Cyber Security Impact on Digitalization and Business Intelligence: Big Cyber Security for Information Management: Opportunities and Challenges (Studies in Big Data, 117) 3031318005, 9783031318009

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Studies in Big Data 117

Haitham M. Alzoubi Muhammad Turki Alshurideh Taher M. Ghazal   Editors

Cyber Security Impact on Digitalization and Business Intelligence Big Cyber Security for Information Management: Opportunities and Challenges

Studies in Big Data Volume 117

Series Editor Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland

The series “Studies in Big Data” (SBD) publishes new developments and advances in the various areas of Big Data- quickly and with a high quality. The intent is to cover the theory, research, development, and applications of Big Data, as embedded in the fields of engineering, computer science, physics, economics and life sciences. The books of the series refer to the analysis and understanding of large, complex, and/or distributed data sets generated from recent digital sources coming from sensors or other physical instruments as well as simulations, crowd sourcing, social networks or other internet transactions, such as emails or video click streams and other. The series contains monographs, lecture notes and edited volumes in Big Data spanning the areas of computational intelligence including neural networks, evolutionary computation, soft computing, fuzzy systems, as well as artificial intelligence, data mining, modern statistics and Operations research, as well as self-organizing systems. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output. The books of this series are reviewed in a single blind peer review process. Indexed by SCOPUS, EI Compendex, SCIMAGO and zbMATH. All books published in the series are submitted for consideration in Web of Science.

Haitham M. Alzoubi · Muhammad Turki Alshurideh · Taher M. Ghazal Editors

Cyber Security Impact on Digitalization and Business Intelligence Big Cyber Security for Information Management: Opportunities and Challenges

Editors Haitham M. Alzoubi School of Business Skyline University College Sharjah, United Arab Emirates

Muhammad Turki Alshurideh Department of Marketing School of Business The University of Jordan Amman, Jordan

Taher M. Ghazal School of Information Technology Skyline University College Sharjah, United Arab Emirates

ISSN 2197-6503 ISSN 2197-6511 (electronic) Studies in Big Data ISBN 978-3-031-31800-9 ISBN 978-3-031-31801-6 (eBook) https://doi.org/10.1007/978-3-031-31801-6 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Paper in this product is recyclable.

Contents

Big Data Impact of Big Data Security on Digital Operations with the Mediating Role of Supply Chain Risk: Evidence from the UAE Transportation and Shipment Industry . . . . . . . . . . . . . . . . Mohammed T. Nuseir, Muhammad Turki Alshurideh, Haitham M. Alzoubi, Barween Al Kurdi, Wasfi A. Alrawabdeh, and Ahmad Al Hamad Modelling Big Data Management for the Finance Sector Using Artificial Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Iman Akour, Barween Al Kurdi, Mohammed T. Nuseir, Haitham M. Alzoubi, Muhammad Turki Alshurideh, and Ahmad Qasim Mohammad AlHamad Role of Big Data Analytics to Empower Patient Healthcare Record Management System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohammed T. Nuseir, Iman A. Akour, Haitham M. Alzoubi, Barween Al Kurdi, Muhammad Turki Alshurideh, and Ahmad AlHamad Integrating Big Data and Artificial Intelligence to Improve Business Growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohammed T. Nuseir, Muhammad Turki Alshurideh, Haitham M. Alzoubi, Barween Al Kurdi, Samer Hamadneh, and Ahmad AlHamad

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Cyber Security The Effect of Cyber Resilience Role in the Relationship of Intelligent Information System on the E-Supply Chain: An Empirical Evidence from the UAE Healthcare Industry . . . . . . . . . . . . Mohammed T. Nuseir, Enass Khalil Alquqa, Haitham M. Alzoubi, Muhammad Turki Alshurideh, Barween Al Kurdi, and Ahmad AlHamad

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Impact of Cyber Security Strategy and Integrated Strategy on E-Logistics Performance: An Empirical Evidence from the UAE Petroleum Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohammed T. Nuseir, Enass Khalil Alquqa, Ata Al Shraah, Muhammad Turki Alshurideh, Barween Al Kurdi, and Haitham M. Alzoubi

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The Mediating Role of Cyber Resilience in the Impact of Innovation Capabilities on Supply Chain Performance: Empirical Evidence from the UAE Petroleum Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 Mohammed T. Nuseir, Samer Hamadneh, Barween Al Kurdi, Muhammad Turki Alshurideh, Haitham M. Alzoubi, and Ahmad AlHamad Impact of Supply Chain Resilience on Competitiveness with the Mediating Role of Supply Chain Capabilities: Empirical Evidence from the UAE Electronics Industry . . . . . . . . . . . . . . . . . . . . . . . . . 129 Mohammed T. Nuseir, Ala’a Ahmad, Enass Khalil Alquqa, Haitham M. Alzoubi, Barween Al Kurdi, and Muhammad Turki Alshurideh Impact of Cyber Security and Risk Management on Green Operations: Empirical Evidence from Security Companies in the UAE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 Barween Al Kurdi, Enass Khalil Alquqa, Mohammed T. Nuseir, Haitham M. Alzoubi, Muhammad Turki Alshurideh, and Ahmad AlHamad Robot-Based Security Management System for Smart Cities Using Machine Learning Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 Ahmad Qasim Mohammad AlHamad, Samer Hamadneh, Mohammed T. Nuseir, Muhammad Turki Alshurideh, Haitham M. Alzoubi, and Barween Al Kurdi Business Digitalization Digital Sustainability and Strategic Supply Chain for Achieving a Competitive Advantage: An Empirical Evidence from Telecommunication Industry in the UAE . . . . . . . . . . . . . . . 183 Enass Khalil Alquqa, Barween Al Kurdi, Haitham M. Alzoubi, Muhammad Turki Alshurideh, Samer Hamadneh, and Ahmad Al Hamad

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Artificial Intelligence Explainable Artificial Intelligence (EAI) Based Disease Prediction Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207 Iman Akour, Mohammed T. Nuseir, Muhammad Turki Alshurideh, Haitham M. Alzoubi, Barween Al Kurdi, and Ahmad Qasim Mohammad AlHamad Intelligent Traffic Congestion Control System in Smart City . . . . . . . . . . . 223 Iman Akour, Mohammed T. Nuseir, Barween Al Kurdi, Haitham M. Alzoubi, Muhammad Turki Alshurideh, and Ahmad Qasim Mohammad AlHamad Automated Sales Management System Empowered with Artificial Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235 Muhammad Turki Alshurideh, Mohammed T. Nuseir, Barween Al Kurdi, Haitham M. Alzoubi, Samer Hamadneh, and Ahmad AlHamad Role of Explainable Artificial Intelligence (EAI) in Human Resource Management System (HRMS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249 Mohammed T. Nuseir, Muhammad Turki Alshurideh, Haitham M. Alzoubi, Barween Al Kurdi, Samer Hamadneh, and Ahmad AlHamad Machine Learning An IoMT-Based Healthcare Model to Monitor Elderly People Using Transfer Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267 Samer Hamadneh, Iman Akourm, Barween Al Kurdi, Haitham M. Alzoubi, Muhammad Turki Alshurideh, and Ahmad Qasim Mohammad AlHamad IoMT-Based Model to Predict Chronic Asthma Disease in Elderly People Using Machine Learning Techniques . . . . . . . . . . . . . . . . . . . . . . . . . 281 Ahmad Qasim Mohammad AlHamad, Mohammed T. Nuseir, Samer Hamadneh, Muhammad Turki Alshurideh, Haitham M. Alzoubi, and Barween Al Kurdi Machine Learning Based Statistical Tools Estimation for Rainfall Forecasting for Smart Cites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295 Mohammed T. Nuseir, Iman Akour, Haitham M. Alzoubi, Muhammad Tu rki Alshurideh, Barween Al Kurdi, and Ahmad Qasim Mohammad AlHamad Machine Learning Empowered House Price Prediction Model . . . . . . . . . 309 Iman Akour, Mohammed T. Nuseir, Muhammad Turki Alshurideh, Haitham M. Alzoubi, Barween Al Kurdi, and Ahmad Qasim Mohammad AlHamad

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Stock Market Price Prediction Using Machine Learning Techniques . . . 323 Mohammed T. Nuseir, Iman Akour, Muhammad Turki Alshurideh, Barween Al Kurdi, Haitham M. Alzoubi, and Ahmad Qasim Mohammad AlHamad Empowering Supply Chain Management System with Machine Learning and Blockchain Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335 Muhammad Turki Alshurideh, Samer Hamadneh, Haitham M. Alzoubi, Barween Al Kurdi, Mohammed T. Nuseir, and Ahmad Al Hamad e-Business The Impact of Information Sharing and Delivery Time on Customer Happiness: An Empirical Evidence from the UAE Retail Banking Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353 Muhammad Turki Alshurideh, Barween Al Kurdi, Enass Khalil Alquqa, Haitham M. Alzoubi, Samer Hamadneh, and Ahmad Al Hamad Investigating the Online Buying Behavior in the UAE Online Retail Industry: The Role of Emotional Intelligence and Customer Perception . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371 Muhammad Turki Alshurideh, Barween Al Kurdi, Enass Khalil Alquqa, Haitham M. Alzoubi, Samer Hamadneh, and Ahmad AlHamad The Mediating Role of Information Sharing in the Effect of Blockchain Strategy Information Security on E-Supply Chain in the UAE Real Estate Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387 Samer Hamadneh, Haitham M. Alzoubi, Enass Khalil Alquqa, Ata Al Shraah, Muhammad Turki Alshurideh, and Barween Al Kurdi Impact of the Internet of Things (IoT) on the E-Supply Chain with the Mediating Role of Information Technology Capabilities: An Empirical Evidence from the UAE Automotive Manufacturing Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 409 Ala’a Ahmad, Mohammed T. Nuseir, Haitham M. Alzoubi, Barween Al Kurdi, Muhammad Turki Alshurideh, and Ahmad Al-Hamad The Impact of Social Media Marketing on Online Buying Behavior via the Mediating Role of Customer Perception: Evidence from the Abu Dhabi Retail Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 431 Barween Al Kurdi, Mohammed T. Nuseir, Muhammad Turki Alshurideh, Haitham M. Alzoubi, Ahmad AlHamad, and Samer Hamadneh

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Impact of Supply Chain 4.0 on Operations Performance with the Mediating Role of Innovation Capabilities: Evidence from the UAE Computer Hardware Industry . . . . . . . . . . . . . . . . . . . . . . . . 451 Ayman Abu-Rumman, Haitham M. Alzoubi, Ata Al Shraah, Muhammad Turki Alshurideh, Barween Al Kurdi, and Ahmad AlHamad Cloud Computing and Blockchain Impact of Blockchain Strategy and Information Sharing on Digital Operations: Empirical Evidence from the UAE Banking Industry . . . . . . 475 Ayman Abu-Rumman, Barween Al Kurdi, Ata Al Shraah, Muhammad Turki Alshurideh, Haitham M. Alzoubi, and Ahmad AlHamad A Critical Review of Cloud Computing Architecture Empowered with Blockchain Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 495 Ahmad Qasim Mohammad Al-Hamad, Samer Hamadneh, Mohammed T. Nuseir, Haitham M. Alzoubi, Barween Al Kurdi, and Muhammad Turki Alshurideh Socio-Technical Management Does Product Differentiation Strategy Mediate the Relationship Between Cost Leadership Strategy and Order-Winners? An Empirical Evidence from UAE Retail Industry . . . . . . . . . . . . . . . . . . . . 509 Ata Al Shraah, Barween Al Kurdi, Enass Khalil Alquqa, Muhammad Turki Alshurideh, Haitham M. Alzoubi, and Samer Hamadneh Does Organizational Culture Moderate the Relationship Between Business Process Reengineering and Business Value in the UAE Banking Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 527 Enass Khalil Alquqa, Ata Al Shraah, Mohammed T. Nuseir, Muhammad Turki Alshurideh, Haitham M. Alzoubi, and Barween Al Kurdi The Impact of Customisation Strategy and Product Variety on Operational Performance in the UAE Construction Industry . . . . . . . 543 Ahmad AlHamad, Barween Al Kurdi, Mohammed T. Nuseir, Haitham M. Alzoubi, Muhammad Turki Alshurideh, and Samer Hamadneh The Impact of Demand Forecasting on Effective Supply Chain with Mediating Role of Strategic Planning in the UAE Pharmaceutical Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 561 Haitham M. Alzoubi, Muhammad Turki Alshurideh, Mohammed T. Nuseir, Barween Al Kurdi, Ahmad AlHamad, and Samer Hamadneh

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The Impact of Team Creativity and Continuous Improvement on Time-to-Market: An Empirical Evidence from the UAE Electronics Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 583 Enass Khalil Alquqa, Muhammad Turki Alshurideh, Barween Al Kurdi, Haitham M. Alzoubi, Ahmad AlHamad, and Samer Hamadneh

Big Data

Impact of Big Data Security on Digital Operations with the Mediating Role of Supply Chain Risk: Evidence from the UAE Transportation and Shipment Industry Mohammed T. Nuseir , Muhammad Turki Alshurideh , Haitham M. Alzoubi , Barween Al Kurdi , Wasfi A. Alrawabdeh, and Ahmad Al Hamad Abstract This research model proposed to empirically examine the impact of big data security on digital operations with the mediating role of supply chain risk in the UAE transportation and shipment industry. This is the first study linking perspectives on big data security and digital operations with a mediating effect of supply chain risk. This research does not claim to be comprehensive; instead, it analyses empirical research and literature to further the conversation by using a conceptual framework for examining the UAE transportation and shipment industry. An online survey was administered to the 226 employees of the UAE’s Dubai transportation and shipment companies. Exploratory research proposed data analysis performed by regression ANOVA using SPSS. Supply chain risk as a mediation link in the research originated as positively significant, whereas there was a direct relationship between big data M. T. Nuseir Department of Business Administration, College of Business, Al Ain University, Abu Dhabi Campus, P.O. Box 112612, Abu Dhabi, UAE e-mail: [email protected] M. T. Alshurideh (B) Department of Marketing, School of Business, The University of Jordan, Amman 11942, Jordan e-mail: [email protected]; [email protected] H. M. Alzoubi School of Business, Skyline University College, Sharjah, UAE e-mail: [email protected] H. M. Alzoubi · B. Al Kurdi Applied Science Research Center, Applied Science Private University, Amman 11937, Jordan e-mail: [email protected] W. A. Alrawabdeh Department of Marketing, School of Business, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan M. T. Alshurideh · A. Al Hamad Department of Management, College of Business, University of Sharjah, 27272 Sharjah, United Arab Emirates e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 H. M. Alzoubi et al. (eds.), Cyber Security Impact on Digitalization and Business Intelligence, Studies in Big Data 117, https://doi.org/10.1007/978-3-031-31801-6_1

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security and digital operations. To support long- and short-term strategic decisionmaking, it is necessary to examine risk management procedures as supply chain and transportation networks develop in a dynamic environment. Organizational risk exposure must be rigorously analyzed using objective, transparent criteria. The costs and advantages of different risk mitigation strategies must be considered with digital criteria. Keywords Big data security · Supply chain risk · Digital operations · UAE transportation and shipment industry

1 Introduction Speed and timeliness are crucial in a real-time economy, and obviously, there are significant ramifications for supply chains, logistics, and the transportation sector. The public transportation industry uses big data analytics to anticipate passenger volumes as precisely as feasible (Alzoubi et al., 2022a). For instance, specific occurrences like inclement weather, holidays, technical difficulties, and consumer feedback from operating transportation operations can be monitored and processed in real time (Akhtar et al., 2022). Similarly, a faster supply chain, logistics planning, keeping up with changing customer expectations and habits, and automation of time-consuming processes allow organizations to take advantage of digital logistics (Alzoubi et al., 2022i; Amrani et al., 2022). So, even though the supply chain crisis is still present and difficult to resolve, industries may look back on their shift to digital logistics during COVID-19 as a positive one (Alzoubi et al., 2022n). In supply chain operations, it can be difficult to distinguish between risk and uncertainty. Uncertainty is characterized by supply chain interruption caused by unreliable and uncertain resources, whereas risk is the result of supply chain procedures balancing supply and demand (Alzoubi et al., 2019; Solfa, 2022; Nasim et al., 2022; Saad Masood Butt, 2022). The outcome of a risk impact its sources should be anticipated (Alshurideh et al., 2020; Hamadneh et al., 2021b). In the digital operation of an organization or system, the operating team should be highly talented and responsible for their duties (Ahmad et al., 2021; Lee et al., 2022). The selection of the digital operation management team should be based on their experience and efficiency (Almaazmi et al., 2020). They should be open to always learning as, in the digitalized world, there is always the scope for invention and up-gradation (Ahmad et al., 2021a; Lee et al., 2022). Therefore, the digital operation specialist or management authority should upgrade their knowledge and technics to adjust to the new and innovative process (Alzoubi et al., 2022e). Digital technologies (such as AI, IoT, automation, and Big Data Analytics) offer the chance to address long-standing urban transportation issues, such as traffic congestion, a lack of customer choice, wasteful use of vehicles and space, and pollution (Al Shebli et al., 2021; Ghazal et al., 2021a, 2021b, 2021c, 2021d; Shamout & Muhammad Alshurideh, 2022).

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In order to deal efficiently with transportation concerns, management can acquire big data security to manage the risk and improve its technological development (T. M. Ghazal et al., 2022; Mondol, 2022). Therefore, this research aims to explore the impact of big data security on digital operations via the mediating effect of supply chain risks. A three-pronged construct-based model was analyzed to incorporate the empirical evidence collected from the UAE transportation and shipment industry.

2 Theoretical Framework 2.1 Big Data Security The current new wave of technology and architectural designs is referred to as big data technologies (Alzoubi et al., 2022o). It is employed to efficiently extract beneficial values from vast volumes of wide-ranging data (Farouk, 2022). Big data technologies and analytical techniques support high-velocity and real-time acquisition, finding, processing, and analysis (Alzoubi et al., 2022d; Ghosh & Aithal, 2022; Radwan, 2022; Tijan et al., 2019). A user-focused presentation and visualization of data and outcomes for assisting decision-making are other crucial components of big data technology and analytical approaches. Both structured and unstructured big data exist today (Aris et al., 2015; Ratkovic, 2022). Unstructured information is not arranged into a predetermined model or format. It includes data gathered from social media sources (Goria, 2022), which help businesses discover more about what clients want (Al Khasawneh et al., 2021b; Aljumah et al., 2021; El Refae et al., 2021; Nuseir et al., 2021). Big data can be obtained from surveys, purchases of goods, electronic check-ins, personal gadgets, and apps, publicly posted remarks on social media and websites, and freely volunteered information (Al Batayneh et al., 2021; Al Kurdi et al., 2021; Alshurideh et al., 2019b; Kurdi et al., 2022). Because intelligent devices feature sensors and other inputs, they may gather data from various situations and occurrences.

2.2 Digital Operations Digital operations are a structural key to today’s world as the entire world is shifting towards digitalization. The invention of cutting-edge technologies and implementations in people’s daily lives worldwide, from personal to organizational and higher administration national levels (Alzoubi & Yanamandra, 2022). Digital operations refer to the centralized operation of an entire system through digital technological implementations (Ahmed & Al Amiri, 2022; Alzoubi, 2022; Alzoubi et al., 2022q; Cole et al., 2019). Today, they are integral to the regulation and operation of all levels of work. While applying digital technical implementations to an organization’s

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management and regulation, monitoring the different sections of its entire process is essential (AlShehhi et al., 2020; Alshurideh et al., 2016, 2017). The digital operation helps management to centralize the entire process by gathering and arranging the information and progress of different sections and thus illustrating overall progress (Alshurideh et al., 2022; Hamadneh et al., 2021a; Lee et al., 2022; Lee, Romzi, et al., 2022). A digital operating system is entirely based on regulating information technology and inputs (Alwan & Alshurideh, 2022a, 2022b; Hammad et al., 2022; Al Kurdi et al., 2022). Therefore, a source’s origin and its informational flow pathway in the organizational digital operation are clarified to the authority (Alzoubi et al., 2022k; Tariq et al., 2022a, 2022b). Thus, the management authority’s tasks and procedures can be smoothed in a digital operation (Ahmad et al., 2021c; Alwan & Alshurideh, 2022a, 2022b). Data transformation from one place to another or from one form to another occurs all the time, and therefore, the transformation’s composition is essential (Annarelli & Palombi, 2021; Salloum et al., 2020).

2.3 Supply Chain Risk Supply chain risks are internal and external uncertainties leading to supply chain hazards. The majority of their internal uncertainties can be predicted using supply chain internal data, gathered using cutting-edge technologies in the customers’ data and information delivery (Alsharari, 2022; Alzoubi et al., 2022p; Schroeder & Lodemann, 2021). Supply chain external environments, like the social, economic, and environmental surroundings, are the source of supply chain external uncertainty (Abuanzeh et al., 2022; Alzoubi et al., 2021g). Scenario analysis is frequently used for risk analysis in supply chain management (Alshurideh, 2022; Kurdi et al., 2022a). That is a method for thinking and communicating, which complements rather than supplants, the managerial mind (Alshurideh et al., 2022; Shamout & Muhammad Alshurideh, 2022). Usually, supply chain logistics are faithful and maintain a loyal relationship with their organizations as their duty is to maintain the information’s safety, privacy, and security. Though information is well-encrypted during a digital transformation and access to it is not easily obtained, a person from the team would know how to do so (M. Alzoubi et al., 2021j). Therefore, the supply chain logistics and medium should be well oriented and regulated to maintain the information’s confidentiality (Kasem & Al-Gasaymeh, 2022; Qasaimeh & Jaradeh, 2022). That is very important, even essential, for retaining customers as they want their loyalty returned by the supply chain authorities (Colicchia et al., 2019).

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2.4 Operational Definitions

Variables

Definition

Reference

Big data security

Big data security refers to all the measures Zage et al. (2013) and tools used to defend against attacks, theft, and other hostile activities, which might harm or adversely influence data and analytical operations

Supply chain risk Supply chain risks include exposures, threats, Mani et al. (2017) and vulnerabilities connected to the products and services moving through the supply chain and the supply chain itself Digital operations Digital operations integrate agility, intelligence, and automation into corporate processes to produce operational models that delight customers and increase productivity

Stevenson and Aitken (2019)

2.5 UAE Transportation and Shipment Industry The UAE transportation and shipment market is anticipated to grow at a compound annual growth rate (CAGR) of over 5%. The region’s continuous and rapid growth of e-commerce and increasing global trade have been significant drivers of the UAE’s transportation and shipment industry’s steady growth. The COVID-19 epidemic damaged all the UAE’s supply chain activities. Road freight was impacted by border sealing, and flight cancellations impacted aviation freight. Due to employee infection problems, businesses had to transition non-operational workers to remote working environments. As a result of the resultant transportation limitations, the industry is only gradually recovering. In the future, vertical market growth is anticipated as a result. The Emirates’ most exemplary trading conditions are provided by Dubai’s location halfway between Asia and Europe, thereby helping the East and West. Dubai has advanced its technology and infrastructure to build an excellent logistics infrastructure and a well-integrated transport system, which will support trade and the e-commerce industry.

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3 Literature Review 3.1 Relationship and Impact of Big Data Security on Supply Chain Risk Understanding the components of organizational big data security receives more attention from practitioners as organizations have begun to recognize the value of big data capabilities for their success and strategy creation (Ali & Xie, 2021; Eli & Hamou, 2022). Scholars have identified two essential components of big data security: the availability of a skilled workforce that can efficiently analyze, assimilate, and use big data to develop actionable information for business decision-making, and the presence of infrastructure to capture big data information from various sources (Alzoubi & Aziz, 2021). Tangible human and intangible resources comprise big data’s security capabilities to secure critical information, which helps to detect supply chain risk early (Alzoubi et al., 2021e; Zage et al., 2013). Therefore, building organizational big data security entails simultaneously building both big data-focused IT infrastructure and human capital to prevent information loss to any fraudulent activity within its supply chain (AlHamad et al., 2022; Alzoubi et al., 2020a; Salloum et al., 2020; Shamaileh et al., 2022). An organization’s big data capabilities allow it to successfully gather, mine, analyze, and visualize data, empowering decision-makers to provide actionable intelligence for decision-making (Ahmad et al., 2021b; Harahsheh et al., 2021). The main benefits for businesses from using big data security are related to their organizational capacity for handling complicated calculations safely and performing important actions regarding client information to strengthen customer trust (Al Kurdi et al., 2022; Hussein et al., 2018). Additionally, it enables businesses to tailor their products and enhance accountability and transparency. Researchers have stated that big data security help firms respond quickly to disruptive events and is crucial for disaster mitigation and enhancing recovery efforts (Alzoubi et al., 2021h). Consequently, an organization’s big data security is a crucial tool for creating a responsive and effective risk management system (Victoria, 2022). Based on the above discussion, the following hypothesis was proposed: H1: Big data security significantly impacts supply chain risk.

3.2 Relationship and Impact of Big Data Security on Digital Operations Analytics and big data technologies rely heavily on flexible digitization (Alzoubi et al., 2021c). Because the computational requirements of analytical systems vary substantially, especially in supply chain management, where systems are largely used for planning purposes, flexible infrastructure can be very cost-effective (Mehmood,

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2021; Mohandu & Kubendiran, 2021). The impact is increased by the growing amount of data needing to be processed and reviewed. As a company’s centralized management of the information’s digital transformation is comprises its digital operation, big data security is, therefore, the most important factor in digital operations (Alolayyan et al., 2022a, 2022b; Naqvi et al., 2021). Data flow has become more exact over time, resulting from its conversion to digital storage and changing storage location (Alzoubi et al., 2020c; Miller, 2021). Big data security is necessary to safeguard the data’s privacy (Alolayyan et al., 2022a, 2022b; Alshurideh et al., 2021a). As administrations also demand some level of secrecy to uphold their reputation and keep their businesses profitable, there is a further reason for maintaining big data security in digital operations (Alzoubi et al., 2020b). However, the impact of big data security on a system’s digital operation is significant, and the level of information intensity can either increase or decrease the digital operation’s precision and effectiveness (Hosseini et al., 2019; Mohd Selamat et al., 2018). A logistics organizational system stores customer and traveler information for future use, which carries a high risk of financial and reputational loss (Alsharari, 2021). Maintenance of this information is simpler but equally important in the age of digital operation (Alzoubi & Ahmed, 2019). The organization’s relationship with its customers or other stakeholders is impacted in addition to the system’s or organization’s digital operation being compromised by an information security incident (Alzoubi et al., 2021f; Hammad et al., 2022). The disclosure of personal information may impact customers, workers, and other stakeholders and the organizational authority is accountable for that (Eli, 2021). It may also impact the organization’s reputation and financial health (Torre-Bastida et al., 2018). Based on the above discussion, the following hypothesis was proposed: H2: Big data security significantly impacts digital operations.

3.3 Relationship and Impact of Supply Chain Risk on Digital Operations The supply chain refers to the regulation of the informational inputs and outputs (AlShurideh et al., 2019a, 2019c). Most business organizations do not develop their own digital operating platforms unless it has vast value, or the organization is linked with national administration or political bodies (Alzoubi et al., 2017). Therefore, different organizations deal in digital operations for customers. While using a supply chain is an essential requirement for any organization dealing with any service or product, there remains a chance of risk (Abuhashesh et al., 2021; Akhtar et al., 2021; Erceg & Sekuloska, 2019). Better inventory planning and supply chain forecasting are made possible by digital operations (Al Kurdi et al., 2020; Kurdi et al., 2020). Businesses access enormous amounts of data and may analyze it almost instantly to predict future demand or changes in the supply chain (Alshurideh et al., 2020, 2021a, 2021b). The advanced

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feature of forecasting the future based on the customer’s data, logistics information, and the nature of its clients can be optimized to enhance business efficiency (Alzoubi & Yanamandra, 2020; Ivanov & Dolgui, 2021; Kashif et al., 2021). Based on the above discussion, the following hypothesis was proposed: H3: Supply chain risk significantly impacts digital operations.

3.4 The Relationship and Impact of Big Data Security on Digital Operations via the Mediating Role of Supply Chain Risk Big data is a new paradigm currently attracting significant attention globally, particularly within the transportation sector. Transport’s data life cycle is improved by combining disruptive technology and novel ideas like the smart city (Torre-Bastida et al., 2018). In this context, big data security is seen as a new commitment by the transportation sector to efficiently handle all the data needed to provide users with a more individualized travel experience as well as safer, cleaner, and more efficient transportation options. Big data primarily includes three key qualities, the 3Vs, volume, variety, and velocity (Singh & Singh, 2019). Vehicles typically gather a sizable amount of data from multiple locations and with various heterogeneous qualities, ultimately resulting in big data that varies in size, volume, and dimensionality (Al-Khayyal et al., 2020; Ghazal et al., 2021a, 2021b, 2021c, 2021d). That would make it possible for national transportation agencies to effectively study and manage intense traffic issues to improve the comfort and convenience of millions of people’s lives. These potential benefits may have recently encouraged the development of large-scale big data platforms for intelligent transportation systems by vehicle manufacturers and shipment companies delivering orders to the end users (Mohandu & Kubendiran, 2021). Transportation and shipment analytics is one of the areas of business that big data is transforming. Logistics provides a fantastic case for big data due to its complexity, dynamic nature, and reliance on several moving pieces, capable of causing bottlenecks at any point in the supply chain. To the advantage of both logistics and shipping companies, big data logistics can be utilized, for instance, to optimize routing, expedite manufacturing operations, and provide transparency to the entire supply chain (Zage et al., 2013). Both shipping companies and third-party logistics providers correspond with supply chain management. Moreover, the use of big data security can prevent supply chain risk by enhancing optimized shipments, financial business forecasts, response rate advertisement, and data analytics from social media to grant the shipment and supply chain procedure more security (Al Khasawneh et al., 2021a; Al-Maroof et al., 2021; Almazrouei et al., 2020). Disruptions to the supply chain and transportation are no longer seen as the sole domain of operational risk management (Ahmed et al., 2021; Alzoubi et al., 2021a).

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Organizations have been prompted to reassess their methods for recognizing and mitigating systemic risks by changes to governance structures following the 2008 global financial crisis and other significant shocks (Khatib et al., 2022). Experts evaluated the disparity between the risk management techniques currently in use and the most crucial ones from the future to determine which ones need improvement (Moeuf et al., 2018; Rahim et al., 2021). Simultaneously, the use of digitization became crucial for transportation concerns and business processes to provide resilience for delivery and overall digital operations. Based on the above discussion, the following hypothesis was proposed: H4: Big data security significantly impacts digital operations via the mediating role of supply chain risk.

3.5 Problem Statement and Research Gap The main difficulties for the transportation and shipment industry are protecting customers and staff, lowering crime and vandalism in transportation corridors, responding promptly to emergencies, resolving liability claims quickly, and ensuring that privacy and local regulations are observed. Investing in a scalable enterprise management system supporting technology surveillance through big data security minimizes supply chain risks and improves digital operations in transportation. Therefore, this research investigates the link between big data security and its impact on digital operations via the mediating effect of supply chain risk to close the gap between practice and theory. The UAE transportation and shipment industry was targeted for empirical evidence.

3.6 General Research Model See Fig. 1.

3.7 Research Hypothesis H1: Big data security significantly impacts supply chain risk in the UAE transportation and shipment industry at (α ≤ 0.05) level. H2: Big data security significantly impacts digital operations in the UAE transportation and shipment industry at (α ≤ 0.05) level. H3: Supply chain risk significantly impacts digital operations in the UAE transportation and shipment industry at (α ≤ 0.05) level.

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Supply Chain Risk H1

H3 H4

Big Data Security

Digital Operations H2

Fig. 1 Conceptual research model

H4: Big data security significantly impacts digital operations via the mediating effect of supply chain risk in the UAE transportation and shipment industry at (α ≤ 0.05) level.

3.8 Research Methodology and Design This research used a quantitative technique to assess the model (Fig. 1), which entailed a survey instrument and applying regression ANOVA to explore the proposed hypotheses. An online survey method based on a questionnaire was chosen because it allowed researchers to collect data and assess the correlations between the different constructs on a large sample size to boost the generalizability of the findings.

3.9 Population, Sample, and Unit of Analysis The data collection from 34 transportation companies located in Dubai UAE, Global Logistics, Move One Moving and Storage, and Emirates Logistics LLC, was gathered from completed questionnaires. A total of 550 questionnaires were emailed, 226 replies of which were analyzed for statistical results. The questionnaire was based on the five-point Likert scale using 26 items dispersed among three variables; nine items were used to measure big data security, nine to measure supply chain risk, and eight to measure digital operations.

Impact of Big Data Security on Digital Operations with the Mediating … Table 1 Demographic data summary

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Items

Description

f

Gender

Male

Job title

%

163

72.1

Female

63

27.9

Transportation officer

80

35.4

SC and logistics officer

57

25.2

Data management officer

30

13.3

IT developer

59

26.1

N = 226, male = 163, female = 63

4 Data Analysis 4.1 Demographic Analysis Two hundred twenty-six employees working in UAE transportation and shipment companies responded to the questionnaire. The male employee respondents numbered 163, whereas the females totaled 63. At 35%, the highest percentage of respondents were designated as transportation officers (Table 1).

4.2 Reliability, Descriptive, and Correlation Analysis The scale’s dependability was first checked. With a total Cronbach loading of 0.884 and high loadings for each individual construct, its reliability was judged to be strong. Table 2 shows the summary of the results, including the descriptive statistics. Big data security’s mean was 2.87, an acceptable level, with an SD at 0.69. The mean value for supply chain risk was also acceptable at 3.02, with an SD at 0.65 being partially acceptable. Finally, the mean for digital operations was also acceptable at 2.95, with an SD of 0.56, the lowest spread near the mean value. Additionally, correlation coefficients were analyzed. At r = 0.83 big data security and supply chain risk showed a high correlation. At r = 0.68, big data security positively correlated with digital operations and, at r = 0.77, supply chain risk and digital operations were highly correlated. Table 2 summarizes the overall results.

4.3 Regression Analysis and Hypothesis Testing The hypothesis testing analyzed by regression analysis using ANOVA showed a positively significant relationship between big data security and supply chain risk for H1 with β = 0.83, t = 22.6, p = 0.000, and R2 = 69%. H2 had a positively significant relationship between big data security and digital operations with β =

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Table 2 Reliability, descriptive, and correlation analysis Construct

No. of items

Cronbach’s alpha

Mean

SD

Big data security

Supply chain risk

Big data security

10

0.88

2.87

0.69

1

Supply chain risk

8

0.86

3.02

0.65

0.834**

1

Digital operations

9

0.88

2.95

0.56

0.681**

0.778**

Digital operations

1

Big data security (M = 2.87, SD = 69%, SC risk M = 3.02, SD = 65%), S digital operations M = 2.95, SD = 56% ** Level of significance at P < 0.05

0.68, t = 1.69, p = 0.000, R2 = 46%. The data for H3 showed a positive significant relationship between supply chain risk and digital operations with β = 0.77, t = 9.06, p = 0.000, and R2 = 60%. Finally, the findings for the H4 indicated a positively significant relationship between big data security and digital operations with the mediating effect of SC risk with β = 0.78, t = 7.34, p = 0.000, and R2 = 60%.

5 Discussion of the Results There is empirical evidence for the managerial insights inherent in the proposed research model with reference to the prior literature. Big data security more greatly impacts supply chain risk by generating sufficient big data to prevent information loss and any fraudulent behavior that might occur within the organizational supply chain. Security involves the simultaneous development of both a big data-focused IT infrastructure and human capital (Mani et al., 2017). Regarding investigation through our statistical analysis and previously defined the big data security, H2 positively and significantly impacts digital operations. Hussein et al. (2018) investigated this as perhaps the strictest security need imposed by transport apps using big data concerns the privacy of the data and information transferred by users through their devices and computers. Table 3 summarized H3 as supply chain risk having a significant impact on digital operations. Some important factors have been discussed analogous to our empirical findings. The use of digital technologies to organize and carry out transactions, communications, and other actions is referred to as digitalization in the supply chain. During the next five years, nearly 90% of businesses anticipate digitalization will give them a competitive edge in the supply chain. Thus, supply chain risk can be optimized with technological adoption to enhance digital operations (Ghadge et al., 2020). Finally, our empirical results demonstrate the significant impact of mediating role of supply chain risk on big data security and digital operations that parallels the literature identified earlier. Big data security has other significant areas of usage in the

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Table 3 Hypothesis testing by ANOVA Hypothesis

Regression weights Standardized coefficients β

R2

Adjusted R2

Sig.

t-value

Hypothesis supported

22.6

Yes

H1

BDS → SCR

0.834

0.696

0.695

0.000

H2

BDS → DO

0.681

0.464

0.462

0.000

1.69

Yes

H3

SCR → DO

0.778

0.605

0.603

0.000

9.06

Yes

H4

BDS*SCR → DO

0.780

0.609

0.605

0.000

7.34

Yes

Dependent variable = digital operations. = 1.64

* Level

of significance (α ≤ 0.05)

** Critical

t-value (df/p)

mobility and transportation sectors. Distribution network architecture has complicated standards in transportation logistics. Rahim et al. (2021) demonstrates that when big data technologies model and analyze distribution centers and capacitate distribution networks, there are numerous opportunities for big data security to adopt digitization. The future is just as vital as the present in all investments and collaborations; thus, it is necessary to guarantee that solutions use transformational, adaptive AI, cloud, and remote on-demand services.

6 Conclusion In the near future, emphasis can be expected on the significant role that big data security can play in enabling organizations to establish resilience capabilities for their supply chain risk. The findings demonstrate that past business disruption experiences do not necessarily prepare management to prevent future disruption. However, if the company builds its big data security skills, it can better use its internal knowledge to prevent future supply chain disruptions and develop efficient digital operations. The findings also show how vital big data security was in boosting the digitization that influenced an organization’s ability to handle supply chain risks. The final lesson of this paper is that if organizations create excellent supply chain risk mitigation competencies, they can assist in achieving their higher technological capabilities.

7 Recommendations/Limitations This research has some limitations. First off, the study largely focuses on UAE transportation and shipment businesses. Asian and European management perspectives may differ on how big data security might help build digital operations and technological developments, but they are not included in the research. Secondly, the scope of the study’s coverage of several industry sectors is rather extensive. Although it

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increases its ability to deliver outcomes with a high degree of generalizability, it also limits its capacity to provide industry-specific knowledge on how big data security might be applied to increase risk resilience. Nevertheless, despite these flaws, the study provides important insights that can be used as a starting point for further research to achieve a deeper understanding.

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Modelling Big Data Management for the Finance Sector Using Artificial Intelligence Iman Akour , Barween Al Kurdi , Mohammed T. Nuseir , Haitham M. Alzoubi , Muhammad Turki Alshurideh , and Ahmad Qasim Mohammad AlHamad Abstract Big Data (BD) is one of the latest commercial and technological concerns in the age of technology. The financial sector faces hundreds of millions of financial transactions daily. It plays a significant role in analyzing big data developments. Tremendous data greatly impacts financial goods services and finding the financial concerns where big data has a substantial impact to investigate. This paper aims to provide an intelligent model for the finance sector utilizing Artificial intelligence (AI) for Big Data management based on these concepts. AI is the knowledge that allows a system to emulate human actions. The fourth modern innovation, distinguished by intelligent and robotic systems powered by BD and machine learning (ML), I. Akour (B) Information Systems Department, College of Computing and Informatics, University of Sharjah, Sharjah, United Arab Emirates e-mail: [email protected] B. Al Kurdi Department of Marketing, Faculty of Business, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan e-mail: [email protected] M. T. Nuseir Department of Business Administration, College of Business, Al Ain University, Abu Dhabi Campus, P.O. Box 112612, Abu Dhabi, UAE e-mail: [email protected] H. M. Alzoubi School of Business, Skyline University College, Sharjah, UAE e-mail: [email protected] Applied Science Research Center, Applied Science Private University, Amman 11937, Jordan M. T. Alshurideh (B) Department of Marketing, School of Business, The University of Jordan, Amman 11942, Jordan e-mail: [email protected]; [email protected] Department of Management, College of Business, University of Sharjah, Sharjah 27272, UAE A. Q. M. AlHamad College of Business Administration, University of Sharjah, Sharjah, UAE e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 H. M. Alzoubi et al. (eds.), Cyber Security Impact on Digitalization and Business Intelligence, Studies in Big Data 117, https://doi.org/10.1007/978-3-031-31801-6_2

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accelerated the development of AI approaches. In this research, the proposed model can overcome big data problems in the finance sector by utilizing AI. The future of BD could see organizations using Big Data Analytics (BDA) to merge analyses from the digital world to create real-world solutions. Keywords Big data · Finance sector · Artificial intelligence

1 Introduction Recently, organizations have collected and stored massive amounts of data, assuming it may be valuable in the future. Managing massive amounts of data and extracting relevant knowledge for decision-making is difficult (Al-Khayyal et al., 2020; Salloum et al., 2020a). There has been an increasing demand for compliance within the financial business, highlighting the need to secure current systems (Al Shebli et al., 2021; Kasem and Al-Gasaymeh, 2022; Naqvi et al., 2021). These can be used for better data storage, tracking, and retrieval. The volume, velocity, and variety of data that most financial institutions rely on have become far more daunting. All of the gamechanging technologies due to data can bring some obvious value (Al Batayneh et al., 2021; Svoboda et al., 2021). Adopting some of the most cutting-edge Big Data technology in the financial sector can assist the banking industry is going beyond the simple choice of cashless payment (Kurdi et al., 2022b; Shah et al., 2020, 2021). Intelligent systems can assist in the automatic learning and enhancement of risk identification, allowing for a better understanding and avoidance (Alshurideh et al., 2021; Alzoubi et al., 2021a, 2021b). Most financial businesses emphasize big data since it will aid in understanding trading, fraud, and risk operations. Fintech firms can assist in particular trading algorithms, outperforming competitors (Muheidat et al., 2021). BD is considered one of the most recent technological accomplishments in practice and academic study. Its uses have been improving and redefining important parts of actual life in recent years, including economic and social life. Big data has sparked debate in a variety of academic fields. Big data has been conceived from various perspectives in the literature (AlSuwaidi et al., 2021; Lv et al., 2022). A BD process is only valuable for the company if it is established to retrieve meaningful knowledge that can be used to assist company decisions. Methodical analytics tools may be applied on top of BD to help achieve that goal, resulting in a hybrid solution that takes advantage of both the information and innovative ML solutions (Amado et al., 2018). Finance Big Data (FBD) is emerging as one of the highly favourable areas of financial administration. It has a significant impact on financial organizations’ business models. Many scholars suggest that BD is transforming banking and business in ways that they can’t yet measure. A novel field of study is emerging to examine quantitative models and econometric techniques for financial studies, which may connect the space between practical finance research and data science (Al-Jarrah et al., 2012; Assad & Alshurideh, 2020). Experts and scientists may recommend

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new finance company models using BD approach. Current sophisticated risk control approaches using ML tools offer visualization tools for the financial market examination, generate novel finance sentiment guides by mining public emotions from huge textual information from social links, and deploy data-based tools in other innovative ways in this fascinating field (Alshurideh, 2022; Alshurideh et al., 2022; Sun et al., 2019). Day by day, the financial business generates a huge amount of information. Labelled data is handled inside a business to deliver critical decision-making information (Alzoubi & Yanamandra, 2020). Unlabelled data accumulate from various sources in ever-growing amounts, presenting substantial analytical ability (Alzoubi et al., 2020). Daily, billions of dollars go through international markets. Experts are tasked with analyzing this information with accuracy, protection, and speed to put up forecasts, identify trends, and expand analytical strategies (Kurdi et al., 2022a). How this information is gained, handled, collected, and interpreted greatly impacts its value (Ben-Abdallah et al., 2021; Kamaruddeen et al., 2022). As a result, intelligent systems are necessary to obtain accurate results. Because older systems can’t control unlabelled and isolated information without IT work, analysts progressively turn to cloud information solutions (Alhamad et al., 2012; Alolayyan et al., 2022; Alshurideh et al., 2020; Kashif et al., 2021; Tariq et al., 2022). Artificial Intelligence has optimized processes and procedures, automated mundane chores, increased customer service, and aided bottom-line financial companie (Alhashmi et al., 2020; AlShamsi et al., 2021; Salloum et al., 2020b; Yousuf et al., 2021). According to company Insider, AI technologies would save banks and financial companies $447 billion by 2023. Most banks (80%) see AI’s ability advantages. Still, the broader effect of COVID-19 has squeezed the banking region and driven more users to accept the digital experience; it’s more important than ever (Al-bawaia et al., 2022; Al Kurdi et al., 2020; Alshurideh et al., 2021a). According to Forbes, ML is used by 70% of financial companies to forecast cash flow activities, change credit ratings, and identify scams (Madakam et al., 2019).

2 Literature Review Many researchers have previously worked in the financial industry on big data management. This section represents some of their work. Timme and Williams initially presented SCF, and later Berger et al. described it from SME financing. They contended that SME.s had difficulty getting loans (Alsharari, 2021) because they lacked strong credit support (Alzoubi et al., 2022a). They advocated a novel financing model in which major corporations or financial organizations regulate transactions to fund difficult-to-finance SMEs (Hamadneh et al., 2021; Hanaysha et al., 2021; Radwan & Farouk, 2021). Until the late twentieth century, when the relevance of capital flow to the whole supply chain became clear, and SCF (Alzoubi et al., 2022b) was established, supply chain management ignored the movement of capital (Lee et al., 2022b; Li et al., 2022; Shamout et al., 2022).

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According to Leloup, introducing AI to the banking industry can restructure industrial links among banks (Al Ali, 2021) and their investors based on objectivity and trust principles. The same pundit sees a “second digital revolution” based on “ethical AI,” (Al-Tahat & Moneim, 2020) which will be reviewed further in the Conversation section as one of the frontiers where AI must be fine-tuned (Alzoubi & Ahmed, 2019; Sadok et al., 2022). This research suggested that the banking sector is a real-time use of big data that would dramatically cut service costs while simultaneously lowering risk (Alnuaimi et al., 2021; Alzoubi et al., 2022c; Lee & Ahmed, 2021). Money lending platforms often use this strategy to guarantee that the data supplied by users is safe, transparent, and dependable (Miller, 2021). When there is no risk, it can evaluate whether or not transactions are fair (Eli, 2021; Mehmood, 2021). Many of these platforms are created by huge financial institutions that can verify and analyze transactions (Alzoubi et al., 2021a, 2021b, 2022a, 2022b, 2022c, 2022d; Joghee et al., 2020). These platforms’ data is made accessible for various purposes, ranging from collecting interest and credit history data and asset categorization to financial structure research and credit quality data (Lee et al., 2022a). In this research, the authors presented that financial AI applications built to predict and deceive depend on mutual observations and are prone to herding, destructive resonance, and tail events (Ali et al., 2022; Farouk, 2021). As a result, the order in which algorithms interact, or collective machine behaviour, becomes crucial to a better understanding of systemic market hazards (Svetlova, 2022). A Jindal et al. introduced a Tensor-based Big Data management (TBDMT) system to diminish data dimensionality acquired in a smart city over the internet (Ghazal et al., 2021, 2022; Zafar et al., 2022). The support vector machine (SVM) Classification Scheme identifies end-users who want to keep their load profile in a certain location (Yanamandra & Alzoubi, 2022). Finally, the mechanism changes the load requirements to optimize the overall load for the smart city data management system (El Khatib et al., 2022; Zhang et al., 2019). The authors in this research present that Business processes, goods and services, and the user experience have all been transformed by AI (Maged Farouk, 2022). For example, autonomous AI systems that do not need human intervention in the finance and banking industry allow banks to increase online banking speed, accuracy, and efficiency (Alshurideh et al., 2022a, 2022b; Lee & Chen, 2022; Neyara Radwan, 2022). This research presents that big data has transformed the financial sector by overcoming many hurdles and acquiring useful insights to increase client comfort and banking experience (Ali et al., 2022a, 2022b; Alzoubi & Aziz, 2021). According to Razin, big data transforms finance in five ways: generating transparency, measuring risk, algorithmic trading, exploiting consumer data, and changing culture (Cruz, 2021; Guergov & Radwan, 2021). In addition, BD considerably impacts economic research and modelling (Hasan et al., 2020). Most of the approaches have been used while employing and constructing several smart as well as intelligent frameworks like machine learning approaches that may

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provide assistance in designing emerging solutions for the rising challenges in designing smart cloud-based monitoring management systems.

3 Problem Statement and Research Contribution The researchers must pay attention to this while implementing their findings because the data form moves away from the original data system and toward semi-structured data. Second, there are various data sources, and the information in the financial sector is derived from various sources, but the number and types of mobile devices are generating more and more data. Each data’s meaning is different, requiring big data to analyze it. Finally, BD will alter in kind during the data collection and sorting process in order to be more effectively used, which will somewhat raise the difficulty of data processing. Our proposed model could overcome big data problems in the finance sector by utilizing AI. The main contribution is that big data is manipulated using AI techniques and analyzing the digital world’s insights to create real-world solutions.

4 Proposed Methodology Recently, Financial companies may embrace big data for applications including developing new income streams through data-driven offerings, giving clients with personalized suggestions, increasing efficiency to achieve competitive advantages, and enhancing security and customer service. Big Data analytics assist finance teams gather the information needed to gain a clear view of key performance indicators (KPIs). Financial services data management refers to the processes, systems, and policies that allow financial organizations to oversee confidential or other sensitive information and ensure continuous compliance with existing and emerging regulations. Financial data management is a set of procedures and guidelines, frequently aided by specialized software, that allows an organization to integrate its financial data, uphold conformance with legal and regulatory requirements for budgeting, and generate comprehensive financial reports. In this article, the researchers are modelling BD management for the finance sector using artificial intelligence that may provide efficient performance while managing the BD being used in the business sectors. Figure 1 is the representation of the proposed model that is comprised of the training as well as the validation phase. In the training phase, the labelled data (transactional data, operational data) and the unlabeled data (social and mobile data and other data like IoT sensors etc.) are collected through the data sources and proceeded to the preprocessing layer in order to mitigate the noisy data by using normalization, using moving average and handling missing values. After the preprocessing layer, the preprocessed data is sent to the fuzzy inference engine (IG). In order to produce

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the fuzzy output, the IG must apply the inference rules to the fuzzy input. The inference norms are utilized specifically to assess the linguistic values and map them to a fuzzy set that needs to be defuzzied to be transformed into a crisp value. After the fuzzy inference engine, the crisp output is tested to determine whether the learning criteria are met or not. In the case of No, the fuzzy inference engine will be retrained, whereas in the case of yes, the data will be collected in the cloud. So the trained data saved on the cloud will be imported from the cloud for prediction purposes using the fuzzy inference engine. It is checked whether the data management in business is found or not in the validation phase. If the answer is no, the operation is discarded; if the answer is yes, the notification will be stated that the management is detected.

Fig. 1 Proposed model

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Table 1 Comparison of the previous approaches Authors

IoT

Big data analytics

Machine learning

Intelligent manufacturing

Rasouli (2020)

Yes

No

No

Yes

Liu et al. (2019)

Yes

No

No

Yes

Chui et al. (2019)

Yes

Yes

Yes

No

Lu and Cecil (2015)

Yes

No

No

Yes

5 Critical Analysis One of the most important sectors of management and governance in the financial industry is finance big data (FBD). It is fundamentally altering business models in the finance sector. This article analyzes four major technologies, IoT, Big Data analytics, Machine Learning, and Intelligent manufacturing, as shown in Table 1. Before today, all these four technologies were not used at a time. In this article, all these technologies are being utilized to develop the model simultaneously, and this proposed model may provide better results. Table 1 compares multiple research works using IoT, big data, machine learning, and intelligent manufacturing. It is clearly shown that all these technologies are not being used simultaneously, but the proposed research work is using all the technologies simultaneously.

6 Discussion The above study clearly proves the proposed technique while predicting anomalies and maintaining each processing node is often required to verify transactions and authenticate user messages in big data of information by the finance sector and associated industries. BD is crucial for business, sales, and other domains in the financial sector. In order to increase the specific application effect of big data in the financial area and advance the growth of our society in a better direction, it is necessary to actively evaluate and comprehend the possibility of using big data in the financial field during the implementation process.

7 Conclusion The growing volume of data from the finance sector is a hurdle requiring tools to fully utilize the information. BD has developed as a field that can deliver possible solutions for data evaluation, knowledge extraction, and advanced decision-making. A major application of BD on service only concentrates on finance, information technologies,

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and advanced-technology fields. This paper presents a model for BD management in finance by employing artificial intelligence. This proposed model might show better performance in order to overcome all data issues of different finance departments like transactional data, operational data, IoT sensor data, and mobile and social data.

8 Limitations and Future Directions Managing big data in the finance sector has been a challenging task. Therefore, It faces many challenges like Regulatory requirements, Data Quality, Data protection, data silos, etc. In this research, the proposed model can overcome these types of big data problems in the finance sector by using Artificial Intelligence. BD enormously affects company plans and tactics. It is a developing tendency to be skilled in the financial sector. In the future, big data could see organizations using Big Data Analytics (BDA) to merge analyses from the digital world to create real-world solutions. This proposed model can leverage better forecasting using BD tools. Organizations may increase their forecasts and incorporate them into the overall strategy.

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Role of Big Data Analytics to Empower Patient Healthcare Record Management System Mohammed T. Nuseir , Iman A. Akour, Haitham M. Alzoubi , Barween Al Kurdi , Muhammad Turki Alshurideh , and Ahmad AlHamad Abstract Big data involves a significant amount of data that may have a remarkable impact on healthcare data records. It has fascinated the imagination of many people over the last two decades due to the enormous potential it holds. Numerous public and private sectors provide the facilitation while maintaining, and analyzing all the data in order to enhance their contributions. Clinical records, personal health records, test results, and the Internet of Medical Things (IoMT) are a few elements of big data sources in the healthcare sector. Big data generated by the medical field is indeed essential in providing universal healthcare. This data has to be properly maintained and examined in order to provide relevant data. In this research, a patient healthcare record management system is developed using Big Data (BD) analytics that may M. T. Nuseir Department of Business Administration, College of Business, Al Ain University, Abu Dhabi Campus, P.O. Box 112612, Abu Dhabi, UAE e-mail: [email protected] I. A. Akour Department of Information Systems, College of Computing and Informatics, University of Sharjah, 27272 Sharjah, United Arab Emirates e-mail: [email protected] H. M. Alzoubi School of Business, Skyline University College, Sharjah, UAE e-mail: [email protected] Applied Science Research Center, Applied Science Private University, Amman 11937, Jordan B. Al Kurdi Department of Marketing, Faculty of Business, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan e-mail: [email protected] M. T. Alshurideh (B) Department of Marketing, School of Business, The University of Jordan, Amman 11942, Jordan e-mail: [email protected] A. AlHamad Department of Management, College of Business Administration, University of Sharjah, 27272 Sharjah, United Arab Emirates e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 H. M. Alzoubi et al. (eds.), Cyber Security Impact on Digitalization and Business Intelligence, Studies in Big Data 117, https://doi.org/10.1007/978-3-031-31801-6_3

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provide assistance to the physicians as well as patients in order to retrieve their health records in real-time. Keywords Big data analytics · Healthcare record · Patient healthcare record management

1 Introduction Big Data is an idea that has tracked down its direction into our regular day to day existences. Enormous Data gives the capability of settling a portion of the world’s most troublesome difficulties, from modern applications to explore in different areas (Taher M ). Enormous Data is without a doubt renowned in most scholastic regions, going from the sociologies to neuroscience, history, humanities (today some of the time known as computerized humanities), and clinical services (Del & Solfa, 2022; Nasim et al., 2022; Sadok et al., 2022). In recent years, big data has grown in popularity all around the world. Almost every field of study, particularly industrial or academic, generates and analyses large amounts of data for a variety of objectives (Alzoubi et al., 2019; Ghazal et al., 2021b). The most significant barrier in dealing with this massive pile of data, which might be structured or poorly organized, is its administration. Given that standard software cannot handle massive data, we want technically sophisticated apps and software that can use rapid and cost-effective high-end processing power for such activities (Akhtar et al., 2021; Alzoubi et al., 2021a; Goria, 2022; Kashif et al., 2021). One of the advantages of employing big data in the health care business is the possibility of boosting client service efficiency (Alshurideh et al., ; Eli and Hamou, 2022; Ghosh & Aithal, 2022). The capacity for processing enormous amounts of data allows health care professionals to examine persons or systems in timely manner (Ghazal et al., 2021a). However, there are certain reservations about storing and reuse data in forms that incorporate big data methods (Akhtar et al., 2022; Alzoubi et al., 2022a, 2022b, 2022c, 2022d, 2022e, 2022f, 2022g, 2022h, 2022i, 2022j, 2022k, 2022l, 2022m; Amrani et al., 2022; Eli, 2021; Nasim et al., 2022). The majority of the impediments arise from privacy and security rules, as well as legal issues, which can exacerbate already-existing inequities across peoples and nations. Furthermore, the primary challenge with big data in biomedical is overcoming the hurdles to sharing and reusing such data for wellbeing research (Alzoubi et al., 2022m; Muheidat et al., 2021). In medical services, enormous information involves different tremendous and confounded informational collections that are trying to look at and handle utilizing run of the mill programming or innovation (Alsharari, 2021; Ghazal et al., 2022a; Mehmood, 2021). Enormous information investigation incorporate dissimilar interoperability, information quality administration, examination, demonstrating, perception, and certification (Altamony et al., 2012; Alzoubi et al., 2021c). Enormous information investigation conveys total information disclosure from the available

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monstrous measure of information (Alzoubi et al., 2022e; Miller, 2021; Victoria, 2022). Large information investigation in medication and medical services, specifically, considers the assessment of tremendous datasets from huge number of patients, the distinguishing proof of bunches and relationships across datasets, and the advancement of expectation models utilizing information mining strategies (Alzoubi et al., 2021a; Eli, 2021; Kasem and Al-Gasaymeh, 2022). Bioinformatics, picture procurement, sensor mental science, clinical informatics, and biomedical exploration are only a couple of the logical disciplines that enormous information examination in medication and medical services may analyze (Tian et al., 2019). The digital healthcare boom has arrived. Developments incorporate not only the collecting and analysis of electronic records of health and individual genetics, but also the collection and analysis of numerous physiological and molecular parameters in patients at a previously unattainable level (Aburayya et al., 2020a, 2020b, 2020c; Ahmed and Amiri, 2022; Alshurideh, 2014; Alzoubi, 2022; Ghazal et al., 2021d; Khatib et al., 2022) Our latest research, in which we strong many people for a median of nearly three years and uncovered several key health findings that affected certain people, demonstrates the significance of large data and ongoing assessment (Aburayya et al., 2020a, 2020b, 2020c; Alhashmi et al., 2020; Hammad et al., 2022). Many health breakthroughs incorporated illness risk prediction using genome sequencing, but the majority involved disease identification before symptoms appeared (Alzoubi et al., 2022m; Alolayyan et al., 2022; Alsharari, 2022; Farouk, 2022). Many of these discoveries had a significant impact, such as the early diagnosis of cardiomyopathy, lymphoma, and two premalignant diseases. However, the patient data transformation is only getting started as more directto-consumer devices become available. that record health data become accessible (Alzoubi et al., 2022f; Chicco & Jurman, 2021; Radwan, 2022; Ratkovic, 2022).

2 Literature Review Many researchers have been worked on multiple patient healthcare record management system using big data analytics. Some of their work is highlighted in this section (ELSamen & Alshurideh, 2012; Ghannajeh et al., 2015). The authors explains the big data strategy to customised management of health care with big data (Alzoubi et al., 2022g). The author concentrated on the virtual physiological human (VPH) in this design, which concerns with talking with the doctor via the technology that serves as our assistant (Alzoubi et al., 2022m; Alzoubi & Yanamandra, 2022; Edward Probir Mondol, 2022). This article discusses the significance of big data application in health care and customized information. The author of that article discussed the approach they used to connect doctors and engineers in order to give a way for the proper development of specialized services for health care (Aburayya et al., 2020a, 2020b, 2020c; Ahmed & Mousa, 2016). They are also concerned with detecting human activity patterns employing big data, and these patterns describe the process of interpreting human feelings and

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actions in order to identify human health patterns (Alshurideh, 2022; Alzoubi & Yanamandra, 2020; Paniagua et al., 2012; Saad Masood Butt, 2022). The advent of big data and the extensive use of e-health records for people has enabled us to explore answers to initially assumed public health concerns (Alzoubi and Aziz, 2021; Alzoubi et al., 2021a). Rather than just extending from a limited set of samples to draw conclusions about a community, we may nowadays use clinical evidence at the statistical level to present a more complete picture. Evaluating real data from huge groups of individuals is a significant departure from traditional health sciences, which focuses on minimizing the consequences of study design mistake (Alzoubi et al., 2017, 2022i). Although clinical trials remain the standard method for establishing the effectiveness of a specific treatment, evaluating the effectiveness of that treatment at the population level, which incorporates real-world conditions, is also important (Alzoubi et al., 2021b; Zhang et al., 2022). Enormous information investigation applications might upgrade patient-focused care by distinguishing sickness flare-ups sooner, producing new bits of knowledge into infection causes, observing the nature of clinical and medical services offices, and giving superior therapy draws near (Alzoubi et al., 2020a, 2020b). Advanced mobile phones are presently phenomenal stages for conveying individualized messages to patients to connect with them in conduct changes that will improve their wellbeing and medical conditions (Akour et al., 2021; Ghazal et al., 2021a, 2021b, 2021c, 2021d, 2021e, 2021f, 2021g, 2021h). Cell phone messages can be utilized to offer clinical and motivational data to patients (Chui et al., 2019; Rashid et al., 2020). Most of the approaches have been used while employing and constructing several smart as well as intelligent frameworks like machine learning approaches (Asif et al., 2021; Chayal & Patel, 2021; Dekhil et al., 2019; Fatima et al., 2020; Muneer & Rasool, 2022), Fuzzy Inference systems (Alshurideh et al., 2022; Asadullah et al., 2020; Fatima et al. 2019; Ihnaini et al., 2021; Saleem et al., 2019), Particle Swarm Optimization (PSO) (Alzoubi & Ahmed, 2019; Iqbal et al., 2019), Fusion based approaches (Gai et al., 2020; Ma et al., 2020; Muneer & Raza, 2022; Sharma et al., 2021; Tabassum et al., 2021), cloud computing (Ghazal et al., 2021e, 2022b; Khan, 2022; Naseer, 2022; Ubaid et al., 2022), transfer learning (Abbas et al., 2020; Alshurideh et al., 2019a, 2019b; Kurdi et al., 2020; Leo et al., 2021) and MapReduce (Asif et al., 2021) that may provide assistance in designing emerging solutions for the rising challenges in designing smart cloud-based monitoring management systems.

3 Problem Statement and Research Contribution In recent years, the healthcare business has faced major problems. Understanding how data, procedures, people, and tools allow care coordination is critical for understanding the origins of bottlenecks in healthcare. As a result, there is a need to comprehend the big data analytics method in healthcare in order to increase effective administration, analysis, and interpretation of big data (Al Kurdi et al., 2021; Alzoubi

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et al., 2021d; Salloum et al., 2020). Big data has the potential to alter the game by opening up new avenues for contemporary healthcare.

4 Proposed Methodology Creating a statistics Patient Health systems Record Management system Using Big Data Analytics, computational tools might be used to investigate various patient trends. However, modelling a patient’s healthcare record is complex, and newer datasets may include management information that are less important or not necessary. This study describes a patient health record management system. Big data models have been shown to be an efficient parallelization strategy for various jobs. Figure 1 demonstrates that patients’ information is moved to the information procurement layer, which stores the information obtained from the data set in its crude structure. The crude information is conveyed to the preprocessing layer, where it eliminates commotion by using standardization, taking care of missing qualities, and moving midpoints. Enormous information investigation reviews a lot of info to uncover stowed away examples, associations, and diverse bits of knowledge. It’s feasible to inspect information and find clarifications from it right away. It is the utilization of state of the art insightful strategies to very enormous, heterogeneous informational collections that contain organized, semi-organized, and unstructured information, as well as information from many sources and sizes working from terabytes to petabytes. Big data examination additionally helps firms in acquiring bits of knowledge from the present monstrous information assets. Monstrous volumes of information are delivered by individuals, organizations, and machines, including web-based entertainment, cloud applications, and machine sensor information. After this, the information is prepared utilizing the AI approach (ANN). Then, at that point, the prepared information is saved money on the cloud, which will be imported from the cloud for expectation purposes utilizing the ML procedure. It is checked regardless of whether an Epidemic is found in the approval stage. Assuming the response is no, the activity is disposed of; on the off chance that the response is indeed, the warning will express that the Epidemic was distinguished.

5 Critical Analysis In healthcare sector, patient Healthcare Record Management System (HRMS) plays a vital role in order to secure patient information. Because patient record is very important for medical specialist as well as patient treatment in the future. In this study, analysis is done on several tools in addition technologies that are used for big data as revealed in Table 1.

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Fig. 1 Proposed methodology Table 1 Comparison of the previous methods Name

Spark SQL

HBase

Hive

Explanation

Spark SQL is an organized data analytics tool built on top of ‘Spark Core.’

Apache HBase is a flexible and decentralized database that is used on front of HDFS to store data

Apache Hive is a relational model and SQL interface for searching, analysing, and summarising massive datasets stored in HDFS

Database model

Relational DBMS

Wide column store

RDBMS

Developer

Apache

Apache

Apache

XML support

No

No

N/A

Server-side scripts No

Yes

Yes

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Table 1 details the main techniques and technologies utilised in big data. The association of several databases and big data systems is shown below, which may give superior performance in order to safeguard patient details.

6 Discussion Some think that connecting medical services supplier frameworks with insurance agency and government associations is the way to making Big Data a reality in medical services. On the off chance that the three organizations can lay out a settlement on protection, legitimate, security, and consistence issues, they will actually want to augment assets and investigate their common information to find designs, accelerate recuperation, and embrace both proof based treatment and precaution medication. The utilization of Big Data investigation was obviously distinguished as a critical undertaking prompting achievement. Utilizing progressed calculations, they can distinguish the risk of hospitalization. One more key issue with regards to medical services information is the utilization of strong computational apparatuses, conventions, and raised equipment in the medical services setting. To accomplish this point, specialists from many fields like science, data innovation, measurements, and arithmetic should team up. The information produced by the sensors can be put away in the cloud using pre-introduced programming apparatuses provided by scientific device merchants. These apparatuses would incorporate info mining and AI sizes made by AI experts to transform data put away as data into data.

7 Conclusion Big Data is the first to improve healthcare throughout the world by giving approaches and solutions to improve particular people’s health, as well as the activity and outcomes of health services. Accuracy in medicine adoption requires vast amounts of data to be continuously monitored quickly in order to establish the best foundation for modifying health improvement for patient avoidance, detection, and illness treatment. Ensuring proper and measurable monitoring of health-related data will be critical for future care systems, and it will necessitate key players cooperating and modifying the performance and design of their systems in order to realise the world’s highest latest offerings and progression future technologies on health. The proposed model in this study would deliver improved performance in patient healthcare records in order to safeguard patient data by utilising various Big Data tools and technologies.

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8 Limitations and Future Directions In today’s digital age, Patient Health Record Management Systems (HRMS) are an essential component of most medical practises. Making the switch from a paper-based to a digital system may enhance not just the quality of work for your workers, but also the patient experience. However, HRMS are not always faultless. It has several disadvantages, such as the fact that HRMS are cumbersome, often incompatible, and vulnerable to cyber-attacks, among other things. Using Big Data analytics, this study creates a system for patient health record management for security considerations. This proposed approach may provide a more secure way for managing patient healthcare records, which is critical for medical professionals as well as patient medical treatment. Big Data Analytics will be able to provide insight into clinical data in the future, enabling for better informed judgments on patient diagnosis and treatment, sickness prevention, and other areas. Big Data Analytics may boost the efficiency of healthcare firms by utilising the data potential.

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Integrating Big Data and Artificial Intelligence to Improve Business Growth Mohammed T. Nuseir , Muhammad Turki Alshurideh , Haitham M. Alzoubi , Barween Al Kurdi , Samer Hamadneh , and Ahmad AlHamad

Abstract With the development of the Internet and technology, a revolution in information technology is taking place in recent years. Currently, In a diverse range of industries, including technology, business, healthcare, the automobile industry, and academia, artificial intelligence (AI) is becoming a major topic. Due to the amalgamation of big data and AI, every industry in the world has undergone dramatic change. Artificial intelligence (AI) is the imitation of human or physical intellect in computational processes such that they can be able to think and behave intelligently. A number of real-world challenges in the business sector can be solved by computer systems more correctly and effectively with AI, as compared to computational systems that M. T. Nuseir Department of Business Administration, College of Business, Al Ain University, Abu Dhabi Campus, P.O. Box 112612, Abu Dhabi, UAE e-mail: [email protected] M. T. Alshurideh (B) · S. Hamadneh Department of Marketing, School of Business, The University of Jordan, Amman 11942, Jordan e-mail: [email protected] S. Hamadneh e-mail: [email protected] M. T. Alshurideh Department of Management, College of Business Administration, University of Sharjah, Sharjah 27272, United Arab Emirates H. M. Alzoubi School of Business, Skyline University College, Sharjah, UAE e-mail: [email protected] Applied Science Research Center, Applied Science Private University, Amman 11937, Jordan B. Al Kurdi Department of Marketing, Faculty of Business, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan e-mail: [email protected] A. AlHamad Department of Management, College of Business Administration, University of Sharjah, 27272 Sharjah, United Arab Emirates e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 H. M. Alzoubi et al. (eds.), Cyber Security Impact on Digitalization and Business Intelligence, Studies in Big Data 117, https://doi.org/10.1007/978-3-031-31801-6_4

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are inflexible and embedded. Many business challenges, including selling, credit card scam recognition, algorithmic exchange, customer facility, portfolio management, merchandise recommendation corresponding to customer demands, and insurance underwriting, are solved and optimized using AI. This research work examines several AI and big data approaches that are now being utilized to promote corporate growth. AI and big data have completely changed the business sector. Keywords Big data · Artificial intelligence (AI) · Business growth

1 Introduction A business is an adventurous body or an organization made up of resources and people that engage in economic, financial, or professional operations intending to turn a profit (Alameeri et al., 2021; Rui et al., 2022) The financial system can be thought of as having a business as its foundation. A business can be as big as a very large set of industries spread across many different nations, or it can be as little as an enterprise that provides its services in a small town (Alzoubi et al., 2021a; Eli & Hamou, 2022; Kashif et al., 2021; Mehmood, 2021). Both a single individual and thousands of individuals may own a business (Ben-Abdallah et al., 2022; Hayajneh et al., 2021). All forms and sizes of businesses, especially the bigger corporations, have been impacted by modern technology (Alshurideh et al., 2019a, 2019b). In actuality, a technology war is being conducted by numerous huge corporations like Google, Facebook, and Amazon. Digital technology is advancing quickly and having a significant impact on every aspect of life today (Akhtar et al., 2021; Alsharari, 2021; Alzoubi et al., 2021b; Kurdi et al., 2020a). It has affected business, medical diagnostics, and many other important fields (Ghazal et al., 2022a). Today’s businesses are increasingly turning to cutting-edge technology to boost their revenues and accelerate growth (Ahmad et al., 2021; Alolayyan et al., 2022). The business landscape and how individuals conduct business have been dramatically revolutionized by AI, data science, big data, and the Internet of Things (IoT). There isn’t a profession today that hasn’t looked into the potential applications of AI (Lee et al., 2022). It is possible to examine how artificial intelligence (AI) and other computational approaches are applied in both the technology and manufacturing sectors (Eli, 2021; Tariq et al., 2022; Victoria, 2022; Yousuf et al., 2021). It is generally known that machines might perform as well as or better than humans in a variety of tasks, such as emotion detection, tacit judgment, and automated tasks. In as little as 10 years, computational technology could replace up to 47% of the employment that exists now globally (Alzoubi & Yanamandra, 2022; Kasem and Al-Gasaymeh, 2022; Ratkovic, 2022). The fast advancements in technology expected in the future years will totally alter the economic environment, which has been significantly impacted by technology (Alzoubi et al., 2021a; Ghannajeh et al., 2015). Therefore, it is crucial to grasp how the most cutting-edge technology is affecting the company (Ahmed & Al Amiri, 2022; Alsharari, 2022;

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Alzoubi, 2022; Alzoubi et al., 2022e). AI has been effectively employed to perform tasks that involve higher-order innovative abilities, such as those performed by journalists, lawyers, lab technicians, paralegals, etc., in addition to automating laborious manual tasks (Al Suwaidi et al., 2021; Edward Probir Mondol, 2022). Many jobs are being displaced by computer technologies like AI and other ones, not only the lowskilled ones like clerical employment. High-skilled employment and jobs requiring the development of such computing systems are in high demand, and this demand is rising quickly (Alzoubi et al., 2021d, 2022d, 2022f; Hamadneh et al., 2021). In their research, Soni et al. analyze 100 AI startups from around the world and present a very intriguing discovery. In 2011, they invested a total of $25.88 million in the 100 businesses they looked at; by 2016, that amount had climbed to $1866.6 million. The funding for these businesses climbed dramatically (7112.52%) in just 6 years. These numbers demonstrate how artificial intelligence as well as big data are influencing the state of the economy today (Alzoubi et al., 2021a, 2021b, 2021d, 2021f, 2021g, 2021h; Matos et al., 2020). Big data and AI are utilized in many facets of the business to increase revenue or to expedite hard work, which helps organizations grow. An automated system may do many tasks that would have taken a person hundreds of hours to complete in a matter of minutes (Alzoubi & Aziz, 2021; Ghazal et al., 2021a, 2021b; Goria, 2022). Artificially intelligent systems are created by many businesses, including cognizance, to enhance business operations and income (Salloum et al., 2020a, 2020b, 2020c). In one instance, the software provider cognizant created an optimal equipment deployment solution utilizing data analysis for a multinational mining firm, which resulted in a capital cost savings of $30 million due to increased system availability. Thus, it is clear that AI and data analysis are widely employed in the current economy to reduce costs, increase profits, and accelerate business growth (Kaplan & Haenlein, 2020; Salloum et al., 2020b).

2 Literature Review Big data analytics and AI have attracted the attention of numerous researchers in an effort to boost business growth. This section, of this research, focuses on a few of their works. Traditional ML techniques for detecting credit card fraud are compared. Credit card transactions from 284,807 European cardholders were utilized as the dataset (Alzoubi et al., 2021e). As a result of the extremely skewed nature of the fraud dataset, oversampling, undersampling, and no sampling procedures were also employed for improved and more precise comparisons (Alzoubi et al., 2021c; Ghosh & Aithal, 2022; Saad Masood Butt, 2022). In their study, the classifiers for logistic regression, k-nearest neighbor, and naive Bayes were contrasted. These are all methods for identifying misuse. The accuracy of the logistic regression classifier was 54.86%, the k-nearest neighbor classifier was 97.69%, and the naive Bayes classifier was

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97.92% accurate. Because it cannot learn nonlinear functions, logistic regression has substantially lower accuracy. In this study, The effectiveness of roughly 12 ML algorithms for detecting credit card fraud is compared by the researchers. They take advantage of a dataset of open credit card transactions (Alzoubi et al., 2020a, 2020b). Additionally, they employ ensemble approaches, which categorize instances via adaptive boosting and majority voting. They test out seven ensemble approaches that rely on a majority vote (Alzoubi & Ahmed, 2019; Del & Solfa, 2022; Nasim et al., 2022). A combination of a Naive Bayes classifier and a neural network provided the best accuracy. The accuracy of this ensemble on the dataset they utilized was 99.941%. Companies typically employ ensemble approaches for fraud detection in the real world. AI may be regarded as a practice of instructing computers to replicate discerning processes in addition can even be done to pretend human actions and can be used to analyze big data (Alzoubi et al., 2017; Amrani et al., 2022). With more data and processing as machine learning takes place, it is recognized that the accuracy of findings obtained will grow; thus, big data and real-time data are essential. The role of information, communication, and technology (ICT) in urban fabrics has received extensive coverage as cities become more digitalized (Akhtar et al., 2022; Alzoubi & Yanamandra, 2020). It has been demonstrated that these technologies have made it possible for governments, municipalities, in addition decision-makers to gather data on a variety of issues. Better urban governance is made possible by its processing and analysis because it enables decision-makers to create suitable and responsive policies and make well-informed choices (H M ). Artificial intelligence (AI) is capable of carrying out this analysis. An essential component of the evolution of an intelligent society is artificial intelligence. It is a field of study that examines how computers replicate, advance, and expand the theory, application, and technology of human intelligence (Alzoubi et al., 2022g). The science behind AI is exceedingly complex; data and computation provide the basis, and algorithms form its heart. Robotics, image and language recognition, natural language processing, expert systems, and other areas are the core branches of artificial intelligence (Khatib et al., 2022). Artificial intelligence is simply a form of information processing that mimics human thought and behavior and can coexist with machines that have information processing capacities that are on par with or even beyond those of humans (Alhashmi et al., 2020; AlSuwaidi et al., 2021). It has developed into a border topic that cuts across the social sciences, technical sciences, and scientific sciences. It is a result of both social and technological advancement (Alzoubi et al., 2022c). It incorporates logical thinking, picture thinking, and inspiring thinking from the standpoint of human thought. The popularisation and application of artificial intelligence subversively change the components and organization of productive forces and frees people from alienated labor and unfair economic relations as it transitions from AI with big data as its core to AI with ML as its core. Most of the techniques have been utilized while employing and building several smart as well as intelligent structures like ML techniques (Alzoubi et al., 2021b, 2022a; Asadullah et al., 2020; Asif et al., 2021; Chayal and Patel, 2021; Fatima et al., 2020; Ghazal et al., 2019, 2022c, 2022d, 2022e; Muneer & Rasool, 2022),

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Fuzzy Inference systems (Alshurideh et al., 2022; Asadullah et al., 2020; Fatima et al., 2019; Ihnaini et al., 2021; Saleem et al., 2019), Particle Swarm Optimization (PSO) (Iqbal et al., 2019), Fusion based approaches (Gai et al., 2020; Ghazal, n.d. Ma et al., 2020; Muneer & Raza, 2022; Sharma et al., 2021; Tabassum et al., 2021), cloud computing (Ghazal et al., 2022b; Khan, 2022; Naseer, 2022; Ubaid et al., 2022), transfer learning (Abbas et al., 2020; Al-Hamad et al., 2021; Alshurideh et al., 2020; Kurdi et al., 2020b) and MapReduce (Asif et al., 2021) that may give assistance in creating emerging solutions for the rising issues in designing smart cloud-based monitoring management systems.

3 Problem Statement and Research Contribution Businesses today confront numerous obstacles to attaining their objectives. The company may be able to improve and optimize repetitive systems and practices, which will enable business could save cash and time. A business issue can be resolved by employing ML to predict customers’ priorities, foresee what they might buy, and predict the likely response of the customers. Boost productivity and efficiency in operations. By using outcomes of cognitive computing, accelerate the decision-making process in company.

4 Proposed Methodology Throughout the initial stage, forecasting a business’s revenue and expenses is more sculpture than technology. Many business owners grumble that creating projections with any level of accuracy takes a lot of time that should be spent marketing rather than planning. Every company, regardless of size, is obliged to create a model for predicting business growth as it moves through its many stages. A model for predicting business growth is put out in this research paper and is depicted in Fig. 1. The proposed approach is distributed into three parts, as shown in Fig. 1. The first step involves gathering or retrieving business-related data from a range of digital business devices, many of which may be poorly organized or entirely unstructured. Data is treated using a pretreatment layer after it has been collected to reduce data noise. Data is sent for preparation, which is the act of gathering, integrating, arranging, and organizing information so it may be utilized in analytics and information visualization functions after it has undergone preprocessing. After that, the prepared data is transmitted for feature engineering. selecting, transforming, and interchanging raw data into features which can be used in supervised learning is the system of feature engineering. It might be essential to make and train improve features if ML is to operate efficiently on new tasks. The data is delivered to big data analytics, which evaluates a lot of data to find hidden patterns, correlations, and other insights, after feature engineering. Data analysis can be used to quickly generate conclusions. Then

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Fig. 1 Proposed methodology

there is the modelling process, in which machine learning algorithms can be utilized to forecast the patient’s lung cancer based on the specified set of parameters. The trained model is retained on the cloud after the data has been modeled, and the trained data is subsequently imported from the cloud for containerization. Containerization is a common software unit that packages up information and all of its addictions to confirm that an effort runs rapidly and constantly in many computer environments. The output is stored in the cloud database after containerization. Then it is imported from the cloud in order to make predictions based on taught patterns, and the prediction of company growth is done using real-time incoming values. It is investigated to see if the patient has lung cancer or not. If the answer is "yes," the indication that business growth has been discovered and the subsequent conclusion are displayed; if the answer is “no,” the procedure will be retrained, and so on.

5 Critical Analysis Data-driven decision-making is becoming a more and more prevalent practice in today’s digital environment. Because it is now simpler than ever to do so and the results are remarkable, businesses big and small are adopting this technology to collect and analyze the data. How big data and AI interact will determine the value of the data that researchers are collecting. AI reduces analysis paralysis while preventing inaccurate interpretations when used with large data by providing clear and relevant direction from enormous data sets quickly. In this study, analysis is done on different methods in which artificial intelligence approaches are used to improve business growth, as revealed in Table 1.

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Table 1 Comparison of the previous methods Name of Author

Year of publication

Methods used

Dataset used

Use cases

Paradarami et al. [28]

2017

Deep ANN + content-based features + collaborative features

Yelp educational dataset

Outcome endorsement

Awoyemi et al. [29]

2017

K-nearest neighour/naive bayes/logistic regression

Credit card business of European cardholders dataset

Fraud recognition

Xuan et al. [30]

2018

Random forest

Credit card transaction dataset of Chinese e-commerce company

Fraud recognition

Randhawa et al. [31] 2018

Adaboost + majority voting, ANN + NB Public

credit card business dataset

Fraud recognition

Roondiwala et al. [32]

LSTM

New York stock conversation dataset

Stock prices forecast utilizing time series information

2017

Table 1 shows the recently completed research work that has been published in the last five years or so. The research demonstrates the wide range of applications for AI in business, from fraud prevention to product discovery.

6 Discussion Integrating big data integration and AI to find growth features of sustainable business models in industries such as the food, automotive, and energy sectors, etc. To comprehend the dynamics of the evolution corridor in the corporate sector, one must first critically reflect on the theoretical frameworks. In actuality, and unlike industries like energy, the food system includes a great number of extremely diverse actors in terms of kind, size, in addition power. The particular of big data in the food industry also necessitates the inclusion of the consumer side as well as societal values like apprehension for the environment, health, and quality of life. This implies that new theoretical and methodological frameworks should be developed for study in every field to account for the uniqueness of these crucial facets of social life.

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7 Conclusion Businesses have a substantial influence on society and philosophy at large. They not only occupy the majority of people’s time, but they also subsidize to crucial technological innovations and developments. Today’s businesses struggle with a variety of problems, including marketing, time management, leadership, and revenue generation. In order to increase corporate efficiency, this research creates a model employing an integrated Big Data and AI approach. AI is deemed to be the fourth industrial innovation. Big data and AI have revolutionized every industry around the globe. It pertains to the modelling of human or animal intelligence in computing systems so that these systems can be made to behave intelligently and think intelligently. Compared to computational processes that are predictable and embedded, computational processes with automatic intellect are much more accurate and effective at resolving a wide range of real-world issues.

8 Limitations and Future Directions The existing economic world has now been successfully driven by AI, as well as it will play a significant role in determining the upcoming. Even if today’s AI is not as sophisticated as humans, it has had a significant influence on the globe. Future AI will likely alter how people view business and how businesses operate. This study can be intelligent to see AI systems that may recruit people into a firm once it reaches a certain level. Artificially intelligent computers may eventually replace CEOs and management firms and their operations. In the future, AI systems may perform all tasks, from creating a business model to releasing it onto the market, making human involvement unnecessary.

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Cyber Security

The Effect of Cyber Resilience Role in the Relationship of Intelligent Information System on the E-Supply Chain: An Empirical Evidence from the UAE Healthcare Industry Mohammed T. Nuseir , Enass Khalil Alquqa, Haitham M. Alzoubi , Muhammad Turki Alshurideh , Barween Al Kurdi , and Ahmad AlHamad Abstract The research was purposefully conducted to empirically assess the impact of intelligent information systems on the e-supply chain with the mediating effect of cyber resilience in the UAE healthcare sector. The research relied on empirical findings in the UAE healthcare sector. The mediating role of cyber resilience distinguishes this research, which has never been discussed in previous literature. A quantitative research technique with causal effect was used. An exploratory, descriptive and analytical design was implemented. A sample of 299 respondents from 39 hospitals in Ajman, UAE, was used for statistical regression analysis using ANOVA and SPSS. Results highlight the importance of intelligent information systems and M. T. Nuseir Department of Business Administration, College of Business, Al Ain University, Abu Dhabi Campus, P.O. Box 112612, Abu Dhabi, UAE e-mail: [email protected] E. K. Alquqa College of Art, Social Sciences and Humanities, University of Fujairah, Fujairah, UAE e-mail: [email protected] H. M. Alzoubi School of Business, Skyline University College, Sharjah, UAE e-mail: [email protected] Applied Science Research Center, Applied Science Private University, Amman 11937, Jordan M. T. Alshurideh (B) Department of Marketing, School of Business, The University of Jordan, Amman 11942, Jordan e-mail: [email protected] B. Al Kurdi Department of Marketing, Faculty of Economics and Administrative Sciences, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan e-mail: [email protected] A. AlHamad Department of Management, College of Business, University of Sharjah, Sharjah 27272, UAE e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 H. M. Alzoubi et al. (eds.), Cyber Security Impact on Digitalization and Business Intelligence, Studies in Big Data 117, https://doi.org/10.1007/978-3-031-31801-6_5

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their direct impact on the e-supply chain, and the indirect effect of cyber resilience and impact on the e-supply chain is found with a higher significance level. The statistical investigation of intelligent information systems, cyber resilience, and e-supply chains limits research for further exploration. Future research should explore these concepts qualitatively, considering the occurrence of complications as digitisation is implemented to achieve an effective e-supply chain. Maintaining a physical barrier make it easier for patients and healthcare professionals to use digital technology. It can allow people to appreciate the benefits of digital health and facilitation for the healthcare sector. Keywords Intelligence information system · Cyber resilience · E-supply chain · Healthcare industry UAE

1 Introduction As the decade progresses, the healthcare industry’s ability to successfully adopt a digitisation process will determine its success in this new environment, even though research on digital transformation in healthcare is still in its early stages (Alzoubi et al., 2020a). According to the World Health Organization (2016), the medical field of “digital health” is evolving rapidly and substantially impacts clinical research, patient cost-effectiveness, and healthcare quality and effectiveness (Alzoubi et al., 2021d). Digital health is defined as a multidisciplinary field to improve the effectiveness of patient monitoring, diagnosis, delivery of long-term care, management, and prevention (Alzoubi et al., 2020d). Digital technology is used in various ways in the healthcare industry, in conjunction with IT and e-services that are primarily geared toward patients (Alzoubi, 2022), such as scheduling appointments with physicians, reviewing waiting lists, receiving appropriate medical care from the comfort of their own homes, sharing information with people who have similar health issues, and gaining access to individualised and accurate health information (Alolayyan et al., 2022b; Alsharari, 2022; Awawdeh et al., 2022b). The healthcare supply chain needs adequate support from IT, given the increasing importance of the electronic transmission of information (Ahmed & Al Amiri, 2022). The healthcare supply chain needs to find radical approaches to improve its cost performance at the functional, intra-organisational, and inter-organisational levels, involving the extended network level. Healthcare organisations are looking for business models that can help them make better strategic decisions so they can develop more efficient healthcare delivery systems that meet stringent financial performance standards (Alhashmi et al., 2020). Before the onset of the Covid-19 pandemic, researchers concluded that digital health methods and toolkits offered an opportunity to maintain and improve healthcare quality (Alzoubi & Ahmed, 2019; Butt, 2022). Digitisation makes healthcare more cost-effective and enables the reinvention of healthcare services to make them more dynamic and technologically adaptable (Nuseir et al., 2020). Therefore, this

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research mainly assesses the impact of intelligent information systems on the esupply chain with mediating effect of cyber resilience that can help provide a better picture of digitisation and its benefits to the UAE healthcare industry.

2 Theoretical Framework 2.1 Intelligent Information System An information system (IS) is a formal, sociotechnical organisational system designed to collect, process, store, and distribute information. The information system mainly comprises people, tasks, technology, and structure (Alzoubi et al., 2020b; Awawdeh et al., 2022a). Most e-commerce enterprises rely on information to communicate with their customers, obtain information from other businesses and other competitive brands (Mondol, 2022), and rely on information from employees in the organisation (Alzoubi et al., 2017). With the growing influence of the Internet and technology in business, most business systems are slowly shifting to internet and computer-driven systems to cope with fierce competition from other brands and pressure from customers who expect to conduct businesses in a digitalised manner (Akhtar et al., 2022; Aljumah et al., 2021; Alzoubi et al., 2021a). The intelligent information system integrates artificial intelligence, database systems, intelligent systems, and information system methodologies (Alzoubi et al., 2021b). This new approach effectively uses data retrieval and manipulation on a large scale to create time efficiency and accuracy.

2.2 Cyber Resilience Cyber resilience is the ability of a company or an enterprise to sustain and grow its operations by preparing for, responding to, and recovering from cyber threats (Alzoubi et al., 2021f). In today’s business world, most business transactions are conducted over the internet using various software designed to handle such operations (Salloum et al., 2020). In a daily transaction, businesses are entrusted with crucial information about their customers (Radwan, 2022), which they store in their databases, and often this data is vulnerable to misuse by unauthorised people (Amrani et al., 2022). In such cases, a business needs to have ways and means to respond to such threats, as failure to do so means loss of customer trust, which can lead to loss of business or revenue (Alzoubi & Yanamandra, 2020; Williams, 2010). Protection is one of the major components of good cyber resilience. It emphasises that a good cyber resilience project should be able to protect data from unauthorised hands (Aljumah et al., 2022a). Other features include the detection and evolution of the system.

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2.3 E-Supply Chains E-Supply chains refer to the system that effectively uses the internet to deliver goods, services, and information from suppliers to customers in an effective and organised manner (Del & Solfa, 2022; Nasim et al., 2022). Every business enterprise relies on the coordination of information, products, services, and information between itself and its customers for effective operations (Alzoubi et al., 2019). In e-commerce, the internet is the centre of every business transaction, and since supply is a common business aspect, it heavily depends on the internet for effective flow and operations. In an E-supply chain, the major aspects are product flow, information flow, and financial flow (Alzoubi et al., 2022c; Ghosh & Aithal, 2022). For instance, an effective Esupply chain facilitates the payment of products from the comfort of a customer’s home, product delivery, and smooth flow of information between the customer and the supplier throughout the process. This shows that an effective E-supply chain system in a business operation helps an enterprise stand out from the crowd and gives it a better chance to expand its operations since it can handle the remote supply of products and services (Alshawabkeh et al., 2021; Alzoubi et al., 2021d, 2022j). These businesses maintained their financial incomes during the covid 19 pandemic since they could interact and deliver products to their customers via the internet.

3 Operational Definitions

Variables

Definition

References

Intelligent information system A set of software and hardware Abudaqa et al. (2020) known as an intelligent information system is designed to involve skilled personnel in the organisation’s decision-making and coordination processes Cyber resilience

The ability of systems that use Ahmadi-Assalemi et al. (2020) or are enabled by cyber resources to prepare for, endure, recover from and adapt to unfavourable circumstances, pressures, attacks, or compromises. Cyber resilience aims to enable the accomplishment of tasks or commercial goals that rely on cyber resources in a hostile cyber environment (continued)

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(continued) Variables

Definition

References

E-supply chain

Business operations that integrate e-business strategies with supply chain processes are referred to as e-Supply Chain. Chain management for e-supplies includes utilising e-business technologies to facilitate and maximise value-adding supply chain operations

Lancaster et al. (2006)

4 Healthcare Industry Description Public and private healthcare providers comprise the healthcare sector in the United Arab Emirates. Federal and emirate-level authorities oversee and regulate government public healthcare services agencies, such as the Ministry of Health in Dubai Health Authority, the Abu Dhabi Health Authority, and the Health Services in Abu Dhabi (SEHA). These organisations often collaborate with foreign healthcare institutions to handle the day-to-day operations of clinics and hospitals across private healthcare providers in the UAE. The New Medical Center is a hospital not run by government clinics and offers comprehensive and specialised care to U.A.E. citizens (Aburayya et al., 2020a). Undoubtedly, these Emirati Private Sector projects, such as the New Medical Centre and Al Noor Hospital, are crucial to the long-term and overall success of the UAE’s medical advancement.

5 Literature Review 5.1 Relationship and Impact of Intelligent Information System on Cyber Resilience Information security in a business enterprise is crucial in ensuring that the business remains profitable and well-fitted to compete with other brands and enterprises (Alzoubi et al., 2022k). Some common cyber-attacks and threats to an organisation target information ranging from customer data to financial data to employee data or any other strategic data (Chen et al., 2011). Some of these attacks primarily emanate from competing brands interested in learning more about other companies to identify vulnerabilities that can help drive them out of the market (Flynn et al., 1995).

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Some researchers have found out that Intelligent Information System has a significant impact on cyber resilience because it emphasises the application of artificial intelligence (Ratkovic, 2022), database systems, and information systems to create a future generation system that can interact and coordinate operations of the organisation, the customers, and the store data effectively (Farouk, 2022; Sibley et al., n.d.). A healthcare sector requires a high-security database to keep patients’ records and provide treatments based on the diagnosis (Nuseir et al., 2021). Integrating the information system methodologies eliminates the possibility of third-party interference or access to the data as it is guided by a set of codes that gives the users access to the stored data (Ahmadi-Assalemi et al., 2020; Alzoubi et al., 2021c; Goria, 2022). Since artificial intelligence can learn and respond to user behaviours and the official use of the data, it is easier to raise the alarm whenever an attack is posed. H1: Intelligent information system has a significant impact on cyber resilience.

5.2 The Relationship and Impact of Intelligent Information System on E-Supply Chain Systems The future of e-supply chain systems lies in the advancement of Intelligent Information systems. The major aspect of the e-supply chain is the coordination of information between the suppliers and the customers (Abudaqa et al., 2020). One of the major features of an effective system is fulfilling the intended purpose at the lowest cost and faster-operating speeds (Alzoubi & Aziz, 2021). By default, customer care in the healthcare sector include helping patients during medical treatments and database management includes storing patient information (Alzoubi et al., 2022e). Brodie (1987) states that with the intelligent information system in business operation (Qasaimeh & Jaradeh, 2022), various aspects such as database management and information systems are integrated to make the information flow and management faster, more effective, and safer. For instance, patients can use the AIS to check the treatment status of different hospitals with the help of artificial intelligence, and the information obtained is effectively stored in the databases for future use (Kasem & Al-Gasaymeh, 2022; Rong et al., 2020). Since an organisation’s growth depends on understanding the customers, the AIS can gain insights from a very large collection of data that can help the organisation to best understand its customers and make effective decisions. H2: Intelligent Information System has a significant influence on e-supply chain systems.

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5.3 The Relationship and Impact of Cyber Resilience Have a Significant Influence on the E-Supply Chain E-supply chains process large amounts of customer data and information due to online transactions and interactions with the business, which also contain sensitive data for the organisation (Akhtar et al., 2021; Colicchia et al., 2019). One major need for customers is to have their data protected from third parties who might try to manipulate it for criminal activities, as some of these data contain the financial details of the customers. Williams (2010) asserts that the failure of an organisation to secure the customers’ data can lead to loss of the customers or, in extreme cases, the customer can sue them in a court of law leading to a reduction in the social appeal of the brand and a possible collapse of the business operations (Nuseir, 2019). Cyber resilience involves a company’s ability to maintain its operations despite increasing cyber threats by detecting, protecting, and effectively responding to cyber threats (Tiirmaa-Klaar, 2016). In an instance where the protection services fail and hackers infiltrate the system, timely detection helps the organisation deploy its best computer experts to respond to the attack, thus, preventing potential data loss (Tiirmaa-Klaar, 2016). With an effective cyber resilience system, e-supply systems are in a better position to expand their healthcare operations as they can evolve according to changes in threats and internet technologies. H3: Cyber resilience has a significant influence on e-supply chains.

5.4 The Relationship and Impact of Intelligent Information System, with the Mediating Role of Cyber-Resilience Intelligent Information System is the backbone of some of the common internetdriven activities in the healthcare sector (Alzoubi et al., 2022i). A major aspect of e-supply is the patient information as daily operations in the healthcare services such as patient records, tokenisation and diagnostic security concerns (Alzoubi et al., 2022f). The digital transformation of the healthcare industry has increased its vulnerability to cyberattacks, considered a major social and organisational threat (Alsharari, 2021; Alzoubi & Yanamandra, 2022; Boddy et al., 2017). Cyber resilience is also an important aspect of the e-supply chain processes because it ensures that business operations run smoothly in the face of pandemics. It also helps the enterprise remain firm against the competitive brands that use dirty business tricks to try to drive each other out of the market (Aljumah et al., 2022b; Ghazal et al., 2022). The flow and the integration of information determine the organisational success because as the business grows, so does the data stored by the customers (Kashif et al., 2021), and without better cyber resilience systems, the data may fall into unauthorised hands (Mehmood, 2021; Urciuoli, 2015). An intelligent information system in healthcare greatly impacts information flow and protection since it involves integrating different systems into a single system that eliminates the possibility of third parties, thus

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reducing the number of loopholes that can leak information (Eli, 2021). With the intelligent information system, the customer is efficiently assisted with any issues that arise by an artificial intelligence system that then stores the information in a database with appropriate data analysis and insights (Abudaqa et al., 2020; Victoria, 2022). This eliminates the need for customer care representatives, who sometimes get valuable customer information through loopholes (Alzoubi et al., 2022m). In many cases, enterprises are faced with the need to evaluate customer data to best understand their needs (Aburayya et al., 2020b; Alshurideh, 2014; Hammad et al., 2022). This is crucial for decision-making and identifying vulnerabilities that require special attention to meet customer needs. Therefore, the COVID-19 pandemic demonstrated the need for data sharing and protection (Ahmad et al., 2021; Alameeri et al., 2021; Garcia-Perez et al., 2022). Digital health systems were found to be ideally suited to offer creative solutions to such a public health emergency (Alshurideh et al., 2014; Alshurideh et al., 2021a). For instance, reliable surveillance systems, wearables for monitoring physiological parameters, or interactive chat services for distributing information on COVID-19 to the general public were quickly created (Al-Dmour et al., 2021b; Taryam et al., 2020). Intelligent Information System makes this process easier and more effective, as they can handle all the data without involving many stakeholders, making the entire process more cyber secure (Akour et al., 2021; Al Khasawneh et al., 2021b; Alshurideh et al., 2021b; Ghazal et al., 2021). Additionally, utilising preventative measures through remote monitoring can address chronic diseases more effectively (Miller, 2021; Zhang et al., 2020). However, as organisations emerge as a new stage of technological growth, technology applications in healthcare can go much further (Alzoubi et al., 2022b; Hamadneh et al., 2021). Unlike intelligent information systems, the goal of cyber resilience is to utilise human creativity in combination with effective, intelligent, and precise machines to produce manufacturing solutions that are both resource-efficient and user-preferred in the e-supply chain (Alolayyan et al., 2022a; Alshurideh, 2022; Alshurideh et al., 2021b; Joghee et al., 2021; Shamout et al., 2022). H4: Intelligent Information System significantly impacts the E-supply chain with the mediating role of cyber-resilience.

5.5 Problem Statement and Research Gap Although the healthcare industry is experiencing great innovations, many problems still need to be solved, particularly those related to heterogeneous data fusion, mobile data transmission and analysis, etc. Healthcare organisation often struggle to implement digitally aligned business strategies. However, in early 2020, the COVID-19 pandemic acted as a compound for the industry’s acquisition of new technology, encouraging the adoption of national plans and global partnerships. There is a need to explore intelligent information systems (Abudaqa et al., 2020; Al-Dmour et al., 2021a; Al Khasawneh et al., 2021a; Alshurideh et al., 2022). Therefore, this research

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Cyber Resilience H1

H3 H4

Intelligent Information System

E-Supply Chain

H2 Fig. 1 Research model

mainly investigates the application of intelligent information systems and cyber resilience to address the security concerns in the E-supply chain in the UAE healthcare industry. It also proposed to evaluate the mediating effect of cyber security to fill the existing gap.

5.6 General Research Model See Fig. 1.

5.7 Research Hypothesis HO1 : Intelligent Information System has a significant impact on Cyber Resilience in the UAE Healthcare Industry at (α ≤ 0.05) level. HO2 : Intelligent Information System has a significant impact on the e-Supply Chain in the UAE Healthcare Industry at (α ≤ 0.05) level. HO3 : Cyber Resilience has a significant impact on the e-Supply Chain in the UAE Healthcare Industry at (α ≤ 0.05) level. HO4 : Intelligent Information System has a significant impact on the e-Supply Chain with the mediating effect of Cyber Resilience in the UAE Healthcare Industry at (α ≤ 0.05) level.

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5.8 Research Methodology and Design An online survey was conducted to incorporate the research variables and gather empirical evidence from the UAE Healthcare industry to evaluate the construct variables (Intelligent Information System, Cyber Resilience & E-supply chain). Moreover, a quantitative research technique Ali et al. (2021) with a causal, exploratory, descriptive and analytical design was used. A convenient cluster sampling technique was used to gather data specifically.

5.9 Population, Sample and Unit of Analysis The research population was selected from the healthcare industry in the UAE. Thirtynine hospitals in Abu Dhabi, UAE, were accessed to gather data from the administrative and management departments of the hospitals. Moreover, about 900 questionnaires were emailed to the (IT Manager, SC Officer, Administrative Manager, Security Manager). After the screening, 309 valid responses were utilised for statistical analysis. An online questionnaire with a 5-point Likert scale was developed to measure the variables. Twenty-three items of the questionnaire were developed, containing “7” items for measuring Intelligent Information System, “9” items for measuring Cyber Resilience & “and 7” items for measuring the E-supply chain.

6 Data Analysis 6.1 Demographic Analysis See Table 1. Table 1 The demographical aspects of the study sample

Items

Description

f

%

Gender

Male

206

68.9

Female

93

38.1

IT manager

120

40.1

Designation

SC officer

97

32.4

Admin manager

45

15.1

Security manager

37

12.4

n = 299, male = 206 (68.9%), female = 93 (38.1%), IT managers 120 (40.1%) higher rate

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Table 2 The reliability and correlation of the study constructs Construct

No. of items

Cronbach’s alpha

Mean

S.D.

Intelligent information system

Intelligent information system

7

0.82

3.15

0.72

1

Cyber resilience

9

0.86

3.05

0.88

0.778**

1

E-supply chain

7

0.81

2.88

0.85

0.761**

0.833**

Cyber resilience

E-supply chain

1

Intelligent information system (M = 3.15, SD = 72%), cyber resilience (M = 3.05, SD = 88%), E-supply chain (M = 2.88, SD = 85%) Level of significance at P < 0.05**

6.2 Reliability, Descriptive and Correlation Cronbach’s Alpha was used to measure the reliability of all constructs and show that the data was valid enough for statistical analysis. α = 0.82 for Intelligent Information system (IIS), Cyber Resilience (CR) α = 0.86 and α = 0.81 is measured for E-Supply Chain (ESC), indicating high validity of the data. To measure the mean and standard deviation of the data, a descriptive analysis was performed using SPSS. The construct is labelled with the mean value for IIs (Mean = 3.15 and SD = 0.72). Cr (Mean = 3.05, SD = 0.88), and finally, the mean for ESC = 2.88 and SD = 0.85 was noted. Table 2 depicts the data summary. The correlation coefficient indicated a significant relationship between IIS and CR r = 0.77, indicating a high correlation at a significance level of P < 0.05**. IIS has a significant impact on CR with a high correlation of r = 0.76, and CR also has a significant impact with a highly correlated value of r = 0.83 at a significance P < 0.05**.

6.3 Multiple Regression and Hypothesis Testing See Table 3.

7 Discussion of the Results The findings derived from study hypotheses H1 to H4 illustrate the findings of H1: IIS has a significant impact on CR with a value of β = 0.77, t-stat = 21.3, a positive statistical impact with a variance level of 60% between the construct. Thus, H1 is

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Table 3 . Hypothesis

Regression weights

β

R2

Adjusted R2

p-value

t-value

Hypothesis supported

H1

IIS → CR

0.778

0.605

0.604

0.000

21.32

Yes

H2

IIS → ESC

0.761

0.579

0.578

0.000

5.91

Yes

H3

CR → ESC

0.833

0.694

0.693

0.000

12.61

Yes

H4

IIS * CR → ESC

0.852

0.726

0.725

0.000

6.50

Yes

Dependent variable = E-supply chain, independent variable = intelligent information system, mediator = cyber resilience *Level of significance (α≤0.05) **Critical t-value (df/p) = 1.64

supported. H2 was examined to provide empirical evidence that showed a significant positive relationship between IIS and ESC β = 0.76, t-stat = 5.91 & R2 = 72%. The results indicate a positive direct relationship between IIS and CR, which supports H2. Moreover, the findings illustrate that the relationship between CR and ECS is positively significant as β = 0.83, t-stat = 12.61, and R2 = 69% variance between the variables is predicted. H3 is supported. Finally, the findings revealed a significant relationship between the mediating role of cyber resilience on IIS and ESC. This demonstrates an indirect positive relationship between cyber resilience and ESC with β = 0.85, t-stat = 6.50 and a variance level of 72% between the constructs. According to the statistical results, H4 is also supported in this research, suggesting a significant positive relationship and that implementing cyber resilience can improve the e-supply chain process.

8 Conclusion The empirical findings conclude the research by emphasising that technologies have the potential to create a variety of cutting-edge healthcare applications that could be collectively referred to as technological healthcare systems. Using augmented reality to support medical decision-making, remote diagnostics, IoT-based medical prescription, digital non-invasive medical treatments, patient’s real-time diagnostics and the management of complex medical procedures through an intelligent information system. Enhancing the sector’s antifragility, or ability to recover more quickly from unforeseen events, macro pressures, and disruptions, would successfully handle cyber resilience. Additionally, intelligent information security is an important feature to implement for a better E-supply chain in healthcare. Advanced technology and cyber resilience can be used to enhance protection. The end users find digital healthcare services more convenient because they find them time-saving and easy to access.

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9 Recommendations/Limitations The need for a deeper understanding of the healthcare sector’s digital transition and its potential for disrupting societies that contend the sector’s digital transformation is necessary and inevitable in the e-knowledge-based era. Based on the research outcomes, there are some limitations in this research. First, the research with causal effect may require more exploration of the variables discussed. Future research should explore the complications of implementing digitisation on the e-supply chain. Second, this research is area specific, which limits the generalizability of the research. It is recommended to explore different cities and states.

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Impact of Cyber Security Strategy and Integrated Strategy on E-Logistics Performance: An Empirical Evidence from the UAE Petroleum Industry Mohammed T. Nuseir , Enass Khalil Alquqa, Ata Al Shraah , Muhammad Turki Alshurideh , Barween Al Kurdi , and Haitham M. Alzoubi Abstract This research examines the impact of cyber-security strategy and integrated strategy implementation on improving e-logistics performance using empirical evidence from the textile industry in the UAE. Empirical evidence from the textile industry to measure the e-logistics performance is timely in the textile industry. This research method, with its consequences, has not been considered in research before. This is a causal, exploratory, descriptive and analytical study using a cluster sampling technique. Data from 301 respondents from the textile industry in Ajman, UAE, was used. A quantitative technique was used to analyse the statistical results. The relationship between cyber security and integrated strategy on e-logistics performance revealed a significant positive relationship. One city-based textile company was M. T. Nuseir Department of Business Administration, College of Business, Al Ain University, Abu Dhabi Campus, P.O. Box 112612, Abu Dhabi, UAE e-mail: [email protected] E. K. Alquqa College of Art, Social Sciences and Humanities, University of Fujairah, Fujairah, UAE e-mail: [email protected] A. Al Shraah Department of Business Administration, Faculty of Economics and Administrative Sciences, The Hashemite University, Zarqa, Jordan e-mail: [email protected] M. T. Alshurideh (B) Department of Marketing, School of Business, The University of Jordan, Amman 11942, Jordan e-mail: [email protected] B. Al Kurdi Department of Marketing, Faculty of Business, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan e-mail: [email protected] H. M. Alzoubi School of Business, Skyline University College, Sharjah, UAE e-mail: [email protected] Applied Science Research Center, Applied Science Private University, Amman 11937, Jordan © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 H. M. Alzoubi et al. (eds.), Cyber Security Impact on Digitalization and Business Intelligence, Studies in Big Data 117, https://doi.org/10.1007/978-3-031-31801-6_6

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considered for data collection. In the future, it is recommended to incorporate further states globally as technology is needed in each sector. Moreover, the current model can be revised with future security risk detection and management techniques. The managerial implications for the textile industrialist include conducting cyber security awareness training for employees and implementing strategies to attain better e-logistics performance and customer satisfaction. Keywords Cyber security strategy · Integrated strategy · E-logistics performance · Textile industry UAE

1 Introduction The UAE is gaining prominence as a major centre of the garment and textile industries due to the increasing demand for apparel. In line with this reputation, the nation is currently renowned for the variety of fabrics it produces for its clothing, outerwear, home textiles, and technical textiles (Alzoubi et al., 2022j). Additionally, the UAE has been particularly successful in attracting textile companies from outside the GCC, increasingly choosing Ajman as the hub of their operations (Radwan, 2022). More than 320 textile-based firms use Ajman Free Zone as a strategic base that enables them to conduct their import and export business with the rest of the globe efficiently (Alzoubi et al., 2022n). The UAE has a significant textile sector, which currently contributes significantly to the national GDP and is one of the key employers. In recent years, businesses worldwide have demonstrated the viability of remote staff and clientele. The textile care sector is not diversified since the environment has evolved (Qasaimeh & Jaradeh, 2022). New business models between textile care and its clients or suppliers have been made possible by technology. Trust is essential for business success in both the physical and digital worlds (Al Kurdi et al., 2021; Alketbi et al., 2020; Al-Khayyal et al., 2020a, 2020b). Based on (Al Kurdi et al., 2020; Alshurideh et al., 2019, 2020; Awadhi et al., 2022; Hammad et al., 2022), To ensure productivity, accessibility, security, and privacy, cyber security plays a significant role in addressing the textile industry’s concerns (Ghazal et al., 2022). To achieve their goal of manufacturing different textile goods for their target market in the right lead time, at the right cost, and with good, acceptable quality, companies pursue different competitive strategies vertically integrated textile manufacturing or horizontally integrated textile manufacturing or a hybrid of these two manufacturing models (Al-Khayyal et al., 2020a, 2020b; Alolayyan et al., 2022; Alshamsi et al., 2021; Alshurideh et al., 2019a; Alshurideh, 2022; Stratton & Warburton, 2003; Alshurideh et al., 2014). To meet the needs of customers or businesses, logistics refers to the management of the movement of goods and services between the point of origin and the point of use. Information, transportation, inventory, warehousing, material handling, packaging, and even security are all integrated into logistics (Alzoubi et al., 2022k; Kasem & Al-Gasaymeh, 2022). The supply chain’s logistics channel adds the value

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of time and location utility. Plant simulation software today can model, analyse, visualise, and optimise the complexity of production logistics, but this technology is constantly evolving (Al Kurdi et al., 2021; Colicchia et al., 2019; Hamadneh et al., 2021). Logistics management plays a crucial part in meeting customer needs in the textile industry because so many phases need to be passed, from raw materials to finished items, before they can be delivered to customers within a certain time (Abuanzeh et al., 2022; Alshurideh, 2022; Joghee et al., 2021; Lee et al., 2022). Therefore, this research focuses on measuring the impact of cyber security and integrated strategy on e-logistics performance in the textile industry in the UAE. The empirical study can help assess the extent to which the industry prefers to implement cyber security and integrated strategy to improve e-logistics performance and gain ultimate organisational performance.

2 Theoretical Framework 2.1 Cyber Security Strategy Cyber security has recently gained much attention and importance. In many research papers, the term cyber security is an all-inclusive term. As defined by the International Telecommunication Unit (ITU), “Cybersecurity is the collection of tools, policies, security concepts, security safeguards, guidelines, risk management techniques, activities, training, best practices, assurance, and technology that can be utilised to secure the cyber environment, organisations, and users’ assets.” Organisational assets include personnel, services, computing devices, telecommunication systems, and a cyber environment (Waseem-Ul-Hameed et al., 2018). Moreover, cybersecurity ensures that the security properties of the organisation’s and user’s assets are attained and maintained against pertinent security hazards in the cyber environment (Alzoubi & Ramakrishna, 2022). For instance, a cyber security breach would directly affect the integrity or availability of information (Ratkovic, 2022). This is a reality for many cyber security-relevant risks associated with a user or an organization (Alshurideh et al., 2021; Salloum et al., 2020). A cybersecurity plan defines how an organisation would maintain and ensure the security of its assets over the next three to five years (Ogbanufe et al., 2021). Certainly, as technology and cyber risks can change unexpectedly, the organisation would need to change its strategy earlier than three years (Butt, 2022). Like a cybersecurity strategy, the cybersecurity plan must be a living document adaptable to the existing risk scenario and the everchanging business environment (Mondol, 2022). Typically, cybersecurity plans are established with a three-to-five-year approach but should be amended and modified as often as possible (Ogbanufe et al., 2021).

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2.2 Integrated Strategy Integration strategies are a crucial element in establishing a competitive business. Businesses can inculcate numerous integration strategies to enhance their influence on supply and distribution or reduce competition (Alzoubi et al., 2022h). Integration strategy is also known as the management control strategy, and as its name says, it allows the firm to control numerous of its processes, such as competitors, suppliers, or distributors. This would assist them in consolidating and expanding their market position and increasing their competitiveness. Integration plans allow businesses to increase their competition, performance or market share by increasing their influence over new segments (Amrani et al., 2022). These areas include supply, distribution and competition and each of these areas requires a unique integration strategy that a business can apply (Alzoubi et al., 2021; Nadeem et al., 2018). The logistics manager must be aware of the role of the accounts payable manager; the sales manager must have knowledge about the human resources manager’s role. This helps departments coordinate and work together, leading to more job efficiency (Ahmad et al., 2021; Alshurideh et al., 2020b; Shamaileh et al., 2022; Taryam et al., 2020). Amalgamating employees refers to cross-training employees to do each other’s jobs. For instance, if the press operator is unwell, another person can take over for a day so that production remains unaffected (Del & Solfa, 2022). Vertical integration refers to a company taking over one or more parts of the supply chain (Alzoubi & Aziz, 2021; Alzoubi, 2022). Possessing control of manufacturing and assembling products but not considering the distribution and sales is referred to as backward integration (Farouk, 2022). Leaving the manufacturing to others and having control over the assembly of products and their distribution is referred to as forwarding integration (Allozi et al., 2021; Ogbanufe et al., 2021). Any businessperson who aims to succeed in operations performs a crucial part in getting things done (Nasim et al., 2022). The integration strategy is also called the management control strategy, and as the name suggests, it allows the business to gain control in many places, such as competitors, suppliers, or distributors. Business-integration strategy is mainly of two kinds that are; horizontal integration and vertical integration (Alameeri et al., 2021; Baabdullah et al., 2019; Ghazal et al., 2021a, 2021b). A horizontal integration strategy is a strategy in which a company controls the supply chain system of different industries that function similarly (Eli & Hamou, 2022). Simply put, a horizontal integration strategy is implied in similar businesses, such as when a fast-food business merges with the chain of a similar business in another country or foreign market (Ogbanufe et al., 2021). Businesses also use vertical integration when facing competition. A vertical integration strategy enables businesses to take control of various stages of supply, distribution, and production (Alshurideh et al., 2019; Ben-Abdallah et al., 2021; Kabrilyants et al., 2021; Kamaruddeen et al., 2022).

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2.3 E-Logistic Performance E-logistics refers to the management of the physical products of a business on an online forum, which can be a website or a marketplace (Hamadneh et al., 2021a). E-logistics contrasts with standard retail logistics, even though both can be complementary (Akhtar et al., 2022). Due to its unique aspects, e-logistics holds a significant value for e-merchants and requires the deployment of certain measures and processes for the e-merchant to get an advantage through optimal flow management (Hamadneh et al., 2021; Qurtubi et al., 2021). E-Logistics provides easy access to each bit of information and aligns with our vehicle and stockroom systems (Eli, 2021). E-Logistics provides urgent and simple data, including the production procedures and network (Alzoubi et al., 2021d). It has a great potential to take care of and control operations (Wu & Chiu, 2018). We would calculate the cargo rates, issue transport orders, track the entire procedure in the warehouse, carefully locate products or outright help for the co-ordinations needs, or regularly track the progress of shipments (Alzoubi et al., 2021a). E-Logistics is "the system of mechanising co-ordination forms and providing a collective satisfaction of the entire production network to both the board administrations and the players of co-ordinations forms (Ghosh & Aithal, 2022). Nowadays, more organisations are inclined towards coordinating the executives as it provides great opportunities to fulfil clients, reduce costs and offer a competitive edge (Kanagavalli & Azeez, 2019).

2.4 Operational Definitions

Variables

Definition

References

Cyber security strategy

A cybersecurity strategy is a comprehensive blueprint for how a business protects its assets over the subsequent three to five years. One needs to revise your approach sooner than three years from now, as both technology and cyber threats can change unexpectedly

Ogbanufe et al. (2021)

Integrated strategy

Businesses can employ integration techniques to increase their competitiveness, efficiency, or market share by extending their influence into other spheres. Supply, distribution, and competition are some examples of these sectors

Taghavi et al. (2021)

(continued)

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(continued) Variables

Definition

References

E-logistics performance E-logistics is the management of all Gunasekaran et al. (2007) physical flows for a business that conducts online sales of items (website, marketplace, etc.). However, traditional retail logistics contrast with e-logistics, although they can work in tandem

2.5 Textile Industry UAE Description After petroleum, the textile industry is the second-largest sector in the UAE. For years, the nation has been linked to petroleum. Now, it is gradually becoming a hub for producing apparel and textiles. UAE uses a wide range of textiles, including woven, non-woven, and knitted items. There are 150 producers of ready-to-wear clothing with permanent locations in Dubai and the Sharjah region. The Middle East is the fourth-largest export of apparel and accessories, accounting for over 5.5% of global trade. Additionally, the UAE has gained much business from other nations relocating their operational centres to Fujairah, one of its Emirates. The GCC textile industry is anticipated to grow by more than 4% during the projection period. (2022–2027). Despite not being promoted or publicised, the United Arab Emirates (UAE) boasts a sizable textile sector in the GCC. It produces goods for local consumption, such as child safety seats, drapes, luggage, and some high-end clothing. However, woven products such as knitwear are its best-selling textiles. After petroleum, the textile industry is now the second-largest industry in terms of employment and income in the UAE. The UAE has a substantial domestic customer base and exports to over 50 nations.

3 Literature Review 3.1 Relationship and Impact of Cyber Security Strategy on E-Logistic Performance In recent years, several studies have discussed how cyber security has changed elogistic performance. As e-logistics has reformed traditional logistics processes, it also brings several risks and challenges (Shamout et al., 2022). Security breaches can seriously affect the whole process. Therefore, firms need to understand why they need a cyber security strategy. Besides firewalls and antivirus programmes, modern

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e-logistics firms use drones and robots for their efficient process (Alzoubi et al., 2020a). The latest cyber security strategies have helped firms facilitate movement, distribution, order fulfilment and storage of goods (Alzoubi et al., 2022c; Goria, 2022). Data integrity is essential for improving the service quality. Another essential component of a firm’s cyber security strategy in the field of logistics is blockchain (Alzoubi et al., 2022c), which is an integral part of Industry 4.0. Several studies have found a positive relationship between a well-developed cyber security strategy and e-logistic performance due to its ability to deal with cyber-attacks and information (Alzoubi et al., 2020b; Brauning et al., 2020). Timely response to these risks improves the efficiency and output of e-logistic firms. H1: Cyber security has a significant positive impact on e-logistics performance.

3.2 Relationship and Impact of Cyber Security Strategy on Integrated Strategy Cyber security strategy is defined as the context for the set of objectives, roles, responsibilities and scope of the risk management process from the perspective of the company’s ICT network. Cyber security policy is part of the firm’s overall strategy and only covers one integral part. Currently, organisations pay attention to operational and cyber security strategies, as most businesses are happening with the help of technology (Alzoubi et al., 2022d; Zheng et al., 2006). Several authors have argued that senior management of companies should create effective security policies. Similarly, evaluation of a holistic approach is equally essential, and new systems and procedures hold great strategic importance and align with the risk management approach. It has been proved that cyber security strategy needs to be integrated at an enterprise level for risk assessment and management plan (Wu & Olson, 2008). The management team and workers are crucial to the company and must be part of the management integration plans (Erceg & Sekuloska, 2019; Mehmood, 2021). Their importance increases significantly when the company needs more time to amalgamate the personnel more intensely in the business functions (Victoria, 2022). For a manager to be proficient, he must understand the business integration, management operations, and the functioning of his department in association with the company (Griffis et al., 2004). Integrated strategy and e-logistics performance: The ultimate goal of an organisation’s strategy is to show better performance. Organisational performance is not only about the financial and operational perspectives (Alshraideh et al., 2017; Ashal et al., 2021). E-logistics firms need to fulfil orders on time; therefore, they need to align their operation time (Qi et al., 2022). A strong strategy with proper implementation guarantees long-term success (Erceg & Sekuloska, 2019). The ultimate objective of a firm’s grand strategy is to achieve long-term sustainable performance (Alzoubi et al., 2022m). Superior performance by e-logistic performance shows how e-logistics performance by firms is highly dependent on integrated strategy (Alzoubi & Ahmed, 2019; Qasaimeh & Jaradeh, 2022).

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Supply chain strategies are designed by expert managers who do so after external and internal environmental analysis. Strong integration between supply chain and business processes results from organisational level strategy (Alzoubi et al., 2022b). The ultimate aim of integrated strategy by organisations is to create customer value and achieve operational excellence (Wu & Chiu, 2018). Recently, the new breed of organisation has been highly dependent on the internet and other connectivity options (Stock et al., 1998). This change in organisational structure and new logistic networks have forced the managers to look for new strategies which help them to gain a competitive advantage in the long run (Alsharari, 2021). Integrating all the organisational and functional strategies is essential as this is the only way to achieve organisational objectives (Al Shraah et al., 2022). Logistics is an essential part of supply chain management, and the managers require external focus to see the impact of integrated strategies on their performance and competitive position (Alzoubi et al., 2022b). H2: Cyber security has a significant positive impact on integrated strategy.

3.3 Relationship and Impact of Cyber Security Strategy and Integrated Strategy on E-Logistic Performance E-logistic performance is based on the extensive use of ICT (Information Communication Technology) and IT capability (Alzoubi et al., 2022g). Additionally, the continuous digital evolution and incredible Internet growth are changing how corporations conduct business with logistic service provider (Akhtar et al., 2021). It is why effective value chain partners In the prospect of e-logistics, the value chain activities are performed on electronic determination, which is part of the firm’s integrated strategy (Alshurideh et al., 2020b). The cyber security strategy leads toward an integrated strategy and ends with a better e-logistic performance (Khatib et al., 2022). Considering this, the ventures must analyse the importance and implementation of value chain activities. As pointed out by Yu et al., ICT and cyber security policy play an essential role within the realm of any business. The role of cyber security strategy is to facilitate the quality of staff service and maintain delivery time at a minimum (Alzoubi et al., 2022h). Eventually, the influence of ICT and cyber security on Firm Performance is highly positive (Alzoubi et al., 2017). Various studies highlight that the contribution of ICT and security strategies to the supply chain and logistic performance is very extensive (Alzoubi & Yanamandra, 2020). Using the latest technologies facilitates logistic operations by decreasing the time capacity (Cheung et al., 2021). The dealing of working processing using computers is time savings. The author has further highlighted that ICT improves companies’ services and products. It also permits the new formation of a partnership between suppliers and consumers by coordinating different websites (Alzoubi et al., 2022e). The process is required to advance the system, keep it safe, and demonstrate authentic payment transaction security procedures (Ahmed & Al Amiri, 2022; Alzoubi, Rehman et al.,

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2022n). It is why ICT create a positive impact on SC (Supply Chain) activities and enhances the industry’s performance. The hypothesis is developed based on previous literature to contribute to the literature. H3: Cyber security and integrated strategy positively impact E-logistics performance.

3.4 Problem Statement and Research Gap Manufacturers of textile and apparel have always felt compelled to combat the troubling decline in market share with innovative offerings that combine low prices, shortened delivery times, and effective services (Eli, 2021). Additionally, authors have commented on the manufacturer’s intention to satisfy customers through responsiveness, quick and increasing quality provision, price, improved lead-time performance, secure delivery, consideration of various sorts of orders with strategic implementation, more selective consumer behaviour, the introduction of customised products with short life cycles, and avoidance of security risks (Brauning et al., 2020; Miller, 2021). The need for security to deliver the product cost-effectively and safely to the end user is an important factor in enhancing customer satisfaction and logistics performance (Kashif et al., 2021). Moreover, in the digital era, the need for technological transactions, buying and selling has driven the industry to be more innovative. In this light, this research mainly aims to assess the impact of cyber security and integrated strategies on e-logistics performance.

3.5 General Research Model See Fig. 1.

3.6 Research Hypothesis H1 : Cyber Security Strategy has a significant impact on E-Logistics Performance in the UAE Textile Industry at (α ≤ 0.05) level. H2 : Integrated Strategy has a significant impact on E-Logistics Performance in the UAE Textile Industry at (α ≤ 0.05) level. H3 : Cyber Security Strategy and Integrated Strategy have a significant impact on E-Logistics Performance in the UAE Textile Industry at (α ≤ 0.05) level.

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Cyber Security Strategy

H1

H3

Integrated Strategy

E-Logistics Performance

H2

Fig. 1 Research model

3.7 Research Methodology and Design The construct measurement associated with this research relied on quantitative research technique and descriptive, exploratory, causal and analytical research design applied with an appropriate cluster sampling technique. Data was conveniently collected from specific textile industries in Ajman, UAE. Primary data was collected through an online survey, while secondary data was obtained from previous literature and research studies.

3.8 Population, Sample and Unit of Analysis The textile industry in the UAE was the target population of the research. The vast industry limits this research sample to a cluster sample from Ajman city in the UAE. The sample of 301 respondents was used after screening the data of 500 questionnaires. The questionnaire was emailed to the industrial management departments (SC Officer, Manager IT, Security Networking Manager, HRD manager). The developed questionnaire contained 21 items on a five-point Likert scale from 5 “strongly agree” to 1 “strongly disagree”. Cyber security strategy was measured with 8 items, an integrated strategy with seven items, and 6 items were used to measure E-logistics performance.

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Table 1 The study demographical aspects Items

Description

f

%

Gender

Male Female

224 77

74.4 25.6

Job status

SC officer Manager IT Security and networking manager HRD manager

100 119 45 37

33.2 39.5 15.0 12.3

N = 301, male = 224 higher number than female respondents, female = 77

4 Data Analysis 4.1 Demographic Analysis See Table 1.

4.2 Reliability, Descriptive and Correlation The reliability measure illustrates the data accuracy for statistical analysis that shows good reliability with α = 0.85 for Cyber Security Strategy, α = 0.82 for Integrated Strategy and α = 0.83 for E-logistics performance. Descriptive statistics show suitable mean values and standard deviations, cyber security strategy Mean = 3.19, SD = 88%. Integrated strategy Mean = 2.80, SD = 77% and E-logistics measured Mean value = 3.33, SD = 81%. Table 2 summarises the results of the correlation coefficients demonstrating a significant positive relationship between cyber security strategy and integrated strategy r = 0.73, P < 0.05. The relationship between cyber security strategy and e-logistics performance r = 0.79, P < 0.05. Integrated strategy and e-logistics showed a significant relationship at level r = 0.81, P < 0.05 significance value. Table 2 provides a summary of the data findings.

4.3 Linear Regression and Hypothesis Testing See Table 3.

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Table 2 Reliability, descriptive and correlation Variables

Cronbach’s α

Mean

S.D.

Cyber security strategy

Cyber security strategy

0.85

3.19

0.88

1

Integrated strategy

0.82

2.80

0.77

0.733**

1

E-logistics performance

0.83

3.33

0.81

0.790**

0.814**

Integrated strategy

E-logistics performance

1

Level of significance P < 0.05**

Table 3 Regression analysis with ANOVA Hypothesis

Regression weights

Standardised coefficients β

R2

Adjusted R2

Sig.

t-value

Hypothesis

H1

CSS → ELP

0.790

0.623

0.622

0.000

9.67

Yes

H2

IS → ELP

0.814

0.662

0.661

0.000

11.78

Yes

H3

CSS * IS → ELP

0.862

0.743

0.741

0.000

3.69

Yes

Dependent variable ELP = E-logistics performance *Level of significance (α ≤ 0.05) **Critical t-value (df/p) = 1.64

5 Discussion of the Results The research findings indicate a significant positive relationship between cyber security strategy and e-logistics performance, with beta value β = 0.79, t-stat = 9.67 and variance in both constructs measured as R2 = 62% depicts a high variance. Thus, H1 is accepted with the positive impact of the latest cyber security strategies that have helped firms facilitate movement, secure goods distribution, and improve order fulfilment and storage of goods (Colicchia et al., 2019). The relationship between integrated strategy and e-logistics performance demonstrates a significant positive β = 0.81, t-stat = 11.78, a positive critical value indicates a significant relationship with a high variance level R2 = 66%, which supports H2 of the research. Based on previous studies, implementing integrated strategies is the only way to improve e-logistics performance to achieve customer satisfaction effectively and cost-efficiently. This is the ultimate way to achieve organisational success (Erceg & Sekuloska, 2019). The statistical findings reveal that the relationship between cyber security strategy and integrated strategy in the textile industry effectively and efficiently improves e-logistics performance. The statistical results show that β = 0.86 and the value for t-stat = 3.69 show a significant positive impact with an increased variance level of 74%. The research findings support the hypothesis developed in this research

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that has identified the prospect of e-logistics. The value chain activities are carried out electronically, which is part of the firm’s integrated strategy. The cyber security strategy leads to an integrated strategy with better e-logistic performance (Cheung et al., 2021).

6 Conclusion The need for innovation in manufacturing industries is considered increasingly important in this technological era. In view of this, managers need to analyse the importance and implementation of value chain activities for e-logistics performance and on-time delivery with secure and effective communication with cyber protection, which can enhance buyer-seller trust and business value. The textile industrialist can address technological advancement with vertical and horizontal integration strategies to achieve an effective supply chain and organisational performance. However, there are always opportunities for risk occurrence in operational management. Regulatory compliance and defined governance objectives are essential for risk mitigation in the textile manufacturing industry. Technologies incompatibility and system differences can create opportunities for intrusion as obsolete technologies are replaced. Therefore, risk analysis should be a top priority to find any vulnerability before opportunities for internal or foreign attacks arise. The textile manufacturing plant may have a successful recovery strategy that a multifunctional team has tried and tested to understand the structured countermeasures required.

7 Recommendations/Limitations The survey findings came up with some limitations to the research, which include the limited geographic area recommended for future consideration of other textile industries worldwide. Moreover, a longitudinal study is recommended as the current research contains cluster sampling with limited respondents. The longitudinal research can help to generalise the responses from the textile industry. Furthermore, future research should incorporate security risk detection and management techniques and awareness training for employees to effectively integrate cyber security strategies.

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The Mediating Role of Cyber Resilience in the Impact of Innovation Capabilities on Supply Chain Performance: Empirical Evidence from the UAE Petroleum Industry Mohammed T. Nuseir , Samer Hamadneh , Barween Al Kurdi , Muhammad Turki Alshurideh , Haitham M. Alzoubi , and Ahmad AlHamad Abstract The research examines the impact of innovation capabilities on supply chain performance with the mediating role of cyber resilience to provide empirical evidence from the petroleum industry in the UAE. Priority to explore supply chain performance with the mediation of cyber resilience stimulated this research to find empirical results that will contribute to the literature concerning the petroleum industry. A descriptive, exploratory, causal and analytical approach using quantitative research technique was used. A sample of 277 respondents was used for data analysis using regression ANOVA. The results show a significant positive impact of innovation capabilities on supply chain performance and an indirect effect of cyber M. T. Nuseir Department of Business Administration, College of Business, Al Ain University, Abu Dhabi Campus, P.O. Box 112612, Abu Dhabi, UAE e-mail: [email protected] S. Hamadneh · M. T. Alshurideh (B) Department of Marketing, School of Business, The University of Jordan, Amman 11942, Jordan e-mail: [email protected]; [email protected] S. Hamadneh e-mail: [email protected] B. Al Kurdi Department of Marketing, Faculty of Business, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan e-mail: [email protected] H. M. Alzoubi School of Business, Skyline University College, Sharjah, UAE e-mail: [email protected] Applied Science Research Center, Applied Science Private University, Amman 11937, Jordan M. T. Alshurideh · A. AlHamad Department of Management, College of Business Administration, University of Sharjah, 27272 Sharjah, United Arab Emirates e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 H. M. Alzoubi et al. (eds.), Cyber Security Impact on Digitalization and Business Intelligence, Studies in Big Data 117, https://doi.org/10.1007/978-3-031-31801-6_7

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resilience identified as a positive and significant impact on supply chain performance. The model identified in the research, which includes cyber resilience, innovation capabilities, and supply chain performance, is considered a limitation, so the authors need to consider other factors in further research. Industrialists may need to adopt technological strategies (cyber resilience and Innovation capabilities) that can enhance recovery rates, reduce costs, and ensure global supply chain security and extensive resources. Keywords Innovation capabilities · Supply chain performance · Cyber resilience · Petroleum industry UAE

1 Introduction When digitisation and exposure to cyber threats are increasing at an unprecedented rate, ensuring effective cyber resilience across the petroleum sector requires cohesive and linked multidisciplinary efforts to build a consistent business and digital enablement (Alsharari, 2022; Alzoubi et al., 2022d). To integrate cyber resilience into business cultures and operating models and adopt a systemic approach to risk management, the World Economic Forum’s initiative on cyber resilience in oil and gas seeks to promote international cooperation and dialogues between public and private sector leaders (Abuanzeh et al., 2022; Awawdeh et al., 2022b; Madi Odeh et al., 2021). The parameter of digital capabilities to address cyber resilience of sustainable characteristics (Hanaysha & Alzoubi, 2022; Lee et al., 2022). Continuous culture of innovation helps improve supply chain operations through using digital equipment, leveraging performance, and preventing cyber threats (Akhtar et al., 2022; Alwan & Alshurideh, 2022a; Tariq et al., 2022). Innovation capabilities in terms of the trust, cloud computing, and open innovation influence supply chain operations to achieve results beyond expectations and address the cyber resilience aspect in the management of petroleum operations (Nuseir et al., 2020). Cyber security is followed by the growing need for a workforce with cyberresiliency attributes. Cybersecurity is a crucial aspect; in this aspect, it has become apparent that modern-day supply chains are largely automated (Alzoubi et al., 2022c). Therefore, incorporating appropriate cybersecurity frameworks offers fantastic opportunities for petroleum companies to attain continuous growth, and innovation in the implemented technological infrastructure is equally important here (Aljumah et al., 2021; Aljumah et al., 2022a; Awawdeh et al., 2022a; Salloum et al., 2020). Innovation capability within organisations impacts supply chain performance and puts companies on the path to bring businesses to the road of success (Alzoubi et al., 2022q). Here, enterprise innovation capabilities improve the supply chain’s overall performance quality (Amrani et al., 2022). The significance of cyber resilience and innovation capabilities in the petroleum sector can include beneficial ideas to enhance the organisation’s supply chain performance (Alzoubi et al., 2022f). This examines

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the impact of innovation capabilities and cyber resilience on the petroleum industry and how these constructs improve the firm’s supply chain performance. This research examines three variables: Innovation Capabilities, Supply chain performance and the mediating role of cyber resilience in attaining Supply chain performance.

2 Theoretical Framework 2.1 Innovation Capabilities Innovation capability refers to the firm’s ability to identify new ideas and transform them into improved or new services, products, or processes that benefit the organisation (Alzoubi et al., 2022a; Alzoubi et al., 2022m). It focuses on introducing new services and products that organisations can adopt to obtain new and innovative products in the long term (Nasim et al., 2022). It is not only about acquiring good ideas but also about implementing appropriate organisational skills and structures to transform these innovative ideas into reality (Almaazmi et al., 2021; Altamony et al., 2012; Cheng & Lin, 2012). Utilising the innovation capacity led by the industry enhances the supply chain’s performance (Goria, 2022). It addresses the aspect of resource-based parameters and gaining a competitive edge in the marketplace to stay ahead of its competitors in terms of process flows and overall business continuity (Alzoubi et al., 2022). The capability of innovation within the premises of organisations positively impacts the performance of the supply chain, leading the organisations to the path of success. Alkalouti et al. (2020), Aljumah et al. (2022b), Alshawabkeh et al. (2021), Alshurideh et al. (2022), Nuseir (2019), Nuseir et al. (2021a, 2021b) point out that innovation capability improves organisations’ performance, specifically in terms of the supply chain in service firms (Ghosh & Aithal, 2022). This improves innovation capabilities and enhances non-financial and financial performance.

2.2 Cyber Resilience Cyber resilience refers to the ability of an organisation to continuously achieve the desired outcome despite various types of cyber events with adverse characteristics. Cyber resilience refers to the evolving prospects that can quickly be quickly identified (Alzoubi et al., 2022; Del & Solfa, 2022). The organisation can enable the acceleration of business as per enterprise resiliency through preparing for and responding to, and executing the recovery aspects of cyber threats (Ahmadi-Assalemi et al., 2020). An organisation considered a cyber-resilient is competent in adapting to unknown threats, crises, challenges, and adversities.

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Moreover, cyber resilience refers to an entity’s ability to continuously deliver the desired outcome despite several cyber events with adverse characteristics (Saad Masood Butt, 2022). Cyber resilience refers to the evolving perspectives found to gain rapid recognition properly (Ghazal et al., 2022). The organisation can enable the acceleration of business as per enterprise resiliency through preparing for and responding to and executing the recovery aspects of cyber threats (Mondol, 2022). A petroleum company that can be considered cyber-resilient can adapt to unknown threats, crises, challenges, and adversities (Alzoubi et al., 2022). Cyber-resilience is based on the strategic approach related to supply chain management within organisations (Management, 2019). Appropriate cyber resilience and risk management strategies are critical in cost-efficiently maintaining business continuity and reliability. They also facilitate recovery and prevent blockages in the event of a disruption; through analysis, adequate measures are incorporated.

2.3 Supply Chain Performance Supply chain performance refers to the extended activities of the supply chain related to meeting the activities in the meeting based on end-user requirements, encompassing the availability of products, timely delivery, and all other essential capacities and inventories within the supply chain for providing the required performance in a responsive approach (Kurdi et al., 2022; Sandhu & Shamsuzzoha, 2018). It has been outlined that supply chain performance is measured from five perspectives (Farouk, 2022). The first perspective is reliability, followed by responsiveness (Alzoubi & Ramakrishna, 2022). The third factor considered is flexibility, along with the assets and costs of the organisations. It covers the factors of logistics in a detailed manner as well (Radwan, 2022). Organisations need to focus on optimising the supply chain’s operations to leverage the organisatio’s growth. Big Data analytics is an advanced factor, as stated by Bahrami and Shokouhyar (2021). This capability, along with the performance of supply, remains interlinked. This helps improve the supply chain’s performance, and the analytics outcomes help attain resilience.

2.4 Operational Definitions

Variables

Definition

Reference

Innovation capabilities

By definition, innovation capability is a company’s capacity to identify novel concepts and develop them into valuable new or better goods, services, or procedures

Cheng and Lin (2012)

(continued)

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(continued) Variables

Definition

Cyber resilience

The ability of systems that use or are Annarelli and Palombi (2021) enabled by cyber resources to foresee, endure, recover from and adapt to adverse conditions, pressures, attacks, or compromises

Reference

Supply chain performance The extended supply chain’s efforts Marinagi et al. (2015) to meet end-customer demands are referred to as supply chain performance. This includes ensuring product availability, on-time delivery, and that the supply chain has all the necessary capacity and inventories

2.5 Petroleum Industry UAE Description The United Arab Emirates (UAE) is one of the top 10 oil producers in the world. The country’s estimated 100 billion barrels of known oil reserves are located in Abu Dhabi, ranked sixth globally, accounting for about 96% of the total reserves. The UAE economy heavily depends on hydrocarbons, with 13% of exports and 30% of GDP coming directly from the oil and gas sector. Oil and gas export revenues, which account for the vast majority of the UAE government’s income, remain an essential source of income for the country. The UAE has prioritised energy transformation as the first Arab nation to adopt a “net-zero” emissions target. Moreover, Abu Dhabi National Oil Company (ADNOC), a global leader in the oil and gas business, operates in all areas of the sector. By 2030, ADNOC hopes to achieve a maximum sustainable production capacity of 5 million barrels.

3 Literature Review 3.1 The Relationship and Impact of Innovation Capabilities on Cyber Resilience Various considerations are required to figure out the relationship and the impact of innovation’s capabilities on cyber resilience (Ratkovic, 2022). Cyber resilience is essential for companies that incorporate advanced technology frameworks and strive for digital transformation (Alzoubi, 2022). The organisation can deliver the desired and expected outcome by preventing adverse events related to the cyber niche

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(Alzoubi et al., 2022). This concept is rapidly gaining recognition, and innovation capabilities are crucial to accomplishing the objectives (Ahmed et al., 2021; Alwan & Alshurideh, 2022b). Annarelli and Palombi (2021) and Nuseir et al. (2021a, 2021b) discussed the elements of digital capabilities in relation to coping with cyber resilience of longterm characteristics (Ahmed & Amiri, 2022). By utilising digital equipment and leveraging the performance, a continuous innovation culture supports the execution of supply chain operations and cyber risk mitigation (Ahmad et al., 2021a, 2021b; Alshurideh et al., 2021). Furthermore, appropriate skills for managing digital parameters are essential to focus on and obtain appropriate outcomes in the long run (Alshurideh, 2022; Al Kurdi et al., 2022). This leads to improving digital functioning and appropriately managing cyber resilience. Technology attains continuous growth and advancement at a rapid rate (Alolayyan et al., 2022b; Alshurideh et al., 2019; Hamadneh et al., 2021a). Here, organisations need to enhance their innovation capabilities to eliminate the possibilities of adverse cyber impacts. The next parameter under consideration is developed by (Aljumah et al., 2022b; Alzoubi et al., 2022; Yeboah-Ofori et al., 2021). In this context, the aspect of security in the supply chain case initiates cyber resilience through the application of machine learning (Alshurideh et al., 2022; Ghazal et al., 2021). It helps predict potential threats that may arise in the process. Based on the literature, a hypothesis has been developed to support it. H1: Innovative capabilities have a significant impact on cyber resilience.

3.2 The Relationship and Impact of Innovation Capabilities on Supply Chain Performance The initial parameter that will be focused on for presenting adequate details is the impact of innovation capabilities on supply chain performance aspects from the perspective of organisations (Alameeri et al., 2021; Obeidat et al., 2020; Qasaimeh & Jaradeh, 2022). The supply chain is a crucial factor in maintaining business continuity and competitiveness (Lee et al., 2022; Shamout et al., 2022). Organisations need continuous innovation and can initiate regular improvement to address the alterations of the dynamic environment (Alsharari, 2021; Alzoubi et al., 2021). The capabilities of innovation generate several impacts on the performance management of the supply chain adequately (Alzoubi et al., 2022; Shamout et al., 2022). The initial aspect under consideration is to present the relationships and the impact of the innovation capabilities on the supply chain’s activities (AlShurideh et al., 2019; Hamadneh et al., 2021a, 2021b). According to Abdullahi et al. (2021), the innovation capabilities in organisations contribute to major improvements in the management of supply chain operations. They are reckoned to leverage the overall level of performance related to the supply chain from the perspective of rapidly developing countries worldwide (Alzoubi & Aziz, 2021). This is because market requirements, demands and trends are

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rapidly changing (Alolayyan et al., 2022a). Therefore, organisations worldwide strive to improve supply chain operations to appropriately fulfil these demands (Alzoubi et al., 2020; Alzoubi et al., 2021). Additionally, industry-led innovation capacity improves supply chain performance. It addresses resource-based characteristics and provides a competitive advantage in the market to stay ahead of the competition regarding model flow and overall business continuity (Amarneh et al., 2021; Shamout et al., 2021). Resources are the key for organisations to execute operations seamlessly. Thus, innovation serves as crucial in these parameters in enhancing supply chain performance (Ghazal et al., 2021a). Ferreira et al. (2021) took an in-depth look at the capacities and competencies related to innovation and found that it improves the quality of supply chain interactions (Kasem & Al-Gasaymeh, 2022). Innovation assists in meeting the supply chain’s objectives in a timely and effective manner (Al Suwaidi et al., 2021; Ghannajeh et al., 2015). This helps maintain the efficiency and effectiveness aspects of the supply chain appropriately to obtain the desired outcomes (Alzoubi et al., 2021e). Bag et al. (2020) looked at several factors that influence innovation in the channel and supply chain improvement from the standpoint of a strategy-based application (Alzoubi et al., 2020b). Value clusters and value co-creation paradigms are integration channels that significantly improve supply chain performance. Several authors have found that innovation capabilities boost the performance of companies, particularly in terms of the supply chain within service enterprises. This promotes innovative capacities and non-financial and financial performance (Alzoubi et al., 2021a). As a result, it helps the companies attain a competitive edge in the market by attracting more customers and appropriately fulfilling their requirements and demands. H2: Innovative capabilities have a significant impact on supply chain performance.

3.3 The Relationship and Impact of Cyber Resilience on Supply Chain Performance The third area under consideration in this aspect focuses on the relationship followed by the impact of the supply chain performance in the case of cyber resilience (Mehmood, 2021). Here, the supply chain performance remains indirectly related to the factor in this case (Alzoubi et al., 2020). In this consideration, it can be presented that the appropriate performance of the supply chain speaks about the presence of advanced frameworks of technology within the organisations. This is due to the availability of adequate technology frameworks, and hence, cyber resilience can be accomplished appropriately (Alzoubi & Ahmed, 2019; Victoria, 2022). According to Shamout (2019), comprehensive data analytics in the supply chain helps to leverage the supply chain’s continual innovation. It also exploits robustness features regarding capabilities, addresses cyber resilience components, removes difficulties, and makes it a useful tool (Al Naqbia et al., 2020; Nuseir et al., 2021a,

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2021b). Data analytics helps in properly assessing the available data in the organisation, conducting adequate data analysis, and identifying enhancement measures to obtain good outcomes in the long run (Alzoubi et al., 2017). Therefore, it helps apply appropriate steps to address the activities of the supply chain by keeping the severe cyber effects from occurring. Davis (2015) focused on enhancing cyber resilience and how to incorporate it into supply chains. It addresses the factors related to information-centric characteristics to improve data protection and proper management of cyber information (Akhtar et al., 2021). Information is the key to improving technology in identifying loopholes and the areas that require appropriate incorporation of measures (Alzoubi & Yanamandra, 2020). Therefore, appropriate automated supply chain operations help in preventing cyber threats. According to McPhee and Khan (2015), cyber resilience in supply chains is an important factor to consider. In the case of supply chains, cyber resilience is extremely important for achieving better results to avoid cyber security issues (Alzoubi et al., 2022; Eli, 2021). This has further been admitted by Khan and Qianli (2017). They stayed concerned with the issue of supply chain-based cyber resilience. It determines the best way to create an agenda to achieve a brighter future in terms of innovation and a properly functioning supply chain. H3: Cyber resilience has a significant impact on supply chain performance.

3.4 The Relationship and Impact of Innovation Capabilities on Supply Chain Performance with Mediating Role of Cyber Resilience The relationship followed by the impact of the innovation capabilities on the parameters of innovation capabilities on the concept of cyber resilience to mediate the aspects of supply chain performance (Alzoubi et al., 2022). These three components remain directly related in this aspect. According to Bahrami and Shokouhyar (2021), Big Data analytics is an important factor. This skill, as well as supply performance, are intertwined. This aids in enhancing the supply chain’s performance and the analytics output to achieve supply chain resilience and innovation. This, as a whole, adequately enhances the organisations’ competitiveness and helps them attain further growth in the long run (Alzoubi et al., 2022). According to (Ahmadi-Assalemi et al., 2020). Big Data analytics is a method for achieving operational excellence and leveraging and improving supply chain performance in organisations’ sustainability considerations (Ghazal et al., 2021b; Shishan et al., 2021). This factor has further been supported by Yeboah-Ofori et al. (2021). They have looked at the variable under examination (Eli & Hamou, 2022). In this context, supply chain security features commence cyber resilience through implementing digital aspects. It aids in anticipating any hazards that may

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arise during the procedure. This, as a whole, would help in attaining the best outcomes in the long run based on necessity. Similarly, the strategic orientation inside businesses’ premises harnesses supply chain capabilities, leads enterprises to achieve an innovation pathway, and enhances the capability to compete successfully and acquire an advantage (Khatib et al., 2022). Furthermore, the complete business process is augmented (Alzoubi et al., 2021c; Ashal et al., 2021). Technological breakthroughs have been discovered to properly prevent cyber resilience threats and help organisations execute operations per Requirement Island to adequately maintain the business flow and the rate of profit earning (Kashif et al., 2021; Puspita et al., 2020). Some authors have explored innovation capacities in ethics, cloud computing, and open innovation (Alzoubi et al., 2022). These characteristics have been found to impact supply chain operations in achieving results that are beyond and above expectations and assessing the organisation’s logistics operations in an adequate manner (Alzoubi et al., 2021d). It manages the factor possibilities of cyber-attacks and issues. It aids in dealing with the possibilities of cyber resilience adequately (Miller, 2021). Overall, the relationship between the three variables is quite intact in today’s business environment. Moreover, the capabilities of innovation help in accomplishing cyber resilience to drive the supply chain performance in the best possible way. This is where it becomes quite essential and mandatory to execute the operations appropriately. H4: Innovation capabilities positively impact supply chain performance with the mediating effect of cyber resilience.

3.5 Problem Statement and Research Gap The digital revolution has augmented the energy transition and sustainability of the oil and gas industry. In this evolution, the petroleum sector depends on not having any weaknesses; thus, cyber resilience is crucial. The promise goes further by creating a set of efforts to introduce cyber-resilience and culture conscious of cyberrisks throughout the energy business. There is a need to determine an appropriate schema creation to attain a better future to innovate and accomplish an appropriately working supply chain by implementing cyber resilience and innovation capabilities to overcome the gap mentioned in previous research (Koroteev & Tekic, 2021).

3.6 General Research Model See Fig. 1.

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Cyber Resilience H1

H3 H4

Innovation

Supply Chain

Capabilities

Performance H2

Fig. 1 Research model

3.7 Research Hypothesis HO1 : Innovation capabilities have a significant positive impact on Supply Chain Performance in the UAE Petroleum Industry at (α ≤ 0.05) level. HO2 : Innovation capabilities have a significant positive impact on Cyber Resilience in the UAE Petroleum Industry at (α ≤ 0.05) level. HO3 : Cyber Resilience has no positive significance on Supply Chain Performance in the UAE Petroleum Industry at (α ≤ 0.05) level. HO4 : Innovation capabilities have a significant positive impact on Supply Chain Performance with the mediating effect of Cyber Resilience in the UAE Petroleum Industry at (α ≤ 0.05) level.

3.8 Research Methodology and Design This research encompassed three variables measured by an online survey. Innovation capabilities, cyber resilience and supply chain performance were mainly measured with quantitative techniques. Present research was cross sectional (Ali et al., 2021). An exploratory, causal and analytical approach with cluster sampling was used. The primary data source was petroleum company management personnel to generate concepts from the literature. Secondary data was used from the previous scientific literature. Quantitatively data (Ali et al., 2020; Perumal et al., 2021) were processed using SPSS software.

The Mediating Role of Cyber Resilience in the Impact of Innovation … Table 1 The sample demographical characteristics

119

Items

Description

f

%

Gender

Male

209

75.5

Female

68

24.5

IT & Development Manager

108

49.0

SC Officer

41

29.2

Cyber Security Manager

81

14.1

HR Manager

49

17.7

Designation

N = 277, Male = 209 (75%), Female = 68 (25%)

3.9 Population, Sample and Unit of Analysis Survey method is one of the important toll to collect the data (Jabeen & Ali, 2022). The survey was conducted to gather data from petroleum companies based in Dubai, UAE, to evaluate the research variables. A total of 13 companies were contacted via email, and 700 questionnaires were sent to the managerial department. Data from 277 valid respondents was used as a sample of the research. A questionnaire was developed on a Five-point Likert scale with 21 items. Six items were used to measure Innovation capabilities, eight to measure Cyber Resilience, and seven to measure Supply Chain Performance. The demographic data included Gender and job designation, respectively.

4 Data Analysis 4.1 Demographic Analysis See Table 1.

4.2 Reliability, Descriptive and Correlation To evaluate the data reliability, a test using Cronbach’s alpha was performed, which showed good reliability of the measured data for Innovation Capabilities = 0.80, Cyber Resilience = 0.87, and for Supply chain performance = 0.83. Table 2 also demonstrates descriptive statistics illustrating the mean value of the data Mean = 2.95 & SD = 72% for “innovative capabilities”. The mean value for “cyber resilience” is M = 3.35 & SD = 88%. Supply chain performance mean is = 3.48 & SD = 95% respectively.

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Table 2 Reliability, descriptive analysis and correlation coefficients Construct

No. of items

Cronbach’s alpha

Mean

S.D

Innovation capabilities

Cyber resilience

Innovation capabilities

6

0.80

2.95

0.72

1

Cyber resilience

8

0.87

3.35

0.88

0.763**

1

Supply chain performance

6

0.83

3.48

0.95

0.770**

0.847**

Supply chain performance

1

Intelligent information system (M = 2.15, SD = 52%), cyber resilience (M = 3.05, SD = 58%), E-supply chain M = 2.48, SD = 45%. -Level of significance at P < 0.05**

Table 3 Linear regression analysis of mediating effect by ANOVA Hypothesis

Regression weights

β

R2

Adjusted R2

p-value

t-value

Hypothesis supported

H1

IC → CR

0.763

0.582

0.581

0.000

19.58

Yes

H2

IC → SCP

0.847

0.717

0.716

0.000

13.37

Yes

H3

CR → SCP

0.770

0.593

0.592

0.000

6.40

Yes

H4

IC*CR → SCP

0.868

0.754

0.752

0.000

3.81

Yes

Dependent variable = supply chain performance, independent variable = innovation capabilities, mediator = cyber resilience, *level of significance (α≤0.05), **critical t-value (df/p) = 1.64

The correlation results show a significant relationship between Innovation Capabilities and Cyber Resilience r = 0.76**, P < 0.05. The relationship between Innovation capabilities and supply chain performance is highly correlated with a value of r = 0.77**, P < 0.05. The results depict a high correlation between Cyber Resilience and Supply chain Performance r = 0.64**, P < 0.05.

4.3 Multiple Regression and Hypothesis Testing See Table 3.

5 Discussion of the Results According to the research findings, the regression analysis depicts the hypothesis testing that validates a significant positive relationship of “Innovative Capabilities statistically” with “Cyber Resilience” β = 0.76, t = 19.58, R2 = 58% for the H1 that

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supports hypothesis 1 of the research. Hypothetically it has proven that innovation capabilities enhance the ability to adopt cyber resilience to adopt the advanced technology in the management of an organisation that stays up to date for the technology adoption in the firm (Ferreira et al., 2021). The results reveal the research’s second hypothesis, which has proven a significant positive relationship between “Innovation Capabilities” and “Supply Chain Performance” β = 0.84, t = 13.37, and R2 = 84%, indicating a high variance and a positive relationship between the constructs. Thus, H2 is also supported in this research. This hypothesis is also supported by Varma et al. (2008) that the new technology in strategic implementation can improve the supply chain process. Their article also indicated the favourable effect of innovation activities on supply chain operations. It creates a platform for sustainable growth relevant to the supply chain and takes the businesses into account in depth. To examine the “Cyber Resilience” with “Supply Chain Performance”, the analysis findings depict a positive significant relationship β = 0.77, t = 6.40, and R2 = 59%, demonstrating a positive relationship that supports H3. The findings reveal a significant relationship and a significant positive impact on mediating the role of cyber resilience on innovation capabilities and supply chain performance β = 0.86, t = 3.81 and R2 = 75%; a high variance shows the increase in implementation of cyber resilience can enhance the capability to adopt innovative strategies. A sustainable supply chain is a trending factor under consideration, and organisations worldwide are looking to improve in the long run and appropriately fulfil ethical goals.

6 Conclusion The research findings show that cybersecurity is still in its infancy, and the demand for a workforce with cyber-resilience characteristics in the petroleum industry is rising. Cybersecurity is an important factor, and the current supply chains are largely automated. As a result, incorporating appropriate cybersecurity frameworks provides firms with amazing opportunities for continued growth and the creation of innovation in existing technical infrastructure. Moreover, appropriate cyber resilience and risk management methods have been acknowledged to play critical roles in costeffectively preserving company continuity and dependability. They also help recover and prevent obstructions along the path of disruption, and necessary measures are integrated via analytics.

7 Recommendations/Limitations The research was proposed with some limitations. First, this research is limited to fewer respondents, and a large number of participants might yield more precise estimates of the comparative weights. Additionally, since the study focused only on Abu Dhabi, UAE, the findings may change slightly when applied to the petroleum

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supply chains in other nations, even though the technique is the same. However, the technique offers a way to conduct a thorough assessment of petroleum supply chains incorporating other factors, such as cyber risks and risk management strategies etc.

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Impact of Supply Chain Resilience on Competitiveness with the Mediating Role of Supply Chain Capabilities: Empirical Evidence from the UAE Electronics Industry Mohammed T. Nuseir , Ala’a Ahmad , Enass Khalil Alquqa, Haitham M. Alzoubi , Barween Al Kurdi , and Muhammad Turki Alshurideh Abstract Purpose To empirically examine the impact of supply chain resilience on competitiveness via the mediating effect of supply chain capabilities by gathering empirical evidence from the electronics industry in the UAE. The impact of supply chain resilience and capabilities on business competitiveness is examined for the first time in this research assessment of supply chain resilience and capabilities in the electronics manufacturing sector in the UAE. It used a hypothetical model M. T. Nuseir Department of Business Administration, College of Business, Al Ain University, Abu Dhabi Campus, 112612 Abu Dhabi, UAE e-mail: [email protected] A. Ahmad University of Sharjah, Sharjah, UAE e-mail: [email protected] E. K. Alquqa College of Art, Social Sciences and Humanities, University of Fujairah, Fujairah, United Arab Emirates e-mail: [email protected] H. M. Alzoubi School of Business, Skyline University College, Sharjah, UAE e-mail: [email protected] Applied Science Research Center, Applied Science Private University, Amman 11937, Jordan B. Al Kurdi Department of Marketing, Faculty of Business, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan e-mail: [email protected] M. T. Alshurideh (B) Department of Marketing, School of Business, The University of Jordan, Amman 11942, Jordan e-mail: [email protected]; [email protected] Department of Management, College of Business Administration, University of Sharjah, 27272 Sharjah, United Arab Emirates © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 H. M. Alzoubi et al. (eds.), Cyber Security Impact on Digitalization and Business Intelligence, Studies in Big Data 117, https://doi.org/10.1007/978-3-031-31801-6_8

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measured quantitatively, that employed an empirical methodology and an organised review of the literature. A set of propositions suggested how supply chain resilience and competitiveness might be simultaneously enabled and constrained by the usage of supply chain capabilities. Data from 34 electronics manufacturing companies was used for statistical analysis. In the presence of supply chain resilience and supply chain capabilities, the findings revealed a positive influence on competitiveness of supply chain resilience and capabilities. The targeted industry is noted as a limitation of the research so that the current research model can be revised for different sectors with additional construct factors, for instance, a digital supply chain and risk management. The stakeholders in the electronics business can use these results for cost-effectiveness, flexibility, early risk detection, and agility to lessen the firm’s local and global risks and achieve a competitive advantage. Keywords Supply chain resilience · Competitiveness · Supply chain capabilities · UAE electronics industry

1 Introduction Resilient supply networks use supply chain (SC) planning to maximize productivity. Supply chain resilience begins with strategic planning, which aligns the whole chain and increases visibility and agility (Alshurideh et al., 2022; Kurdi et al., 2022). Supply and demand can be better understood and production synchronized via SC planning (Ahmed et al., 2021; AlShurideh et al., 2019). It is possible to predict problems better and minimize the effect of SC interruptions by using this integrated, forwardlooking strategy (Alzoubi et al., 2022c; Gölgeci & Kuivalainen, 2020). Moreover, to deliver and produce services and products to customers, various necessary activities are coordinated by SC management (Aslam et al., 2020; Del & Solfa, 2022). Transporting or packaging, farming, designing, and manufacturing are some examples of SC activities. SC management can achieve several business goals (Hamadneh et al., 2021a, 2021b; Lee et al., 2022). Product quality can be improved by controlling manufacturing processes, and the risks of lawsuits and recalls can be reduced by supporting the organization to develop a strong consumer brand (Abu Zayyad et al., 2020; Alshurideh et al., 2015; Alzoubi et al., 2021). Customer service can be built using controls over shipping procedures without simultaneously neglecting the expensive time and shortages of inventory oversupply (Aslam et al., 2019; Madi Odeh et al., 2021). Several opportunities are provided for companies by SC management so that profit margins can be improved (Alzoubi et al., 2022m; Ghazal et al., 2021a). This is essential for international operations and companies (Alzoubi et al., 2021b). Product design can be influenced by the SC’s capability creation process, which is its primary aim. According to SC models and strategy, there is a ramp-up in cost-efficient and smooth products (Alzoubi et al., 2021g; Shamout et al., 2022a).

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From previous research and identified gaps, this research has developed a conceptual model that represents the relationship and impact of supply chain resilience on competitiveness per the mediating role of supply chain capabilities.

2 Theoretical Framework 2.1 Supply Chain Resilience An SC that can recover and resist is known as a resilient SC. In simple terms, it can be said that its capacity to resist or neglect the effects of an SC disruption and its ability to recover quickly are termed supply chain resilience (AlShamsi et al., 2021; Alshurideh et al., 2022). Within the SC, various operational areas can face issues due to operations (Alzoubi et al., 2021e; Chowdhury et al., 2019; Saad Masood Butt, 2022). For example, global SCs, logistics, and global chain workforces witnessed a global and far-reaching impact of Covid-19, which included worldwide disasters. It is not enough for supply networks to merely withstand and recover from adversity. Foreseeing the future’s threats and possibilities is made possible by contemporary SC procedures and cutting-edge technology (Alolayyan et al., 2022a, 2022b; Hamadneh et al., 2021a, 2021b). A flexible contingency plan and reacting swiftly to operational issues are essential to good SC management (Alzoubi et al., 2021f). To genuinely withstand interruption, an SC must be capable of foreseeing and preventing it (Emenike & Falcone, 2020). However, organizations face issues in evaluating a balance between the analysis of economies and scenarios to reward them with additional costs that reduce risk (Alolayyan et al., 2022a, 2022b; Alzoubi et al., 2021). Companies can gain enormous benefits by attaining resilient SCs. During the significant disruptions, the everyday environments are added with significant value. Customer needs are responded to, and high service levels are maintained through a resilient SC when control is levied on costs and net working capital (Al-Dmour et al., 2021; Edward Probir Mondol, 2022; Ghazal et al., 2021b; Hasan et al., 2022). Understanding and using data are the keys to a supply chain’s resilience. SC robustness improves significantly when a company has the digital technologies to monitor and make sense of the big data (Alzoubi et al., 2022p). Machines using artificial intelligence can bring together information from all around the company and the globe (Emenike & Falcone, 2020). Consumer comments may be studied in conjunction with news stories, rival activity, sales records, and customer feedback to identify patterns and possibilities (Aburayya et al., 2020; Alshurideh, 2019; Alshurideh et al., 2020). It is possible to automate and improve operations in real-time by listening to the connected devices inside the system (Ali & Gölgeci, 2019; Alzoubi et al., 2020b; Ashurideh, 2010).

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2.2 Competitiveness An organization’s ability to provide customers with more valuable products and services than its competitors is competitiveness (Neyara Radwan, 2022). Marketing plays an integral aspect in competitiveness by constantly searching for a sustainable competitive advantage, and is portrayed in the research by Añaña et al. (2018). With competitiveness, companies gain an advantage by offering better products and services and, simultaneously, ensuring better profits (Alzoubi et al., 2022i). Competition between organizations is a global issue. Assisted by global strategies, companies often tend to lead not only in domestic markets but also in foreign ones (Altamony et al., 2012; Alzoubi et al., 2022k; Obeidat et al., 2021). To become competitive and successful, organizations must opt for permanent change and often become less competitive when they do not opt for innovation—and subsequently disappear from the market (Maged Farouk, 2022; Yeniyurt et al., 2019). Successful management ensures that effective responses to changes can be made. If organizations have implemented various new rules within the last two decades, they have been internally trained to perform successfully in the market (Alzoubi, 2022; Alzoubi et al., 2022o). They must attain flexibility, and to remain competitive and opt for the changes; responses must be made quickly (Alshurideh et al., 2020; Shamout et al., 2022b). To assess competitiveness, measures can be more or less calculated with the help of consensus (Ghazal et al., 2022). Two disciplines outline the measurement tools for competitiveness, the strategic management school, which emphasizes the organization’s strategy and structure, and neoclassical economics.

2.3 Supply Chain Capabilities Translating the objectives, goals, and visions into understandable, integrated priorities and easily communicated actions is the primary struggle for the heads of SC strategy and chief supply chain officers (CSCOs) (Alzoubi et al., 2022l). They deal with the natural complexity of the SC and strategy, and derive a simple way to articulate and codify their plans (Rajaguru & Matanda, 2019). These CSCOs must access the SC’s resources and capabilities for the SC’s desired future and current states (Ahmed & Al Amiri, 2022). These resources and capabilities are to be found within data, processes, technology, skills, and organizations. These SC resources and capabilities then close these gaps. Within their SCs, organizations can precisely delineate their activities (Alzoubi et al., 2022g). Their supply chain capabilities include processes within the management of their supplier relationship, supply risk, and SC planning. As the sub-process for a new product is developed, the SC capability creation process can be located in tasks, roles, targets, milestone criteria, key performance indicators, and governance models for new product development.

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Within this category, various SC operation factors involve asset management, manufacturing, and inbound logistics (Aslam et al., 2020). Profitable promises can be made to customers, and the company can orchestrate end-to-end SCs with the help of demand and supply capabilities (Alzoubi & Ramakrishna, 2022). Various processes such as advanced operations and sales planning, product life cycle management, and control towers enable such actions (Yeniyurt et al., 2019). Learning and monitoring are included within capacities as per the customers’ needs. A specific part is also held within this by demand sensing, forecasting, and demand management (Alsharari, 2022). Companies provide the products and services demanded by customers through processes of e-commerce, multi-channel fulfillment and logistics (Alshurideh, 2022; Alshurideh et al., 2012; Kurdi et al., 2020).

2.4 Operational Definitions

Variables

Definition

Reference

Supply chain resilience

A supply chain’s ability to withstand attack and bounce back defines it as robust. That entails being able to prevent or significantly reduce the impact of most supply chain interruptions

Cheng and Lin (2012)

Competitiveness

The capacity of a business to manufacture products or provide services with favorable quality-price ratios that ensure strong profitability while winning customers from rival businesses defines its competitiveness. Competitiveness guarantees the company’s long-term viability

Annarelli and Palombi (2021)

Supply chain capabilities Supply chain capabilities show how Marinagi et al. (2015) resilient and adaptable the supply chain is to interruptions. Maximum supply chain capacity is measured at any given time by a fixed amount as a percentage of total capacity

2.5 UAE Electronics Industry As more electronic gadgets adopt wireless connectivity, the size of the consumer electronics sector in the United Arab Emirates is expanding quickly. The market in

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the UAE is driven by rising R&D expenditures in consumer electronics and technological improvements, as well as rising acceptance of wearable electronic devices. Innovations like the proliferation of IoT in fitness bands and the rising demand for intelligent gadgets present a chance for market expansion in this nation. Over the forecast period, the worldwide consumer electronics market is anticipated to increase at a compound annual growth rate (CAGR) of 2.91%. The main drivers of the growth of the worldwide consumer electronics market are changing lifestyles, the rise of the middle class, and a growing propensity to use intelligent electronic gadgets. Additionally, the expanding number of Internet users and high consumer disposable income will probably accelerate the development of electronic devices.

3 Literature Review 3.1 Relationship and Impact of Supply Chain Resilience on Competitiveness A company’s ability to develop a defensive position against its rivals is its competitive advantage. To differentiate and measure the firm against other organizational competitors are critical (Mehmood, 2021). The measures for this include on-time delivery, flexibility, competitive cost, appropriate quantity, and high quality (Dubey et al., 2021). Companies have prioritized time-based competition as an integral aspect of competitive advantage. The SC must become aligned, adaptable, and agile so that the company can gain a competitive advantage (Alzoubi et al., 2021). The dynamic capability view (DCV) can consider reconfiguring organizational resources and developing accurate capability. The firm’s competitive advantage and performance can be justified with a unifying DCV framework and a grounded resource base (Qasaimeh & Jaradeh, 2022). The intangible and tangible resources and organizational assets help the firm to develop a competitive advantage and the firm’s performance; they create financial, human, reputational, organizational, physical, and technical resilience (Alzoubi et al., 2022h). Organizations can modify their practices using strategic routines, integrated activities, and a pattern of collective activities, within its dynamic capabilities (DC). Then, with the changing environment, competitive advantage can be gained and sustained, and new resource configurations can be achieved (Alzoubi & Aziz, 2021; Emenike & Falcone, 2020). However, there are arguments that an organization’s SC can be developed to mitigate vulnerabilities in an unexpected DC situation (Nasim et al., 2022). The DC approach can be apt when organizations ask how they could gain a competitive advantage during a period of uncertainty. The sources of wealth creation can be investigated this way (Ghazal et al., 2021c). However, according to a grounded DVC, supply chain resilience can often be considered an organizational resource (Abeysekara & Wang, 2019).

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Due to this, corporate boards or management have suddenly inclined towards these (Dubey et al., 2021). Despite not having adopted SC risk management tools broadly, the company has gained offshore, lean, and brittle SCs helped by a specific focus on asset efficiency and operating margins (Alzoubi et al., 2021h). SCs are built with high redundancy levels since they become more expensive and resilient. The bottom line can have a subsequent short-term negative impact (Amrani et al., 2022; Emenike & Falcone, 2020), which has often been resisted by the company’s shareholders and corporate board. However, it can be said that in today’s complicated world, competitive advantage and differentiation can be gained by organizations through the key source of the SC (Alzoubi et al., 2021c). The company’s primary focus is developing SCs since the products can be sent more economically, effectively, and quickly than their rivals can (Kasem & Al-Gasaymeh, 2022). Based on the above discussion, the following hypothesis is proposed: H1: Supply chain resilience has a significant impact on competitiveness.

3.2 Relationship and Impact of Supply Chain Resilience on Supply Chain Capabilities The ability of a firm to coordinate and execute its various tasks to execute operational activities such as operations planning, logistics, and distribution, the supply chain capability (Alzoubi et al., 2017). Knowledge of the routines and processes is rooted (Liu et al., 2017) in this. This capability refers to a collection of routines and a highlevel routine, which include organizational competencies and routines for responding to unforeseen events and often tend to impact the SC’s ability to execute and perform (Hosseini et al., 2019; Vorobeva Victoria, 2022). One significant but evolving concept is the firm’s resilience, which primarily focuses on capability. When environmental disruptions occur, they are absorbed by the SCs; however, the firm’s performance might or might not suffer (Akhtar et al., 2022; Alzoubi & Ahmed, 2019). Many researchers have agreed that the notion of resilience is often linked with flexibility. Organizations can widely use SCs to manage unexpected events and environmental changes (Alzoubi et al., 2020a). Supply chain resilience, an integral position, is marked by agility (Abuanzeh et al., 2022; Can Saglam et al., 2020). After the disruptive event, capability is managed by the resilient SCs so that they can return to their improved or primary state (Ghosh & Aithal, 2022). Thus, it can be said that after disruptions, a firm’s capability to return to its regular business operations is called resilience rather than flexibility and agility (Eli, 2021). To have an effective SC, the organization must learn practical lessons from the disruption and adopt a new operational structure instead of returning to its original state (Ebraheem, 2017). When organizations reach a new stable situation or when sustainability is witnessed in the primary business, resilience can be witnessed within the SCs (Goria, 2022).

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Kumar’s definitions and descriptions of flexibility can be equally applied to resilience. The notion of change is involved, and the changes are responded to (Alzoubi & Yanamandra, 2020; El Baz & Ruel, 2021; Miller, 2021). To explain flexibility to an organization, he proposed a stimulus–response framework. He stated that three related dimensions specify the ease of response to changes which reveal the level of flexibility. They include the scope, cost, and time of the responses (Alsharari, 2021). However, the organization’s effort, cost, and time taken to change its organizational structure and workflow of processes are called supply chain resilience (Ali & Gölgeci, 2019). Multidimensionality is the primary focus of past research since resilience is the most affected related concept used in the description of flexibility (Alzoubi et al., 2022d; Eli & Hamou, 2022). With this, range and temporal dimensions are the most fundamental. The range of options, both unplanned and planned, sit within the range dimension and explain the notions of versatility and robustness in response to the disruption and events arising from the environment (Alzoubi et al., 2022n). To respond to the disrupted SCs, the time taken by companies sits within the temporal dimension. The ability of the firm to adapt in the provided time demonstrates resilience, which can be easily measured by efficiency and responsiveness. Based on the above discussion, the following hypothesis is proposed: H2: Supply chain resilience has a significant impact on supply chain capabilities.

3.3 Relationship and Impact of Supply Chain Capabilities on Competitiveness Competition can occur when the industry’s costs are lower than those of rival firms. A country’s competitiveness usually occurs at a country level (Alzoubi et al., 2022f). However, there is no competition if one country’s competitiveness increases, because another country’s competitiveness will not decrease if it still has access to domestic competition (Attia & Essam Eldin, 2018). Supply chain capabilities develop some essential relationships for the organization. Supply chain capability allows the production of products in an appropriate quantity and at a proper location for the goods, warehouses, manufacturers, and providers (Khatib et al., 2022). That way, the costs of the system can be reduced when facility-level necessities are met. The globalized environment is preserved when multinational companies manage this by applying it in IT. Additionally, with the help of other supply chain capability factors, five essential procedures can be identified in the SC (Dubey et al., 2019), selling, delivering, buying, storing, and creating. The primary methods of business study are lacking when competitive advantage development and planning are the primary emphases (Akhtar et al., 2021; Alzoubi et al., 2022j). According to Attia and Essam Eldin (2018), the term competitiveness is used when original marketplaces establish contact with the company’s competitors for product growth design and upgrade its status.

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The supply chain capabilities can be considered an aspect of a sophisticated process in the manufacturing industry as they permit the superior fulfillment of a request for the company’s goods (Kashif et al., 2021). The price of logistics is thus reduced and covers the whole cycle of the organization when product delivery, manufacturing, and raw materials are purchased with modern technologies. Timely distributions can be made. The preparation structure can be grown. Costs can be decreased. Warehouse inventory can be improved, and offers to request conformity can be checked by activities within the SCs (Attia & Salama, 2018). Competition in the market can be decreased through this process. IT tools to improve the firm’s competitive advantage and build radically transformed relationships with clients and providers. However, the indirect and direct influences of competitive priorities using an SC strategy on institutional performance were examined by Attia and Essam Eldin (2018). To ensure this goal is attained, four dimensions are required to measure the competitive priorities of cost, distribution, value, and flexibility. They were used to measure SC strategies. The study provides a new framework of performance-driven and competitive advantage that impact SC strategy and supply chain capabilities procedures. Based on the above discussion, the following hypothesis is proposed. H3: Supply chain capabilities have a significant impact on competitiveness.

3.4 The Relationship and Impact of Supply Chain Resilience on Competitiveness with the Mediating Role of Supply Chain Capabilities SC risk management aims to attain resilient and robust logistics networks or SCs— even during severe disruption. Firms can gain sustainability through SC risk management. Supply chain capabilities often involve two significant terms, resilience and robustness, because the risks in SCs can be easily mitigated by applying different connotations of those terms. In this vein, resilience is the capacity of the company to retain and adapt, whereas robustness is the capacity of the company to sustain and resist (Liu et al., 2017). Efficiency involves the firm’s ability to get accustomed to new situations without hampering performance or increasing costs. In contrast, its responsiveness is the time required to adapt to those changed situations. Two new terms were invented (Scholten et al., 2020), ease of exit and new capability. Resilience analysis is the main aim for every organization since they are the focus of only the significant metrics and dimensions. Other past research has stated that to achieve resilience, an integral role is played by robustness. The ability of an organization’s system to convert its primary state to a more desirable new state after the disruptions is resilience. The unexpected events involving control and connectedness are recovered from, responded to, and prepared for with the adaptive ability of the firm within the SC context. However, researchers have

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agreed that resilience can be obtained within the SCs by accessing responsiveness, collaboration, flexibility, visibility, redundancy, and responsiveness. Unforeseeable events can be efficiently dealt with by resilient SCs rather than robustness, where a low probability characterizes a high consequence. The initial stage of mild impact is compared within the disruption to the other extreme, a significant full effect (Dubey et al., 2019). The duration and performance level of the disruptions are considered within this approach because the magnitude of risk effects in these stages can be evaluated. In the primary stage of disruption, a crucial role is played by robustness because of the decrease in well-prepared logistics networks and risk awareness and the elimination of the possible risks within the company. Within the expected performance level, a constraint is faced since the disruptions can be controlled and withstood by a robust SC to a fair degree (Can Saglam et al., 2020). Various factors reduce the risk’s impact and occurrences, including collaborative risk preparation, visibility within anticipation, flexibility, and outsourcing quality control. The risk effects can be reduced by resilience by cutting down the time taken by those disruptions. A firm’s competitive position faces substantial influence from the high levels of demand and technological and environmental uncertainties. Various stages of competitive advantage can be conferred throughout the different stages of the risk management capacity within which these uncertainties exist. A crucial capability is formed by risk management in the SC through which differentiation and cost reduction can be gained to compete with company competitors. Based on the above discussion, the following hypothesis is proposed. H4: Supply chain resilience has a significant impact on competitiveness with the mediating role of supply chain capabilities.

3.5 Problem Statement and Research Gap Businesses are more exposed to disastrous risks because of the unpredictability and potential losses brought on by natural disasters. A more vulnerable situation exists for manufacturing companies due to global sourcing and supply activities. To investigate these consequences and fill the gap mentioned in prior research (Chowdhury et al., 2019), this research sought to provide a conceptual framework for understanding the significance and impact of a firm’s supply chain resilience on competitiveness with a mediating effect provided by supply chain capabilities.

3.6 General Research Model See Fig. 1.

Impact of Supply Chain Resilience on Competitiveness …

H1

Supply chain capabilities

139

H3

H4

Supply chain resilience

Competitiveness H2

Fig. 1 Conceptual research model

3.7 Research Hypothesis H1: Supply chain resilience has a positive significant impact on supply chain capabilities in the UAE electronics industry at (α ≤ 0.05) level. H2: Supply chain resilience has a positive significant impact on competitiveness in the UAE electronics industry at (α ≤ 0.05) level. H3: Supply chain capabilities have a positive significant impact on competitiveness in the UAE electronics industry at (α ≤ 0.05) level. H4: Supply chain resilience has a positive significant impact on competitiveness through the mediating effect of supply chain capabilities in the UAE electronics industry at (α ≤ 0.05) level.

3.8 Research Methodology and Design The designated methodology for this current research focused on quantitative research with a descriptive, exploratory, and causal research design. The sampling technique was convenient cluster sampling to diversify the comprehensive population sample limited to one city. The online data survey was conducted by sending emails to the correspondents.

140 Table 1 The sample demographical aspects

M. T. Nuseir et al. Items

Designation

f

%

Gender

Male

196

72.1

Female

76

27.9

Product Development Manager

76

27.9

SC Manager

81

29.8

Marketing and Sales Manager

41

15.1

IT Manager

74

27.2

Job status

N = 272, Male = 72% Female = 28%

3.9 Population, Sample and Unit of Analysis The population of this research comprised the 34 electronics manufacturing companies targeted to gather data in Abu Dhabi, UAE. A sample of 272 respondents was accumulated for overall analysis. The data was gathered through an online questionnaire sent to the executive departments of electronic manufacturing companies. The questionnaire was organized on a five-point Likert scale using 24 items to measure the research construct.

4 Data Analysis 4.1 Demographic Analysis The demographic data contain “Gender” and “Designation,” representing the 196 male and 76 female respondents, whereas the majority were SC Managers, with the highest percentage of 81% (Table 1).

4.2 Reliability, Descriptive, and Correlation To ensure data reliability, Cronbach’s Alpha illustrated good reliability of the constructs. Nine items were tested to check the reliability of supply chain resilience = 0.88, eight items were tested for competitiveness which indicated the data is reliable enough = 0.86, and seven items were tested for supply chain capabilities = 0.83 that, indicating data is reliable enough for further analysis. The data was further tested for descriptive analysis that depicted the mean for supply chain resilience as M = 2.78, SD = 71%, and the mean for competitiveness as M = 2.73, SD = 79%, an acceptable level of mean. Lastly, the mean for supply chain capabilities was M = 2.50, SD = 69%. Table 2 illustrates the summary of the results.

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Table 2 The correlation coefficient and reliability findings Construct

No of Cronbach’s Mean S.D. SC Competitiveness SC items alpha resilience capabilities

SC resilience

9

0.88

2.78

0.71 1

Competitiveness 8

0.86

2.73

0.79 0.789**

1

7

0.83

2.50

0.69 0.719**

0.745**

SC capabilities

1

Supply chain resilience (M = 2.78, SD = 71%, Competitiveness M = 2.73, SD = 79%, Supply chain capabilities M = 2.75, SD = 69%) Level of significance at P < 0.05**

Table 2 demonstrates that the correlation coefficient findings indicated a high correlation between supply chain resilience and competitiveness r = 0.789** , P = 0.000 at P < 0.05** . The relationship between supply chain resilience and capabilities was highly correlated with r = 0.719** , P = 0.000 at level P < 0.05** . Supply chain capabilities positively correlate with Competitiveness r = 0.745, P = 0.000 at significance level P < 0.05** , respectively.

4.3 Regression and Hypothesis Testing Considering the above statistical results, the hypothesis for the research presented the relationship of supply chain resilience with supply chain capabilities. For H1, the findings revealed a significant positive relationship between supply chain resilience and supply chain capabilities as β = 745, P = 0.000, t = 18.36, with the variance level R2 = 55%. The results for H2 described the relationship of supply chain resilience with competitiveness as positively significant as β = 0.79, P = 0.000, t = 9.21, and R2 = 63%, a high level of variance that indicates support for H2 in the following analysis. The relationship between supply chain capabilities and competitiveness for H3 was declared positively significant as β = 0.81, P = 0.000, t = 11.01, and R2 = 67%, which reveals a positive relationship with high variance. Furthermore, the mediation role of supply chain capabilities in the impact of supply chain resilience on competitiveness for H4 was demonstrated as positively significant as β = 0.86, P = 0.000, t = 4.00, and R2 = 74%, which shows a high variance (Table 3).

5 Discussion of the Results Although many glitches occur, a stronger value position and macroeconomic condition can be obtained. According to the literature and current statistical analysis, this research accepts H1. Various authors argue that to measure the organization and differentiate it from other company competitors is critical. The measures of this competition include on-time delivery, flexibility, competitive cost, appropriate

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Table 3 Hypothesis testing using ANOVA Hypothesis

Regression weights

Standardized coefficients β

R2

Adjusted R2

Sig

t-value

Hypothesis supported

H1

SCR → SCC

0.745

0.555

0.554

0.000

18.36

Yes

H2

SCR → COM

0.798

0.636

0.635

0.000

9.21

Yes

H3

SCC → COM

0.819

0.670

0.669

0.000

11.01

Yes

H4

SCR * SCC → COM

0.866

0.749

0.747

0.000

4.00

Dependent variable = Competitiveness * Level of Significance (α ≤ 0.05)** , for the whole study factors ** Critical t-value (df/p) = 1.64, for the whole study factors

quantity, and high-quality manufacturing that can protect an organization from SC risks (Añaña et al., 2018). Some authors have directed that the supply chain capabilities can be considered an aspect of a sophisticated process in the manufacturing industry. Supply chain capabilities permit the superior fulfillment of a request (Yang et al., 2021) for the company’s goods. The price of logistics is also thereby reduced. Purchasing product delivery, manufacturing, and raw materials with modern technologies covers the organization’s entire cycle. Timely distributions can be made. The preparation structure can be grown. Costs can be decreased. Warehouse inventory can be improved, and offers to request conformity can be checked by activities within the SC. According to different studies, a resilient SC can quickly recover from a disruption to a more desirable level or at least the original performance level since responsiveness and adaptability are the outcomes that achieve organizational competitiveness (Aslam et al., 2020).

6 Conclusion The proposed research model has been statistically validated using SPSS. Findings prove that supply chain resilience and capabilities such as agility, collaboration, and engineering are the primary drivers of company competitiveness. To attain sustained competitiveness, current research has shown that firms must manage SC risks effectively. However, supply chain resilience’s significance can significantly impact organizational competitiveness via flexibility, cost-effectiveness, and risk management. Moreover, electronics companies should adjust the restructuring priorities of their SC and manufacturing operations from ‘just in time’ to ‘just in case’ and shift their goals and priorities toward resilience-building alternatives. SC operations might involve on-demand fabrication, virtual inventory, and risk detection employing supply chain resilience and capabilities.

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7 Recommendations and Limitations A large sample of businesses outside the electronics industry can be used to describe the research limitations. Future studies may attempt to validate and apply the same conceptual approach. Small- and medium-sized businesses in an economy could also be the focus of future research using other research constructs, such as risk management and digital SC.

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Impact of Cyber Security and Risk Management on Green Operations: Empirical Evidence from Security Companies in the UAE Barween Al Kurdi , Enass Khalil Alquqa, Mohammed T. Nuseir , Haitham M. Alzoubi , Muhammad Turki Alshurideh , and Ahmad AlHamad Abstract To use empirical evidence from the UAE security industry to measure an organization’s green operations impacted by cyber security and risk management. The security industry has not been previously targeted for research measuring cyber security, risk management, and green operations. This research uniquely contributes to the literature and knowledge for future research. The data from 77 securityproviding companies from Sharjah City UAE was used for statistical analysis. A quantitative research technique was applied with convenient cluster sampling. A B. Al Kurdi Department of Marketing, Faculty of Business, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan e-mail: [email protected] E. K. Alquqa College of Art, Social Sciences and Humanities, University of Fujairah, Fujairah, United Arab Emirates e-mail: [email protected] M. T. Nuseir Department of Business Administration, College of Business, Al Ain University, Abu Dhabi Campus, P.O. Box 112612, Abu Dhabi, UAE e-mail: [email protected] H. M. Alzoubi School of Business, Skyline University College, Sharjah, UAE e-mail: [email protected] Applied Science Research Center, Applied Science Private University, Amman 11937, Jordan M. T. Alshurideh (B) Department of Marketing, School of Business, The University of Jordan, Amman 11942, Jordan e-mail: [email protected]; [email protected] Department of Management, College of Business Administration, University of Sharjah, 27272 Sharjah, United Arab Emirates A. AlHamad Department of Management, College of Business, University of Sharjah, 27272 Sharjah, United Arab Emirates e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 H. M. Alzoubi et al. (eds.), Cyber Security Impact on Digitalization and Business Intelligence, Studies in Big Data 117, https://doi.org/10.1007/978-3-031-31801-6_9

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descriptive, exploratory, and causal research design was applied in the research. The findings reveal a significant positive association between cyber security and risk management with green operations. The limitations are that respondents were derived from one sector in one city, and thus lost generalizability. Future research is recommended to target other sectors’ manufacturing and retailing industries from diverse locations. Security management requires optimization of supply chain operations and awareness of security and protection needs, and the subsequent employee training, privileged access implementation, monitoring, detection, and strategic implementation to avoid cyber attacks. Keywords Cyber security and risk management on green operations · UAE security industry

1 Introduction In today’s technology environment, new hazards can emerge every hour of every day. The risk of a hacker targeting a business increases with internet connectivity. Businesses and governments worldwide are extremely concerned about cyber threats and cybercrime (Alzoubi et al., 2022g). If businesses don’t have a good cybersecurity plan, there are serious financial and brand repercussions (Ghazal et al., 2021a; Salloum et al., 2020). Globally, the importance and popularity of cybersecurity have increased. In some policy papers, more than 50 nations have previously disclosed their official stances on cyberspace, cybercrime, and cyber security (Alzoubi et al., 2021d; Ratkovic, 2022). However, the definitions lack unanimity and clarity (Mondol, 2022). Cybersecurity for assessment is the term used to describe the collection of procedures, safeguards, risk management plans, protocols, technologies, processes, and training to secure a company’s user assets and online infrastructure (AlShamsi et al., 2021; Von Solms & Van Niekerk, 2013). Moreover, all human endeavors involve some level of risk, which necessitates effective risk management techniques for, among other things, profit maximization, loss reduction, and the safety of people and property (Gurtu & Johny, 2021; Kamaruddeen et al., 2022). Cyber risk describes a risk related to cyber activities (Alzoubi et al., 2019). Cyber risk is one of the most challenging and quickly changing concerns that modern businesses must address. Besides this, going green benefits more than just the environment—it may also benefit the company. Establishing more environmentfriendly practices can have several beneficial effects on business, from cost savings to superior competitive advantages (Altamony et al., 2012; Alzoubi et al., 2021e). The evaluation framework of green operations, cyber security, and risk management used in this research are based on empirical evidence and literature that summarizes the findings with beneficial outcomes for the security industry.

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2 Theoretical Framework 2.1 Cyber Security Cybersecurity prevents malicious breaches into networks, computers, servers, mobile devices, electronic systems, and data. Cybersecurity depends on the precautions people can take and the decisions they make while setting up, running, and using systems and the internet. Cyber security evaluation has been attempted numerous times, and numerous frameworks have been built (Alzoubi et al., 2022h; Radwan, 2022). The frameworks that initially functioned well at the time of development now face various challenges (Boddy et al., 2017). Additionally, traditional cyber security methods include a never-ending detection cycle and reaction to new threats and weaknesses (Kashif et al., 2021). This temporary fix demonstrates the limitations of many aspects of the current cyber security paradigm and the need for an improved strategy (Thakur et al., 2016).

2.2 Risk Management Risk management identifies, assesses, and restricts the danger to an organization’s resources and financial success (Akhtar et al., 2021; Alshurideh et al., 2020; Alzoubi et al., 2022k). These risks may arise from several circumstances, such as monetary instability, contractual commitments, technological difficulties, inadequate strategic planning, accidents, and natural disasters (Alshurideh et al., 2021; Kurdi et al., 2020; Yang et al., 2021). An organization must plan and implement strategies to deal with risks and opportunities if it wants to comply with the demands of the latest standard (Alshraideh et al., 2017; Shamout et al., 2022). Similarly, the foundation for enhancing the efficiency of the quality management system, achieving the best results, and preventing harmful effects considers both risk and opportunity (Abeysekara & Wang, 2019; Al Kurdi et al., 2021; Naqvi et al., 2021).

2.3 Green Operations Green operations refer to the incorporation of organizational environmental management strategies into production and procedures (Liu et al., 2019) to increase corporate sustainability (Alzoubi et al., 2022i, 2022l). Existing research has identified it as a crucial instrument for enhancing business environmental performance and achieving sustainable development objectives. In order to balance and increase both financial success and pollution reduction (Butt, 2022), green operations strongly emphasize product- and process-oriented environmental initiatives (Alshurideh et al., 2019a; Shishan et al., 2021; Tariq et al., 2022). Product stewardship, another name for the

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product-oriented environmental practice of green operations, aims to lessen the environmental burden by using less hazardous and nonrenewable materials in developing products (Alzoubi et al., 2022p; Del & Solfa, 2022; Eli, 2021). It also considers the environmental impact of product design, packaging, and materials (Ghazal et al., 2022). It explicitly encourages the use of recyclable parts and packaging, recycling and reusing product components, and eco-design (Akhtar et al. 2022; Khatib et al., 2022; Nasim et al., 2022; Wong et al., 2012). In the security sector, providing products and service development requires cost-effective service with safety that enhances customer trust and business sustainability.

3 Operational Definitions Variables

Definition

Reference

Cyber Security

Cyber security is the prevention harmful assaults on computers, servers, mobile devices, electronic systems, networks, and data

(Von Solms & Van Niekerk, 2013)

Risk Management

Risk management processes include (Colicchia et al., 2019) the identification, assessment, and control of risks to an organization’s resources and revenues. The risks can arise from various factors, such as economic instability, legal obligations, technology issues, strategic management errors, accidents, and natural disasters

Green Operations

Incorporating corporate environmental management practices into business production and operations creates green operations, which aim to increase company sustainability. According to recent studies, it is a crucial instrument for enhancing corporate environmental performance and achieving sustainable development objectives

(Chawla et al., 2020)

3.1 UAE Security Industry The UAE cyber security market is anticipated to expand remarkably throughout the forecast period. Rising cyber threats to enterprises drive the UAE cyber security market. Another driving factor for the market is the growing requirement to

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secure sensitive documents and data from various complex and sophisticated attacks, including malware and ransomware. Additionally, over the coming few years, it is anticipated that technical breakthroughs like integrated security solutions and next-generation security solutions, together with the rising use of cutting-edge technologies like AI and IoT, will generate an attractive potential for market expansion (Al-Khayyal et al., 2020; Al Batayneh et al., 2021; Alzoubi et al., 2022; Ghazal et al., 2021). However, the high price of cyber security solutions has resulted in declining adoption rates, particularly among SMEs (small and medium enterprises). Due to factors like the lack of skilled cybersecurity professionals in businesses, the rising Bring Your Own Device (BYOD) trends, adoption of cloud-based services, and security requirements, the market for UAE security system integrators is predicted to expand at a robust compound annual growth rate (CAGR) between 2022 and 2027. The SME segment, measured by organizational size, is anticipated to experience the most considerable CAGR growth during that time due to the rise in the sophistication of cyberattacks and the frequency of data breaches.

4 Literature Review 4.1 Relationship and Impact of Cyber Security on Green Operations The purpose of green operations is to guarantee the quality and environmental compliance of the inputs (such as metals and electronic components) and outputs of electronics manufacturers (e.g., finished products, carbon emissions, and waste) (Chawla et al., 2020; Nasim et al., 2022). In order to balance and enhance both financial success and pollution reduction, and focus on cyber security risk factors, green operations strongly emphasize environmentally friendly techniques that are processand product-oriented (Alzoubi et al., 2022m). Product stewardship aims to lessen the environmental burden by using less hazardous and nonrenewable materials in product development while considering the environmental impact of product design, packaging, and materials (Alsharari, 2021; Alzoubi et al., 2022e). In particular, it encourages using recyclable parts and packaging, recycling, and reusing product components with eco-design (Alzoubi et al., 2022o). Cybersecurity is “the prevention of loss or damage to IT hardware, software, and the data stored on the systems (Goria, 2022; Miller, 2021).” A holistic approach to cyber security is necessary, taking into account people, processes, and physical and technological security (Alzoubi & Yanamandra, 2020; Ogbanufe et al., 2021). Globally, the importance and popularity of cybersecurity have increased. More than 50 countries, according to Thakur (2016), have released numerous strategy documents providing an overview of cyberspace, cybercrime, and cyber security (Thakur et al., 2016). Based on the above discussion, the following hypothesis was proposed:

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H1: Cyber security significantly impacts green operations.

4.2 Relationship and Impact of Risk Management on Green Operations The design activities, creation, and delivery of goods and services of a corporation fall under the operating function, which is relatively concerned with risk factors (Alzoubi et al., 2017). Therefore, they encompass manufacturing- and logisticsrelated operations and microeconomic sources of risk (Ahmed et al., 2021; Alzoubi et al., 2021; Lee et al., 2022). However, operational risk additionally originates from external macroeconomic factors (Mehmood, 2021), such as the risks related to theft and cyber-attack, which have the potential to influence the productivity and effectiveness of organizational operations (Alzoubi & Yanamandra, 2022; Pandey et al., 2020). Security devices protect companies and security (Alzoubi & Ahmed, 2019; Alzoubi et al., 2020; Ghosh, & Aithal, 2022). Additionally, as risk and variability are essential components of value generation, businesses must discover strategies to understand and mitigate their effects. Furthermore, local and global communities are putting increasing pressure on security manufacturers to reduce the risk that their supply chain processes entail (Alshurideh et al., 2022; Ben-Abdallah et al., 2021; Hamadneh et al., 2021). The early addition of high-quality modifications and implementation of security practices to a product manufacturing process can help to eliminate risk factors (AlShurideh et al., 2019b; Kurdi et al., 2022; Lee et al., 2022). Risk management assists in the smooth production and delivery of the products or services to the end user (Alzoubi et al., 2021a, 2022l; Eli & Hamou, 2022; Monostori, 2018; Victoria, 2022). Based on the above discussion, the following hypothesis was proposed: H2: Risk management significantly impacts green operations.

4.3 Relationship and Impact of Cyber Security and Risk Management on Green Operations Taking advantage of potential possibilities, cyber security, and risk management also raises the chances that a business will accomplish its goals cost-effectively (Kasem & Al-Gasaymeh, 2022). Managing cyber security risk can help identify and comprehend risk ratings of occurrences and put the appropriate processes or controls in place (Alzoubi et al., 2022j; Qasaimeh & Jaradeh, 2022). That ensures that the company functions within acceptable risk tolerance limits, even if it does not eliminate all hazards (Chawla et al., 2020). This is a continual process rather than an isolated occurrence (Alsharari, 2022). Cybercrimes include identity theft, hacking, virus propagation, computer fraud, and other related incidents. Politically motivated

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and non-politically motivated cybercriminals can be distinguished (Alzoubi et al., 2021f; Alzoubi, 2022). Extremist organizations engage in political cybercrime to exploit the internet to spread false information, launch cyberattacks, make money, or organize terrorist attacks on the ground (Amrani et al., 2022; Von Solms & Van Niekerk, 2013). The primary justification for green operations is that companies can outperform their rivals in terms of business potential if they properly handle environmental challenges (Ahmed & Al Amiri, 2022; Alzoubi et al., 2021c). However, evaluating green initiatives is challenging, requiring not just the weighing of costs and benefits but also operational and environmental performance (Alzoubi et al., 2022f; Alzoubi & Aziz, 2021). Green operation efforts can improve organizational and environmental performance and security concerns (Ashal et al., 2021; Chawla et al., 2020; Ghazal et al., 2021d). Organizations may simultaneously experience changes to both their internal and external operations (AlMehrzi et al., 2020; Alshurideh et al., 2015; ELSamen & Alshurideh, 2012; Alzoubi et al., 2021h; Farouk, 2022; Mehrez et al., 2021). In order to successfully implement new strategies, organizations may need to restructure and enhance a number of their current operational processes, such as the advanced implementation of cyber security operations during the delivery of their product or services to reduce the risk of loss, fraud, theft, and cyber attacks (Durowoju et al., 2020). Based on the above discussion, the following hypothesis was proposed: H3: Cyber security and risk management significantly impact green operations.

4.4 Problem Statement and Research Gap Cybercrimes include identity theft, hacking, virus propagation, computer fraud, and other related incidents. Managing cyber security risk helps identify and comprehend the risk ratings for occurrences and put the proper processes or controls in place to ensure that the company works at acceptable risk tolerance levels, even if it does not eliminate all hazards during operations (Bojanc & Jerman-Blažiˇc, 2008). Revolutionary organizations that protect their operations from security risks by producing and delivering the products safely may improve their green operations (Cheung et al., 2021). This research’s objective is to investigate the impact of cyber security and risk management on green operations by finding empirical evidence and highlighting the previously provided gap with the help of the literature and empirical data derived from the UAE security industry.

4.5 General Research Model See Fig. 1.

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Cyber Security

H1

H3 Green Operations

Risk Management

H2

Fig. 1 Conceptual research model

4.6 Research Hypothesis H1: Cyber security significantly impacts green operations in the UAE security industry. H2: Risk management significantly impacts green operations in the UAE security industry. H3: Cyber security and risk management significantly impact green operations in the UAE security industry.

4.7 Research Methodology and Design The research emphasized investigating cyber security and risk management in green operations using quantitative techniques with a descriptive, exploratory, and analytical design to clarify the empirical results. Moreover, a convenient cluster sampling technique was applied within a specific population area. An online survey was conducted to gather data through emails for primary data collection.

4.8 Population, Sample, and Unit of Analysis The security sector was selected as the targeted population of the research. Seventyseven security-providing (product and services) companies were accessed by email

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to their administrative departments with a total of 700 questionnaires, from which accurate data was received from 273 after a screening process. The questionnaire used a five-point Likert scale with 29 items to measure each research construct. A demographic section included the participant’s “gender” and “designation.”

5 Data Analysis 5.1 Demographic Details The demographic data for the current survey showed the total number of respondents, n = 273, of which there were 169 (71%) males and 68 (29%) females. Moreover, the respondent’s designations showed that a high proportion of them, 82 (34.6%), included cyber security managers.

5.2 Reliability, Descriptive, and Correlation Before additional validation analysis, the statistical reliability was evaluated using Cronbach’s Alpha, which indicated the cyber security α = 0.80, the risk management α = 0.84, and the green operations α = 0.83. The descriptive statistics data showed the extent of acceptance with the cyber security mean = 2.86 and SD = 62%, the risk management’s mean = 3.32 and SD = 69%, and green operations mean = 3.90 and SD = 72%. Table 1 also demonstrates the correlation coefficients at a significance level p < 0.05. The findings depicted a high correlation between cyber security and risk management r = 0.84, p < 0.05**. The correlation between cyber security and risk management was depicted as r = 0.72 at level p < 0.05**. The relationship between risk management and green operations was significant and highly correlated as r = 0.79 at level p < 0.05**. Table 1 illustrate the summary of the findings. Table 1 Reliability, descriptive, and correlation coefficients Construct

No. of items

Cronbach’s alpha

Mean

S.D.

Cyber security

Risk management

Cyber security

6

0.80

2.86

0.62

1

Risk management

9

0.84

3.32

0.69

0.841**

1

Green operations

7

0.83

3.90

0.72

0.722**

0.793**

Green operations

1

Cyber security (M = 2.86, SD = 62%, Risk management (M = 3.32, SD = 69%), Green operations M = 3.90, SD = 72%. Level of significance at P < 0.05**

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Table 2 Hypothesis testing through ANOVA Hypothesis

Regression weights

Standardized coefficients β

R2

Adjusted R2

p-value

t-value

Hypothesis supported

H1

CS → GO

0.722

0.521

0.519

0.000

2.58

Yes

H2

RM → GO

0.793

0.629

0.628

0.000

8.77

Yes

H3

CS*RM → GO

0.800

0.639

0.636

0.000

8.24

Yes

Dependent variable = Green operations, Independent variable = Risk management and cyber security, *Level of significance (α ≤ 0.05**), Critical t-value (df/p) = 1.6

5.3 Regression Analysis, and Hypothesis Testing The regression analysis by ANOVA reveals several notable findings. For H1, cyber security was confirmed to have a significant positive relationship on green operations (β = 0.72, p = 0.000, t = 2.58, R2 = 52%), indicating a positive relationship concerning the summary mentioned in Table 2. For H2, risk management was confirmed to have a significant positive relationship with green operations, with β = 0.79, p = 0.000, t = 8.77, and R2 = 62%, which indicates a positive critical value with a reasonable variance of the construct. For H3, cyber security and risk management were confirmed to have a significant positive relationship on green operations with β = 0.80, p = 0.000, t = 8.24, and R2 = 63% (Table 2).

6 Discussion of the Results The research showed that the hypothesized model is consistent with past investigations. Using information technology tools effectively ensures that firms will continue to grow over time. Future supply chain operations will be autonomous and include the Internet of Things, sensors, and artificial intelligence to identify end-to-end real-time information operations to prevent cyber risks (Pandey et al., 2020). Thus, H1 and H2 are supported. According to this current research, H3 is accepted. Past research rarely investigated cyber risk connected to cyber activities. However, one of the most challenging and quickly changing concerns that modern enterprises must deal with is a cyber risk to smoothly operate their manufacturing and marketing process compared to their opponents (Ogbanufe et al., 2021). This research formulates a foundation for evaluating all constructs for that purpose. It lays the groundwork for more studies in this field, which has benefited the research community.

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7 Conclusion Coordinating cyber security with risk management is crucial for any organization to attain operational development. Cyber security is significant in assisting against external and internal threats and preventing security risks. Cybersecurity seeks to guarantee an organization’s execution and maintenance to safeguard it and its user’s assets from security hazards in the cyber environment. The security industry is vigilant due to various new and growing cybersecurity threats. The data and assets of businesses, governments, and people are always at risk due to more complex assaults involving malware, phishing, machine learning, and artificial intelligence, cryptocurrencies. In the attempt to risk management, the security companies may require employee training, monitoring, detection, and managing to develop improved green operations.

8 Recommendations/Limitations After reaching the specified findings, there remain a few acknowledged limitations in the current research. Firstly, the number of respondents from one specific city and sector limits the results. It is recommended that future studies incorporate more industries spreading globally. Secondly, a comprehensive concept of cybersecurity has been investigated with limited information. It is recommended that future studies expand the study construct, for instance, with the mediating effect of strategic implementation.

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Robot-Based Security Management System for Smart Cities Using Machine Learning Techniques Ahmad Qasim Mohammad AlHamad , Samer Hamadneh , Mohammed T. Nuseir , Muhammad Turki Alshurideh , Haitham M. Alzoubi , and Barween Al Kurdi Abstract Recently, smart cities are developing more slowly, gathering plenty of data and communication skills to improve service worth. Despite the smart city concept offerings many beneficial services, security management is still a significant problem because of shared threats and activities. The security aspects of smart cities should be constantly assessed to remove the unnecessary events employed to improve the superiority of the facilities to solve the issues. This study shows how robots are used in the smart city to manage privacy-related problems and actively learn how to forecast the superiority of facilities. Today, smart city development depends heavily on advancing technologies like the Internet of Things (IoT), Artificial Intelligence A. Q. M. AlHamad College of Business Administration, University of Sharjah, Sharjah, United Arab Emirates e-mail: [email protected] S. Hamadneh · M. T. Alshurideh (B) Department of Marketing, School of Business, The University of Jordan, Amman 11942, Jordan e-mail: [email protected]; [email protected] S. Hamadneh e-mail: [email protected] M. T. Nuseir Department of Business Administration, College of Business, Al Ain University, Abu Dhabi Campus, P.O. Box 112612, Abu Dhabi, UAE e-mail: [email protected] M. T. Alshurideh Department of Management, College of Business, University of Sharjah, 27272 Sharjah, United Arab Emirates H. M. Alzoubi School of Business, Skyline University College, Sharjah, UAE e-mail: [email protected] B. Al Kurdi Department of Marketing, Faculty of Economics and Administrative Sciences, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan e-mail: [email protected] H. M. Alzoubi Applied Science Research Center, Applied Science Private University, Amman, Jordan © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 H. M. Alzoubi et al. (eds.), Cyber Security Impact on Digitalization and Business Intelligence, Studies in Big Data 117, https://doi.org/10.1007/978-3-031-31801-6_10

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(AI), Blockchain, and Geospatial Technology. Machine learning, a branch of artificial intelligence, excels in security management systems. The proposed model may overwhelm the security challenges and presents how to keep and obtain their necessary robot-based security solutions by providing maintaining security services. Keywords Robotic systems · Smart cities · Security systems · Machine learning

1 Introduction By 2050, the United Nations (UNs) predicts that about 68% of people will stay in city areas and that 55% prefer to settle in urban areas. Increased metro migration is caused by the effects of urbanization, work pressure, and political policymakers. People are encouraged to manage economic stability, communities, and sustainability in order to maximize and improve lifestyle quality (Thangavel, 2018). In order to solve the issues with metro migration, smart cities are being developed. Various information communication techniques and software systems are used to do this (Aljumah et al., 2021; Alshurideh et al., 2014; Alyammahi et al., 2021). In order to meet people’s demands, advanced smart cities need more procedures and deliberate technological adoption strategies (Ghazal et al., 2022; Sweiss et al., 2021; Xu et al., 2020). The security aspects must be considered when designing smart cities with the aid of the techniques (Alshurideh et al., 2021; Ghazal et al., 2021a, 2021b; Kurdi et al., 2022a, 2022b, 2022c). Generally, advancements are made according to plan (Alzoubi et al., 2020); the metropolis takes any residual issues with smart products into account. Security is one of the important aspects of smart cities (Alshurideh et al., 2020; Ratkovic, 2022) because security-related factors can have a significant impact (Al Batayneh et al., 2021; Al Shebli et al., 2021). For instance, low-security intelligent cities are increasing the ease (Obaid, 2021) with which external servers can get into the process and access, disrupting system control and distracting mobile communication (Aburayya et al., 2020; Naqvi et al., 2021). The analysis demonstrates unequivocally that security-based design is necessary for smart cities (Ahmad et al., 2021; Alshurideh, 2019; Alshurideh et al., 2022; Zhang et al., 2017). Intelligent robots are utilized in smart cities to uphold protection to obtain the smart model’s safety component. An additional stable person must relocate to a municipal space because of the rapid changes in everyday life, full utilization, and consumption of cities (Farouk, 2021). Robots, which are artificial humans, are employed to control and stable human movement and lifestyle (Ghazal et al., 2021a). The machines examine the environment, handle human tasks, and provide comprehensive information about particular problems (Tiddi et al., 2020). Instead of moving on to the machines, they should comprehend human domains like healthcare, community, financial, and electoral information (Lee et al., 2022a), that further aids in establishing essential protection in intelligent cities (AlHamad et al., 2022a, 2022b; Hammad et al., 2022). The artificial intelligence and computer science lab

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that created the flexible and manufactured smart robots successfully developed the robots’ capability (Ali et al., 2022) to cooperate with intelligent city processes and receive response on smart layout and assistance (Al Hamad, 2016; Alhashmi et al., 2020; Salloum et al., 2020a, 2020b). Because smart cities are made up of numerous sensors and smart devices, the automatons are also managing large data that is gathered by intelligent sensor gadgets (AlShamsi et al., 2021; Yousuf et al., 2021). In order to manage the quality of services over a given time period, smart cities require a continuous learning process (Mondol, 2021). In order to predict valuable services, smart computers are created and incorporated into smart cities for these purposes (Alnuaimi et al., 2021; Alsharari, 2022; Hamadneh et al., 2021). Robots may monitor smart cities, but other important concerns include their features, services, security, and privacy (Shamout et al., 2022). The public safety, momentum, and medical sectors will need safety in 2024 due to security concerns and cyberattacks affecting about 44% of the sectors. The components of smart cities include various gadgets, highly complicated system boards, water waste, manufacturing, places, house automation, telemedicine, and electronic authority procedures. In order to counter threats and intermediate attacks, these industries need increased security (Rashid et al., 2020). ML approaches are utilized in smart city functions to employ and install efficient, smart systems that can survive attacks and threats. Even though machine learning techniques enable the development of innovative, intelligent cities, security and privacy concerns arise when data is transferred to the cloud. False data injection impacts smart systems, reducing system dependability and modifying the system’s functionality (Torrey & Shavlik, 2009).

2 Literature Review Many researchers have worked for smart cities in robot-based security management systems by employing machine learning techniques. Some of their works are highlighted in this section. The importance of maintaining data security in smart cities was highlighted by Berkel et al. in their research on developing data system design. Information and communication technologies (ICT) are utilized to construct smart cities (Zafar et al., 2022). They are used to help users authenticate when accessing data and services within the infrastructure of these cities (El Khatib et al., 2022). Multiple stakeholders contribute to smart cities’ quality, and ongoing security lapses necessitate security measures. Consequently, the threat analysis and enterprise architecture model are developed to reduce security concerns and effectively control the range of smart cities (Alzoubi et al., 2022a). The threat information model, which reduces external attacks, is used in many smart city projects to measure the system’s effectiveness (Al Kurdi et al., 2022b; Zhang et al., 2021). Farahat et al. examined the different data protection issues in smart homes and smart cities because data information is becoming increasingly popular (Alshurideh

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et al., 2022; Alzoubi & Aziz, 2021). Education, mobility, government, e-health, and home services will become more intelligent (Alzoubi, 2021; Alzoubi et al., 2021; Edward Probir Mondol, 2022; Kashif et al., 2021). The security concepts needed by developed smart cities to manage data privacy and security when dealing with communication processes. Smart cities use secure wi-fi data broadcasting during the data transmission process to increase security (AlHamad et al., 2014; Alzoubi et al., 2022b; Ghazal et al., 2021a, 2021b). According to this study, a frequent issue in large city regions is high-level vehicle intensity. Providing an effective and safe direction is a never-ending battle in a smart city setting (Eli, 2021). Installing an intelligent traffic management process is one of the primary objectives because it makes it possible to resolve the problem. Different skills may be utilized to improve such systems (Hanaysha et al., 2021a; Saad Masood Butt, 2022), ranging from modest and less difficult ones, such as car direction-finding and traffic handle, to extremely difficult ones, such as multiple closed-circuit television cameras (CCTV), all combined with real-time information (Alzoubi & Ahmed, 2019), DL approaches, and computer vision (CV), to yield a real-time decisionmaking process that provides direction (Alhamad et al., 2021)and data for further examination. A Boston case is an example of smart traffic (Magaia et al., 2021). The authors suggest that because domestic robots resemble humans externally, they are also referred to as humanoid robots (Mehmood et al., 2019). The humanoid Robot’s motion planning presents the most significant difficulty (Alhamad et al., 2022a). Planning algorithms for motion include those that identify the Robot’s ecofriendly deals in order to keep equilibrium and communication (Hanaysha et al., 2021b). In order to address the issue of daily inventory updates (Victoria, 2022), a robot inventory assistant named Andy Vision was developed (Radwan, 2022). The Robot has active antennas underneath its hoody and a mobile-based sonar to help it prevent roadblocks (Aziz & Aftab, 2021). Related object concept aids the Robot’s ability to tally the pieces in the shop and notify staff colleagues when merchandise is away from the sale or missing (Ahmed & Al Amiri, 2022). It assists the shops by pointing out the user’s requirements (Kurdi et al., 2022c; Liu et al., 2020). This study’s authors (Ponnusamy & Alagarsamy, 2021) offer an affordable walkin sensor jacket and open source applications for social humanoid robots using the Internet of Things (Akhtar et al., 2021; Lee et al., 2022b). The jacket includes vision, orientation, size, temperature, contact, temperature sensors, and a wireless communication module. The IoT feature enables the Robot to communicate locally and online with people (Kurdi et al., 2022a). As a result of its modular architecture, developers can easily add, remove, or update complex behaviours. Most of the approaches have been used while employing and constructing several smart as well as intelligent frameworks (Alzoubi & Yanamandra, 2020) like machine learning approaches that may provide assistance in designing emerging solutions for the rising challenges in designing smart cloud-based monitoring management systems (Ghazal et al., 2021a).

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3 Problem Statement and Research Contribution Multiple Internet of things (IoT) gadgets have been connected to handle data security. The IoT gadgets are interlinked with the different smart cities facilities like power grids, public water systems, traffic control, waste management, public transportation so on. The main challenge is to secure the data on the cloud, so this proposed model is to protect data by robot-based security using blockchain technique and contributed this study as well.

4 Proposed Methodology Every firm in the modern world uses the most recent technology and creates robotbased models to boost productivity and reduce the need for human labor. Therefore, industries are developing autonomous systems. Self-driving, robotic, and artificial intelligence (AI) technologies are all used in robotic systems, which are automated guards used for surveillance and security activities. Patrolling, reporting, monitoring, investigating, and intruder detection are all capabilities of these systems. Today, Industrial networks are probably becoming an even more attractive target for cyber-attacks in the future because of increasing connectivity to outside facilities and sites. The future automated management processes might be installed in highly connected surroundings where they can connect to remote machines like cloud computing or manufacturing systems. Computer security poses a growing threat to the security of the robotic systems at the centre of this novel change in the solid digitization tendency in production plants. Even before products are sold in large quantities, it is anticipated that the Robot Operating System (ROS) may have security problems that would need to be resolved. In this research work, a secure robotic management system is being introduced that may represent the most familiar susceptibilities of ROS, assault vectors to develop those and many techniques to ensure ROS and the same processes. This research shows how to ensure ROS on a function level and defines a solution incorporated quickly into the ROS core. The proposed model is shown in Fig. 1.

Fig. 1 Proposed model

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It is described in Fig. 1 that the robotic information’s sensed through the robotic management system and forwarded to the e-health gateway device. The e-health gateway device is utilized to collect data and store it in the data collection database. After data collection, the data is passed to the blockchain layer. The blockchain layer is used in the robotic management system to preserve and exchange robotic information through the robotic management system. Blockchain applications can accurately identify severe and dangerous mistakes in the robotic field. After blockchain, the data is forwarded to the data preprocessing to minimize noisy data. And then, the preprocessed information’s passed for the classification, where machine learningbased approaches may be applied to diagnose the disease, and it is tested whether the learning criteria are met or not. In the case of No, the classification process is retrained, whereas in the condition of Yes, the trained outcome will be gathered on the cloud. After this process, the trained outcome will be imported from the cloud for predicting purposes, and it will be checked that if the robotic management is found, the message will be displayed that secure management is found.

5 Empirical Analysis In smart cities, there are many challenges faced by people and improving their lifestyles such as infrastructure must be a foundational element, smart city IT infrastructure must be agile and flexible to scale, cities need effective and efficient data processing and analytics, cities must protect residents’ data from assuaging privacy concerns, political differences should be a roadblock to smart city deployments and public and private sector organizations need to coordinate and security. Table 1 shows that the deep belief network achieves maximum accuracy of 97.81 and a miss rate of 2.19%. This article analyzes the security of smart cities using machine learning techniques, and a robot base model is developed. This proposed model may perform better than the deep belief network. Table 1 Comparison of the previous AI approaches

Authors

Approach

Accuracy

Miss-rate (%)

Shahan et al. (2021)

Deep belief network

97.81

2.19

Khan et al. (2022)

Artificial neural network

93.60%

6.4

Ali et al. (2022)

Fusion approach 93.33%

6.67

Abbas et al. (2020)

Convolutional neural network

93.1%

6.9

Ghazal et al. (2022)

Fused machine learning

90.70%

9.30

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It is shown in Table 1 that some previous AI approaches are utilized to develop robot-based systems with their performances in terms of accuracies and miss-rate.

6 Discussion The above analysis demonstrates the proposed approach while predicting anomalies and detecting obstacles in smart cities. With the help of the Collected information, the machine learning empowered with the blockchain concept successfully trains the system to effectively capture the anomalies in the situation. The proposed model may overwhelm the security challenges and presents how to keep and obtain their necessary robot-based security solutions by providing security services.

7 Conclusion This article examines the security idea in smart cities employing ML techniques. Initially, cobalt security robots are cohesive with the smart city since they efficiently capture adjacent information, interests, and patrol in a smart city. Previously, The robotic information is processed by applying the self-determining modular neural network, which includes the probabilistic and deep stack networks. In this research, the proposed model performs better than a self-determining modular neural network from a security perspective in smart cities.

8 Limitations and Future Directions Better space utilization, less traffic, cleaner air, and more effective municipal services all contribute to a higher quality of life in smart cities. Additionally, smart cities offer additional prospects for employment, economic growth, and greater ties to the local community. In smart cities, Robot based security management system has many challenges such as poor user authentication scheme in terms of security and privacy, lack of firewall protection, inefficient Intrusion Detection Systems (IDSs), etc. In this research, the proposed model has a blockchain-based secure approach by using machine learning to overwhelm these challenges and presents how to keep and gain their needed Robot based protection solutions by providing maintaining security services (Confidentiality, Message/Device integrity and Device/Data availability), Strong user authentication scheme in terms of security and privacy, enhancing security policy, Smart Cooperation with non-cryptographic solutions, strong password and dynamic hashing process, defining privileges, real-time monitoring, security check and up to date systems. In the future, it is recommended that the various security aspects, services, and best practices be put in place to ensure solid and safe robotic

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systems while keeping the needed performance and superiority of service. These robot-based security management systems will also be very effective for smart cities like firmware integrity and secure boot, mutual authentication, security monitoring, analysis and security lifecycle management, etc.

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Business Digitalization

Digital Sustainability and Strategic Supply Chain for Achieving a Competitive Advantage: An Empirical Evidence from Telecommunication Industry in the UAE Enass Khalil Alquqa, Barween Al Kurdi , Haitham M. Alzoubi , Muhammad Turki Alshurideh , Samer Hamadneh , and Ahmad Al Hamad Abstract The research seeks to discuss and examine impact of digital sustainability and strategic supply chain on competitive advantage. It also attempts to establish a model to mitigate the empirical evidence from the Telecom industry in the UAE. Empirical evidence expresses a contemporary perspective that can increase the competitive advantage through digital sustainability and a strategic supply chain in the Telecom sector, which has not been considered in research to date. The research was conducted through an online survey with a convenient sampling technique and incorporated the descriptive, exploratory, causal and analytical research design. A valid sample of 312 participant data was used to measure results through regression, ANOVA. Key findings revealed that digital sustainability is positively correlated E. K. Alquqa College of Art, Social Sciences and Humanities, University of Fujairah, Fujairah, UAE e-mail: [email protected] B. A. Kurdi Department of Marketing, Faculty of Business, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan e-mail: [email protected] H. M. Alzoubi School of Business, Skyline University College, Sharjah, UAE e-mail: [email protected] Applied Science Research Center, Applied Science Private University, Amman 11937, Jordan M. T. Alshurideh (B) · S. Hamadneh Department of Marketing, School of Business, The University of Jordan, Amman 11942, Jordan e-mail: [email protected]; [email protected] S. Hamadneh e-mail: [email protected] M. T. Alshurideh · A. A. Hamad Department of Management, College of Business Administration, University of Sharjah, Sharjah 27272, UAE e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 H. M. Alzoubi et al. (eds.), Cyber Security Impact on Digitalization and Business Intelligence, Studies in Big Data 117, https://doi.org/10.1007/978-3-031-31801-6_11

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with competitive advantage. Besides, it was examined that a strategic supply chain is significantly associated with competitive advantage. The research has limited generalisability as the data was only collected from telecommunications companies in Dubai. A divergent industry population should be considered for future research. Incorporating digital sustainability measures into the telecommunication companies and strategic supply chain management can strengthen their competitive advantages and meet customer demands. Keywords Digital sustainability · Strategic supply chain · Competitive advantage · Telecommunication industry in the UAE

1 Introduction Currently, the business sectors are facing sudden changes in the way they conduct their business activities, resulting in huge losses if they fail to cope with the changes. The adoption and innovation of technology in strategic planning reflect organisational competitiveness (Almaazmi et al., 2021; Ghannajeh et al., 2015; Obeidat et al., 2020). A strategic supply chain needs to be implemented to become more competitive and innovative (Alzoubi et al., 2022a, 2022b, 2022c; Shamout et al., 2022). This concept is becoming increasingly important in various industries to gain a competitive advantage. Significant increases in the supply chain can be achieved by designing and implementing strategic supply chain networks (Alzoubi et al., 2022a, 2022b, 2022f, 2022g; Hamadneh et al., 2021a, 2021b). To achieve a high competitive advantage, organisational resources, objects, and parameters must emphasise the capabilities of a strategic supply chain network (Alshurideh et al., 2022; Ebraheem, 2017). Consequently, organisations need to improve their strategies and methods and increase market competition (Awawdeh et al., 2022b; Hamadneh et al., 2021a, 2021b). Organisations that successfully establish their strategic supply chain stand out from their competitors in several ways, including higher sales, increased profits, and higher customer retention due to better customer experiences (Alolayyan et al., 2022a, 2022b; Alshurideh et al., 2012; Alzoubi et al., 2022n). SSCM assists businesses in accurately forecasting consumer demand, satisfying that need quickly and reliably, and increasing supply chain productivity (Demeter & Gelei, 2004). This ultimately leads to lower supply chain costs, faster market responsiveness, and improved supply chain efficiency and effectiveness (Al-bawaia et al., 2022; Alshurideh et al., 2020). In the Telecom sector, innovativeness and quick service delivery are priorities, which can enable the organisation to acquire advanced technology with strategic implementation (Alshurideh, 2022; Alzoubi et al., 2022o; Ashal et al., 2021; Eli & Hamou, 2022). The research focuses on the sustainability and strategic implementation of the supply chain to achieve a competitive advantage in the telecom industry

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(Al-Dmour et al., 2021; Alaali et al., 2021; Alzoubi et al., 2021a, 2022b). The empirical evidence can help in identifying the required factors and customer requirements and their fulfilment according to the market trends.

2 Theoretical Framework 2.1 Strategic Supply Chain The supply chain refers to the management that manages the production cycle, from the organisation of raw materials to their production, ensuring smooth delivery (Alzoubi et al., 2022b; Nuseir et al., 2020). The strategic supply chain system is further defined by theories such as resource-based theory, stakeholder theory, transaction cost theory, institutional theory, and resource dependence theory (Alshraideh et al., 2017; Ben-Abdallah et al., 2021). These theories have described supply chain management strategies as follows (Halldorsson et al., 2007): Resource-based theory: This resource-based theory focuses on the relationship between the organisation’s internal features and its representations. This theory enables the organisation to enhance performance by providing occasional, valuable, and hard-to-copy resources that help the firm run its business through this competitive advantage (Halldorsson et al., 2007; Shamout et al., 2021). Stakeholder theory: The theory emphasises the decision-making purposes for outsourcing and supplier strategies. This stakeholder theory focuses on creating value for stakeholders and shareholders that help the company gain competitive advantages in the market (Alzoubi et al., 2022s; Halldorsson et al., 2007). Transaction cost theory: The transaction cost theory is based on the economic theory, which leads the firm to optimise its economic structure by reducing costs and supporting the development of supply chain management (Halldorsson et al., 2007). Institutional theory: The institutional theory helps the organisation to adopt new and advanced strategies for effective business in the competitive market while introducing new strategies for innovation. This theory focuses on the efficiency of the technology used in the organisation and creates an organised structure for better improvement of supply chain management (Halldorsson et al., 2007). Resource dependence theory: The theory states in today’s competitive world, a company requires extensive resources for effective business. Thus, this theory helps the organisation acquire various resources for supply chain development (Halldorsson et al., 2007).

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2.2 Competitive Advantages The competitive advantages help an organisation to produce better products to run its business more effectively than its competitors. Porter’s Five Forces theory has been used to analyse these competitive advantages in business sectors as described (Ali & Anwar, 2021). Competition in the business: The number of competitors in business reduces a company’s strength which can affect the business, whereas a small number of competitors gives the company strong growth and huge profits (Gerard & Bruijl, 2018; Miller, 2021). Potential of new entrants: The potentiality of the new entrants can cause a company to generate low profits because the new entrants reduce the company’s strength by entering the business sphere (Ali & Anwar, 2021; Alzoubi et al., 2022j, 2022k). Power of the suppliers: The suppliers are the strength of a company, as the number of suppliers determine the company’s profits. Fewer suppliers would become a threat as companies have to rely on them, which makes them increase their inputs, which leads to losses in the company (Gerard & Bruijl, 2018). Power of customers: A considerable number of customers helps the company to build its strength by making profits without compromising customer satisfaction (Awawdeh et al., 2022a). The threat of substitute products: The substitution of products dramatically threatens the company’s future as it has the chance to take its place among customers, which leads to a great loss for the business (Aljumah et al., 2021; Nuseir et al., 2021).

2.3 Digital Sustainability Data experts define digital sustainability as constantly updating and modifying content, responding to the changing technological environment (Kashif et al., 2021), and maintaining access to digital resources (Bradley, 2007). As long as a society and a socio-technical system still exist and wish to maintain and care for the information stored, digital sustainability acknowledges that the responsibility for access is shared by those in the present and the users of a future time (2019b; Aljumah et al., 2022a; Alshurideh et al., 2019a, 2019b; Alzoubi et al., 2022a, 2022b). This time may be as near as tomorrow or in the dimly perceived future. The theoretical background of the sustainability issues in the business sphere that determines the essence, function, and utility of the same in the current business context (Cricelli & Strazzullo, 2021): Theory of corporate social responsibility: Social responsibility of the business organisations and its impact on enhancing the living standards of customers while simultaneously addressing the environmental concerns of contemporary society

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(Alshawabkeh et al., 2021; Alshurideh et al., 2019a, 2019b). The theory states that the function of sustainability is to ensure the social positioning of corporate organisations by prompting them to undertake social roles (Bradley, 2007; Shishan et al., 2021). Stakeholder Theory: The responsibility of the companies is to develop stakeholder relationships with the organisational culture, which helps regulate the supply chain management of the firms (Eli, 2021). The theory recognises the significance of sustainable measures to ensure the development of this relationship in the organisational culture with technological development (Abuhashesh et al., 2021; Al Kurdi et al., 2021; Hamadneh & Al Kurdi, 2021; Odeh et al., 2021; Stürmer, 2014). Corporate sustainability: To determine the company’s position in its operation’s corporate or business environment. The theory specifies the role of sustainability in determining strategies that give the company competitive advantages and strengthen its position in the current market (Abu Zayyad et al., 2021; Stürmer, 2014).

2.4 Operational Definitions

Variables

Definition

References

Digital sustainability

The frequent use of technology in (Hajishirzi et al., 2022) company operations to improve the environment is known as digital sustainability. People worldwide are trying to reduce the environmental damage that can be caused by sustainability and digital technology, which has led to the current movement

Strategic supply chain

SSCM is defined as the integration (van der Westhuizen & Niemann, of supply chain organisations and 2022) activities strategically, operationally, and technologically through interactions, procedures, and information sharing, to attain a competitive edge

Competitive advantage A situation or scenario that favours (Ali & Anwar, 2021) or enhances a company’s competitive position Maintaining a competitive advantage requires expansion

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2.5 Industry Description (Telecommunication Industry in the UAE) The increase of end-user industry applications and expansion in sectors such as the Internet of Things, cloud, data centres, and 5G are driving the growth of the UAE’s telecom sector. Additionally, the country is experiencing an increase in internet usage due to industry expansion, such as video on demand (VOD). Numerous businesses are introducing cutting-edge internet plans to fulfil the rising demand for internet services and win over a large portion of the market (Alzoubi et al., 2020a, 2020b, 2020c; Nuseir et al., 2021a, 2021b). During the forecast period, the UAE telecom market is anticipated to see a CAGR of 3.5%. (2022–2027). Established rules and government programmes support the area’s highly developed telecommunications industry. The UAE telecom market is consolidated, with large players holding most of the market share, including Etisalat, du, Thuraya Telecommunications Company, Al Yah Satellite Communications, and Virgin Mobile UAE. Additionally, individual participants are setting up the 5G network by collaborating with different 5G infrastructure suppliers worldwide. Considering high competitiveness, it is necessary to investigate the criteria of technological development in this competitive market (Awadhi et al., 2022; Nuseir, 2019).

3 Literature Review 3.1 Relationship and Impact of Strategic Supply Chain on Competitive Advantages According to research, the strategic supply chain can positively impact the organisation’s competitive advantages as this resource-based theory conducts the competitive advantages, including technological skill, strategies, and knowledge (Al Mehrez et al., 2020; Al-Maroof et al., 2021; Qasaimeh & Jaradeh, 2022; Youn et al., 2013). Supply chain management adopts many strategies and ideas to cope with the organisation’s situational changes and the global market, which impact the company’s competitive advantages (Kamaruddeen et al., 2022; Lee et al., 2022). Competitive advantages are the process through which an organisation creates a justifiable position over its competitors in the market, thus, improving the organisational performance through active operations (Alzoubi et al., 2022p). In the current business environment, the competition transfers from organisations to their supply chains, as the supply chain management determines the costs of the inputs, imposing a significant effect on the business profits (Almazrouei et al., 2020; Alzoubi et al., 2022r, 2022m; Nasim et al., 2022; Youn et al., 2013).

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Several authors have pointed out that supply chain management avails popularity as it becomes the source of competitive advantage in the business sectors (Alzoubi et al., 2022l). The business industry has adopted the supply chain management to keep pace with technological advancements to survive in this ever-changing technologyoriented world (Alzoubi et al., 2022a, 2022b, 2022c; Nurova & Freze, 2021). Moreover, this supply chain enhances the chances of producing quality products in time, thereby delivering them to its customers in the scheduled period (Aljumah et al., 2022b). Through this management, Big Data exploitation develops competitive advantages for the business, which positively affects the operational performance of the business organisation (Alzoubi & Yanamandra, 2022). Furthermore, the adaptability of supply chain management refers to the changes adopted during the changes in the structural environment of the business, which has influenced the competitive advantages for surviving in the constantly changing technological world (Alzoubi et al., 2022f; Khatib et al., 2022). The supply chain performs a major role in reducing costs, managing the limited period, and satisfying the demands of the customers (Alzoubi et al., 2022d, 2022e). Organisations need to change their strategies, technologies, and skills in the constantly changing world to represent the organisation as defensible in front of its competitors in the existing market (Attia & Essam Eldin, 2018; El Refae et al., 2021). The theory, relying on resources and knowledge, helps avail competitive advantages (Alzoubi & Yanamandra, 2020; Ghazal et al., 2022). To survive and run its business in this competitive world, the organisation needs to manage the changes in supply chain management (Mehmood, 2021), which greatly affects the competitive advantages from the evidenced relationship intent to support this research hypothesis. H1: Strategic supply chain has a significant impact on competitive advantage.

3.2 Relationship and Impact of Digital Sustainability on Competitive Advantage A study on supply chain management sustainability refers to the organisational achievements in the economic and environmental world of the competitive business market (Kasem & Al-Gasaymeh, 2022). This sustainability emphasises the business operations that help improve the organisation’s economic and environmental performances (Alameeri et al., 2021; Saberi et al., 2019). Social sustainability needs to keep environmental performance first on the list (Victoria, 2022). A sustainable supply chain needs to identify the problems within the organisation, therefore adopting necessary measures to resolve them and taking the responsibilities of the social environment (Akhtar et al., 2021). Similarly, to make the suppliers follow sustainable methods, the organisations have taken preventive measures such as recognising the risk factors and solving them with suitable solutions to improve the business (Alawneh & Hattab, 2009; Alzoubi et al., 2022h, 2022i). However, supply chain management dramatically

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impacts organisational and sustainability performances, which leads the organisation to create a defensible position in this highly competitive world (Altamony et al., 2012; Del & Solfa, 2022). Sustainable supply chain management aims to provide an active and effective supply base that leads the company to a sky-high level (Sobb et al., 2020). An effective, sustainable supply chain management increases productivity while minimising the costs of the inputs as well as maximising the profits of the company (Alzoubi et al., 2022q). Organisational performances should occur under the limits of the environmental and social sphere to create sustainability (Ratkovic, 2022). Financial sustainability comes in third place as environmental and social sustainability are the most important things to be considered (Alzoubi et al., 2017; Alzoubi et al., 2022m, 2022n). The sustainable supply chain helps to improve the quality of the products, minimises the costs, and creates a better agile and sustainable business (Alzoubi, 2022). Supply chain management can improve organisational performance, thereby achieving the organisational goals without compromising the customer’s satisfaction and the environmental atmosphere (Lee et al., 2022). It is said that effective technological development is the most important for the growth of the business sectors, which directs the organisation to generate and add value to the business in this highly competitive world (Alzoubi & Ahmed, 2019; Radwan, 2022). A sustainable supply chain expands the durability, flexibility, and affordability of the products among its consumers, which leads the company to survive in this competitive business market. Regarding the presented literature, the literature supports the hypothesis of this research. H2: Digital sustainability has a significant impact on competitive advantage.

3.3 Impact of Strategic Supply Chain Management and Sustainability on Competitive Advantages Strategic supply chain management plays a crucial role in creating competitive advantages for the companies by regulating the financial activities and enhancing the production level of the organisations (Alzoubi et al., 2021b; Farouk, 2022). Green management, of late, has won rising popularity in the supply chain management activities of organisations in the current business environment owing to the rising environmental concerns of people across the globe (Goria, 2022). The introduction of green management through the implementation of sustainability measures in the organisational culture thus plays a vital role in developing relationships between environmental programs of orientation and the business operation of the organisations (Alzoubi et al., 2020a, 2020b, 2020c). Some authors have argued that green management of organisations can enhance the companies’ market positioning by fostering financial management and enabling them to play active roles in fulfilling social responsibilities (Edward Probir Mondol, 2022; Papke-Shields & Boyer-Wright, 2017).

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Implementing sustainability measures in developing strategic supply chain management of the organisations can help increase the companies’ competitive advantages by enabling them to incorporate more partners in their scheme of operation by developing trust among them (Abuanzeh et al., 2022; Alzoubi et al., 2021e; Saad Masood Butt, 2022). The global social changes have accompanied changes in the customer purchasing pattern, with more buyers preferring to purchase ecofriendly products (Ghosh & Aithal, 2022). This can potentially promote transformations in the supply chain management system of the organisations, causing them to implement more sustainable measures in the existing pattern, which in turn develops the competitive advantages of the organisations (Alsharari, 2022; AlShurideh et al., 2019c). Analysing the impact of sustainability issues on the development of the competitive advantages of business organisations, several authors have remarked that the relationship between sustainability and firm performance is directly proportional, making it a crucial element in fostering firm productivity (Ahmed & Amiri, 2022; Alzoubi et al., 2020a, 2020b, 2020c). The sustainability measures in strategic supply chain management ensure creating stakeholder values that enhance the market share of the products produced by the company and thereby create competitive advantages for it (Alzoubi et al., 2021c). Incorporating these measures further enables the creation of organisational innovations that add to customer satisfaction with the services offered, thereby expanding its customer base and elevating its market positioning (Akhtar et al., 2022). Additionally, retaining innovation in the supply chain management of business organisations has remarked that green or sustainability measures play an essential role in fostering uniqueness in this aspect (Amrani et al., 2022; Demeter & Gelei, 2004; Alzoubi et al., 2021g). Tracing changes in the supply chain models of the business organisations from 1990 to the age of globalisation has observed that the current business environment has led to the occurrence of large-scale uncertainties in the supply of the resources of production owing to the emergence of strict national and international laws monitoring the actions of the companies (Alzoubi & Aziz, 2021). However, the literature suggested a significant relationship between digital sustainability and strategic SC to attain a competitive advantage in support of the proposed hypothesis of this research. H3: Digital sustainability and strategic supply chain significantly impact competitive advantage.

3.4 Problem Statement and Research Gap The telecommunications sector experienced a rapid global expansion in a matter of decades. Today’s communication technologies provide users several advantages on both an individual and corporate level. In addition to these benefits, modern communications promote economic expansion. The UAE’s telecoms industry is selected for empirical research to evaluate the strategic development within the management and

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Digital Sustainability

H1

H3

Strategic Supply Chain

Competitive Advantage

H2

Fig. 1 Research model

the extent to adopt digital sustainability to become competitive. To incorporate the gap identified in previous research, this research will contribute evidence of technology acquisition and criteria to implement the strategic supply chain for a better consumer experience.

3.5 General Research Model See Fig. 1.

3.6 Research Hypothesis HO1 : Digital Sustainability has no statistical impact on Competitive Advantage in the UAE Telecommunication Industry at (α ≤ 0.05) level. HO2 : Strategic Supply Chain has no statistical impact on Competitive Advantage in the UAE Telecommunication Industry at (α ≤ 0.05) level. HO3 : Digital Sustainability and Strategic Supply Chain have no statistical impact on Competitive Advantage in the UAE Telecommunication Industry at (α ≤ 0.05) level.

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3.7 Research Methodology and Design Literature have used quantitative research in their research methodology sections (Jabeen & Ali, 2022; Ali et al., 2020). In order to assess the research variables, this research used a quantitative empirical research strategy as the research method to evaluate experimentally managers’ awareness of the idea of competitive advantage through digital sustainability and strategic supply chain. The research design was selected as descriptive, exploratory, causal and analytical with a convenient sampling technique. To gather data online, a questionnaire survey was conducted.

3.8 Population, Sample and Unit of Analysis The Telecom industry is the targeted population of the research. Through the distribution of a questionnaire to a sample of the major telecom companies in Dubai, UAE, empirical data have been gathered from 312 telecom companies’ respondents through email, and data were analysed using regression analysis. Questionnaire is one of the mostly used tool to collect the data (Ali et al., 2020, 2021). The questionnaire was based on a five-point Likert scale from1 “strongly disagree” to 5 “strongly agree”. The variable assessment was made with 28 items, with nine items used for the “Digital Sustainability” measurement. 10-items were used to measure the “Strategic Supply chain”, and 9 items were used for the “Competitive Advantage” assessment.

4 Data Analysis 4.1 Demographic Analysis

Items

Description

F

%

Gender

Male

222

72.2

90

28.8

Female Job status

IT manager

139

44.6

Sales manager

98

31.4

SC manager

40

12.8

Retail marketer

35

11.2

N = 312, Male = 72.2%, Female = 28.8%

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Table 1 . Construct

No. of items

Cronbach’s alpha

Mean

SD

Digital sustainability

Strategic supply chain

Digital sustainability

6

0.78

2.75

0.67

1

Strategic supply chain

7

0.83

3.43

0.65

0.806**

1

Competitive advantage

6

0.80

3.17

0.55

0.655**

0.745**

Competitive advantage

1

Digital Sustainability (M = 3.45, SD = 77%, Strategic Supply Chain M = 3.11, SD = 57%, Competitive Advantage M = 3.51, SD = 58%. Level of significance at P < 0.05**

4.2 Reliability, Descriptive and Correlation Table 1: For reliability assessment, Cronbach’s alpha was applied to determine the sample’s validity, indicating good reliability of Digital sustainability = 0.78, Strategic supply chain = 0.83 and = 0.80 for competitive advantage. On the other side, the descriptive analysis shows that the mean value for digital sustainability as M = 2.75 and S.D. = 67%. The mean value for the strategic supply chain is M = 3.43, SD = 0.65%, whereas the mean value for competitive advantage is 3.17 and SD = 55%. The correlation coefficients depict a strong correlation with significant results of digital sustainability with a competitive advantage as r = 0.65, P < 0.05. The correlation is high for digital sustainability and strategic supply chain r = 0.80, at a significant P < 0.05. The strategic supply chain relationship with competitive advantage indicates a positive correlation r = 0.74 at a significance level of P < 0.05. Table 1 illustrate the overall summary of the tests.

4.3 Multiple Regression See Table 2.

5 Discussion of the Results H1: Research findings for the proposed model reveal a significant positive association between “Digital Sustainability” with “Competitive Advantage” with a value (β = 0.65, t-stat = 2.47); a positive finding support H1 with moderate variance R2 = 43%. Previous studies point out that technology adoption and

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Table 2 Regression analysis and hypothesis testing through ANOVA Hypothesis

Regression weights

Standardised coefficients β

R2

Adjusted R2

Sig.

t-value

Hypothesis

H1

DS → CA

0.655

0.430

0.428

0.000

2.47

Yes

H2

SSC → CA

0.745

0.555

0.553

0.000

9.73

Yes

H3

DS * SSC → 0.751 CA

0.561

0.563

0.000

9.13

Yes

* Level of Significance (α ≤ 0.05) ** Critical t-value (df/p) = 1.64

sustainability can boost organisational performance that ultimately reforms the competitive advantage (Hajishirzi et al., 2022). H2: The findings reveal a significant positive association between “Strategic Supply Chain” with “Competitive Advantage” at level (β = 0.74, t-stat = 9.73) and variance as R2 = 55% between both variables. In light of past literature, the strategic approach for enhancing business wellness and customer satisfaction improves the organisation’s competitive advantage (Georgiadis et al., 2005). H3: The proposed hypothesis aimed to determine the relationship that indicates a significant positive relationship between “Digital Sustainability” and “Strategic Supply Chain” with “Competitive Advantage” at the level (β = 0.75, t-stat = 9.13 with a high variance of R2 = 56%). The results are based on P < 0.05 level of significance. Some previous research focused on investigating competitive advantage impacting innovation and found significant results (Youn et al., 2013). Previous literature has paid less attention to the impact of digital sustainability and strategic supply chain on competitive advantage. Incorporating these concepts in Telecom Industry can help adopt an innovative plan for digital sustainability and strategic planning to gain a competitive advantage.

6 Conclusion The conclusion of the proposed research, which addresses the decision-making process for the entire supply chain, including distribution and product/service planning, can be improved through a strategic supply chain. Thus, a strategic supply chain improves the organisation’s capacity by maintaining digital sustainability to launch new products to market and improve existing products productively and effectively. Customer data and information is discussed and shared in real-time through the supply chain members, accelerating the process of quality improvement and product innovation while reducing time to market with digital sustainability. Moreover, it has the potential to significantly enhance customer demand forecasting, product planning, and optimisation, and coordinate organisational resources with the development of consumer value. A better understanding of the research findings for the telecom

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sector can improve operational efficiency and help enterprises achieve the desired business value.

7 Recommendations/Limitations A key concept initiated in this research with multiple perspectives encourages organisational management to acquire digital systems and new strategic implementation in the supply chain to become competitive. However, this research has some limitations, including limited generalizability due to a single focused city. Future studies could replicate this method using multiple cities or states (or multiple methods), such as, the impact of strategic dimensions of the supply chain and their effects on organisational competitiveness and from the perspective of information technology development.

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Artificial Intelligence

Explainable Artificial Intelligence (EAI) Based Disease Prediction Model Iman Akour , Mohammed T. Nuseir , Muhammad Turki Alshurideh , Haitham M. Alzoubi , Barween Al Kurdi , and Ahmad Qasim Mohammad AlHamad

Abstract Early disease prediction is a critical area of focus in healthcare. Identifying diseases at an early stage can increase the chances of successful treatment and reduce healthcare costs. Artificial Intelligence (AI) techniques like NLP, speech recognition, and machine vision can be used to predict and diagnose diseases. However, traditional AI methods can be error-prone. Explainable AI (EAI) techniques can reduce detection errors and improve prediction accuracy. This study proposes an EAI model for disease prediction using eSHAP. ESHAP can explain how a model arrives at a prediction, making it easier to understand and validate. The proposed model may provide better performance in accurate disease prediction. AI and EAI techniques I. Akour Information Systems Department, College of Computing and Informatics, University of Sharjah, Sharjah, United Arab Emirates e-mail: [email protected] M. T. Nuseir Department of Business Administration, College of Business, Al Ain University, Abu Dhabi Campus, P.O. Box 112612, Abu Dhabi, UAE e-mail: [email protected] M. T. Alshurideh (B) Department of Marketing, School of Business, The University of Jordan, Amman 11942, Jordan e-mail: [email protected]; [email protected] Department of Management, College of Business, University of Sharjah, 27272 Sharjah, United Arab Emirates H. M. Alzoubi School of Business, Skyline University College, Sharjah, UAE e-mail: [email protected] Applied Science Research Center, Applied Science Private University, Amman, Jordan B. Al Kurdi Department of Marketing, Faculty of Business, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan e-mail: [email protected] A. Q. M. AlHamad College of Business Administration, University of Sharjah, Sharjah, United Arab Emirates e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 H. M. Alzoubi et al. (eds.), Cyber Security Impact on Digitalization and Business Intelligence, Studies in Big Data 117, https://doi.org/10.1007/978-3-031-31801-6_12

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have significant potential to revolutionize disease prediction, early detection, and treatment, ultimately leading to improved health outcomes for patients. Keywords Explainable artificial intelligence (EAI) · EAI based model · Disease prediction

1 Introduction Disease prediction analysis holds significant importance in the healthcare sector. A disease can be described as a state of physical or mental distress, dysfunction, or even mortality resulting from a series of events (Alzoubi & Yanamandra, 2020; Shu et al., 2018). The impact of diseases on an individual’s well-being can be profound, leading to significant changes in their lifestyle and daily routines (Alshurideh et al., 2021; Nuseir et al., 2021a, 2021b). Pathological processes encompass the investigation of underlying disease causes, which are usually identified through warning signs and symptoms (Alnazer et al., 2017; Alshurideh et al., 2020a, 2020b; Rehman et al., 2022). Diagnosis involves the process of determining a disease based on a thorough analysis of its signs and symptoms, whereas identification entails the recognition of a disease centered on a patient’s hints as well as signs (Alzoubi et al., 2022b). To obtain the information required for the accurate prediction and diagnosis of a disease, a physical examination of the patient is typically required (Aburayya et al., 2020a, 2020b). This procedure usually involves conducting at minimum one identification procedure, such as investigative basic healthcare exams, to obtain essential information about a patient’s health condition (Alhashmi et al., 2020; Alshurideh, 2014). To arrive at an accurate diagnosis, a healthcare professional follows a series of steps, collecting and analyzing as much relevant data as possible (Alolayyan et al., 2022; Ghazal et al., 2021). Disease diagnosis and prediction are critical and challenging processes that healthcare professionals undertake to arrive at an accurate conclusion (). These procedures are often time-consuming and demand meticulous data gathering to ensure precision (Alshurideh et al., 2021; Nuseir et al., 2021a, 2021b). The aim is to minimize ambiguity in health diagnosis by leveraging empirical evidence to measure a patient’s disease accurately (Alzoubi et al., 2022a, 2022b, 2022c; Ghazal et al., 2021). This enables care providers to develop effective treatment plans that address the patient’s specific health needs (Hammad et al., 2022; Kurdi et al., 2022). Errors in the diagnostic system can cause delays or even lead to the overlooking of proper treatment for patients suffering from thoughtful medical disputes (Lee et al., 2022). Unfortunately, not every doctor is an expert in each zone of medication (Abualigah et al., 2022). Therefore, there was a need for an automated system that could combine the expertise of medical professionals with the precision of machine algorithms (Aburayya et al., 2020a, 2020b, 2020c; AlHamad et al., 2014; Alshurideh, 2022; Alshurideh et al., 2020a, 2020b). In order to ensure that the diagnostic process yields accurate results while also minimizing costs (Radwan & Farouk, 2021), an appropriate

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decision support system is necessary (Alameeri et al., 2021; Taryam et al., 2020). Categorizing disorders based on various factors is a complex task for human professionals (Miller, 2021), but the use of AI can aid in identifying and managing such cases (Tan et al., 2021). In the field of medicine, various AI techniques are currently being employed to accurately diagnose and predict illnesses (Aburayya et al., 2020a, 2020b). Dindorf et al. (2021) stated, AI is a crucial element in the realm of computing that enables machines to become smarter (Nuseir et al., Salloum et al., 2020a, 2020b; Svoboda et al., 2021). Slightly smart system should have the aptitude to acquire (Al AlShamsi et al., 2021; Shebli et al., 2021; Yousuf et al., 2021). AI employs different techniques that are based on learning, such as deep learning, machine learning, and other related methods (Al Batayneh et al., 2021; Naqvi et al., 2021; Yang et al., 2022). Though, there are quiet approximately difficulties with exact disease forecast (Alomari et al., 2019). To overcome these challenges, a idea identified as “Explainable Artificial Intelligence (XAI)” has been familiarized (Alhashmi et al., 2020; Alshurideh, 2014). XAI encompasses a set of methods and systems that enable individuals to comprehend and trust the results and output produced by Machine Learning (ML) techniques (Akour et al., 2021; Al Kurdi et al., 2021; Ali et al., 2021a, 2021b; N. Ali et al., 2022a, 2022b; Al Raza et al., 2022; Asif et al., 2021; Aslam et al., 2021; Dekhil et al., 2019; Fatima et al., 2020; Ghazal et al., 2022b; Khan et al., 2021; Salloum et al., 2020b). XAI encompasses a range of methods and systems that facilitate people’s comprehension and trust in the results and efficiency generated by Machine Learning (ML) techniques (Alolayyan et al., 2022; Ghazal et al., 2021). XAI assists in the identification of model accuracy, fairness, transparency, and decision-making outcomes supported by AI (Farouk, 2021; Ghazal et al., 2022a; Al Kurdi et al., 2022). Furthermore, XAI outlines the AI model, its anticipated effects, and potential biases to ensure complete transparency (Hammad et al., 2022; Kurdi et al., 2022). In essence, XAI strives to make AI models comprehensible to the human mind and capable of earning the trust of users by providing clear explanations of how decisions are made (Al Ali, 2021; Alnuaimi et al., 2021; Alshurideh et al., 2022). There are three fundamental steps that serve as the foundation of XAI. The first step is the scope of interpretability, which allows for the understanding of a model’s complete logic (global) or a certain choice or forecast (local) (Hamadneh et al., 2021). In XAI, the second step is the applicability of interpretability, which can be utilized for any AI algorithm or a specific type/class of algorithms (either model-specific or model-agnostic). The third step is the interpretability timing, which involves explaining a model after it has been trained and is unrelated to its internal structure (post hoc), or alternatively, when models are inherently easy to comprehend (intrinsic). Essentially, XAI aims to make AI models interpretable and comprehensible to humans, and this involves determining which interpretability methods are applicable to a given algorithm or class of algorithms and when to apply these methods to maximize effectiveness (Alsinglawi et al., 2022).

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2 Literature Review XAI is an essential aspect of AI, which focuses on developing methods for explaining how learning algorithms, derived models, or knowledge-based inference techniques work (A. ; Shamout et al., 2022). Several researchers have employed XAI to predict diseases (Guergov & Radwan, 2021). This segment acmes a scarce of their the whole thing (Alzoubi & Ahmed, 2019; Eli, 2021; Joghee et al., 2020; Lee & Ahmed, 2021): One application of XAI is predicting the effects of respiratory cleft involvement on postoperative cardiorespiratory complications and length of hospital stay for patients with non-small-cell lung tumors in the primary phase (El Khatib et al., 2022), Li et al. employed a physical feature selection method along with multivariate logistic regression (LR) (Alzoubi et al., n.d.; Kashif et al., 2021). Logistic regression is another useful method for analysis (Alhamad et al., 2022; Alsharari, 2021; Alzoubi et al., 2020; Mehmood, 2021). For example, the authors utilized binary logistic regression to investigate the ability of oxygen desaturation as well as heart rate to predict noteworthy postoperative cardiorespiratory complications in patients through non-small cell lung tumors (Al-Tahat & Moneim, 2020; Guergov & Radwan, 2021) (Alhamad et al., 2021; Muneer & Rasool, 2022; Zafar et al., 2022). Amoroso and colleagues proposed an XAI framework to improve breast cancer treatment (Ali et al., n.d.). The results of the experiment demonstrated that the method could identify the critical medical attributes of patients as well as their envisioned oncological actions (Abualigah et al., 2022), particularly after applying the alliance and dimension lessening techniques (Alzoubi et al., 2021; Cruz, 2021; Kasem, 2022). Dindorf et al. (2021), developed an interpretable spinal posture classifier that is independent of any specific pathology (Aburayya et al., 2020a, 2020b, 2020c; AlHamad et al., 2014; Alshurideh, 2022; Alshurideh et al., 2020a, 2020b). The authors employed LIME to explain the predictions of the machine learning classifier (Alzoubi et al., 2021; Cruz, 2021; Kasem, 2022), which was trained using Support Vector Machine (SVM) and Random Forest (RF) algorithms (Alhamad et al., 2022; Alsharari, 2021; Alzoubi et al., 2020; Mehmood, 2021). EI-Sappagh et al. introduced an RF model for recognizing Alzheimer’s disease (AD) and motion patterns in Zhang et al.’s study (2022). The authors utilized SHAP to identify the critical features for the classifier before building it Zhang et al. (2022), (Hamadneh et al., 2021). The authors then utilized a fuzzy rule-based system (Alameeri et al., 2021; Taryam et al., 2020), where SHAP enabled local explanations for feature influences in specific patient identification/movement prediction descriptions (Al Ali, 2021; Alnuaimi et al., 2021; Alshurideh et al., 2022). Furthermore, the use of the fuzzy rule-based approach can lead to the creation of forms in natural language (Guergov & Radwan, 2021), making it more comprehensible for both healthcare providers and patients to understand the AI model (Farouk, 2021; Ghazal et al., 2022a; Al Kurdi et al., 2022). (Tan et al., 2021) Sarp et al. proposed a CNN-based framework for classifying chronic wounds, and subsequently utilized the XAI technique LIME to expound on the CNN centered model that employed Transfer Learning (TL) and yielded satisfactory results in terms of precision (95%), recall (94%), as well as F1-score (94%)

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(Nuseir et al., 2021a, 2021b; Salloum et al., 2020a, 2020b; Svoboda et al., 2021). The distinctive wound image, along with the heatmap generated by LIME, could provide clinicians with visual insights into the model’s decision-making process (Radwan & Farouk, 2021). Tan et al. developed a logical neural network (LNN) to diagnose fenestral otosclerosis using progressive High-Resolution Computed Tomography (HRCT) bone pieces (Miller, 2021). Chen et al. developed an explainable medical identification model using Electronic Medical Record (EMR) documents (Ali et al., n.d.). The model utilized entity-aware CNN networks and ensembles of Bayesian networks to achieve a Top-3 forecast accuracy rate of more than 88 percent (Al AlShamsi et al., 2021; Shebli et al., 2021; Yousuf et al., 2021). In other words, the model was able to accurately predict the top three outcomes with high precision, thanks to its use of these advanced techniques (Al Batayneh et al., 2021; Naqvi et al., 2021; Yang et al., 2022). The proposed model can help in medical identification and decision-making (Lee et al., 2022). The model’s explainability was achieved through the Bayesian network (BN) by linking diseases and symptoms (Alzoubi et al., 2022a, 2022b, 2022c; Ghazal et al., 2021). The model’s explanation was evaluated by three qualified surgeons who reviewed the connections extracted from the medical knowledge graph (Ali et al., 2022a, 2022b; Alzoubi et al., 2022a; Farouk, 2022; Khubrani, 2021). Badiganti et al. (2022) proposed a deep learning model to identify COVID-19 patients who are at risk of requiring mechanical ventilation (MV) within 24 h of hospitalization (Alzoubi et al., 2022b). Utilizing all relevant patient information is crucial for medical research. This includes demographic data (Alomari et al., 2019), medication information, laboratory results, indications and signs, and all medical procedures (Akour et al., 2021; Al Kurdi et al., 2021; Ali et al., 2021a, 2021b, 2022a, 2022b; Ali Raza et al., 2022; Asif et al., 2021; Aslam et al., 2021; Dekhil et al., 2019; Fatima et al., 2020; Ghazal et al., 2022b; Khan et al., 2021; Salloum et al., 2020b). The comprehensive collection of this information can help researchers develop accurate models and make better predictions (Filipow et al., 2022). In medical research, where the consequences of inaccurate predictions can be dire, the use of all available information is critical (Alsinglawi et al., 2022). Hence, it is essential to consider all relevant patient data in order to make informed decisions and achieve the best possible outcomes (Alnazer et al., 2017; Alshurideh et al., 2020a, 2020b; Rehman et al., 2022). The data was attributed using the devotion method and compared using ML and DL models (Ali et al., 2022a, 2022b; Shamout et al., 2022). The DBNet outperformed the models with an AUC of 0.80 and an F1 score of 0.798 (Alzoubi & Yanamandra, 2020; Shu et al., 2018). Many intelligent frameworks, such as machine learning, have been used to construct solutions for the emerging challenges of designing smart cloud-based monitoring management systems.

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3 Problem Statement and Research Contribution By examining the advantages and drawbacks of artificial and human intelligence, they will be able to uncover answers where they now lack acceptable approaches. Because contemporary AI/ML systems demand massive volumes of data, training on local datasets may quickly reach its limits. Early disease prediction is a critical challenge in clinical data analysis since it has the potential to save human lives by detecting diseases at an early stage. In order to overcome, researchers are to contribute with perfect precision via learning on anonymous data or risk severe consequences, the need to examine methods to learn from other models instead of other data in the disease prediction.

4 Proposed Methodology As the world population continues to grow rapidly, many individuals face health problems ranging from illnesses to environmental issues that increase their risk of developing diseases. Various data mining and neural network approaches have been utilized to assess the severity of cardiac diseases in individuals due to the complexity of diagnosing illnesses caused by risk factors such as high blood pressure, high cholesterol, diabetes, and irregular heart rates. These health problems pose significant challenges for accurate diagnosis, and as a result, advanced technologies like data mining and neural networks are employed to aid in the assessment of cardiac disease severity. ML has proven to be an effective tool for decision-making and prediction based on the vast amounts of data generated by the healthcare industry. In summary, health issues are prevalent due to the population increase, and advanced technologies such as ML are useful in managing the complex health data generated. In this research work, explainable artificial intelligence is being proposed in recent developments of disease prediction to predict the disease in real-time. In order to diagnose any kind of ailment, EAI is needed in the healthcare industry. Although it has been demonstrated that AI-powered systems can execute specific analytical jobs better than humans, the lack of interpretability is still a point of contention. This has inspired the concept of explainable AI in an effort to increase human comprehension, lessen bias, and inspire trust in machine conclusions. Figure 1 is indicating that disease prediction data is transferred to the data acquisition layer, that is storing the data acquired from the input layer in the database in raw form (Alzoubi & Ahmed, 2019; Eli, 2021; Joghee et al., 2020; Lee & Ahmed, 2021). The raw data is then delivered to the preprocessing layer (El Khatib et al., 2022), where it removes noise utilizing normalization, handling missing values, and moving averages (Alzoubi et al., n.d.; Kashif et al., 2021). After this, the preprocessed data is trained using the Explainable Artificial Intelligence (EAI) approach. After the EAI approach, it is checked whether the trained outcome is according to the learning rate or not (Al-Tahat & Moneim, 2020; Guergov & Radwan, 2021). In the case of no the

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Fig. 1 Proposed model

raining procedure is retrained and in the case of yes the trained outcome is stored on the cloud (Alhamad et al., 2021; Muneer & Rasool, 2022; Zafar et al., 2022). Then in the validation, the trained data saved on the cloud will be imported for prediction purposes using the EAI technique. It is checked whether the disease from the given set of parameters is found or not. If the answer is no, the operation is discarded; if the answer is yes, the notification will state that the disease is detected.

5 Empirical Analysis Globally, chronic diseases and ailments are becoming more prevalent. Because of an aging population and changes in socioeconomic behavior, the frequency of these common and costly long-term health conditions is constantly increasing. People are becoming less mobile as the middle class grows and urbanization accelerates. This study analyzes previous works on predicting different diseases by using XAI in the last two years; their results are highlighted in Table 1. This research presents a model by using the EAI approach to predict disease and may show better accuracy than previous techniques. Table 1 represents the results of the previous published approaches using explainable artificial intelligence (XAI) methods with their accomplishments in terms of accuracy and miss rate.

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Table 1 Comparison of previous published approaches Authors

Approaches

XAI method

Accuracy (%)

Miss-rate (%)

(Mirchi et al., 2020)

SVM

Virtual operative assistant

92

8

(Aljameel et al., 2021)

SMOTE

SMOTE with oversampling

90

10

(Zhao et al., 2020)

ECNNs

Bayesian network ensembles

88.8

11.2

(Kroll et al., 2020)

NB

Context-free grammar (CFG)

81.5

8.5

(Yoo et al., 2020)

XGBoost

SHAP

78.9

11.1

6 Discussion The above segments show the obtained outcome while predicting anomalies and maintaining the prediction model with the help of explainable AI. The ultimate objective is to achieve a degree of accessible intelligence that can comprehend data in the context of an application activity, making machine decisions clear, easy to interpret, and explainable. Whereas machine learning algorithms require hundreds of thousands of training sets, a human analyst is sometimes presented with only a few input variables. Interestingly in the background of recent progressions in AI, this work presents a model to predict disease by using Explainable Artificial Intelligence (EAI). The proposed model may provide better performance in accurate disease prediction by using eSHAP technique.

7 Conclusion This research highlights that explainable artificial intelligence (EAI) based systems help discover the cloaked information in an assembly of disease data that may be used to analyze and anticipate the future conduct of diseases. In the field of EAI, methods are being developed to explain predictions produced by artificial intelligence (AI) systems. In this work, XAI is explored as a method that AI-based systems might utilize to analyze and identify disease, and a suggested strategy is proposed to establish sustainability. In (Filipow et al., 2022), the authors predict disease using the XG Boost algorithm and achieve an accuracy of 95%. This research develops a disease prediction model using Explainable Artificial Intelligence (EAI). This proposed model may provide better performance using eSHAP technique.

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8 Limitations and Future Directions Numerous ML techniques have been employed in the medical field to predict different diseases. Various traditional medical systems are employed for accurate disease prediction but face many challenges regarding less accuracy due to smaller datasets, inappropriate techniques, etc. This proposed model may be very helpful for the prediction of disease. In the future, more disease datasets can be utilized for EAI methods, and AI systems can be used to evaluate and examine the presentation of different diseases. There also will be an unmatched human–machine synthesis rather than the vision of machines taking over the planet. According to Kurzweil, artificial intelligence will match human skills by 2045, and they will be able to connect our brains to the cloud by 2030.

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Intelligent Traffic Congestion Control System in Smart City Iman Akour , Mohammed T. Nuseir , Barween Al Kurdi , Haitham M. Alzoubi , Muhammad Turki Alshurideh , and Ahmad Qasim Mohammad AlHamad

Abstract Congestion in the domain of transportation typically refers to an overload of vehicles on a certain stretch of road at a specific moment, resulting in speeds that are slower, sometimes significantly slower, than normal or “free flow” norms. Congestion-free traffic has been the main goal for a decade, and many methodologies have been embraced to make blockage-free streets. Sadly, street traffic is constrained by antiquated traffic lights (tri-variety signals) no matter the persistent exertion committed to creating and further developing the traffic stream. This study adopts a clever model to control congestion utilizing AI (ML) strategies. These ML procedures show cutting-edge execution to monitor and prevent congestion. The

I. Akour Information Systems Department, College of Computing & Informatics, University of Sharjah, Sharjah, United Arab Emirates e-mail: [email protected] M. T. Nuseir Department of Business Administration, College of Business, Al Ain University, P.O. Box 112612, Abu Dhabi CampusAbu Dhabi, United Arab Emirates e-mail: [email protected] B. Al Kurdi Department of Marketing, Faculty of Business, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan e-mail: [email protected] H. M. Alzoubi School of Business, Skyline University College, Sharjah, United Arab Emirates e-mail: [email protected] Applied Science Research Center, Applied Science Private University, Amman 11937, Jordan M. T. Alshurideh (B) Department of Marketing, School of Business, The University of Jordan, Amman 11942, Jordan e-mail: [email protected] M. T. Alshurideh · A. Q. M. AlHamad Department of Management, College of Business Administration, University of Sharjah, Sharjah 27272, United Arab Emirates e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 H. M. Alzoubi et al. (eds.), Cyber Security Impact on Digitalization and Business Intelligence, Studies in Big Data 117, https://doi.org/10.1007/978-3-031-31801-6_13

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proposed model implements fit in making secure correspondence organizations and safeguarding information spillage to keep away from traffic jams. Keywords Intelligent system · Traffic congestion control system · Smart city

1 Introduction As indicated by comprehensive investigations, the congest out and about is expanding over the long haul. Street congestion has turned into a critical issue, particularly in metropolitan regions (Al Batayneh et al., 2021; Ghazal et al., 2021). Thus, it is a significant issue that various nations address at an alternate level. Numerous nations have a unique division to control the clog out and about. Transportation is an essential spine of any country (AlSuwaidi et al., 2021; Hasan et al., 2022). It assumes a fundamental part in the development of financial aspects (Ratkovic, 2022). As an exchange, correspondence, and trade between individuals rely upon it, the fast expansion in rush hour gridlock influences the street’s limit (Aburayya et al., 2020; Al Shebli et al., 2021; Ma et al., 2015). These days, this matter is broadly examined in arranging a brilliant city. In a bright town, gridlock has become essential in the past ten years because wise urban communities mean working on conventional individuals’ lives (Ghazal et al., 2022; Obaid, 2021). Subsequently, scientists are creating numerous techniques to give manageable traffic to the city. Other than this, it is likewise acknowledged that gridlock in metropolitan urban areas directly results from ill-advised signal administration (Hanaysha et al., 2021b; Ramakrishna & Alzoubi, 2022). Signals provide static timing to both blocked streets as well as non-clogged roads. Accordingly, clogged streets call for more significant investment to lessen traffic risks as the period builds and the vehicles line the street (Ahmed & Amiri, 2022; Mondol, 2021). At times, this massive line of traffic likewise influences the other connection streets straightforwardly connected with the crowded street (Nafi et al., 2015). The conventional traffic board framework was planned a very long while back. The number of vehicles was negligible then, at that point, and the customary framework was adequate to deal with the traffic with the accessible innovation proficiently (Alzoubi, 2022; Hanaysha et al., 2021a). Obviously, with the enormous expansion in the number of vehicles and the failure to expand the sizes and the number of streets in numerous urban communities, there is a requirement for more brilliant arrangements (Alzoubi, 2021a; Lee et al., 2022; Victoria, 2022) that utilize ongoing and most refined innovations to embrace savvy traffic on the board frameworks (Radivojevi´c et al., 2021). ITMS framework will offer many types of assistance that the conventional framework can’t provide. Web of Vehicles (IoV) or associated vehicles is a new exploration setting that permits the improvement of many promising applications in Smart Cities in light of Intelligent Transportation Systems (ITS) (Ali et al., 2022a; El Khatib

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et al., 2022; Ghazal et al., 2021; Zafar et al., 2022). IoV will be conveyed generally in Smart Cities.us; rather than sending committed framework and exceptionally complicated and costly heritage frameworks to control and oversee traffic, why not utilize the fundamental structure block of future Smart Cities with practically no additional intricacy and give a superior presentation (Alsharari, 2022; Butt, 2022), thus tackling a large portion of the issues in the old framework (Drapalyuk et al., 2020). ML is an artificial consciousness application that naturally permits frameworks to gain from information and settle on choices without human help (Alhamad et al., 2012, 2013; Alhashmi et al., 2020; AlShamsi et al., 2021; Alzoubi et al., 2022c; Nuseir et al., 2021; Yousuf et al., 2021). ML empowers frameworks to achieve as a matter of fact and recuperate without being expressly modified (Alshurideh et al., 2020a; Salloum et al., 2020). ML can motorize and build the proficiency of smart gridlock control frameworks (TCCSs) while diminishing voyaging costs all the more productively and precisely in a dependable manner (Saleem et al., 2022). Information combination consolidates information from one and different sources with lacking crude information to assemble exact, far-reaching, and bound together element data (Alshurideh et al., 2022; Alzoubi et al., 2022a; Rehman et al., 2022). At the choice level, the combination delivers a solitary choice in the wake of consolidating choices from different sources to make a more canny choice on activity b (Ali et al. 2022b). Dynamic information and ML can assist with making better decisions by taking the information examples of different calculations (Hejazi et al., 2021; Khamees et al., 2021; Yousef et al., 2019).

2 Literature Review The superior part of the investigations utilize just IoT gadgets and give techniques to anticipate clogs. The different inscription uses different folklore to answer. They proposed a framework for foreseeing transport blockage utilizing the profound learning strategy. A global positioning system (GPS) is used to determine the location of the vehicle structure. GPS records the location, timing, and speed of the connections. This recorded information is subsequently sent as a contribution to the brain structure, which gives the anticipation regarding the blockage. The estimate is based on RNN-RBM brain network simulations. For congestion anticipation, the creators propose an artificial consciousness-based framework. Modeling Smart Road Traffic Congestion Control Using Artificial Back Propagation Neural Network is the name given to the suggested model (Alnazer et al., 2017; Alzoubi & Aziz, 2021). Following the clog expectation, the framework displays the notification on the car LCD and provides an alternate route using Google maps (Alhamad et al., 2022; Alzoubi et al., 2022c). A calculation to deal with the activity of a solitary conventional traffic signal sign for a crossing point with fourway streets is introduced by (Händel et al., 2014), proposing changing the traffic as per the traffic condition. Albeit the proposed calculation is professed to be versatile,

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it thinks about fixed times of days, like the review introduced in (Bautista et al., 2019). Essentially, a schedule-based history data approach is proposed (Ali et al., 2021; Nesmachnow et al., 2019). An astonishing review choosing the best charging station for electric vehicles as per the traffic condition to limit driving time is introduced (Alzoubi & Ahmed, 2019; Joghee et al., 2020; Kurdi et al., 2022; Li et al., 2016). An equal calculation to synchronize crossing points in huge and thick zones proposes further developing the normal speed-based Bus Rapid Transit (Nesmachnow et al., 2019). A comparable report in light of a cross-breed heuristic methodology is introduced by (Cabezas et al., 2019). At last, (Cabezas et al., 2019), the creators propose involving the speed of vehicles at the convergence as an improvement boundary for traffic signal control. In Wu et al. (2016), Specialists established a stage based on Wireless Sensor Networks (WSNs) to collect, deconstruct, and keep city traffic data The smart city transportation framework is more adaptive and effective than current city transportation frameworks (Alzoubi et al., 2020; Aziz & Aftab, 2021; Mondol, 2022). WSNs are inappropriate for connectivity as they are developed for limited applications and are far costly to construct. The authors of (Ghazal et al., 2021) investigate Usagebased Insurance (UBI) and cell phone-based estimate algorithms used to create a vehicle traffic light system (Khan, 2021; Mehmood et al., 2019). This engineering is intended to monitor, monitor, and filter traffic flow (Alzoubi 2021b; Eli and Hamou, 2022). This structure has seven startups, beginning with actual mobile phones and servers and concluding with the overall business system at the top. (Händel et al., 2014). Likewise, The creators (Htike et al., 2014) utilized K-means grouping calculations for traffic information clogs. Because of fluffy grouping’s benefits, (Nafi et al., 2015), scientists involved the C-means calculation for bunching traffic clog. Additionally, finding the most limited way can be defined as an ideal issue (Akhtar et al., 2021; Alshurideh et al., 2020b; Shamout et al., 2022). A few strategies can be utilized to settle this issue. Straight writing computer programs is a proper calculation for these sorts of issues because of their straightforwardness and exactness (Alhamad et al., 2021). Most of the approaches have been used while employing and constructing several smart as well as intelligent frameworks like machine learning approaches that may provide assistance in designing emerging solutions for the rising challenges in designing smart cloud-based monitoring management systems.

3 Problem Statement & Research Contribution Traffic issues are common in smart cities; it has emerged in terms of information gathering related to the number of entities that collect traffic-related data, from road traffic operators such as public transportation companies, private taxi companies, and public traffic management authorities such as city authorities, and planning institutions. Some other stages are major concerns and are growing in traffic data,

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such as gathering, storage, aggregation, exchange, and processing of real-time large amounts of data with real user attraction, and putting contributed effort by using the proposed model is to overcome the above challenges with ML techniques.

4 Proposed Methodology Congestion is a critical test in metropolitan urban communities prompting collected traffic. With the headway in the clever web of vehicles, new advancements and conventions have been created to anticipate congestion and use this traffic-related information for blockage expectation and ID. This examination has featured the ML way to deal with recognizing gridlock because of numerous boundaries. ML might give live traffic forecasts progressively, future traffic expectations, and momentary traffic expectations in light of late perceptions and authentic information. In this exploration, a gridlock checking framework has been proposed to foresee the clog progressively in a superior manner. In this proposed study, every traffic entering and exiting smart homes is seen and recorded as considered. The additional thing is these traffic flow photos and videos are then uploaded to a cloud database for additional processing and analysis. All of the picture and video datasets are linked and transferred using rapid wireless transmission, i.e. LiFi, which is more reliable and faster than traditional wireless transmission techniques. It is addressed in Fig. 1 that the proposed congestion guess approach relies upon the information assortment from numerous info sensors associated with the vehicles. Then, in the following stage, the gathered information from the traffic on the board is sent for preprocessing, where the missing information is dealt with utilizing standardization. The preprocessed information is continued component designing. Highlight designing is choosing, controlling, and changing crude information into highlights that can be utilized in managed learning. To make AI function admirably on new errands, planning and preparing better highlights may be important. After the element designing, the information is sent for grouping. Grouping calculations are utilized to sort information into a class or class. It very well may be performed on both organized and unstructured information. Order can be of three sorts: parallel, multiclass, and multilabel. Then, at that point, in the last step after grouping, it is checked whether the exactness and miss pace of the proposed framework is found.

5 Empirical Analysis Traffic congestion has grown to be a major issue in smart cities over the past few decades, which not only hurts people’s everyday lives but also impedes steady economic and societal growth. Air pollution, travel time, and economic losses all rise as a result of traffic congestion. Governments work harder than ever to control

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Fig. 1 Proposed model

and alleviate traffic congestion, but the effort is challenging due to the complexity of the issue; in particular, it is challenging to predict. Some of previous work regarding traffic congestion as highlighted in Table 1. In this research, an intelligent model is developed to control traffic congestion in smart cities that overcome all these issues regarding traffic congestion. Table 1 is showing that Ayesha et al. is showing better results as compared to the (Tamimi & Zahoor, 2010) and (Yousef et al., 2019).

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Table 1 Comparison of previous research works Authors

Accuracy

Miss-rate

Tamimi and Zahoor (2010)

76.1

23.9

Yousef et al. (2019)

90.6

9.4

Aid et al. (2019)

95.84

4.16

6 Discussion Both traffic congestion and traffic accidents have increased. Intelligent transportation systems handled the problem of short communication delays between automobiles and roadside units, smooth traffic flow, and road safety in the proposed model. The purpose of this research is to provide drivers with unique services that allow them to remotely monitor traffic flow and the number of automobiles on the road to avoid traffic jams. This project uses machine learning technology to develop a fusionbased intelligent traffic congestion control system for vehicular networks to alleviate traffic congestion. This post offers suggestions for improving traffic rate and reducing congestion.

7 Conclusion The purpose of this task is to make the fixed and preset traffic signal action dynamic. The study presents a novel way for directly making signal timing proportionate to road congestion at any moment. The proposed Intelligent Smart Traffic Congestion Control System based on AI might address the shortcomings of current traffic congestion control. This article shows that ML classification algorithm-based excellent judgment version may provide efficient results while predicting the road traffic congestion that provide assistance to the citizens while scheduling their routine work.

8 Limitations and Future Directions Congestion is a central problem worldwide. Disappointment of markers, inferior policing horrible site guest control, have caused congestion. Consequently, it requires extreme investment to deal with the site roadblock inconvenience. Consequently, It is a requirement for a clever model to take care of these issues. A few conventional models show better execution in checking and controlling clogs. However, they face a few difficulties like less secure correspondence organizations and information spillage in the public cloud because of access of every individual and so forth. This exploration proposes a smart model to control congestion utilizing AI. This proposed

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model achieves better protected correspondence and keeps information from utilizing a confidential cloud.

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Automated Sales Management System Empowered with Artificial Intelligence Muhammad Turki Alshurideh , Mohammed T. Nuseir , Barween Al Kurdi , Haitham M. Alzoubi , Samer Hamadneh , and Ahmad AlHamad

Abstract Businesses are using autonomous monitoring and surveying systems in increasing numbers since they enhance the efficiency of the business overall and dramatically cut the expense of hiring employees. Additionally, by eliminating human-related issues, an automated system increases the accuracy of data that is collected. An autonomous system that can do tasks without human effort and saves production costs is therefore crucial. The machine learning (ML) techniques used in this research are used to develop a robot-based system for sales management. Using software robots or artificial intelligence (AI) as workers, automated robotic processes is a new type of business process automation technology. All major Internet businesses are based on the groundwork of machine learning (ML), a subfield of artificial intelligence. Robots have been employed by big organizations for decades to M. T. Alshurideh (B) · S. Hamadneh Department of Marketing, School of Business, The University of Jordan, Amman 11942, Jordan e-mail: [email protected] S. Hamadneh e-mail: [email protected] M. T. Nuseir Department of Business Administration, College of Business, Al Ain University, P.O. Box 112612, Abu Dhabi CampusAbu Dhabi, United Arab Emirates e-mail: [email protected] B. Al Kurdi Department of Marketing, Faculty of Business, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan e-mail: [email protected] H. M. Alzoubi School of Business, Skyline University College, Sharjah, United Arab Emirates e-mail: [email protected] A. AlHamad Department of Management, College of Business Administration, University of Sharjah, 27272 Sharjah, United Arab Emirates e-mail: [email protected] H. M. Alzoubi Applied Science Research Center, Applied Science Private University, Amman, Jordan © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 H. M. Alzoubi et al. (eds.), Cyber Security Impact on Digitalization and Business Intelligence, Studies in Big Data 117, https://doi.org/10.1007/978-3-031-31801-6_14

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enhance output, reduce expenses, speed up manufacturing, and improve quality standards. Small organizations can now use a variety of robots to improve their operations with ever-increasing simplicity. Keywords Automated system · Sales management system · Machine learning techniques

1 Introduction Pressure from the competition is always growing for manufacturers. Rapid changes brought on by global social, legal, and technical advancements are one reason for this. The requirements placed on the items to be created, their production, as well as the technology and methods used, are therefore continually rising (Alshurideh et al., 2020a, 2020b; Kurdi et al., 2020). A new era of practical autonomous systems has arrived as a result of consistent advancements in the field of intelligent and interactive robotics during the past 20 years (Alshurideh et al., 2020a, 2020b; Shamout et al., 2022). Shortly, personal and service robots will be found in offices, homes, schools, hospitals, and factories (Alzoubi et al., 2021b). They will function as “personal assistants,” serve as security for the building, sweep the floors, watch out for kids, prepare copies, and generally improve our quality of life. A closed system within a bigger unit might be thought of as a robot-based production cell (system) (workshop) (Alzoubi et al., 2022g). The system can be explained by dimensioning its key components, indicating the relationships between them, and defining the system’s structure (Al Mehrez et al., 2020; Alshurideh, 2022; Yousuf et al., 2021). These relationships include equipment for loading and unloading workpieces, grippers (end effectors), the working area and range of IR, the loading capacity, the controlled coordinates of IR and MT, and more (Da Silva & Reali Costa, 2021). The prevalence of Shelf Out of Stock (SOOS) issues is a serious issue in the retail sector. Planogram design, which depicts how stock-keeping units (SKUs) are organized on shelves, is frequently closely tied to SOOS events. Retailers experience roughly 4% sales losses due to the 8% global average out-of-stock rate (Alzoubi et al., 2022e, 2022j; Khatib et al., 2022). Out-of-stock circumstances can occur for some causes, but the main one is poor shelf replenishment techniques (surveying and restocking), which cause 70–90% of occurrences to result in SOOS. Another 10–30% are caused by issues in the supply chain, which result in store-OOS. Promotional activities also have a significant impact on consumer behavior and SOOS, which has an impact on total retail turnover (Alzoubi et al., 2022a, 2022b, 2022k; Madakam et al., 2019). The retail sector is going through enormous change: from bar-code to RFID technology, from experience-driven policies to data-driven procedures, and from physical versus web to an omnichannel strategy (Alzoubi et al., 2022i; Butt, 2022; Hamadneh et al., 2021; Mondol, 2022). As a result, there are many benefits accessible for both merchants and customers, but they can only be exploited in situations where

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shelf assortment, promotion optimization, and inventory management are frequent and precise (Al-Dmour & Al-Shraideh, 2008; Alshurideh et al., 2019; Alzoubi et al., 2022a, 2022b, 2022k; Farouk, 2022; Radwan, 2022; Shannak et al., 2012). Along with marketing initiatives and supply chain management automation, all data-driven operations are dependable and helpful for handling SOOS issues (Vrontis et al., 2022). A branch of artificial intelligence referred to as machine learning can infer associations from data without explicitly describing them (Alzoubi et al., 2021d; Ghazal et al., 2021a, 2021b). ML approaches are frequently applied in the commercial world. Financial analysis can be used to improve accuracy and look for contradictions in historical data (Alsharari, 2022; Alzoubi et al., 2022h; Assad & Alshurideh, 2020; Ghazal et al., 2022a; Ratkovic, 2022; Shah et al., 2021). Additionally, ML is employed in loan underwriting, algorithmic trading, portfolio management, and insurance fraud detection. Additionally, chatbots can be used to increase sentiment and provide better customer support (Akour et al., 2021; Alshurideh et al., 2019; Alzoubi & Yanamandra, 2022; Eshghi & Kargari, 2019; Ghazal et al., 2021d; Salloum et al., 2020).

2 Literature Review Machine learning techniques have been used by many researchers to develop autonomous systems. This section highlights some of their work. Sequential Floating Forward Search and Fisher Projection techniques were employed by Picard et al. to classify eight sentiments with an accuracy of 81%. Lisetti and Nasoz used the KNN, Discriminant Function Analysis, and Marquardt Backpropagation algorithms to distinguish between six emotions, achieving correct classification accuracies of 71, 74, and 83%, respectively (Naoum, 2012; Sarker, 2019). Numerous claims where real-world information variables are accessible have used artificial neural networks (Ahmed & Amiri, 2022; Alzoubi, 2022; Alzoubi et al., 2021a, 2022f; Qasaimeh & Jaradeh, 2022). An artificial neural network (ANN) is a computational structure made up of nodes, or artificial neurons, associated with artificial synapses that pass signals from the input layer through one or more hidden layers to the output layer (Alzoubi & Aziz, 2021; Alzoubi et al., 2021f). The socalled learning effect from considering examples and the ability to work with large amounts of data are the method’s key benefits (Atutxa et al., 2019; Eli, 2021; Kasem and Al-Gasaymeh, 2022). In order to create an algorithm that is precise and effective for analyzing customers’ expenses and costs in history to see the same functionalities the customers could introduce in the future, Ching- Seh (Mike) Wu, Pratik Patil, and Saravana Gunaseelan conducted research on sales on Black Friday (discounted days) predictions (Alzoubi

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et al., 2020a, 2020b, 2021c; Victoria, 2022). This study built a predictive model using a variety of machine learning approaches, including regression and neural networks and compared the prediction accuracy and performance (Alsharari, 2021; Alzoubi et al., 2017; Mehmood, 2021). Several platforms and algorithms were utilized in this technique to produce the best-performing prediction; in this instance, 7 (seven) machine learning algorithms were used (Kroll et al., 2020). In the telecoms industry, Irfan Ullahet al. suggested a model of churn prediction utilizing classification and clustering methods to identify churn consumers and display affecting aspects of churn customers (Akhtar et al., 2022; Alzoubi & Yanamandra, 2020; Ghosh & Aithal, 2022; Miller, 2021). By gathering knowledge and ranking attribute correlation filters, features are chosen. The first model uses a classification method to order customer turnover data, and the approach, Random Forest, performs well, accurately classifying 88.63% of the cases (Ullah et al., 2019). A Japanese supermarket’s sales were recently predicted using deep learning. A stacked denoising autoencoder was specifically used in the method to produce deep high-level features, which were then input into a long short-term memory network to predict future sales (Alzoubi et al., 2020a; Amrani et al., 2022; Eli & Hamou, 2022; Goria, 2022). Sales forecasting is primarily a regression issue than a time series one. Regression procedures can frequently produce findings that are superior to time series methods, according to practice. Patterns in the time series can be discovered using machine learning methods (Akhtar et al., 2021; Alzoubi & Yanamandra, 2020; Kashif et al., 2021; Nasim et al., 2022). Using supervised machine-learning techniques, we can identify complex patterns in the dynamics of sales. The most well-known ones are some of the tree-based ML approaches, such as Random Forest and Gradient Boosting Machine (Del & Solfa, 2022; Li et al., 2022). Most of the approaches have been used while employing and constructing several smart as well as intelligent frameworks like machine learning approaches (Asif et al., 2021; Chayal & Patel, 2021; Dekhil et al., 2019; Fatima et al., 2020; Ghazal et al., 2022a, 2022b, 2022c, 2022d; Muneer & Rasool, 2022), Fuzzy Inference systems (Alshurideh et al., 2022; Asadullah et al., 2020; Areej et al., 2019; Ihnaini et al., 2021; Saleem et al., 2019), Particle Swarm Optimization (PSO) (Iqbal et al., 2019), Fusion based approaches (Gai et al., 2020; Ma et al., 2020; Muneer & Raza, 2022; Sharma et al., 2021; Tabassum et al., 2021; Ghazal, n.d.), cloud computing (Ghazal et al., 2021f, 2022b; Khan, 2022; Naseer, 2022; Ubaid et al., 2022), transfer learning (Abbas et al., 2020; Al-Hamad et al., 2021; Al Kurdi et al., 2021; Alzoubi et al., 2021d) and MapReduce (Asif et al., 2021) that may provide assistance in designing emerging solutions for the rising challenges in designing smart cloud-based monitoring management systems.

3 Problem Statement and Research Contribution Researchers specifically look at how autonomous decision-making tools would have to be employed to comply with the no delegation theory and with laws governing due

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process, anti-discrimination, and open government. Although administrative agencies are just now starting to utilize machine learning algorithms, it is now a good time to carefully consider the possibilities for machine learning’s application by governmental organizations given the widespread worry about the robotic control of other aspects of life.

4 Proposed Methodology Robotics and AI have been proposed as solutions for everything from the Internet of Things to self-driving cars, and now machine learning has introduced a robotic sales associate. Running a sale is something that most salespeople and business development managers are familiar with. In this study, machine learning is suggested as a remedy for this drawback. A salesperson can spend 50% less time on menial activities thanks to machine learning, which frees them up to concentrate on more important things like improving the customer experience for current clients or cultivating relationships with prospects. The proposed research work’s approach is shown in Fig. 1. According to the specification, a robot uses an IoT device to gather client data, which is then sent to the gateway device. The gateway device is utilized in order to collect data from IoMT devices and transmit it to the training layer. To anticipate the management of sales, a machine learning-based approach is used in the training process, and it is then tested to see if the learning requirements have been satisfied. With the use of machine learning (ML), which is a form of artificial intelligence (AI), software programs can predict outcomes more accurately without having to be explicitly instructed to do so. If the response is no, the diagnostic system process must be retrained; if the response is yes, the trained result will be saved on the cloud. Following this procedure, the cloud-imported training results will be used for diagnosis, and it will be determined whether or not robotic sale management is present. If the response is no, the process will be abandoned; if the response is yes, a notice indicating that the process is predicted will be shown.

5 Discussion Considering its concentration on accuracy and the government’s pressing need to make the best use of its limited resources to avert threats, machine learning is an excellent choice for automating these types of judgments. However take note that in this situation, switching to a machine-learning strategy may result in a qualitative decline in human engagement. Under machine learning, the analysts would no longer decide in advance which factors should be included in the agency’s risk models; in fact, they would not even establish any risk models in the sense of developing equations detailing precisely how various variables can affect pipeline risk. Additionally,

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Fig. 1 Proposed methodology

machine learning does not provide a clear method of articulating why every pipeline portion has to be examined or shut down. Since ML is computerized, it can also automate decisions that are now done by humans, like sending out inspections or even monitoring and evaluating.

6 Conclusion In this research, a robot-based system is developed with the help of machine learning techniques for the sales department. Any system’s effectiveness depends on how it is used. So it is anticipated that the technology will be deployed in environments that fulfill those requirements. The system will aid in sales management by tracking and storing pertinent data essential for efficient sales management, given the requisite upkeep. By using robotics and artificial intelligence in the sales department, businesses will save time and money. When used for activities that they can accomplish more effectively, more consistently, and with higher quality than people, robotic systems increase productivity. Numerous sales-related tasks, such as creating quotes, confirming inventory availability, placing orders with the warehouse, billing, database inputs, analytics, and performance reporting, can also be handled by robot-based systems.

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7 Limitations and Future Directions The cost of purchasing a robot is often high. Evaluate all automated robotic costs, involving installation and configuration, when researchers are conducting the study for your business case for acquisition. Operations, servicing, and programming for industrial robots must be complex. Despite an increase, there are now few professionals who possess these skills. Although some production labor costs may be reduced by using industrial robots, they do have recurring expenditures like maintenance. You should also take into account the costs associated with maintaining cyber security for your robot and any other IoT-linked devices that are connected in its vicinity. Robots will develop a variety of solutions to further streamline organizational operations when combined with AI. Better services will be provided for complex and repetitive business operations. The rate of learning increases when intelligent technologies are combined with bots. The program can examine the data provided in real-time during processing when coupled with technologies like Big Data and IoT.

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Role of Explainable Artificial Intelligence (EAI) in Human Resource Management System (HRMS) Mohammed T. Nuseir , Muhammad Turki Alshurideh , Haitham M. Alzoubi , Barween Al Kurdi , Samer Hamadneh , and Ahmad AlHamad Abstract Human resources are one of an organization’s most precious assets, and they are likely to develop distinctive and dynamic elements that increase its competitive benefit in an ever-evolving market context. Eliminating human participation and validating the participant’s information are vital in the recruitment process in order to locate a competent employee for a corporation. Additionally, it is critical for the human resource management (HRM) process to understand how well or effectively people perform, as well as the risk of job dissatisfaction. Using Explainable Artificial Intelligence, this study creates a smart Human Resource Management System (HRMS) that may maximise the efficiency of an organisational setting (EAI). It is essential in many industries, including healthcare, electricity, agriculture, and so forth, but especially in business. This proposed model shows better performance

M. T. Nuseir Department of Business Administration, College of Business, Al Ain University, Abu Dhabi Campus, P.O. Box 112612, Abu Dhabi, United Arab Emirates e-mail: [email protected] M. T. Alshurideh (B) · S. Hamadneh Department of Marketing, School of Business, The University of Jordan, Amman 11942, Jordan e-mail: [email protected]; [email protected] S. Hamadneh e-mail: [email protected] H. M. Alzoubi School of Business, Skyline University College, Sharjah, United Arab Emirates e-mail: [email protected] Applied Science Research Center, Applied Science Private University, Amman 11937, Jordan B. Al Kurdi Department of Marketing, Faculty of Economics and Administrative Sciences, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan e-mail: [email protected] M. T. Alshurideh · A. AlHamad Department of Management, College of Business Administration, University of Sharjah, 27272 Sharjah, United Arab Emirates e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 H. M. Alzoubi et al. (eds.), Cyber Security Impact on Digitalization and Business Intelligence, Studies in Big Data 117, https://doi.org/10.1007/978-3-031-31801-6_15

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while providing assistance to recruit their employees in the business sector, especially in human resource management (HRM). Keywords Human resource management system (HRMS) · Explainable artificial intelligence (EAI) · Role of explainable artificial intelligence (EAI)

1 Introduction Human resource (HR) is main resources of any association which empowers accomplishing authoritative objectives and targets. Human Resource Management (HRM) influences the general effectiveness of the association (Al-Hamad et al., 2021; Alshurideh et al., 2022) It very well may be distinguished a quick development in Artificial Intelligence (AI) and Machine Learning (ML) advancements in HRM processes, with the improvement of cloud advancements as of late (Alzoubi et al., 2020a, 2022e; Ghazal et al., 2022a). HRM divisions of associations are confronting a difficult undertaking of enlisting accurate capacity who can take care of in various issue space and accomplish objectives it inside course of events (Alzoubi et al., 2021g, 2022e, 2022i; Eli and Hamou, 2022; Miller, 2021; Shamaileh et al., 2022). Picking a very capable possibility for a task position with precise and confirmed subtleties will assist an association with accomplishing wanted efficiency (Akhtar et al., 2022; Alshurideh et al., 2015; Alzoubi et al., 2022g; Kurdi et al., 2020). Moreover, having thought regarding the representative’s appropriateness to the ongoing asset pool, execution and the likelihood of leaving the organization will help administrative positions and leaders to take right choices (Albreiki et al., 2021; AlShehhi et al., 2021; Alsuwaidi et al., 2021; Alzoubi & Yanamandra, 2022; Kurdi and Letters, 2020; Zhu et al., 2016). The power-driven development in HRM can be followed back to the modern transformation, mechanical progressions had essentially adjusted either physical or mental administrations (Alameeri et al., 2021a; Shamout et al., 2022). Contemporary turns of events, be that as it may, are progressively giving options in contrast to HR in capabilities customarily requiring human collaboration and correspondence, in this manner changing both the authoritative designs and the idea of work (Alzoubi et al., 2021b, 2022a; Ghazal et al., 2021a; Alzoubi et al., 2021j). Humanoid administration robots and man-made consciousness bots, for instance, are progressively drawing in industry consideration (Alsharari, 2021; Amrani et al., 2022; Mehmood, 2021; Nasim et al., 2022). These insightful “creatures” have reformed conventional human asset capabilities, giving developing qualities and possibilities to HRM yet additionally considerable difficulties including position explicit outdated nature (Akhtar et al., 2021; Al-bawaia et al., 2022; Alzoubi et al., 2022b; Kashif et al., 2021). Simultaneously, profound learning calculations, brilliant articles and Internet of Things (IoT) remain valuable for organizations working across borders as they can cultivate more useful coordination and participation (Alzoubi & Aziz, 2021; Alzoubi et al., 2022k, 2022l; Ghazal et al., 2021d; Ghosh & Aithal, 2022; Nasim et al., 2022) Additionally,

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the presentation of electronic human asset data frameworks and other novel advancements offer a few potential chances to enhance and diminish the expense of HRM capabilities including, among others, the assessment of occupation candidates and representative execution examinations (Aburayya et al., 2020; Al-Dhuhouri et al., 2021; Alshurideh, 2022; Vrontis et al., 2022). HRM encapsulated by mechanical progressions is progressively the focal point of universally situated HRM studies (Eli, 2021; Victoria, 2022). Surprisingly, researchers underscore how data advancements are evolving HRM-related rehearses by presenting e-enrollment, e-preparing or e-capability the board, contributing decidedly to HRM administration quality in both neighborhood and worldwide associations (Alkalha et al., 2012; Ghazal et al., 2021d; Kabrilyants et al., 2021) As these advancements are presenting new entertainers like social robots to HRM rehearses, they open various conceivable outcomes and backing different HRM administrations. Predictable with this view, a few examinations feature the manners by which PC supported plan, assembling and interaction arranging are computerizing many undertakings and improving viability and speed (Alzoubi et al., 2021b, 2021d; Kasem and Al-Gasaymeh, 2022; Qasaimeh & Jaradeh, 2022). Most outstandingly, a rising collection of information relates to HRM as an empowering influence of mechanical change and development at a worldwide level through work rearrangement, for example, working circumstances and representative preparation (Al Mehrez et al., 2020; Nuseir et al., 2021; Yabanci, 2019). Improvements in software engineering have prompted the prospering of trend setting innovations, like reasonable man-made consciousness (EAI) (Ahmed and Amiri, 2022; Alsharari, 2022; Alzoubi et al., 2021i; Alzoubi, 2022). This domain is comprising the pinnacle of current PC innovation and is setting out new open doors for organizations and bringing about different developments. Computer based intelligence innovation is additionally prompting elementary changes (Alameeri et al., 2021b; Altamony et al., 2012). Such a variation could recently be seen in a secondary peculiarity, HRM. These major variations in HRM we are going through are exceptionally worth of inspection (Amjad et al., 2019; Aslam, 2022).

2 Literature Review Multiple researchers have been previously worked on human resource management system by utilizing artificial intelligence techniques. Some of their work are highlighted in this section. The authors focused on a review for involving data mining measures in HRM. They breaking down the aftereffects of show evaluation (Alzoubi et al., 2021a, 2021e; Ratkovic, 2022). It is upto the association to intensify the technique for examination to get the proportion of similarity of for all intents and purposes carrying out with the evaluation process targets (Alzoubi & Ahmed, 2019; Alzoubi et al. 2020b, 2021c, 2022f, 2022j). That’s what to achieve, they have utilized a few procedures of information removal. This study was performed on information mining strategies give a

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more noteworthy importance in human asset exercises to blow up representative’s presentation examination (Abraham et al., 2019). As of late, numerous associations have significantly encountered the utilization of PC frameworks in HRM. The blooming utilization of PCs in HRM showed up during the 1980s. During the time, the scattering of PCs brought about the PC based human asset data frameworks (HRISs) (Alzoubi et al., 2017, 2022c; Alzoubi & Yanamandra, 2020; Farouk, 2022; Mondol, 2022; ). As far as time concerns, with the growth of the Internet as a occupational strength, and with the advancement in IT, HRISs turned into a center point for overseeing HR exercises (Yabanci, 2019). The proposed engineering is the quantity of secret layers in the brain organization and can arranged accord to. To accomplish superior execution, every neuron has a handling unit with the goal that they work in equal (Abiodun et al., 2019; Al Naqbi et al., 2020; Alaali et al., 2021; Radwan, 2022). The Human Resource Information System (HRIS) uses AI as an application. In HRIS, with the advancement of human-PC collaboration elements of AI, there is additionally opportunities for administrators to further develop the executives effectiveness by utilizing AI (Alzoubi et al., 2022e, 2022m). “HRIS is a methodology for gathering, putting away, keeping up with, recovering and approving information required by an association about its HR, faculty exercises, and association unit qualities” (Butt, 2022). HRIS can help the essential preparation with data for workforce market interest conjectures; managing candidate capabilities; improvement with data on preparing; and assessing execution with data, etc. (Rimba et al., 2020). Without uncertainty, human asset the executives (HRM) is one of the organization works that has encountered tremendous changes throughout the course of recent many years (Alzoubi et al., 2022h; Khatib et al., 2022). Starting from the start of the 1980s, a tremendous writing has been created requiring a more essential job for HR (Del & Solfa, 2022; Goria, 2022; Sehar et al., 2017). Most of the approaches have been used while employing and constructing several smart as well as intelligent frameworks like machine learning approaches (Asif et al., 2021; Chayal and Patel, 2021; Dekhil et al., 2019; Fatima et al., 2020; Ghazal et al., 2022e; Muneer & Rasool, 2022), Fuzzy Inference systems (Areej et al., 2019; Asadullah et al., 2020; Ihnaini et al., 2021; Saleem et al., 2019), Particle Swarm Optimization (PSO) (Iqbal et al., 2019), Fusion based approaches (Gai et al., 2020; Ma et al., 2020; Muneer & Raza, 2022; Sharma et al., 2021; Tabassum et al., 2021; Ghazal, n.d.), cloud computing (Ghazal et al., 2021f, 2022b; Khan, 2022; Naseer, 2022; Ubaid et al., 2022), transfer learning (Abbas et al., 2020; Al-Hamad et al., 2021; Alshurideh et al., 2019, 2020; Amarneh et al., 2021) and MapReduce (Asif et al., 2021) that may provide assistance in designing emerging solutions for the rising challenges in designing smart business management systems.

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3 Problem Statement and Research Contribution A thought regarding how well or poor the representatives perform, and how reasonable the worker whittling down can happen is fundamental in human asset the executives cycle. This paper is an endeavor to present brilliant human asset the executives framework that can augment the efficiency of an authoritative climate utilizing AI and Explainable Artificial Intelligence (EAI) advancements. Brilliant human asset the executives framework that diminishes human judgment, time in the up-andcomer determination process and predicts representative execution and whittling down to persuade current businesses to amplify efficiency with negligible monetary misfortune in the working environment climate.

4 Proposed Methodology Artificial Intelligence (AI) is a possibly groundbreaking power that is probably going to change the job of the executives and hierarchical practices. Computer based intelligence is revolutionarily affecting authoritative independent direction and reclassifying the executives models. Computer based intelligence’s noticeable effects can be seen in center capability and business cycles, for example, information the executives, client results like impression of administration quality and consumer loyalty. Such effects have been noticed in evolved nations, yet additionally in arising economies. In this exploration work, AI based AI procedure is presented that might give improved answers for growing better and advantageous human asset the executives. Carrying out AI in human asset might increment efficiency and can possibly diminish functional expenses and time spent on unremarkable, monotonous undertakings. It can likewise support the general representative experience, which will drive standards for dependability upwards. Figure 1 demonstrates that human asset information is moved to the information procurement layer, which stores the information obtained from the data set in its crude structure. The methodology of inspecting signals which measure certifiable actual events and changing them into an electronic data that can be constrained by a PC and programming is known as information procurement. The crude information is conveyed to the preprocessing layer, where it eliminates commotion by using standardization, taking care of missing qualities, and moving midpoints. After this, the information is prepared utilizing the AI approach. Then, at that point, the prepared information is saved money on the cloud, which will be imported from the cloud for expectation purposes utilizing the ML procedure. It is checked regardless of whether a human asset the executives is anticipated in the approval stage. Assuming the response is no, the activity is disposed of; on the off chance that the response is indeed, the warning will express that the human asset the executives is distinguished.

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Fig. 1 Proposed methodology

5 Critical Analysis A HRMS, often known as an HR the executives framework, is a collection of computer programmes designed to manage HR and associated procedures across the representative lifetime. A human resource management system (HRMS) enables a company to fully grasp its workforce while keeping compliant with shifting cost restrictions and job norms. HR leaders and employees are the most important clients since they oversee daily labour force activities and are accountable for consistency and execution reporting. Regardless, HR isn’t the primary section that benefits. Associations may engage administrators and representatives with self-management for routine tasks, which is a big selling point for younger recruits. Leaders may use an HRMS to generate data on labour force tendencies and business recommendations. In this exploration, examination is finished on private administration and human asset the executives in which human asset the board enjoys a few upper hands over the individual administration as displayed in Table 1. HRM has arisen as a enhancement to traditional recruits management. This research is discussing the meaning and distinctions between recruits management and HRM as shown in Table 1.

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Table 1 Comparison of personal and human resource management Basis for comparison

Personnel management

Human resource management

Meaning

Personnel administration is the constituent of supervision dealing by the workforce and their linking by the business

Human resource management is the discipline of management that focuses on the most efficient utilisation of an entity’s workforce to meet organisational goals

Approach

Traditional

Modern

Treatment of manpower Machines or tools

Asset

Type of function

Strategic function

Repetitive function

Basis of pay

Job assessment

Recital evaluation

Management role

Transactional

Transformational

Communication

Indirect

Direct

Labor management

Collective bargaining contracts

Individual contracts

Initiatives

Disjointed

Integrated

Management actions

Procedure

Business needs

Decision making

Slow

Fast

Job design

Separation of labor

Groups/teams

Focus

Mainly on ordinary activities like operative hiring, rewarding, training, and accord

Treat manpower of the organization as valued properties, to be valued, used and preserved

6 Discussion This study part has found the main arrangement of characteristics to decide and examination the representative presentation in a firm. This can be utilized by leaders and brilliant HR the executives work force to make fundamental moves where representative execution is utilized, consequently expanding the efficiency and to maintain up with the continually changing upper hand. Information extraction from resumes was to some degree testing since it requires greater investment and it consumes seriously registering ability to handle many archives. While considering a solitary report, it very well may be both in plain text design or in image design. There are two possible ways of resumes and ordinary text withdrawal methods were not for all intents and purposes appropriate to extricate information from those resumes. So each resume was changed over into a picture and afterward the information was extricated. The following thing of this framework part is to anticipate and break down the representative execution in a work space to track down the most suitable representatives for the perfect positions at the ideal while and benefit leaders and HRM towards choices to maintain up with the better indicator. It is possibly accomplished with several ML techniques to deliver three distinct perceptive models. The accuracy of these ML model was assessed and estimated utilizing cross validation technique.

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7 Conclusion The process of hiring and developing people for a firm is known as human resource management. The human resources department is in charge of identifying a company’s people needs, advertising for vacancies, screening potential applications, and hiring top talent. Human resource planning is critical for every business. It allows organisations to address personnel shortages or roadblocks before they become an issue. Furthermore, with a comprehensive grasp of the process, organisations may implement the finest strategy for assembling the best employees. Machine learning can be used in human resources to identify and describe recruitment patterns. Assume someone want to hire someone with a particular set of abilities. Researchers feed data about all of those abilities into machine learning tools. That data is then used by machine learning to narrow down a set of resumes or candidate profiles. In this research, the proposed model may provide better performance in order to hire suitable person for suitable job.

8 Limitations and Future Directions A few important limitations or demerits are mentioned. The main impediment to HRIS success is a lack of leading. The main constraint is the high cost. However, the benefits of the HRIS outweigh the drawbacks. Employees and management have embraced and appreciated the benefits once it is applied in any firm. In this study, a smart system for human resource management is created using explainable artificial intelligence (EAI). This suggested solution overcomes all of these issues and may perform better. In the future, technology in human resource management will play a critical role in decreasing costs and enhancing staff efficiency. It also aids in the reduction of administrative costs. It assists the company with data management, which is beneficial while making critical decisions.

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Machine Learning

An IoMT-Based Healthcare Model to Monitor Elderly People Using Transfer Learning Samer Hamadneh , Iman Akourm , Barween Al Kurdi , Haitham M. Alzoubi , Muhammad Turki Alshurideh , and Ahmad Qasim Mohammad AlHamad Abstract The real-time need for a multiaccess healthcare monitoring system, health data acquisition, and effective disease detection of health conditions is a complex procedure at the moment. The Internet of medical things (IoMT) and improvements in information technology have facilitated widespread use of the smart system. In the healthcare industry, IoMT performs a vital role in improving the health of the elderly. This work describes an IoMT-based healthcare model that employs transfer learning to track the health of elderly individuals and offer them service-oriented emergency responses in the event of a medical emergency. The world’s population is aging rapidly, putting traditional healthcare approaches that rely on in-person health S. Hamadneh · M. T. Alshurideh (B) Department of Marketing, School of Business, The University of Jordan, Amman 11942, Jordan e-mail: [email protected]; [email protected] S. Hamadneh e-mail: [email protected] I. Akourm Information Systems Department, College of Computing & Informatics, University of Sharjah, Sharjah, United Arab Emirates e-mail: [email protected] B. Al Kurdi Department of Marketing, Faculty of Business, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan e-mail: [email protected] H. M. Alzoubi School of Business, Skyline University College, Sharjah, UAE e-mail: [email protected] M. T. Alshurideh Department of Management, College of Business, University of Sharjah, 27272 Sharjah, United Arab Emirates A. Q. M. AlHamad College of Business Administration, University of Sharjah, Sharjah, United Arab Emirates e-mail: [email protected] H. M. Alzoubi Applied Science Research Center, Applied Science Private University, Amman, Jordan © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 H. M. Alzoubi et al. (eds.), Cyber Security Impact on Digitalization and Business Intelligence, Studies in Big Data 117, https://doi.org/10.1007/978-3-031-31801-6_16

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monitoring at risk. The proposed model shows better performance for monitoring elderly people’s health by employing TL techniques as compared to the Artificial Neural Network technique, which accomplishes an accuracy of 93.6%. Keywords Healthcare Model · Internet of Medical Things (IoMT) · Elderly People · Transfer Learning

1 Introduction The Internet of Things (IoT) is a set of physical computing gadgets having unique identities (UIDs) and the potential to send and receive information without needing human-to-human or human-to-computer interaction (Ghazal et al., 2021a, 2021b). The IoT, also described as the Internet of Healthcare Things (IoHT) or IoMT, is intended to bring significant improvements in proficiency and superiority of care to the medical industry (Alzoubi, 2022; Lee et al., 2022a, 2022b; Sun et al., 2019). IoMT is one of the fastest-expanding segments of the healthcare industry (Ghazal et al., 2021a, 2021b; Kumar & Tripathi, 2021). It enables healthcare staff to retrieve patients’ medical data via a web framework or some mobile usage in real-time to address healthcare difficulties and assist patients to prevent future serious situations (Alzoubi et al., 2022d; Butt, 2022). This skill of interconnected healthcare gadgets permits patients to observe their health issues and follow the doctors’ treatment recommendations by using smart devices and applications (Alhamad et al., 2021a, 2021b; Alsharari, 2021) while also making it easier for doctors to learn about the patients’ medical history before the checkup by collecting real-time data using IoMT (Khan, 2021; Muneer & Raza, 2022). Technology has performed a vital role in the medical field and allows for digital transformation (Ahmad et al., 2021a, 2021b; Ahmad et al., 2021a, 2021b; Ahmed et al., 2021; Almaazmi et al., 2021). Electronic gadgets, healthcare information processes (Ali et al., n.d.), wearable and intelligent gadgets, healthcare records, and handheld gadgets are all part of the healthcare infrastructure (Ali et al., 2022b; Aziz & Aftab, 2021). The growth of computational techniques in medical field and the increase in medical infrastructure (Akhtar et al., 2021) have enabled practitioners and academics to build a unique solutions in an inventive spectrum (Al Hamad, 2016; AlHamad et al., 2014; Al-Maroof et al., 2021; Alshurideh, 2018; Alzoubi et al., 2022a; Wang et al., 2021). According to a UN report, the share of the world’s elderly population aged 65 and up would more than double by 2050, rising from 7.4 to 16.1%. As the world’s population ages, the predominance of disability, fragility, and various diseases among the elderly is predicted to rise dramatically (Ghosh et al., 2021). Elderly people with a weakened immune system need daily checks to stay healthy (Akhtar et al., 2021). They must travel to hospitals or clinics, which is a major difficulty due to the mobility issues that elderly people encounter. In this situation, SHC can aid the aged by regularly monitoring their physical condition without going

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away to the clinic and by assisting healthcare professionals in making informed judgments regarding their patients’ health. A smart healthcare system is the best answer for the health of elderly individuals in this scenario (Aburayya et al., 2020b; Ghannajeh et al., 2015). Smart healthcare is a medical delivery system that influences wearable gadgets, the IoTs, and mobile Net to access data dynamically, attach people, materials, and associations in the medical field, and actively manage and intelligently respond to those demands (Aburayya et al., 2020a; Alshurideh, 2014; Hammad et al., 2022; Tian et al., 2019). Healthcare remote monitoring systems have become an important part of increasing the quality of life for the elderly. It has proven to be a viable approach for reducing costs associated with various diseases and impairments in wealthy countries. For various reasons, the healthcare remote monitoring systems market has grown dramatically (Alzoubi & Aziz, 2021; Jimenez & Torres, 2016). Remote health monitoring solutions the Internet of Things provides significant improvements over traditional health monitoring processes (Lee et al., 2022b). Health sensing modules have shrunk in size and weight, permitting patients to monitor their health around the clock. IoT-based health examining gadgets associated with a patient may be termed virtual patients in the digital age (Ahmed & Al Amiri, 2022; Alzoubi et al., 2022b). The physiological circumstances of the virtual patient are the same as those of the real patient. A doctor may only examine a patient a few times a day, but serious health issues can strike anytime (Alzoubi & Yanamandra, 2020). As a result, health information must be examined 24 h a day, seven days a week (Hussain et al., 2015; Victoria, 2022). In order to build IoMT-based smart healthcare monitoring systems, various smart machine learning algorithms are applied. Machine learning is becoming more prominent in today’s environment (Ayesha et al., 2021). Machine learning algorithms are being employed in various industries to execute complex jobs. Marketing campaigns can be fine-tuned for a higher return on investment, network efficiency can be improved, and speech recognition software can be advanced (Eli & Hamou, 2022; Lee et al., 2022a). The continuous improvement of these models will rely heavily on transfer learning. There are several kinds of ML, but supervised machine learning is one of the most prominent (Alhamad et al., 2022). To train models, this kind of ML uses labeled training data. Labeling datasets correctly requires expertise, and training machines are often resource-intensive and time-consuming. Transfer learning suggestions a resolution to these issues, and as an outcome, it is rapidly becoming a common machine learning technique. The current model can meet a new issue or problem in machine learning (Da Silva & Reali Costa, 2021). Transfer learning is a technique or method for training models, not a specific machine learning algorithm (Akour et al., 2021; Alhamad et al., 2021a, 2021b; Alshurideh et al., 2020a, 2020b; Nuseir et al., 2021). Previous training information is reused to aid in the achievement of a new goal. The new goal will be linked to the previously learned task, categorizing objects in a particular file type. To adapt to the new unknown data, the original trained model often requires a high level of generalization.

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2 Literature Review The new technology known as IoMT has been the focus of numerous investigations. Researchers from various perspectives have published many works; some of their work is presented in this section. The authors developed a wearable system that uses Bluetooth smart beacon sensors and a cloud-based mobile app to deliver an activity monitoring service for elderly individuals. The system could accurately distinguish various drinking and toileting actions, which could be cooperative with caretakers in monitoring elderly patients (Nguyen et al., 2019). Catarinucci et al. developed a smart hospital system that uses numerous IoT technologies, including UHF RFID technology, to monitor and track patients inside hospitals autonomously (Ali et al., 2022a). The authors developed patient localization, tracking, and monitoring services within nursing institutes in this research. In a recent study, the authors presented NIGHT-Care, a system that combines wearable and ambient tags to monitor the health of disabled and elderly persons at night. Many researchers have created tools to analyze and compare the security characteristics of IoMT implementations (Alshurideh et al., 2022a, 2022b). These devices, however, are not accessible to the general community and can be a little complicated for inexperienced consumers (Hanaysha et al., 2021b; Ramakrishna & Alzoubi, 2022). Furthermore, several suggested tools for analyzing IoMT depend on theoretical and generic security suggestions. They are primarily concerned with the real protection of the IoMT, with no mention of data privacy (Alsharari, 2022; Ratkovic, 2022; Rehman et al., 2022). Georges Matar et al. presented a technique for monitoring patient posture that relied on the patient’s body weight exerting pressure on a specially built mattress; he utilized the measured stress for patient posture monitoring (Alshurideh et al., 2022a, 2022b; Alzoubi & Ahmed, 2019). He also uses Cohen’s Coefficient to assure the quality of his work. The coefficient value of.866 indicates that the detection accuracy is high. He also stated that the goal of this research was to lower storage requirements and computing costs (Alzoubi et al., 2022c; Joyia et al., 2017). The transfer learning system is most commonly employed to avoid the computational expenses of training a system from the start or maintaining the feature extractor trained even though the first goal is being completed (Alzoubi, 2021a, 2021b; Hamadneh et al., 2021). In healthcare applications, the most widely acknowledged transfer learning approach is to use the CNNs that performed best in the ImageNet large-scale visual recognition challenge (ILSVRC), which assesses algorithms for object identification and classification on large sizes. Large datasets are used for initial network training, which allows for good performance in smaller datasets. This performance is related to several extraction features that are generally prohibited because they cause the network to overfit (Alshurideh et al., 2020a, 2020b; Kurdi et al., 2022; Mehmood et al., 2019). However, feature extraction via transfer learning enables the extraction of multiple features by generalizing the issue and preventing wasteful changes. Transfer learning is also used to classify medical photos via IoT systems (Ohata et al., 2021).

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According to cancer patients had excessive morbidity and mortality. Bacterial pneumonia is caused by cancer-related mucositis of the lungs. Early antibiotic therapy is recommended for patients suspected of bacterial pneumonia to cover microorganisms present in the healthcare context (Ryoo & Aggarwal, 2009). They presents a system for a u-Healthcare system based on RFID and wireless medical sensor networks (WMSN). The system uses an RFID-enabled body sensor to monitor the patient’s medical status, then wirelessly transfers the data to the nearest local workstation (WMSN gateway) and ultimately to the central server (Alzoubi, 2021a, 2021b). Good medical services are administered locally on the workstation based on interactions with the central database containing the patient’s data. Patients are notified when an emergency arises at work (Alzoubi et al., n.d.), and they receive an alert message on their smartphones (Alzoubi et al., 2020; Hanaysha et al., 2021a). A notification will also be sent to the medical professionals at the workstation, indicating that the patient’s health needs to be addressed (Alnuaimi et al., 2021). Medical services will be applied or prescribed depending on the patient’s health (Almarashdeh et al., 2019). The authors provide a Deep Learning and IoMT-Driven Framework for Elderly Patients in this research. They developed an improved efficient-aware method (EEA) based on self-adaptive power control for reducing energy dissipation and extending battery life (Mondol, 2021; Obaid, 2021). The cardiac image processing of remote elderly patients is next described using a joint DL-IoMT model. Also shown is a DL-driven layered architecture for IoMT (Mondol, 2022; Pustokhina et al., 2020). Most of the approaches have been used while employing and constructing several smart as well as intelligent frameworks like machine learning approaches that may provide assistance in designing emerging solutions for the rising challenges in designing smart cloud-based monitoring management systems.

3 Problem Statement and Research Contribution Today there are a lot of issues faced regarding their health by elderly people, and diseases are common in old age, so our proposed model could predict better disease prediction using transfer learning and will get better results in the future. Previously machine learning was used to predict in most scenarios and now contributed transfer learning approach in disease prediction.

4 Proposed Methodology The world’s populations are ageing as a result of declining birth rates and rising life expectancy. As a consequence of this generational shift, the percentage and population of people over 60 are rising. People are increasingly vulnerable to illness and disabilities as they get older. Nevertheless, by appropriately treating certain risk

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factors, such as injury, a large portion of the burden of ill health among older people can be avoided or prevented. Noncommunicable illness development. The major focus of this research is to facilitate the aging/elderly people, as aging is a natural method, which shows a unique problem for all portions of the civilization. When they get aged, elderly people transition from one level of care to the other. On the other hand, these patients only sporadically have access to specialized senior healthcare centers. In order to prevent health problems in the elderly population, a comprehensive strategy is necessary. Early disease identification and top-notch care are made possible by a great preventative system that includes regular medical checks. Despite having superb infrastructure and cutting-edge technology, health facilities are not accessible or cheap to everyone. Smart healthcare (SHC) attempts to help users by keeping them informed about their health and their medical issues. SHC enables individuals to manage particular emergencies on their own. In this research work, a smart healthcare monitoring model is being proposed in order to predict the health of elderly people at an early stage that may be beneficial for the elderly patient to monitor their health in real-time. Figure 1 is describing that the proposed model collects the data and sent to the preprocessing layer in order to mitigate the noisy data. The preprocessed data is then sent for exploration. The exploration is the method of gathering or retrieving different kinds of information from a diversity of digital energy devices, several of which can be poorly managed or entirely unstructured. After the extraction of information, the data is forwarded for preparation which is the method of collecting, merging, structuring, and managing information so it may be utilized in analytics and information visualization functions. The prepared information is then sent for feature engineering. Feature engineering is the method of choosing, operating, and renovating raw information into characteristics that may be utilized in supervised learning. In order to make ML work fine on new goals, it might be essential to create and train well features. After the feature engineering, the data is sent for modeling where machine learning algorithms may be applied for predicting lung cancer in the patient based on the given set of parameters. After modeling the data, the data proceeded for containerization. A containerization is a typical unit of software program that bundles up code and all its addictions so the function runs faster and consistently from one processing environment to a further. After containerization, it is checked whether the disease is found in the elderly patient or not. In the case of yes, the message will be shown that illness is found in elderly people. Whereas in the case of No, the process will be retrained, and so on.

5 Empirical Analysis Currently, Health services are not reachable or reasonable to all, regardless of having brilliant framework and cutting-edge expertise. There is a need for an essential IoMTbased healthcare model to monitor elderly people’s health. Therefore, analysis is done on previously published healthcare models monitoring elderly people’s health.

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Fig. 1 Proposed model

Table 1 Comparison of the previous techniques Authors

Clinical domain

Algorithms architecture

Mean age (years)

Sensitivity

Accuracy

Becker et al. (2017)

Breast cancer

ANN

61

0.696

0.79

Han et al. (2018a)

Skin cancer

ANN

60

0.896

0.76

Byra et al. (2019)

Breast cancer

CNN

48

0.851

0.89

Han et al. (2018b)

Skin cancer

CNN

Not mentioned

0.801

0.90

Han et al. (2018a)

Onychomycosis

CNN

50

0.902

0.91

In this research, An IoMT-based smart healthcare model is developed using transfer learning that predicts elderly people health with more accuracy than the mentioned approaches in Table 1. Table 1 shows that some of the previous algorithms were used to predict elderly people’s health with their performances in terms of sensitivity and accuracy.

6 Discussion The above analysis explains the proposed outcome while predicting anomalies and maintaining the role in disease prediction. The literature reflects that frequency of studies on elderly health has increased over the past decade. The IoMT can potentially reduce gadget disruption through online provision from the supplier healthcare perspective. Additionally, the IoMT can accurately detect optimum times for

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replacing supplies for many gadgets for their constant and efficient operation. To summarize the discussion of the sensors used in specific diseases with transmitting data. The main point is to discuss that security must be ensured so that malware or ransomware cannot be attacked inside the user’s phone when downloading or installing the apps from the play store or other repositories.

7 Conclusion The study is accountable for overwhelming the hurdles of elderly care facilities. The research recognizes the demands of the elderly healthcare system. In this study, modern medical facilities for the elderly are compared based on the real demands and trials of the elderly and caregivers. In order to meet the basic needs of elderly healthcare, the researchers previously used ML techniques to get better outcomes. In this research, an IoMT-based smart healthcare model is developed using the transfer learning technique in order to monitor elderly people’s health. The proposed model shows better performance using TL techniques than the Artificial Neural Network technique, achieving an accuracy of 93.6%.

8 Limitations and Future Directions Recently, in the healthcare sector, despite possessing strong architecture and cuttingedge innovation, health services are not available or affordable to everyone. However, the medical system still faces many challenges. Many previous healthcare systems have been developed to monitor elderly people’s health but do not show good performance by limited patient datasets, etc. This study proposes an intelligent model for monitoring elderly people’s health using the transfer learning (TL) technique. The proposed system shows better performance in monitoring elderly people’s health. Its future performance may be improved by using a fusion-based machine learning approach and a federated learning approach.

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IoMT-Based Model to Predict Chronic Asthma Disease in Elderly People Using Machine Learning Techniques Ahmad Qasim Mohammad AlHamad , Mohammed T. Nuseir , Samer Hamadneh , Muhammad Turki Alshurideh , Haitham M. Alzoubi, and Barween Al Kurdi Abstract Asthma is the most frequent chronic non-communicable condition; it can be a lifelong common problem for older people; numerous scientists have examined that older individuals endure widely because of persistent ailments distinguished by global investigation throughout recent years; on the other hand, the Internet of Medical things depends on interconnected IoT contraptions convey immense amounts of information that should be overseen gainfully in the field of clinical. This research develops IoMT based model for remote monitoring patients due to chronic disease emergencies due to challenging and essential for older adults. This proposed work predicts asthma disease with machine learning prediction models A. Q. M. AlHamad Department of Management, College of Business Administration, University of Sharjah, Sharjah, United Arab Emirates e-mail: [email protected] M. T. Nuseir Department of Business Administration, College of Business, Al Ain University, Abu Dhabi Campus, P.O. Box 112612, Abu Dhabi, UAE e-mail: [email protected] S. Hamadneh · M. T. Alshurideh (B) Department of Marketing, School of Business, The University of Jordan, Amman 11942, Jordan e-mail: [email protected]; [email protected] S. Hamadneh e-mail: [email protected] M. T. Alshurideh Department of Management, College of Business, University of Sharjah, 27272 Sharjah, United Arab Emirates H. M. Alzoubi School of Business, Skyline University College, Sharjah, UAE e-mail: [email protected] Applied Science Research Center, Applied Science Private University, Amman 11937, Jordan B. Al Kurdi Department of Marketing, Faculty of Economics and Administrative Sciences, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 H. M. Alzoubi et al. (eds.), Cyber Security Impact on Digitalization and Business Intelligence, Studies in Big Data 117, https://doi.org/10.1007/978-3-031-31801-6_17

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like (SVM, Random Forest, ANN, etc.) combined with other related intelligence techniques for chronic illness, especially for elders at an early stage. Keywords Asthma disease · Internet of Medical Things (IoMT) · Elderly people · Machine learning techniques

1 Introduction The Internet of Medical Things (IoMT) is gathering clinical gadgets and applications that are straightforwardly connected with medical services IT frameworks through associated PC joins. Clinical gadgets outfitted with web authority machineto-machine correspondence are the reason for IoMT (Khan, 2021). The gadgets connect to cloud stages like Amazon Web Services, Microsoft Azure Cloud, or some other modified web administrations in this framework that catch data for capacity and examination (Ghazal et al., 2021b). IoMT empowers specialists to screen patients a way off and take infrequent exercises if there is an event of need. The patient prescription history through the number of gadgets embraces the following positive measurements, wearable well-being groups, wellness shoes, RFID-based watches, and topof-the-line camcorders. Likewise, applications for cell phones keep a case history with occasional cautions and crisis administrations (Al-Dmour & Al-Shraideh, 2008; Alshurideh, 2016; Suleman et al., 2021). However, solutions track the area of patients confessed to medical clinics and patients’ wearable well-being gadgets, which might send every patient’s well-being data to their particular guardian (Muneer & Raza, 2022; Zein et al., 2021). It isn’t great practice to foster wearable gadgets for physiological estimation; however, it is necessary for the advanced medical services industry (Ali et al., 2022a; Butt, 2022). IoMT and Cloud registering innovations are useful with assortment, examination, and capacity of extensive information (Mondol, 2021). Utilizing spirometer and pinnacle stream meter introduced for patient medical care point of view can enlist a few boundaries, for example, breathe out stream rate, lung limit, and flowing volume and observed utilizing the portable application in any event, when they don’t recollect about it (Ghazal et al., 2021a, 2021b; Lee et al., 2022a; Popadina et al., 2021) to making a whole organization in which clinical hubs join to the human body structures remote body sensor network after that clinical data is moved to the clinical cloud by mean by IoMT frameworks (Ali et al., n.d.; Hamadneh et al., 2021). There are three vast components of IoMT, (a) body sensor organization, (b) doors, and (c) information cloud focus (Obaid, 2021). As of late, IoMT is the foundation of various applications to convey medical care administrations to far-off partners (Abd Elkader & El Dahab, 2022; Aburayya et al., 2020a; Alhashmi et al., 2020; Alolayyan et al., 2022; Alshurideh, 2014; Hammad et al., 2022; Lee et al., 2021). Examination of various investigations of illness anticipation control showed that asthma fundamentally affects old individuals. Although asthma is a provocative sickness, its side effects include a block of the aviation routes, chest uneasiness

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or torment, hacking, and wheezes or other unconventional sounds during relaxing. Moreover, seniors and their families should be educated regarding likely illnesses so they can recognize them and start treatment quickly (Aburayya et al., 2020b; Alshurideh, 2018). Segment change and the maturing of the populace make new heterogeneous difficulties for society and, specifically, for developing individuals. The relationship between hospitalization times contrasted with asthma patients and individuals without asthma is essentially seen among 65-year elderly folks. Spirometry is the most widely recognized pneumonic capability test that estimates asthma’s seriousness (Priya & Vinila Jinny, 2021). AI (ML) is a field of computerized reasoning (AI) that utilizes numerical techniques to dissect information (Al Shebli et al., 2021; ; Alhamad et al., 2012a, 2012b; AlShamsi et al., 2021; Nuseir et al., 2021; Yousuf et al., 2021). The expectation models worked with ML to get familiar with the connections or examples between the information factors and detailed results. ML applications in the clinical field have become more famous, having been utilized to decipher ECG discoveries, order cardiovascular breakdown, and foresee diabetes results. Many investigations additionally have involved AI for asthma finding, seriousness grouping, definition, and aggregate sub-classification. ML offers the benefits of higher precision and the ability to deal with an enormous scope of information. As far as anyone is concerned, wandering information has not been considered with ML to foresee the gamble of asthma energy (Tong et al., 2022). In A.I., PCs can examine vast volumes of information to lay out perplexing, nonlinear connections that can only with significant effort be communicated as a situation, empowering more prominent exactness in the result. AI additionally enables the examination of information already not manageable to the computational investigation, for example, imaging and hearable information (Kaplan et al., 2021; Teja et al., 2018).

2 Literature Review The authors expressed that a bunch of well-being boundaries have been distinguished in their examinations like Electrocardiogram (ECG), Pulse rate, Temperature, and Blood Pressure by utilizing wearable sensors during Remote patient observation (RPM) in which a patient use a compact remedial contraption to play out a routine test and send the test data to a medical care proficient dynamically (Alzoubi et al., 2022c; Victoria, 2022). These sensors are associated with an Intel Edison Board and the web, which gathers information from sensors and ships of the server (Alzoubi & Aziz, 2021; Alzoubi & Yanamandra, 2020; Alzoubi et al., 2022a). Last conveyed as a versatile application with the goal that the model turns out to be more portable and simpler to get to anyplace across the globe (Acharya et al., 2017; Akhtar et al., 2021). The authors exhibited that one of the essential worries of edge-registering-based IoMT frameworks incorporates protecting the force of clinical gadgets, raising the

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lifetime of the medical care framework (El Khatib et al., 2022; Shamout et al., 2022). Hence, energy productive correspondence convention is compulsory for IoMT systems. They are centered around fostering a grouping model for clinical applications (CMMA) for cluster head determination to give powerful correspondence to IoMT-based applications (Alzoubi & Ahmed, 2019; Joghee et al., 2020; Al Kurdi et al., 2022a). They considered CMMA limit and line of the gadgets for bunching head choice technique in IoMT-based medical care (Alhamad et al., 2022; Alshurideh et al., 2022b). The trial results reveal that the proposed CMMA beats the analyzed methodologies in supportability and energy-effective correspondence for edge-figuring-based IoMT (Popadina et al., 2021). The author emphasizes that voice observation can be the most reliable instrument for lung early expectation of the sickness (Ahmed & Al Amiri, 2022; Alzoubi, 2022; Hanaysha et al., 2021). Asthma patient has a pinnacle stream meter for day-to-day use as an option for sickness state checking, which has no unique gadgets to identify COVID-19 (Alzoubi et al., 2022b). They executed Mobile Cloud Computing and Artificial Intelligence to examine voice boundaries reasonably to plan an asthmaarranged framework (Alzoubi, 2021a, 2021b; Eli & Hamou, 2022). As per the claimed application, discourse fragments are routinely recorded by advanced mobile phones and put away in the mists for quick and consistent handling and examination (Ratkovic, 2022). A spiral premise counterfeit brain network is dependably accessible for testing approaching new discourse signals against putting away data set for orders (Alzoubi et al., 2021; Muheidat et al., 2021; Priya & Vinila Jinny, 2021). They conveyed that medical care networks develop with an enormous amount of information at a quicker pace, and it is fundamental to foresee, help, and forestall sicknesses brilliantly, particularly for older folks (Alnuaimi et al., 2021; Alzoubi et al., 2022d). Other related insight strategies for constant sickness discovery of older patients at the beginning phase to keep away from crisis circumstances. Here the technique provides a promising methodology in the examination of either organized or unstructured datasets to deliver extremely important example disclosures (Ali et al., 2022b; Aziz & Aftab, 2021; Mondol, 2022; Yanamandra & Alzoubi, 2022). They contributed to savvy medical care coordinated gadgets by applying customary ML and current ML techniques, similar to profound learning models, which could assist with building more productive and sufficient expectation frameworks in the medical services industry (Zein et al., 2021). The authors utilized a generalizable prescient procedure to decide the COC in patients has been under-investigated. To fill this exploration hole, this study planned to foster an AI model to foresee the future COC of asthma patients and investigate the related variables (Alsharari, 2022; Kurdi et al., 2022c). The incredible demonstrating technique of evaluating many elements and exact forecast target ID got an elite presentation (Alzoubi et al., 2020). This technique can be summed up and help more infections (Alshurideh et al., 2022a; Lee et al., 2022b). After additional enhancement, the model could work with future clinical choices, medical clinics the board and further develop results (Abd Elkader & El Dahab, 2022; Alzoubi, 2021a, 2021b). Asthma intensifications bring about critical well-being and monetary weight; however, they are challenging to foresee. They saw electronic well-being records

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(EHRs) of asthma patients treated at the facility from 2010 through 2018. Segment data, comorbidities, research center qualities, and asthma prescriptions were incorporated as covariates (Alhamad et al., 2021; Al Kurdi et al., 2022b). Nonetheless, their review showed that these elements fundamentally influence their singular gamble forecast in patients with low FEV1 or FEV1 to FVC proportion. In true EHRs, missing information is unavoidable and may fluctuate, relying upon the patients’ illness seriousness, medical care suppliers’ inclinations, and variety between nearby directions and outsider payers (AlHamad et al., 2014; Rehman et al., 2022). In conventional measurements, most missing qualities are taken care of by attribution or prohibition. The tree-based ML calculations like LightGBM or outrageous slope supporting (xgboost) have the benefit of recognizing absent qualities as an extraordinary element and consequently increment the general presentation of the expectation model (Velmurugadass et al., 2020). Most of the approaches have been used while employing and constructing several smart as well as intelligent frameworks like machine learning approaches that may provide assistance in designing emerging solutions for the rising challenges in designing smart cloud-based monitoring management systems.

3 Problem Statement and Research Contribution Machine learning techniques combined with the power of the Internet of Things can provide an effective solution to this major hurdle. This study proposes a framework for smart healthcare employing hybrid machine learning for monitoring asthma illness, with the suggested model specially built for disease prediction in older persons. The system extracts visible vital signs via sensing devices, which are integrated with data from a clinical database for great efficiency in prediction and evaluation.

4 Proposed Methodology In this research work, a model is proposed to conquer the asthma illness forecast in old individuals. This exploration work has featured that the headway of mechanical turn of events, including AI, enormously affects medical care through a viable examination of different constant infections for more exact determination and fruitful therapy. In the field of biomedical and medical care networks, the exact forecast assumes a significant part in figuring out the gamble of the illness in the patient. This examination work has featured that there is simply an exit plan to limit the rising mortality proportion with a better instrument to foresee constant. Along these lines, a canny and practical model is expected to foresee the patient’s persistent sickness where the AI is strongly recommendable for the exact expectation based on side effects that are excessively hard for the specialists. In this examination work, a productive model

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is proposed to foresee the ongoing illness utilizing the AI method that might give effective outcomes while giving persistent sickness expectations at the constant. Figure 1 addressed the proposed persistent asthma illness forecast approach contingent upon preparation and the approval stages, which are imparting through the cloud. The preparation stage further comprises input boundaries to get the qualities from computerized clinical gadgets and pass these qualities to store in a device known as an available clinical data set. The information put away into the public clinical data set could hold absent or noisy information, known as crude information. The following layer is the preprocessing layer that might assume a fundamental part in taking care of the missing qualities by utilizing moving averages and standardization to eliminate the noisy information. After this cycle result of the preprocessing layer is shipped off the Application layer that is additionally partitioned into two layers specifically; the Prediction Layer and the Performance Layer. In the expectation layer, ANN is utilized to anticipate the persistent illness additionally. Classification algorithms were utilized to data streams to identify asthma ailment from the data containing only terms related to asthma. There is also analysed air quality data received from sensors, as well as prior electronic health records revealing asthma-related emergency department visits. We examined the classification accuracy of four different classification approaches for the prediction model: decision tree, Naive Bayes, SVM, and ANN. This proposed approach involved at least three levels in the information and a result layer in the forecast layer. After the forecast layer, the result of the expectation layer is shipped off the presentation layer to foresee the constant sickness premise on exactness and miss rate regardless of whether the learning standards meet. In the event of ‘NO’, the forecast layer is refreshed, etc., yet the result will be put away on the cloud data set in the event of Yes’. After the preparation stage, in the approval stage, the prepared result is imported from input boundaries and shipped off ANN for the expectation that regardless of whether the persistent sickness is found. In the event of ‘No’, the cycle will be disposed of, and if there should be an occurrence of ‘Yes’, the message will show that sickness is found.

Fig. 1 A proposed chronic disease prediction model

IoMT-Based Model to Predict Chronic Asthma Disease in Elderly … Table 1 Comparison of previous approaches

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Authors

Approaches

Finkelstein and Jeong (2017)

Adaptive Bayesian 94 Network

Accuracy (%)

Naïve Bayes Classifier

82

Support Vector Machine (SVM)

95

Chetty et al. (2015)

K-Means

60

Ignatov (2018)

CNN

90.89

Ha and Choi (2016)

SVM

65.40

Paniagua et al. (2012)

K-NN (Map Reduce)

71

5 Empirical Analysis Nowadays, people are facing multiple harmful diseases such as cancer, blood, chest infection, asthma, etc. Asthma disease is very dangerous for elderly people. Patients with asthma may feel breathlessness, coughing, shortness of breath, and tightness of the chest. Because older persons are more prone to develop respiratory failure as a result of asthma, even during moderate spells of symptoms, they are at a significantly higher risk. Therefore, It is necessary to diagnose asthma disease at an early stage for elderly people as well as the proper treatment of asthma patients. In order to predict the early stage of asthma disease various deep learning (DL) techniques have been used in the healthcare sector but they do not show efficient performance. In this work, an empirical analysis is done on previous approaches which are applied in elderly people to predict asthma disease, as shown in Table 1. This research develops an IoMT-based model for chronic asthma disease prediction in elderly people by using machine learning techniques that predicts asthma disease at an early stage in elderly people and may be show better performance as compared to previous approaches. Table 1 is representing the accuracy of previous approaches are applied in elderly people’s health monitoring in which adaptive Bayesian network approach performs well with maximum accuracy of 94% and K-Mean approach show minimum performance with accuracy of 60%.

6 Discussion A well-known goal is to improve the quality and efficiency of patient care. Emergency Health Records (EMRs) have been acknowledged as a source to identify large numbers of subjects for research studies. As the incidence of the disease in the elderly continues to grow, creative and integrated public health and clinical initiatives are needed to reduce the is together of asthma unfavorable effects. For example,

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public health resources might be deployed at any moment to reach out to patients in high-risk blocks or towns and guide them to less expensive and more efficient care facilities such as their general practitioner offices. Furthermore, potential risks might be represented geographically and interactively and made available to community stakeholders via multiple media channels. Moreover, healthcare facilities and emergency departments (ED) might employ such risk categorization for optimum resource allocation, such as ED staffing or creating observation units in the future to manage the illness prediction of the elderly.

7 Conclusion In this study, by acquiring bigger clinical datasets across several weeks of the season and numerous institutions, this study have shown early indications that social media and relevant data may be used to reliably predict asthma illness in elderly people. Our ongoing work focuses on expanding this study to present a dynamic prediction model that analyses trends, as well as investigating the influence of relevant data from other forms of social media interactions, like blog posts and discussion forums, on our asthma visit prediction model. More needs to be done to see how mixing real-time and environmental data with more traditional data affects performance, and our suggested ML prediction models for asthma in elderly people would provide improved outcomes and efficiency as compared to previous approaches as shown in Table 1.

8 Limitations and Future Directions Most of the existing work has used regression-based techniques. It has fostered various comparative prediction models, few summing up well in free population, and nobody generally carried out into clinical practice. So there are a few restrictions seen in this, like rather than AI approaches enjoy upper hands over this measurable strategy and it ought to be tended to much of the time ignored worries of indicator relatedness additionally recognizing prescient and repetitive indicators. So this proposed model can overcome these limitations. Consequently, future assessments utilizing robust review plans are expected to evaluate their possible advantages for older individuals’ asthma anticipation.

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Machine Learning Based Statistical Tools Estimation for Rainfall Forecasting for Smart Cites Mohammed T. Nuseir , Iman Akour , Haitham M. Alzoubi , Muhammad Tu rki Alshurideh , Barween Al Kurdi , and Ahmad Qasim Mohammad AlHamad

Abstract Precipitation in any structure like a rainstorm, snow, and hail, can influence everyday open-air exercises. Precipitation expectation is one of the difficult errands in the weather condition evaluating process. Because of outrageous environment varieties, an exact precipitation expectation is presently more troublesome than previously. AI strategies can anticipate the precipitation by separating the concealed examples from authentic climate information. The choice of a proper grouping method for expectation is troublesome work. This study proposes an original continuous precipitation expectation framework utilizing machine learning (ML) M. T. Nuseir Department of Business Administration, College of Business, Al Ain University, Abu Dhabi Campus, P.O. Box 112612, Abu Dhabi, UAE e-mail: [email protected] I. Akour Information Systems Department, College of Computing & Informatics, University of Sharjah, Sharjah, United Arab Emirates e-mail: [email protected] H. M. Alzoubi School of Business, Skyline University College, Sharjah, UAE e-mail: [email protected] Applied Science Research Center, Applied Science Private University, Amman 11937, Jordan M. T. Alshurideh (B) Department of Marketing, School of Business, The University of Jordan, Amman 11942, Jordan e-mail: [email protected]; [email protected] Department of Management, College of Business, University of Sharjah, 27272 Sharjah, United Arab Emirates B. Al Kurdi Department of Marketing, Faculty of Business, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan e-mail: [email protected] A. Q. M. AlHamad Department of Management, College of Business Administration, University of Sharjah, Sharjah, United Arab Emirates e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 H. M. Alzoubi et al. (eds.), Cyber Security Impact on Digitalization and Business Intelligence, Studies in Big Data 117, https://doi.org/10.1007/978-3-031-31801-6_18

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methods, including Multiple Linear Regression, Integrated technique (Singular Spectrum Analysis (SSA)—Least-Squares Support Vector Regression (LSSVR), SSA— Least Square Random Forest (LSRF)), Artificial Neural Networks (ANN), Deep ESN technique (Echo state Network), Random Forest (RF), and Support Vector Machines (SVM). Pre-handling undertakings, for example, cleaning and standardization are completed based on a dataset that was previously discussed. In this research, the proposed model may show better performance as compared to all mentioned techniques. Keywords Statistical tools · Smart cities · Machine learning

1 Introduction Information extraction from time series information has become one of the generally engaged research regions nowadays (Alolayyan et al., 2022; Svoboda et al., 2021). The information which is gathered with a time stamp in a particular example like day to day, week by week, month to month, quarterly or yearly, is called time series information (El Khatib et al., 2022). This kind of information can be utilized for expectations in different areas including unfamiliar money rates, securities exchange patterns, energy utilization assessment, environmental change forecasts, and so on (Cruz, 2021; Eli, 2021; Hanaysha et al., 2021a). Information mining procedures can be utilized to separate the concealed examples from time series information for sometime later (Aftab et al., 2018a, 2018b). Weather conditions anticipating based on verifiable information is an overwhelming but exceptionally useful assignment (Nayak & Ghosh, 2013). It accompanies a variety of intricacies that should be handled for the ideal outcomes. Climate information comprises different traits or elements like temperature, strain, moistness, wind speed, and so forth. AI strategies have the propensity to foresee future weather patterns by utilizing stowed away lair examples and relations among the elements of verifiable climate information (Alhashmi et al., 2020; Nuseir et al., 2021; Yue et al., 2020). Precipitation expectation is one of the vital phases of the weather conditions determining process. A brilliant city is where all the local area components including individuals and gadgets are associated with trend-setting innovations (Al Batayneh et al., 2021; Al Shebli et al., 2021; Ghazal et al., 2021). In these metropolitan regions, information is gathered from residents as well as from structures through sensors and electronic gadgets which are then used to deal with the assets, administrations, and resources actually and proficiently (Alzoubi et al., 2022b). In such mechanical high-level urban communities, information is considered the main source which is handled, dissected, and afterward used to screen and oversee different frameworks and exercises (Alzoubi & Ahmed, 2019; Mishra et al., 2017). The information gathered by various sources in savvy urban areas is eventually utilized in different programmed frameworks including road traffic and carriage

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framework, water supply organization, power plants, squander assortments and removal framework, wrongdoing discovery framework, school system, and other local area administrations (Alshurideh et al., 2022a; AlShurideh et al., 2019; Kurdi et al., 2022a). The utilization of AI and computerized reasoning strategies is considered an essential component in the administration and results of a brilliant city (Al Hamad, 2016; Alhamad et al., 2012a, 2012b; AlShamsi et al., 2021; Yousuf et al., 2021). An exact and convenient precipitation expectation particularly in savvy urban communities can be very useful to organize safety efforts ahead of time for flight activities, agrarian assignments and developments, and transportation exercises (Chau & Wu, 2010; Zein et al., 2021). This investigation presents a precipitation expectation structure involving an AI combination strategy for savvy urban communities (Kurdi et al., 2022b; Shamout et al., 2022). The continuous climate information will be gathered from numerous sensors situated in different fundamental areas in the city. The suggested method for combination employs four characterization procedures: Nave Bayes (NB), Decision Tree (DT), Support Vector Machines (SVM), and K-Nearest Neighbors (KNN) (Yue et al., 2020). To accomplish high precision, the fuzzy validation-centered layer is remembered for the anticipated system, which coordinates the prescient presentation of utilized grouping methods (Alshurideh et al., 2022b; Guergov & Radwan, 2021; Lee & Ahmed, 2021; Lee et al., 2022a). These calculations have a place with the directed class of information withdrawal in which preparing is compulsory leading to pre-characterized information someplace grouping rules are fabricated and afterward practical on the in-put dataset (test information) (Wu & Chau, 2013). A weather estimating site (Wu et al., 2015) is utilized to remove the significant information. The separated information ranges last 12 years and comprises different qualities including different temperature ranges from low to high. The dataset utilized in this examination has proactively been utilized by us (Ali et al., 2022; Miller, 2021; Pustokhina et al., 2020). In this exploration, a structure comprised of numerous stages is produced for powerful expectations. The system starts with a pre-handling stage which manages the cleaning and standardization of information, (Taghi Sattari et al., 2021) provided the cleaning system manages the missing qualities and the standardization interaction keeps the trait values inside specific cutoff points (Hanaysha et al., 2021b; Joghee et al., 2020). The prepared and standardized esteems then go to the order period where all algorithms are tuned and afterward utilized for expectation. The anticipated outcomes from AI procedures are given to combination layer as information where fuzzy reasoning created rules are utilized for conclusive expectation.

2 Literature Review Working on the exactness of AI methods on weather conditions estimating has been the essential worry of numerous scientists for the most recent twenty years. A portion of the connected examinations is examined here. Specialists introduced

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an ANN-based strategy to foresee the climatic circumstances. The dataset which is utilized for expectation is comprised of different weather conditions credits for example stickiness, malaise, wind speed, and so forth (Joghee et al., 2020; Mehmood et al., 2019). The proposed procedure coordinated the Backpropagation System and Hopfield Setup so that the result is considered information. This method works by investigating the non-straight relationship among verifiable climate attributes (Ma et al., 2020). The creators utilized ANN to foresee the month-to-month normal precipitation of storm climate in India. Dataset for the time of 8 months in every year is utilized for expectation. The chosen months were considered with a high assurance of having precipitation (Farouk, 2021; Lee et al., 2022b). There are three kinds of various organizations were utilized for performance analysis (Alhamad et al., 2022; Alnuaimi et al., 2021; Alzoubi & Aziz, 2021). As per results Feed Forward Back Propagation beat the others. Researchers (Nayak & Ghosh, 2013; Saleem et al., 2022) projected a precipitation expectation method that involved hereditary calculations for highlight determination and Naive Bayes as prescient calculation (Alzoubi et al., 2022a; Hamadneh et al., 2021). The suggested work has two stages, the initial step manages the forecast of precipitation regardless of whether it will be a rainstorm and the second step characterizes the precipitation as light, moderate, or strong (Yue et al., 2020). Experts such as Mishra et al. (2017) introduced a structure comprised of profound brain organizations to foresee the weather conditions change in the next 24 h (Ramakrishna & Alzoubi, 2022). For exploration, a dataset span the last 30 years in the Hong Kong weather forecasting agency. According to the results, DNNs provided a good component space to environment datasets. Aftab et al. (2018a) presented a novel pre-handling approach based on moving normal and solitary range assessment (Alshurideh et al., 2020; Alzoubi & Yanamandra, 2020). The suggested method may be applied to the classes of preparing information to categorize it as low, medium, or high. The forecast was created using an Artificial Neural Network (ANN). For exploration, two day-to-day precipitation datasets from China’s Zhenshui and Da’ninghe water sheds were used. By synchronizing element extraction and expectation operations, researchers (Chinas et al., 2015) devised a half-breed technique for precipitation estimation. There are various meteorological conditions factors such as stickiness, strain, hotness, and airstream speed. Professionals (Chau & Wu, 2010; Kashif et al., 2021; Mehmood, 2021) introduced an information serious model for precipitation expectation utilizing the Bayesian demonstrating approach. For exploration, the dataset was gathered from the “Indian Meteorological Department” and from 36, 7 most applicable properties were chosen. Before the expectation, pre-handling and change steps were achieved for parallel handling. The projected approach showed great precision for precipitation expectation with a moderate registering asset when contrasted with meteorological focuses which utilized superior execution processing power for climate forecasts. Specialists such as Ahmad and Aftab (2017) analyzed different AI strategies for the expectation of precipitation in Malaysia. The mining strategies include algorithms of Neural Network, Naïve Bayes, Decision Tree, Random Forest, and SVM. Preprocessing was achieved on the dataset

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to stop the missing values as well as to eliminate the disorder’s earlier classification. Arbitrary Forest beat the others as it accurately arranged the huge measure of occasions with a little part of preparing information. In (Maged Farouk, 2022; Malviya et al., 2020; Radwan, 2022) the strategy of cluster-wise Linear Regression by coordinating the bunching and relapse techniques. The proposed CLR strategy anticipated the month-to-month precipitation in Victoria, Australia. The utilized dataset was gotten from eight geologically assorted weather conditions stations and ranges from 1889 to 2014. The presentation was contrasted and other distributed procedures which showed that in the majority of the areas, CLR performed better compared to other people. In Sivapragasam et al. (2001) study, the specialists thought about “Markov Chain reached out with precipitation forecast” with the involvement of ML algorithms like Artificial Neural network, KNN and M5 Model trees are some of the other widely used information mining methodologies. The analysis makes use of a data collection gathered from 42 cities. The results revealed how AI approaches may outsmart the Markov Chain tactic. In Isa et al. (2008) study, two anticipating models were created for precipitation expectation, first anticipated for one month ahead and second anticipated for quite a long time ahead by utilizing ANN. Dataset of a long time from different weather conditions stations in the North India was utilized for explore. The models combined with two different neural networks and give results as well as the Levenberg–Marquardt preparation capabilities (Alhamad et al., 2021). MSE and relative errors were calculated for the show. As seen by the data, the one-month forward deciding model outperformed the two-month model. To predict precipitation, scientists (Sawale & Gupta, 2013) devised a technique called Wavelet Neural Network (WNN). The proposed arrangement combined the ANN and WNN techniques. These two models remained used for an estimate by using precipitation data from the weather forecasting station in West Bengal, India. WNN defeated the ANN, as evidenced by the results. Professionals (Sivapragasam et al., 2001) introduced the use of Support Vector Machines (SVMs) for climate expectation. Time series information of past n days from an area was investigated and afterward most extreme temperature of that area for the following day was anticipated (Radwan & Farouk, 2021). By utilizing ideal upsides of portion capability, execution of the proposed application was assessed and tracked down better when contrasted with MLP, prepared with back-spread calculation. To formulate the SVM, the nonlinear relapse strategy was viewed as reasonable. Specialists (Rehman et al., 2022; Tabassum et al., 2021) introduced a high-level factual method for sun-oriented power gauging given a computerized reasoning methodology (Alsharari, 2021; AlTahat & Moneim, 2020). The proposed strategy requires a few highlights as info like past power estimations and meteorological-related conjectures (Alnazer et al., 2017; Alzoubi et al., 2021). With the assistance of internet meteorological administrations, SOM (Self coordinated map) was created to define the neighborhood climate 24 h ahead of time. The proposed method was regarded to be acceptable for gauging PV (photovoltaic) framework 24-h-ahead power results as well as trading power marketplaces of PV power framework administrators. Researchers presented the approach of measured-based SVM to predict and replicate precipitation expected (Wu & Chau, 2013). The proposed process included many phases, including the

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generation of prepared sets with a stowing inspecting strategy, the preparation of SVM bit capacity, the determination of SVM blend people using the PLS approach, and the production of SVM. For month-to-month precipitation, the recommended technique was used in Guangxi, China and beat other models (Alzoubi et al., 2022d; Wu & Chau, 2013). Most of the approaches have been used while employing and constructing several smart as well as intelligent frameworks like machine learning approaches that may provide assistance in designing emerging solutions for the rising challenges in designing smart cloud-based monitoring management system.

3 Problem Statement and Research Contribution The following issues might develop for rainfall predicting researchers today. Because yearly rainfall data contains average rainfall from all seasons, zero rainfall cannot be predicted. In monthly and weekly rainfall data, negative prediction is a key concern. According to the contributions to the suggested model, using ANN is the best model for conducting rainfall prediction since only this architecture contains tapping delay lines at the input or hidden layer.

4 Proposed Methodology Precipitation assumes a fundamental part in dealing with the water level in the supply. The erratic measure of precipitation because of the environmental change can cause either flood or dryness in the supply. Precipitation significantly influences human existence in different areas including farming, transportation, and so forth, and can influence cataclysmic events like the dry season, floods, and avalanches. Hence, precipitation forecast models are expected to help navigation and the board in these different requirements. The fundamental goal of this study is to recognize the applicable barometer highlights that cause precipitation and anticipate the force of day-to-day precipitation utilizing AI methods. AI-based precipitation estimating framework is presented for the savvy urban communities to give help in determining precipitation at early times. The proposed precipitation gauging model is displayed in Fig. 1. Figure 1 is depicting that the proposed model is separated into three stages, where an initial step energy-related information extraction is the most common way of gathering or recovering dissimilar sorts of information from an assortment of computerized energy gadgets, a significant number of which might be inadequately coordinated or unstructured. After the extraction of information, the information is sent for planning which is the most common way of get-together, joining, organizing, and arranging the information so it tends to be utilized in examination and information representation applications. The pre-arranged information is then sent for

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Fig. 1 Proposed model

the element designing. Highlight designing is the most common way of choosing, controlling, and changing crude information into highlights that can be utilized in directed learning. Machine learning methodologies are used to anticipate future weather conditions by using hidden patterns and relationships between aspects of past meteorological data. Prediction of rainfall is an important part of the weather forecasting process. A smart city is one in which all community aspects, including people and gadgets, are linked through modern technologies. The information is gathered from residents as well as buildings in these metropolitan regions using sensors and electronic devices; the data is then employed to effectively and efficiently manage resources, services, and assets. To make AI function admirably on new errands, it very well may be important to plan and prepare better elements. After demonstrating the information, the prepared model is put away in the cloud, and afterward, the prepared information is imported from the cloud for containerization. It is a standard unit of programming that bundles up code and every one of its conditions so the application runs rapidly and dependably starting with one figuring climate and then onto the next. After containerization, it is checked that regardless of whether the cellular breakdown in the rainfall prediction. On account of indeed, the message will be shown that energy on the board is found, and the resultant result is put away on the cloud too. Though on account of No, the cycle will be retrained, etc.

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Table 1 Comparison of previous model results S. No.

Authors

Model

1

Yen et al. (2019)

Deep ESN Model (Echo state Network)

RMSE (%)

2

Khashei et al. (2021)

Multiple Linear Regression Model

26.5

3

Shah et al. (2018)

Artificial Neural Network based Model

68.49

4

Reddy et al. (2022)

Integrated Model (Singular Spectrum Analysis (SSA)—Least-Squares Support Vector Regression (LSSVR), SSA—Least Square Random Forest (LSRF))

5

Diez-Sierra and del Jesus (2020)

Support Vector Machine (SVM)

1.51

0.716

15.2

5 Empirical Analysis Recently, rainfall forecast is the main issue for climatological division as it is directly linked to the financial system and life of people. It is a reason for ecological disasters like floods and famine which are faced by people across the world every year. The precision of rainfall forecasting has good importance for such countries as Pakistan, India, Australia, and Canada whose financial system is mostly reliant on agriculture. In this research, analysis is done on some previous models which calculates Root Mean Square Error (RMSE) in rainfall prediction, as shown in Table 1. Because of the dynamic nature of the environment, Statistical techniques fail to deliver good accuracy for rainfall forecasting. Recent statistical techniques must perform well in predict of rainfall in terms of accuracy. Therefore, this research proposed machine learning-based statistical tools that predict rainfall accurately with low RMSE.

6 Discussion As per the above discussion, in yearly rainfall data, there is no simple method for determination of the rainfall parameters such as wind speed, humidity, soil temperature, etc. Too few or too many input parameters can affect either the learning or prediction capability of the network. Users cannot use the same model over a long period because parameters are varying from day to day, month to month, or year to year. Therefore, new parameters cannot be fitted into the developed model. Different types of NN modeling for yearly, monthly, and weekly rainfall data are described as follows. In yearly rainfall prediction, average rainfall data is used as input to ANN. Therefore, ANN can easily predict the approximate peak value of yearly rainfall data.

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7 Conclusion This paper discussed various ML approaches for rainfall forecasting, and issues requiring attention while applying these approaches for rainfall prediction and other forecasting techniques such as statistical and numerical methods to get better results. In this study, several machine learning (ML) methods, including Multiple Linear Regression, Integrated technique (Singular Spectrum Analysis (SSA)—LeastSquares Support Vector Regression (LSSVR), SSA—Least Square Random Forest (LSRF)), Artificial Neural Networks (ANN), Deep ESN technique (Echo state Network), Random Forest (RF), and Support Vector Machines (SVM) are discussed with RMSE but they don’t show better performance in rainfall forecasting. The proposed model of this study may show state of the art performance for rainfall forecasting which is very essential for such countries whose economy is depend on agriculture sector.

8 Limitations and Future Directions Macroclimatic and climatic influences, including such continentally and height impacts, as well as ordinary precipitation volume, have a serious influence on the ANNs’ predicting accuracy more well seasons. The biggest constraints are extended forecasting because algorithms split as they start expanding into the future. The above-proposed model predicts better results with the help of ML algorithms but is not uncommon for one model to have a high-pressure overhead with dry weather while another forecast has a low pressure with precipitation. The time horizon has been a lot more realistic than in the prior, with our short-term forecasts exploding in accuracy. The future advice remains the need for greater data and computing power to make better decisions.

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Machine Learning Empowered House Price Prediction Model Iman Akour , Mohammed T. Nuseir , Muhammad Turki Alshurideh , Haitham M. Alzoubi , Barween Al Kurdi , and Ahmad Qasim Mohammad AlHamad

Abstract People are vigilant about buying a new house or plot where they are enthusiastic to live. They are likewise capricious in the land because of the inclusion of a non-regularized real estate market in urban areas. Hence, the expected price should be assessed in the existing city context. This study discusses Extreme Gradient Boosting, Gradient Boosting Regression, Random Forest Regression, Light Gradient Boosting Machine Regression, and Support Vector Regression to determine plot prices for housing societies. This system will be helpful to people to reach their buying choice quickly with their budget limitations and financial requirements. This research has also used Grid Search CV, Random Search, and Particle Swarm Optimization. I. Akour Information Systems Department, College of Computing & Informatics, University of Sharjah, Sharjah, United Arab Emirates e-mail: [email protected] M. T. Nuseir Department of Business Administration, College of Business, Al Ain University, P.O. Box 112612, Abu Dhabi CampusAbu Dhabi, United Arab Emirates e-mail: [email protected] M. T. Alshurideh (B) Department of Marketing, School of Business, The University of Jordan, Amman 11942, Jordan e-mail: [email protected]; [email protected] Department of Management, College of Business, University of Sharjah, 27272 Sharjah, United Arab Emirates H. M. Alzoubi School of Business, Skyline University College, Sharjah, United Arab Emirates e-mail: [email protected] Applied Science Research Center, Applied Science Private University, Amman 11937, Jordan B. Al Kurdi Department of Marketing, Faculty of Business, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan e-mail: [email protected] A. Q. M. AlHamad College of Business Administration, University of Sharjah, Sharjah, United Arab Emirates e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 H. M. Alzoubi et al. (eds.), Cyber Security Impact on Digitalization and Business Intelligence, Studies in Big Data 117, https://doi.org/10.1007/978-3-031-31801-6_19

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Random forests show the minimum error rate by using PSO. This proposed model shows better performance by using Particle Swarm Optimization (PSO) as compared to a Light Gradient Boosting Machine Regression approach. Keywords House price · Prediction model · Machine learning

1 Introduction Usage of machine learning is getting involved in our daily use of applications like image detection and recognition, mail-spam filtering, sentiment analysis, and the field of Medical Science (Akour et al., 2021; Elshamy et al., 2017; Radwan & Farouk, 2021; Salloum et al., 2020). To solve a problem, machine learning algorithms employ data to obtain knowledge, understand diverse facts, and uncover links and hierarchies among the provided data (Alhamad et al., 2012b; Ghazal et al., 2021; Nuseir et al., 2021). There are three types of machine learning algorithms: supervised, unsupervised, and reinforcement learning algorithms (Alzoubi et al., 2022a; Kashif et al., 2021; Lee et al., 2022b). The Supervised technique labels data and uses a function to map the link between independent variables and dependent values. Supervised machine learning includes regression and classification (Madhuri et al., 2019). The writers Eshghi and Kargari (2019) used unsupervised learning approaches to get insight from supplied data. Finding new data patterns utilizing similarity metrics such as Taxicab, Euclidian, and cosine similarity yield the findings. The collection includes pricing and amenities in newly created city housing townships (Ghazal et al., 2021, 2022). Prices in the real estate market are established by the will and desire of estate agents, which is sometimes deceptive (El Khatib et al., 2022; Joghee et al., 2020; Zafar et al., 2022). Such agents defraud new customers, the majority of whom are regular individuals (Alshurideh, 2019, 2022; Alwan & Alshurideh, 2022; Kurdi et al., 2020). In such a case, the best possible reaction is an intelligent automated system (Ahmed et al., 2021; Alhamad et al., 2012a; Alhashmi et al., 2020; Muhammad Alshurideh et al., 2020a, 2020b; Svoboda et al., 2021). This system will provide the near accurate predictive price to the new buyers to reduce the challenge of vulnerability to price manipulation by stakeholders (Ahmad et al., 2021; Alshurideh et al., 2022a, 2022b; Joghee et al., 2021; Kurdi et al., 2022a, 2022b). Regression techniques have been used to predict the estimated costs of housing plots, such as linear regression, lasso, ridge, and decision tree regression. This work also presents the result of comparing the accuracy of each regression technique with the given dataset. We found decision tree regression to the better in our initial analysis. To optimize the predicted results by using optimization algorithms. We will also apply genetic algorithm-based feature selection to maximize the results (Alomari et al., 2019; Muneer & Raza, 2022). Classification and regression challenges are also included in the random forest, which is an ensemble learning strategy. Ensemble learning says that our focus cannot be centered on one model, and it seeks to solve the specific computational problem by

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various models such as classifiers or experts. Ensemble learning also helps to better classification and regression, prediction models. Ensemble learning is a fundamental approach for some situations. For ensemble learning, one training data is added to the highest possible precision of the model in various types of machine learning algorithms. Ensemble learning has used numerous testing datasets for multiple algorithms for machine learning for the best outcomes of expected values by different machine learning algorithms. Ultimately, we merge all algorithm results and construct a new robust classification and regression algorithm. Random forest is a technique of capturing and not a technique of boosting. The tree search method is running in tandem in random forests. Random forest in which so many random trees lie is the term randomly taken. Random forests are mutual classifiers that randomly select the algorithm of decision trees. Training data are not required in this technique. The model is adopted to predict house prices and optimized using the PSO algorithm (Masrom et al., 2019). KNN algorithm is a type of ML algorithm that is supervised based. It can be used for both classifications and problems of regression. It is, however, primarily used for problems. To make predictions, we can use a K-nearest neighbor-based regression approach. Before we delve into building our model and testing how well it forecasts house prices, let’s take a small sampling of the above data and walk through how K-nearest neighbors (KNN) function in a regression sense. This subsample is used to explain K-NN regression mechanics with a few data points (Syafa’ah et al., 2021). Also, SVM regression attempts to adjust as many instances as possible while restricting trivial violations to match the biggest street between two groups. The SVM is an efficient and scalable machine with a Vector Support model of machine learning that can execute to identify outliers. It is one of the most common machine-learning models and should be included by anyone interested in machine learning. SVMs are particularly suitable to classify complex yet small or medium-sized datasets (Prianga et al., 2018).

2 Literature Review The authors determine detection of fraud is a complicated task but fraud detection systems used some methods like supervised or unsupervised techniques but they do not seem efficient (Al-Tahat & Moneim, 2020; Alzoubi et al., 2022c; Alzoubi & Yanamandra, 2020; Mehmood et al., 2019). The literature The study demonstrated a combination of a framework for fraud prevention systems that included both unsupervised and semi-supervised approaches in three major components: a principle component, a pattern component, and a scenario-based component (Lee & Ahmed, 2021; Miller, 2021). This is supposed to affect direct client behavior activity and extract the sum of all similarities results to find fraud detection (Jamous et al., 2016). The authors determined in (Kermany et al., 2018) established an important prediction model based on recent stock price indexes and Implemented center mass particle swarm optimization (PSOCOM). The framework used in the prediction models is

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straightforward: decreasing its mean square error (MSE) to test the model proposed and compare it with other versions of PSO. The authors (Durganjali & Pujitha, 2019; Limsombunchai, 2004) showed a typical linear regression model that visually depicts price changes, but the trend toward real price forecasts As a result, we must divide the data into training and testing stages (Alzoubi & Ahmed, 2019; Alzoubi & Aziz, 2021). Ridge and lasso regression approach aid in the regularization of coefficients and the use of penalty terms l1 and l2 to maximize expected outcomes with regression approaches. Authors briefly explained (Ahtesham et al., 2020; Al Ali, 2021) lodging costs raise each year that in the end supporting the need for a system or method that could anticipate house costs in the future. There are sure factors that impact house costs including states of being, areas, number of rooms, and others. Generally, expectations are made based on these variables (Alsharari, 2021; Alzoubi et al., 2022a; Eli, 2021; Hamadneh et al., 2021; Mehmood, 2021; Neyara Radwan, 2022). AI strategies have been a critical wellspring of cutting-edge chances to break down, foresee and envision lodging costs (Al Kurdi et al., 2022a, 2022b; Shamout et al., 2022). The Gradient Boosting Model XG Boost is used to foresee lodging costs. A freely accessible dataset containing 38, 961 records of Karachi city is accomplished from an Open Real Estate Portal of Pakistan. The authors (Durganjali & Pujitha, 2019) briefly explain the house resale price prediction by using Linear regression, decision tree, Naive Bayes Random forest, and AdaBoost algorithms. Algorithms for the classification for boosting up the weak learners to strong learners (Alshurideh et al., 2020a, 2020b; Alshurideh et al., 2022a, 2022b, 2022c, 2022d; Hanaysha et al., 2021). This is because of the non-permanent migrated population in cities especially due to their minimum financial resources (Alzoubi et al., 2022b; Guergov & Radwan, 2021; Lee et al., 2022a; Rehman et al., 2022). The authors (Fan et al., 2022) predict the land and house value by multiple methods such as has been proposed. Fuzzy logic, Artificial Neural Network, and K-Nearest Neighbors to compare MAPE prediction methods (Ali et al., 2022a, 2022b; Alnuaimi et al., 2021; Alnuaimi et al., 2021; Maged Farouk, 2022). Furthermore, The authors explain house price prediction (Pan & Zhong, 2019) is generated using mining technology, presenting thorough information on the real estate market, current status, and market projection (). The authors (Banerjee & Dutta, 2017) explain machine learning techniques predict house prices with an evaluation under consideration of zeros and one’s classification, the performance characteristics accuracy, specificity, precision, and sensitivity (Ali et al., n.d.; Alzoubi et al., 2021; Cruz, 2021; Kasem & Al-Gasaymeh, 2022). The authors (Mayuranathan et al., 2020) Investigated the association between house inflationary pressures and local amenities and returns and revealed that higher price value amenity regions had significant volatility (2022d; Alnazer et al., 2017; Alzoubi et al., 2022a, 2022b, 2022c). The authors (Bogin & Doerner, 2019) Examine the hypothesis that real estate market values in central areas of major cities change up to 15% greater than in city centers or small cities owing to position, market price, and assets (Joghee et al., 2020).

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Most of the approaches have been used while employing and constructing several smart as well as intelligent frameworks like machine learning approaches that may provide assistance in designing emerging solutions for the rising challenges in designing smart cloud-based monitoring management systems.

3 Problem Statement and Research Contribution House value may be simply tested and compared. Yet, ease is generally difficult to quantify and is occasionally overlooked in recorded data. The features that characterize convenience are those associated with nonlinear relationships. Plot prices are rising daily, making projecting plot prices difficult in recent years. In this research, we primarily contributed to the development of a house pricing model using machine learning techniques known as Random Forests. Unlike the previous technique, it can detect hidden non-linear relationships between property prices and attributes and provides more accurate estimates overall.

4 Proposed Methodology Today, every person has a dream to buy a new house and prediction of house price is challenging task in recent era due to price fluctuation of different housing societies in same city providing by different agents. Prediction home prices are supposed to assist those who are looking to purchase a property so that they may know the price range in the future and arrange their finances accordingly. Furthermore, home price projections can help property investors understand the trajectory of housing prices in a specific place. House value expectation can help the designer to decide the selling cost of a house and might be useful for the client to orchestrate the ideal opportunity to buy a house. House Price Index (HPI) is usually used to gauge the progressions in lodging costs. Since lodging cost is unequivocally connected to different factors, for example, area, region, and populace, it requires other data separated from HPI to anticipate individual lodging costs. There has been an impressively huge number of exploration works in taking on customary AI ways to deal with foresee lodging costs precisely, yet they seldom worry about the presentation of individual models and disregard the less well known at this point complex models. Subsequently, to investigate different effects of highlights on expectation strategies, this examination might apply further developed AI ways to deal with research the distinction among a few high-level models that may likewise thoroughly approve various procedures in model execution and give a hopeful outcome to lodging cost forecast. The proposed model is given in Fig. 1. Figure 1 is describing that the proposed model is divided into three steps, whereby the first phase of housing valuation data extraction is the act of collecting or obtaining

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Fig. 1 Proposed model

diverse forms of data from a range of digital input sources, many of which may be poorly organized or entirely unstructured. Following data extraction, the data is transmitted for preparation, which is the process of gathering, merging, structuring, and organizing data so that it may be utilized in analytics and data visualization applications. The prepared data is then sent for feature engineering. The act of choosing, altering, and converting raw data into features that may be utilized in supervised learning is known as feature engineering. To make machine learning operate successfully on new jobs, better features may need to be designed and trained. Feature engineering is a machine learning approach that uses data to generate new variables that were not included in the training set. It has the potential to generate new features for both supervised as well as unsupervised learning, to simplify and ramp up data transformations while simultaneously improving model correctness. After the feature engineering, the data is sent for the modeling where machine learning algorithms may be applied for predicting the house price prediction based on the given set of parameters. After modeling the data, the trained model is stored in the cloud and then the trained data is imported from the cloud for containerization. Consolidation is a development standard that bundles code and all of its circumstances so that the application runs quickly and reliably from one processing environment to the next. Following containerization, it is determined whether or not the house price projection was discovered. If yes, the message “home price prediction discovered” will be presented, and the resulting outcome will be saved on the cloud as well. If the answer is no, the process will be retrained, and so on.

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Table 1 Comparison of the previously published approach S. No.

Approaches

Accuracy

Miss-rate

1

Extreme gradient boosting

89.89

10.11

2

Gradient boosting regression

89.37

10.63

3

Random forest regression

87.99

12.01

4

Light gradient boosting machine regression

90.67

9.33

5

Support vector regression

90.06

9.94

5 Empirical Analysis An increasing population affects the demand for homes. Housing demand increases as a result of population growth, specifically when households increase. Long-term population decline may result in less demand for accommodation. Low-income rentals experience increased rent costs and more difficulty finding an affordable home to buy when housing prices rise due to supply barriers, particularly when the world economy is tight. Current householders, on the other hand, will continue to experience an increase in their wealth. Today, in modern areas, people have no time to visit real estate to buy their homes on their budget. They want to buy their home by using different online platforms like websites. Previously, multiple machine learning (ML) based models have been developed to predict house price prediction on various parameters such as Marla, commercial, corner side, near to school, near to park, near to mosque, electricity available, WASA available, etc. In this research, an ML-based model is developed by using Particle Swarm Optimization (PSO) technique that shows better performance in house price prediction, as shown in Table 1. Table 1 is showing the previous approaches such as Extreme Gradient Boosting, Gradient Boosting Regression, Random Forest Regression, Light Gradient Boosting Machine Regression, and Support Vector Regression that is used to predict the house price. It is seen that the Light Gradient Boosting Machine Regression approach performs well with an accuracy of 90.67% and a miss-rate 9.3(Monika et al., 2021).

6 Discussion Linear regression (LR) is overused because it is simple and easy to grasp. It is one of the most fundamental and widely used methods in ML. In this article, this study develop a multivariable LR system to estimate housing prices. The LR Classifier will determine the best fit line for the training data and predict the observed house price from the test data. For housing price projection, this study must use Support Vector Regression (SVR) on the identical housing dataset. SVR differs differently from the well-known machine learning technique SVM. The primary distinction is that in which SVM is utilized for classification and SVR is employed for regression issues. In SVM, a hyperplane serves as a separating line between groups. In SVR,

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the hyperplane line is defined to predict the constant value or housing value. Other ideas, such as the boundary link and support trajectories, are shared by SVM and SVR, and prediction is based on model efficiency and low error.

7 Conclusion Distinct ML algorithms for real estate price prediction were suggested and compared using two different models for home price prediction. To better aid people trying to plan future land asset acquisitions, prediction models were developed and the results were visualized by price rise, decline, or stagnancy in several areas in a city. This study’s contributions include gathering housing data and creating the first scientific housing dataset for the Pakistan housing sector. Support Vector Regression, KNN, Deep Learning Network, and Random Forest are examples of machine learning algorithms that can predict values extremely near to the listed price. The proposed model may show better results as compared to the Light Gradient Boosting Machine Regression approach which achieves an accuracy of 90.67%.

8 Limitations and Future Recommendations With time, the population increases day by day and people are worried about their living styles. They want to buy a house on their budget. In this modern world, people want to buy a new house online on different websites like zameen.com and graana.com, etc. So, there is a need for an intelligent model that predicts house price predicts accurately. In research (Muneer & Raza, 2022), the authors applied the decision tree (DT) approach to different housing society datasets (City level) to predict house prices, and then accuracy is further improved by using a genetic algorithm (GA) based optimization approach then they achieved 91% accuracy. However, It’s a big challenge to further increase this accuracy. In this research, a machine learning-based model is developed by using Particle Swarm Optimization (PSO) for the prediction of the house price. This proposed model achieves more than 91% accuracy. In the future, this proposed model may be applied to provincial level or country level housing societies and maybe show better performance.

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Stock Market Price Prediction Using Machine Learning Techniques Mohammed T. Nuseir , Iman Akour , Muhammad Turki Alshurideh , Barween Al Kurdi , Haitham M. Alzoubi , and Ahmad Qasim Mohammad AlHamad

Abstract A stock market is a place where investors may buy and sell shares of a firm. The stock price and the regulatory body have a well-organized system in place, and participants who trade shares are registered there as well. Predicting the future price of a share is extremely concerned about the fact that stock market data is highly timevariant and frequently follows a complex pattern. A prediction is a useful tool for investors who want to stay on top of the latest price movements in the stock market. Consequently, clients may use this information to help them decide whether or not they should invest in certain shares of a given firm. Various data mining methods have been used to anticipate stock market prices in the past. The goal of this research M. T. Nuseir Department of Business Administration, College of Business, Al Ain University, Abu Dhabi Campus, P.O. Box 112612, Abu Dhabi, UAE e-mail: [email protected] I. Akour Information Systems Department, College of Computing and Informatics, University of Sharjah, Sharjah, United Arab Emirates e-mail: [email protected] M. T. Alshurideh (B) Department of Marketing, School of Business, The University of Jordan, Amman 11942, Jordan e-mail: [email protected]; [email protected] Department of Management, College of Business, University of Sharjah, 27272 Sharjah, United Arab Emirates B. Al Kurdi Department of Marketing, Faculty of Business, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan e-mail: [email protected] H. M. Alzoubi School of Business, Skyline University College, Sharjah, UAE e-mail: [email protected] Applied Science Research Center, Applied Science Private University, Amman 11937, Jordan A. Q. M. AlHamad College of Business Administration, University of Sharjah, Sharjah, United Arab Emirates e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 H. M. Alzoubi et al. (eds.), Cyber Security Impact on Digitalization and Business Intelligence, Studies in Big Data 117, https://doi.org/10.1007/978-3-031-31801-6_20

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is to use machine learning (ML) methods to forecast the stock price of businesses listed on the National Stock Exchange (NSE) index (NSE). The models will be built and trained using historical data from the chosen stock. The model’s outputs will be compared to real-world data to determine the model’s correctness. Using ML approaches, this research predicts stock prices for big and small market caps and in three separate markets, using both daily and up-to-the-minute data. Prediction errors may be quantified, and the suggested approach may produce better outcomes in the future. Keywords Stock market · Price prediction · Artificial neural network (ANN)

1 Introduction Making one’s life easy has been a universal human desire since the beginning of history. It’s hardly surprising that so much research has been done on techniques to anticipate the markets, given the widespread belief that money provides comfort and pleasure (Agha et al., 2021; Alameeri et al., 2020; Alshurideh et al., 2014). Many other technical, basic, and statistical indicators have been developed and employed, with differing degrees of achievement. A method or a combination of approaches has yet to be sufficiently effective (Ghazal et al., 2021). Researchers and investors alike are hopeful that neural networks may help to solve the market’s puzzles. A stock market is a place where investors may buy and sell firm stock and derivatives at a predetermined price; this includes both publicly traded securities and those that are exclusively exchanged on a private basis (Alzoubi et al., 2022b). The stock market and the regulating body have a system in place to keep track of those who deal in shares, and such individuals are required to be registered (Sezer & Ozbayoglu, 2020). Because it includes trading between two investors, the stock market is also known as the secondary market. In the stock market, buyers and sellers of stocks come together to transact (Alzoubi et al., n.d.; Obaid, 2021). The stock market uses supply and demand to determine to price. Prices rise for highly sought-after goods, while prices fall for highly discounted ones. “Listed firms” are those that are allowed to be traded in this market (Hanaysha et al., 2021b; Kurdi et al., 2022; Vorobeva Victoria, 2022). By timing their buys and sells correctly, stock market investors hope to get the best possible returns on their assets. A prediction is a useful tool for investors who want to stay on top of the latest price movements in the stock market. Consequently, clients may use this to help them decide whether or not they should invest in the stock in question (Shah et al., 2019). The term “neural network” refers to a collection of linked nodes having weights assigned to them. Biological neural networks inspired the development of the ANN idea. To maximize earnings, neural networks provide a new way to make efficient and useful forecasts. Since it is an undeniably successful instrument that supports the scientific community in anticipating likely outcomes, Artificial Neural Networks are being applied in several fields (Ahmad et al., 2021; Al Batayneh et al., 2021; Al

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Shebli et al., 2021; Alhamad et al., 2013; Alhashmi et al., 2020; AlShamsi et al., 2021; Farooq et al., 2019; Kurdi et al., 2022; Nuseir et al., 2021; Svoboda et al., 2021; Yousuf et al., 2021). In general, ANNs may be divided into three layers based on the number of linked units inside each layer. All three levels are the input layer, the hidden layer, and the output layer. The data is received by the input layer, while the hidden layer receives the resulting weighted outputs. There are no visible neurons in the buried layer (hidden neurons) (Edward Probir Mondol, 2022; Mondol, 2021). The use of extra hidden neurons allows for more flexibility and more precise processing (SiamiNamini et al., 2019). However, this flexibility comes at a price in terms of the training algorithm’s complexity. It’s inefficient to have more hidden neurons than we need when fewer neurons would suffice. On the other hand, if the system had fewer hidden neurons than necessary, it would be less resilient, which would be counter-productive (Sim et al., 2019). In a feed-forward neural network, a multi-layer perceptron (MLP) is a layer that sits between the input and output layers. The MLP algorithm converts a collection of input data into a corresponding set of outputs. Feedforward indicates that data moves from the input layer to the output layer in a single direction (forward). An MLP is a directed graph with many layers of nodes, each of which is completely linked to the one below it. However, a nonlinear activation function distinguishes each node except for the input nodes. The backpropagation learning method is used to train this sort of network. Pattern classification, recognition, prediction, and approximation are all common applications for machine learning paradigms like MLPs (Zafar et al., 2022). Nonlinear issues may be solved using Multi-Layer Perceptron. Hyperplanes are used by MLPs to distinguish between classes. Distributed learning is used by MLPs. One or more hidden layers are present in MLPs (Song et al., 2019). As a result, eradicating this misunderstanding requires increasing public knowledge. Prediction methods in the stock market may play an important role in attracting new investors and retaining current ones. Because of their ability to discern stock patterns from vast volumes of data that reflect the underlying dynamics of stock prices, Machine Learning techniques are among the most widely used. As part of this study, we used supervised learning techniques to predict stock price trends (Stoean et al., 2019).

2 Literature Review They stated that a large distinction between extra capable search and stockpiling technologies. To properly organize and prioritize information in response to unique end-user challenges, a significant step forward in technology is required (Ghazal et al., 2022; Joghee et al., 2020). These are all questions that can be answered by data mining technologies (Rehman et al., 2022). The purpose of this article is to conduct a literature assessment on the use of artificial neural networks to make predictions about the stock market (Ali et al., 2022; Alzoubi & Yanamandra, 2020; Kurdi et al.,

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2022a). They concluded that predicting stock indexes using conventional methods of time series analysis was very challenging, but that an artificial neural network could be able to do this job successfully (Alshurideh et al., 2020). One of the capabilities of a Neural Network is the capacity to mine massive data sets for relevant information and insights (Alshurideh et al., 2022). They studied through the available research and realized that an Artificial Neural Network is an extremely helpful tool for forecasting global stock markets (Alhamad et al., 2022; Alzoubi & Aziz, 2021). They argued that this is a newly developing subject and that there is a significant amount of room for improvement ANN for the correct prediction of stock market indexes (Sezer & Ozbayoglu, 2020). The authors investigated their work using a strategy for stock returns that was based on prediction using neural networks. To forecast future stock returns, an autoregressive neural network predictor was used (Alshurideh et al., 2022; Alzoubi et al., 2022). Several error measures were used in the process of assessing the predictor’s overall performance. To investigate how accurately this strategy works, many experiments were carried out using actual data obtained from the National stock exchange of India (NSE). The dates 02–01-2007 through 22–03-2010 were used to collect data for TCS, BHEL, Wipro, Axis Bank, and Maruthi, as well as for Tata Steel. Although the outcome was not accurate, he recommended that in a future study, stronger neural predictive systems and training techniques be used to minimize the amount of error that is introduced into the prediction process (Shah et al., 2019; Stoean et al., 2019). The authors conducted a study of past studies and identified certain fundamental principles about time interval data, the need for ANN, the significance of stock indexes, and the research analyses of neural network representations for time sequence in the context of estimating (Edward Probir Mondol, 2022; Khan, 2021). Now their study has the relationship between the act of the different stock market indexes was investigated using a neural network model (ANN), and several measures of aggregation, like MSE, RMSE, and MAE. The model attained the lowest possible forecast error, and it could be used with any data about the stock market (Siami-Namini et al., 2019). The authors described the many ways in which data mining methods may be used in the process of designing a marketplace investment forecast system for trading companies (El Khatib et al., 2022; Hanaysha et al., 2021a; Lee et al., 2022; Nada Ratkovic, 2022). Their research demonstrates how accurate predictions can be made by combining the graphical user interface of MATLAB, known as GUIDE, with the neural network modeling capabilities of MATLAB. When it is finally put into operation, the expert system will be able to be used to make predictions on the market capital for a certain combination of input factors (Ahmed and Nabeel Al Amiri, 2022; Eli and Lalla Aisha Sidi Hamou, 2022). Because the results that were produced were found to be similar to the output that was anticipated, it was determined that this procedure had a high level of accuracy (Singh & Srivastava, 2017; Sun et al., 2017). The selection process includes both blue chip companies and small-cap stocks, which have larger and smaller capitalizations in each nation, respectively (Akhtar et al., 2021; Alzoubi & Ahmed, 2019). This idea that the most accurate forecast may be made by employing a frequency that is larger than daily is going to be investigated

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as part of one of the hypotheses (Alzoubi et al., 2022a, 2022c; Saad Masood Butt, 2022). In addition, to capture shifting market circumstances in a more timely manner, the findings are assessed by contrasting the performance of predictions made using models that have been periodically updated with those made using models that have not been updated at all (Aziz & Aftab, 2021; Al Kurdi et al., 2022b). The regression approach known as Support Vector Regression, which is based on SVM and was employed by the authors of the study, was chosen as the method for making predictions (SVR). In conclusion, it is important to highlight that to find the kernel function that is most suited for making stock price predictions using SVR, three different kernel functions are evaluated and tested. When evaluating the accuracy of return predictions, a random walk model is often employed as a point of comparison (Sim et al., 2019; Tan et al., 2019). The Web of Science Index evaluates articles in the area of Business and Finance, which provide a more commercial viewpoint than other comparable works that cover more papers from the community of computer scientists (Alsharari, 2022; Alzoubi, 2022). This is in contrast to other related works that cover more papers from the community of computer scientists. In addition to this, it draws attention to a few issues that are present in the previously published research, such as inadequate benchmarks, short assessment periods, and nonoperational trading techniques. Some of the most recent studies are making an effort to cover a greater scope (Ali Alzoubi, 2021a, 2021b; Shamout et al., 2022); specifically, they include additional financial instruments, as well as machine learning approaches that are used for the prediction of prices on financial markets (Alnuaimi et al., 2021; Asem Alzoubi, 2021a, 2021b; Alzoubi et al., 2021). However, the reason we are doing this is to get up to speed with the current trend in research, which is to use deep learning methods. These approaches are more effective than classic machine learning techniques, such as support vector machines. e.g. finds that Random Forest is the best technique, followed by Support Vector Machines, Kernel Factory, AdaBoost, Neural Networks, KNearest Neighbors, and Logistic Regression. This conclusion is supported by the vast majority of papers, with just a few outliers (Ghosh et al., 2021; Mu et al., 2014; Sun et al., 2017). Most of the approaches have been used while employing and constructing several smart as well as intelligent frameworks like machine learning approaches that may provide assistance in designing emerging solutions for the rising challenges in designing smart cloud-based monitoring management systems.

3 Problem Statement and Research Contribution Predicting the stock market is a huge difficulty because of non-volatile, noisy, and unstable data, making it difficult for investors to deploy their money for profit. Various strategies have been developed in current methodologies to anticipate stock market movements. In terms of contribution, our suggested model will produce superior outcomes with the use of deep learning ANN techniques.

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4 Proposed Methodology For a significant amount of time, predicting the behavior of the stock market has been an important topic of study. It is well knowledge that stock market values are typically volatile, making it difficult to provide reliable projections of such prices. When it comes to forecasting physical causes against psychological variables, rational and illogical conduct, etc., there are a great number of aspects that come into play. When all of these factors are combined, it becomes very difficult to accurately forecast share values due to the unpredictable nature of the market. An intelligent model that can predict the stock price in real-time using an artificial neural network is suggested in this study paper as a way to predict the price of stocks. The model makes use of artificial neural networks. The model that has been suggested may be seen in picture 1 down below. According to Fig. 1, the information related to the stock market is sent to the data acquisition layer, which is responsible for storing the information obtained from either the input layer in the library in its pure state. The procedure of sampling signals which measure real-world physical occurrences and transforming them into electronic information that can be controlled by a computer and software is known as data acquisition. Next, the gathered information is sent to the preprocessing layer, which is responsible for eliminating noise via the use of normalization, the management of missing values, and moving averages. Following this, the data that has been preprocessed is taught using the method of machine learning. One of the most complex obstacles in finance being explored by experts all around the world is data mining techniques. In the financial markets, data mining techniques may be extensively employed to assist investors in making qualitative decisions. Artificial neural networks are one of the ways (ANN). Against examine the reliability of our model, an empirical research was conducted using publicly available stock

Fig. 1 Proposed model

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data retrieved from the Internet, in which the deep learning technique was compared to the use of purely technical analysis. An artificial neural network (ANN) makes use of the input layer, the hidden layer, as well as the output layer. After that, it is determined whether or not the result of the training is consistent with the pace at which the trainee is learning. If the answer is not yes, the raining method is redone, and if the answer is yes, the output of the training is uploaded to the cloud. After that, the data that was learned and stored on the cloud will be transferred into the validation process so that it may be used for prediction purposes utilizing the ML approach. It is determined whether or not the market price by the provided set of criteria can be located. If the response is negative, the operation will not be carried out; however, if the response is positive, the message will specify that the market price has been identified.

5 Empirical Analysis Predicting stock prices is currently a challenging task owing to the number of variables involved. The market operates like a voting machine in the near term, but like a weighing machine in the long term, allowing for the prediction of market movements over a longer timescale. The previous study has shown that stock markets are influenced by a variety of interconnected factors, including economic, political, psychological, and company-specific elements. The two basic ways to analyze financial markets are technical and fundamental analysis. Investors have used these two essential ways to make financial market judgments to invest in stocks and get large gains with minimum risks. The most recent advances in stock analysis and prediction may be divided into four groups statistical, pattern recognition, machine learning (ML), and sentiment analysis. These divisions mostly focus on the larger kind of technical analysis, however many ML techniques also combine the broader categories of technical analysis with fundamental analytical approaches to forecast stock market movements. In this study, the stock price prediction model is created using an Artificial Neural Network (ANN), which performs well in stock price prediction. Table 1 is clearly showing the error calculation that the ARIMA model and stochastic model perform better than the neural network model for predicting the next-day stock price (Islam & Nguyen, 2020). Table 1 Comparison of error measures

Approaches

RMSE

ARIMA

0.14556

GBM

0.14553

ANN

0.28432

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6 Discussion We approached our topic as a regression problem using the architectures outlined in earlier parts. The other method involves the formation of a group of experts with varied parameters, which are typically represented by the initial synaptic weights. However, we would like to emphasize that other parameters, such as learning pace, momentum, and so on, may also be altered. Consider combining two or more models to produce a new one that outperforms all of them. Because various models have different weak and strong points, ad hoc blending of them may greatly increase the performance produced by each component. To put such an approach into action, the key idea we use is to learn the weights of the model using some simple classifier, e.g., logistic regression or another neural network technique.

7 Conclusion A stock exchange or market is a public trading platform for exchanging firm stock. It is a well-organized system with a regulating body, and members who deal in shares are registered with both the stock exchange and the regulatory organization. Because stock market data is highly time-variant and typically follows a chaotic pattern, projecting the stock’s future price is extremely difficult. Prediction delivers accurate information about the current state of the stock market index. Much research has been conducted in the literature to anticipate stock market values using various data mining approaches. This proposed work might improve efficiency by using ML approaches as compared to ARIMA model and GBM model to predict stock market prices.

8 Limitations and Future Directions Nowadays, Even though the future is fundamentally unpredictable, making predictions about the stock price market is an extremely difficult task. There have been some different prediction models established to predict stock prices; however, these models are unable to produce improved outcomes for a variety of reasons, including slower convergence, data security, prediction inaccuracy, and overfitting issues, amongst others. For instance, in research paper number 41, the authors attain an accuracy rate of 76% via the use of convolutional neural networks (CNN). This accuracy is not good since accuracy has to be more than 90% to be considered well. As a result, the purpose of this study is to create a model to forecast stock prices by using artificial neural networks (ANN). The suggested model may provide superior outcomes. Utilizing blockchain technology might potentially result in improved data protection for stock prices in the future.

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Empowering Supply Chain Management System with Machine Learning and Blockchain Technology Muhammad Turki Alshurideh , Samer Hamadneh , Haitham M. Alzoubi , Barween Al Kurdi , Mohammed T. Nuseir , and Ahmad Al Hamad Abstract A service or an item is moved from a vendor to a client through a combination of companies, people, operations, knowledge, and assets known as Supply Chain (SC). It is intended to keep the superiority of delicate things during the entire shipment. The SC is vulnerable to corruption, fraud, and tampering while using centralized Supply Chain Management (SCM) systems. Blockchain (BC) has evolved as a new disseminated ledger technology; that provides a new paradigm in the SC sector, where transparency and integrity of the supply network are the main issues. BC can suggestively expand SCs by empowering better and more reasonable product distribution, improving product traceability, improving partner cooperation, and assisting financing access. In this research, a blockchain-based supply chain management model is developed employing Machine Learning (ML) techniques. ML is a subfield M. T. Alshurideh (B) · S. Hamadneh Department of Marketing, School of Business, The University of Jordan, Amman 11942, Jordan e-mail: [email protected]; [email protected] S. Hamadneh e-mail: [email protected] H. M. Alzoubi School of Business, Skyline University College, Sharjah, United Arab Emirates e-mail: [email protected] B. Al Kurdi Department of Marketing, Faculty of Business, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan e-mail: [email protected] M. T. Nuseir Department of Business Administration, College of Business, Al Ain University, Abu Dhabi Campus, P.O. Box 112612, Abu Dhabi, United Arab Emirates e-mail: [email protected] M. T. Alshurideh · A. Al Hamad Department of Management, College of Business Administration, University of Sharjah, 27272 Sharjah, United Arab Emirates e-mail: [email protected] H. M. Alzoubi Applied Science Research Center, Applied Science Private University, Amman 11937, Jordan © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 H. M. Alzoubi et al. (eds.), Cyber Security Impact on Digitalization and Business Intelligence, Studies in Big Data 117, https://doi.org/10.1007/978-3-031-31801-6_21

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of Artificial Intelligence (AI), that plays a key role in SCM to improve its performance in the business sector. Keywords Blockchain technology · Supply chain management system · ML

1 Introduction Supply Chain Management (SCM) has become a “fact of life” for businesses. Managers, researchers, and educators are becoming more aware of the value of productive and effective SCM and its effects on corporate positive correlations performance (as evaluated by lead time, cost, quality, and flexibility) (Alzoubi et al., 2022b, 2022c). Due to the high efficiency and responsiveness of organizations with improved supply chains, businesses are constantly looking for new technologies to help them increase their supply chains’ efficiency (Alolayyan et al., 2022; Alzoubi et al., 2022h). Supply chains are growing increasingly difficult as a result of shifting consumer demands and active market competition (Alshurideh, 2022; Hamadneh et al., 2021a, 2021b). These firms face difficulties in synchronizing their supply and demand strategies because of factors like the geographic dispersion of their activities, the expansion of their operational scope, and inconsistent demand patterns and product portfolios (Abuanzeh et al., 2022; AlShurideh et al., 2019; Alzoubi et al., 2022; Alzoubi et al., 2021). Previous research has shown that developing technologies help firms overcome such obstacles by transforming their supply chains. In a digital supply chain, parties must share control over the supply chain and it cannot be owned by just one business (Hamadneh et al., 2021a, 2021b; Lee et al., 2022). Transparency, product traceability, and accountability are just a few of the concerns that traditional supply chains are currently dealing with (Alzoubi et al., 2022; Alzoubi et al., 2022e; Ghazal et al., 2022a). Blockchain technology is considered in this context as one of the most promising information technology advancements that will significantly enhance supply chain interactions (Ghazal et al., 2022; Lee et al., 2022) Blockchain is one of six "mega-trends" in computing that the Globe Economic Forum believes will have a significant impact on the world in the upcoming ten years. Using blockchain technology (BCT), data may be stored in discrete blocks that can subsequently be shared and disseminated among all relevant supply chain participants. Blocks are then added chronologically (Alzoubi & Yanamandra, 2022). In general, the blockchain architecture enables businesses to track and trace their products, creating a highly secure transaction environment that aids in resolving trust difficulties (Velmurugadass et al., 2020). BCT is currently gaining traction across businesses, and an increasing number of businesses are looking into how to adapt it to their needs. The sociological concept known as "technology implementation" analyses how people embrace or accept technology based on the psychological and demographic traits of identified adopter groups (Alshurideh et al., 2019a; Alzoubi et al., 2021a, 2022g). The implementation of BCT in SCs is influenced by the user’s demographic and psychological traits

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(Akhtar et al., 2022; Ghosh & Aithal, 2022). The trust factor, which is connected to four areas including increased data protection, formal verification, supply chain digitalization, and disintermediation, is what primarily drives blockchain adoption (Alzoubi & Aziz, 2021). Organizations should do thorough feasibility assessments before implementing the blockchain because it has not yet reached its optimal maturity level. A blockchain supply chain can help businesses in maintaining track of pricing, period, location, superiority, authorization, and other appropriate data to monitor and handle more effectively (Amrani et al., 2022). The accessibility of this information within BC can enhance perceptibility and obedience over outsourced contract trade, lower losses from grey market in addition forged products, upsurge the traceability of material supply chains, and possibly strengthen an organization’s position as a pioneer in ethical engineering (Alzoubi et al., 2020, 2021 HYPERLINK "sps:refid::bib24|bib36|bib25|bib26|bib27|bib37|bib28" ). By increasing supply chain clearness, let down risk, boosting effectiveness, and managing the supply chain more effectively overall, blockchain-driven supply chain novelties have the potential to harvest enormous business value (Benaddi et al., 2020). Machine Learning (ML) (Chayal & Patel, 2021; Sarikaya et al., 2014), is a branch of Artificial Intelligence (AI). In SCM, ML is essential. In order to increase corporate efficiency, SCM employs several machine learning techniques.

2 Literature Review In order to track supply chain performance, BCT has been developed by numerous researchers. This section highlights some of their work. The other popular ML method that finds non-compensatory and non-linear correlations in research models is the artificial neural network (Al Kurdi et al., 2021). According to the literature, artificial neural networks can produce predictions that are more accurate than those made using conventional regression-based methodologies (Del & Solfa, 2022; Edward Probir Mondol, 2022; Butt, 2022; Sawale & Gupta, 2013). Kshetri also highlighted the function of the blockchain in SCM and evaluated early supply chain sector instances (Goria, 2022; Nasim et al., 2022). The examples show what motivates supply chain firms to achieve their goals of cost, quality, speed, reliability, risk mitigation, sustainability, as well as flexibility (Alzoubi & Ahmed, 2019; Alzoubi et al., 2017; Alzoubi et al., 2019; Radwan, 2022). Additionally, factors that have been identified as influencing blockchain acceptance comprise the number of companies (a sustainable blockchain ecosystem), the skills of the participants, and the intensity of industry competition (Velmurugadass et al., 2020). In this research, the authors reviewed BCT and its potential applications in the supply chain systematized a complete list of barriers (including system-related, intra and inter-organizational, as well as external barriers), and suggested some strategies for overcoming those significant hurdles (Benaddi et al., 2020; Ratkovic, 2022).

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Blockchain is a decentralized ledger architecture that depends on a consensus and communication protocol to protect the ledger’s integrity through linked blocks with cryptographically time-stamped representations of transactions (Alzoubi & Yanamandra, 2020). A private blockchain and a public blockchain can be found in the blockchain system. The private blockchain is an exclusive network that is run and managed by a group of registered users (Alsharari, 2022; Alzoubi, 2022; Farouk, 2022). While anybody can participate in the production of blocks and the consensus mechanism on a permissionless blockchain, only parties who have registered can do so in an empowered blockchain. As a result, the public blockchain is less secure, less anonymous, and less transparent because it depends on the honesty of the participants (Alsharari, 2021). The private blockchain is also more secure, highly customizable, better scalable, and has an improved access management system. In other words, the private blockchain is more effective than the public blockchain (Ahmed & Al Amiri, 2022; Alzoubi et al., 2022d; Khatib et al., 2022; Qasaimeh & Jaradeh, 2022). As a result, in the system that is being presented, they employ Hyperledger Fabric, a public blockchain that is used for creating blockchain-based applications (Zyskind et al., 2015). These days, the development of the ML algorithm presents a technique to extract hidden information from a vast amount of data and build a predictive model to support a claim. The key component of any prediction system that affects the performance and outcome of the system is the prediction algorithm (Akour et al., 2021; Alshurideh et al., 2020; Alzoubi et al., 2022f). Computer science, energy management, speech recognition, computer vision, and other fields frequently use deep neural networks (DNN) (Eli, 2021; Kasem and Al-Gasaymeh, 2022; Victoria, 2022). The concert of the system is improved by a number of researchers employing DNN to construct a prediction model using a number of algorithms, including data mining and text mining. The LSTM is a well-known machine learning technique used for processing, classifying, and making predictions using time-series data (Hwang et al., 2017; Kashif et al., 2021; Pustokhina et al., 2020). This study introduces FeneChain, a blockchain-based energy trading system that is built on the Industrial Internet of Things (IIoT) (Akhtar et al., 2021; Eli & Hamou, 2022). The FeneChain is a safe distributed energy platform built on industry 4.0 that enhances and controls building energy management. They emphasize the cuttingedge functionality in microgrid power generation (Magaia et al., 2021; Mehmood, 2021; Miller, 2021). Most of the techniques have been utilized while retaining and developing many smart as well as intelligent frameworks like ML techniques (Alzoubi et al., 2021b, 2022a; Asif et al., 2021; Chayal & Patel, 2021; Fatima et al., 2020; Ghazal et al., 2019; ; Ghazal et al., 2022c, 2022d; ; Muneer & Rasool, 2022), Fuzzy Inference systems (Fatima et al., 2019; Asadullah et al., 2020; Ihnaini et al., 2021; Saleem et al., 2019), Particle Swarm Optimization (PSO) (Iqbal et al., 2019), Fusion based approaches (Gai et al., 2020; Ma et al., 2020; Muneer & Raza, 2022; Sharma et al., 2021; Tabassum et al., 2021; Ghazal, n.d.), cloud computing (Ghazal et al., 2021c; Ghazal et al., 2022b; Khan, 2022; Naseer, 2022; Ubaid et al., 2022), transfer learning (Abbas et al., 2020; Alshurideh et al., 2019b; Ashal et al., 2021; Kurdi et al., 2020) and

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MapReduce (Asif et al., 2021) that can provide help in designing developing solutions for the growing issues in manipulative smart cloud-based monitoring management system.

3 Problem Statement and Research Contribution A supply chain is a structured, methodical network among a business in addition its suppliers that harvest a certain product and sells it to the consumer to minimize costs and keep market competitiveness. The phases and parties tangled in getting a product from its initial phase to the final customer are all covered by Blockchain when it is used in supply chain management (SCM), starting with the supply of raw materials and their transformation into manufactured goods, moving those goods in the market, and allocating them to the final consumer. Due to its security, increased visibility, traceability, and profitability, the blockchain is a ground-breaking expertise that can revolutionize SCM even with only partial implementation.

4 Proposed Methodology Cooperation in the supply chain refers to the planned and concerted movement of parts from the vendor to the factory, the industry to the distributor, the distributor to the retailer, and the retailer to the final consumer. The reduction of inventory errors is the main objective of a successful supply chain system. Business intelligence architectures and their modeling are now widely employed in a variety of application domains, from complicated systems architecture to the creation of decision support tools, ranging from supply chain in addition industry to military, business service, health, as well as wealth management. In order to foresee the existence of SCM that may help a company while going from controlling specific functions to integrating all activities along the entire SC, a BC-based SCM system is utilized. The proposed model has two modules, blockchain, and ML, as shown in Fig. 1. First, a number of digital input devices are utilized to gather the information, many of which can be poorly organized or entirely unstructured. The information gathered is directed to the blockchain layer and kept in a data container. Blockchain perchance decreases communication or data handover errors by delivering all members in a given supply chain access to the similar information. The data is then passed on to the preprocessing layer, where noise is eliminated via normalization, handling missing values, and moving averages. When the data become available, it is sent for preparation, which is the gathering, merging, arranging, and organization of the data so that it may be utilized in analytics and data visualization applications. After that, the prepared data is transmitted for feature engineering. Indicating, altering, and adapting raw data into features that may be exploited in supervised learning is the technique of feature engineering. It may be significant to create in addition train

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Fig. 1 Proposed methodology

better features if machine learning is to perform efficiently on new jobs. Following feature engineering, the data is split into 70% training data and 30% testing data. A trained model is predicted using a ML algorithm employed to train data. The trained model is then evaluated on test data to forecast the outcome, and the output is also recorded in the cloud database. The trained result is then exported from the cloud for forecasting; if SCM is not anticipated, the procedure will end; if it is forecasted, a message indicating that supply chain management is found will be displayed.

5 Critical Analysis Blockchain is currently supporting corporate change in a range of international businesses. A higher level of trust encourages efficiency by minimizing duplication of effort. Blockchain is transforming a variety of industries, including the supply chain, food delivery, financial services, public administration, retail, and more. Blockchain helps firms minimize costs by removal of suppliers, brokers, and other third parties who previously handled the work that blockchain can do. Businesses can gain from improved trust, security, and transparency thanks to blockchain’s unique qualities, among other things. Therefore, analysis is done on the basis of five ML algorithms and their advantages are disadvantages are shown in Table 1. Table 1 offers a brief impression of these 5 ML algorithms to support a clear empathetic of these algorithms between SCM researchers in addition practitioners. In Table 1, the first column as of the left lists the names of 10 ML algorithms; the second column presents the general usage of the 5 ML algorithms; in addition the third and fourth column précis their advantages and disadvantages therefore.

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Table 1 Brief overview of 5 ML algorithms Name

General usage

Advantage

Disadvantage

Decision Tree (DT)

Discriminant models; multi-regression (DMMR) and categorization; standardized highest Likeli hood Guess

DT

DMMR and categorization; standardized highest Likelihood Guess

Random forest (RF)

Categorization

1. Oblivious to missing and strange values 2. Maximum accuracy of training outcomes 3. Relative Bagging may join to a tiny simplification fault

1. Over-fitting for huge information noise Responsive to the characteristics with various values

K-means

Clustering; categorization

1. Simple and speedy 2. Minimum difficulty

1. Only utilized when collection mean values have been specified 2. The real line given by group K is responsive to the initial values 3. Penetrating to noise and outliers

K-Nearest neighbor (KNN):

DMMR; categorization 1. Easy for categorization and regression, especially for non-linear categorization 2. Minimum complexity Resistant to outliers

1. Necessary to preset K 2. Impotent to resolve large distorted data sets

Logistic regression (LR)

Regression

Poor fitness and accuracy

1. Easy to use 2. Simple measurement 3. Little storage properties

6 Discussion The global market for supply chain services is enormous, and it will continue to expand over time everywhere in the world, especially along the African coast and in some areas of developing nations. Any product we see in our workplace, home, or industry travels through several middlemen before arriving to us from the factory where it was made. Manufacturers, procurement personnel who serve as quality inspection officers, distributors, suppliers, etc. are all part of that chain. That indicates that there are twelve intermediates in the process. As a result, a lot of information

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may be lost, or any intermediaries may simply manipulate that information in a way that serves their own interests. In addition, there are several companies involved in the supply chain. Therefore, the difficulty is that everyone has conflicting interests, and for a reason, one vendor should have to trust everyone. The supply chain in the global sustainable business is affected negatively by these vulnerabilities, which cause a clear problem in the chain. The largest industry for blockchain right now is the supply chain. Supply chains are being made more safe, accessible, and client for sustainable business with the use of blockchain. Therefore, based on the standards of necessity, the viability of blockchain technology has opened doors in manufacturing and supply chain management (SMC). In terms of manpower, investments, and sheer size, it gave rise to the logistics and handling sector of the global economy.

7 Conclusion This research focused on supply chain management (SCM), which is the control of the movement of goods in addition services and comprises all procedures that transform raw materials into final goods. Utilizing BCT entails the proactive optimization of a company’s supply-side operations to enhance consumer value and attain a modest edge in the market. To meet the rising demand for goods, the supply chain makes extensive use of blockchain (BC). Blockchain possibly lessens communication or data handover errors by providing all participants in a assumed supply chain access to the similar information. In this study, ML techniques are applied to SCM in order to upsurge the effectiveness of the supply chain in terms of automated feature inspections for vigorous management, real-time visibility to enhance consumer understanding, streamlining construction preparation, and minimizing cost and response times.

8 Limitations and Future Directions Today’s supply chains increasingly take the shape of complicated networks because of recent advancements. Systems for SCM confront a number of difficulties. They include an absence of visibility among the upstream in addition downstream parties, a lack of flexibility to rapid variations in demand, a deficiency of cost-control measures, a deficiency of dependence on safety stakeholders, and poor risk management in the supply chain. Current blockchain-based systems offer a complete audit trail of communications (i.e., data exchange) between multiple blockchain parties. However, because blockchain-based solutions take into account all of the interacting components, they fall short of providing appropriate transparency to its interacting stakeholders and customers. In addition, information about the company operations of interacting partners will strengthen blockchain’s ability to protect SCM systems by

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ensuring that stakeholder communication, including smart contracts, complies not only with the rules contained in those contracts but also with the correlating company operations.

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e-Business

The Impact of Information Sharing and Delivery Time on Customer Happiness: An Empirical Evidence from the UAE Retail Banking Industry Muhammad Turki Alshurideh , Barween Al Kurdi , Enass Khalil Alquqa, Haitham M. Alzoubi , Samer Hamadneh , and Ahmad Al Hamad Abstract To provide empirical evidence on improved customer happiness with the impact of information sharing and delivery time in the retail banking sector in the UAE. The concepts of improved banking performance, information sharing and delivery time to increase customer happiness are barely assessed in this research. Typically, this research is a contemporary contribution to the retail banking sector in UAE. A quantitative research technique was used through an online survey with 273 respondents working in retail banking (branches) based in Fujairah, UAE. A descriptive exploratory, causal and analytical design were used to analyse the proposed hypothesis. The results revealed evidence that information sharing and delivery time have a significant positive impact on customer happiness. Proposed research has measured one model containing information sharing, delivery time and customer M. T. Alshurideh (B) · S. Hamadneh Department of Marketing, School of Business, The University of Jordan, Amman 11942, Jordan e-mail: [email protected]; [email protected] S. Hamadneh e-mail: [email protected] B. Al Kurdi Department of Marketing, Faculty of Business, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan e-mail: [email protected] E. K. Alquqa College of Art, Social Sciences and Humanities, University of Fujairah, Fujairah, United Arab Emirates e-mail: [email protected] H. M. Alzoubi School of Business, Skyline University College, Sharjah, UAE e-mail: [email protected] Applied Science Research Center, Applied Science Private University, Amman 11937, Jordan M. T. Alshurideh · A. Al Hamad Department of Management, College of Business Administration, University of Sharjah, 27272 Sharjah, United Arab Emirates e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 H. M. Alzoubi et al. (eds.), Cyber Security Impact on Digitalization and Business Intelligence, Studies in Big Data 117, https://doi.org/10.1007/978-3-031-31801-6_22

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happiness. Many different classifications might be considered in future. For instance, customers consider being population & e-banking advantages. A great source of knowledge for professionals looking to put strategies into the organisational system. The research also offers a wide range of implications for the banking sector that could improve customer care services. Keywords Information sharing · Delivery time · Customer happiness · Retail banking sector UAE

1 Introduction Sharing information has become essential to succeed in the age of competition. Information sharing is the act of one person or organisation sharing information with another person or organization (Ahmad et al., 2021; Brodie, 1989). Different types of information sharing exist, including personal, employee-based, and organizationalbased (Alolayyan et al., 2022b; Alzoubi et al., 2019; Tchamyou & Asongu, 2017). The banking industry’s information sharing system is one of the critical variations in the business where management frequently enhances its functioning system by exchanging critical information in reliable ways. In the banking sector, regulating financial outcomes is vital (Alzoubi et al., 2020a; Eli & Hamou, 2022), and the process of sharing knowledge with operational management and customers has greater significance in attaining customer satisfaction (Alshraideh et al., 2017; Alshurideh, 2014; Hayajneh et al., 2021; Kurdi et al., 2020; Owen, 1993). Furthermore, the sensitive information exchange system for the banking sector is in charge of precisely and quickly relaying all reliable information to banking management and stakeholders (2022m; Hanaysha & Alzoubi, 2022). Employee interaction significantly impacts the provision of banking services and the strength of client relationships (Al Kurdi et al., 2020a, 2020b; Alzoubi et al., 2022m; Ghazal et al., 2022). Additionally, the impact of delivery time signifies customer happiness (Aljumah et al., 2022b; Eli, 2021; Alzoubi et al., 2021f; Victoria, 2022). It is best suited to the banking sector, which provides information to their customer and delivers services on time to attain client satisfaction (Al-Khayyala et al., 2020; Alshurideh et al., 2012; Alzoubi et al., 2020a, 2020b, 2020c, 2020d). To investigate this model, this research is conducted to analyse the three variables, information sharing, delivery time and customer happiness in the banking sector in UAE.

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2 Theoretical Framework 2.1 Information Sharing One of the significant differences in the business where management often improves its functional system through exchanging pertinent information in trustworthy ways is the banking sector’s information sharing system (Beck et al., 2014; Kurdi et al., 2020a, 2020b; Leo et al., 2021). The banking industry is one of those sectors where the process of sharing knowledge with operational management and customer is just as crucial as controlling financial outcomes (Alzoubi et al., 2021a, 2021b, 2021c, 2021d, 2021e; Awawdeh et al., 2022b). The banking industry’s sensitive information sharing system is in charge of accurately and promptly transferring all trustworthy information to banking management and stakeholders (Al Ali, 2021; Beck et al., 2014). In order to develop high standards of services like investing relaxation into the banking industry, flexible financial outcomes, privatization (Alzoubi et al., 2021b; Alzoubi et al., 2021g; Alzoubi & Ramakrishna, 2022; Miller, 2021; Qasaimeh & Jaradeh, 2022), direct lending, capital account opening, easy regulations (Alsharari, 2021; Kasem & Al-Gasaymeh, 2022; Mehmood, 2021), and stock market development, the information sharing system reduces restrictions on customers’ ability to accumulate their assets (Abuhashesh et al., 2021; Al-bawaia et al., 2022; Alkitbi et al., 2021; Alshurideh, 2016a, 2016b; Alshurideh et al., 2019a, 2019b).

2.2 Delivery Time Delivery time is the window of time during which a physical commodity, product, or service must be delivered to a certain location (2022m; ; Li & Lee, 1994; Sanjuq, 2014). The timely completion of tasks in every business and financial sector of the economy depends on the services institutions offer their clients (Ghannajeh et al., 2015; Nuseir et al., 2020). Therefore, the quick services provided by the organisation according to the client’s expectations can increase satisfaction and the competitive advantage (Alzoubi et al., 2022g; Farouk, 2022; Ratkovic, 2022), particularly in complicated businesses like the banking sector (Alzoubi et al., 2022j; 2022k), where the desire for information sharing and customer satisfaction in order to receive timely delivery services is essential (Al-Maroof et al., 2021; Alshurideh, 2022; Alshurideh, 2022; Alzoubi et al., 2022c; Davamanirajan et al., 2002).

2.3 Customer Happiness Individual judges their level of contentment by comparing their current situation to expectations (Alzoubi et al., 2021e; Alzoubi & Aziz, 2021; Khatib et al., 2022;

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Owen, 1993). Numerous factors influence customer satisfaction; thus, businesses must recognise these elements and evaluate their significance in light of consumers’ wants to thrive over the long term in a competitive environment (Akhtar et al., 2021; Alwan & Alshurideh, 2022b; Ghazal, Alzoubi, Ali, et al., 2021; Kashif et al., 2021; Neyara Radwan, 2022). Customer satisfaction breeds loyalty, and repeat business is an organisation’s capacity for long-term business sustainability (2022m; Alketbi et al., 2020; Alzoubi et al., 2022a, 2022b, 2022c, 2022d, 2022e, 2022f, 2022g, 2022h, 2022i, 2022j, 2022k, 2022l; Chaouali et al., 2020). Only through all-around sustainable aspects where he can relate to the product and service experience can a consumer develop loyalty. The banking and financial development sector relies on customer retention and flow (Awawdeh et al., 2022a, 2022b). The customer satisfaction and happiness are related to better financial results and creating the huge customer retention (Aljumah et al., 2021; Alsharari, 2022; Alzoubi, 2022; Alzoubi et al., 2022f). The customer satisfaction and happiness are the large component and satisfactory competitive advantage for banking sector (Alshamsi et al., 2020; Alshurideh, 2016a, 2016b; Ashurideh, 2010; Niedermeier et al., 2019).

2.4 Operational Definitions

Variables

Description

References

Information Sharing

The voluntary act of making information owned by one entity available to another entity is known as information sharing

(Al Ali, 2021)

Delivery Time

The length of time it takes for purchased items to be (Li & Lee, 1994) delivered to their intended location

Customer Happiness

Consumer happiness is the general contentment a customer experiences when using a company’s goods and services

(Shin et al., 2019)

2.5 Industry Description The central bank is the main body in charge of financial regulation in the UAE. The UAE Banks Federation’s membership of 52 operating local and foreign banks contributes to the country’s strong financial sector. There are 22 national banks among them. The UAE Central Bank regulates new business, new technologies, and all banking activity within the nation. In 2021, the top 10 banks in the UAE reported a 5% year-over-year gain in total assets worth AED 2989 billion and a 42% increase in net income. According to data from the central bank, gross bank assets were AED 2.9 billion as of July 2022. The UAE’s GDP is mainly derived from financial

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and insurance operations, accounting for 8.6% of total GDP. The banking sector contributes at a high level to the UAE economy. Customer satisfaction and service providers are a competitive advantage for the banking sector (Aburayya et al., 2020; Al Kurdi et al., 2021; Aljumah, Nuseir, et al., 2022a, 2022b). This study focused on measuring customer happiness through information sharing and delivery time in the retail banking sector in UAE.

3 Literature Review 3.1 Relationship and Impact of Information Sharing on Customer Happiness Information sharing and customer satisfaction have been investigated as inter-related with positive outcomes for the organisation (Al-Dmour et al., 2021; Al Ali, 2021; Sultan et al., 2021). Different authors argued that customer management expectations are the fundamental components forming a company’s brand and reputation in the market (Alzoubi et al., 2022l; Amrani et al., 2022; Hamadneh & Al Kurdi, 2021; Sweiss et al., 2021; Tariq et al., 2022). For customer asset management, the banking industry virtually always offers services of the same calibre and functionality. A small product or service adjustment is not considered significant (Ahmed & Amiri, 2022; Akhtar et al., 2022; Owen, 1993). The customer services offered to increase client satisfaction and enjoyment with banking services proved the only source of competitive advantage (Alshurideh et al., 2015; Alwan & Alshurideh, 2022a, 2022b). H1: Information sharing has a significant impact on customer happiness.

3.2 Relationship and Impact of Delivery Time on Customer Happiness Customers have high expectations for the services their bank will provide (Aljumah et al., 2022b; Alzoubi et al., 2022d). Customers constantly demand that bank employees focus on providing timely solutions to the issues they need. Customer happiness is the most critical policy in the banking sector (Al Alshurideh, 2013; Joseph & Mcclure, 1999; Khasawneh et al., 2021). Consequently, the banks train their staff to handle customer integration favourably (Edward Probir Mondol, 2022; Saad Masood Butt, 2022). The development of technology and its application have made it feasible for the banking sector to provide services to its clients quickly and professionally (Alshawabkeh et al., 2021; M. Alshurideh et al., 2017; Alzoubi & Yanamandra, 2020). The prior studies investigated a prompt response to a customer’s expectations can win their satisfaction.

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H2: Delivery time has a significant impact on customer happiness.

3.3 Relationship and Impact of Information Sharing and Delivery Time on Customer Happiness Customer satisfaction has been seen as highly influenced by information sharing and delivery times (AlShehhi et al., 2021; Nuseir, 2019). Previous research has investigated that a satisfied client requires quick services according to his requirement (Leong et al., 2014; Odeh et al., 2021). Strong coordination between employees and customers is a type of interpersonal service that banks build (Alameeri et al., 2020; Nuseir et al., 2021). The digital and online banking procedures prevent clients from having a consistent online banking connection (Del & Solfa, 2022; Goria, 2022) and experience linked to their happiness and contentment (Alolayyan et al., 2022a; Alzoubi et al., 2022c; Norton et al., 2010). H3: Information sharing and delivery time significantly impact customer happiness.

3.4 Problem Statement and Research Gap Customer care services provided by banks and their expenditure of pertinent resources on staff training and development strongly interlink with customers’ happiness and satisfaction (Al-Khayyal et al., 2020; Alzoubi et al., 2022h; Hasan et al., 2022). In order to integrate digital and online banking services, the banking sector has made a substantial contribution to the market by providing quick services to clients. (Alzoubi et al., 2022e) In order to meet the needs of the customers, who demand high-quality services rapidly from their institutions (Alzoubi et al., 2022d, 2021g), this research will analyse the factors that can improve and increase customer happiness (Alzoubi & Ahmed, 2019; Alzoubi et al., 2020; 2021b). Consequently, this research is aimed to analyse customer happiness with the impact of information sharing and delivery time. Empirical evidence can help to explore the relationship among different variables.

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3.5 General Research Model

Information Sharing

H1 H3

Delivery Time

Customer Happiness

H2

3.6 Research Hypothesis HO1 : Information sharing has a significant impact on customer happiness. HO2 : Delivery time has a significant impact on customer happiness. HO3 : Information sharing and delivery time significantly impact customer happiness.

3.7 Research Methodology and Design The research variables (Information Sharing, Delivery Time and Customer Happiness) are proposed to examine empirically using descriptive, exploratory, causal and analytical research design with quantitative research techniques. Past studies have used different sampling techniques to collect the data from respondents i.e., probability and non probability sampling (Ali et al., 2020; Perumal et al., 2021). The cluster sampling technique was applied to gather data from bank branches in Fujairah, UAE. An online research survey was conducted to gather data from the employees working in retail banking.

360 Table 1 The study’s demographical aspects

M. T. Alshurideh et al. Items

Description

f

%

Gender

Male Female

199 74

72.9 27.1

Designation

Branch manager Relationship manager Customer services officer Marketing manager

33 83 128 29

12.1 30.4 46.9 10.6

n = 273, Male = 199, 72.9% Female = 74.27.1%

3.8 Population, Sample and Unit of Analysis The banking sector is the targeted population of the research. One hundred eight branches located in Fujairah UAE are accessed through email. A total of 900 questionnaires were sent via email, and 273 were received with a valid sample size for analysis. Past studies have used survey to reach the respondents (Jabeen & Ali, 2022; Ali et al., 2021). The survey correspondents are (branch managers, relationship managers, customer services officers, and marketing managers). An online questionnaire evolved with a five-point Likert scale containing 32 questions to measure the variables. Nine items were used to assess the information sharing, 11 were used to assess delivery time, and 12 were used to assess customer happiness. The questionnaire is inclusive of demographic data, gender and position (Table 1).

4 Data Analysis 4.1 Demographic Analysis 4.2 Reliability, Descriptive Analysis, Correlation The Cronbach Alpha values indicating for information sharing (IS) α = 0.71, Delivery time (DFT) α = 0.84, and customer happiness (CH) α = 0.73, which is considered good reliability. The implementation of descriptive analysis indicating for IS (M = 27.7, SD = 6.91), (M = 37.9, SD = 7.21) for DT and (M = 41.6, SD = 7.69) for CH is considered to be the great extent to agreeableness. The correlation coefficients depict a significant positive relationship, IS with DT r = 0.812, P < 0.05 indicates a highly positive correlation, and IS with CH indicates a significantly correlated at level r = 0.685, P < 0.05, and results for DT with CH are considered to be positively significant r = 0.766, P < 0.05. Table 2 shows the combined findings.

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Table 2 Reliability, descriptive and correlation coefficients Variables

Cronbach’s Alpha

Mean

SD

Information Sharing

Delivery Time

Information Sharing

0.71

27.78

6.91

1

Delivery Time

0.84

37.97

7.21

0.812**

1

Customer Happiness

0.73

41.68

7.69

0.685**

0.766**

Customer Happiness

1

Level of significance at **P < 0.05, *P < 0.001

4.3 Regression and Hypothesis Testing 5 Discussion of the Data The model is assessed by regression weights to test the hypothesis. The findings of the data analysis indicate that all hypotheses proposed are supported. Table 3 shows each hypothesis’s summary at a significance level of P < 0.05. H1: Information sharing (IS) has a significant positive impact on customer happiness (CH) (Supported). The findings indicate a significant positive impact of IS on CH (β = 0.68, t = 2.79) that revealed a significant positive relationship with a variance of R2 = 47%. The extent of sharing accurate information to the customer can lead to higher customer satisfaction (Kumar, 2021). H2: The proposed hypothesis 2 is also supported in the analysis that indicates (β = 0.76, t = 9.32) a significant positive impact of DT on C with a variance of 58%. So H2 is supported. In the technological era, everybody needs quick services within a limited time, and it has been analysed that shorter delivery times can increase the customer’s happiness, specifically in the retail banking sector. H3: The findings revealed a significant positive impact of IS and DT on CH. The data analysis depicts regression analysis (β = 0.77, t = 6.92), R2 = 59% variance among the variables. The relationship between these variables is exposed to be significant. Table 3 Regression analysis with ANOVA Hypothesis

Regression weights

β

df

R2

t-stat

Adjusted R2

p-value

Hypothesis supported

H1

IS → CH

2

0.68

0.47

2.79

0.467

0.000

Yes

H2

DT → CH

271

0.76

0.58

9.32

0.585

0.000

Yes

H3

IS*DT → CH

273

0.77

0.59

6.92

0.595

0.004

Yes

Level of Significance (α ≤ 0.05), ** Critical t-value (df/p) = 1.64 *

R2

= 47%,

R2

= 58%,

R2

= 59%

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Hence, this research findings could help the retail banking sector improve customer care by improving information sharing and prompt delivery time of the services.

6 Conclusion A conclusion to this research suggests the service sector may follow and effectively implement all of its strategies, including accurate information sharing, providing quick services and reliability to achieve customer happiness. Managers might consider the demands of the bank’s clients in addition to the bank’s profit-related goals. This research carried out a great asset of knowledge to the banking sector, specifically in the UAE, that could help grab customer attention by providing required information efficiently. Moreover, it could be sure to offer the appropriate services at the appropriate times to keep the client’s retention and satisfaction.

7 Recommendations/Limitations It is essential for a marketing manager in the banking sector to closely adhere to all the elements of prompt services that satisfy a customer’s needs. All frontline employees should receive customer service training that can assist in dealing with clients by providing accurate information on quicker nodes to get customer satisfaction. The bank’s basic competency in client satisfaction would be strengthened this way. This research aimed to highlight the elements that could impact retail banking customers’ happiness. Some limitations are noted; firstly, the impact of delivery time and information sharing on customer happiness is analysed; other variables need to be explored in future studies, for instance, e-banking services and customer response.

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Investigating the Online Buying Behavior in the UAE Online Retail Industry: The Role of Emotional Intelligence and Customer Perception Muhammad Turki Alshurideh , Barween Al Kurdi , Enass Khalil Alquqa, Haitham M. Alzoubi , Samer Hamadneh , and Ahmad AlHamad Abstract The current research seeks to investigate retail online buying behaviour with the emerging role of emotional intelligence and customer perception in the retail industry in the UAE. To better understand how customers’ emotions and perceptions impact online purchases. It is believed that this study will make a particularly useful contribution to the management of the online retail industry. Data from 248 valid respondents from retail supermarkets in Sharjah, UAE, are used to test the hypothesis through regression. A descriptive, causal and analytical research design was used with a convenient cluster sampling technique. Research findings reported a significant direct impact of emotional intelligence and customer perception on improving online buying behaviour. A geographical limitation is listed, and the business context is limited for generalising the results. Customer intentions and motivations for online buying should be explored for future research. Online retailers can implement the M. T. Alshurideh (B) · S. Hamadneh Department of Marketing, School of Business, The University of Jordan, Amman 11942, Jordan e-mail: [email protected]; [email protected] S. Hamadneh e-mail: [email protected] M. T. Alshurideh · A. AlHamad Department of Management, College of Business Administration, University of Sharjah, 27272 Sharjah, United Arab Emirates B. Al Kurdi Department of Marketing, Faculty of Business, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan e-mail: [email protected] E. K. Alquqa College of Art, Social Sciences and Humanities, University of Fujairah, Fujairah, United Arab Emirates e-mail: [email protected] H. M. Alzoubi School of Business, Skyline University College, Sharjah, UAE e-mail: [email protected] Applied Science Research Center, Applied Science Private University, Amman 11937, Jordan © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 H. M. Alzoubi et al. (eds.), Cyber Security Impact on Digitalization and Business Intelligence, Studies in Big Data 117, https://doi.org/10.1007/978-3-031-31801-6_23

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evaluation of previous purchases to determine how convenient customers find online shopping. This will assist the managers in growing their customer base while identifying and removing major barriers through emotional intelligence and recognising customer perceptions. Keywords Emotional intelligence · Customer perception · Online buying behaviour · Retail Industry UAE

1 Introduction E-commerce has provided both businesses and consumers access to a technological world. Companies are attempting to exploit the potential of the internet to attract customers and retain them by building enduring relationships (Alshamsi et al., 2020; Alshurideh, 2019a, 2019b). In this context, the management of the retail company needs to observe the customer’s emotions during each purchase (Alshurideh et al., 2012a, 2012b; Mashaqi et al., 2020). The ability to comprehend and control one’s own emotions and those around oneself is referred to as emotional intelligence, or EI (Alzoubi et al., 2020a, 2020b; Alzoubi et al., 2021d; Hejase et al., 2018; Butt, 2022). People with higher levels of emotional intelligence are more aware of their emotions, understand the meaning behind their emotions, and are also more aware of how their emotions can affect others (Mondol, 2022; Qasaimeh & Jaradeh, 2022). Emotional intelligence is a significant personality trait for leadership because it enables individuals to lead effectively and efficiently (Al-Dhuhouri et al., 2021a, 2021b; Alshurideh, 2016, 2019a, 2019b; Kurdi et al., 2020). Moreover, understanding customer perception has become difficult for marketers in the modern era of intense business competition. Customer perception is a key component of a strategic marketing strategy because it affects how users, buyers, and payers interact with products and services (Alshurideh, 2022; Alzoubi et al., 2022a, 2022b, 2022c, 2022d, 2022e, 2022f, 2022g, 2022h, 2022i, 2022j, 2022k, 2022l, 2022m, 2022n, 2022o, 2022p). Additionally, customer perception provides a summary that gives businesses insight into how their clients view their goods and services (Alwan & Alshurideh, 2022a, 2022b; Kurdi et al., 2020). Therefore, businesses may use consumer perception feedback to address any flaws in their goods or services and improve them. One of the many diverse consumer behaviours is online shopping (Akhtar et al., 2022; Alzoubi et al., 2022a; Victoria, 2022). Customers use this behaviour when browsing online stores’ websites to make specific purchases of services or commodities (Alshurideh et al., 2012; Alzoubi et al., 2022n; Mashaqi et al., 2020). These factors greatly emphasise online sellers in the retail industry as they try to observe and identify what a customer wants to purchase remotely (Alshurideh et al., 2022; Hamadneh et al., 2021; Kurdi et al., 2022). Therefore, this research attempts to measure the impact of emotional intelligence and customer perception on online buying behaviour.

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2 Theoretical Framework 2.1 Emotional Intelligence Emotional intelligence is a relatively new field recently attracting considerable international attention (Alzoubi et al., 2022l; Ghazal et al., 2022; Mohammad & Turki, 2020; Zafar et al., 2021). Furthermore, it is the ability to feel and express emotions, integrate emotions into thoughts, comprehend, and control one’s own emotions and those of others (Al-Dhuhouri et al., 2021a, 2021b; Henry, 2017). Concerning a consumer, this term is defined as consumer emotional intelligence (Alzoubi & Ramakrishna, 2022; Alzoubi et al., 2022i; Amrani et al., 2022), which refers to an individual’s ability to effectively use emotional information to achieve the desired consumer outcome (Farouk, 2022; Ratkovic, 2022). When making online purchases, organisations need to comprehend customer behaviour and the choices customers make when making their next purchase (Alzoubi et al., 2022m; Obeidat et al., 2019; Tariq et al., 2021). Thus, managers need to understand the emotional intelligence associated with increased sales and buyer retention (AlShurideh et al., 2019; Alzoubi et al., 2021a, 2021b, 2021c, 2021d, 2021e, 2021f).

2.2 Customer Perception Customer perception is based on attitudes, beliefs, and past positive or negative experiences (Alshurideh et al., 2021; Ghouri et al., 2017). In online buying behaviour, customer perception plays a significant role because if the customer experience deteriorates, they would probably not prefer to shop online from the same retailer (Ghosh & Aithal, 2022; Nasim et al., 2022). Amazon is a perfect example of how Jeff Bezos has recognised the needs and psychology of his customers and is catering to those needs by offering required products (Al Kurdi et al., 2022; Joghee & Alshurideh, 2021). Understanding the market demand is essential to recognise the customer’s perception of a particular product. The customer’s perspective of the items can be easily comprehended once the need has been recognised.

2.3 Online Buying Behavior To sustain a pleasant emotion, a particular consumer may be able to apply internal cognitive control mechanisms (i.e., emotional intelligence and regulation techniques) (Habib & Qayyum, 2018). Due to its accessibility and convenience, online buying behaviour has drawn particular attention (Goria, 2022; Radwan, 2022). Online

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buying has changed how businesses operate. Businesses’ growth greatly influences technology because customers have a wide range of options while shopping online, and their behaviour toward a specific product is crucial. One of the several diverse consumer behaviours is online shopping (Ahmed & Al Amiri, 2022; Alzoubi et al., 2022c, 2022p). Additionally, e-commerce websites are becoming more intelligent and customer-focused through emotion-based web technologies (Alwan & Alshurideh, 2022a, 2022b). They actively stimulate their customers’ senses by using web technologies to increase the probability that customers will want to buy something (Eli, 2021; Kasem & Al-Gasaymeh, 2022; Rajan, 2019).

2.4 Operational Definitions

Variables

Description

References

Emotional intelligence

The ability to understand, manage, and (Kidwell et al., 2008) express one’s emotions and act wisely and compassionately in interpersonal interactions

Customer perception

Customer perception refers to consumers’ (Mehta & Kumar, 2012) thoughts, emotions, and assumptions about a brand. It fosters customer retention, loyalty, brand recognition, and reputation

Online buying behavior The process by which customers utilise the (Liao et al., 2010) internet to find, select, purchase, use, and dispose of goods and services is known as online buying behaviour. Online shopping has become increasingly popular, primarily because consumers find it convenient and easy to shop from the comfort of their homes or workplaces

2.5 Industry Description The UAE leads the Gulf Cooperation Council (GCC) in e-Commerce, with a market growth of 53% in 2020 and record e-Commerce sales of $3.9 billion, representing 10% of total retail sales. This is mainly due to the COVID digital. According to the Dubai Chamber of Commerce and Industry, the UAE’s population is expected to have access to the internet and mobile devices at a rate of about 100% by 2025.

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3 Literature Review 3.1 Relationship and Impact of Emotional Intelligence on Online Buying Behaviour The literature review focuses on determining the relationship between emotional intelligence and online buying behaviour. (Alzoubi et al., 2022j; Tariq et al., 2021) suggested that emotional benefits (i.e., the presence of emotional information) facilitate access to categorical knowledge about emotions and the many experiences associated with this knowledge (Alzoubi et al. 2022b; Alzoubi et al., 2022o). Based on this emotional data, the brand can then be evaluated positively or adversely (Alshurideh et al., 2012). The notion of emotional trade-off challenges has been used to study online buying behaviour (Alzoubi et al., 2022h; Henry, 2017). Emotional intelligence affects online buying behaviour because it shapes one’s attitude toward purchases (Alzoubi et al., 2022k; Eli & Hamou, 2022; Khatib et al., 2022; Obeidat et al., 2019). A crucial aspect of global motivation is that the purchase is made for motivation and personal progress rather than for recognition or reward (2021f; Alzoubi et al., 2021b). However, when buying online customers with various levels of emotional intelligence (Akhtar et al., 2021; Alzoubi & Aziz, 2021), and a psychological state interact with the e-commerce website and choose what to purchase (Kashif et al., 2021). Hence, previous studies have investigated that emotional intelligence positively impacts online buying behaviour, which supports the research’s hypothesis relatively well. H1: Emotional intelligence has a significant impact on online buying behaviour.

3.2 Relationship and Impact of Customer Perception on Online Buying Behaviour The relationship between customer perception and online buying behaviour was studied in a different context that determined the customer intention to buy goods or services because they believe they are valuable (Alzoubi et al., 2020b; Alzoubi et al., 2020d; Mehta & Kumar, 2012). Retail companies are said to be successful due to their customers’ satisfaction, repurchase intention, and loyalty (Alshurideh et al., 2012; Rajan, 2019) because once they have positive online buying experiences, buyers make a sustained decision to use that channel again (Alzoubi, et al., 2020a). The previously reviewed research found a positive relationship between customer perceptions emphasising online buying by shoppers based on experiences (Alsharari, 2022; Alzoubi, 2022; Ghouri et al., 2017). The proposed hypothesis of this research shows a significant impact. H2: Customer perception has a significant impact on online buying behaviour.

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3.3 Relationship and Impact of Emotional Intelligence and Customer Perception on Online Buying Behaviour Tariq et al. (2021) point out that the presence of emotional intelligence stimulates online behaviour, giving people the impression and motivation to shop online. Lim and Kim (2020) argue that customer perception and emotional intelligence influence consumers’ online shopping behavior (Alzoubi & Ahmed, 2019). The rationale behind the awareness of customers’ emotions is that retailers quickly create opinions to establish and maintain a transactional relationship with customers (Alzoubi et al., Alzoubi et al., 2021a, 2021b, 2021c, 2021d, 2021e, 2021f; Danish Habib & Qayyum, 2018). Moreover, the quality of service and business must be maintained and conducted across multiple online platforms (Mehmood, 2021; Miller, 2021), as transactional relationships are crucial in e-commerce (Alketbi et al., 2020; Alzoubi & Yanamandra, 2020; Alzoubi et al., 2017; Sweiss et al., 2021). Long-term B2B and B2C relationships can be established and maintained if the service provider demonstrates empathy and fully comprehends customers’ motivations and buying behaviour (Al-bawaia et al., 2022; Al-Dmour et al., 2021; Alsuwaidi et al., 2020; Liao et al., 2010). H3: Emotional Intelligence and customer perception significantly impact online buying behaviour.

3.4 Problem Statement and Research Gap Customers’ perceptions are difficult to modify after establishing an opinion about a product and reacting toward a specific product where emotional intelligence is identified (Ghazal et al., 2021; Nazir et al., 2022). However, several aspects exist to determine consumer behaviour to grow an online business. Retail companies prefer to understand customers based on previous experience. Therefore, it is crucial to analyse customers’ online buying behaviour between the impact of emotional intelligence and perception by targeting the internet and social media (Al Aljumah et al., 2021; Khasawneh et al., 2021; Kurdi et al., 2022) More purposively, this research investigates the gap in previous research that can assist in focusing on factors (emotional intelligence and customer perception) that can help to influence consumer buying behaviour (Kurdi et al., 2022; Obeidat et al., 2012; Zhang et al., 2022).

3.5 General Research Model See Fig. 1.

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Emotional Intelligence

377

H1

H3

Customer Perception

Online Buying Behavior

H2

Fig. 1 Research model

3.6 Research Hypothesis HO1 : Emotional Intelligence has a statistical impact on Online Buying Behaviour in the UAE Retail Industry at (α ≤ 0.05) level of significance. HO2 : Customer Perception has a statistical impact on Online Buying Behaviour in the UAE Retail Industry at (α ≤ 0.05) level of significance. HO3 : Emotional Intelligence and Customer Perception have a statistical impact on Online Buying Behaviour in the UAE Retail Industry at (α ≤ 0.05) level of significance.

3.7 Research Methodology and Design The assessment of the proposed research model (impact of emotional intelligence and customer perception on online buying behaviour) is based on a quantitative technique and applies a descriptive, causal and analytical research design with convenient cluster sampling. Moreover, to obtain responses, an online questionnaire was used to collect primary data. Secondary data were obtained from previous literature and the internet.

3.8 Population, Sample and Unit of Analysis In order to authenticate the developed hypothesis, this research targeted the population of the retail industry based in Sharjah, UAE. Six hundred and fifty questionnaires

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Table 1 The study’s demographical aspects Items

Description

f

%

Gender

Male

178

71.8

Job status

Marketing and advertising manager

Female

70

28.2

112

45.2

Sales manager

75

30.2

Social media and digital marketer

33

13.3

IT manager

28

11.3

n = 248, Male = 71.8%, Female = 28.2%

were distributed online via email to the marketing and administrative departments of the largest supermarkets in Sharjah. After screening, a total of 248 valid responses were utilised for analysis. A simple structured questionnaire was developed to evaluate the variables, containing 29 items. Eight questions were used to measure “emotional intelligence”, 11 were used to measure “customer perception”, and 10 items were used to measure “online buying behaviour”. The questionnaire also included demographic items on “gender”, “Job Status”, and “Experience”. A questionnaire was designed using a Five-point Likert scale to obtain responses.

4 Data Analysis 4.1 Demographic Analysis The research findings on demographic data show that a convenient number of male and female respondents work in supermarket branches and shop headquarters. The summary of the data is shown in Table 1.

4.2 Reliability, Descriptive Analysis, Correlation The reliability of the data was evaluated by measuring Cronbach’s alpha. The results for the constructs based on five items each, EI = 0.76, CP = 0.82, and OBB = 0.89, shows the validity of the data. Descriptive analysis reveals the mean value of the respondents based on the five-point Likert scale “neutral to agree”, ranging from Mean = 3.46, to SD = 81% for emotional intelligence. Mean = 3.69, SD = 73% more extent to agree for customer perception. For online buying behaviour, mean = 3.52, SD = 69% neutral to agree. The correlation coefficients reveal a high correlation with a significant impact of emotional intelligence on online buying behaviour r = 0.694**. Emotional intelligence with customer perception also indicates a high

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Table 2 The constructs’ correlations and reliability Construct

Cronbach’s α

Mean

S.D

Emotional intelligence

Emotional Intelligence

0.76

3.46

0.81

1

Customer Perception

0.82

3.69

0.73

0.833**

1

Online Buying Behavior

0.89

3.52

0.69

0.694**

0.768**

Customer perception

Online buying behavior

1

Level of significance at P < 0.05**

Table 3 Regression analysis and hypothesis testing using ANOVA Hypothesis

Regression weights

Standardised coefficients β

Adjusted R2

R2

Sig

t-value

Hypothesis

H1

EI → OBB

0.694

0.480

0.590

0.000

8.49

Yes

H2

CP → OBB

0.768

0.588

0.482

0.000

2.42

Yes

H3

EI*CP → OBB

0774

0.596

0.600

0.000

7.34

Yes

Level of Significance (α ≤ 0.05), ** Critical t-value (df/p) = 1.64 *

correlation with r = 0.933**. In contrast, customer perception is highly correlated with online buying behaviour r = 0.768**. All results are based on a significance level of P < 0.05. Table 2 summarises the findings.

4.3 Regression Analysis and Hypothesis Testing See Table 3.

5 Discussion of the Data H1: The analytical results indicate that there are positive relationships between the constructs under study. The results support H1, and shows a significant positive relationship. The impact of “emotional intelligence” on “online buying behaviour” is (β = 0.694, t = 8.49) with a variance value of R2 = 59%. The results support H1. Consumers with high emotional intelligence tend to seek superior attributes rather than compulsive buying value. They have a greater impact on increased intention to buy online. This finding supports previous research findings that found that online buying behaviour increases with high emotional intelligence (Lim & Kim, 2020).

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H2: The results reveal that a comprehensive approach to “customer perception” has a significant positive impact on the “online buying behaviour” of customers (β = 0.768, t = 2.42) variance level R2 = 48%. The results support H2. The results interpretation can be made in relation to previous literature that reported that customers’ thoughts, emotions, and beliefs about a product are crucial for increasing customer retention, loyalty, brand awareness, and reputation (Kumar Singh et al., 2013). H3: The empirical results indicate that the predicted and proposed hypothesis support the results, ‘emotional intelligence” and “customer perception” have a significant positive impact on “online buying behaviour” of customers with (β = 0.774, t = 7.34) and variance of R2 = 60%. The findings of this research supports H3. Similarly, previous research by (Banu & Jayam, 2021; Zafar et al., 2021) reported that digital marketers and business managers need to explore consumer demand by examining their past shopping experiences and marketing trends.

6 Conclusion Customer engagement include focusing on client expectations, receiving and responding to customer feedback, and being open to improvement, which are crucial for e-business growth. Results indicate that emotional intelligence and customer perceptions can significantly improve online buying behaviour for retail businesses. Moreover, utilising customer and business data can improve business decisions and enable more accurate predictions of future demand. Emotional intelligence can help today’s online retailers deliver an optimised consumer experience on and off their e-commerce websites and through digital marketing. Objectively, emotional intelligence in retail creates an environment that customers enjoy interacting with.

7 Recommendations/Limitations Despite making significant contribution to the literature and online retail business management, the current research has some shortcomings. First, this research is limited to a geographic area, which is online businesses. Second, the population of the research is limited to the management level. For future research, it is recommended that the same construct be studied in the context of customers who prefer to shop online. Third, future research should be conducted at an advanced level, such as the role of artificial intelligence in the growth of e-commerce.

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The Mediating Role of Information Sharing in the Effect of Blockchain Strategy Information Security on E-Supply Chain in the UAE Real Estate Industry Samer Hamadneh , Haitham M. Alzoubi , Enass Khalil Alquqa, Ata Al Shraah , Muhammad Turki Alshurideh , and Barween Al Kurdi Abstract To empirically analyse the adoption of the e-supply chain with blockchain strategy in the advanced technological era, it is essential to assess if the mediating role of information sharing supports the relationship in the real estate industry in the UAE. Implementation of Blockchain and E-supply chain with mediating effect of Information Sharing has never been considered investigated in previous research, specifically in the Real Estate Industry. A quantitative technique is used to obtain empirical evidence. The targeted population of the research is the employees working S. Hamadneh · M. T. Alshurideh (B) Department of Marketing, School of Business, The University of Jordan, Amman 11942, Jordan e-mail: [email protected]; [email protected] S. Hamadneh e-mail: [email protected] M. T. Alshurideh Department of Management, College of Business Administration, University of Sharjah, Sharjah 27272, United Arab Emirates H. M. Alzoubi School of Business, Skyline University College, Sharjah, UAE e-mail: [email protected] Applied Science Research Center, Applied Science Private University, Amman 11937, Jordan E. K. Alquqa College of Art, Social Sciences and Humanities, University of Fujairah, Fujairah, United Arab Emirates e-mail: [email protected] A. Al Shraah Department of Business Administration, Faculty of Economics and Administrative Sciences, The Hashemite University, Zarqa, Jordan e-mail: [email protected] B. Al Kurdi Department of Marketing, Faculty of Economics and Administrative Sciences, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 H. M. Alzoubi et al. (eds.), Cyber Security Impact on Digitalization and Business Intelligence, Studies in Big Data 117, https://doi.org/10.1007/978-3-031-31801-6_24

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in 21 real estate companies based in Fujairah, UAE. A valid sample of 278 respondents was used for data analysis by regression and hypothesis testing. Blockchain technology is directly associated with the e-supply chain and information system. The indirect impact of Information sharing has also been significantly linked with blockchain and the e-supply chain. The E-supply chain adoption strategy has been considered a limitation in this research; however, exploring various aspects of the E-supply chain in the real estate industry while implementing a blockchain strategy is recommended. The real estate sector needs to recognise the value of blockchain technology and the E-supply chain adoption as it develops and reviews its current business models, procedures, and strategies to create transparent transactions and build up customer trust and business development. Keywords Blockchain · Information sharing · E-supply chain · Real estate industry UAE

1 Introduction Due to the Covid-19 pandemic, which has continued into the first quarter of the year, the real estate market in the UAE is experiencing a significant change in consumer behaviour. In contrast to off-plan units, completed residential units are now more popular in the real estate market. Based on figures issued by the Dubai Land Department, sales throughout the year’s first quarter reflected this, with finished units accounting for 70% of all transactions. The property dealings with advanced criteria eased the transactions between buyers and sellers (Alzoubi et al. 2022i, 2022j). The use of supply chain in real estate is about the management of people, places, things, procedures, suppliers, and resources are supply chain management in the context of real estate (AlShurideh et al., 2019; Alzoubi et al., 2021a, 2021b, 2021c, 2021d, 2021e, 2021f; Hamadneh et al., 2021a, 2021b). The SCM oversees all aspects of buying, selling, or renting a piece of land, a structure, or a dwelling (Alshurideh, 2022; Alzoubi et al., 2021e). In the advanced era, the supply chain’s use has turned into an electronic supply chain that assists customers by providing legitimate information online, maintaining the data source systematically and managing properties, assets and overall transactions (Alolayyan et al., 2022a, 2022b; Shamout et al., 2022a, 2022b). Additionally, blockchain technology can be used for real estate or land transactions to speed up and safeguard real estate deals (AlShamsi et al., 2021; Alzoubi et al., 2022a, 2022b, 2022i, 2022j). Currently, the ability to create a private blockchain for land transactions is required the security purpose (Alzoubi et al., 2022i, 2022j; Ghazal et al., 2021a, 2021b). The individuals or individuals who are interested in using blockchain for land transactions must sign up for or register with the platform. Only a registered user with access to the blockchain can buy or sell real estate. Blockchain smart contracts facilitate the land transaction process (Alzoubi et al., 2022m; Alzoubi & Ahmed, 2019; Alzoubi et al., 2022; Yousuf et al., 2021). When

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the buyer and seller have reached an understanding of the terms and conditions (landrelated specifics) and the buyer is ready to move through with the deal, the land can be transferred with the help of various software used by real estate companies (LionDesk, BoldLeads, Zillow Premier Agent and Propertybase etc.). Furthermore, blockchain and e-supply chains can tremendously impact real estate transactions. However, information sharing is a crucial aspect when using technology/blockchain (Hamadneh et al., 2021a, 2021b; Kurdi et al., 2022; Shamout et al., 2022a, 2022b). According to Li and Zhang (2008), the information has six categories. These are resources, planning, process, order, product and inventory (Alzoubi & Yanamandra, 2020; Alzoubi et al., 2017;). Whang and Lee have pointed out different types of shared information and their significant benefits (Alzoubi et al., 2022a). The payment cycles can be reduced, the quality of customer service can be improved, and labour costs can be reduced when the status of orders can be shared (Alshurideh, 2014; Ghazal et al., 2021a, 2021b; Madi Odeh et al., 2021). The transparency in information provided to the buyers can enhance business sales and trust. The real estate industry requires legality and valid information for its customers (AlDmour et al., 2021; Hasan et al., 2022; Butt, 2022). Based on the need for blockchain technology and information sharing, this research aims to obtain the findings that may be incorporated into the research solution to benefit real estate agencies.

2 Theoretical Framework 2.1 Blockchain Strategy Organisations and people who do not trust each or agree to certain factors and record information about the company without the involvement of any third-party authority can easily use blockchain technology within their organisation. The way organisations can carry out transactions online and through which information is shared can be revolutionised with the help of blockchain technology (AlShamsi et al., 2021; Alzoubi et al., 2022g, Khatib et al., 2022; Utami, 2021). Organisations tend to trust their data with the help of blockchain technology. It is integral for blockchain technology to become energy-efficient and sustainable. Blockchain technology should be compatible anywhere with the possible support of Europe’s strong privacy regulations and data protection (Alzoubi et al., 2022e). These collective verifications of the ecosystem provide considerable speed, security and traceability. A set of protected information blocks consists of a blockchain where the blocks are sequentially chained. An immutable ledger is together formed by them and distributed equally within the participating nodes (Mettler & Hsg, 2016).

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2.2 E-Supply Chain The Internet plays an essential role in e-supply chain management to ensure that the value-added activities are carried out so that a good return on investment can be gained and the customers’ needs can be met through the products manufactured by the company (Alzoubi et al., 2020b, 2020c; Lee et al., 2022). In an efficient and organised way, from the suppliers, the company’s information, services, and goods are delivered to the customers with the help of the business processes and the Internet under the e-supply chain management (Alzoubi et al., 2022l; Del & Solfa, 2022). There are various players within e-supply chain management, including customers, suppliers, companies, manufacturers, retailers, distributors and logistics (Nasim et al., 2022; Taghipour et al., 2021). Technology may be used to successfully manage the supply chain. Advanced analytics tools that evaluate data from various company divisions are well-equipped with software solutions (Alzoubi et al., 2022c). A more adaptable organisation that can make decisions proactively to maximise business results can be built using an e-supply chain. Here are some examples of how the real estate sector might benefit from supply chain management solutions: • • • • • •

Assets and properties management Planning and Forecasting Management of files and documentation electronically Sourcing and Procurement Managing Vendors and Purchase Orders Managing orders, locations and franchises

All the order processing activities can be coordinated with the help of e-supply chain management at the customer level (Alzoubi et al., 2021b, 2021g; Ghosh & Aithal, 2022). The list of such activities includes entry into the order processing system, the process of order generation, material and production forecast, prioritisation and order acceptance (Aloqool et al., 2022). It is responsible for financial activities and material-related activities (Alzoubi et al., 2020a; Alzoubi et al., 2021f). Fund transfer, invoicing, accounting, and production are a few examples of financial activities and production, delivery, distribution, scheduling, and material are a few examples of material-related activities.

2.3 Information Sharing A highly secure technology can be made out of the Blockchain with the help of the permanent availability of transaction history and decentralised systems. This technology has attained the trust of most organisations by exchanging the most authentic information (Goria, 2022). In this modern era, the most effective transformational technology is the Heralded (Alzoubi et al., 2021a, 2021b, 2021c, 2021d, 2021e, 2021f). Sooner or later, few impacts will be witnessed on the business through

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extensive information sharing (Nakasumi, 2017). Future growth can be unlocked by the company executives acquiring better knowledge and mind-boggling scope of the opportunities of this technology (Alzoubi & Aziz, 2021; Alzoubi, 2022). Transactions such as contracts, patents, money or anything that holds value within the company included within the firm’s assets are tracked by them through the ledgers as well as the existence of required information can assist in better transactions. Moreover, two flows of information comprise information sharing (Alzoubi et al., 2022d). These are the information provided to the company’s suppliers and the information that the suppliers provide to the company. Data quality plays a major role in information sharing (Alzoubi et al., 2022f; Amrani et al., 2022; Marinagi et al., 2015). Even though there are various documents on the pros of information sharing, good quality information sharing can add value to the shared information.

2.4 Operational Definitions

Variables

Definition

References

Blockchain

Blockchain is a decentralised, immutable database that makes it easier to track assets and record transactions in a corporate network. An asset may be physical (such as a home, car, money, or land) or intangible (intellectual property, patents, copyrights, branding)

(Utami, 2021)

Information sharing Information sharing is the exchange, collection, (Alzoubi et al., 2020b) use, or disclosure of personal information by one public body or another organisation or organisation for a specific purpose E-supply chain

Business operations that integrate e-business (Taghipour et al., 2021) strategies with supply chain processes are referred to as e-supply chain. Chain management for e-supplies includes. Utilising e-business technologies to facilitate and maximise value-adding supply chain operations

2.5 Industry Description The residential real estate market in the UAE is anticipated to grow at a CAGR of more than 8% from 2022 to 2027. The COVID-19 epidemic caused the residential real estate market to collapse as people were compelled to stay at home and the government-imposed lockdowns. However, experts predict that residential real estate prices in the UAE will continue to rise in 2022, supported by positive economic

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reforms and a stepped-increased vaccination campaign that has sped up the recovery from the coronavirus-caused slowdown (Ghazal et al., 2022). The UAE residential property market is improving as consumers migrate to bigger homes with better amenities after softening owing to a three-year oil price collapse that started in 2014 over concerns about oversupply and the subsequent pandemic.

3 Literature Review 3.1 The Relationship and Impact of Blockchain Strategy on the E-Supply Chain Regarding writing and reading, access to this ledger can be restricted or unrestricted. Modification is used to protect shared information. This is because it is easy to detect any alterations within the systems so that the organisations can be alert. This information is considered to be immutable, which is once recorded in the Blockchain (Mettler & Hsg, 2016). However, the way administrative control mechanisms are maintained and regulated digitally has been changed with blockchain technology’s help (Alzoubi et al., 2022n). The data is transformed into digital codes in the blockchain, where shared databases are stored, the issues of revisions and deletion are eliminated, and higher transparency can be gained. In every payment, deal, and interactive and commercial activity within digital recording lies the secret ability of the Blockchain (Akhtar et al. 2022; Alzoubi & Yanamandra, 2022). Transparency is the transparent and straightforward circulation of information outside and inside the company (Baabdullah et al., 2019) The information can be entirely and timely disclosed by reading the transaction of the stakeholders. The issues that the e-supply chain members can face can be mitigated through transparency when they are given the ability to track the products full-time in the esupply chain (Alzoubi et al., 2022h; Taghipour et al., 2021). To regain better ideas and trust of the customers and impressions of the e-supply chain violations of the manufacturers, transparency is provided by the companies. The environmental impacts on the products, values and attention of the products can also be understood through the transparency in the e-supply chains. Customer satisfaction can be increased with the help of transparent e-supply chains. When increasing sales and quality feedback are provided, guidance can be provided to them by developing effective initiatives. The location of the products’ processing and production along with the products’ route can be identified by the organisations with the help of this factor (Alzoubi et al., 2021c; Utami, 2021). In contemporary world, the challenging environment can be handled effectively with the help of the prime mechanism under the flexible e-supply chains (Alsharari, 2021). When the disruptions can be effectively adapted and responded to by the firms, it can be said that flexibility has been attained. In support of the literature, the

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proposed research assumed the hypothesis that can help to contribute more empirical findings in the literature. H1: Blockchain has a significant impact on the e-supply chain.

3.2 The Relationship and Impact of Blockchain Strategy on Information Sharing Fraud can emerge and often be misused in a property transaction. These deficiencies can be solved with the help of Blockchain. The data can be recorded and maintained with the help of a data system so that individuals and multiple organisations can be allowed to share the same information of the company in real-time, whereas the issues of control, privacy and security can be mitigated (Cole et al., 2019). The private or public distributed digital transaction ledger is created through the Blockchain data structure, which ensures that these are shared within a distributed network of computers rather than on a single provider (Baabdullah et al., 2019). The e-supply chains have become very dynamic and complex with the intense competitive pressure in the market and the rapid development of economic globalization (Kashif et al., 2021). This is because organisations face more demanding customers with better service and better-customised products, which can be gained from an acceptable cost and speed (Taghipour et al., 2021). The primary focus of the companies is now on the core functions so that they can easily adapt to market changes and remain competitive. Development of advanced value chains, outsourcing and open innovation are the few collaborative and collective efforts so that companies can move along them. The reliability of the data and information provided by the trade partners within a central authority or e-supply chain is known as data trust. Appropriate data often plays the role of a catalyst in information sharing so that the efficiency of the supply chain is improved (Alzoubi et al., 2022b; Eli & Hamou, 2022). An essential organisational strategy is information sharing since transaction costs can be reduced (Bayramova et al., 2021). The information shared within the members of the e-supply chain includes ownership, the environmental impact of the products, product specification, location of data and the state of the product. Beyond decision-making processes, firms can see the importance of information sharing. These include an increase in logistic planning and profit margin. Collaborative work can also be infused among the workers within an e-supply chain (Al Shraah et al., 2022). From the start to the end of the e-supply chains, information is being transferred constantly. Thus, an increase in the volume of information has also been witnessed. The buyers and firms can often be confused as to which data must be trusted due to the large volume of information distributed within the e-supply chains (Eli, 2021). There are no such verifications made on whether the information is accurate or not (Nakasumi, 2017). The approved transactions are recorded throughout the supply chain in a tamper-evidence environment. Evidence and apparent outcomes will be

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left behind if any person alters the existing transactions approved by all the members (Akhtar et al., 2021). To automate and digitise the processes, blockchain technology can opt for the help of smart devices and the Internet of things (IoT) to share and collect data with other members in real-time (Marinagi et al., 2015). Through this, the efficiency increases and the transparency improve in an e-supply chain. The researcher focused on the significant influence of the e-supply chain. Based on the proven relationship, this research has proposed a hypothesis to include findings from the research. H2: Blockchain has a significant impact on information sharing.

3.3 The Relationship and Impact of Information Sharing on the E-Supply Chain Typically, sufficient inventory is present in the organisation when required. The company cannot face any surplus or shortage of inventory through this (Mehmood, 2021). The company’s reputation can be hampered if there is an inventory shortage (Ahmed et al., 2022). The company’s funding can be unnecessarily blocked through excess inventory (Marinagi et al., 2015). The company can gain a competitive advantage over its competitors. On-time deliveries are increased, and just-in-time delivery can be increased with the help of which satisfaction of the customers can be gained (Miller, 2021). Such companies provide enhanced customer services since the revenues increase and the cycle time is reduced. Activities such as decision making, order fulfilment, distribution and warehouse, forecasting, order management and demand planning are improved through e-supply chain management (Qasaimeh & Jaradeh, 2022; Victoria, 2022). Several authors have evidenced its close collaboration and coordination in the e-supply chains. Information sharing among the members of the e-supply chain is the basic foundation. For escalating the operational performance of the suppliers, the two integral factors are the close buyer–supplier relationship and the prerequisite for knowledge sharing included within information sharing as per the rushed research (Qi et al., 2022). The linkages amongst members of an e-supply chain are provided by information with the help of which all the activities can be synchronised across the e-supply chain. The efficiency of the e-supply chain can be increased by information sharing with the help of smoothing production, and inventories can be reduced (Ratkovic, 2022). According to Lewis’s study, outsourcing performance can be affected by information sharing in an e-supply chain. Lewis found a significant link between outsourcing performance and information sharing (Edward Probir Mondol, 2022). The extent to which the companies share distinct types of information with their partners is called information intensity. The information shared flows from the customer or demanded information, retailer and distributor information, manufacturer information, and supplier information to an e-supply chain (Radwan, 2022).

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The information that can be shared with the customers can be measured within the research. In the e-supply chains, if the information is shared, the real power of information can be evident. The level to which the information is shared to meet the organisation’s needs is known as information quality (Farouk, 2022). Several aspects such as reliability, timeliness, accuracy and adequacy of the information are encompassed within information quality. Several studies have provided empirical evidence regarding the importance of information quality in e-supply chain management. Severe issues in e-supply chains have been created by distorted and delayed information, which has been proven in extensive research in supply chains (Alsharari, 2022; Kasem & Al-Gasaymeh, 2022; Li, 2002). Propagation of distorted information can be gained when they tend to move up in the e-supply chains. In light of the presented evidence, this supports the hypothesis developed for the research. H3: Information sharing has a significant impact on the e-supply chain.

3.4 The Relationship and Impact of Blockchain Strategy on E-Supply Chain with Information Sharing In uncertain situations, faster responses can be made by an organisation with flexible e-supply chains. Through this flexibility, the ability of the firm to present the services and products effectively and quickly is also increased. Across the entire supply chain, the level of competition has expanded in the globalising world. The business processes are outsourced by the organisations in today’s economic situation when they can attain flexible e-supply chain. Through this, the visibility and control of different logistics operations are lost. At all goods delivery stages, the company can attain the visibility of logistics systems and integrated transportation with the help of digital technologies from the manufacturers to end-users (Alwan & Alshurideh, 2022; Taghipour et al., 2021; Tariq et al., 2022a, 2022b). The information must be shared by the firms so that it can be quickly applied to the firms present in the chains to provide flexible e-supply chains (Alshurideh et al., 2021; Alzoubi et al., 2022i, 2022j, Tariq et al., 2022a, 2022b). However, to ensure if the chains are acting as a whole or not, various information is required by the organisation. The vulnerabilities and connectivity must be accessed and identified to ensure that the hampered trust changes can be reduced in the e-supply chains. The supply chain partners must be identified under the first step of the risk assessment process. The next step is to develop a supply chain map to view the significant information flows (Zhang et al., 2021). For sharing information in the e-supply chain, various factors are driven by blockchain technology, within which the most important factor is trust development. In an observable and time-stamped manner, the transactions are confirmed and logged in in a blockchain. When all the parties have approved the transaction, these can neither be deleted nor changed by any party. To ensure that the data is secured and integrated, transparency, efficiency, security and traceability within the e-supply chain transactions must be

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maintained through blockchain technology. On the changes, the records cannot be changed by anyone, which can ensure the safety of the data gathered (Golosova & Julija, 2018). On the Blockchain, the decisions and transactions of all the supply chain members can be documented and recorded. These transactions are accurate and known to all the chain members. The e-supply chain members can monitor the activities leading to controversial business results in such a situation. When accurate data is provided to the organisations, trust can be gained. The companies can gain many opportunities with the help of the development of information technology so that seamless integration can be achieved with the members of the e-supply chains at a low cost. All the members of the e-supply chains can access a wide range of technologies (Durowoju et al., 2020). These technologies include the Internet, extensible mark-up language, radio frequency identification, mobile computing, electronic data interchange (EDI), and wireless application protocol. The integration of information sharing and technologies is the major issue that is being faced with the use of these technologies. Considering previous literature, these factors may assist in current research that investigates the impact of blockchain on the e-supply chain and the information-sharing sources to make a strong relationship. Requires. H4: Blockchain significantly impacts the e-supply chain with the mediating effect of information sharing.

3.5 Problem Statement and Research Gap Based on the existing problems regarding traditional real estate management, this research will consider the empirical analysis of the implementation of Blockchain and E-supply chain with a mediating role of Information sharing. Some issues may be found in the previous literature (Pankratov et al., 2020). There are issues with slow strategic implementation and inefficient systems, and transparency. This research aims to analyse the potential effects of implementing blockchain technology in the real estate industry while providing the best information sharing with clients. The proposed research aims to investigate and cover the gap in past research that may come up with valuable findings for the literature and the real estate industry.

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3.6 General Research Model

Information Sharing H1

H3 H4

Blockchain

E-Supply Chain

H2

3.7 Research Hypothesis HO1 : Blockchain has a statistical impact on Information Sharing in the UAE Real Estate Industry at (α ≤ 0.05) level. HO2 : Blockchain has a statistical impact on the e-Supply Chain in the UAE Real Estate Industry at (α ≤ 0.05) level. HO3 : Information Sharing has a statistical impact on the e-Supply Chain in the UAE Real Estate Industry at (α ≤ 0.05) level. HO4 : Blockchain has a statistical impact on the e-Supply Chain with the mediating effect on Information Sharing in the UAE Real Estate Industry at (α ≤ 0.05) level.

3.8 Research Methodology and Design The variables assessment was conducted through empirical analysis incorporating the quantitative research technique. Due to the research’s exploratory nature, a descriptive, analytical and causal design was applied to figure out the findings. To measure the variables, the questionnaire was used to collect data online. The secondary data was used from literature to clear the variable concept and interlinking with the real estate industry.

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3.9 Population, Sample and Unit of Analysis This research targeted the employees of real estate companies based in Fujairah, UAE. A total of 21 real estate agencies were accessed to collect the data as they are expected to use the e-supply chain and blockchain technology to help evaluate this research results. A total of 700 questionnaires were sent via email to the (Sales Executive, Real Estate Agents, Real Estate Brokers and IT Managers). Two hundred seventy-eight questionnaires were received with valid results for further analysis by correlation, regression and hypothesis testing. For the assessment of Blockchain, Information Sharing and E-supply chain, a 24 items questionnaire was developed containing each of them as (8 items to assess Blockchain), (7 items to assess Information sharing) & (9 items used to assess the E-supply chain). A Five-Point Likert scale was used with the demographic section, including Gender, Experience and Designation.

4 Data Analysis 4.1 Demographic Analysis See Table 1. Table 1 The study’s demographical aspects

Items

Description

F

%

Gender

Male

203

73

75

27

Female Designation

Experience

Sales executive

108

38.8

Real estate agent

100

36.0

Real estate broker

40

14.4

IT manager

30

10.8

1–3 years

38

13.7

3–6 years

71

25.5

6–10 years

117

42.1

52

18.7

10–15 or above

N = 278, Male = 203, Female = 75, Sales Executive with high number of 108, 42.1% employees with 6–10 years of experience

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Table 2 The study correlation and reliability analysis Construct

No of items

Cronbach’s α

Mean

S.D

Blockchain

Information E-supply sharinsg chain

Blockchain

8

0.86

3.47

0.91

1

Information Sharing

7

0.83

2.92

0.76

0.766**

1

E-Supply Chain

6

0.83

3.09

0.59

0.795**

0.837**

1

BLC M = 3.47, SD = 91%, IS M = 2.92, SD = 76%, ESC M = 3.09, SD = 59% Significance level at **P < 0.05.

4.2 Reliability, Descriptive and Correlation The whole sample of the employees was measured to validate the data using Cronbach’s Alpha, which indicated a good reliability analysis of the data with Blockchain = 0.86, Information Sharing = 0.83 and E-supply chain = 0.83 respectively. The descriptive analysis indicates the result of mean for the BLC = 3.47, SD = 61%, IS Mean = 2.92, SD = 46%, and ESC Mean = 3.09, SD = 59%. Moreover, the variable relationship was measured through correlation coefficients representing the relationship of BLC with IS as high correlated r = 0.766 with a significant value at level P < 0.05. The correlation shows the relationship of BLC with ESC as highly correlated with the significant value r = 0.795, P < 0.05. The relationship of ESC with IS is also highly correlated with the value of r = 0.837 and significance level at P < 0.05. Table 2 illustrate the summary of the results.

4.3 Multiple Regression See Table 3.

5 Discussion of the Results The research findings reveal that the impact of “Blockchain strategy” on “Information Sharing” has a significant positive relationship with (β = 0.777, t = 8.06). The variance between the variables predicted is R2 = 58%, which indicates a high dependency. Thus, H1 is supported. The empirical assessment of “blockchain strategy” and its impact on “E-supply chain” demonstrate a significant positive relationship (β = 0.795, t = 21.79), indicating a positive relationship with a variance of R2 = 68%. This supports H2. Moreover, the relationship between “Information Sharing” and “E-supply chain” is positively associated with each other (β = 0.837, t = 25.47),

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Table 3 Regression analysis with ANOVA, Hypothesis testing Hypothesis

Regression weights

Standardised coefficients β

Adjusted R2

R2

Sig

H1

BLC → IS

0.766

0.586

0.587

0.000

8.06

Yes

H2

BLC → E-SC

0.795

0.631

0.632

0.000

21.79

Yes

H3

IS → E-SC

0.837

0.700

0.701

0.000

25.43

Yes

H4

Mediating effect of IS BLC*IS → E-SC

0.871

0.756

0.758

0.000

4.90

Yes

t-value

Hypothesis supported

BLC=Blockchain, IS=Information sharing, E-SC=E-Supply Chain. *Level of Significance (α≤0.05) **Critical t-value (df/p) = 1.64

indicating a positive relationship and significance level at P < 0.05. The variance between IS and ESC shows the highest level of R2 = 70%. Thus, the H3 is also empirically supported. Additionally, the mediating effect of “Information Sharing” between the “Blockchain” and “E-supply chain” has a significant relationship (β = 0.871, t = 4.90). A positive impact of the mediator represents a high association between the Independent and dependent variable of this research. The variance of the “information sharing” is high R2 = 75%. This indicates that information sharing can significantly strengthen the relationship between blockchain and the e-supply chain, specifically in the real estate industry. Thus, H4 is supported. The results can significantly contribute to future research and supplement literature.

6 Conclusion The research findings found a significant aspect of the Real Estate Industry that validated the use of blockchain can eliminate the transparency issue. Customers can obtain the required information through the organisation’s database. The database of the registration department’s system, which provides information on real estate, ensures the accuracy and validity of the data that is made available to the buyer through the platform. Buyers and sellers can transact directly on the blockchain, which eliminates the need for intermediary linkages and their associated costs.

7 Recommendations/Limitations In the actual world, there is still few supply chain that uses blockchain technology. Additionally, most cases lack conventional design processes, making the validation

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of blockchain solutions challenging. However, for the proposed research, the data was collected from different companies in real estate industries based in one city in the UAE. Covering the excessive geographical area is recommended to increase the generalizability of adopting a blockchain strategy using the e-supply chain. Additionally, it is anticipated that future research may include qualitative techniques besides quantitative ones and attempt to gather data from various contexts and countries.

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Impact of the Internet of Things (IoT) on the E-Supply Chain with the Mediating Role of Information Technology Capabilities: An Empirical Evidence from the UAE Automotive Manufacturing Industry Ala’a Ahmad , Mohammed T. Nuseir , Haitham M. Alzoubi , Barween Al Kurdi , Muhammad Turki Alshurideh , and Ahmad Al-Hamad Abstract To analyse the mediating effect of IT Capabilities on the Internet of Things and the e-supply chain using empirical evidence from the automotive industry in the UAE. First, an empirical analysis of the development of IoT-enabled smart systems for vehicles is presented, coupled with a study of how technology has changed to support IoT connectivity with the e-supply chain. A quantitative technique with a causal, analytical and descriptive research design using an appropriate cluster sample was used to investigate the research variables. A valid sample of 38 Dubai-based automotive companies was utilised for data analysis using regression and hypothesis A. Ahmad University of Sharjah, Sharjah, UAE M. T. Nuseir Department of Business Administration, College of Business, Al Ain University, Abu Dhabi Campus, P.O. Box 112612, Abu Dhabi, UAE e-mail: [email protected] H. M. Alzoubi School of Business, Skyline University College, Sharjah, UAE e-mail: [email protected] Applied Science Research Center, Applied Science Private University, Amman 11937, Jordan B. Al Kurdi Department of Marketing, Faculty of Business, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan e-mail: [email protected] M. T. Alshurideh (B) Department of Marketing, School of Business, The University of Jordan, Amman 11942, Jordan e-mail: [email protected]; [email protected] M. T. Alshurideh · A. Al-Hamad Department of Management, College of Business Administration, University of Sharjah, Sharjah 27272, UAE e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 H. M. Alzoubi et al. (eds.), Cyber Security Impact on Digitalization and Business Intelligence, Studies in Big Data 117, https://doi.org/10.1007/978-3-031-31801-6_25

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testing. The findings show that the proposed model is efficient and suitable for the automotive sector. E-supply chain management methods in the automotive industry are improved and depend on IT and IoT. Data from only a few automotive companies were utilised, limiting the vast knowledge and experiences of this industry. It is recommended that future research be conducted on a global scale. From a strategic perspective, the research shows that implementation with a better understanding of smart production systems (IoT) interaction can better support automotive manufacturers in solving their control, safety, and other operational problems. Keywords Internet of Things (IoT) · E-supply chain · Information technology capabilities · Automotive manufacturing industry UAE

1 Introduction The concept of the Internet of Things (IoT) has entered numerous industries due to rapid social growth, and it has connected things and things to things. To attain a higher level of automotive information, the concept of automobile manufacture “IoT” has been introduced to the manufacturing industry (H. M. Alzoubi, In’airat, et al., 2022). The automotive manufacturing sector links the two major strategic growing industries of the “IoT” and the intelligent automobile. The burgeoning Internet of Things (IoT) is a boon to the automotive sector, offering a wide range of opportunities to creatively develop, build, and improve seamless services for user convenience. IoT has developed and changed dramatically over time in various automotive applications (H. M. Alzoubi, Ali, Septyanto, et al., 2022). Automotive companies are integrating technology as part of their business strategy and vision. IoT devices collect enormous amounts of data, which is then used for quick and accurate decision-making (Ahmad, Alshurideh, & Al Kurdi, 2021a, 2021b, 2021c; M. Alshurideh, 2022; H. M. Alzoubi, Alshurideh, Kurdi, et al., 2022e, 2022f; Ghadge et al., 2022; Harahsheh et al., 2021). Moreover, organisations can strengthen their capabilities and business decisions by consistently using business intelligence (Alshurideh et al., 2020a, 2020b, 2021; Alzoubi et al., 2021a, 2021b, 2021c, 2021d, 2021e, 2021f, 2021g, 2021h). Manufacturing companies are expected to benefit greatly from IoT and its direct and indirect benefits to improve the e-supply chain (Alshurideh 2022; Alzoubi, Alshurideh et al., 2022b; Alzoubi, Kurdi et al., 2022c). However, information technologies are anticipated to play a significant role in automotive manufacturing firms that join IT-based techniques to improve manufacturing desired by customers (Alsuwaidi et al., 2020; Altamony et al., 2012; H. M. Alzoubi, Ghazal, Svoboda, et al., 2021f). Furthermore, the automotive supply chain will be highly electronically integrated in the future; this is referred to as the “e-supply chain”. Online access to sales orders and market changes is provided throughout the supply chain (Abuanzeh et al., 2022; H. M. Alzoubi, Joghee, Alshurideh, et al., 2021g; Shamout et al., 2022). Order management, sophisticated forecasting systems, scheduling features, and virtual marketplaces are

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part of the electronic interface between customers and vendors (Awawdeh, Shahroor, et al., 2022b; Hamadneh, Keskin, et al., 2021a). This research offers a comprehensive literature review on the implementation of IoT technology in the automotive industry, focusing on the development of information technology capabilities, connectivity, and applications. It is motivated by the growing significance of the automotive e-supply chain. First, an empirical analysis of the development of IoT-enabled smart systems for vehicles is presented, coupled with a study of how technology has changed to support IoT connectivity and applications.

2 Theoretical Framework 2.1 Internet of Things The Internet of Things refers to physical objects equipped with sensors, software, processing capabilities, and many other technologies. It connects and exchanges data and relevant information with other systems or devices over the Internet or other communication networks. The devices include simple household items and other industrial tools (H. M. Alzoubi & Yanamandra, 2020). Currently, more than seven billion IoT devices are connected, and this number is expected to increase to 22 billion by 2025 (H. M. Alzoubi & Ramakrishna, 2022; Kirk, 2015). The IoT is one of the most powerful technologies in the world. Home appliances that can be connected now include kitchen appliances, thermostats, and new cars (H. M. Alzoubi, Alshurideh, Al Kurdi, et al., 2022e, 2022f). Widespread applications of IoT for industrial purposes include smart manufacturing, smart power grid, connected logistics, connected assets and gadgets, smart cities and much more (M. Alzoubi, Ghazal, et al., 2021e, 2021f; Rahim et al., 2021). Certain technologies have made IoT a grand success today. Among the technologies, the notables include connectivity, platforms for cloud computing, ingress to certain low-cost technology and artificial intelligence (Ghazal, Al Shebli et al., 2021a; Alhashmi et al., 2020; AlShamsi et al., 2021; Nuseir et al., 2020; Yousuf et al., 2021).

2.2 E-Supply Chain Electronic supply chain management or E-SCM is related to manufacturing. E-supply chain is a series of value appending activities enabled by the Internet. This business process enables the production of goods or services through value appending activities (Alsharari, 2022; H. M. Alzoubi, Lee, Romzi, et al., 2022o; Taghipour et al., 2021). The value-added technologies in the e-supply chain primarily include product development and its design, supply of raw materials, manufacturing of the objected products, packaging of the manufactured products, delivery of the

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products to consumers and after-sale services to maintain good quality services (Hamadneh et al., 2021a, 2021b; Wolff & Geiger, 2000). The electronic supply chain primarily coordinates an organisation or a company between its suppliers, partners and consumers (A. Alzoubi, 2022; H. M. Alzoubi, Rehman, Saleem, et al., 2022p; Awawdeh, Ananzeh, et al., 2022a). The supply chain has gradually evolved into an electronic supply chain due to the internet globalisation of the planet (AlShurideh et al., 2019; Alshurideh, Gasaymeh, et al., 2020b). The success of this chain depends on the capabilities of existing partners involved in the supply chain who are responsible for implementing the necessary strategies (Alolayyan et al., 2022a, 2022b; Alzoubi, Lee, Azmi, et al., 2022n). The partners and the parties involved in the esupply always strive to enhance their performance to meet the business and customer requirements (Al Kurdi et al., 2020; Alwan & Alshurideh, 2022; Kurdi et al., 2020).

2.3 Information Technology Capabilities IT capabilities or information technology capabilities refer to the propensity of a company or an organisation to determine whether information technology meets its business needs (Al Suwaidi et al., 2021; Almaazmi et al., 2020; Edward Probir Mondol, 2022). Companies deploying information technology seek to ameliorate their business processes cost-effectively (H. Alzoubi & Alnazer, N., Alnuaimi, 2017). It also aims to offer maintenance for a long time and support for systems based on IT (Alzoubi, Alshurideh et al., 2021a, 2021b, 2021d; Alzoubi et al., 2021a, 2021b, 2021c, 2021d, 2021e, 2021f, 2021g, 2021h). Basheer et al. (2016) stated that information technology capabilities are mainly the potential to leverage various IT resources for impalpable benefits. IT capabilities of a company consist of certain components that include IT strategy, IT processes, IT metrics, IT organisation and assets or infrastructure (Kabrilyants et al., 2021; Tariq et al., 2022; Vorobeva Victoria, 2022). Capabilities, structure and knowledge are the main components of an IT organisation (H. Alzoubi & Ahmed, 2019; Goria, 2022). Infrastructures or assets of a company include software and hardware, applications, tools, database and networks (Aljumah et al., 2021; H. Alzoubi, Alhamad, Alshurideh, et al., 2022a; Ghazal, Alzoubi, et al., 2021c). Potential organisations create value by employing a distinctive combination of the abovementioned components.

2.4 Operational Definitions

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Variables

Definition

Internet of Things (IoT)

The network of physical items, or Liu et al., (2012) “things,” that are implanted with sensors, software, and other technologies to communicate and exchange data with other devices and systems over the Internet is referred to as the Internet of Things (IoT)

Information technology capabilities

IT Capabilities are characterised as the Shahzad et al., (2020) capacity to deploy combinations of IT-based resources and other resources that add value to operational goals

E-supply chain

E-supply chain refers to business activities incorporating e-business approaches into supply chain processes. E-supply chain management includes the application of e-business (Internet-based) technologies to support and optimise value-adding activities in the supply chain

Reference

Lancaster et al., (2006)

2.5 The Description of the UAE Automotive Manufacturing Industry The automotive sector is one of the key drivers of the UAE’s economic expansion. The sector is essential to the economy as the nation looks to the future and is currently the second-largest automotive market in the Gulf Cooperation Council (GCC). The UAE government’s emphasis on the growth of the automotive sector demonstrates how it intends to use it to support the country’s economic expansion. As Dubai enters a new era of expansion, it is also anticipated that it will become the automotive hub in the region, growing through the export and re-export of vehicles to the Middle East.

3 Literature Review 3.1 The Relationship and Impact of the Internet of Things and Information Technology Capabilities The relationship between IoT and information technology capabilities is emphasised. The impact of these two factors is evident in today’s business and market

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world. Due to extreme globalisation, leading companies have changed how they communicate by implementing technological systems (H. M. Alzoubi, Ahmed, et al., 2020a; Ghosh & Aithal, 2022). Like the connection between the IoT and e-supply management, information technology and the IoT are closely related. The IoT is a fruitful product of enabling information technology capabilities (Aljumah, Nuseir, et al., 2022a; Ghazal, Alshurideh, et al., 2021b). The IoT is primarily things oriented. In order to establish the IoT, competent information technology is required (H. M. Alzoubi, Kurdi, Alshurideh, et al., 2022m). Information technology is primarily the potential use of computers to create, process, assimilate, and exchange information electronically (H. M. Alzoubi, Joghee, et al., 2020b; Bayramova et al., 2021; Eli & Lalla Aisha Sidi Hamou, 2022; Stoyanova et al., 2020). The IoT is a system related to computing devices, digital machines, mechanical machines, humans and animals tagged with UIDs or unique identifiers. Information technology has made the IoT possible (Akhtar et al., 2021; H. M. Alzoubi, Alnuaimi, Ajelat, et al., 2021c). It is the product of information technology capabilities (Del & Solfa, 2022). The availability of budget-friendly and less powerful sensor technologies, good connectivity, computing platforms, machine learning, analytics and artificial intelligence technologies have helped the IoT succeed (Mehmood, 2021; Pulevska-Ivanovska & Kaleshovska, 2013). These components are the outcomes of information technology capabilities (H. Alzoubi, Alshurideh, Kurdi, et al., 2022b). IoT is rightly regarded as an emerging paradigm in the information technology sector that has recently changed the business style (H. M. Alzoubi, Lee, Romzi, et al., 2022o; Nasim, S. F. et al., 2022). One is incomplete without another in the information technology domain. Based on the literature, the hypothesis can be supposed to support this research. H1: IoT has a significant impact on IT Capabilities.

3.2 The Relationship and Impact of the IoT and the E-Supply Chain Emerging global markets have led to different competition in the market. The IoT is a major development in information technology (H. M. Alzoubi, Ghazal, Kamrul Hasan, et al., 2022k). Nowadays, the IoT has a major impact on e-supply management (H. M. Alzoubi, Vij, Vij, et al., 2021h; Sobb et al., 2020). The SCOR (Supply-chain operations reference) model is a distinguished framework that helps to understand the supply chain in terms of the IoT (Eli, 2021). The IoT brings many capacities to sustain e-supply chain management in today’s market. Many researchers accept this SCOR model as it can link supply processes and certain performance metrics (Abdel-Basset et al., 2018; Amrani, A. Z., Urquia, I., & Vallespir, 2022). IoT technologies related to the e-supply chain include IT enablers, source, make and finally, deliver (H. M. Alzoubi, Hanaysha, et al., 2022i). IT enablers consider the information layer, transmission layers such as mobile networks, service layer and interface

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layer (Alshawabkeh et al., 2021). These are the pillars of IT enablers that impact the e-supply chain. The source is another essential process by which the companies acquire raw materials and other services (H. M. Alzoubi, Ghazal, Hasan, et al., 2022k; Saad Masood Butt, 2022). A successful e-supply chain organizes its sourcing ventures with good strategies in the e-supply chain (Nuseir et al., 2021). The manufacturing step refers to the manufacturing process of items. Recently, manufacturing companies have been inclined to adopt automation systems (H. M. Alzoubi, Zafar, Zhilin, et al., 2022q). The final process is about delivering the manufactured products, mainly related to logistics. Van der Westhuizen and Niemann (2022) pointed out that potential logistics planning involves the smooth flow of products and services to consumers (Akhtar, A., Bakhtawar, B., & Akhtar, 2022). The e-supply chain and the IoT are now interlinked. Without the right IoT services, the e-supply chain will likely be affected, as this is an important aspect of information technology. The identified relationship helps to prove this research hypothesis developed for empirical investigation. H1: IoT has a significant impact on the E-supply chain.

3.3 The Relationship and Impact of E-Supply Chain and Information Technology Capabilities The business world has transformed into a digital economy. Information technology has brought various aspects to the business and marketing world. The dynamics have drastically changed in recent years with the blessing of information technologies (H. M. Alzoubi, Ghazal, Ali, et al., 2021e). Companies now primarily rely on their information technology capabilities to improve their business (M. N. Alolayyan et al., 2022b; Nuseir, 2019). Information technology is all about data management related to the company’s business. Basheer et al. (2016) stated that this information gradually leads to automation control (Ghazal et al., 2022; John Kasem & Anwar Al-Gasaymeh, 2022). Information technology also helps in sharing data in the industry (H. M. Alzoubi, El Khatib, Ahmed, et al., 2022g). As the progression continues with the help of information technology capabilities, the e-supply chain begins changing the process of resource allocation and control between organisations and even industries (H. M. Alzoubi, Elrehail, et al., 2022h). The principles of e-supply chain management also heavily depend on the company’s information technology capabilities (Aljumah, Shahroor, et al., 2022b; Miller, 2021). In their article, Agyabeng-Mensah et al. (2019) assert that the e-supply chain is certain business activities that incorporate electronic business strategies into the supply chain processes (Kashif et al., 2021). Depending on the data processed by the information technology and its analysis, a company decides how to upgrade its e-supply management process, which is the main pillar of an organisation in the

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globalised era of technology (H. M. Alzoubi, Ghazal, Ali, et al., 2022i). The highlighted literature identifies the positive impact of IoT on IT capabilities that supports this research hypothesis. H3: E-Supply Chain has a significant impact on IT Capabilities.

3.4 The Relationship and Impact of the IoT and E-Supply Chain with the Mediating Role of Information Technology Capabilities The IoT and e-supply chain are two major and comparatively new aspects in this era of significant globalisation. The IoT, as discussed earlier, is a fruitful product of information technology capabilities. Similarly, the e-supply chain dramatically depends on a company’s information technology capabilities (G. Ahmed & Nabeel Al Amiri, 2022; Neyara Radwan, 2022). Information technology plays a mediating role in the impact of the IoT on the e-supply chain. Taghipour, Huang et al., (2021) opined that IoT is a revolutionary aspect of every major industry (M. Alzoubi, Hanaysha, et al., 2021). The IoT shows its real potential in the domain of the e-supply chain. Tracking and surveilling are two primary aspects of e-supply management regarding IoT deployment. These two objectives enable a company’s management to keep track of the cargoes and supplies. With the help of information technology capabilities, managing the major companies’ tracks and the IoT is a boon in this domain (Qasaimeh & Jaradeh, 2022). (Maged Farouk, 2022) highlight that real-time tracking of location, monitoring storage conditions, predicting the arrival and movement of the products, locating goods and materials in warehouses and improving contingency planning are some major factors that enable companies to use IOT technology in the e-supply chain (H. M. Alzoubi & Aziz, 2021; Nada Ratkovic, 2022). It is impossible without the information technology capabilities, which are a boon and godsend, acting the role of mediator. Based on previous studies, the identified relationship supports this research hypothesis. H4: IoT significantly impacts E-supply Chain with the mediating role of Information Technology capabilities.

3.5 Problem Statement and Research Gab The globalisation of the market and production due to the globalisation of the automotive sector, not only the business itself is managed, but also the consumers, suppliers, and other related resources. However, manufacturing companies use the Internet to establish a virtual extended supply chain to meet these demands. Electronic commerce solutions are one way to manage cross-border supply chain activities,

Impact of the Internet of Things (IoT) on the E-Supply Chain …

H1

Information Technology Capabilities

417

H3

H4

Internet of Things

E-Supply Chain H2

Fig. 1 Research model

which also support the reengineering of supply chain processes for the business. There is still a need to investigate the requirements of IoT and analyse the IT capabilities for the e-supply chain in the automotive industry. There are few studies that evaluate the results of implementing IoT and IT Capabilities. Therefore, this research will fill the gap identified by previous research (Bose et al., 2022).

3.6 General Research Model See Fig. 1.

3.7 Research Hypothesis HO1 : The Internet of Things has a significant impact on Information Technology Capabilities in the UAE Automotive Industry at (α ≤ 0.05) level. HO2 : The Internet of Things has a significant impact on the e-Supply Chain in the UAE Automotive Industry at (α ≤ 0.05) level. HO3 : Information Technology Capabilities have a significant impact on the e-Supply Chain in the UAE Automotive Industry at (α ≤ 0.05) level. HO4 : The Internet of Things has a significant impact on the e-Supply Chain with the mediating effect of Information Technology Capabilities in the UAE Automotive Industry at (α ≤ 0.05) level.

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3.8 Research Methodology and Design As used by past studies (Ali et al., 2021; Perumal et al., 2021), To assess the research model with the three variables of IoT, ITC and E-supply chain, a quantitative research technique with analytical, descriptive, causal and exploratory research design was used. In order to determine the population, an appropriate sampling technique was used, and an online survey was developed to collect primary data for empirical evidence. Literature was primarily used to conceptualise and prove relationships.

3.9 Population, Sample and Unit of Analysis The automotive industry is the target population of the proposed research. Thirtyeight of the largest automotive companies based in Dubai were selected as the research sample. Two hundred and seventy-eight respondents with accurate data were used for the analysis. In contrast, the online questionnaire (Ali et al., 2020; Jabeen & Jawad Ali, 2022) was distributed through emails to the (IT managers, marketing and SM developers, SC managers and financial managers) to gather relevant data. The online questionnaire was developed with a 24-items scale using a five-point Likert scale. From 5–1-Strongly Disagree to Strongly Agree. The IoT was assessed with 8 items, ITC with 7 items, and ESC with a 9-item scale. Demographic details were recorded with “Gender” and “Designation”.

4 Data Analysis 4.1 Demographic Analysis The demographic data indicates a higher number of male participants, 171 (72.5%) and a lower number of female participants, 65 (27.5%) and a high number of participants designated as IT managers of automotive companies, 110 (46.6%) (Table 1).

4.2 Reliability, Descriptive and Correlation An initial test to analyse the reliability of the data was performed using Cronbach’s Alpha for IoT = 0.86, IT Capabilities = 0.88, and E-supply chain = 0.84, which indicated high data reliability. The descriptive statistics show the mean value for IoT (M = 2.77 and SD = 81%), for IT Capabilities (M = 3.01 and SD = 72%), and the

Impact of the Internet of Things (IoT) on the E-Supply Chain … Table 1 The study’s demographical characteristics

419

Items

Description

F

%

Gender

Male

171

72.5

Female

65

27.5

IT manager

110

46.6

Marketing and SM developers

73

30.9

SC manager

29

12.3

Financial manager

24

10.2

Job status

N = 236 Table 2 The constructs correlation and reliability Construct

No. of items

Cronbach’s Alpha

Mean

S.D

Internet of Things

IT capabilities

Internet of Things

8

0.86

2.77

0.81

1

IT capabilities

9

0.88

3.01

0.72

0.830**

1

E-supply chain

7

0.84

2.89

0.88

0.749**

0.831**

E-supply chain

1

Level of significance P < 0.05**

values for E-supply chain show (M = 2.89 and SD = 88%). Table 2 illustrates the data summary. Table 2 shows the results of the correlation coefficients indicating a positive, highly correlated relationship between IoT and IT Capabilities r = 0.830**, IoT has a significant positive relationship with E-supply chain with a high correlation r = 0.749** and the relationship between IT Capabilities and E-supply chain is highly correlated with a significant impact r = 0.831**. All values are significantly based on the P < 0.05 level.

4.3 Multiple Regression and Hypothesis Testing See Table 3.

5 Discussion of the Results The above analysis depicts a significant relationship between the Internet of Things (IoT) and IT Capabilities (ITC) at the level (β = 0.83, P = 0.000, t-stat = 0.22.78), a significant positive relationship with variance R2 = 68%. The empirical evidence

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Table 3 The hypotheses testing Hypothesis

Regression weights

β

R2

Adjusted R2

p-value

t-value

Hypothesis supported

H1

IoT → ITC

0.830

0.689

0.688

0.000

22.78

Yes

H2

IoT → ESC

0.749

0.560

0.558

0.000

2.94

Yes

H3

ITC → ESC

0.831

0.690

0.563

0.000

10.49

Yes

H4

IoT*ITC → ESC

0.838

0.701

0.699

0.000

6.33

IoT = Internet of Things, ITC = Information Technology Capabilities, ESC = S-supply Chain, Dependent Variable = E-Supply Chain, *Level of Significance (α ≤ 0.05) **Critical t-value (df /p) = 1.64

support H1. The authors acknowledged that the IoT is widely considered an emerging paradigm in the field of information technology that has recently changed how businesses are conducted (Kirk, 2015). A significant relationship between IoT and E-supply chain is empirically confirmed (β = 0.74, p = 0.000, t-stat = 2.94) with a variance prediction of R2 = 56%, supporting H2. The literature indicates that as an essential component of information technology, the e-supply chain will likely be impacted without the appropriate IOT services (Abdel-Basset et al., 2018). Additionally, the impact of IT capabilities on the e-supply chain is significant (β = 0.83, p = 0.000, t-stat = 10.49) with an overall variance of R2 = 69%. The results support H3. Various researches have found that an organisation makes decisions and continuously improves its e-supply management process, which plays a key role in the globalised era of technology, by relying on and evaluating the data processed by information technology (Agyabeng-Mensah et al., 2019). The data findings show that the relationship between IoT and the E-supply chain with the mediating effect of IT capabilities is significant (β = 0.83, p = 0.000, t-stat = 6.33), with a high variance between the predicted variables as R2 = 70%. The indirect positive impact of the mediating variable (ITC) on IoT and ESC emphasises the information technology as necessary for successful IoT implementation and Esupply chain in the automotive industry through a variety of identification codes, automatic identification, data collection, and wireless sensor networks, and the IoT enables connectivity between different electronic devices, sensors, and machines. Cloud computing could make this information instantly accessible to all supply chain participants. These research findings can contribute to the literature and provide important insights for future research.

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6 Conclusion In conclusion, IT and IoT play a significant role in implementing an advanced esupply chain. Significant efforts have been made to digitise the automobile sector; nevertheless, connecting manufacturers to larger supply chain operations is the next stage in digitising automotive production. Although not explicitly stated, it underscores the importance of leveraging IoT and information technology to significantly increase the effectiveness and efficiency of automotive supply chains.

7 Recommendations/Limitations This research is limited to a spatial perspective, focusing on a specific geographic location and could benefit from a larger sample size. The data was only collected from UAE automotive companies based in Dubai, where there has been some development in technology adoption. It would be interesting to broaden the geographic focus of the study to include the perspectives of the automobile industry in developing and underdeveloped countries.

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The Impact of Social Media Marketing on Online Buying Behavior via the Mediating Role of Customer Perception: Evidence from the Abu Dhabi Retail Industry Barween Al Kurdi , Mohammed T. Nuseir , Muhammad Turki Alshurideh , Haitham M. Alzoubi , Ahmad AlHamad , and Samer Hamadneh Abstract This empirical investigation aimed to examine social media marketing’s impact on online buying behavior via its mediating effect on customer perception in the Abu Dhabi retail industry in UAE. A descriptive, exploratory, causal, and analytical design was used with a quantitative technique and practical cluster sampling. The empirical analysis showed the importance of customer perception as the mediator in the model. As a result, this research proposed the development of an exceptional retailer-customer relationship. For retailers, that can contribute to the literature and future research-related retail industry in UAE. Regression and hypothesis testing were employed to statistically analysis a sample of 266 respondents working in Abu B. Al Kurdi Department of Marketing, Faculty of Business, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan e-mail: [email protected] M. T. Nuseir Department of Business Administration, College of Business, Al Ain University, P.O. Box 112612, Abu Dhabi Campus, Abu Dhabi, UAE e-mail: [email protected] M. T. Alshurideh (B) · S. Hamadneh Department of Marketing, School of Business, The University of Jordan, Amman 11942, Jordan e-mail: [email protected]; [email protected] S. Hamadneh e-mail: [email protected] H. M. Alzoubi School of Business, Skyline University College, Sharjah, UAE e-mail: [email protected] Applied Science Research Center, Applied Science Private University, Amman 11937, Jordan M. T. Alshurideh · A. AlHamad Department of Management, College of Business Administration, University of Sharjah, Sharjah 27272, UAE e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 H. M. Alzoubi et al. (eds.), Cyber Security Impact on Digitalization and Business Intelligence, Studies in Big Data 117, https://doi.org/10.1007/978-3-031-31801-6_26

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Dhabi retail stores. The findings revealed both the direct and significant impact of social media marketing and the indirect and significant mediating effect of customer perception on online buying behavior. Unidimensional model measurement is noted as a limitation of the research. Future research is recommended to explore a multidimensional model with a deep analysis of the same construct. Due to the large user bases of common social media sites like Facebook, Twitter, Instagram, and YouTube, businesses will likely discover many customers and followers online. Retailers need to be flexible and consider this when establishing appropriate strategies to improve social media usage to boost sales; it is crucial to concentrate on elements of design for both store and user experience. Keywords Social media marketing · Customer perception · Online buying behavior · Retail industry in UAE

1 Introduction E-commerce is one of the critical concerns in business today. E-commerce converts business practices and old marketplaces into new forms, enhances communication, and promotes open trade on a national and international scale (El Refae et al., 2021; Nuseir et al., 2021; Sami Alkalha et al., 2012). E-shopping is an innovative purchase strategy with many benefits and is now used for the vast majority of transactions worldwide (Al Dmour et al., 2014; Al-Khayyala et al., 2020; Mashaqi et al., 2020). The advantages of online shopping for businesses include business growth, increased sales and income, cost savings, an indirect relationship between buyer and seller, increased transaction speed, lower advertising costs, and relationships with customers or other businesspeople (Al-Khayyal et al., 2020; Alzoubi et al., 2021; Alzoubi et al., 2022a, 2022b, 2022c, 2022d). It is also available at any time and location. By contrast, the growing trend of social media marketing significantly impacts consumer propensity for online purchasing (Al Kurdi et al., 2021; Alshurideh et al., 2019). Communication between consumers and marketers has evolved due to the internet, mainly social media, as it offers unique features for companies and consumers to achieve satisfaction (Aljumah et al., 2021; Al-Maroof et al., 2021; Almazrouei et al., 2020; Alzoubi et al., 2022e, 2022f, 2022g, 2022h). Additionally, when consuming a product or using a service, customer perception of a company essentially derives from their opinion about such items or services (Al Khasawneh et al., 2021a; Zhang & Yu, 2020). Additionally, it generates a summary that gives businesses insight into how their clients view their goods and services (Al Alshurideh, 2022; Khasawneh et al., 2021b). Therefore, businesses can use consumer perception feedback to address any flaws in their goods or services to improve them (Alzoubi et al., 2020a, 2020b, 2020c; Madi Odeh et al., 2021). For the analysis of consumer feedback, one consumer behavior is the act of making purchases online (Al-Dmour et al., 2021; Al Kurdi & Alshurideh, 2021). Customers use this when browsing an online store website to make specific purchases of services

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or commodities (Alzoubi et al., 2022i, 2022j, 2022k, 2022l, 2022m; Alzoubi et al., 2022r; Obeidat et al., 2019; Sweiss et al., 2021). Social media promotions could help organizations accomplish various marketing goals, including raising consumer awareness, enhancing their knowledge, modifying their perceptions, and inspiring them to make purchases (Lee et al., 2022; Tariq et al., 2022a, 2022b; Victoria, 2022). This process enables a business to grow and create a better customer purchasing experience (Alshurideh et al., 2012; Tariq et al., 2022a, 2022b). Therefore, this research focused on empirically analyzing the retail industry in UAE to investigate the relationship provided in the literature and authenticate the significance of social media marketing for online buying behavior as mediated by the impact of customer perception.

2 Theoretical Framework 2.1 Social Media Marketing Globally, social media and networking websites have grown in recent years. One example is Facebook, which reportedly has more than a billion active members (Alzoubi et al., 2022n, 2022o; Kimiagari & Malafe, 2021). Peer interaction on social media, an advanced customer socialization method, significantly influences customer choices and marketing tactics (Eli et al., 2022; Ghazal et al., 2022). According to consumer socialization theory, consumer interaction affects consumer cognitive, affective, and behavioral perspectives (Alalwan, 2018; Alshurideh, 2019). Social media has altered traditional marketing strategies like advertising and promotion with its distinctive features and enormous popularity (Alkitbi et al., 2020; Alshurideh, 2016). Social media has also changed how consumers behave, from how they gather information to how they behave after making purchases, including how they use the internet and express their discontent (Ahmad et al., 2021; Alwan & Alshurideh, 2022a, 2022b; Hammad et al., 2022).

2.2 Customer Perception Attitudes, beliefs, and prior experiences form the foundation for customer perception, which could be either favorable or unfavorable (Alzoubi et al., 2022o, 2022p; Ghouri et al., 2017), depending on the customer experience (Alzoubi et al., 2022q; Kasem & Al-Gasaymeh, 2022). Consumer perception is essential in online purchasing since a customer with a negative experience will probably not choose to shop online or from the same company again (Alshurideh, 2014; Alwan & Alshurideh, 2022a, 2022b). Once the customer’s need is identified, their perception of a product can be easily understood, and the service provider can offer the product (Alketbi et al., 2020; Pütter,

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2017). It is essential to detect a consumer’s previous purchasing experience because of its enormous impact on businesses (Alshurideh et al., 2020; Alzoubi et al., 2022a, 2022k). Suppose retailers are unable to recognize their customer’s needs (Alshurideh et al., 2022; Alzoubi et al., 2022i). In that case, things might go awry for them as the company might not be able to enhance its profits and sales in the market or best utilize social media marketing for business growth.

2.3 Online Buying Behavior Because of ubiquity and convenience, online purchasing behavior has drawn analysis. Online buying has changed the way businesses operate; they have advanced thanks to the usage of technology (Croes & Bartels, 2021; Kurdi et al., 2020). Because customers have a wide range of options while shopping online, their behavior is crucial (Alzoubi et al., 2022f; Qasaimeh & Jaradeh, 2022). The key to effective leadership is to observe client behavior and identify their pressure spots (Alameeri et al., 2020; Alzoubi & Ramakrishna, 2022; Harahsheh et al., 2021; Kashif et al., 2021). The perfect strategies used to target an audience through social media marketing can always encourage sales and result in positive consumer buying behavior. With the help of social media, online behavior is continually stimulated, which motivates people to shop online.

2.4 Operational Definitions

Variables

Definition

References

Social media marketing Social media marketing (SMM) uses social networking sites to generate more leads for your business and brand exposure. Social networking sites with the most prevalent social media marketing are Facebook, Instagram, LinkedIn, Twitter, Pinterest, Tumblr, and Snapchat

(Alalwan, 2018)

Customer perception

(Jiang et al., 2013)

Customer opinions, feelings, and presumptions about a brand are referred to as customer perception. It is essential for boosting customer retention, loyalty, and brand reputation

Online buying behavior Online shopping behavior refers to how consumers (Joshi et al., 2018) use the internet to find, choose, purchase, use, and dispose of goods and services. Because people mostly find it convenient and easy to compare costs from the comfort of their home or office, online shopping has grown in popularity over time

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3 Literature Review 3.1 The Relationship and Impact of Social Media Marketing on Customer Perception Social media platforms give businesses the chance to engage and communicate with both current and potential clients, foster deeper familiarity with client relationships, and develop the critical meaningful relationships with clients so essential in today’s business environment (Alzoubi et al., 2019). Client loyalty can vanish at the slightest mistake, and negative experiences with a company can be widely exposed online. Consumer perception of online shopping and vendors was examined by (Alzoubi et al., 2022e, 2022q; Zafar et al., 2021), who found that those perceptions appeared to be based on underlying aspects of control and convenience, trust and security, affordability, ease of use, and effort/responsiveness (Alzoubi et al., 2022a; Miller, 2021). Using these dimensions as a segmentation base, seven categories can be identified: the unconvinced, the security conscious, the undecided, the convinced, the complexity avoiders, the cost-conscious, and the apprehensive customer service (Alzoubi et al., 2022c, 2022f, 2022h; Vakulenko et al., 2019). Social Media Marketing engages customers in the attractive strategy of advertisement, which enables them to buy online (Alzoubi et al., 2022k). Providing a good quality product can produce returning buyers and generate positive feedback for business development (Eli, 2021; Alzoubi et al., 2021a, 2021b). Investigation of social media marketing has shown that it can influence a customer’s intention to buy (Chen & Lin, 2019). Businesses worldwide spend much time and money on social media platforms to advertise their products, so concerns about the viability of their efforts and the potential to draw in new customers are always present (Akhtar et al., 2021). Based on the above discussion, we propose the following hypothesis: H1: Social Media Marketing positively and significantly impacts customer perception.

3.2 The Relationship and Impact of Social Media Marketing on Online Buying Behavior When purchasing something online, customers frequently make snap selections. Online shoppers benefit from easy product availability, simple purchasing, a lack of peer pressure, and no delivery effort. Therefore, impulsive purchases account for around 40% of all internet purchases (Voramontri & Klieb, 2019). Due to the rapid development of social commerce, users of social media platforms like Facebook, Twitter, and Pinterest can now easily make unplanned or unnecessary purchases while viewing posts on these platforms. On these networks, shoppers can discover intriguing ties to online retailers (Alzoubi & Aziz, 2021). Impulsive purchasing

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is inevitable in these situations, particularly regarding social commerce (Ebrahim, 2020; Mehmood, 2021). Surprisingly, little attention is paid to the existing knowledge in the literature on social commerce. From a service provider’s perspective, social media marketing significant affects online purchasing behavior (Alzoubi et al., 2022q). The business is aware of its customer’s perceptions and decision-making process; detecting consumer demand via artificial intelligence can increase sales (Alzoubi et al., 2021a, 2021b). Increased sales result in higher earnings for the retailer, which helps business development and customer propensity to buy a product in the future (Alsharari, 2021). Moreover, (Meyer et al., 2018) demonstrated how marketing strategy and consumer behavior are related. They claim strategy is about boosting the likelihood and regularity of consumer activity (Ghazal et al., 2021a, 2021b). Understanding the needs and wants of the customer is essential for success in this endeavor (Alzoubi et al., 2022d, 2022f, 2022j). On the other side, the expectation-confirmation model focuses on post-purchase behavior (Alsharari, 2022). Research on consumer behavior is frequently employed to explain consumer pleasure and repeat purchases (Ghosh & Aithal, 2022). The essence of this paradigm is the satisfaction created by the discrepancy between expectation and experienced performance (Kimiagari & Asadi Malafe, 2021). Based on the above discussion, we propose the following hypothesis: H2: Social Media Marketing positively and significantly impacts online buying behavior.

3.3 The Relationship with and Impact of Customer Perception on Online Buying Behavior Building perception is essential in the e-commerce industry. Because customer perception and online shopping are closely tangled, service providers engage with clients and help them make informed decisions when purchasing at their stores or locations (Ahmed & Amiri, 2022; Alzoubi et al., 2022). This is crucial in the retail industry because e-commerce relies on customers being able to interact with brands via digital media, having their awareness raised, and routinely making their own product choices (Alzoubi & Yanamandra, 2020). Helping customers increases retail revenue, and earnings give firms a competitive advantage over their rivals. (Alzoubi, 2022; Goria, 2022; Jiang et al., 2013) stated that customer attitudes toward online buying are positively impacted by the perceived convenience provided by internet vendors because they view the internet as a tool that efficiently improves the results of their shopping experiences (Ratkovic, 2022; Nasim et al., 2022). Youth marketers have many opportunities for online sales, as youth are the critical consumers who have historically used the internet to purchase goods, according to (Alzoubi et al., 2017; Farouk, 2022; Zhang & Yu, 2020). Some authors looked at the connection between age and online purchasing and discovered that younger consumers reported doing more of their shopping online (Croes & Bartels, 2021). Additionally, they

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discovered that younger customers did more online product searches and were more inclined to concur that purchasing online was more practical. Based on the above discussion, we propose the following hypothesis: H3: Customer perception positively and significantly impacts online buying behavior.

3.4 The Relationship and Impact of Social Media Marketing on Online Buying Behavior with Mediating Role of Customer Perception Meyer et al., (2018) evaluated the quality of relationships between social media marketing (SMM) and online buying behavior based on customer perception (CP). They found that the online shopper behavior is influenced by CP and SMM (Radwan, 2022). The rationale is that a service provider who comprehends a customer’s use of social media may quickly engender an opinion and thereby establish and maintain a relationship with the consumer (Alzoubi & Ahmed, 2019). Because the quality of the service and the business must be maintained and conducted over several online platforms, relationships are crucial in e-commerce (Alzoubi et al., 2020a; Amrani et al., 2022). A proper and long-lasting relationship can be created and maintained if the service provider demonstrates empathy and completely comprehends the reasoning behind the customer’s purchasing behavior (Del & Solfa, 2022; Mondol, 2022). Empathy is the most crucial component of healthy partnerships since it enables business owners and clients to forge (Kimiagari & Asadi Malafe, 2021) that partnership. Suppose the business owner (marketer) cannot understand their customers’ buying patterns. In that case, the business might fail numerous times because the most important thing is to understand how the customers will react in the future and identify their reasoning. Researchers believe that SSM has greatly impacted the e-commerce industry (Alzoubi et al., 2020; Butt, 2022). Thus, service providers must maintain that healthy relationship by understanding the concept of SMM. Social media is beneficial because it is less expensive to use than developing offline marketing campaigns. Because many people utilize social media, getting new clients is also simpler (Alzoubi et al., 2021a, 2021b). Additionally, networks that share content, like Facebook, rely on producing and disseminating various online tools, like blogs, social media posts, and videos (Akhtar et al., 2022). These initiatives aim to draw potential clients and inform them about available goods or services. Based on the above discussion, we propose the following hypothesis: H4: Social media marketing positively and significantly impacts online buying behavior via the mediating role of customer perception.

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H1

Customer Perception

H3

H4 Social Media Marketing

Online Buying Behavior H2

Fig. 1 Conceptual research model

3.5 Problem Statement and Research Gap Because online buying has become incredibly popular, it is vital to identify the most significant research gap from the past ten years, with some problems still to be investigated (Hyun et al., 2022). To explore some of the factors identified in the gap, this paper dedicated research to empirically investigate SMM’s impact on online buying behavior (OBB) via the mediating effect of CP. While SMM enables people to purchase from home, there is a need to discover the opportunities and impacts of SMM on online purchasing and cover the previously identified gap. This research will examine CP’s mediating of social media marketing and online buying behavior.

3.6 General Research Model See Fig. 1.

3.7 Research Hypotheses H1: Social Media Marketing positively and significantly impacts Customer Perception in the Retail Industry UAE at (α ≤ 0.05) level. H2: Social Media Marketing positively and significantly impacts Online Buying Behavior in the Retail Industry UAE at (α ≤ 0.05) level. H3: Customer Perception positively and significantly impacts Online Buying Behavior in the Retail Industry UAE at (α ≤ 0.05) level.

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H4: Social Media Marketing positively and significantly impacts Online Buying Behavior via the mediating effect of Customer Perception in the Retail Industry UAE at (α ≤ 0.05) level.

3.8 Research Methodology and Design To discover the empirical findings, the research was used to assess the variables “Social Media Marketing,” “Customer Perception,” and “Online Buying Behavior.” The research survey was assessed with a quantitative research technique and exploratory, causal, and analytical research methods. The sampling technique was chosen for convenient cluster sampling. An online survey was conducted aimed at gathering primary data.

3.9 Population, Sample and Unit of Analysis The population of the research was the retail industry in UAE. The sample contains the data of 44 retail superstores located in Abu Dhabi, UAE. The total sample size for data analysis consisted of 266 respondents after screening the data from 550 responses. The questionnaire was developed using a 5 point Likert scale ranging from strongly agree to strongly disagree. A questionnaire containing 24 items was used to assess the variables respectively. Seven items were used to assess Social Media Marketing, 10 items to assess Customer Perception, and 7 items were used to assess Online Buying Behavior.

4 Data Analysis 4.1 Demographic Analysis The participant demographic information from the online survey is shown in Table 1. According to the data, there were 182 male and 84 female respondents out of 266 respondents. Those who contributed the most were 98 IT managers.

4.2 Reliability, Descriptive & Correlation The pilot testing for the data gathered evaluated the validity of the data and showed Cronbach Alpha’s value as = 0.85 for social media marketing, = 0.86 for customer

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Table1 Participant’s demographic information

Items

Description

f

%

Gender

Male

182

68.4

Female

84

31.6

Job status

IT manager

98

36.8

SC manager

78

29.3

Sales manager

33

12.4

Retail marketer

57

21.4

N = 266, Male = 182 and female = 84

Table2 Reliability, descriptive analysis and correlation coefficients Construct

No of items

Cronbach’s alpha

Mean

SD

Social media marketing

Customer perception

Social media marketing

7

0.85

3.18

0.72

1

Customer perception

10

0.86

3.02

0.61

0.807**

1

7

0.85

3.82

0.70

0.771**

0.822**

Online buying behavior

Online buying behavior

1

Social Media Marketing (M = 3.18, SD = 72%, Customer Perception M = 3.02, SD = 61%, Online Buying Behavior M = 3.82, SD = 70%) Level of significance at P < 0.05**

perception, and = 0.85 for online buying behavior, indicating good reliability. The descriptive analysis had the mean results for SMM as M = 3.18 and SD = 0.72, the mean for CP = 3.02 and SD = 0.61, and the mean value predicted for OBB = 3.82 & SD = 0.70, respectively. Table 2 shows the correlation coefficients for the relationship of SMM with CP as highly correlated with r = 0.807 and positively significant at P < 0.05**. The relationship between SMM and OBB is highly correlated with r = 0.771 and positively significant at level P < 0.05**. Lastly, the relationship between OBB and CP has a high correlation with a significant relationship r = 0.822 at P < 0.05**.

4.3 Regression Analysis and Hypothesis Testing The hypothetical model was analyzed in this research to authenticate the developed hypothesis through statistical results. H1 describes the relationship of SMM with CP and shows a significant relationship with β = 0.807** at level P < 0.05, t = 22.16,

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Table3 Linear regression and hypothesis testing through ANOVA Hypothesis

Regression weights

Standardized coefficients β

R2

Adjusted R2

Sig

t-value

Hypothesis supported

H1

SMM → CP

0.807

0.651

0.649

0.000

22.16

Yes

H2

SMM → OBB

0.771

0.595

0.593

0.000

5.48

Yes

H3

CP → OBB

0.822

0.676

0.673

0.000

10.20

Yes

H4

SMM*CP → OBB

0.842

0.710

0.707

0.000

8.46

Yes

Dependent variable = Online Buying Behavior *Level of Significance (α ≤ 0.05**) **Critical t-value (df/p) = 1.64

and variance predicted as R2 = 65%. The statistical findings illustrate a significant positive relationship between the variables. Secondly, H2 describes the relationship between SMM and OBB as β = 0.771**, P < 0.05 and t = 5.48 with the variance noted R2 = 59%. H3 describes the relationship between CP and OBB as positively significant at β = 0.822**, P < 0.05 and t = 10.20 and noted a high variance R2 = 67%. H4 describes the relationship of SMM and OBB with the mediating effect of CP being positively significant at β = 0.842**, P < 0.05, t = 8.46, and a high variance prediction of R2 = 71%.

5 Discussion of the Results To ensure robust research findings, it is confirmed that these findings are analogous to the previously investigated research that verifies a positive relationship of SMM with CP. According to the earlier literature, SMM involves the customers in an enticing strategy of advertisement that enables them to make an online purchase. By offering a high-quality product, businesses can increase their chances of customers returning and positive customer reviews (Vinerean et al., 2013). The findings supported the current hypothesis H1. The measurement of H2 revealed the significant positive relationship between SMM and OBB. Several authors argued that retailers need to be aware of the variables influencing CP. They must grasp the e-commerce consumer influence on online behaviors of comfort and content (including atmosphere, information, product display, and featured content) (Voramontri & Klieb, 2019). The next step was to determine whether CP acts as a mediator between SMM and OBB. The mediation analysis for H4 supported that perspective about CP. After all, it identified that a service provider might quickly form an opinion about a customer and, based on that view, establish and sustain a relationship with that customer by understanding how they use social media (Voramontri & Klieb, 2019). The empirical data findings revealed a strong and significant relationship between a model construct supporting the past literature and an outstanding contribution to the literature and future research.

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6 Conclusion The current research results evaluated well-understood study fields and are conducive to investigating their novel potential. The positive relationship between SMM and OBB indicated that excessive internet use enables most people to buy online from the comfort of their homes. According to the research survey, most customers are motivated by social media ads and marketing that attract and enable them to purchase. Additionally, it can be said that effective SMM can enhance sales and business development by creating a positive CP. Another consequence is the receipt of positive consumer feedback. Exploring and improving SMM strategies can thus assist in achieving a business’s goals.

7 Recommendations/Limitations This study offers several contributions but has a few restrictions. First, one might contend that flows are multidimensional constructs, whereas this research was focused on a single aspect of each construct. It is recommended for future research to profoundly investigate the factors influencing SMM that expand business and business strategies. Secondly, this research covered the retailer’s responses; future research is recommended to investigate customer responses of the influence to SMM on buying decisions.

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Impact of Supply Chain 4.0 on Operations Performance with the Mediating Role of Innovation Capabilities: Evidence from the UAE Computer Hardware Industry Ayman Abu-Rumman , Haitham M. Alzoubi , Ata Al Shraah , Muhammad Turki Alshurideh , Barween Al Kurdi , and Ahmad AlHamad Abstract The purpose is to investigate the impact of supply chain 4.0 on operations performance with a mediating effect of innovation capabilities. Empirical evidence was collected from the UAE computer hardware industry. The adoption of Industry 4.0, including its technological transformation’s drivers and inhibitions, is examined in terms of its effect on operations performance through model analysis. A new conceptual framework is investigated for the deployment of Industry 4.0 in supply chains, giving an empirical foundation for this relationship in the UAE computer and hardware industry. To validate the empirical evidence, a quantitative technique was used for the research containing exploratory, causal, and analytical design. A sample A. Abu-Rumman Department of Business Administration, Business School, Al Ahliyya Amman University, Amman, Jordan H. M. Alzoubi School of Business, Skyline University College, Sharjah, UAE e-mail: [email protected] Applied Science Research Center, Applied Science Private University, Amman 11937, Jordan A. Al Shraah Department of Business Administration, Faculty of Economics and Administrative Sciences, The Hashemite University, Zarqa, Jordan e-mail: [email protected] M. T. Alshurideh (B) Department of Marketing, School of Business, The University of Jordan, Amman 11942, Jordan e-mail: [email protected] B. Al Kurdi Department of Marketing, Faculty of Business, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan e-mail: [email protected] A. AlHamad Department of Management, College of Business, University of Sharjah, Sharjah 27272, UAE e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 H. M. Alzoubi et al. (eds.), Cyber Security Impact on Digitalization and Business Intelligence, Studies in Big Data 117, https://doi.org/10.1007/978-3-031-31801-6_27

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of 259 respondents was employed for statistical analysis using SPSS. A significant correlation with operations performance was verified for supply chain 4.0, and a substantial correlation was verified for the indirect influence of innovative skills. The single construct limits the research, which future research with a dimensional approach can counter. The primary advantage for manufacturers is to create cuttingedge supply chain 4.0 strategies that will boost productivity by utilizing IoT, robotics, and advanced technological aspects to compete in the market and enhance operations performance. Keywords Supply chain 4.0 · Operations performance · Innovation capabilities · UAE computer hardware industry

1 Introduction Cognitive automation is the basis of the supply chain 4.0, or the fourth revolution, Industry 4.0, implemented in a company. Implementing digital technology enables a firm to make better decisions and achieve collaboration (Al-Khayyal et al., 2020; Ghazal et al., 2021a, 2021b, 2021c, 2021d). Collaboration allows it to work collectively on specific tasks and achieve better efficiency and productivity (Alzoubi et al., 2022a). Introducing this advanced technology provides it with beneficial and efficient management that adds value to the company (Alyammahi et al., 2020; Madi Odeh et al., 2021). The role of supply chain 4.0 enhances productivity in the company’s industry by introducing advanced technology that helps to analyze databases and make active and practical decisions that support the company’s growth (Frederico et al., 2020). Additionally, the operations performance role is to monitor the production process to maintain the standards for the organization and its goods (Alshurideh, 2022; Alzoubi et al., 2021a, 2021b, 2021c, 2021d, 2021e, 2021f). It can also decide the organization’s service operations as well as observe the trends, advanced techniques, and strategies for a more effective and active operations performance that will allow the company to add to its revenue (Battistoni et al., 2013). The operations performance ensures the tracking of the resources and databases, thereby controlling and directing the whole business sector to a level where advanced operational strategies can generate high revenues for the business (Alolayyan et al., 2022a, 2022b; Alshraideh et al., 2017; Alshurideh et al., 2022). For survival in this modern world, every business sector needs to be innovative, upto-date, and skilled, which, additionally, helps the community to advance. The role of innovation has both interior and exterior outcomes (Al Naqbi et al., 2020; Al Suwaidi et al., 2021; Altamony et al., 2012; Alzoubi et al., 2021a, 2021b, 2021c, 2021d, 2021e, 2021f). By advancing the company’s employees and methods, the company enhances the economic growth of both the company and its environment (Alzoubi et al. 2022; Ammari et al., 2017; Ghazal et al., 2021a, 2021b, 2021c, 2021d). It impacts the market value of the business and its products throughout the international

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market (Ferreira et al., 2021). Innovation necessitates not only human, mental, and resource innovation, organizational assets that lead to sky-high achievement these days (Almaazmi et al., 2020; Alzoubi et al. 2022; Kabrilyants et al., 2021). This research was conducted to gather empirical data from the UAE computer hardware industry. It extensively defines the relationship and significance of supply chain 4.0 on operations performance via the mediating role of innovation capabilities. This research aims to contribute to the literature and provide vast knowledge for industrialists and future researchers.

2 Theoretical Framework 2.1 Supply Chain 4.0 The supply chain performs a significant role in enhancing business for an organization by arranging the raw ingredients, transforming them into the final products, and distributing them to its customers (Alzoubi et al. 2022; Hamadneh et al., 2021). With technology’s advance, business sectors use supply chain 4.0, an independent variable for better improvement of the quality of their products and productivity (Cole et al., 2019; Ghazal et al., 2022). In order to survive in the competitive business sectors globally, it uses this to improve the design of its products and their quality, thereby increasing customer satisfaction internationally (Al-Zu’bi et al., 2012; Alzoubi et al., 2019; 2022; Ghannajeh et al., 2015). Supply chain 4.0 works independently in the organization. Applying new and advanced strategies advances the organization’s business models, which take it to the front line (Joghee et al., 2021; Shamout et al., 2022). Supply chain 4.0 helps solve the issues and challenges it faces during its growth. By introducing the Industry 4.0 system into supply chain management, a business enhances its resilience, which increases profits and makes the it grow, thereby building its ability to cope with new business challenges in a competitive business market (Alzoubi et al., 2021a, 2021b, 2021c, 2021d, 2021e, 2021f; Alzoubi & Ramakrishna, 2022). This advanced supply chain has introduced more accuracy and flexibility in business sectors as well as organizing and planning supply management system structures and models (Frederico et al., 2020; Hamadneh et al., 2021a, 2021b). The aim of applying this advanced technology is to turn the linear, traditional supply chain management system into an independent management system that takes care of the sourcing (Alzoubi et al., 2022), manufacturing, and quality of the products without compromising the profitable growth of the company or its customer satisfaction (Alshurideh et al., 2022; Alzoubi et al., 2022). Supply chain 4.0 develops the relationship between its organization and its customers by providing them with quality products and a suitable delivery system.

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2.2 Operations Performance Operations performance refers to an operation’s cost-reduction, which leads to a more profitable business while maintaining the quality of its goods (Alzoubi et al. 2022; Lee et al., 2022; Tariq et al., 2022). Operations performance enhances the products’ flexibility, scalability, and durability and reduces costs for the organization’s benefit (Alshurideh et al., 2021; Nahmens & Bindroo, 2011). Operations performance influences organizational business objectives in both the interior and exterior spheres (Alzoubi et al., 2022a, 2022b, 2022c, 2022d, 2022e, 2022f, 2022g, 2022h, 2022i, 2022j, 2022k, 2022m, 2022n). This dependent variable’s performance relies on the final products being of good quality, and consumer satisfaction for generating business profits (Alolayyan et al., 2022a, 2022b; Sami Alkalha et al., 2012). Furthermore, operations performance involves the entire organization’s work to achieve organizational goals and lead the business to the frontline (Alzoubi et al., 2021a, 2021b, 2021c, 2021d, 2021e, 2021f). Operations performance improves the service system by including business intelligence, strategies, data collection, and analysis of the processes that reduce the risk of loss and increase the chances of product availability and durability (Alameeri et al., 2021; Nuseir et al., 2021). To run a business in a highly competitive world, operations performance helps generate revenue with efficient supervisory skills (Nawanir et al., 2013). These can be developed by reviewing previous operations, producing an organized chart of upcoming projects, resolving issues, and regularly motivating employee performance (Alzoubi & Aziz, 2021). Improving skills and introducing advanced technologies avoids company fall backs.

2.3 Innovation Capabilities The capability to innovate new ideas, strategies, plans, and business models enhances the company’s chance of survival in the advanced technological world without any significant challenges (Herrera, 2016). Innovation helps the company make decisions that will advance its business strategies, ideas, and skills for making profits and reducing the manufacturing costs of its products (Ghazal et al., 2021a, 2021b, 2021c, 2021d). It is the process of renovating the business by adopting original, innovative concepts to achieve the business objectives and goals for a better future for the company (Alzoubi et al. 2022; Obeidat et al., 2021). Business has adopted the idea of innovation for motivating employees, keeping them updated and skilled in their work field, and thereby generating products of good quality and durability that satisfy their consumers (Alzoubi et al., 2021). Achieving that leads a company to expand its business in the local market and internationally. A business organization must have innovation capabilities for its methodology, productivity, and manufacturing process to increase revenue and achieve its business goals, commensurate with efficiently fulfilling its customers’ needs (Alzoubi et al.

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2020). The critical role of the innovation process is to remain relevant and updated in the competitive business market, so it can focus on the issues that become barriers to company success (Alzoubi et al., 2020a, 2020b). Moreover, innovation is the crucial factor that fulfils every individual’s needs while acknowledging the constant change in consumer needs and the competitive, innovative business world (Alzoubi & Ahmed, 2019; Puspita et al., 2020). Innovation in business includes anticipating market values, opening new opportunities for business growth, and remaining active and relevant in the current world.

2.4 Operational Definitions

Variables

Definition

Supply chain 4.0

Industry 4.0 technology like IoT (internet of Bag et al. (2019) things), AI (artificial intelligence), cloud, and big data are all included in the upgraded supply chain known as supply chain 4.0. It integrates cutting-edge artificial intelligence algorithms, business intelligence tools, data sciences, and other cutting-edge technology to improve supply chain management significantly

References

Operations performance Operations performance is best understood as Battistoni et al. (2013) the ability of several business units to work together more effectively and produce more output. In other words, all the company departments cooperate to achieve particular corporate goals Innovation capabilities

Innovation capability refers to a company’s capacity to recognize novel concepts and develop them into valuable new or improved goods, services, or procedures

Bag et al. (2020)

2.5 UAE Computer Hardware Industry The size of the global computer hardware market is projected to rise from $1129.39 billion in 2021 to $1215.76 billion in 2022 at a compound annual growth rate (CAGR) of 7.6%. The COVID-19 effect, which had previously led to restrictive containment measures like social isolation, remote work, and the closure of businesses that created operational difficulties, has primarily caused the market to expand as businesses restructure their operations and recover from it. The computer hardware market is projected to reach $1568.25 billion in 2026 at a CAGR of 6.6%.

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The computer hardware market comprises companies (organizations, sole proprietors, and partnerships) that sell computers, laptops, tablets, and storage devices for computers, peripheral equipment, and other types of computer hardware. Servers and processors are part of the computer hardware business, but embedded systems for autos and factories are not. These manufacturing companies require frequent innovation in production to achieve a competitive advantage. As a result, this industry has been selected for research to provide better information regarding supply chain 4.0, innovation capabilities, and their impact on operations performance.

3 Literature Review 3.1 Relationship and Impact of Supply Chain 4.0 on Innovation Capabilities A company’s innovation capabilities are vital to draw the attention of more customers by launching innovative products and upgrading existing ones for better sales and productivity (Alzoubi et al., 2020a, 2020b). Industries are struggling to balance ecological effects on the business and the welfare of the people and achieve cost benefits (Saetta & Caldarelli, 2020). Utilizing the technological growth and implementation of supply chain 4.0, companies often achieve better results and innovation in their business operations (Alzoubi et al., 2017). Companies can handle the overall performance of the supply chain by utilizing these technologies. These authors stated that the pressure of stakeholders and capabilities for implementing innovation is beneficial to creating circular economy practices (Alzoubi et al., 2022a, 2022b, 2022c, 2022d, 2022e, 2022f, 2022g, 2022h, 2022i, 2022j, 2022k, 2022m, 2022n). Company managers and leaders have to focus on the stakeholders’ requirements so that continuation of business processes is possible and achieved in the long run (Ahmed et al., 2021; Shamout, 2019). Implementing the supply of supply chain 4.0 beneficially affects the global network and improves factory set-up so that the company can exchange information and maintain control over its activities. Industry 4.0 effectively enhances customer experience so that the company can achieve better results from its business operations (Alzoubi & Yanamandra, 2020). Companies can make better decisions and maintain transparency leading to cost and manufacturing costs by implementing technological advancements in their businesses (Al-Madi et al., 2021; Ghazal et al., 2021a, 2021b, 2021c, 2021d). Their organizations maintain agility so that changes can be adopted and implemented quickly to achieve better results. According to several authors, robotics has been used widely in companies to obtain objectives and fulfill customer demands (Alzoubi et al., 2022a, 2022b, 2022c, 2022d, 2022e, 2022f, 2022g, 2022h, 2022i, 2022j, 2022k, 2022m, 2022n). However, the companies utilize manufacturing engineering, big data, 3D printing etc., to increase their efficiency in implementing

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innovation (Khatib et al. 2022). Using innovative processes is beneficial for facing disruptions and other issues in supply chain management (Ali & Xie, 2021). Furthermore, supply chain 4.0 relates to Industry 4.0 implemented in supply chain management in various industries. Supply chains directly impact their operations and enhance efficiency. The companies can maintain agility so that when their customers’ changing demands are identified they adapt to those changes and enhance productivity and profitability (Alzoubi et al., 2022a, 2022b, 2022c, 2022d, 2022e, 2022f, 2022g, 2022h, 2022i, 2022j, 2022k, 2022m, 2022n). Supply chain 4.0’s advanced technologies, including blockchain, AI, and IoT, are widely used to achieve those objectives. Supply chains are crucial for companies to meet their objectives and satisfy their customers to enhance productivity and profitability (Sobb et al., 2020). Based on the above discussion, the following hypothesis was proposed: H1: Supply chain 4.0 significantly impacts innovation capabilities.

3.2 Relationship with and Impact of Supply Chain 4.0 on Operations Performance Supply chain and operations performance are interlinked. Supply chain 4.0 benefits a company in integrating business operations with intelligent technology to complete tasks within its deadline and offer customers superior quality products and services. Utilizing supply chain 4.0 benefits the company’s operation management process to assure productivity optimization (Nawanir et al., 2013). Digital technologies are emerging and are widely used in companies to bring about industrial transformation, which beneficially manages their operations and increases productivity (Al Kurdi et al., 2021; Alshurideh et al., 2021). Supply chain 4.0 allows organizations to reduce the processing time and the cost of production, maintain flexibility in business operations, offer superior services to customers, and so on (Alzoubi et al. 2022a, 2022b, 2022c, 2022d, 2022e, 2022f, 2022g, 2022h, 2022i, 2022j, 2022k, 2022m, 2022n). The authors have focussed on cloud-based systems of ERP (Enterprise Resource Planning) and RFID-enabled (Radio Frequency Identification) systems. These technologies can be implemented to assure collaboration and maintain business operation flexibility and transparency. The authors Garcia-Perez et al. (2022) have concluded that the relationship between supply chain 4.0 and operations management is crucial to continue business activities locally or globally. Per Frederico’s 2020 statements, information technology plays an essential role in improving business processes. The effect of Industry 4.0 is associated with technological advancement in business supply chains as their implementation effectively improves performance within supply chain processes for procurement, manufacturing, management of inventory, and process automation. The business environment is becoming complicated, along with the changing demands of the customers, so increasing automation in business operations enhances business efficiency and

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quality. Such results were achieved from the implementation of supply chain 4.0 to enhance organizational and operational perfection (Frederico et al., 2020). Ghadge et al. (2020) stated that the implementation of Industry 4.0 has been beneficial and indicates significant results in managing supply chain activities (Kashif et al., 2021). That technological implementation effectively creates new opportunities for further development of supply chain activities. Several companies are effectively implementing robotic technologies for automating supply chain management operations that include big data analytics to properly assist companies in collecting and managing valuable massive amounts of data to optimize the performance of their business processes (Akhtar et al., 2021). The implementation of big data analytics in the business process is beneficial for increasing flexibility and organizational agility along with the customization of products (Ghadge et al., 2020). However, a company can organize the activities of supply chain management by using various advanced technologies like the IoT, robotic technology, and big data analytics. Using these technologies is beneficial for transforming linear business operations into integrated ones, reducing costs, and making the production process more responsive to identifying customer demands (Eli et al., 2022). Supply chain 4.0, also referred to as Industry 4.0, is beneficial for positively impacting global supply chain management, by allowing the companies to operate globally and offer products to vast target audiences in various nations (Alsharari, 2021). The implementation of supply chain 4.0 in companies is beneficial for achieving innovation in the manufacturing process and transforming the various business processes to better achieve their objectives. Based on the above discussion, the following hypothesis was proposed: H2: Supply chain 4.0 significant impacts operations performance.

3.3 Relationship and Impact of Innovation Capabilities on Operations Performance Many business management researchers and professionals have focused on innovation capabilities to analyze its impact on operations performance. Innovation capability is a necessary ability of the business firm to design new products and services to meet the organizational goals and objectives (Miller, 2021). It is also beneficial to satisfy the employees and customers and to improve engagement and performance. As Silva et al. (2019) noted, a “higher level of innovation in the product and service design leads to a higher level of operations performance.” The organization utilizes product innovation to develop innovative and attractive products to fulfill customer expectations and increase product availability in the market (Goria, 2022). However, innovation capabilities also help the business company attract customers on a large scale and improve internal and external business performance (Mehmood, 2021). The company gets a chance to establish a relationship with stakeholders, which is beneficial for enhancing operational efficiency and performance (Nasim et al., 2022).

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Organizational operations performance is also increased when innovative ideas are used in the business process (Eli, 2021). As the internal operation of the business is dependent on process management, integrating innovation capabilities in the process is advantageous to restructuring the business operation and adjusting it according to market trends and organizational needs (Al Shraah et al., 2022; Hayajneh et al., 2021; Victoria, 2022). This type of organizational innovation benefits the design of new management methods. Managers use their innovative skills and knowledge to critically analyze their business operations and make strategies that intensify business performance (Kasem et al., 2022). A higher level of operations performance requires a higher level of managerial innovation. An effective relationship between managerial innovation and operational management performance is established (Alicke et al., 2016). Financial performance is also impacted by innovation capabilities. The import and export of goods and services are also refined using innovative ideas when managers structure the business process using innovation capabilities and create effective plans to improve product sales in the market and profitability (Ghosh & Aithal, 2022; Qasaimeh & Jaradeh, 2022). The innovation also impacts external sources of the business operation and management, leads to the collaboration of business networks with other business groups, and helps the organization to internationalize products and services (Ahmed & Al Amiri, 2022). Employees and managers get a chance to learn new skills and knowledge with other business groups and from different organizational cultures, which is beneficial to boost the performance of operation management and expand the business in the global market (Bag et al., 2020). Accessibility of external sources is also increased, which can effectively improve the intensity of knowledge to modify the operation. IC also influences the performance of marketing. Innovative skills help the marketing department to increase research and help in event organization (Alsharari, 2022). A strong relationship with potential customers is established, which helps the business firm to resolve business conflicts. Innovation capabilities are also used to mitigate the impact of globalization, competition, and the revolution of technology and knowledge (Akhtar et al. 2022). Innovation in business organizations generates opportunities for organizational growth and the betterment of operations performance. Based on the above discussion, the following hypothesis was proposed: H3: Innovations capabilities significantly impact operations performance.

3.4 The Relationship and Impact of Supply Chain 4.0 on Operations Performance via the Mediating Role of Innovation Capabilities Supply chain 4.0 dramatically affects businesses concerning customer satisfaction, strategies, organizing business models, and structures to improve productivity (Basheer et al., 2016). The supply chain manages productivity, and it becomes time

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to operate a better consumer service system (Alzoubi, 2022). The organization needs to reduce its cost of manufacturing and sourcing the products to gain profits without compromising customer satisfaction. Supply chain 4.0 management and the company operations performance lead to producing high-quality goods, keeping the customer demands in mind while directing the company to approach problem-solving strategies for better performance (Ratkovic, 2022). Adopting the advanced technology of Supply chain 4.0 helps the company to remain innovative and updated in a technologically oriented world. This supply chain reduces the gap between the customers and the organization through an active supply system that generates products within a short period and thereby delivers them to their consumers as early as possible (Amrani et al., 2022). In the words of Adeniran (2012), innovation in products, services, and business techniques leads the business sector to rearrange its strategies, business models, and structures to remain active in a competitive market (Farouk, 2022). Innovations help the organization collect and analyze both primary and secondary data, which creates awareness in the business sectors about the upcoming challenges and issues; problems in the future are then addressing solutions. Aslinda et al. (2019) have stated that innovation capabilities have played a significant role in the growth of businesses without harming the environment or work. Both the supply chain 4.0 technology and the operations performance impact innovation capabilities (Nasim, et al., 2022). Likewise, the supply chain affects the company’s operations performance (Aslinda et al., 2019; Radwan, 2022). Operations performance covers the activity and areas of improvement important for the innovation of processes and management systems. It focuses on the speed of the working process and is measured by innovative tools and technology (Mondol et al., 2022). This aspect of the organization helps restructure product development and the sale of goods and services (Del & Solfa, 2022). An effective process is also beneficial to increasing supply chain management and strengthening relationships with customers, investors, and other stakeholders (Bag et al., 2020). Integration of operations performance and innovation capabilities is also favorable for business globalization and boosts brand value and recognition (Butt, 2022). Business disputes are also effectively resolved using innovation capabilities because it works like a bridge between the supply chain management and the operations performance. Based on the above discussion, the following hypothesis was proposed: H4: Supply chain 4.0 significantly impacts operations performance with mediating role of innovation capabilities.

3.5 Problem Statement and Research Gap By offering a thorough method of supply chain management resulting from significant supply chain integration, information exchange, and transparency, the deployment of Industry 4.0-enabled skills is projected to improve operations performance

Impact of Supply Chain 4.0 on Operations Performance …

H1

Innovation Capabilities

461

H3

H4

Operations Performance

Supply Chain 4.0 H2 Fig. 1 Conceptual research model

significantly. Besides all the investigated aspects of supply chain 4.0, there is still a need to explore the criteria to adopt innovation capabilities while implementing supply chain 4.0. Therefore, to consider the previously provided research gap (Ralston & Blackhurst, 2020), this research aims to explore the impact of supply chain 4.0 on operations performance with a mediating effect of innovation capabilities in the UAE computer hardware industry.

3.6 General Research Model See Fig. 1.

3.7 Research Hypothesis HI: Supply chain 4.0 significantly impacts innovation capabilities in the UAE computer hardware Industry at (α ≤ 0.05) level. H2: Supply chain 4.0 significantly impacts operations performed in the UAE computer hardware Industry at (α ≤ 0.05) level. H3: Innovation capabilities significantly impact operations performance in the UAE computer hardware industry at (α ≤ 0.05) level. H4: Supply chain 4.0 significantly impacts operations performance via the mediating effect of innovation capabilities in the UAE computer hardware Industry at (α ≤ 0.05) level.

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3.8 Research Methodology and Design An online survey, along with literature, served to identify the most appropriate solutions to supply chain 4.0 and innovation capabilities imperatives for achieving operations performance. The research was quantitative with an exploratory and causal design. A cluster sampling technique was used to simplify the population size.

3.9 Population, Sample, and Unit of Analysis This research used 133 computer manufacturing companies based in the UAE as the research population. They were accessed by email and an online survey that qualified this research to collect 259 valid respondents for the statistical analysis. The design of the online questionnaire was based on the Five Point Likert scale, with 27 items divided into each construct. Ten items were used to measure supply chain 4.0, with eight items used to measure operations performance and nine items to measure innovation capabilities.

4 Data Analysis 4.1 Demographic Analysis The demographic details of the respondents reveal a large number of male employees (72%) and 28% female with a high number of IT developers from the computer and hardware industry (Table 1). Table 1 The study’s demographical aspects

Items Gender Job status

Description

f

%

Male

186

71.8

Female

73

28.2

IT developers

87

33.6

Production manager

55

21.2

SC manager

35

13.5

HR manager

82

31.7

N = 259, Male = 72%, Female = 28%

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Table 2 The study correlation and reliability Construction

No. of items

Cronbach’s Alpha

Mean

S.D

Supply chain 4.0

Innovation capabilities

Supply Chain 5 4.0

0.75

3.10

0.73

1

Innovation Capabilities

6

0.81

3.04

0.59

0.847**

1

Operations Performance

6

0.83

3.87

0.78

0.740**

0.864**

Operations performance

1

Supply Chain 4.0 (M = 3.10, SD = 73%, Innovation Capabilities (M = 3.04, SD = 59%), Operations Performance M = 3.87, SD = 78%. Level of significance at P < 0.05**

4.2 Reliability, Descriptive Statistics and Correlation Table 2 shows the data reliability, measured by pilot testing, and the descriptive statistics that demonstrate the extent of the acceptance level of the mean and standard deviation for each construct. Defining the relationships between the variables and correlation coefficients was performed and showed a high correlation between supply chain 4.0 and innovation capabilities, supporting the literature that identifies how an advanced supply chain can enhance a firm’s adoption criteria for innovation capabilities. The supply chain is highly correlated with the operations performance depicted in Table 2. The correlation of innovation capabilities with operations performance also defines a high correlation between the constructs.

4.3 Regression Analysis and Hypothesis Testing Regression analysis was employed to test the hypothesis that indicated the results of H1: supply chain 4.0 has a significant positive impact on innovation capabilities as β = 0.74 and t = 17.62, whereas the value of R2 = 54% indicates an adequate level of variance between the constructs. The data findings relating to H2 revealed a significant positive relationship between supply chain 4.0 and operations performance as β = 0.84 and t = 12.45 with a variance prediction R2 = 71%, demonstrating a high variance between the variables. The analysis for H3 showed a significant positive impact of innovation capabilities on operations performance as β = 0.86, t = 14.17 and R2 = 74%. The critical value and the beta also represent a positive impact. The data analysis of H4 depicted a significant positive relationship between supply chain 4.0 on operations performance with mediating effect of innovation capabilities as β = 0.91, t = 2.15, and R2 = 84%, which demonstrated a higher level of variance among all constructs of the research (Table 3).

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Table 3 The hypotheses testing Hypothesis

Regression Weights

Standardized Coefficients β

R2

Adjusted R2

p-value

t-value

Hypothesis supported

H1

SC4.0 → IC

0.740

0.547

0.546

0.000

17.62

Yes

H2

SC4.0 → OP

0.847

0.718

0.717

0.000

12.45

Yes

H3

IC → OP

0.864

0.746

0.745

0.000

14.17

Yes

H4

SC4.0*IC → OP

0.919

0.842

0.841

0.000

2.15

Yes

Dependent variable = Operations Performance, Independent Variable = Supply Chain4.0, Mediator= Innovation Capabilities, *Level of Significance (α ≤ 0.05** **Critical t-value (df/p) = 1.64

5 Discussion of the Results That statistical analysis supported the alignment of our findings with past research. Several studies argue that companies utilize manufacturing engineering, big data, 3D printing, and others to increase their efficiency in implementing innovation. Using innovative processes is beneficial for facing disruptions and other issues in supply chain management (Tan et al., 2016). According to prior research, supply chain 4.0 implementation and resilience are effective ways to enhance supply chain operations in business. Implementing supply chain 4.0 is beneficial for integrating the business environment (Ghadge et al., 2020). H3 identifies the relationship between innovation capabilities and operations performance. The literature indicates that the integration of innovation capabilities in the process is advantageous to restructuring business operations and adjusting them as per the requirements of market trends and organizational needs (Andersén, 2021). According to the above analysis, H4 is also supported by this research as the mediating effect has been empirically proven significant. As mentioned in the literature, integrating operations performance and innovation capabilities is favorable for business globalization and boosts brand value and recognition. Business disputes are also effectively resolved using innovation capabilities because it works like a bridge between supply chain management and operations performance (Shcherbakov & Silkina, 2021).

6 Conclusion Product innovation, methodologies, and services provide the ability to analyze the performance of a business so it can achieve its goals without any resistance. The operations performance leads the company to adopt more innovative technologies, thoughts, and methods. Furthermore, the innovations also affect supply chain 4.0 and operations performance. To generate high revenue and survive in a technologyoriented world, a business needs innovative strategies and advanced supply chain

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management to ensure financial and practical performance. Business operations include activities that enhance operations performance and accomplish innovation capabilities. Business operations are also used to manage a business’s internal and external activities that concern its innovation capabilities. Business operations performance systems develop a strategic plan to utilize the innovative product and process design to intensify organizational performance and improve the implementation of supply chain 4.0 practices.

7 Recommendations and Limitations This research has provided significant findings but has a few limitations, such as the single-construct research employed for it. A dimensional construct is required for future research to make it more elaborate and explore each construct’s extensive factors. A longitudinal study is also recommended because many companies and states can be explored in future research using the same hypothetical model.

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Cloud Computing and Blockchain

Impact of Blockchain Strategy and Information Sharing on Digital Operations: Empirical Evidence from the UAE Banking Industry Ayman Abu-Rumman , Barween Al Kurdi , Ata Al Shraah , Muhammad Turki Alshurideh , Haitham M. Alzoubi , and Ahmad AlHamad Abstract Using empirical evidence from the banking industry in the UAE, this paper investigates blockchain strategy implementation and the impact of its information sharing on digital operations. The implementation of a blockchain strategy and information sharing to improve digital operations mark the first attempt to contribute to banking sector research. A conceptual model was created and verified with empirical evidence and existing literature about blockchain, information sharing, and digital operations. To gather information, a questionnaire survey instrument was created and distributed to supply chain managers. Seventy-seven companies from a range of industries contributed the data. Data analysis for the study included both exploratory A. Abu-Rumman Department of Business Administration, Business School, Al Ahliyya Amman University, Amman, Jordan e-mail: [email protected] B. Al Kurdi Department of Marketing, Faculty of Business, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan e-mail: [email protected] A. Al Shraah Department of Business Administration, Faculty of Economics & Administrative Sciences, The Hashemite University, Zarqa, Jordan e-mail: [email protected] M. T. Alshurideh (B) Department of Marketing, School of Business, The University of Jordan, Amman 11942, Jordan e-mail: [email protected]; [email protected] M. T. Alshurideh · A. AlHamad Department of Management, College of Business Administration, University of Sharjah, 27272 Sharjah, United Arab Emirates e-mail: [email protected] H. M. Alzoubi School of Business, Skyline University College, Sharjah, UAE e-mail: [email protected] Applied Science Research Center, Applied Science Private University, Amman 11937, Jordan © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 H. M. Alzoubi et al. (eds.), Cyber Security Impact on Digitalization and Business Intelligence, Studies in Big Data 117, https://doi.org/10.1007/978-3-031-31801-6_28

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and causal approaches. Regression analysis was performed to measure the hypothesis. Implementing a blockchain strategy significantly impacts digital operations; information sharing is also noted as significant in improving digital operations. Recent research has established a connection between the proposed variables and their implications. This can be revised by examining more geographic areas. Additionally, it has been discovered that not many studies on the banking industry take a blockchain perspective. Blockchain experts, IT developers, and bankers can place their trust in the blockchain network, track all transactions, confirm them, and lower the risks associated with credit and capital management. Quick transaction settlement with intelligent contracts can increase digital operations and business efficiency. Keywords Blockchain strategy · Information sharing · Digital operations · UAE Banking Industry

1 Introduction An innovative wave of disruptive technologies known as “blockchain” has recently emerged in our society and extended across several industries. Blockchain technology was once only applicable to the manufacturing industry, and many industrial units have seen improvements in their capabilities and performance because of blockchain strategy implementation (Ghazal et al., 2021a, 2021b, 2021c; Kurdi et al., 2022). Many service sectors, from banking to telecom, also anticipate benefits from blockchain digital technology (Alzoubi et al., 2021a, 2021b, 2021c). These digital technologies have enabled many new business models (Garg et al., 2021). In developing nations like the UAE, where banks and other financial institutions are seen as the backbone of contemporary civilization, they serve as a stimulant for economic progress (Alzoubi et al., 2022a, 2022b, 2022d, 2022f, 2022g, 2022i, 2022j). The UAE banking industry is dealing with problems like rising operational expenses, an uptick in fraudulent transactions, and difficulties maintaining transparency (Kashif et al., 2021). The banking system needs to explore robust technologies to avoid fraud and carry out transactions quickly (Al Kurdi et al., 2020; Al-bawaia et al., 2022). Additionally, the system must maintain openness for its users and regulators and cost-effectiveness in its operations (Ghazal et al., 2022). The banking industry has embraced several platforms powered by technologies to achieve this (Akhtar et al., 2021). By automating, simplifying, and upgrading banks’ conventional business processes, blockchain can eliminate intermediaries, improve transaction transparency and traceability, and reduce risks (Cole et al., 2019). According to the “Business Research Company” report it is expected the creative use of blockchain technology will allow the global banking industry to save up to $20 billion by 2022. Blockchain technologies are viewed as new, exciting, and significantly affecting business and society. Before its considerable value was realized, blockchain in banking and investment-related solutions endured a period of

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skepticism (Eli & Hamou, 2022). In contrast, it has been considered the most effective source of information sharing between businesses and their clients (Durowoju et al., 2020). A more convenient way to communicate with clients and deliver critical information is through digital operations that enable banking sectors to manage their time cost-effectively (Alzoubi et al., 2022a, 2022b, 2022d). This research explores the impact of blockchain strategy and information sharing on digital operations and hopes to find the most pertinent knowledge for contributing to the literature and future research.

2 Theoretical Framework 2.1 Blockchain Strategy Blockchain has shown sufficient promise to transform how people communicate information and conduct online transactions by increasing their faith in data in a previously unavailable or impossible way (AlShamsi et al., 2021; Alsharari, 2021). To achieve organizational objectives, a blockchain strategy has been developed to meet the digitalization objectives while embracing regulatory and legal framework values (Tijan et al., 2019). Environmental stability is the gold standard for this strategy: such technology can be used sustainably and must be energy efficient (Alshurideh et al., 2019). As a result, the deployment of blockchain must be compatible with sound privacy rules and improved data security; blockchain technology must help to strengthen and respect the digital identity system, which “involves supporting a logical, pragmatic, decentralized, and self-sovereign identification architecture and being compliant with e-signature requirements such as eIDAS.” (Electronic IDentification, Authentication and trust Services) Blockchain technology provides high-level cyber security (Mehmood, 2021). Blockchain adheres to a secure design paradigm, making it a unique and sound system that allows business transactions to be trusted and transparent in a consortia corporate environment without needing a third party or centralized authority (Alzoubi et al., 2022; Ghazal et al., 2021a, 2021b, 2021c). Blockchain strategy plays an effective and efficient function in promoting legal certainty, which could be used to create high levels of sustainability, and could support standards (Treiblmaier, 2018).

2.2 Information Sharing Information sharing transfers high-quality knowledge or information within a collaborative supply chain amongst partners (Alolayyan et al., 2022a, 2022b; Svoboda et al., 2021). Therefore, “information sharing” may also be referred to as “knowledge sharing” or “information integration (Miller, 2021).” From the banking sector’s

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perspective, the connections appear to revolve around information sharing; each supply chain participant and the entire supply chain collectively profit significantly from it. Information sharing lowered supply chain vulnerability. Supplier expenses decreased by 1–35% by exchanging inventory information (Alzoubi & Ramakrishna, 2022; Sibley et al., n.d.). It has been established that information sharing across supply chain participants can dramatically reduce both inventory and related expenses (Alshurideh et al., 2022; Hamadneh et al., 2021a, 2021b). Information sharing in the banking industry can also massively impact the building of strong connections with customers and provide them with the best pieces of information to secure business operations (Alzoubi et al., 2022a, 2022b, 2022c, 2022d, 2022e, 2022f, 2022g, 2022h, 2022i, 2022j; Vorobeva Victoria, 2022). Sharing customer data among businesses is frequently essential for ensuring people’s safety and that they receive the best services (Altamony et al., 2012; Alzoubi et al., 2021a, 2021c, 2021d, 2021e, 2021f, 2021h; Alzoubi et al., 2021a, 2021c, 2021d, 2021e, 2021f, 2021h, 2021i). The business always ensures it only ever communicates the relevant information with the appropriate organization if it has a legal justification (Ghosh & Aithal, 2022). An example of information sharing in banking is the sharing of credit data among financial organizations, which has the potential to decrease or enhance the likelihood of banking crises (Tchamyou & Asongu, 2017).

2.3 Digital Operations Digital operations examine the vertical digitalization and integration of all organizational processes, from product creation and purchasing through manufacturing, distribution, and customer support, in order to consistently execute the supply chain (including manufacturing and replenishment) according to real-time consumer demand signals and the delivery of profitable and sustainable outcomes throughout the ecosystem (Eli, 2021; Ghazal et al., 2022a, 2022b, 2022c, 2022d, 2022e, 2022f, 2022g, 2022h, 2022i, 2022j, 2022k, 2022l; Goria, 2022). “The concept of infusing business processes with the agility, intelligence, and automation to digital operation is a process that is based on digital transformation and involves the use of many systems and resources (Hasan et al., 2022a, 2022b, 2022c, 2022d, 2022e, 2022f, 2022g, 2022h, 2022i, 2022j, 2022k, 2022l).” Furthermore, that will “develop operational models that delight consumers and increase performance.” (Seidmann & Sundararajan, 1997). Digital operations focus on detecting and responding while effectively facilitating dynamic optimization and learning. They ensure the beginning of a new era of business discipline to raise organizational visibility (Ahmad et al., 2021a, 2021b; Alshurideh et al., 2021a, 2021b; Harahsheh et al., 2021). Digital operations involve a holistic technique that enables technology linked with enterprises to deliver suitable parameters and value in a real-time setting (Kasem & Al-Gasaymeh, 2022; Alzoubi et al., 2021a, 2021b, 2021c, 2021d, 2021e, 2021f, 2021g, 2021h, 2021i). Put another way, a digital operation is more flexible than a digital support function (Alwan &

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Alshurideh, 2022a, 2022b; Hammad et al., 2022; Al Kurdi et al., 2020). According to (Wu & Lai, 2019), operational executives progressively attempt to improve efficiency by directly explaining and automating digital products and ensuring scalability and controllability across multiple factors.

2.4 Operational Definitions

Variables

Definition

Reference

Blockchain strategy A blockchain is a shared, distributed Cheng and Lin (2012) database or ledger between computer network nodes. It serves as an electronic database for storing data in digital form. Blockchain innovation fosters confidence without the necessity for a reliable third party by ensuring the fidelity and security of a data record Information sharing Information sharing transfers high-quality Annarelli and Palombi (2021) knowledge or information within a collaborative supply chain amongst partners Digital operations

Digital operations incorporate agility, Marinagi et al., (2015) intelligence, and automation into corporate processes to provide operational models that delight consumers and boost productivity

2.5 UAE Banking Industry Banks are quickly automating processes to provide seamless customer service in a virtual environment by utilizing digital transformation techniques like blockchain, artificial intelligence, and machine learning. A sizeable number have adopted predictive analytical models to analyze big data sets to deliver faster and more effective decision-making processes for business, investment, and credit decisions. The financial services industry in the UAE is changing and implementing new business resilience mechanisms in the hope that the Covid-19 pandemic will be contained and economies will overcome difficulties. The CEOs of local banks are focusing on various crucial areas, such as digitalization, regulatory concerns, corporate governance, and risk management.

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3 Literature Review 3.1 Relationship and Impact of Blockchain Strategy on Digital Operations Blockchain technology has interlinked with the digital operations of technological operations (Stevenson & Aitken, 2019). A blockchain with transparent, distributed systems may be more conducive to operational partnering than strategic partnering (Qasaimeh & Jaradeh, 2022). Organizations may look for short-term connections due to information visibility, including cost, materials, capabilities, and performance measurements (Alzoubi & Aziz, 2021). Finding the ideal partnership will require flexibility (Ahmed & Amiri, 2022). Because they reveal more details about other potential interactions, which can be advantageous to organizations, these shorter-term pressures could enhance environmental uncertainty and meet the current digitization needs (Akhtar et al., 2022; Khatib et al., 2022a, 2022b, 2022c, 2022d, 2022e, 2022f, 2022g, 2022h, 2022i, 2022j, 2022k, 2022l). According to prior research, blockchain strategy and its implementation in the banking sector require a digital system that enables customers to be up-to-date and connected with their banking services (Francisco & Swanson, 2018). E-transactions and instant transactions require a blockchain strategy and the advanced implementation of digital operations to become competitive in the market. Based on the above discussion, the following hypothesis was proposed: H1: Blockchain strategy significantly impact digital operations.

3.2 Relationships and the Impact of Information Sharing on Digital Operations Information sharing involves the transfer of acute knowledge or information for a beneficial purpose, and internet-based technology opens several doors for business innovation (Alsharari, 2022; Ghazal et al., 2021c; Nasim et al., 2022). Organizations are adopting information sharing systems to boost their growth, and provide better customer service (Amrani et al., 2022; Cole et al., 2019; Tariq et al., 2022). Adopting information sharing directly impacts digital operations as both are technology-based and require an advanced technological system facilitating customer services and managing business operations (Alzoubi et al., 2022a, 2022b, 2022c, 2022d, 2022e, 2022f, 2022g, 2022h, 2022i, 2022j, 2022k, 2022l; Baabdullah et al., 2019). An information sharing system provides complete support to digital operations. Information sharing requires the accuracy of customer data and blockchain provides those requirements (Cole et al., 2019; Del & Solfa, 2022). Confidential information sharing provides a sense of security and confidence to organizations, suppliers, and customers

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about the safety and security of their information (Alzoubi et al., 2021a, 2021b, 2021c, 2021d, 2021e, 2021f, 2021g, 2021h, 2021i). The productivity of organizations has been improved by implementing an information sharing system (Sibley et al., n.d.). Technology-based processes are mostly interconnected with each other and support operations and also provide a sense of security for digital operations management (Alzoubi et al., 2020; Lancaster et al., 2006). Operations management always requires data to make decisions, and the information security system provides sufficient data for that; digital operations cannot work effectively without the support of technological advancement (Abuanzeh et al., 2022; Alolayyan et al., 2022a, 2022b). Since the handling and control of complete commercial banking activities occur within the time frame and accuracy of the data, the banking industry’s information sharing system impacts quality assurance (Alzoubi et al., 2020a, 2020b. Alzoubi et al., 2022k; Butt, 2022). Its application of the information-sharing mechanism aids in the timely development of risk management assessments and functional properties for the financial sector (Büyükkarabacak & Valev, 2012). Information technology and digitalization are integrated with the banking industry’s informationsharing infrastructure to offer a symbiotic relationship between information sharing and risk management. Based on the above discussion, the following hypothesis was proposed: H2: Information sharing significantly impacts digital operations.

3.3 Relationship and Impact of Blockchain Strategy and Information Sharing on Digital Operations Various studies define blockchain as a decentralized database of records or a shared public ledger of all completed digital events or transactions; its records are synced and not modifiable (Mettler & Hsg, 2016). Bitcoin correlates to its blockchain because it was created as a digital currency online (Alzoubi, 2022). Both are different sides of the same reality; the blockchain is a database of input/output transactions agreed upon by all parties (Alzoubi Edward Probir Mondol, 2022; et al., 2022a, 2022b, 2022c, 2022d, 2022e, 2022f, 2022g, 2022h, 2022i, 2022j, 2022k, 2022l), containing serious information that enables the banking sector to keep, share, or utilize the information for customer care services (Al Shraah et al., 2022; Shamout et al., 2022). A more considerable lack of trust results from inefficient transactions, fraud, theft, and underperforming supply chains, which necessitated improved information exchange and verifiability, specifically in the banking sector (Alzoubi et al., 2020a, 2020b; Ratkovic, 2022). It is simple to lose or change expensive and valuable information whose provenance would ordinarily depend on paper certifications and receipts (Joghee et al., 2021; Lee et al., 2022; Lee et al., 2022; Nakasumi, 2017). Data sharing between partners or customers may face new difficulties due to different privacy policies for information and data usage and release to the customer (Ghazal et al., 2021b; Radwan, 2022; Salloum et al., 2020). A supply chain network

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should create and manage information-sharing rules and procedures due to the openness of information provided by blockchain technology (AlShurideh et al., 2019; Alshurideh et al., 2022; Hamadneh et al., 2021a, 2021b). Collaboration between business partners is eventually hampered by a lack of explicit norms for information exchange and digitization (Alshurideh, 2022; Nakasumi, 2017). The adoption of blockchain to produce sustainable values is disrupted by a lack of coordination and good communication across banking partners with disparate and even incompatible operational aims and priorities (Alzoubi et al., 2017; Farouk, 2022). Building a personalized relationship with clients and efficiently responding to their needs will no longer be a challenging endeavor to complete within the confines of a digital operation (Ahmad et al., 2021a, 2021b; Stevenson & Aitken, 2019). It is worth mentioning that the digital operations investment is cost-effective in demonstrating the value of visibility (Almaazmi et al., 2020; Alwan & Alshurideh, 2022a, 2022b). The digital platform, digital tools, digital content, asset management, search engine optimization, data analytics, tag management, digital customer interaction center, and agile processing are some of the most frequently utilized digital operations (Ahmad et al., 2021a, 2021b; Alshurideh et al., 2021a, 2021b). Digital operations have undoubtedly opened new avenues for better and longerterm management of business goals and objectives (Ahmad et al., 2021a, 2021b; Awadhi et al., 2022). When sharing accurate information with a targeted customer, focusing correctly on digital operations is critical for effectively managing prospects and concerns (Alzoubi & Yanamandra, 2020). Based on the above discussion, the following hypothesis was proposed: H3: Blockchain strategy and information sharing significantly impact digital operations.

3.4 Problem Statement and Research Gap The financial sector is overwhelmed with mountains of paperwork, risky data breaches, and unnecessary procedures, all of which have contributed to its enormous losses and consumer lack of trust (Nurova & Freze, 2021). Blockchain technology can significantly help to solve these issues in the banking sector. Investigating blockchain and information sharing can increase knowledge regarding digital operations expansion and the significance of implementing digitalization in the financial sector. In order to fill the gap from previous research, this research will identify blockchain and information sharing in the banking industry (Bayramova et al., 2021; Nakasumi, 2017).

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Blockchain Strategy

H1

H3

Digital Operations

Information Sharing

H2

Fig. 1 Conceptual research model

3.5 General Research Model See Fig. 1.

3.6 Research Hypothesis H1: Blockchain strategy significantly impacts digital operations in the UAE banking industry at (α ≤ 0.05) level. H2: Information sharing significantly impacts digital operations in the UAE banking industry at (α ≤ 0.05) level. H3: Blockchain strategy and information sharing significantly impact digital operations in the UAE banking industry at (α ≤ 0.05) level.

3.7 Research Methodology and Design Three variables are measured in this research using a quantitative technique. The research design selected for this current research is exploratory, descriptive, causal, and analytical. A convenient sampling technique was applied to segment the sample for convenience. The empirical analysis required online survey data, whereas the literature study enabled clarification of the variables.

484 Table 1 The study’s demographical characteristics

A. Abu-Rumman et al. Items

Description

f

Gender

Male

Job title

%

211

74.0

Female

74

26.0

Relationship Manager

83

29.1

Business Development Officer

77

27.0

Sales Executive

39

13.7

Branch Manager

86

302

N = 285, Male = 211, Female = 74

3.8 Population, Sample, and Unit of Analysis The security industry in the UAE was the targeted population for the study. Eightyeight security product and service-providing companies were accessed to gather data. A valid sample of 285 respondents was used after screening the 635 questionnaires received. The questionnaire was sent to the branch managers, sales executives, business development officer, and relationship managers to get feedback about using a blockchain strategy for customer service and data management. A questionnaire containing 27 items was employed to measure all constructs, and a five-point Likert scale was applied to gather responses on a priority basis.

4 Data Analysis 4.1 Demographic Data See Table 1.

4.2 Reliability, Descriptive, and Correlation Table 2 illustrates the data’s reliability by independently describing each variable’s reliability through Cronbach’s Alpha. Blockchain is measured at 0.88, information sharing at 0.86, and the digital operations at 0.88, which validate the data for further analysis. Descriptive statistics also described the mean value for blockchain strategy as M = 3.10 and SD = 74%. The mean estimated for information sharing = 2.71 and SD = 53%. The digital operations mean = 3.34 and its SD = 69% respectively. Table 2 also illustrates the correlation coefficient results indicating a high correlation between blockchain strategy and information sharing at r = 0.81, p = 0.000. The relationship of blockchain and digital operations was r = 0.685. In contrast, the relationship between digital operations and information sharing was also highly

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Table 2 The constructs correlation and reliability Construct

No. of items

Cronbach’s alpha

Mean

SD

Blockchain strategy

Information sharing

Blockchain strategy

10

0.88

3.10

0.74

1

Information sharing

8

0.86

2.71

0.53

0.814**

1

Digital operations

9

0.88

3.34

0.69

0.685**

0.736**

Digital operations

1

Blockchain strategy (M = 3.10, SD = 74%, information sharing (M = 2.71, SD = 53%), digital operations M = 3.34, SD = 69%. Level of significance at P < 0.05**

correlated with r = 0.736, p = 0.000. All results were based on the significance level of P < 0.05.

4.3 Regression Analysis and Hypothesis Testing Findings exposed a significant positive relationship of blockchain with digital operations, and the statistical findings noted β = 0.68, P = 0.000, t = 3.74, and R2 = 46%. A positive critical value and a significant relationship in the current analysis showed the acceptance of H1. H2 has restrained the relationship of information sharing with digital operations and the statistical results is noted as β = 0.732, P = 0.000, t = 7.82, and R2 = 54%. The overall hypothesis results support the current hypothesis for this research. H3 defined the relationship between blockchain strategy and information sharing impact on digital operations as β = 0.751, P = 0.000, t = 9.28, and R2 = 56%. High variance and positive critical value depict a significant positive relationship of both constructs with digital operations. Table 3 illustrates the summary of the results. Table 3 Hypothesis testing through regression analysis (ANOVA) Hypothesis

Regression weights

Standardized coefficients β

R2

Adjusted R2

p-value

t-value

Hypothesis supported

H1

BLC → DO

0.685

0.469

0.467

0.000

3.74

Yes

H2

IS → DO

0.732

0.542

0.540

0.000

7.82

Yes

H3

BLC*IS → OP

0.751

0.564

0.560

0.000

9.28

Yes

Dependent variable = digital operations, independent variable = blockchain strategy & information sharing * level of significance (α ≤ 0.05** **Critical t-value (df/p) = 1.64

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5 Discussion of the Results Despite the expanding body of information in this field, increased exclusion of digital operations could result from blockchain’s ability to create visibility and traceability. The findings of the empirical investigation thus draw attention to a few crucial topics. A summary of the findings indicates that within an organization, it is quite likely that businesses will be interested in maintaining some kind of information asymmetry that can improve the organization’s digital operations (Stevenson & Aitken, 2019). This will maintain competitive advantages and further lower the risk of information leakages. Based on these research results and the prior literature, H1 is supported. The results for H2 are also positively significant. Most technology-based processes are related to one another and support operations, providing a sense of security for digital operations management. Digital operations need data to make choices, and the information security system provides operational management sufficient data for that. This research aimed to maintain an open mind about the application of blockchain by recognizing its potential to enhance digital organizational activities while calling attention to the worries and criticisms surrounding it. It can, therefore, be argued that H3 is accepted.

6 Conclusion This research aims to gauge the perceived advantages of disruptive blockchain technology, which significantly influences digital operations by delivering accurate and secure information to banking industry customers. The research’s empirical findings may assist strategic banking in adopting a blockchain strategy to provide confidential data securely to its customers. The digitization of operations can also assist in improving banking performance by providing better customer care services expeditiously and safely via phone applications or online portals.

7 Recommendations/Limitations Various methods will expand on these empirical findings. One potential extension is to analyze the relationship between customer satisfaction with blockchain strategy and information sharing in greater depth. Current research linking proposed variables can be repeated by covering other geographical areas. Moreover, it explored a limited number of studies conducted on the banking sector from the perspective of blockchain. Future research may consider the banking industry and its criteria for technological adoption.

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A Critical Review of Cloud Computing Architecture Empowered with Blockchain Technology Ahmad Qasim Mohammad Al-Hamad , Samer Hamadneh , Mohammed T. Nuseir , Haitham M. Alzoubi , Barween Al Kurdi , and Muhammad Turki Alshurideh Abstract Blockchain technology (BCT) is a dispersed ledger with data records that are shared among the network’s nodes and contain all the specifics of the transactions that have taken place. Consensus mechanisms are used to confirm every transaction made in the system, and once data has been stored, it cannot be changed. Bitcoin, a well-known digital currency, is supported by blockchain technology, a prerequisite technology. Another term used with blockchain is known as cloud computing. It uses a network of internet-connected servers to gather, organize, and process data instead of a local server or a single machine. This study thoroughly analyses cloud computing architecture based on blockchain, and 12 articles from reputable journals are discovered and evaluated after applying various filters and searching global databases. Data integration, trust, and privacy are the three areas of cloud security A. Q. M. Al-Hamad · M. T. Alshurideh Department of Management, College of Business Administration, University of Sharjah, Sharjah 27272, United Arab Emirates e-mail: [email protected] S. Hamadneh · M. T. Alshurideh (B) Department of Marketing, School of Business, The University of Jordan, Amman 11942, Jordan e-mail: [email protected]; [email protected] S. Hamadneh e-mail: [email protected] M. T. Nuseir Department of Business Administration, College of Business, Al Ain University, Abu Dhabi Campus, P.O. Box 112612, Abu Dhabi, UAE e-mail: [email protected] H. M. Alzoubi School of Business, Skyline University College, Sharjah, UAE e-mail: [email protected] Applied Science Research Center, Applied Science Private University, Amman 11937, Jordan B. Al Kurdi Department of Marketing, Faculty of Business, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 H. M. Alzoubi et al. (eds.), Cyber Security Impact on Digitalization and Business Intelligence, Studies in Big Data 117, https://doi.org/10.1007/978-3-031-31801-6_29

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examined in this study. The findings demonstrate that blockchain offers a productive platform in this area. Keywords Blockchain technology · Cloud computing · Cloud computing architecture

1 Introduction Developed from large-scale, distributed computing technology, cloud computing is a properly specified field of research. The processing burden on clients has diminished due to cloud computing. Numerous benefits exist, including lower computer hardware and preservation costs, global accessibility, elasticity with a highly robotic system, and simple scalability (Guergov & Radwan, 2021). Many large corporations like IBM, Google, Amazon, and Microsoft have embraced the cloud. Many new applications, including Google App Engine, Google Cloud Platform, etc., are prototypes. It offers the convenience of a pay-per-use system and an adaptable IT framework that is available via the Internet on mobile gadgets. Although the cloud offers a wide range of useful services, businesses are reluctant to adopt it because of privacy concerns. The cloud’s significant drawbacks include security concerns and difficulties (Divya Mounika & Naresh, 2020). BCT is the potential of the businesses striving for safety and secrecy advancements (Almaazmi et al., 2020; AlShamsi et al., 2021). Blockchain is a decentralized archive that stores chain-like, tamper-evident data without a main repository (B. Kurdi et al., 2022a, 2022b). Nodes are the components or users of the BCT (Alzoubi, 2021). Blockchain offers a dispersed network where all connected nodes actively participate in the data validation and verification process (Ghazal et al., 2021). Cryptography will be used to encrypt the information collected in the Block Chain (BC) (B. Al Kurdi et al., 2022b). Each block has a timestamp, an encoded hash, and a hash of the block before it is in the group over which it will link. As a result, the blockchain’s information is tamper-evident (Maged Farouk, 2022). The blockchain provides data protection, and since participating users will be confirmed in the network, there is no longer any privacy concern (Shrimali & Patel, 2021). By integrating with blockchain technology, this study can ease worries about the safety and secrecy of the information and promote the growth of cloud computing (Ghazal et al., 2022). It enhances data security, increases service accessibility, and manages cloud data. The explanation of three parameters like data integrity, trust, and privacy (Rehman et al., 2022) by combining blockchain technology with cloud computing is reviewed in Table 1 (Abdulqadir et al., 2021). BCT research is developing quickly in all aspects. Even though there can be systematic evaluations of the BC approach in various fields, this study’s systematic literature reviews (SLRs) are very distinctive since there hasn’t been a thorough investigation into how blockchain fits into cloud computing. This study attempts to compile and integrate the analysis of pertinent articles. The article’s structure will

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Table 1 Summarization of review articles References

Year

Publication

Journals

Trust

Privacy

He, K. (2020)

2021

Springer

State and Corporate No Management of Region’s Development in the Conditions of the Digital Economy

Data integrity

No

No

Benaddi and Ibrahimi (2020)

2020

Springer

ICA3PP

Yes

No

No

Huang et al. (2020)

2020

Elsevier

Journal of Parallel and Distributed Computing

Yes

No

No

Wei et al. (2020)

2020

IEEE

IEEE Access

Yes

No

No

Benaddi and Ibrahimi (2020)

2020

Elsevier

Future Generation Computer Systems (FGCS)

No

No

No

Rimba et al. (2020)

2020

IEEE

IEEE Access

No

No

Yes

Velmurugadass et al. (2020)

2020

Springer

Information Systems Frontiers

No

Yes

No

Raja et al. (2020) 2020

Elsevier

Materials Today: Proceedings

No

No

No

Ashik et al. (2020)

2020

IEEE

IEEE Access

No

No

No

Wilczy´nski and Kołodziej (2020)

2020

IEEE

ICECCE

No

No

No

Zhu et al. (2019) [19]

2020

Elsevier

Simulation Modelling Practice and Theory

No

No

No

Ali et al. (2021a, 2021b)

2019

Elsevier

FGCS

No

Yes

No

therefore be reviewed in the paragraphs that follow. The review papers of this field have been examined in this study, and their flaws and shortcomings, as well as the necessity of presenting them, have been discussed.

2 Literature Review Many researchers have previously worked on blockchain-based cloud computing infrastructure. Some of their works are present in this section. The authors indicate that cloud computing provides multiple benefits by combining servers, sources, and data centers through the Internet (H. M. Alzoubi

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et al., 2022c). These facilities operate under a pay-per-use model (Alhamad et al., 2022). The facilities are offered anywhere in the globe (Alzoubi & Aziz, 2021; Radwan & Farouk, 2021) and at a significantly reduced cost (Yanamandra & Alzoubi, 2022), enhancing employee collaboration (Abuanzeh et al., 2022; Al Kurdi et al., 2020; Kurdi et al., 2020). The cloud is simple to manage because its software is updated automatically (Shamout et al., 2022). The cloud-based documents will also be under the service user’s control (Amjad et al., 2019). The authors of this study present that cloud computing has five main characteristics. The self-service model known as on-demand self-service (Alnuaimi et al., 2021) allows users to impromptu offer network storage resources (Al Kurdi et al., 2021; Kabrilyants et al., 2021). In order to support various client platforms (Alzoubi & Yanamandra, 2020), large network access provides service throughout the link that can be retrieved with standard processes (Alnazer et al., 2017; Alsharari, 2021). By utilizing a multi-tenant model, resource pooling offers the majority of its computing resources to many customers in order to meet their demands (H. ; Kasem & Al-Gasaymeh, 2022). When resources are preserved, sustained, and improved by metering abilities (Cruz, 2021), the service is said to be measured (B. Al Kurdi et al., 2022a). Elastic scalability can adjust IT possessions as necessary to satisfy shifting requirements (H. M. Alzoubi et al., 2022b). For instance, a request can automatically scale to meet demand when more servers are required (Chirit, a˘ , 2022). The authors demonstrate how Blockchain technology was launched alongside Bitcoin (El Khatib et al., 2022). The digital currency known as Bitcoin was first introduced in 2008 by a person using the alias “Satoshi Nakamoto.” His white article “Bitcoin: A Peer to Peer Electronic Cash System” presents people to straight online payment between two parties without using a middleman (Zyskind et al., 2015). In this study, the authors present that the BC is an unbreakable digital record that may be automated to evidence economic transactions and virtually anything with a value (Alshurideh et al., 2021; Eli & Hamou, 2022; Mayuranathan et al., 2020). It is used to track economic transactions (Ali et al., n.d.). Blockchain technology eliminates the need for government intervention and fraud completely (Hanaysha et al., 2021; Khan, 2021) because of consensus validation. By eliminating the need for a middleman (Alzoubi & Ahmed, 2019), instant transactions can be carried out without incurring transaction fees (N. ). These attributes enhance economic effectiveness (Alzoubi et al., 2021). Another research shows that numerous Blockchain-Cloud applications can be used in daily activities to enhance our information’s security and safety (Al Ali, 2021; H. M. Alzoubi et al., 2022a; Lee & Ahmed, 2021; Miller, 2021; Zafar et al., 2022). Blockchain-Cloud offers services to a variety of industries such as banking, healthcare, realestate etc. (Al-Tahat & Moneim, 2020; T. M. Ghazal et al., 2021a, 2021b; Joghee et al., 2020). In addition to maintaining the validated data, this integration can give us greater storage flexibility (M. T. Alshurideh et al., 2022a). The network’s resilience increases while monitoring network access authorization (Al-Dmour et al., 2021; Alshurideh, 2022; Kurkin et al., 2021). Most of the approaches have been used while employing and constructing several smart (Khai Loon Lee et al., 2022) as well as intelligent frameworks like machine

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learning approaches (M. T. Alshurideh et al., 2022b; Asem Alzoubi, 2022) that may provide assistance in designing emerging solutions for the rising challenges (Mehmood, 2021) in designing smart cloud-based monitoring management systems (A. Ali et al., 2022a, 2022b).

3 Problem Statement and Research Contribution IoT data is generally transmitted over time in all connected devices using cloud computing (Alshurideh et al., 2022a, 2022b, 2022c; Ghazal et al., 2021a, 2021b; Lee et al., 2022). There are five possible hurdles and anomalies faced: standardization, security vulnerability, privacy leakage, intelligence, and resource management. This review suggests and evaluates after applying various filters and searching with data integration, trust, and privacy security examined in this study. The findings demonstrate that blockchain offers a productive platform in this area. Our contribution is to monitor and study the best possible approach to secure the data with the help of blockchain and cloud computing.

4 Proposed Methodology Blockchain technology (BCT) is a shareable, unchangeable database that makes it easier to track resources and transaction data in a corporate network. An asset may be material or immaterial. On a blockchain network, practically anything of value may be recorded and traded, lowering risk and increasing efficiency for all parties. BCT is the essential technology after Bitcoin, a standard numerical Cryptocurrency. “Cloud computing uses a system of online servers presented on the web to collect, handle, and process information, instead of a limited server or a particular computer.” It even looks many issues such as data protection, management, conformity, and consistency. This research presented many of the essential issues the cloud looks and suggested solutions by combing it with BCT, as indicated in Fig. 1. Figure 1 shows that a communication layer and a gaeteway device are supposed to be used in information interaction processes. The gateway device is used to collect data through IoMT devices and passed to the blockchain-based cloud layer to gain high-protection levels like user verification with deals and data privacy with the cryptography. Throughout the information exchanges through the blockchain-based cloud layer, the records are encoded and attached to the blocks by a consent system in the BC network. Also, BC can create a protected peer-to-peer link while enabling seamless communications among ubiquitous information for service management, value exchange and collaborative trust. The information from different sources can achieve efficient interconnection through the cloud, enabling secure service management, value exchange and collaborative trust within the communication layer. Blockchain

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Fig. 1 Proposed model

is adopted to support peer-to-peer collaboration among clouds with high-security levels.

5 Critical Analysis Table 1 highlights many problems in cloud computing framework and appropriate work from many researchers of different international journals. Only three facets were utilized in this evaluation, the main topics reviewed by these essential researchers. The reseaarchers are listed in no specific order and just reviewed the research papers highlighted in cloud computing and BCT. Here, an effort is made to evaluate parameters like trust, privacy, and data integrity in articles. Moreover, the chosen articles are classified according to the parameters, and their research results are shown in Table 1. Table 1 shows the comparison of three parameters as data integrity, trust, and privacy and presents that cloud computing and blockchain-based articles have a major issue in security.

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6 Discussion The overhead portion clearly demonstrates the achieved results while forecasting irregularities. keeping every blockchain nodule is rarely needed to authenticate transactions and validate user messages, that can need large computing and storing assets. BC also needs cloud services’ bandwidth and energy assets to execute the mining system. Furthermore, current BC designs remain scalability problems from the perspective of throughput, storage and networking. So this review study will collect more information about cloud computing and suggest that things should be simplified with blockchain technology.

7 Conclusion and Future Directions An established method that has been employed for a long time is known as cloud computing. Cloud computing is deploying computer services over the web, including systems, storage, databases, connection, software, analysis, and knowledge. This provides for speedier innovation, adaptable resources, and economies of scope. Cloud computing services allow customers and designers to manage assets and virtually load the Internet. Cloud computing is used by millions of people worldwide to share and store data. Today, Cloud computing services have faced many challenges regarding security, data storage, and speed. With the aid of BCT, cloud computing services such as security, data integrity, and trust have improved. This research work has analyzed 12 research articles, highlighted the BC and cloud computing challenges regarding three parameters (Data integrity, trust, and privacy), and compared one another in light of the necessity to integrate blockchain and cloud computing. The results highlight that the major issue with cloud computing is data privacy, which scholars have attempted to recover. In the future, data privacy may be solved by using private blockchain technology in which data is accessible only to authorized persons. Because BC is one of the business technologies that is currently receiving the most attention is blockchain. From finance and cybersecurity to intellectual property and healthcare, blockchain technology can bring about significant change and open up new opportunities. The future company practices will undergo a fundamental change according to business models facilitated by blockchain technology. Due to the rapidly digitized global economy and the decentralization of business models and stakeholders made possible by blockchain, its effects on commerce will be gamechanging.

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Socio-Technical Management

Does Product Differentiation Strategy Mediate the Relationship Between Cost Leadership Strategy and Order-Winners? An Empirical Evidence from UAE Retail Industry Ata Al Shraah , Barween Al Kurdi , Enass Khalil Alquqa, Muhammad Turki Alshurideh , Haitham M. Alzoubi , and Samer Hamadneh Abstract In the past decade, the importance of the mediating effect of cost leadership strategies on improving order winners has not been analysed. This research will provide a high level of knowledge to retail industry players. To empirically investigate the relationship between cost leadership and product differentiation in the United Arab Emirates retail industry to improve order winners. A quantitative, descriptive, and exploratory research design was used with a convenient sampling technique that A. Al Shraah Faculty of Economics and Administrative Sciences, Department of Business Administration, The Hashemite University, Zarqa, Jordan e-mail: [email protected] B. Al Kurdi Department of Marketing, Faculty of Business, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan e-mail: [email protected] E. K. Alquqa College of Art, Social Sciences and Humanities, University of Fujairah, Fujairah, United Arab Emirates e-mail: [email protected] M. T. Alshurideh (B) · S. Hamadneh Department of Marketing, School of Business, The University of Jordan, Amman 11942, Jordan e-mail: [email protected]; [email protected] S. Hamadneh e-mail: [email protected] M. T. Alshurideh Department of Management, College of Business Administration, University of Sharjah, Sharjah 27272, United Arab Emirates H. M. Alzoubi School of Business, Skyline University College, Sharjah, UAE e-mail: [email protected] Applied Science Research Center, Applied Science Private University, Amman 11937, Jordan © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 H. M. Alzoubi et al. (eds.), Cyber Security Impact on Digitalization and Business Intelligence, Studies in Big Data 117, https://doi.org/10.1007/978-3-031-31801-6_30

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helped to analyse data from 241 valid respondents through regression with ANOVA. The research results indicate the acceptance of the proposed hypothesis that there is a significant direct relationship between cost leadership strategy and product differentiation, and cost leadership significantly impacting order winners. In contrast, the indirect effect of product differentiation helped to increase firm performance by implementing a cost leadership strategy. This study was limited to examining cost leadership strategy, order winners, and product differentiation. Future research is recommended to comprehensively examine longitudinal research with a SWOT analysis of the retail industry using cost leadership strategies. High-quality products with low prices can maintain customer loyalty and competitive advantage. The retail industry needs technological adaptations that influence market trends with high customer demands. Keywords Cost leadership strategy · Order winners · Product differentiation · Retail industry in the United Arab Emirates

1 Introduction In the modern world, every company relies on cost-efficient production to increase its sales and gain a competitive advantage (Altamony et al., 2012; Obeidat et al., 2021). Selling goods and services at the lowest prices while maintaining a high level of quality allows companies to use the cost leadership strategy to gain competitive advantage, higher revenues, or both (Mehmood, 2021). Increasing operational efficiency, gaining exclusive access to raw materials, leveraging economies of scale, developing special relationships with suppliers, customers, or distributors, and learning on the store floor can help reduce production or operating costs (Ali & Anwar, 2021; Alshurideh, 2022; Shamout et al., 2021). Cost leaders tend to be integrated into proprietary, high-value-added components and services. As a result, they can operate at the highest levels of efficiency along multiple links in the value chain (Alwan & Alshurideh, 2022a, 2022b). Retailers, who often have large market shares, are cost leaders and benefit from several economies of scale in the retail industry (Akhtar et al., 2021). Using technology for product development can positively impact business performance, making product differentiation key to increasing customer loyalty and competitive advantage (Alolayyan et al., 2022; Alshurideh et al., 2019a, 2019b, 2021; Hamadneh et al., 2021; Tariq et al., 2022a, 2022b). A feature that is used to persuade customers to buy is called an order winner (Eli & Hamou, 2022; Miller, 2021). If a company wants to improve order winners, it should develop requirements significantly different from its competitors (Ghazal et al., 2022). Therefore, the retail industry in the United Arab Emirates was selected to measure business efficiency by strategically implementing cost leadership strategies through product innovation (Kashif et al., 2021).

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2 Theoretical Framework 2.1 Cost Leadership Strategy A comprehensive approach for a company defines a critical component of the corporate strategy to operate as a cost-effective organization (Al-Dmour et al., 2021; Alaali et al., 2021). The motivations for this strategy include the company’s low-cost products of high quality (Abuanzeh et al., 2022; Baabdullah et al., 2019). Investment in production equipment, quality management, careful monitoring, cost-saving measures (Victoria, 2022), and expertise are all critical components of the cost leadership approach (Al-Dhuhouri et al., 2021; Alameeri et al., 2020; AlShehhi et al., 2021; Harahsheh et al., 2021; Madi Odeh et al., 2021; Valipour et al., 2012).

2.2 Order Winners The qualities that a company’s products should exhibit even before product development are order winners and qualifiers (Alzoubi & Ramakrishna, 2022). Therefore, it is critical to consider order winners and qualifiers when developing a plan (Alsharari, 2021). The selection of order winners and qualifiers is the most crucial step for a company’s development and performance (Khan & Qianli, 2017).

2.3 Product Differentiation This strategy is about creating outstanding products or exceptional services, relying on customers’ brand loyalty (Abu Zayyad et al., 2020; Aljumah et al., 2021; Alwan & Alshurideh, 2022a; Tariq et al., 2022a, 2022b). A company may offer higher quality, better performance, or more differentiating features, which would justify a price increase (Boehe & Cruz, 2010; Qi et al., 2022). Companies that excel at product differentiation often invest heavily in research and development to increase their innovation capabilities (Farouk, 2022; Kasem & Al-Gasaymeh, 2022) and improve their ability to keep pace with their competitors’ innovations (Al-Khayyal et al., 2020a; Al Suwaidi et al., 2020; Almaazmi et al., 2021; Ghannajeh et al., 2015; Nuseir et al., 2021; Zu’bi et al., 2012).

2.4 Industry Description The Dubai Chamber of Commerce and Industry forecasts retail sales in the UAE will increase 6.6% annually to AED 258 billion (US $70.5 billion) by 2025. After

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two years of economic uncertainty, consumer spending is rising along with retail sales. According to experts, the UAE will see growth in 2022, with total household spending rising to AED 463.3 billion (US $126 billion) from AED 447.3 billion (US $121 billion) in 2021. Emerging trends such as webrooming, showrooming, digital personalisation, in-store tracking, and greater use of social media are expected to significantly change the retail industry. Choosing a retail industry according to strategic execution can help achieve ultimate business goals.

3 Literature Review 3.1 Relationship and Impact of Cost Leadership Strategy on Product Differentiation Cost leadership companies must strictly manage costs, avoid excessive spending on marketing or innovation, and lower the prices at which they offer their goods (Correia et al., 2020; Eli, 2021). Companies that excel in product differentiation typically engage in extensive research and development activities to expand their innovation capabilities (Alsharari, 2022) and improve their ability to keep pace with their competitors’ advances (Ray Gehani, 2013). Therefore, corporate efficiency involves the higher capacity to invest in product differentiation by implementing a cost leadership strategy to increase sales and gain a competitive advantage (Alzoubi, 2022; Goria, 2022). H1: Cost leadership strategy has a significant impact on product differentiation.

3.2 Relationship and Impact of Cost Leadership Strategy on Order Winners Using leverage effects to improve management effectiveness, which lenders can monitor, will benefit companies that plan to improve cost leadership strategy to achieve higher order profit (Al-Bawaia et al., 2022; Ashal et al., 2021; Lee et al., 2022; Taryam et al., 2020). According to previous research, lender monitoring will significantly limit managers’ opportunistic actions by limiting the resources available for uncontrolled spending (Ali & Anwar, 2021; Ghosh & Aithal, 2022). Therefore, debt control is essential for companies that want to be efficient (Alzoubi & Aziz, 2021; Butt, 2022). Companies seeking cost leadership must strictly manage costs and limit their potential financial risk for marketing and innovation (Al Naqbi et al., 2020; Alameeri et al., 2021). H2: Cost leadership strategy has a significant impact on order winners.

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3.3 Relationship and Impact of Product Differentiation Strategy on Order Winners Considering the organisational benefits, a company can gain from a differentiation strategy. The ability of a company to differentiate itself from its competitors in a crowded market is one of the main advantages (Ghazal et al., 2021; Qi et al., 2022). Therefore, customers are willing to pay a higher price for such a unique product and become less price sensitive (Akhtar et al., 2022; Valipour et al., 2012). Moreover, differentiation strategy can be divided into sub-strategies in response to the ongoing development and complexity of the business environment. For example, differentiation through product innovation, focus on customers’ needs, or marketing and image management can enhance business performance (Haque et al., 2021). H3: The product differentiation strategy has a significant impact on order winners.

3.4 Relationship and Impact of Cost Leadership on Order Winners with Mediating Impact on Product Differentiation In order to develop a business strategy and compete successfully, Porter and Advantage (1985) established a framework (Ratkovic, 2022). He argued that a firm must decide whether to compete as the lowest cost producer in its industry (i.e., cost leadership) or to offer unique products in terms of quality (Ahmed & Al Amiri, 2022), physical characteristics, or product-related services (i.e., product differentiation strategy) (Qasaimeh & Jaradeh, 2022). Moreover, he emphasised that a company’s ability to purposefully select a set of activities (Amrani et al., 2022) that provide a particular mix of value to its customers is at the heart of its business strategy (Jermias, 2008). H4: Cost leadership significantly impacts order winners through product differentiation.

3.5 Problem Statement and Research Gap The goal of the cost leadership approach is to consistently reduce costs while providing customers with the lowest price for a given set of goods or services (Al-Khayyal et al., 2020b; Alshurideh, 2013). Striking the right balance between quality and price is critical to lowering the cost of cost leadership because, at some point, customers will no longer associate deteriorating quality with lower prices (Al Dmour et al., 2014; Alshurideh et al., 2019a, 2019b; Nasim et al., 2022; Wilke & Zaichkowsky, 1999). For this reason, a retail industry was selected to analyse the implementation of cost leadership strategies to improve order profits

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H3

H4

Cost Leadership Strategy

Oder Winners

H2

Fig. 1 Research model construct

and customer satisfaction (Del & Solfa, 2022). One of the most important strategies a retail company should consider is adopting market leadership to increase order profits (Alzoubi & Ahmed, 2019; Alzoubi & Yanamandra, 2020). In addition, previous studies have not clarified enough characteristics of cost leadership strategies, including their impact on order winners (Alzoubi et al., 2017; Mondol, 2022). Over many years, progress has been made in developing cost leadership strategies and methods to improve contract profits in order-winner to improve business performance (Radwan, 2022). Therefore, this research was conducted to identify the gap in participation in strategic implementation to improve business performance in the retail sector in the United Arab Emirates.

3.6 General Research Model See Fig. 1.

3.7 Research Hypothesis H1: Cost Leadership Strategy has no statistical impact on Product Differentiation Strategy in the Retail Industry in UAE at (α ≤ 0.05) level. H2: Cost Leadership Strategy has no statistical impact on Organisational Performance at Retail in UAE at (α ≤ 0.05) level. H3: Product Differentiation Strategy has no statistical impact on Organisational Performance in the Retail industry in UAE at (α ≤ 0.05) level.

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H4: Cost Leadership Strategy has no statistical impact on Organisational Performance with the mediating effect of Product Differentiation Strategy at the Retail Industry in UAE at (α ≤ 0.05) level.

3.8 Research Methodology and Design A quantitative approach was used in this research. The exploratory, descriptive and analytical research design was used to evaluate the research variables. In contrast, a convenient sampling technique was used, and the sample size was clustered for the city of Abu Dhabi in the United Arab Emirates. The primary data source was a questionnaire to obtain empirical data. SPSS was used to measure the data with correlation and regression.

3.9 Population, Sample and Unit of Analysis The target population of this research is the retail industry in UAE. On the other hand, the sample consists of 88 retail companies located in the UAE city of Abu Dhabi, e.g., Walmart, IKEA, Carrefour, and MH-Alshaya were used as research samples. The online questionnaire was emailed to 750 respondents (logistics managers, advertising and marketing managers, inventory managers); 241 respondents received valid data for analysis. A 26-question online questionnaire with seven items measuring cost leadership strategy was developed. Ten items were used to measure product differentiation, and nine were used to measure order winners. The questionnaire was developed on a five-point Likert scale ranging from 1 for “strongly disagree” to 5 for “strongly agree.”

4 Data Analysis 4.1 Demographic Analysis See Table 1.

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Table 1 Demographic data Items

Description

f

Male

%

Gender 173

71.8

Female

68

28.2

18–25

14

5.8

26–35

60

24.9

36–45

115

47.7

52

21.6

111

46.1

Advertising and marketing manager

75

31.1

Inventory manager

30

12.4

Other employees

25

10.4

Age

46 and above Designation Logistics manager

(n = 241), Male = 71.8%, Female = 28.2%

4.2 Reliability Analysis, Descriptive and Correlations Coefficients The results of data analysis showed good reliability and validity of data for further analysis. CLS α = 0.80, PDS α = 0.85, and OW α = 0.83 show high data reliability. The mean value is in the range of (M = 21, SD = 53%) for CLS, (M = 33, SD = 70%) for PDS, (M = 31, SD = 58%) for OW of the data. Table 2 shows a high correlation coefficient between the variables. CLS with PDS showed high correlation (0.836) ** with significance level at P < 0.05, PDS with OW showed high correlation (0.800) ** with significance level at P < 0.05, and CLS with OW highly correlated (0.713) ** with significance level P < 0.05. Table 2 Reliability, descriptive and correlation coefficients summary Variables

α

Mean

Std deviation

CLS

Cost leadership strategy

0.80

2.1

0.53

1

Product differentiation strategy

0.85

3.3

0.70

0.836**

1

Order winners

0.83

3.1

0.58

0.713**

0.800**

PDS

OW

1

Cronbach’s Alpha for CLS = 0.80, PDS = 0.85, OW = 0.83 (M = 2.1, SD = 53%), PDS (M = 3.3, SD = 70%), OW (M = 3.1, SD = 58%), *P < 0.001, **P < 0.05

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Table 3 Linear regression analysis Regression weights

β

R2

Adjusted R2

p-value

CLS → OW

0.713

0.508

0.506

0.000

PDS → OW

0.800

0.640

0.638

0.000

CLS → PDS → OW

0.804

0.646

0.634

0.000

CLS = Cost Leadership Strategy, OW = Order Winner, PDS = Product Differentiation Strategy Significance level at **P < 0.05 Dependent Variable = Order Winners

Table 4 Hypothesis testing using ANOVA Hypothesis Model weights

Standardised R2 beta coefficients

Adjusted Sig R2

t-value Status

H1

CLS → PDS

0.836

0.700 0.698

0.000 23.5

Supported

H2

CLS → OW

0.713

0.508 0.506

0.003 2.08

Supported

H3

PDS → OW

0.800

0.640 0.638

0.000 9.26

Supported

H4

CLS → PDS → OW 0.804

0.646 0.643

0.000 9.22

Supported

* Significance Level (α ≤ 0.05) ** Critical t-value (df/p) = 1.64

4.3 Regression Analysis Linear regression analysis indicates how strongly one variable is related to another. The data analysis showed a significant relationship between CLS and OW at P < 0.05 and β = 0.713. The results show the strong relationship of PDS with OW at the significance level P < 0.05, β = 0.800 indicates a positive significance level, while the mediating effect of PDS showed a significant relationship at the level P < 0.05, β = 0.804. Table 3 shows the summary of the data.

4.4 Hypothesis Testing See Table 4.

5 Discussion of the Data The research evaluates the overall fit of the model by indicating hypothesis support. The results of the data analysis indicate a significant relationship between CLS and PDS with positive β = 0.863 and t = 23.5; a higher critical value indicates

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a significance level of the variable. Thus, H1 was accepted. The diversification of companies’ investments to increase sales through the strategy of cost leadership and the improvement of order winners through innovation has a great advantage for the competitiveness of companies (Al Shraah et al., 2022; Correia et al., 2020). CLS and OW showed a significant relationship with positive β = 0.713 and t = 2.08, indicating a supportive relationship, so H2 is also accepted. The cost leadership strategy aims to leverage the company’s industry-leading, low-cost product offerings to maintain product quality and improve organisational performance (Mahfoud et al., 2017). While PDS with OW also showed significant influence at P level < 0.05, β = 0.800. t = 9.26. Thus, H3 is also accepted. Product differentiation due to the strong emphasis on innovation means that firms must undertake riskier ventures with undeveloped products. On the other hand, innovation will likely favour customer demand and increase firm performance. The mediating relationship shows a significant influence of PDS mediation with CLS on OW β = 0.804, t = 9.22 shows a significant positive influence. H4 is also accepted. The literature has examined the importance of product innovation in enhancing firm performance (AlMehrzi et al., 2020; Awadhi et al., 2021; Grinerud et al., 2021). Table 4 shows a summary of the hypothesised results. This study contributes to the literature to provide a more comprehensive understanding of cost leadership strategies.

6 Conclusion The research findings suggest the following conclusions. First, cost leadership strategy positively affects order winners, and the retail industry encourages the adoption of cost leadership strategies by saving costs and improving product differentiation. Second, the differentiation strategy has a significant and favourable effect on order winners. Third, the cost leadership strategy has a certain positive effect on business performance. The UAE’s large retail industry gains more customers for better business performance.

7 Recommendations/Limitations Based on the analysis, the research findings are expected to help retail industry owners, especially retail companies, choose a strategy to compete effectively under the circumstances, business conditions, and the market. They might choose the differentiation strategy because it can enhance business performance and give them a competitive advantage. The results of this study can serve as a guide for organisations to assist, especially in choosing the most competitive strategy to enhance business growth and performance. Research needs to focus on future studies that consider generalizability and longitudinal research that needs to be conducted in the

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future. This research is limited to the population. Future research should examine market trends (a SWOT analysis) if the cost leadership strategy is adopted.

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Does Organizational Culture Moderate the Relationship Between Business Process Reengineering and Business Value in the UAE Banking Industry Enass Khalil Alquqa, Ata Al Shraah , Mohammed T. Nuseir , Muhammad Turki Alshurideh , Haitham M. Alzoubi , and Barween Al Kurdi Abstract The objective is to identify the proposed model containing moderating effect of organisational culture on the relationship between business process reengineering and business value for the banking sector in the UAE. The business reengineering process and its strategical implementation with moderating effect of organisational culture to improve business value have never been considered before for the research, specifically in the banking sector of the UAE. A total of 112 bank branches in Dubai UAE were selected as the research population. A quantitative research E. K. Alquqa College of Art, Social Sciences and Humanities, University of Fujairah, Fujairah, United Arab Emirates e-mail: [email protected] A. Al Shraah Faculty of Economics and Administrative Sciences, Department of Business Administration, The Hashemite University, Zarqa, Jordan e-mail: [email protected] M. T. Nuseir Department of Business Administration, College of Business, Al Ain University, Abu Dhabi Campus, P.O. Box 112612, Abu Dhabi, UAE e-mail: [email protected] M. T. Alshurideh (B) Department of Marketing, School of Business, The University of Jordan, Amman 11942, Jordan e-mail: [email protected]; [email protected] Department of Management, College of Business Administration, University of Sharjah, Sharjah 27272, United Arab Emirates H. M. Alzoubi School of Business, Skyline University College, Sharjah, UAE e-mail: [email protected] Applied Science Research Center, Applied Science Private University, Amman 11937, Jordan B. Al Kurdi Department of Marketing, Faculty of Business, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 H. M. Alzoubi et al. (eds.), Cyber Security Impact on Digitalization and Business Intelligence, Studies in Big Data 117, https://doi.org/10.1007/978-3-031-31801-6_31

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technique and cluster sampling were used with descriptive, causal, and exploratory designs. A valid sample size of 263 respondents was used to evaluate the model and moderator using SPSS and Hayes Process Macro. The findings revealed a positive significant direct relationship between business process reengineering and business value and a positive significant direct relationship between organisational culture and business value. In contrast, the moderating effect of organisational culture was found to strengthen the relationship between business process reengineering and BV. Data collected in the banking sector only limits the generalizability. Future research should explore other sectors and the different BPR constructs. Redesigning a business is crucial with time expansion; this research will benefit the banking sector by enhancing the knowledge to significantly boost performance in essential areas, including service, quality, cost, and speed. Keywords Organizational culture · Business process reengineering · Business value · Banking sector in the UAE

1 Introduction A financial entity, typically a bank, provides its clients with banking and other financial services. Banks are part of the financial services industry and crucial to world economies (Al-Jarrah et al., 2012; Assad & Alshurideh, 2020b). They serve an essential role in fostering financial development (Al-Gasaymeh et al., 2015; Assad & Alshurideh, 2020a). Banks’ care for capital growth enables economies to expand and prosper (Alsharari, 2021; Puntilo, 2009; Shah et al., 2020). Banks act as a middleman between asset providers and clients because businesses and governments require money to operate (Butt, 2022; Mehmood, 2021). An authoritative understanding between the bank and the client is necessary for banking legislation (Al-Bawaia et al., 2022; Shah et al., 2021). The customer is any substance for which the bank grants permission to conduct a record or activity (Al Kurdi et al., 2020a; Mondol, 2022). The criteria for success in the banking sector depend on the implementation of managerial strategies that assist in creating business value and trust (Alkalha et al., 2012; Qi et al., 2022). Business process reengineering is the practice of replicating a core business process to increase product yield, improve product quality, or reduce costs (AlKhayyal et al., 2020; Zu’bi et al., 2012). It typically entails examining organisational work processes, identifying wasteful or trashy procedures, and figuring out how to eliminate or replace them (Abdolvand et al., 2008; Alolayyan et al., 2022; Alshamsi et al., 2020). Innovation was created quickly; several organisations began completing business process reengineering activities. The implementation of this strategy can allow us to analyse the banking progress (Akhtar et al., 2022; Baabdullah et al., 2019). Organisational culture is about covert beliefs, values, and communication styles that contribute to an organisation’s exceptional social and mental state (Abuhashesh

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et al., 2021; Madi Odeh et al., 2021). Culture depends on common viewpoints, attitudes, habits, and unwritten and written rules that have developed over time and are considered important (AlShehhi et al., 2021; Hamadneh et al., 2021). This research will assess the organisational culture’s moderating impact on the relationship between business process reengineering and business value (Alshurideh, 2022; Ashal et al., 2021). The ultimate purpose of the research is to analyse the business value, which entails the motivation behind the organisation to make and convey value proficiently (Akhtar et al., 2021; Kashif et al., 2021; Farouk, 2022) so that it will create benefit after expense. Moreover, this research will contribute to the literature by assisting the banking sector process reform, information technology, and organisational performance.

2 Theoretical Framework 2.1 Business Process Reengineering Business process reengineering is a management technique that increases the productivity of business processes (AlShurideh et al., 2019; Amrani et al., 2022; Ghosh & Aithal, 2022; Obeidat et al., 2021). The best way for organisations to achieve BPR is to look at their business processes with a “fresh start” perspective and determine how to effectively develop them to enhance how they lead their businesses (Grover et al., 1995). To achieve significant improvements in cost, quality, speed, and administration, business processes must be fundamentally reexamined and completely overhauled (Al Mehrez et al., 2020; Altamony et al., 2012; Del & Solfa, 2022; Hamadneh et al., 2021). BPR combines a method for increasing business development with a method for significantly improving business procedures, enabling an organisation to become a much more solid (Ghazal et al., 2021) and fruitful competitor in the competitive market (Abuanzeh et al., 2022; Anand et al., 2013; Kabrilyants et al., 2021; Shamout et al., 2021; Shra’ah, 2009).

2.2 Organizational Culture The ability to absorb the organisational effectiveness occurs through business culture (Goria, 2022; Nasim et al., 2022). Organisational culture is the covert beliefs, values, and communication styles that contribute to an organisation’s exceptional social and mental state (Alzoubi & Yanamandra, 2020). The business’s founder and administrative staff impact organisational culture due to their involvement in strategic decision-making (Imran et al., 2021).

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2.3 Create Business Value Business value drives a company to produce and deliver value in a way that generates profit after incurring costs (Alwan & Alshurideh, 2022; Alzoubi et al., 2017; Ramirez et al., 2010). It is essential to understand that value creation is the foundation of all businesses, whether they are successful or not (Al Kurdi et al., 2020b). The broadest sense of the phrase is that value is created via labour (Alzoubi & Ahmed, 2019). The goal of a business is to create value with work efficiency (Alzoubi, 2022), transfer that value to customers, and retain a portion of that value as profit. Business value is real and tangible (Soh & Markus, 1995).

2.4 Operational Definitions

Variables

Description

References

Business process reengineering

BPR is a method for interactively aligning work processes with customer expectations to meet long-term organisational goals

Guha et al. (1993)

Business culture

Organisational culture refers to the beliefs, norms, and customs that all employees must adhere to achieve a common objective

Imran et al. (2021)

Business value

Business value is the whole worth of Soh and Markus (1995) a company, including all of its tangible and intangible assets

2.5 Industry Description Currently, there are 23 local and 28 international banks serving the market in the UAE, which has a very fragmented banking industry. Banks hold most of all domestic assets with corporate offices in Abu Dhabi and Dubai. The central bank is the main body in charge of financial regulation in the UAE. In the UAE, there are 30 foreign banks and 22 domestic banks. The 60% of assets in the banking sector are held by the country’s five largest banks, which are larger banks. Furthermore, the proposed research aimed to assess how banks develop a business value for customers with business process reengineering and whether business culture has a moderating impact on strengthening their relationships.

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3 Literature Review 3.1 Relationship and Impact of Business Process Reengineering on Business Value A decade of studies shows that Business Process Reengineering (BPR) has been extensively discussed in the literature, but there is still a lot of misconception about what BPR is and how it differs from other change programs, especially among managers (Kasem & Al-Gasaymeh, 2022; O’Neill & Sohal, 1999; Qasaimeh & Jaradeh, 2022; Shra’ah, 2009). As the foundation of competition evolves from cost and quality to flexibility and responsiveness, the significance of process management is widely recognized (Ahmed & Al Amiri, 2022; Ratkovic, 2022). Business Process Reengineering (BPR) was initially described as the role that process management may play in establishing a sustainable competitive advantage (Radwan, 2022) and creating business value (Alsharari, 2022; Grover et al., 1995; Nuseir et al., 2021). The prior studies indicate a positive relationship between business process reengineering and business value (Miller, 2021; Ramirez et al., 2010; Victoria, 2022). H1: Business process reengineering has a significant impact on the business value.

3.2 Relationship and Impact of Organisational Culture on Business Value Generally, organisational culture (OC) refers to the norms, values, beliefs, and interactions between employees. Certain structures gauge these values and beliefs (Imran et al., 2021). Several studies show a significant impact of cultural variables on business performance. Cultures influence how individuals should be handled based on their preferences and values (Alzoubi & Ramakrishna, 2022; Eli & Hamou, 2022; Ghazal et al., 2022). Additionally, it affects the functional areas of distribution, sales, and marketing. It might impact how a business assesses and chooses to enter a new market (Ramirez et al., 2010; Yarbrough et al., 2011). H2: Organisational culture has a significant impact on the Business Value.

3.3 Moderating Impact of Organisational Culture on the Relationship Between Business Process Reengineering and Business Value Business Process Reengineering influences the strategic development of organisational management and the implementation of strategies to achieve business value.

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Based on several authors (Eli, 2021), the main goal of business process management is to rethink processes to improve performance from the customer’s perspective (Al Suwaidi et al., 2020; Fetais et al., 2022). Business performance and customer value can be achieved by implementing management strategies efficiently (Alzoubi et al., 2019). Some researchers investigated a lack of comprehension of the BPR and its connections to other elements influencing the character of work and its settings, such as the use of IT systems and organisational culture can harm the organisational performance that supports implementation (Anand et al., 2013; Fetais et al., 2022; Grover et al., 1995; Qi et al., 2022). This research investigated organisational culture’s moderating impact on the relationship between BPR and business value. However, there is no evidence of organisational culture’s moderating impact on strengthening or weakening the relationship between BPR and BV. H3: Organisational culture moderates the relationship and impact between Business process reengineering and Business Value.

3.4 Problem Statement and Research Gap Business process reengineering is needed because in the dynamic environment; reengineering creates value for the customers. It creates a culture of change and learning to satisfy the end goal of value creation. In the baking industry, change is significant. This industry is dynamic and requires the best use of technology to be implemented for better results. Competition is key which is possible only if the organisation has implemented the advanced tools for the operations. This research needs to test the hypothesis to determine how business process reengineering helps the banking sector to create value for the customers and if business culture moderates the relationship between BPR and BV. Prior studies have not discussed the factor of organisational culture as a moderator. This research will examine whether OC’s moderating effect strengthens or weakens the relationship between BPR and BV.

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3.5 General Research Model

Business Process Reengineering

H1

Business Value

H3 H2 Organization Culture

3.6 Research Hypothesis HO1 : Business process reengineering has a statistically impact on Business value in the Banking Industry at (α ≤ 0.05) level of significance. HO2 : Organisational culture has a statistical impact on Business value in the Banking Industry at (α ≤ 0.05) level of significance. HO3 : Organisational culture moderates the relationship and impact between Business process reengineering and Business value in the Banking Industry at (α ≤ 0.05) level of significance.

3.7 Research Methodology and Design To assess the empirical analysis of the relationship between business process reengineering and business value with moderating impact of business culture, the research aims to collect data from the UAE’s banking sector. To assess the variables, a survey questionnaire was used in a descriptive, explanatory, causal, and analytical research design. A cluster sampling technique was adopted due to the industry’s magnitude; thus, the banks in Dubai were used as a sample. The primary data was obtained from an online survey. The demographic, reliability, descriptive, correlation, regression, and hypothesis testing were all analysed using SPSS and the moderating impact of the Hayes Process Macro’s used through SPSS.

534 Table 1 Demographic statistics

E. K. Alquqa et al. Item

Description

f

Gender

Male

Age

191

72.6

Female

72

27.4

18–25

14

5.3

26–35

66

25.1

36–45

127

48.3

45 and above Designation

%

56

28.1

121

46.0

Branch manager

77

29.3

Regional bank manager

34

12.9

HR manager

31

11.8

Relationship manager

(N = 263), Male = 191, Female = 72

3.8 Population, Sample and Unit of Analysis For the purpose of this research, the banking sector in the UAE was selected as the research population, and 112 bank branches were targeted to collect the data. Two hundred sixty-three responses with accurate data were received, and 800 questionnaire emails were sent to the correspondents (managers, branch managers, regional managers etc.). A 34-item questionnaire was developed to measure the BPR, BC and BV. To assess the business process of reengineering, 13 items were used. Ten items were used to measure the business culture, and 11 items were used to assess the business value. A five-point Likert scale, from 1 strongly disagree to 5 strongly agree, was designed to collect responses.

4 Data Analysis 4.1 Demographic Analysis See Table 1.

4.2 Reliability, Descriptive and Correlation Coefficients The reliability analysis through Cronbach’s Alpha shows a good reliability value for performing further tests. BPR = 0.83, BV = 0.84 and OC = 0.70 indicated that the data is reliable enough to execute the further analysis. Descriptive analysis shows the most likely agree to the questionnaire items with a value of (M = 4.15 SD =

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Table 2 Reliability, descriptive and correlation Variables

Cronbach’s alpha

Mean

Std deviation

BPR

Business process reengineering

0.83

4.15

0.90

1

BV

BC

Business value

0.84

3.44

0.67

0.852**

1

Organizational culture

0.70

3.89

0.69

0.694**

0.624**

1

Correlation coefficients significant level at **P < 0.05, *P < 0.001

Table 3 Moderation effect of OC on the relationship between BPR and BV Relationship

β

R2

SE

t-value

p-value

BPR → BV

0.593

0.512

0.161

2.73

0.000

OC → BV

0.118

0.430

0.168

2.01

0.000

Moderating effect (BPR * OC) → BV

0.881

0.776

0.004

2.08

0.000

SE = standard error, significant level at **P < 0.05

90%) for BPR. (M = 3.44, SD = 67%) for BV shows the good extent to agree. (M = 3.89, SD = 69%) for OC indicates that the majority strongly agree. Correlation depicts the strong relationship of BPR with BV by 0.852 and significant at level **P < 0.05. OC results indicated a high correlation with BV by 0.624 and a significance level at **P < 0.05. The value for BPR and OC also shows a significant positive relationship with 0.694 and a significance value at level **P < 0.05 (Table 2).

4.3 Moderator Analysis The data was analysed for moderating effect by Hayes Process Macros using SPSS. The results assessed the moderating role of organisational culture (OC) on the relationship between business process reengineering (BPR) and business value (BV). The results reveal a significant moderating impact of OC on the relationship between BPR and BV (β = 0.69, t = 2.78, P = 0.0002), supporting H3. The moderation analysis summary is presented in Table 3.

4.4 Hypothesis Testing The research hypothesis results indicate the significant positive impact of BPR on BV (β = 0.593, P = 0.000), and H1 is supported here. The H2 also showed a significant positive impact (β = 0.118, p = 0.000); thus, H2 is also accepted. The H3 revealed

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Table 4 Hypothesis analysis Hypothesis

Regression weights

β

R2

Adjusted R2

p-value

Hypothesis

H1

BPR → BV

0.593

0.512

0.411

0.000

Supported

H2

OC → BV

0.118

0.430

0.233

0.000

Supported

H3

OC * BPR → OW

0.881

0.776

0.433

0.000

Supported

a moderating effect of OC on the relationship between BPR and BV (β = 0.881, p = 0.000). H3 is also accepted in this research. The summarised Table 4 is shown.

5 Discussion of the Data Based on the research findings, business process reengineering (BPR) significantly impacts business value BV. Table 4 shows hypothesis testing results that demonstrate BPR significantly impacts BV by (β = 0.59, t = 2.73), indicating a high impact. Several studies show that one of the various programs that can result in effective organisational change is process reengineering, which ultimately enables business value and performance (Nkomo & Marnewick, 2021). However, the statistical analysis indicates that OC has a significant relationship with BV (β = 0.11, t = 2.01). Previous literature shows that the technique used in organisational assessments determines the need for organisational transformation, identifies subcultures, and assesses how well mergers and acquisitions might fit to achieve business value (Jondle et al., 2014). The third hypothesis, OC as moderator, was assessed to get the statistical results that show a significant impact of moderator on BPR and BV by (β = 0.88, t = 2.08), which reveals a significance level. Organisational culture affects how managers perceive and respond to their surroundings with the strategic implementation to improve business value (Alaali et al., 2021; AlShehhi et al., 2021). Hence, this research investigated the OC as a moderator to strengthen the relationship between BPR and BV.

6 Conclusion Research findings concluded that reengineering helps organisations implement noticeable changes in the manner and speed of their response to client requests. An organisation can change from having a standard-driven and occupation-focused structure to one that promotes and legitimately focuses on the customer through reengineering. The banking sector can adopt the BPR strategy to enhance customer services and create business value by achieving strategic goals.

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7 Recommendations/Limitations It is proposed that future research should expand the suggested approach to cover several services and production companies and how they prefer to implement the BPR to gain a competitive edge. This research contributes to the literature and provides a great asset of knowledge concerning the increasing business value and strategic implementations. This research has some limitations for future research; a comprehensive analysis with longitudinal research is recommended to prevent generalizability; it is also necessary to develop new constructs for BPR determined by the sector. Future research should consider different elements of BPR.

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The Impact of Customisation Strategy and Product Variety on Operational Performance in the UAE Construction Industry Ahmad AlHamad , Barween Al Kurdi , Mohammed T. Nuseir , Haitham M. Alzoubi , Muhammad Turki Alshurideh , and Samer Hamadneh Abstract The research primarily focuses on analysing the impact of customisation strategy and product variety on operational performance in the UAE construction industry. Customisation strategy implementation by providing product variety validated the increase in operational performance for the UAE construction industry and has proved a contemporary contribution to the research. The data of 34 construction companies based in Sharjah was utilised. A quantitative approach employed a practical cluster sampling and a descriptive, causal, and exploratory research design. A valid sample size of 282 respondents was used to assess the model by regression and ANOVA. The study found that customisation strategy has a significant impact and direct relationship with operational performance, and product variety has a positive direct relationship with a significant impact on an operational strategy A. AlHamad · M. T. Alshurideh Department of Management, College of Business Administration, University of Sharjah, 27272 Sharjah, United Arab Emirates e-mail: [email protected] B. Al Kurdi Department of Marketing, Faculty of Business, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan e-mail: [email protected] M. T. Nuseir Department of Business Administration, College of Business, Al Ain University, Abu Dhabi Campus, P.O. Box 112612, Abu Dhabi, UAE e-mail: [email protected] H. M. Alzoubi School of Business, Skyline University College, Sharjah, UAE e-mail: [email protected] Applied Science Research Center, Applied Science Private University, Amman 11937, Jordan M. T. Alshurideh (B) · S. Hamadneh Department of Marketing, School of Business, The University of Jordan, Amman 11942, Jordan e-mail: [email protected]; [email protected] S. Hamadneh e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 H. M. Alzoubi et al. (eds.), Cyber Security Impact on Digitalization and Business Intelligence, Studies in Big Data 117, https://doi.org/10.1007/978-3-031-31801-6_32

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that improves the organisation’s competitiveness. Analysis of customisation strategy, product variety and operational performance is limited to concluding research widely. In the manufacturing industry, lean construction is recommended for analysing operational performance for future research. Operational performance leads to every organisation’s objective to achieve a competitive advantage. Increased operational performance through customisation strategy can benefit the overall construction industry. Keywords Customisation strategy · Product variety · Operational performance · The construction sector in the UAE

1 Introduction The construction industry is perceived as an economic sector of any country that contributes the largest portion to the economy (Ali & Rahmat, 2010). The industry plays a vital role in the nation’s economic development, but it currently faces various challenges that impact venture objectives and the economy’s steady growth (Alzoubi et al., 2022i). Construction is a high-risk industry that includes planning, building, developing, changing, maintaining, fixing, and ultimately destroying structures (Alzoubi et al., 2022o; Awawdeh et al., 2022b; Kasem & Al-Gasaymeh, 2022). It also entails civil engineering work, mechanical and electrical engineering, and other similar tasks. One of the industries that blast the most is the construction industry (Alzoubi et al., 2022e). The entire construction procedure can be grouped into different fragments, such as the construction of structures: Residential, mechanical, business, and different structures (Alzoubi et al., 2019; Butt, 2022; Ghazal et al., 2022). Overwhelming and civil engineering construction: the building of sewers, streets, roadways, scaffolds, burrows, and different tasks (Mondol, 2022). Specific exercises such as carpentry, painting, plumbing, and electrical work (Altamony et al., 2012; Mehrez et al., 2021; Nuseir et al., 2020; Zu’bi et al., 2012). While dealing with the construction industry, the customisation goal can easily be attained because developers and builders have freedom of customisation at a maximum level (Alzoubi et al., 2020c; Da Rocha et al., 2015; Farouk, 2022; Radwan, 2022; Sezer, 2016). They are closer to customers’ needs and have a good perception to satisfy the needs through product variation (Almaazmi et al., 2021; Alzoubi et al., 2022a; Alzoubi & Ramakrishna, 2022; Ghannajeh et al., 2015). They have choices to build villas, apartments, townhouses or units etc. (Alzoubi et al., 2022h; Amrani et al., 2022; Del & Solfa, 2022). This research investigates three variables, including customisation strategy, product variety and operational performance in the construction industry in the UAE (Al Suwaidi et al., 2020; AlShehhi et al., 2021; Awawdeh et al., 2022a; Madi Odeh et al., 2021). The empirical analysis will demonstrate the impact of customisation strategy and product variety on operational performance.

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2 Theoretical Framework 2.1 Customisation Strategy Customisation is the area or customer-specific strategy where the focus is high on the cross-border differences while making product provisions (Akhtar et al., 2022; Alzoubi et al., 2021b; Nahmens & Bindroo, 2011). Customisation refers to a countryspecific product technique in global marketing that focuses on cross-border differences in target customers’ wants and needs, suitably altering items to make them compatible with local market conditions (Aljumah et al., 2021b; Alzoubi et al., 2022f, 2022j; Nasim et al., 2022). Mass customisation is a manufacturing strategy that combines custom goods’ adaptability and personalisation with the cheap unit costs associated with mass production (Ahmed & Al Amiri, 2022; Alzoubi et al., 2021a; Ratkovic, 2022). While maintaining costs that are more or less the same as those of mass-produced items, mass customisation allows a customer to design specific features of an item (Alzoubi & Aziz, 2021; Da Rocha et al., 2015).

2.2 Product Variety Product variety can be explained by the number of products or services a company offers to meet the needs and demands of various customers in a particular industry (Alzoubi et al., 2021c; Ghosh & Aithal, 2022; Lyons et al., 2020). How many variations of a product a provider advertises will depend on how many product variations are provided by competitors and how much of the market is divided (Aljumah et al., 2022a; Alzoubi et al., 2022k; Ghazal et al., 2021). When selecting a product variety (Alsharari, 2022; Alzoubi, 2022), the company should consider positioning its brands to serve its target market segments without experiencing excessive brand duplication (Alsharari, 2021; Alzoubi et al., 2022c; Goria, 2022) in any category and the additional costs of producing small volumes of many assortments with a resulting loss of institutionalisation economies (Alzoubi et al., 2020d; Lee et al., 2022; Mehmood, 2021; Trattner et al., 2019).

2.3 Operational Performance Several approaches measure the performance of an organisation to determine whether it is improving admirably (Alzoubi et al., 2017; Battistoni et al., 2013). The most well-known tactic is to consider the gross or net profit. Nevertheless, this is not often reliable for assessing an organisation’s performance (Alzoubi et al., 2022m; Ashal et al., 2021). The operational performance areas that an organisation seeks to enhance

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to achieve its corporate strategy are known as operational performance objectives (Al Kurdi et al., 2020; Alzoubi et al., 2022c; Fetais et al., 2022).

2.4 Operational Definitions

Variables

Definition

References

Operational performance

Operational performance is best described as the capacity of various company units to collaborate more successfully and yield more output

Razak et al. (2015)

Customisation strategy

Customisation is an advanced approach to addressing shifting consumer needs that helps businesses improve customer interaction

Sunikka and Bragge (2008)

Product variety

Product variety is defined as the number of product variants a provider advertises based on the degree of market fragmentation and the number of product variants that rivals offer

Landahl and Johannesson (2018)

2.5 Industry Description Worldwide, the different sectors of the construction business exhibit differential development design. The construction industry makes about 1/tenth of the global GDP, a significant contribution—massive potential for creating a vast amount of business. Approximately 7% of the global labour force is employed in construction. In 2020, the UAE construction market was valued at USD 101.45 billion, and it is projected to reach a value of USD 133.53 billion by 2027, registering a CAGR of 4.69% between 2022 and 2027. The UAE economy is centred on the construction industry; thus, the industry’s forecast for rapid growth in the future is quite optimistic (Alshawabkeh et al., 2021). The building industry significantly influences the development and growth of the nation’s economy.

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3 Literature Review 3.1 Relationship and Impact of Customisation Strategy on Operational Performance The construction and design processes face several obstacles as product variety is increased by a company to meet the customer needs and demands (Alzoubi et al., 2022b; Jensen, 2020; Victoria, 2022). Industrialised builders provide their customers with various conventional models and degrees of customisation, ranging from a fixed set of options (mainly the substitution of raw materials or components) to distinctive home designs (Qasaimeh & Jaradeh, 2022). Industrialised homes are created as “ready-to-live” volumes or 3D modules (with integrated cabinetry, electrical installations, and other finishing) that are then brought to the construction site and put together to create a finished house (Alzoubi et al., 2020b; Fogliatto et al., 2012). The customisation strategy allows the builders to work based on the foundation of a business’s strategy for assisting customers in making purchase decisions is one way to increase customer satisfaction (Akhtar et al., 2021) through personalisation and enhance operational performance (Alzoubi & Ahmed, 2019; Alzoubi et al., 2020a; Kurdi et al., 2020; Nahmens & Bindroo, 2011). H1: Customisation Strategy has a significant impact on operational performance.

3.2 Relationship and Impact of Product Variety on Operational Performance Product variety decisions focus on how to create and produce goods with the necessary level of customer choice. However, the full effects of product variety can only be realised by expanding this focus across several business operations (Alzoubi & Yanamandra, 2020; Lyons et al., 2020). The degree of customisation increases with the customer’s involvement in a product’s production and operational procedures (Al-Dhuhouri et al., 2021; Alsuwaidi et al., 2020; Nuseir et al., 2021). Additionally, a high degree of customisation often leads to various products that enhance operational performance so that the construction industry essentially completes the tasks or provides services in the most cost-effective way possible without compromising the quality standards, productivity, or profit (Al Naqbi et al., 2020; Alyammahi et al., 2021; Alzoubi et al., 2022l; Salvador et al., 2002). H2: Product variety has a significant impact on operational performance.

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3.3 Relationship and Impact of Customisation Strategy and Product Variety on Operational Performance Manufacturers perceive customisation as another client demand that tests their ability to maintain costs, product quality, and production speed (Eli, 2021). The entire process of estimating, producing, delivering, and managing a product can be disrupted by changes resulting from product modification, making it extremely difficult to properly manage operations (AlMehrzi et al., 2020; Alzoubi et al., 2022n; Eli & Hamou, 2022; Fogliatto et al., 2012; Nahmens & Bindroo, 2011). Several authors have investigated how various levels of product customisation and product variation affect particular business process performance metrics (Alzoubi et al., 2022d; Kashif et al., 2021; Miller, 2021). Measures of cost, time, quality, and flexibility (or reactivity) within the production environment at construction organisations, including productivity, which is related to time, will be characterised as operational performance (Alaali et al., 2021; Alzoubi et al., 2022g; Khatib et al., 2022; Trattner et al., 2019). The deep literature study reveals the dependency of customisation strategy and product variety on operational performance and the significant relationship that demonstrates the customisation strategy increases the product variety that ultimately impacts organisational and operational performance (Nuseir, 2019). H3: Customisation strategy and product variety significantly impact operational performance.

3.4 Problem Statement and Research Gap The construction industry is prosperous but dynamic and has a significant relationship with a particular country’s economy. The UAE construction industry is an essential source of attracting investors. This sector was severely affected after the global financial crisis but has started growing again. This sector of the economy is dynamic but very much versatile. It always has room to attract an extensive portfolio of customers considering their demands and choices (Aljumah et al., 2022b). Product customisation can be offered through various offerings, and this industry has the edge over others in attracting a large portion of the customers. In the construction industry, customisation is easy to attain where the builders have a decent range of product variety. However, while providing variety, the performance of the operations must never be compromised. If the performance is compromised, customers would never prefer to invest in this sector. Buying houses is necessary, so it must be addressed with care. In the current research, the importance of operations performance, which is influenced by customisation and product variety, is assessed empirically, contributing to the literature and adding value to the research.

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3.5 General Research Model

Product Customization

H1 H3

Product Variety

Operational Performance

H2

3.6 Research Hypothesis HO1 : Product customisation has a statistical impact on operational performance in the UAE construction industry at (α ≤ 0.05) level of significance. HO2 : Product variety has a statistically impact on operational performance in the UAE construction industry at (α ≤ 0.05) level of significance. HO3 : Product customisation and product variety have a statistically impact on operational performance in the UAE construction industry at (α ≤ 0.05) level of significance.

3.7 Research Methodology and Design According to Nuseir (2019) and Aljumah et al. (2022a, 2022b), there are two types of studies i.e. quantitative (Awawdeh et al., 2022a) and qualitative. This study was cross sectional (Aljumah et al., 2022a; Jabeen & Ali, 2022). An empirical analysis assessed the variables as discussed by Aljumah et al. (2021a) which are: customisation strategy, product variety and operational performance, from the construction industry in UAE. An online survey questionnaire was utilised in a descriptive, explanatory, causal and analytical research design to evaluate the variables (Ali et al., 2021; Aljumah et al., 2021b). Due to the magnitude of the industry size, a cluster sampling technique was used for Dubai in UAE. Statistical analysis was employed

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to assure the accuracy of data gathering. The online survey helped gather primary data for the research. The SPSS was used to analyse the demographic, reliability, descriptive, correlation, regression and hypothesis testing.

3.8 Population, Sample and Unit of Analysis The study population was the construction industry in the UAE, in which 34 construction companies based in Dubai were accessed for the research sample. To assess the defined variables, the online survey questionnaire with a total of 900 was sent via email to the employees (Construction managers, site supervisors, and project managers). Two hundred eighty-two responses with data validity were used for analysis. A 28 items questionnaire was developed with an online survey to assess the variables (9 items used to measure customisation strategy), (9 items used to measure product variety) and (10 items used to measure operational performance). A fivepoint Likert scale was used (from 5 = strongly agree to 1 = strongly disagree) and added demographic data such as gender, age and designation to specify the correspondents.

4 Data Analysis 4.1 Demographic Analysis See Table 1. Table 1 Demographic data

Item

Description

f

Gender

Male

Age

272

96.4

Female

10

3.6

18–25

14

5.0

26–35

72

25.5

36–45

135

47.9

61

21.6

45 and above Designation

%

Construction manager

128

45.4

Site supervisor

83

29.4

Foreman

38

13.5

Project manager

33

11.7

N = 282, Male = 96.45%, Female = 3.6%

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Table 2 Validity, descriptive and correlation summary Variables

Cronbach’s alpha

Mean

Std deviation

CS

Customisation strategy

0.88

3.94

0.90

1

PV

Product variety

0.85

4.45

0.86

0.845**

1

Operational performance

0.70

4.96

0.77

0.701**

0.662**

OP

1

CS = Customization Strategy (M = 3.9, SD = 90%), PV = Product Variety (M = 4.4, SD = 86%), OP = Operational Performance (M = 4.9, SD = 77%) *P < 0.001, **P < 0.05

4.2 Reliability, Descriptive and Correlation The reliability analysis indicates the Cronbach’s Alpha value with enough reliability for CS = 0.88, PV = 0.85 and OP = 0.70, (7) items used for each variable to check the reliability. Descriptive analysis shows the M = 3.94, SD = 90% for customization strategy, (M = 4.45, SD = 86%) for product variety and (M = 4.96, SD = 77%) for operational performance. The correlation coefficient results show a significant relationship between CS and PV r = 0.84**, PV with OP also indicates a highly correlated relationship with r = 0.66** and results indicate a high correlation and significant relationship between CS and OP with r = 0.70**. Table 2 shows the summary of the results.

4.3 Linear Regression Regression analysis results indicate the dependency and relationship of each variable. The regression results with ANOVA show the significant relationship of customisation strategy (CS) with operational performance (OP) (R = 0.867, P = 0.000) and 52% variance impact. The relationship of product variety (PV) with operational performance is highly significant (R = 0.774, P = 0.000) and has a 75% variance impact. The results indicate a significant relationship between CS and PV highly influential on OP with (R = 0.783, P = 0.004) and 62% variance showed on each variable. Table 3 shows the summary of the data analysis.

4.4 Hypothesis Testing See Table 4.

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Table 3 Linear regression Regression weights

R

R2

Adjusted R2

p-value

2

0.867

0.522

0.511

0.000

df

CS → OP

Regression

PV → OP

Residual

280

0.774

0.750

0.420

0.000

CS * PV → OP

Total

282

0.783

0.628

0.545

0.004

CS = Customization Strategy, PV = Product Variety, OP = Operational Performance, *P < 0.001, **P < 0.05

Table 4 Hypothesis testing using regression coefficients Hypothesis

Variable paths

B

t-value

p-value

Hypothesis

H1

CS → OP

0.867

2.51

0.000

Supported

H2

PV → OP

0.774

3.42

0.000

Supported

H3

CS * PV → OP

0.783

3.51

0.004

Supported

OP = Operational Performance is DV, Significance level at *P < 0.001, **P < 0.05

5 Discussion of the Data H1: The data analysis results indicate a significant impact of customisation strategy on operational performance with (β = 0.867, t = 2.51 and p = 0.000) results supporting the H1. Different authors have investigated that the willingness to customise the construction based on the customer’s desire can decrease the operational cost by maintaining quality standards and improving operational performance (Gallo et al., 2021). H2: The data analysis reveals the statistically significant impact for the second hypothesis, product variety has a significant impact on operational performance (β = 0.774, t = 3.42, P = 0.000), so H2 is also supported in this research. Based on the literature study, the authors suggested that an option of product variety (the number of components used in production) provided to a client can increase the confidence level in choosing the desired structure, which increases cost efficiency and organisational performance (Landahl & Johannesson, 2018). H3: Impact of customisation strategy and product variety on operational performance is statistically significant by (β = 0.783, t = 3.51 and P = 0.004). H3 is also supported in this research. A similar finding of previous research investigated the positive relationship between customisation strategy and product variety on operational performance (Jensen, 2020; Landahl & Johannesson, 2018; Nahmens & Bindroo, 2011; Salvador et al., 2002).

6 Conclusion The conclusion underlines the significance of the present study for the construction industry. Implementing the mass customisation strategy in this sector can benefit from product modularity in house-building design and overall construction projects.

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The primary contribution of this research will benefit the construction companies that would be required to build cost-effectively by maintaining quality standards, winning the client, competitive advantage and organisational profitability. Building contractors must strike a balance between standardisation and customer happiness to increase their company’s production. Moreover, the results of the current study may contribute to a better understanding of the applicability of mass customisation strategies in the construction industry. It is anticipated that it would be helpful to builders looking to cater to certain client needs while managing operational performance.

7 Recommendations/Limitations The operational performance was extensively analysed with the impact of customisation strategy and product variety. The research findings will assist the construction industry in implementing the strategies requiring a quality standard with low cost, lean production, customer satisfaction, employee engagement and competitive advantage. The research is limited to investigating customisation, product variety and operational performance. Future research should focus on the importance of lean construction in other geographical areas. In future longitudinal research, it is recommended to come up with more accurate and generalised results.

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The Impact of Demand Forecasting on Effective Supply Chain with Mediating Role of Strategic Planning in the UAE Pharmaceutical Industry Haitham M. Alzoubi , Muhammad Turki Alshurideh , Mohammed T. Nuseir , Barween Al Kurdi , Ahmad AlHamad , and Samer Hamadneh Abstract The research assesses the relationship and impact of demand forecasting in achieving an effective supply chain, mediating the impact of strategic planning in the pharmaceutical manufacturing industry in the UAE. An empirical investigation of the importance of demand forecasting and strategic planning for an effective supply chain within the UAE pharmaceutical industry contributes something new to the literature. Data from 15 Pharmaceutical companies based in Abu Dhabi, UAE, was used for data analysis. A quantitative technique was followed, and descriptive, causal and analytical research design was used. A convenient cluster sampling technique was applied. Two hundred sixty-three valid questionnaires were analysed using correlation, linear regression and ANOVA. The findings showed a significant direct H. M. Alzoubi School of Business, Skyline University College, Sharjah, UAE e-mail: [email protected] H. M. Alzoubi · S. Hamadneh Applied Science Research Center, Applied Science Private University, Amman 11937, Jordan e-mail: [email protected] M. T. Alshurideh (B) · S. Hamadneh Department of Marketing, School of Business, The University of Jordan, Amman 11942, Jordan e-mail: [email protected]; [email protected] M. T. Alshurideh · A. AlHamad Department of Management, College of Business Administration, University of Sharjah, Sharjah 27272, United Arab Emirates e-mail: [email protected] M. T. Nuseir Department of Business Administration, College of Business, Al Ain University, Abu Dhabi Campus, P.O. Box 112612, Abu Dhabi, UAE e-mail: [email protected] B. Al Kurdi Department of Marketing, Faculty of Business, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 H. M. Alzoubi et al. (eds.), Cyber Security Impact on Digitalization and Business Intelligence, Studies in Big Data 117, https://doi.org/10.1007/978-3-031-31801-6_33

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relationship and impact of demand forecasting in achieving an effective supply chain. At the same time, the indirect impact through the mediating role of strategic planning was found to be significant. The research domain focused on the relationships among three variables; demand forecasting, strategic planning, and effective supply chain. However, the investigation is limited to the pharmaceutical industry in UAE during 2022. Demand forecasting is essential for a sustainable supply chain. At the same time, organisations in general and pharmaceuticals in specific who seek to improve the supply chain’s effectiveness are recommended to implement and practice proper strategic planning and demand forecasting. Keywords Demand forecasting · Strategic planning · Effective supply chain · Pharmaceutical industry in the UAE

1 Introduction Demand forecasts refer to the estimated process that includes analysing sales data across different times or years of work to set potential expectations for the desires of customers and consumers (Feizabadi, 2022). Most industrial companies rely on this feature to determine the number of goods and products that consumers may demand in the specified future, helping predictions of the demand for determining trading schedules, profit margins, and other administrative characteristics that contribute to the management of industries’ sales (Almaazmi et al., 2020). Whereas strategic planning is an essential part of corporate and industrial management and organisational management, the aim of strategic planning is to set priorities, employ resources, and deal with material elements in the organisation and human resources from workers and stakeholders (Awawdeh, Shahroor, et al., 2022a). This component significantly helps determine common goals and access the desired results (Alshurideh, 2016; Alshurideh et al., 2021). It helps to take organised decisions and determine the company’s directions (Alshurideh, 2022; Nuseir et al., 2020). Besides participating in setting plans, effective planning contributes to its success (Awawdeh, Ananzeh, et al., 2022b). Additionally, an effective supply chain can be defined as the ability to improve the performance and standards of companies (Hamadneh, Keskin, et al., 2021a; Krishnamurthy & Yauch, 2007; Shamout et al., 2022). Furthermore, the pharmaceutical industry is like a science that has contributed to changing peoples’ lives throughout time, apart from the ability of this industry to continuously develop and produce various products (Aljumah et al., 2021; Al-Zu’bi et al., 2012). These research variables show relationships among each concerning the UAE’s pharmaceutical industry, such as forecasts of demand, which are one of the most crucial factors that help pharmaceutical firms achieve high supply chain effectiveness, such as the presence of so-called seasonal diseases like influenza (Aljumah et al., 2022a, 2022b; Alshurideh et al., 2018; Ghazal et al., 2021a, 2021b; Svoboda et al., 2021). Manufacturers work to increase the supply of medications for these seasonal diseases during the viral, such as amantadine and rimantadine antivirals (Alshawabkeh et al.,

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2021; Lee et al., 2022; Lee, Romzi, et al., 2022). Successful strategic planning offers analysis and forecast of the market conditions and contributes to pharmaceutical manufacturing development.

2 Theoretical Framework 2.1 Demand Forecasting Demand forecasting is a technique used to set expectations in the market for a service or product; this technique is mainly dependent on the current market situation, and this process is not carried out randomly but instead relies on planning, study, and a scientific basis must consider the criteria of incidents and facts related to the prediction (Roozbeh Nia et al., 2021). The external and internal aspects of the market conditions and past sales analysis that ultimately help to assess the consumer demand are closely related to labour supply chains (Ghosh & Aithal, 2022; Nasim, et al., 2022), besides consumer buying habits, and give an indication of external factors that affect sales in the future, all of which works based on data collection (Ghannajeh et al., 2015; Nuseir et al., 2021). In the present research, three dimensions of demand forecasting will be assessed to identify the impact of demand forecasting on an effective supply chain (Radwan, 2022); Internal and external market conditions, past sales analysis, and external sales factors (Almaazmi et al., 2020; ELSamen & Alshurideh, 2012; Madi Odeh et al., 2021; Rožanec et al., 2021).

2.2 Strategic Planning Strategic planning entails formulating and executing plans to achieve long-term organisational objectives (Ratkovic, 2022). It is also an effort to account for environmental dynamics, complexity, and potential future occurrences (Alaali et al., 2021; Fletcher, 2020). Strategic planning comprises various aspects applied for organisational effectiveness (Abuanzeh et al., 2022; Allozi et al., 2021; Nuseir, 2019). Among these include collecting data, ensuring all viable solutions are carefully considered, making the business examine its environment, inspiring fresh thinking, boosting inspiration, and promoting internal contact and communication (Aljumah et al., 2022a, 2022b; Alyammahi et al., 2021; Ashal et al., 2021; Goria, 2022). Planning for the long term is crucial for both large and small organisations. Strategic planning is something that no organisation should avoid doing because it enables them to take advantage of future opportunities and foresee potential threats (Altamony et al., 2012; Del & Solfa, 2022; Kasem & Al-Gasaymeh, 2022; Obeidat et al., 2020; Sosiawani et al., 2015). Three dimensions are defined to assess an effective supply chain in this research: formalisation, time horizon, strategic instruments and

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control frequency (Alolayyan et al., 2022a, 2022b; Alshurideh et al., 2022; Suklev & Debarliev, 2012). The dimensions contribution in this research would help to reach a conclusion to analyse its impact on the effective supply chain in the pharmaceutical industry.

2.3 Effective Supply Chain To enhance the performance of each link of the chain, a supply chain’s design and management often aim for the best global performance (AlShurideh et al., 2019; Demeter & Gelei, 2004). Strong techniques are required to enable managers who make decisions at various supply chain levels to assess potential strategies’ effects on an organisation’s performance before implementing them in the real world (Aljumah, Shahroor, et al., 2022a, 2022b; Alzoubi, Joghee, et al., 2021a). The basic goal of supply chain modelling is to minimise or maximise an objective function by identifying choices and trade-off solutions that simultaneously address several objectives (Alolayyan et al., 2022a, 2022b; Salvador et al., 2002). Moreover, there are two dimensions, “Customer relationship and corporate culture” (Edward Probir Mondol, 2022; Kashif et al., 2021), to be assessed in this research to reach a specific conclusion regarding effective manufacturing strategies and supply chain improvements (Aburayya et al., 2020; Akhtar et al., 2021; Al Kurdi et al., 2020; Demeter & Gelei, 2004; Mehmood, 2021).

2.4 Operational Definitions

Variables

Definition

Reference

Demand forecasting

Demand forecasting is the practice of estimating future customer demand over a predetermined period

(Zhu et al., 2021)

• Internal and external market conditions

The term “internal environment” refers to internal factors and circumstances that may impact a firm’s operations. All exogenous forces with the potential to influence an organisation’s performance, profitability, and functionality are collectively referred to as the external environment

(Kessides, 1990)

(continued)

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(continued) Variables

Definition

• Past sales analysis

It provides insights into the past, (Haque et al., 2021) present, and future performance of a business and can be used to help you forecast trends, identify opportunities for growth, and develop a strategic action plan for your company

Reference

• Current market factors

The overall conditions within a defined market affect all properties within that market

(Kessides, 1990)

Strategic planning

Strategic planning is the skill of developing detailed business plans, putting them into action, and assessing the outcomes in light of a company’s overarching long-term objectives or aspirations

(Papke-Shields & Boyer-Wright, 2017)

• Formalisation

Formalisation is the extent to which written policies, rules, procedures, job descriptions, and other documentation outline the activities that should be (or should not be) taken in a specific situation

(Samad & Ahmed, 2021)

• Time horizon

The time horizon is the period considered while creating an organisation’s strategic plan

(Samad & Ahmed, 2021)

• Frequency of control

Frequency control is a process of maintaining the stability of a power system

(Samad & Ahmed, 2021)

Effective supply chain

Customers, partners, suppliers, and vendors are some of the major stakeholders in a supply chain, and a successful supply chain satisfies or surpasses their actual demands

(Christopher & Towill, 2001)

• Customer relationship

Customer relations is the term used to describe how organisations interact with customers and develop long-term relations

(Stamoulis et al., 2002)

• Corporate culture

The values and practices that guide how a company’s management and employees interact and handle outside the business are referred to as corporate culture

(Shahzad et al., 2012)

(continued)

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(continued) Variables

Definition

Reference

2.5 Industry Description Based on a report from Abu Dhabi holding company ADQ, the UAE’s pharmaceutical sector is anticipated to increase by 27% between 2021 and 2025 as the country strives to become a regional pharmaceutical hub. According to an ADQ FWD white paper titled Redefining Regional Pharma, the local pharmaceutical sector is anticipated to grow three times from its 2011 level to reach $4.7 billion in value by 2025. In the UAE, 32 medicine manufacturing companies produce medicines. From the four production units in 2010, this represents a significant increase. Four pharma companies specialise in medical devices, and two make disinfectants. Among those, 14 make human pharmaceuticals. Various pharmaceutical firms in the United Arab Emirates are large distributors of drugs and medical supplies rather than producers. Due to the UAE’s political stability and pro-foreign investment government policies, there are increasing pharmaceutical businesses there. Along with Egypt and Saudi Arabia, the UAE is quickly rising to prominence in the Middle East’s pharmaceutical industry.

3 Literature Review 3.1 Relationship and Impact of Demand Forecasting on Strategic Planning Demand forecasting helps companies to define and monitor the data needed to develop and improve the strategic business plan (Akhtar, et al., 2022; Amrani et al., 2022; Farouk, 2022). Strategic planning assists in forecasting demand interaction and suggests using demand forecasting for strategic decision-making (Eli et al., 2022; Victoria, 2022). Hence, the strategic plan begins with knowing the current trends and then forecasting the future of these trends, besides defining the future you desire by setting goals (Ahmed et al., 2021; Alsharari, 2022; Nikolopoulos, 2021; Qasaimeh & Jaradeh, 2022). This indicates the support of the first hypothesis; for example, if the company wants to implement a new strategy, then forecasting demand is its way of determining potential expectations from that environment (Alzoubi, 2022). Strategic plans assist in getting rid of business risks, so planning and demand forecasts are related activities (Fletcher, 2020; Ghazal et al., 2021a, 2021b; Nikolopoulos, 2021). To determine the future, the organisation should need a step of prediction; therefore,

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forecasting demand has been proved in literature as one of the complementary steps for strategic planning. H1: Demand forecasting has a significant impact on strategic planning.

3.2 Relationship and Impact of Demand Forecasting on Effective Supply Chain A couple of decades presented several studies emphasising that an effective supply chain provides a strong competitive advantage concerning inventory and sales management (Ahmed & Nabeel Al Amiri, 2022; Razak et al., 2015; Saad Masood Butt, 2022; Shah et al., 2021). This helps pharmaceutical manufacturers develop and consolidate their position in the market among many similar products with sustainable efficiency. Faulkner and Valerio (2001) specify that sharing demand information contributes to two levels in the success of an effective supply chain: • The ability to note the level of the company’s performance in the market (Alzoubi, Ahmed, et al., 2020). • Share information with suppliers and merchants (Alzoubi, Ghazal, Ali, et al., 2021b). Predicting demand provides ease in general administrative work such as budget and financial management and production that assist in efficiently managing supply chain management (Alsharari, 2021). H2: Demand forecasting has a significant relationship with effective supply chain.

3.3 Relationship and Impact of Strategic Planning on Effective Supply Chain To transform raw materials into finished goods and transport them to ultimate customers, a supply chain is a complex network that runs from suppliers to customers and involves resources, people, technology, activities, and information (Eli, 2021; Gunasekaran et al., 2019; Alzoubi et al., 2021a, 2021c; Miller, 2021). Suppliers, factories, warehouses, distribution centres (DCs), and clients typically make up the various levels of a supply chain (Shakhour et al., 2021; Zhu et al., 2013). Pharmaceutical companies are highly involved in an effective supply chain process that requires healthy strategic planning to meet the customer requirement and fulfil the manufacturing criteria through an effective supply chain (Shamaileh et al., 2022; Zhu et al., 2021). Different authors suggest that strategic planning will improve business efficiency by increasing supply chain performance. The literature study supports the current hypothesis of the research that shows a significant relationship between

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strategic planning and an effective supply chain (Al-Dmour et al., 2021; Alshraideh et al., 2017; Alzoubi & Yanamandra, 2020; Boulton et al., 1982). H3: Strategic planning has a significant relationship with effective supply chain

3.4 Relationship and Impact of Demand Forecasting on Effective Supply Chain with Mediating Impact of Strategic Planning The studies dealt with pharmaceutical companies in India. They mentioned the challenges faced by these companies and how they handle the high-cost expenditures due to the lack of transportation infrastructure. This can be avoided when developing strategic plans and studying demand forecasts, the region’s status, and the market (Zhu et al., 2021). Reducing the high costs and avoiding the supply chain risks mainly requires strategic planning for an effective operation (Alzoubi & Ahmed, 2019; Alzoubi, Joghee, et al., 2020). This confirms the support of the fourth hypothesis of the research. Undoubtedly, successful management of strategic planning, besides the correct prediction of demand (Alzoubi et al., 2017), helped reduce costs for customers and suppliers in a way that helps achieve an effective supply chain, and consequently, companies that are effective in supply chains are the most successful in the business world today (Grinerud et al., 2021; Khasawneh et al., 2021; Nikolopoulos, 2021). The inability to relate it with the supply chain and strategic planning may cause inefficiency, poor performance, less production, and a slow process of responding to changes in the market Zhu et al., 2021). H4: Demand forecasting has a significant impact on effective supply chain with mediating effect of strategic planning.

3.5 Problem Statement and Research Gap To analyse the role of strategic planning on the effective supply chain in drug manufacturing companies through demand forecast is crucial to reach a specific strategy. The main research problem is the importance of clarifying the relationship between the three variables they are demand forecasts, strategic planning and an effective supply chain in the pharmaceutical industry because a successful pharma business relies on medicine manufacturing based on the demand of consumers and impact on the effective supply chain to achieve competitive advantage. Conventional approaches in business have become unacceptable; maintaining a sound strategy in the company with the possibility of developing it and providing a basis for predicting market demand would positively affect the supply chain.

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Strategic Planning • • •

Formalization Time Horizon Frequency of control

H1

H3 H4

Demand Forecasting • • •

Internal & external market conditions Past sales analysis

Effective Supply Chain •

H2

Current market factors

•

Customer Relationship Corporate culture

Fig. 1 Research model

3.6 General Research Model See Fig. 1.

3.7 Research Hypothesis HO1 : Demand forecasting has no statistical impact on strategic planning in the UAE pharmaceutical industry at (α ≤ 0.05) level. HO2 : Demand forecasting has no statistical impact on the effective supply chain in the UAE pharmaceutical industry at (α ≤ 0.05) level. HO3 : Strategic planning has no statistical impact on the effective supply chain in the UAE pharmaceutical industry at (α ≤ 0.05) level. HO4 : Demand forecasting has no statistical impact on effective supply chain with the mediating effect of strategic planning at the UAE pharmaceutical industry at (α ≤ 0.05) level.

3.8 Research Methodology and Design The research aims to collect data from the drug manufacturing industry in the UAE to evaluate the empirical analysis of demand forecasting and strategic planning’s impact

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on an effective supply chain. Survey questionnaire is one of the method to reach the respondents (Ali et al., 2020; Jabeen & Ali, 2022). A descriptive, explanatory, causal and analytical research design utilised a survey questionnaire to evaluate the variables. The number of pharmaceutical companies in the UAE is limited, so a cluster sampling technique is used because the industry has great privacy and sensitivity. To achieve these findings, the data were analysed with reliability, descriptive, correlation and multiple regression by ANOVA through SPSS.

3.9 Population, Sample and Unit of Analysis A drug manufacturing company represents the population of this research in the United Arab Emirates with 15 companies. However, the number of pharmaceutical companies in the UAE is limited, so this research focused on reaching a maximum number of companies to collect data. This study used questionnaire to get the feedback from respondents (Ali et al., 2021; Perumal et al., 2021).The data collection instrument was an online questionnaire sent among the administrative departments of the companies through emails. Two hundred sixty-three valid responses were received and used for analysis out of 650 questionnaire emails. The questionnaire was developed on a Five-point Likert scale to gather the responses (from 1-strongly disagree to 5-strongly agree). To assess the variables, the data were collected using 35 items scale. A 12-item scale was used to measure demand forecasting and its dimensions (Internal and external market conditions, past sales analysis and external sales factor). The 12 items were used to assess strategic planning (Formalization, time horizon, frequency of control) and 11 items were used to assess SC (customer relationship, corporate culture).

4 Data Analysis 4.1 Demographic Analysis The questionnaire was sent online to the administrative department of each company with specified (gender) demographic data that shows male = 189(189%) and female = 74(28.1%) (Table 1). Table 1 Demographic data

Item

Description

f

%

Gender

Male Female

189 74

71.9 28.1

n = 236, Male = 71.9%, Female = 28.1%

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Table 2 Validity, descriptive and correlation summary Variables

Cronbach’s Alpha

Mean

Std deviation

DF

Demand forecasting

0.86

3.80

0.86

1

SP

Strategic planning

0.86

4.13

0.79

0.854(**)

1

Effective supply chain

0.69

3.57

0.64

0.686(**)

0.619(**)

ESC

1

DF = Demand Forecasting (M = 38.0, SD = 86%), SP = Strategic Planning (M = 41.3, SD = 79%), ESC = Effective Supply Chain (M = 35.7, SD = 64%) *P