Intelligent Data Analytics in Business: Select Proceedings of ICDABM 2022 (Lecture Notes in Electrical Engineering, 1068) 981995357X, 9789819953578

Citation preview

Lecture Notes in Electrical Engineering 1068

Kiran Chaudhary Mansaf Alam Narayan C. Debnath   Editors

Intelligent Data Analytics in Business Select Proceedings of ICDABM 2022

Lecture Notes in Electrical Engineering Volume 1068

Series Editors Leopoldo Angrisani, Department of Electrical and Information Technologies Engineering, University of Napoli Federico II, Napoli, Italy Marco Arteaga, Departament de Control y Robótica, Universidad Nacional Autónoma de México, Coyoacán, Mexico Samarjit Chakraborty, Fakultät für Elektrotechnik und Informationstechnik, TU München, München, Germany Jiming Chen, Zhejiang University, Hangzhou, Zhejiang, China Shanben Chen, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China Tan Kay Chen, Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore Rüdiger Dillmann, University of Karlsruhe (TH) IAIM, Karlsruhe, Baden-Württemberg, Germany Haibin Duan, Beijing University of Aeronautics and Astronautics, Beijing, China Gianluigi Ferrari, Dipartimento di Ingegneria dell’Informazione, Sede Scientifica Università degli Studi di Parma, Parma, Italy Manuel Ferre, Centre for Automation and Robotics CAR (UPM-CSIC), Universidad Politécnica de Madrid, Madrid, Spain Faryar Jabbari, Department of Mechanical and Aerospace Engineering, University of California, Irvine, CA, USA Limin Jia, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China Janusz Kacprzyk, Intelligent Systems Laboratory, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Alaa Khamis, Department of Mechatronics Engineering, German University in Egypt El Tagamoa El Khames, New Cairo City, Egypt Torsten Kroeger, Intrinsic Innovation, Mountain View, CA, USA Yong Li, College of Electrical and Information Engineering, Hunan University, Changsha, Hunan, China Qilian Liang, Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA Ferran Martín, Departament d’Enginyeria Electrònica, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, Spain Tan Cher Ming, College of Engineering, Nanyang Technological University, Singapore, Singapore Wolfgang Minker, Institute of Information Technology, University of Ulm, Ulm, Germany Pradeep Misra, Department of Electrical Engineering, Wright State University, Dayton, OH, USA Subhas Mukhopadhyay, School of Engineering, Macquarie University, NSW, Australia Cun-Zheng Ning, Department of Electrical Engineering, Arizona State University, Tempe, AZ, USA Toyoaki Nishida, Department of Intelligence Science and Technology, Kyoto University, Kyoto, Japan Luca Oneto, Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genova, Genova, Genova, Italy Bijaya Ketan Panigrahi, Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi, India Federica Pascucci, Department di Ingegneria, Università degli Studi Roma Tre, Roma, Italy Yong Qin, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China Gan Woon Seng, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore Joachim Speidel, Institute of Telecommunications, University of Stuttgart, Stuttgart, Germany Germano Veiga, FEUP Campus, INESC Porto, Porto, Portugal Haitao Wu, Academy of Opto-electronics, Chinese Academy of Sciences, Haidian District Beijing, China Walter Zamboni, Department of Computer Engineering, Electrical Engineering and Applied Mathematics, DIEM—Università degli studi di Salerno, Fisciano, Salerno, Italy Junjie James Zhang, Charlotte, NC, USA Kay Chen Tan, Department of Computing, Hong Kong Polytechnic University, Kowloon Tong, Hong Kong

The book series Lecture Notes in Electrical Engineering (LNEE) publishes the latest developments in Electrical Engineering—quickly, informally and in high quality. While original research reported in proceedings and monographs has traditionally formed the core of LNEE, we also encourage authors to submit books devoted to supporting student education and professional training in the various fields and applications areas of electrical engineering. The series cover classical and emerging topics concerning: • • • • • • • • • • • •

Communication Engineering, Information Theory and Networks Electronics Engineering and Microelectronics Signal, Image and Speech Processing Wireless and Mobile Communication Circuits and Systems Energy Systems, Power Electronics and Electrical Machines Electro-optical Engineering Instrumentation Engineering Avionics Engineering Control Systems Internet-of-Things and Cybersecurity Biomedical Devices, MEMS and NEMS

For general information about this book series, comments or suggestions, please contact [email protected]. To submit a proposal or request further information, please contact the Publishing Editor in your country: China Jasmine Dou, Editor ([email protected]) India, Japan, Rest of Asia Swati Meherishi, Editorial Director ([email protected]) Southeast Asia, Australia, New Zealand Ramesh Nath Premnath, Editor ([email protected]) USA, Canada Michael Luby, Senior Editor ([email protected]) All other Countries Leontina Di Cecco, Senior Editor ([email protected]) ** This series is indexed by EI Compendex and Scopus databases. **

Kiran Chaudhary · Mansaf Alam · Narayan C. Debnath Editors

Intelligent Data Analytics in Business Select Proceedings of ICDABM 2022

Editors Kiran Chaudhary Department of Commerce Shivaji College University of Delhi New Delhi, India

Mansaf Alam Department of Computer Science Jamia Millia Islamia New Delhi, Delhi, India

Narayan C. Debnath Department of Software Engineering Eastern International University Thu Dau Mot City, Vietnam

ISSN 1876-1100 ISSN 1876-1119 (electronic) Lecture Notes in Electrical Engineering ISBN 978-981-99-5357-8 ISBN 978-981-99-5358-5 (eBook) https://doi.org/10.1007/978-981-99-5358-5 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 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 Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Contents

Examining Socially Responsible Investing Behaviour of Individuals Based on Expert Opinion Using DEMATEL . . . . . . . . . . . . . . . . . . . . . . . . . Kiran Mehta, Ramesh Kumar, Renuka Sharma, and Vishal Vyas

1

Research on the Ecological Environment Evaluation of Technological Talents in the Shuangcheng Economic Circle of Chengdu Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hong Liu, Amit Yadav, Li Qin Hu, Md. Mehedi Hassan, Swarnali Mollick, and Dinesh Talwar

25

Current Landscape and Future Prospects of Community Healthcare Information System: A Conceptual Study W.R.T. Indian Healthcare . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Priyanka Gandhi and Sonal Pahwa Fake News Detection Using Machine Learning . . . . . . . . . . . . . . . . . . . . . . . Ajay Agarwal, Shashank Mishra, and Sartaj Ahmad Status of Basic Digital Tools Awareness Among Women Vegetable Vendors and Consequences-Hyderabad Local Markets . . . . . . . . . . . . . . . . K. Athhar Shafiyah and Shaik Kamaruddin Influence of Covid-19 on Punjab Textile Industry . . . . . . . . . . . . . . . . . . . . . Rajni and Palak Bajaj A New Transformation for Manufacturing Industries with Big Data Analytics and Industry 4.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nabeela Hasan and Mansaf Alam Analysis for Detection in MANETs: Security Perspective . . . . . . . . . . . . . . Taran Singh Bharati

39 51

61 75

85 97

Exploring the Role of Business Intelligence Systems in IoT-Cloud Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 Manzoor Ansari and Mansaf Alam v

vi

Contents

Leading a Culturally Diverse Team in the Workplace . . . . . . . . . . . . . . . . . 119 Rajni Bala An Integrated Secure Blockchain and Deep Neural Network Framework for Better Agricultural Business Outcomes . . . . . . . . . . . . . . . 127 Disha Garg and Mansaf Alam ASCII Strip-Based Novel Approach of Information Hiding in Frequency Domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 Ravi Saini, Gaurav Kumar, Kamaldeep Mintwal, Rajkumar Yadav, and Rainu Nandal Investigating the Mediating Effect of eWOM While Exploring a Destination in Uttarakhand: A Tourist Perspective . . . . . . . . . . . . . . . . . . 151 Anuj, Rajesh Kumar Upadhyay, Himanshu Kargeti, Madhur Thapliyal, and Himani Kargeti Investigating the Factors that Lead Towards Intention to Use Mobile Commerce Among Higher Education Students . . . . . . . . . . . . . . . . 161 Himanshu Kargeti, Himani Kargeti, Rajesh Kumar Upadhyay, and Anuj Strategic Plans for Market Campaign Using Machine Learning Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 Aashita Chhabra, Kiran Chaudhary, and Mansaf Alam Gap Analysis of Electricity Demand and Supply in Perspective of PROSUMERS of 5 KW Solar Rooftop Plants in Uttarakhand . . . . . . . 187 Pratibha Verma, Abha Sharma, and Mukesh C. Verma

About the Editors

Dr. Kiran Chaudhary is an Associate Professor in the Department of Commerce at Shivaji College, University of Delhi. She has fourteen years of Teaching and research experience. She has completed a Ph.D. in Marketing (Commerce) from Kurukshetra University, Kurukshetra, Haryana. Her area of research includes Big Data analytics, artificial intelligence, cyber security, marketing, human resource management, organisational behaviour, business, and corporate law. She has published a book on Probability and Statistics. She has published several research articles in reputed International Journals and the Proceedings of reputed International conferences. She delivered various National and International invited talks. She chaired a session at various International Conferences. She recently got an international patent (Australian) on An Artificial intelligence-based smart dustbin. Prof. Mansaf Alam has been working as a Professor in the Department of Computer Science, Faculty of Natural Sciences, Jamia Millia Islamia. He is a Young Faculty Research Fellow, DeitY, Government of India and Editor-in-Chief of the Journal of Applied Information Science. He has published several research articles in reputed International Journals and Proceedings at reputed International conferences published by IEEE, Springer, Elsevier Science, and ACM. His area of research includes Artificial Intelligence, Big Data Analytics, Machine Learning and Deep Learning, Cloud Computing, and Data Mining. He is a reviewer of various journals of International repute, like Information Science, published by Elsevier Science. He is also a member of the program committee of various reputed International conferences. He recently got an International Patent (Australian) on An Artificial intelligence-based smart dustbin to his credit. Prof. Narayan C. Debnath is the founding Dean of the School of Computing and Information Technology at Eastern International University Vietnam, Director and Past President of the International Society for Computers and their Applications (ISCA) at Winona, Minnesota, USA, and former Professor and Chairperson of Computer Science at Winona State University, Winona, Minnesota, USA. He is

vii

viii

About the Editors

presently working as Head, Department of Software Engineering at Eastern International University Vietnam. He has published over 450 research papers, ten books, and edited 20 Journals and Conference Proceedings. He has given over 300 Technical Presentations and attended Over 190 conferences. He has also organised and chaired over 80 sessions and completed various research projects. He received various International honours and awards and delivered various invited talks.

Examining Socially Responsible Investing Behaviour of Individuals Based on Expert Opinion Using DEMATEL Kiran Mehta, Ramesh Kumar, Renuka Sharma, and Vishal Vyas

Abstract The purpose of this research is to discover the individual investor characteristics that influence their non-economic investing objectives in India. Using an integrated research approach that relies on exploratory factor analysis, the study identifies the factors from the standpoint of individual investors. From the standpoint of fund managers, these aspects were evaluated further. And for this purpose, an expert-based system is analysed to determine the most influential feature or characteristic of individual investors on their choice of non-economic goals. DEMATEL, a tool for multi-criteria decision modelling, is used to analyse the views of experts. The study’s conclusions are applicable to the development of responsible investing solutions for individual investors by asset management companies. The findings will bring about consciousness, interest, and knowledge about the importance of responsible investing behaviour among investors and fund managers who come from a variety of investment backgrounds. Keywords Socially responsible investment · EFA · Non-economic Goals · DEMATEL · Indian investor behaviour · JEL Codes G1 · G40

1 Introduction and Background During the years 1995–2007, there was a considerable increase in investments made into socially responsible investments [4, 84, 85]. To be more precise, this remarkable expansion is seen in the markets of the USA and the UK [100]. Companies may improve their reputations by allocating funds to initiatives that promote social K. Mehta · R. Sharma (B) Chitkara Business School, Chitkara University, Punjab, India e-mail: [email protected] R. Kumar Department of Commerce, Shivaji College, University of Delhi, New Delhi, India V. Vyas ABV-IIITM, Gwalior, Madhya Pradesh, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 K. Chaudhary et al. (eds.), Intelligent Data Analytics in Business, Lecture Notes in Electrical Engineering 1068, https://doi.org/10.1007/978-981-99-5358-5_1

1

2

K. Mehta et al.

responsibility. Investors who have a long-term view, such as institutional investors, are pleased when a company’s ethical and moral practices provide value to their investment. They express this satisfaction by acknowledging the company in question. It encourages companies to act responsibly to the community [35]. Researchers have explained the idea of socially responsible investments by referring to them interchangeably as ethical investments, responsible investments, sustainable investments, and ESG criteria (which stands for environment, social, and governance criteria) [31, 58]. These funds are not like other types of investments often used [54]. Investors are considering non-financial ratings due to the rise in popularity of socially responsible investing (SR) funds, which affects the performance of SR funds [7, 20, 55]. Trading guidelines based on technical analysis or mean-reversion methods might assist responsible investors in improving their financial returns via investments in SR funds [100]. Researchers have attempted to uncover the hedging capabilities of gold, renewable energy, and oil to cover the risk associated with the sustainability index of Brazil due to the growing interest of investors in socially responsible investments [45]. This interest has prompted researchers to make this effort [45]. Additionally, the SR funds can entice value investors who are not too concerned with the potential for high financial returns [25]. The managers’ skill plays a significant role in determining the risk-adjusted returns of SR funds. Suppose investors can invest in the previous best socially responsible equity-centred mutual fund. In that case, there is no incongruity between pursuing higher ethical values and higher financial returns, according to research based on 3920 socially responsible equity-centred mutual funds conducted between 2000 and 2018 [64]. There have been several instances in which it was discovered that the return on socially responsible investments was higher than the market benchmark, however, these discoveries have not been proven to be constant throughout various periods. According to [76], socially responsible investing (SR) funds are seen as an intelligent alternative for investors since their decisions are focused on financial benefits and take a company’s reputation, responsible behaviour, and good governance practices. The funds strive for financial and non-financial returns when making their investments in socially responsible businesses and organisations. Several different mutual funds’ financial performances and the abilities of fund managers working for responsible funds have been scientifically investigated by researchers [40, 70, 71]. The investors’ perceptions of these funds’ risk profile and market share lead them to choose socially responsible investments. Conventional investment portfolios are bested by the performance of the SR funds [38, 49, 53]. Sometimes, there is not a substantial difference in the performance of conventional funds and SR funds or SR funds and green funds. Such cases can be witnessed when comparing conventional funds to SR funds or green funds [23, 63, 65, 66, 84, 85]. Statman [98] found evidence that socially responsible (SR) funds have a track record of underperforming the market benchmark in the US market. Evidence that is comparable to this has also been acquired in other countries. Ronneboog et al. [85] provided evidence that the SR funds in 17 countries throughout Asia, Europe, North America, and Oceania have underperformed their investment objectives. Investors are interested in the financial benefits, but their

Examining Socially Responsible Investing Behaviour of Individuals …

3

perspectives on these monetary advantages are influenced by the responsible behaviour of the firms in which they are investing. The financial benefits are essential for investors. Investors willing to have a higher premium on financial returns will often not differentiate between “sin” stocks and “non-sin” companies as carefully as possible. When this kind of situation arises, the importance that retail investors place on the responsible behaviour of businesses affects the patterns of volatility and return associated with the equities in which they are investing. An exciting finding is obtained through research-based in the USA. The research revealed that investors prefer financial gain. However, once they receive a dividend from a company that engages in unethical business practices, they rebalance their portfolio and switch to companies that engage in ethical business practices [24]. Furthermore, there is evidence suggesting that responsible funds perform better during times of crisis compared to other current portfolios [77]. The research findings indicated that the responsible investors look at the historical performance of these funds along with the inflow of new capital. Investors affiliated with socially responsible funds are often more loyal than other investors because these investors value non-financial rewards and financial gains. In addition to this, it occurs because there are not enough alternatives to socially responsible funds accessible [12, 14, 19, 78, 83]. According to the findings of research that focused on socially responsible funds in the USA, the decision of investors to put their money into socially responsible funds did not contribute to the steady inflow of capital. Furthermore, it was discovered that investors who put their money into religious funds had the worst skills in the US market for selecting socially responsible funds. In contrast, investors who put their money into environmental funds reported the strongest positive association between financial gains and the flow of funds [69]. The data that has been published by previous research has mainly concentrated on the return performance of socially responsible investments in the markets of the USA, the UK, and Europe. There is a dearth of research evidence that reveals individual investors’ behaviour about socially responsible investing. In the case of India, it is also noticeable that there is an insufficient amount of research evidence. As a result, the present study has focused on analysing the attitudes and behaviours of individual investors regarding socially responsible investing. Iyer and Kashyap [47] devised a scale that considers five factors that influence the choice that an individual investor makes for non-economic reasons. Using this scale, the authors determined five primary factors influencing how people engage in socially responsible investing behaviour. These ideas include collectivism, religion, consumerism, an attitude towards the environment, and risk tolerance. Two subcategories make up the characteristic of risk tolerance known as individual risk affinity and individual risk propensity. Their study looked at the effectiveness of social investment as a potential mediating factor. The research work done by [47] has been expanded upon in terms of the study’s scope. This extension was accomplished by considering broader categories of investors to investigate the factors that influence individuals’ choice of non-economic goals. The response of fund managers is also obtained to study the causal relationship between non-economic goals and factors determining

4

K. Mehta et al.

the socially responsible behaviour of individual investors. This constitutes an additional development in the analysis, which brings us one step closer to answering the research question. In other words, the emphasis of the present research is now on the characteristics of individual investors that impact their non-economic investment motives, particularly in the Indian setting. Individual ethical or non-economic behaviour is influenced by several elements that have been examined in this research. In addition, these criteria were evaluated from the standpoint of fund managers. For this purpose, an expertbased system is analysed to determine individual investors’ most noteworthy features or characteristics in selecting non-economic aims. Based on the above context, the next part discusses the existing research about people’s socially responsible investing behaviour.

2 Review of Literature The authors have categorised the available research on the socially responsible behaviour of investors into six categories, which are discussed further in this article.

2.1 Religion and Conducting Oneself in a Socially Responsible Manner Many characteristics influence the investors’ attitudes towards socially responsible investments. The social responsibility (SR) funds target those customers who are environmentally sensitive, socially conscious, devout, and less materialistic. These clients are members of a culture or society that prioritises ethical behaviour [11, 44, 47, 68, 90, 102]. In the past, researchers have published conclusions that directly oppose one another. According to [106], it has been discovered that an investor’s economic decision and the religion that he or she adheres to are incompatible with one another. On the other hand, it has been discovered that an individual’s religion and the economic decision of an individual can drive one another [61]. The religious conviction of an investor is intimately tied to the ethical behaviour of that investor. It has been found that a person’s ethical decisions are strongly influenced by their religious beliefs and practices [94]. Because a person’s religion influences his values and attitude, it is mirrored in his character and every sort of decision that he makes [32, 61, 90, 97]. An individual who adheres to a specific religion considers himself as having a higher moral standing. Similarly, a person’s religious beliefs might impact investment decisions [56, 68, 79]. For example, an individual motivated by religion will refrain from investing in businesses that produce items and services that fall into the “bad list” or “sin”

Examining Socially Responsible Investing Behaviour of Individuals …

5

category. Companies that produce bad things for the environment are included in this category. Similarly, investors who follow the Islamic religion are unlikely to be interested in investing in businesses that use meat or alcohol in their manufacturing process. There is evidence provided by previous research in the markets of Malaysia and India showing that the consumers behave more loyal to a firm that produces goods and services that are approved by the consumers’ religious beliefs [1, 46, 48, 101]. According to the study’s findings, there is a correlation between religious observance and ethical behaviour in the workplace [68, 102]. Investors’ religious belief also plays a vital role in their ethical choices. The investments that are authorised by the religion of investors are regarded as ethical investments. Investors choose to put their money into businesses that provide goods and services that are not prohibited by the religion of the stakeholders and are following moral norms [75]. In the same vein, the return on Shariah equities was shown to be greater than the return on non-Shariah stocks in the instance of India. It was also revealed that the risk associated with Shariah portfolios was lower than that associated with conventional portfolios [26]. Research evidence is available supporting the performance of stocks and funds that adhere to Shariah or the principles of Islamic finance [16]. According to [2], the risk associated with Islamic stocks is determined to be lower in the instance of Malaysia, and the momentum effect can be seen in the performance of Islamic stocks from 1974 to 2014 [60, 72]. In addition, stock returns were shown to be somewhat more significant during Ramadan, with relatively more minor risk than other times of the year [3, 6, 16]. The research was based on 14 Muslim nations (2012). In contrast, research conducted in Saudi Arabia and based on Islamic investment principles demonstrated a negative influence on stock performance between 2003 and 2011 [67]. Al-Hajieh et al. [5] and Białkowski et al. [15] found findings comparable for the Middle Eastern markets and the Turkish market.

2.2 Collectivism and Socially Responsible Behaviour Similarly, an individual’s actions and choices are also impacted by the culture and the group to which he or she belongs. There is accessible information demonstrating that individualism and collectivism both have a distinct influence on the social behaviour of people in a variety of nations [29, 42, 57, 81, 82, 95]. According to Shavitt and his colleagues’ research, individuality may be broken down into four distinct aspects (2006). All four of these characteristics may be present in individuals, but how they manifest varies greatly depending on the community. The tendency to be independent and the prominence of competition are related to horizontal and vertical individualism. However, horizontal and vertical collectivism is related to social relations with the equality of individuals and social relations with superiors. Among these four characteristics, horizontal and vertical individualism are related to the tendency to compete with one another. Such evidence is also accessible in India, where the author has demonstrated the influence of culture on family life, community

6

K. Mehta et al.

engagement, and individuals’ capacities for empathy [57]. Compared to American investors, Indian investors are characterised by a higher degree of collectivism due to their cultural background. Individuals in India who belong to the same religion, caste, language, and cultural background look out for each other. The collective actions of these social groups affect the attitudes of the individuals who are a part of them. Indians who belong to the same religion, caste, language, and cultural background look out for each other. The collective actions of these social groups affect the attitudes of the individuals who are a part of them. It is because of its wide cultural variety [27]. Both East and West have distinctive cultures, each of which plays a role in shaping the mentality of people living in their respective regions. Western culture leans more towards individualism and materialism due to more significant progress in science and technology. On the other hand, the influence of collectivism may be shown to have a more significant effect on eastern countries [39, 74]. The effect of culture on investors’ trading behaviour was seen, specifically in how they responded to risk [59].

2.3 Materialism and Socially Responsible Behaviour The practice of placing an excessive value on material items is a hallmark of the materialistic worldview. The degree to which civilisations are materialistic also varies from one another, influencing how individuals feel about themselves [10, 11, 86]. The acquisition of material possessions may signify a variety of things to various people depending on the person. People who are very concerned with their material possessions have a greater propensity to spend more money on their consumption to demonstrate their social standing. Individuals’ pursuit of non-economic objectives has been proven to have an inverse link with materialism [22, 89].

2.4 Environmental Attitude and Socially Responsible Behaviour Even environmentally conscious investors are concerned about the state of the environment. Responsible investors would instead put their money into environmentally conscious businesses [17, 80, 99], resulting in firms having grown more environmentally conscious. There is a correlation between the way investors feel about the environment and the ethical priorities in their businesses [104]. The investors’ concern for the environment compels businesses to implement various environmentally friendly policies and procedures [8]. According to [105], customers value environmentally friendly and green business activities.

Examining Socially Responsible Investing Behaviour of Individuals …

7

2.5 Risk Tolerance and Socially Responsible Behaviour The predisposition of an investor to take risks determines the level of risk he or she is willing to tolerate [30]. The word risk is defined in different ways [50, 52, 96]. Because of the rising fragility of the financial universe, there is an imperative need to do ongoing research on the investors’ levels of comfort with financial risk [103]. The investor’s comfort level with financial risk has a role in the choices made about their retirement plan, portfolio, and other investments [41]. Both the investors’ willingness to take financial risks and their actual behaviour in taking risks may be used to classify the investors [51]. The risk that is associated with investing in socially responsible avenues may be found in both the positive and negative aspects of the market. The risk tolerance attitude of the investors has an impact on the behaviour of the investors [9, 93].

2.6 Social Investing Efficacy and Socially Responsible Behaviour Responsible investors’ conviction that their actions would compel and pressure corporations to embrace green policies beneficial to the environment, society, and governance. Additionally, it guides their investment choice in SR funds. This particular activity or conviction is referred to as social investing efficacy [18, 28, 47]. In addition, research has concentrated on the effectiveness of social investing as a quality of people making responsible investment choices [18, 28, 88]. The firmness with which a person feels that their investment methods might influence company behaviour is known as social investing efficacy (SIE. When investors have such a mindset, they are in a position to communicate their social ideals to firms by approving suitable investment strategies. These kinds of features force investors to make a compromise between their financial and their non-financial priorities. The strength of the connection between investor characteristics and the pursuit of non-economic aims is impacted by the power exerted by SIE. Due to rising societal pressures stemming from public disclosures, institutions such as pension funds, mutual funds, and other organisations are reluctant to hold equities with divergent ethical ideals from those of shareholders and customers [43, 62, 92]. Due to the implicit promise made to stakeholders, socially responsible companies have more substantial cash holdings than other organisations. Due to the implicit promise made to stakeholders, socially responsible businesses have more cash on hand, increasing their ability to spend more on CSR-related initiatives [21]. Individual investors’ non-economic or ethical investment selections are influenced by religiosity, collectivism, materialism, risk tolerance, environmental attitude, and social investing efficacy. The research issue that has to be answered here is whether or not these are the factors that determine investors’ non-economic aspirations. In

8

K. Mehta et al.

addition, the research question is whether these factors influence investors’ noneconomic goals. Also another consideration is how these factors are positioned from the fund managers’ viewpoint. Precisely, the research under consideration intends to achieve the following two objectives: 1. To investigate the factors influencing Indian investors’ socially responsible investment behaviour. 2. To determine the ranking of several factors affecting investors’ non-economic objectives based on expert opinion.

3 Data Inputs and Research Methods The research being considered is based on primary data. The core data was gathered through offline and online surveys. The present research used [47] standardised questionnaire on responsible investment behaviour, which included 40 questions assessed on a five-point Likert scale. After that, the questionnaires that were not filled out were rolled out, and the data was cleaned up using several different criteria. The target respondents consisted of investors with three years of financial experience. The data collection took eight months to complete. Table 1 shows the demographic characteristics of the respondents. The present study extends the scope of the research conducted by [47], consequently, exploratory factor analysis was applied to identify the components and items of the study. For this purpose, a sample of 723 respondents was used. Then, based on Table 1 Demographic characteristics of the sample Gender

No. of resp.

Occupation

No. of resp.

Male

429

Business

73

Females

294

Govt. job

89

Total

723

Pvt. job

397

Financial services/ financial markets

117

Other

47 723

Qualification Undergraduate

27

Total

Undergraduate with a business/management professional degree (BBA, B.Com. etc.)

34

Experience

Postgraduate with a professional management degree (MBA, CFA, MIB, MFC, etc.)

472

Less than 1 year

186

Postgraduate

108

1 year to 3 year

352

Other

82

3 to 5 years

103

More than 5 years

82

Total

723

Total

723

Examining Socially Responsible Investing Behaviour of Individuals …

9

the factors found through EFA, an expert-led analysis was done. For this, experts from the business world to fund managers were sought out. These experts had been in their fields for more than 20 years. Twelve experts could be contacted for their opinion on the relationship between the eight components retrieved from EFA. DEMATEL, an expert-based system, was used since the research objective was to measure the influence of numerous characteristics on the non-economic goal of an individual investor. In the early 1970s, Geneva’s Battelle Memorial Institute (BMI) provided this approach [33, 34, 36, 37, 73]. The hierarchical structure was utilised to tackle complex issues involving the interaction between groups using the model [91]. In the present context, the EFA-identified components were further investigated using expert opinion to identify cause and receiver variables using DEMATEL.

4 Results of Exploratory Factor Analysis Principal component analysis has been used to perform exploratory factor analysis. The first tests, the KMO and Bartlett’s tests, showed that EFA could be used on the sample survey. Table 2 shows that the KMO is 0.840 and that Bartlett’s Test of Sphericity is significant. Table 3 shows the results of Rotated Component Matrix. As shown in Table 3, all of the observed variables can be put into one of eight main categories. The standard questionnaire gives more names for these variables. It’s important to note that items with factor loadings of less than 0.4 were taken out, and the final EFA results were based on 36 items that fit into 8 structures. Please look at Appendix 1 for a list of individual items and factors/constructs. The eigenvalue matrix was able to explain 70% of the total difference. Through DEMATEL, these eight factors were looked at more closely to see how they affect each other and to find out which factor has the biggest effect on how investors choose to invest their money in ways that aren’t just about making money. In Appendix 2, you can read about the steps that DEMATEL takes. By performing steps 1, 2, and 3, Tables 4, 5, and 6 were produced, accordingly. Table 7 shows the outcomes of step 4; based on these calculations, the relationship (Di − Rj) and prominence (Di + Rj) were determined (see Table 7). The sum of received and given effects determines the degree of prominence, which refers to a factor’s direct or indirect link. This connection illustrates the factor’s net contribution to the system. The positive value of (Di − Rj) reflects its influence on other factors, Table 2 KMO and Bartlett’s test

Kaiser–Meyer–Olkin measure of sampling adequacy

0.840

Bartlett’s test of sphericity

Approx. Chi-square

15,322.092

df

630

Sig.

0.000

10

K. Mehta et al.

Table 3 Rotated component matrix 1 VAR00023

0.862

VAR00022

0.833

VAR00020

0.805

VAR00024

0.769

VAR00021

0.754

VAR00018

0.751

VAR00025

0.699

VAR00019

0.657

2

VAR00035

0.851

VAR00034

0.843

VAR00032

0.841

VAR00033

0.831

VAR00036

0.738

VAR00037

0.706

3

VAR00001

0.844

VAR00003

0.839

VAR00005

0.814

VAR00002

0.805

VAR00004

0.786

4

VAR00007

0.848

VAR00012

0.826

VAR00010

0.821

VAR00009

0.818

5

VAR00014

0.833

VAR00015

0.823

VAR00016

0.815

VAR00017

0.705

6

VAR00028

0.891

VAR00026

0.889

VAR00027

0.879

7

VAR00031

0.877

VAR00030

0.858

VAR00029

0.835

8

VAR00040

0.866

VAR00039

0.846

VAR00038

0.825

Extraction Method: Principal component analysis Rotation Method: Varimax with Kaiser normalisationa a Rotation converged in 6 iterations

0

2.83

2

2.75

2.42

2.42

2.08

2.58

Environmental attitude

Materialism

Individual risk propensity

Collectivism

Individual risk affinity

Collectivism

Religiosity

Non-economic goals

Environmental attitude

Table 4 Direct-influence matrix

2.58

2.5

2.25

2.83

2.92

2.25

0

2.5

Materialism

2.67

2.33

2.58

2.25

2

0

2.42

2.58

Individual risk propensity

2.08

2.42

3

2.08

0

2.67

2.5

2.08

Collectivism

2.92

2.33

2.67

0

2.67

2.5

2.58

2.5

Individual risk affinity

2.58

2.42

0

3.25

2.42

2.67

2.58

2.58

Social investing efficacy

2.08

0

2.33

2.83

2

2.25

2.67

2.33

Religiosity

0

2.75

3.5

2.67

2.33

2.58

2.5

2.58

Non-economic goals

Examining Socially Responsible Investing Behaviour of Individuals … 11

0

0.15

0.106

0.145

0.128

0.128

0.11

0.137

0.903

Environmental attitude

Materialism

Individual risk propensity

Collectivism

Individual risk affinity

Social investing efficacy

Religiosity

Non-economic goals

Total

Environmental attitude

0.943

0.137

0.132

0.119

0.15

0.154

0.119

0

0.132

Materialism

Table 5 Next step of direct-influence matrix

0.89

0.141

0.123

0.137

0.119

0.106

0

0.128

0.137

Individual risk propensity

0.89

0.11

0.128

0.159

0.11

0

0.141

0.132

0.11

Collectivism

0.96

0.154

0.123

0.141

0

0.141

0.132

0.137

0.132

Individual risk affinity

0.978

0.137

0.128

0

0.172

0.128

0.141

0.137

0.137

Social investing efficacy

0.872

0.1101

0

0.123

0.15

0.106

0.119

0.141

0.123

Religiosity

1

0

0.145

0.185

0.141

0.123

0.137

0.132

0.137

Non-economic goals

0.925

0.89

0.991

0.969

0.903

0.894

0.956

0.907

Total

12 K. Mehta et al.

1.5

1.7

1.58

1.63

1.71

1.74

1.58

1.65

Environmental attitude

Materialism

Individual risk propensity

Collectivism

Individual risk affinity

Social investing efficacy

Religiosity

Non-economic goals

Environmental attitude

Table 6 Total relationship matrix

1.71

1.65

1.79

1.78

1.69

1.64

1.63

1.67

Materialism

1.63

1.57

1.72

1.68

1.57

1.46

1.66

1.6

Individual risk propensity

1.61

1.57

1.73

1.67

1.47

1.58

1.66

1.58

Collectivism

1.75

1.67

1.84

1.68

1.7

1.68

1.78

1.7

Individual risk affinity

1.76

1.7

1.74

1.86

1.72

1.71

1.81

1.73

Social investing efficacy

1.58

1.43

1.68

1.68

1.55

1.54

1.65

1.57

Religiosity

1.67

1.74

1.93

1.87

1.75

1.74

1.84

1.76

Non-economic goals

Examining Socially Responsible Investing Behaviour of Individuals … 13

14

K. Mehta et al.

Table 7 Degree of influence Dimensions

Di

Ri

Di + Ri

Environmental attitude

FACT_1

13.1217

13.0761

26.1978

Di − Ri 0.0457

Net cause

Materialism

FACT_2

13.7253

13.5569

27.2822

0.1683

Net cause

Individual risk propensity

FACT_3

12.9544

12.907

25.8614

0.0473

Net cause

Collectivism

FACT_4

13.0813

12.8805

25.9618

0.2007

Net cause

Individual risk affinity

FACT_5

13.9311

13.8077

27.7388

0.1234

Net cause

Social investing efficacy

FACT_6

14.1694

14.0251

28.1945

0.1443

Net cause

Religiosity

FACT_7

12.8888

12.6675

25.5564

0.2213

Net cause

Non-economic goals

FACT_8

13.3607

14.3118

27.6725

− 0.951

Net receiver

whilst the negative value indicates the factor’s effect on others. Using the DEMATEL technique, all dimensions are broken down into two subcategories: “cause” and “effect.” Accordingly, the five components were divided into two distinct groups (Di − Rj). Exhibit 7 demonstrates that variables with positive values belong to the cause group and directly influence the other variables. In contrast to the non-economic aims element, which has a negative value, this factor is a receiver. Religiosity (0.2213), collectivism (0.0707), materialism (0.1683), and social investing efficacy (0.1443) have shown the most significant causal value among these seven variables. The impact of FACT 8, namely the non-economic aims of individual investors, has been found. Utilising prominence and relation values as coordinate sets, an impact relationship map or cause-and-effect diagram was constructed (see Table 8). If we divide the sum of prominence and relationship by 8, the total number of elements and the mean coordinates will be obtained. The degree of influence among criteria for each dimension is shown in Table 9.

5 Conclusion and Implications of Study This research is one of a kind since it takes an integrated approach from the point of view of investors and fund managers to investigate the different aspects that determine a person’s ethical or non-economic behaviour. The findings were collected using EFA and DEMATEL, both of which provided eight parameters that describe the socially responsible behaviour of Indian investors. The data were then analysed. Individual risk propensity, social investing efficacy, individual risk affinity, religiosity, environmental attitude, collectivism, and materialism all affect investors’ responsible behaviour. The present study will raise understanding, curiosity, and consciousness

Examining Socially Responsible Investing Behaviour of Individuals …

15

Table 8 Impact relationship map InvRisk Affinity

Collectivism. 0.123422129

0.200714774

Materialism 0.168334687 EnvAtt.

0.045650432

NonEcoGoal 0.221296072 Religiosity 0.04732717

IndvRisk Propensity

0.14427257 SIE

among investors and fund managers from diverse investment backgrounds on the importance of ethical investing behaviour. The findings of DEMATEL indicate that seven of the eight dimensions of the research serve as causative factors. In contrast, people’s non-economical or ethical aims serve as impact variables. The three variables mentioned above most strongly influence individuals’ non-economic goals. These include religiosity, collectivism, and the effectiveness of social investment. In order to attain useful insights of the responsible behaviour of investors based on their demographic background, it is possible to investigate further these findings based on a variety of demographic variables. Socially responsible investing is a developing trend among individual investors [13, 83, 87]. The study’s conclusions have both management and theoretical ramifications. Fund managers, practitioners advocating ethical finance, and individual portfolio managers may benefit from the causal link shown by this research. The result gained from the investigation may add to the scholarly literature on behavioural finance theories about individuals’ attitudes towards responsible investment. Moreover, among other elements of behaviour, the data revealed in support of religiosity, collectivism, and social investing effectiveness might be studied further in future studies. These characteristics may have a more significant impact on countries with smaller communities and families, like India. Friends and religion influence an individual’s behaviour.

16

K. Mehta et al.

Table 9 Degree of relationship and effect for criteria of each dimension Dimensions

Di

Ri

Di + Ri

Di − Ri

Environment attitude

Dimensions

Di

Ri

Di + Ri

Di-Ri

Individual’s risk affinity

FAC_E1

12.14

12.03

24.17

0.11

FAC_RA1

11.48

11.46

22.95

0.02

FAC_E2

12.75

12.66

25.41

0.08

FAC_RA2

12.21

11.83

24.04

0.38

FAC_E3

11

10.9

21.9

0.1

FAC_RA3

10.92

11.32

22.24

-0.4

FAC_E4

12.04

12.33

24.36

-0.29

FAC_S1

11.43

11.41

22.84

0.01

Materialism

SIE

FACT_M1

15.11

15.05

30.17

0.06

FAC_S2

12.52

12.39

24.91

0.13

FACT_M2

15.29

15.8

31.1

-0.51

FAC_S3

11.7

11.51

23.21

0.19

FACT_M3

15.05

15.04

30.09

0.01

FAC_S4

11.9

11.72

23.62

0.18

FACT_M4

15.36

15.23

30.59

0.13

FAC_S5

10.88

11.51

22.4

-0.63

FACT_M5

16.12

15.93

32.05

0.19

FAC_S6

11.63

11.52

23.16

0.11

FACT_M6

15.86

15.76

31.62

0.11

Religiosity

FACT_M7

15.41

14.66

30.07

0.75

FAC_R1

12.14

12.03

24.17

0.11

FACT_M8

15.61

16.35

31.96

-0.73

FAC_R2

12.75

12.66

25.41

FAC_R3

11

10.9

21.9

0.1

FAC_R4

12.04

12.33

24.36

-0.29

7.06

13.31

-0.82 0.55

Individual’s risk propensity FAC_RP1

6.25

7.06

13.31

-0.82

FAC_RP2

7.23

6.67

13.9

0.55

Non-economic goals

FAC_RP3

6.2

5.94

12.14

0.26

FAC_N1

FAC_C1

19.82

21.11

40.93

-1.28

FAC_C2

21.55

21.4

42.96

0.15

FAC_C3

21.28

21.23

42.51

0.05

FAC_C4

22.01

21.57

43.58

0.44

FAC_C5

21.43

20.78

42.21

0.65

Collectivism

6.25

FAC_N2

7.23

6.67

13.9

FAC_N3

6.2

5.94

12.14

0.08

0.26

Appendix 1: List of Factors/Constructs and Items

Items considered by [47]

Items considered in present study

Abbreviation used in EFA

Abbreviation used in DEMATEL

Name of factor

Code used for factor

1

Individual should sacrifice self-interest for the group that they belong to

Yes

VAR00001

FAC_C1

Collectivism

FACT_4

2

Individuals should stick with the group even through difficulties

Yes

VAR00003

FAC C2

(continued)

Examining Socially Responsible Investing Behaviour of Individuals …

17

(continued) Items considered by [47]

Items considered in present study

Abbreviation used in EFA

Abbreviation used in DEMATEL

3

Group welfare is more important than individual success

Yes

VAR00005

FAC_C3

4

Individuals should pursue their Yes goals only after considering the welfare of the group

VAR00002

FAC_C4

5

Group loyalty should be encouraged even if individuals suffer

Yes

VAR00004

FAC_C5

6

I feel connected with nature and the world around me

Yes

VAR00007

FAC_E1

7

I think of myself as an environmentalist

Yes

VAR00012

FAC_E2

8

I feel a moral obligation to help protect the environment in whatever way I can

Yes

VAR00010

FAC_E3

9

I am really not very interested in environmental issues

Yes

VAR00009

FAC_E4

10

I believe that all living things have an equal right to survive

Deleted

VAR00008

–

11

I believe harm to our environment is a serious problem

Deleted

VAR00011

–

12

I take responsibility to protect the environment around me

Deleted

VAR00006

–

13

I would describe myself as very religious

Deleted

VAR00013

–

14

If more Indian used their religion, they would make better choice

Yes

VAR00014

FAC_R1

15

Spiritual values guide me in making important decisions

Yes

VAR00015

FAC R2

16

My religious beliefs help me recognize the dignity and welfare of people

Yes

VAR00016

FAC_R3

17

I am guided by my religion to ensure that my actions do not intentionally harm others

Yes

VAR00017

FAC_R4

I8

I pay a lot of attention to the objects my friends own

Yes

VAR00018

FACT Ml

19

I like to keep my life materially simple

Yes

VAR00019

FACT M2

20

Owning things is not very important to me

Yes

VAR00020

FACT_M3

21

I do not have all the things needed to enjov life

Yes

VAR00021

FACTM4

Name of factor

Code used for factor

Environmental attitude

FACT1

Religiosity

FACT 7

Materialism

FACT_2

(continued)

18

K. Mehta et al.

(continued) Items considered by [47]

Items considered in present study

Abbreviation used in EFA

Abbreviation used in DEMATEL

22

I measure my achievements through my material possessions

Yes

VAR00022

FACTM5

23

My possession speaks a lot about my status

Yes

VAR00023

FACTM6

24

I like to impress people with my material possession

Yes

VAR00024

FACT_M7

25

I will feel happier if I own more things

Yes

VAR00025

FACTM8

26

I avoid high risk activities

Yes

VAR00026

FAC RA1

27

I believe in taking a lot of risk

Yes

VAR00027

FAC RA2

28

I adopt an investment strategy so as to reduce risk

Yes

VAR00028

FAC RA3

29

I find investing in risky funds to be thrilling

Yes

VAR00029

FACRP1

30

I am a conservative investor

Yes

VAR00030

FAC RP2

31

I prefer to invest in risky individual stocks over mutual or managed funds

Yes

VAR00031

FAC_RP3

32

I believe my investments have a positive impact on the environment

Yes

VAR00032

FAC_S1

33

I think my investments have a favorable effect on community welfare

Yes

VAR00033

FAC_S2

34

My investments will make managers more responsive to social and community needs

Yes

VAR00034

FAC_S3

35

I want my investments to enhance society’s welfare

Yes

VAR00035

FAC_S4

36

I think my investments will improve the condition of the ecosystem

Yes

VAR00036

FAC_S5

37

My investments will have a positive bearing on corporate governance

Yes

VAR00037

FAC_S6

38

The mam goal of my investment strategy is advancing social agendas

Yes

VAR00038

FAC_N 1

39

I aim to promote environmental causes through my investment decisions

Yes

VAR00039

FAC_N2

40

I never invest in what I deem as firms ‘bad’ for society

Yes

VAR00040

FAC_N3

Name of factor

Code used for factor

Individual

FACT 5

Individual risk propensity

FACT3

Social investing FACT_6 rfficacy

Non-economic goals

FACT_8

Examining Socially Responsible Investing Behaviour of Individuals …

19

Appendix 2 Step 1: Preparing the direct-influence matrix At first, the raw data is collected from the experts. Each expert is asked to score the degree of direct influence between two factors based on pair-wise comparison. The following formula is used for step 1. zi j =

m 1 ∑ k x m i=1 i j

(1)

The total number of experts is represented by m, the factors considered for comparison are represented by i and j, particular number of expert are shown with k and x denotes the score or response. Step 2: Normalizing direct-influence matrix For this, formula (2) and (3) mentioned hereunder was used. D =λ∗ Z

(2)

where [

1 1 λ = Min ∑n || || , ∑n || || zi j max 1 ≤ i ≤ n j=1 z i j max 1 ≤ i ≤ n i=1

] (3)

Step 3: Achieve the total-relation matrix In the third step, total relation matrix (T ) is attained by using following formula, where I indicate identity’ matrix. T = D(I − D)−1

(4)

Step 4: Producing impact relationship map Once, the result for total-relation matrix is obtained then sum of rows (Di) and columns (Rj) is calculated. Rj signifies the total received direct or indirect effects by jth factor from rest of the factors, the value of Di describes the total direct or indirect effects of ith factor on rest of the factors. Step 5: Cause and effect analysis At the final stage, a threshold value is calculated for the understanding of causal relationships among the factors. The threshold value is the average of the elements in T matrix that has been calculated using the following formula (5).

20

K. Mehta et al.

∑n α=

∑n

t=1

α = 1.675

j=1

[ti ]

N (5)

The T i,j values greater then a value of 1.675 can be observed in Table 6. It helps us in visualizing the causal relationships among the factors and the arrows from each factor were drawn accordingly.

References 1. Abu-Alhaija AS, Nerina R, Hashim H, Jaharuddin NS (2018) Religion in consumer behaviour research: the significance of religious commitment and religious affiliation. Int J Econ Comm Manage VI(1):245–258 2. Akhtar S, Jahromi M (2017) Risk, return and mean-variance efficiency of Islamic and nonIslamic stocks: evidence from a unique Malaysian data set. Account Fin 57:3–46 3. Akhtar S, Akhtar F, Jahromi M, John K (2017) Impact of interest rate surprises on Islamic and conventional stocks and bonds. J Int Money Financ 79:218–231 4. Aktas N, Eric DB, Cousin JG (2011) Do financial markets care about SRI? Evidence from mergers and acquisitions. J Bank Finance 35:1753–1761 5. Al-Hajieh H, Redhead K, Rodgers T (2011) Investor sentiment and calendar anomaly effects: a case study of the impact of Ramadan on Islamic middle eastern markets. Res Int Bus Financ 25(3):345–356 6. Al-Hajieh H, Redhead K, Rodgers T (2011) Investor sentiment and calendar anomaly effects: a case study of the impact of Ramadan on Islamic middle eastern markets. Res Int Bus Financ 25:345–356 7. Ammann M, Bauer C, Fischer S, Muller P (2018) The impact of the Morningstar sustainability rating on mutual fund flows. Euro Fin Manag (in Press) 8. Barnea A, Heinkel R, Kraus A (2005) Green investors and corporate investment. Struct Chang Econ Dyn 16:332–346 9. Baxi PV (2011) Cross cultural differences in financial risk-taking: India and the United States. Available at http://deepblue.lib.umich.edu/bitstream/handle/2027.42/85313/pvbaxi. pdf; jsessionidD65A0E48511122D7A91DB097F5E18865?sequence 10. Belk RW (1983) Worldly possessions: issues and criticisms. In: Bagozzi RP, Tybout AM (eds) Advances in consumer research, vol 10. Association for Consumer Research, Michigan, pp 514–519 11. Belk RW (1984) Three scales to measure constructs related to materialism: reliability, validity, and relationships to measures of happiness. In: Kinnear T (ed) Advances in consumer research, vol 11. Association for Consumer Research, Utah, pp 291–297 12. Benson KL, Humphrey JE (2008) Socially responsible investment funds: investor reaction to current and past returns. J Bank Fin 32(9):1850–1859 13. Berry T, Junkus J (2013) Socially responsible investing: an investor perspective. J Bus Ethics 112. https://doi.org/10.1007/s10551-012-1567-0 14. Bialkowski J, Starks LT (2016) SRI funds: investor demand, exogenous shocks and ESG profiles. In: Laura T (ed) Starks at 25th finance forum. Working paper, Unversity of Canterbury. Available at https://ir.canterbury.ac.nz/handle/10092/12492 15. Białkowski J, Bohl MT, Philipp KP, Wisniewski TP (2013) Do mutual fund managers exploit the Ramadan anomaly? Evidence from Turkey. Emerg Markets Rev 15:211–232 16. Bialkowski J, Etebari A, Wisniewski T (2012) Fast profits: investor sentiment and stock returns during Ramadan. J Bank Finance 36:835–845. https://doi.org/10.1016/j.jbankfin.2011.09.014

Examining Socially Responsible Investing Behaviour of Individuals …

21

17. Bidappa R, Kaul TM (2011) Indian consumers conscious of environmental benefits of sustainable practices. Available at http://www.nielsen.com/in/en/news-insights/pressroom/2011/ind ian-consumers-conscious-of-environmental-benefits.html 18. Block LG, Keller PA (1995) When to accentuate the negative: the effects of perceived efficacy and message framing on intentions to perform a health-related behavior. J Mark Res 32:192– 203 19. Bollen NP (2007) Mutual fund attributes and investor behavior. J Fin Quant Anal 42(03):683– 708 20. Cellier A, Chollet P, Gajewski JF (2016) Do investors trade around social rating announcements? Euro Fin Manage 22(3):484–515 21. Chang CH, Chen SS, Chen YS, Peng SC (2019) Commitment to build trust by socially responsible firms: evidence from cash holdings. J Corp Finan 2019. https://doi.org/10.1016/ j.jcorpfin.2019.03.004 22. Chatterjee A, Hunt JM, Kernan JB (2000) Social character of materialism. Psychol Rep 86:1147–1148 23. Climent F, Soriano P (2011) Green and good? The investment performance of US environmental mutual funds. J Bus Ethics 103(2):275–287 24. Colonnello S, Curatola G, Gioffré A (2019) Pricing sin stocks: ethical preference vs. risk aversion. Eur Econ Rev 118:69–100 25. Derwall J, Koedijk K, Ter Horst J (2011) A tale of values-driven and profit-seeking social investors. J Bank Fin 35(8):2137–2147 26. Dharani M, Hassan MK, Paltrinieri A (2019) Faith-based norms and portfolio performance: evidence from India. Glob Fin J. https://doi.org/10.1016/j.gfj.2019.02.001. Accepted 5 Feb 2019 27. Dhawan N, Roseman IJ, Naidu RK, Rettek SI (1995) Self-concepts across two cultures: India and the United States. J Cross Cult Psychol 26:606–621 28. Diamond WD, Iyer ES (2007) Creating effective direct mail charitable solicitations: the effects of enclosures and different appeals. J Nonprofit Public Sect Mark 18:81–100 29. Diltz JD (1995) Does social screening affect portfolio performance? J Invest 4:64–69 30. Dimas HP, Celso FL, Ricardo PCL (2014) Socially responsible stock funds in Brazil. Int J Managerial Fin 10(4):432–441 31. Eccles NS, Viviers S (2011) The origins and meanings of names describing investment practices that integrate a consideration of ESG issues in the academic literature. J Bus Ethics 104(3):389–402 32. Essoo N, Dibb S (2004) Religious influences on shopping behavior: an exploratory study. J Mark Manag 20:683–712 33. Falatoonitoosi E, Leman Z, Sorooshian S, Salimi M (2013) Decision-making trial and evaluation laboratory. Res J Appl Sci Eng Technol 5(13):3476–3480. https://doi.org/10.19026/rja set.5.4475 34. Fontela E, Gabus A (1976) The DEMATEL observer. DEMATEL 1976 Report. Battelle Geneva Research Centre, Switzerland Geneva 35. Fu X, Tang T, Yan X (2019) Why do institutions like corporate social responsibility investments? Evidence from horizon heterogeneity. J Empirical Fin. https://doi.org/10.1016/j. jempn.2019.01.010. Accepted 29 Jan 2019 36. Gabus A, Fontela E (1972) World problems, an invitation to further thought within the framework of DEMATEL. Battelle Geneva Research Centre, Switzerland, Geneva 37. Gabus A, Fontela E (1973) Perceptions of the world problematique: communication procedure, communicating with those bearing collective responsibility (DEMATEL report no. 1). Battelle Geneva Research Centre, Switzerland Geneva 38. Galema R, Plantinga A, Scholtens B (2008) The stocks at stake: return and risk in socially responsible investment. J Bank Fin 32(12):2646–2654 39. Ger G, Belk RW (1996) Cross-cultural differences in materialism. J Econ Psychol 17(1):555– 577

22

K. Mehta et al.

40. Gil-Bazo J, Ruiz-Verdu P, Santos AAP (2010) The performance of socially responsible mutual funds: the role of fees and management companies. J Bus Ethics 94(2):243–263 41. Hanna SD, Gutter MS, Fan JX (2001) A measure of risk tolerance based on economic theory. Fin Counseling Plann 12(2):53–60 42. Hofstede G (1980) Culture’s consequences: international differences in work-related values. Sage, Beverly Hills CA 43. Hong H, Kostovetsky L (2012) Red and blue investing: values and finance. J Financ Econ 103(1):1–19 44. Hui CH, Triandis HC (1986) Individualism-collectivism: a study of cross-cultural researchers. J Cross Cultural Psychol 17:225–248 45. Iglesias-Casal A, López-Penabad MC, López-Andión C, Maside-Sanfiz JM (2019) Diversification and optimal hedges for socially responsible investment in Brazil. Econ Model 85:106–118. https://doi.org/10.1016/j.econmod.2019.05.010 46. Islam T, Chandrasekaran U (2016) Effect of religiosity on ecologically conscious consumption behaviour. J Islamic Market 7(4):495–507 47. Iyer ES, Kashyap R (2009) Non economic goals of investors. J Consumer Behavior 8(5):225– 237 48. Izberk-Bilgin E (2012) Infidel brands: unveiling alternative meanings of global brands at the nexus of globalization, consumer culture, and Islamism. J Consum Res 39(4):663–687 49. Jansson M, Biel A (2014) Investment institutions’ beliefs about and attitudes toward socially responsible investment (SRI): a comparison between SRI and non-SRI management. Sustain Dev 22(1):33–41 50. Kahneman D, Tversky A (1974) Judgment Under uncertainty: heuristics and biases. Science 185:1124–1131 51. Kannadhasan M (2015) Retail investors’ financial risk tolerance and their risk-taking behaviour: the role of demographics as differentiating and classifying factors. IIMB Manag Rev 27:175–184 52. Kannadhasan M, Nandagopal R (2010) Influence of decision-makers’ characteristics on risk analysis in strategic investment decisions. J Mod Account Audit 6(4):38–44 53. Kempf A, Osthoff P (2007) The effect of socially responsible investing on portfolio performance. Eur Financ Manag 13:908–922 54. Kempf A, Osthoff P (2008) SRI funds: Nomen est Omen. J Bus Financ Acc 35(9–10):1276– 1294 55. Kim J-B, Li B, Liu Z (2018) Does social performance influence breadth of ownership? J Bus Fin Account (Forthcoming) 56. Klein TA, Laczniak GR, Santos NJC (2017) Religion-motivated enterprises in the marketplace: a macro marketing inquiry. J Macro-Market 37(1):102–114. https://doi.org/10.1177/ 0276146716674050 57. Kulkarni SP, Hudson T, Ramamoorthy N, Marchev A, Kondakova PG, Gorskov V (2010) Dimensions of individualism-collectivism: a comparative study of five cultures. Curr Issues Bus Law 5:93–109 58. Kurtz L (2005) Answers to four questions. J Investing 14(3):125–139 59. Lee S, Switzer LN, Wang J (2018) Risk, culture and investor behavior in small (but notorious) Eurozone countries. J Int Fin Markets Inst Money. https://doi.org/10.1016/j.intfin.2018.12.010 60. Li B, Shan Ee M, Rashid M (2016) Is momentum trading profitably from Shari’ah compliant stocks? Rev Fin Econ 31(1):56–63 61. Lindsay DM (2007) Faith in the halls of power: how evangelicals joined the American elite. Oxford University Press, New York 62. Luo A, Balvers RJ (2017) Social screens and systematic boycott risk. J Fin Quant Anal 52(1):365–399 63. Mallett J, Michelson S (2010) Green investing: is it different from socially responsible investing? Int J Bus Res 15(4):395–410

Examining Socially Responsible Investing Behaviour of Individuals …

23

64. Matallín-Sáez JC, Soler-Domínguez A, de Mingo-López DV, Tortosa-Ausina E (2019) Does socially responsible mutual fund performance vary over the business cycle? New insights on the effect of idiosyncratic SR features. Bus Ethics: Eur Rev 28(1):71–98. https://doi.org/10. 1111/beer.12196 65. Matallín-Saez JC, Soler-Domínguez A, Tortosa-Ausina E, Mingo-Lopez DV (2019) Ethical strategy focus and mutual fund management: performance and persistence. J Clean Prod 213:618–633 66. McLachlan J, Gardner J (2004) A comparison of socially responsible and conventional investors. J Bus Ethics 52:11–25 67. Merdad HJ, Hassan MK, Hippler WJ (2015) The Islamic risk factor in expected stock returns: an empirical study in Saudi Arabia. Pacific-Basin Fin J 34:293–314 68. Mokhlis S (2006) The effect of religiosity on shopping orientation: an exploratory study in Malaysia. J Am Acad Bus 9(1):67–74 69. Munoz F (2019) The ‘smart money effect’ among socially responsible mutual fund investors. Int Rev Econ Financ 62:160–179 70. Muñoz F, Vargas M, Vicente R (2014) Fund flow bias in market timing skill. Evidence of the clientele effect. Int Rev Econ Fin 33(C):257–269 71. Munoz F, Vicente R, Ferruz L (2015) Stock-picking and style-timing abilities: a comparative analysis of conventional and socially responsible mutual funds in the US market. Quant Fin 15(2):345–358. https://doi.org/10.1080/14697688.2013.832833 72. Narayan PK, Phan DHB, Narayan S, Bannigidadmath D (2017) Is there a financial news risk premium in Islamic stocks? Pac Basin Fin J 42:158–170 73. Naser BM, Mahdi S, Amir SA, Hami Y, Seyed HH (2010) Strategic environment analysis using DEMATEL method through systematic approach. Manage Sci Eng 11(2–3):95–105 74. Nisbett RE (2003) The geography of thought. Free Press, New York, NY 75. Al-Khazali O, Lean HH, Samet A (2014) Do Islamic stock indexes outperform conventional stock indexes? A stochastic dominance approach. Pac Basin Financ J 28:29–46 76. O’Rourke A (2003) The message and methods of ethical investment. J Clean Prod 11(6):683– 693 77. Paul K (2017) The effect of business cycle, market return and momentum on financial performance of socially responsible investing mutual funds. Soc Responsibil J 13(3):513–528 78. Peifer JL (2010) Morality in the financial market? A look at religiously affiliated mutual funds in the USA. Soc Econ Rev 9(2):235–259 79. Phau I, Kea G (2007) Attitudes on university students toward business ethics: a cross-national investigation of Australia, Singapore and Hong Kong. J Bus Ethics 72:61–75 80. Poloinsky MJ, Mintu-Wimsatt AT (1995) Environmental marketing: strategies, practice, theory, and research. Haworth Press, Binghamton, NY 81. Ramamoorthy N, Gupta A, Sardessai RM, Flood PC (2005) Individualism-collectivism and attitudes towards human resource systems: a comparative study of American, Irish and Indian MBA students. Int J Human Resour Manage 16(5):853–870 82. Ramamoorthy N, Kulkarni S, Gupta A, Flood P (2007) Individualism-collectivism orientation and employee attitudes: a comparison of employees from the high-technology sector in India and Ireland. J Int Manag 13:187–203 83. Renneboog L, Ter Horst J, Zhang C (2011) Is ethical money financially smart? Nonfinancial attributes and money flows of socially responsible investment funds. J Fin Intermediation 20(4):562–588 84. Renneboog L, Ter Horst J, Zhang C (2008) The price of ethics and stakeholder governance: the performance of socially responsible mutual funds. J Corp Finan 14(3):302–322 85. Renneboog L, Ter Horst J, Zhang C (2008) Socially responsible investments: institutional aspects, performance, and investor behavior. J Bank Fin 32(9):1723–1742 86. Richins ML, Dawson S (1992) A consumer values orientation for materialism and its measurement: scale development and validation. J Consum Res 19:303–316 87. Riedl H A, Smeets P (2017) Why do investors hold socially responsible mutual funds? J Finance 72(6):2505–2550. https://doi.org/10.1111/jofi.12547

24

K. Mehta et al.

88. Rogers RW (1975) A protection motivation theory of fear appeals and attitude change. J Psychol 91:93–114 89. Sagiv L, Schwartz SH (2000) Value priorities and subjective well-being: direct relations and congruity effects. Euro J Soc Psychol 30(2):177–198 90. Sasaki JY, Kim HS (2011) At the intersection of culture and religion: a cultural analysis of religion’s implication for secondary control and social affiliation. J Pers Soc Psychol 101(2):401–414 91. Shahraki AR, Paghaleh MJ (2011) Ranking the voice of customer with fuzzy DEMATEL and fuzzy AHP. Indian J Sci Technol 4(12):1763–1772 92. Sharma R, Mehta K, Vyas V (2020) Responsible investing: a study on non-economic goals and investors’ characteristics. Appl Fin Lett 9(SI):63–78. https://doi.org/10.24135/afl.v9i2.245 93. Shavitt S, Lalwani AK, Zhang J, Torelli CJ (2006) The horizontal-vertical distinction in cross-cultural consumer research. J Consum Psychol 16:325–356 94. Siala H (2013) Religious influences on consumers’ high-involvement purchasing decisions. J Serv Mark 27(7):579–589 95. Sirmon DJ, Lane PJ (2004) A model of cultural differences and international alliance performance. J Int Bus Stud 35:306–319 96. Slovic P, Baruch F, Lichtenstein S (1982) Why study risk perception? Risk Anal 2:83–93 97. Sood J, Nasu Y (1995) Religiosity and nationality: an exploratory study of their effect on consumer behavior in Japan and the United States. J Bus Res 34:1–9 98. Statman M (2005) Normal investors, then and now. Financ Anal J 61:31–37 99. Tripathy SN, Panda S (2003) Fundamentals of environmental studies. Vikas Publications, Delhi 100. Urquhart A, Zhang H (2019) The performance of technical trading rules in socially responsible investments. Int Rev Econ Fin. https://doi.org/10.1016/j.iref.2019.05.002. Accepted 21 May 2019 101. Vyas V, Mehta K, Sharma R (2020) Investigating socially responsible investing behaviour of Indian investors using structural equation modelling. J Sustain Fin Invest 1–23. https://doi. org/10.1080/20430795.2020.1790958 102. Vitell SJ, Paolillo JGP, Singh J (2005) Religiosity and consumer ethics. J Bus Ethics 57(2):175–181 103. Yao R, Sharpe DL, Wang F (2011) Decomposing the age effect on risk tolerance. J Socio-Econ 40(6):879–887 104. Yencken D (2000) Attitudes to nature in the east and west. In: Yencken D, John F, Helen S (eds) Environment, education and society in the Asia-Pacific: local traditions and global discourses. Routledge, London 105. Zavali M, Theodoropoulou H (2018) Investigating determinants of green consumption: evidence from Greece. Soc Responsibility J 14(4):719–736 106. Zerubavel E (1991) The fine line: making distinctions in everyday life. The Free Press, New York, NY

Research on the Ecological Environment Evaluation of Technological Talents in the Shuangcheng Economic Circle of Chengdu Region Hong Liu, Amit Yadav, Li Qin Hu, Md. Mehedi Hassan, Swarnali Mollick, and Dinesh Talwar

Abstract This paper takes 22 cities in Sichuan-Chongqing region as examples, builds a comprehensive evaluation index system for the ecological environment of scientific and technological talents on the basis of existing research, uses the mean square error weighting method to determine the weight of each index, and uses the comparative analysis method to analyze the Chengdu-Chongqing double-city economic circle. The current situation and shortcomings of the cities in the country proposed countermeasures and suggestions to improve the comprehensive ability of scientific and technological talents in the ecological environment. Keywords Chengdu-Chongqing area dual-city economic circle · Ecological environment of scientific and technological talents · Mean square error weighting

H. Liu Department of Human Resource Management, Chengdu University of Technology, Chengdu, China e-mail: [email protected] A. Yadav (B) College of Engineering, IT and Environment, Charles Darwin University (CDU), Darwin, Australia e-mail: [email protected] L. Q. Hu Department of E-Commerce, Chengdu Neusoft University, Chengdu, China e-mail: [email protected] Md. Mehedi Hassan Computer Science and Engineering, North Western University, Khulna, Bangladesh e-mail: [email protected] S. Mollick Computer Science and Engineering, Northern University of Business and Technology, Khulna, Bangladesh D. Talwar Department of Corporate and Digital Development, Darwin, Australia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 K. Chaudhary et al. (eds.), Intelligent Data Analytics in Business, Lecture Notes in Electrical Engineering 1068, https://doi.org/10.1007/978-981-99-5358-5_2

25

26

H. Liu et al.

Fig. 1 Schematic diagram of the spatial pattern of Cheng-yu urban agglomeration

1 Introduction This article aims to build a comprehensive evaluation index system for the ecological environment of scientific and technological talents in the Chengdu-Chongqing double-city economic circle on the basis of existing research [1], in-depth analysis of the degree of ecological environment integration of scientific and technological talents in cities (regions) within the economic circle, and adopts a horizontal comparative analysis method determining the region that will assume the role of the third growth pole in the Chengdu-Chongqing double-city economic circle [2, 3]. The specific spatial pattern of Chengdu-Chongqing urban agglomeration is shown in Fig. 1.

2 Analysis of the Status Quo of Scientific and Technological Personnel in Chengdu 2.1 Distribution Characteristics of Scientific and Technological Talents The distribution of R&D personnel varies greatly among cities (Source: 2020 Sichuan Statistical Yearbook and 2020 Chongqing Statistical Yearbook), as it shows R&D investment in Sichuan and Chingqing in 2019. It can be seen from Table 3 that compared with other cities, the distribution of R&D personnel in Chongqing, Chengdu, and Mianyang accounts for 84.2% of the total R&D population, and the corresponding R&D investment intensity reaches 10.38%. The data of the Sichuan Statistical Yearbook 2020 shows the number of employed

Research on the Ecological Environment Evaluation of Technological …

27

persons by three strata of industry in Chengdu-Chongqing Economic (Source: 2020 Sichuan Statistical Yearbook and 2020 Chongqing Statistical Yearbook).

2.2 Characteristics of Technology Talent Flow According to the investment of scientific and technological talents in the Shuangcheng Economic Circle of Chengdu-Chongqing area, the scientific and technological talents in the economic circle are mainly concentrated in Chengdu and Chongqing [4]. The total number of R&D personnel has reached more than 70%. The flow of researchers in the institution where statistical stables in terms of scientific research and technical personnel of Sichuan research and development institutions (Source: 2020 Sichuan Statistical Yearbook).

3 Related Works 3.1 Related Theories The ecology of scientific and technological talents is an organic life system; the ecological environment of scientific and technological talents is a concept formed by introducing ecology into the science of talents [5].

3.2 Research Status Juan (2018) summarized the attractiveness of urban talents into three target levels by constructing the urban attractiveness evaluation index system and evaluation model [6–9].

4 Construction of Evaluation Index System for Ecological Environment of Sci-Tech Talent 4.1 Evaluation Index Determination The evaluation index system has 5 first-level indicators, 20 secondary indicators, as shown in Table 1.

28

H. Liu et al.

Table 1 Ecological environment evaluation index system for scientific and technological talents Aim

Guidelines

Evaluation index The level of system of scientific economic and talents development (A) ‘ecological environment’

Science and technology talent investment (B)

Technology talent environment (C)

Technology talent guarantee (D)

Index

Unit

Indicator Attributes

Per capita gross regional product (A1)

Billion

+

Urbanization rate (A2)

%

+

Per capita disposable income of urban residents (A3)

Yuan

+

Total import and export (A4)

Ten thousand yuan

+

R&D/GDP (B1)

%

+

R&D personnel growth rate (B2)

%

+

R&D expenditure growth rate (B3)

%

+

Full-time equivalent of R&D personnel (B4)

Man-year

+

R&D personnel (B5)

Person

+

Creation and inventions patent applications (C1)

Item

+

Number of industrial enterprises above designated size (C2)

Item

+

Average wage of scientific research, and technical service employment in all units (C3)

Yuan

+

Scientific and technological achievements registration items (C4)

Item

+

Collection rate of % basic pension insurance for urban workers (D1)

+

(continued)

Research on the Ecological Environment Evaluation of Technological …

29

Table 1 (continued) Aim

Guidelines

Natural ecological environment (E)

Index

Unit

Indicator Attributes

Collection rate of % basic medical insurance for urban workers (D2)

+

Per capita housing area of urban residents (D3)

+

Squaremeter

Decline rate of % criminal cases filed (D4)

_

Sewage treatment rate (E1)

%

+

Urban road area per capita (E2)

Square meter

+

Park green area per Square meter capita (E3)

+

4.2 Data Sources The index data involved in this article comes from “Sichuan Statistical Yearbook (2020)”, “Chongqing Statistical Yearbook (2020)”, Sichuan Intellectual Property Service Center, Sichuan Province’s 2019 Scientific and Technological Achievements Statistical Analysis Report, and Sichuan Province’s Scientific and Technological Expenditures Statistics in 2019 Statistical data and information disclosed in the bulletin and the official websites of cities at all levels [10]. This paper uses the dispersion standardization method to normalize the original data and map the final data to the interval [0,1].

4.3 Index Weight Determination The weighting methods of multi-objective evaluation indicators are generally divided into subjective weighting methods and objective weighting methods. The specific steps are shown in formulas 1, 2, and 3 [11]. n 1∑ Calculate the index Mean a j : a j = zi j n i=1

Calculate the mean square error of each indicator,

(1)

30

H. Liu et al.

┌ | n |∑  zi j − a j σj: σj = √

(2)

i=1

σj Calculate indicator weight: ω j = ∑m j=1

σj

(3)

Note: z i j is the standardized value; i is the number of samples; j is the number of indicators.

5 Comprehensive Evaluation of Ecological Environment of Sci-Tech Talents in Chengdu-Chongqing Region 5.1 Comparative Analysis This paper selects the original data of 21 cities in Sichuan Province and 22 cities in Chongqing for calculation and calculates according to the five major economic zones in Sichuan Province [7]. The results are shown in Table 2. By sorting the comprehensive scores, the final ranking results shown in Table 3 are obtained. As shown in Table 2: Chengdu (0.145782075), Chongqing (0.13150809), and Mianyang (0.094609924) rank in the top 3 comprehensively in the comprehensive evaluation results of the ecological environment of scientific and technological talents in the Chengdu-Chongqing double-city economic circle. It ranks the second two in Chengdu, but the score gap reaches 0.05. Chengdu ranks first with a comprehensive score of 0.14005419, and Aba Prefecture ranks last. The score is 0.007872969, with a gap of 0.13218123 [12]. In Table 3 shows the final ranking of the ecological environment of scientific and technological talents based on the outcome shown in Table 2.

5.2 Third Growth Pole Table 4 shows the scoring results of the five major economic zones in Sichuan in terms of the ecological environment of scientific and technological talents [13]. In order to determine the third pole, this article uses the data analysis software SPSS 26.0 to perform cluster analysis on each city shown in Fig. 2. According to the cluster pedigree map, the sample data can be basically divided into five categories. The first category is Chengdu and Chongqing. The collection rate of urban employee pension insurance in the four cities has reached over 96%,

0.029150236

0.044394826

Ziyang

Meishan

Panxi economic zone

Northeast Sichuan economic zone

0.032012808

0.026739852

0.014010369

Guangan

Dazhou

Bazhong

0.105742414

0.030116958

Nanchong

Panzhihua

0.025233241

Guangyuan

0.05559026

0.039152931

Yaan

0.040398566

0.057133106

Leshan

Yibin

0.03941388

Suining

Neijiang

0.063319224

Mianyang

0.052579147

0.065760467

Deyang

Luzhou

0.200321681

Chengdu plain economic zone

0.052629068

0.146628305

Chengdu

Chongqing

Zigong

0.200321681

Weights

South Sichuan economic zone

The level of economic development

Region

0.012021042

0.032334245

0.048996854

0.0977401

0.035269102

0.025790424

0.055707694

0.064028081

0.050733865

0.022918698

0.013353558

0.021269192

0.058784382

0.040488367

0.031178222

0.107843678

0.049664734

0.14005419

0.133127668

0.251581689

Science and technology talent investment

0.023343294

0.002126198

0.014310504

0.019444347

0.011181964

0.008055834

0.021266904

0.012690291

0.009852194

0.017110347

0.022602354

0.0085912

0.004435852

0.009198869

0.023650988

0.027551408

0.012637232

0.138870167

0.172768872

0.192460195

Technology talent environment

0.093494685

0.069714558

0.124729372

0.138910523

0.164205557

0.165286509

0.112868381

0.122916735

0.160690286

0.119805117

0.175442219

0.103576386

0.169910752

0.102813179

0.11095426

0.172868901

0.161864701

0.147805269

0.112404842

0.211995429

Technology talent guarantee

Table 2 Comprehensive evaluation of the ecological environment of scientific and technological talents

0.077912735

0.07254154

0.05651119

0.131157818

0.075978652

0.077451519

0.075516841

0.073248349

0.067239299

0.053397663

0.083184079

0.106134357

0.131177227

0.076155162

0.102944188

0.089419104

0.11054145

0.08602842

0.080494661

0.143641007

Natural ecological environment

(continued)

0.059711337

0.036549601

0.054996867

0.083032779

0.0627827

0.05925879

0.064018898

0.06322263

0.068916536

0.05266996

0.065744404

0.050046794

0.078348712

0.056136476

0.058600001

0.094609924

0.078293011

0.145782075

0.13150809

–

Overall rating

Research on the Ecological Environment Evaluation of Technological … 31

Northwest Sichuan ecological area

Region

Table 2 (continued)

0.029039101

0.012194814

Ganzi

0.011569903

Aba

Liangshan

The level of economic development

0.0224174

0.007872969

0.036591764

Science and technology talent investment

0.016093151

0.023956308

0.022038061

Technology talent environment

0.162159384

0.16322485

0.19310716

Technology talent guarantee

0.012588604

0.055412602

0.030158125

Natural ecological environment

0.047365272

0.054970936

0.061034748

Overall rating

32 H. Liu et al.

2

2

17

9

Pan zhi hua

13

3

21

21

10

Level of economic 1 development

1

2

11

12

8

1

Technology talent guarantee

Region Rank

Final ranking

Level of economic 22 development

12

Technology talent environment

Natural ecological 7 environment

Liang shan

Science and technology talent investment

Science and technology talent investment

Technology talent environment

Technology talent guarantee

Natural ecological 21 environment

6

1

2

1

Final ranking

Chongqing

Chengdu

Region rank

11

5

20

16

19

14

Guang yuan

6

3

3

3

5

3

Mianyang

5

18

5

15

12

15

Sui ning

2

12

10

4

14

4

Guangan

12

20

18

11

6

16

Le shan

1

4

21

6

13

5

Yaan

18

13

13

10

18

17

Da zhou

3

9

15

9

4

6

Deyang

19

7

4

22

17

18

A ba

17

10

17

8

9

7

Luzhou

Table 3 Summary of the final ranking of the ecological environment of scientific and technological talents

20

15

11

17

8

19

Zi gong

8

2

7

20

10

8

Meishan

4

19

19

19

16

20

Zi yang

14

16

9

7

7

9

Yibin

22

8

12

18

21

21

Gan zi

15

14

14

5

11

10

Neijiang

16

22

22

14

20

22

Ba zhong

13

6

16

13

15

11

Nanchong

Research on the Ecological Environment Evaluation of Technological … 33

0.0502

0.0256

0.0586

0.0206

➂

➃

➄

5

2

4

3

1

0.0151

0.0243

0.048

0.0483

0.0578

5

4

3

2

1

0.02

0.0226

0.011

0.0152

0.0309

Score

3

2

5

4

1

Rank

Technology talent environment

0.1626

0.1433

0.1325

0.129

0.1431

Score

1

2

4

5

3

Rank

Technology talent guarantee

0.034

0.054

0.0827

0.0673

0.0981

Score

5

4

2

3

1

Rank

Natural ecological environment

0.0511

0.0603

0.0593

0.0622

0.0784

Score

5

3

4

2

1

Rank

Overall rating

➀ is the Chengdu Plain Economic Zone; ➁ is the Southern Sichuan Economic Zone; ➂ is the Northeast Sichuan Economic Zone; ➃ is the Panxi Economic Zone; ➄ is the Northwest Sichuan Ecological Zone

0.0673

➁

Rank

Score

Score

Rank

Science and technology talent investment

Level of economic development

➀

Region

Table 4 Comprehensive scoring results of the five major economic Zsnes in Sichuan

34 H. Liu et al.

Research on the Ecological Environment Evaluation of Technological …

35

Fig. 2 Cluster analysis

and the collection rate of urban employee basic medical insurance has reached over 99% [14].

6 Upgrade Countermeasures 6.1 Increase Investment in Science and Technology to Increase the Attractiveness of Talent Flow Cities in the Chengdu-Chongqing double-city economic circle, especially those outside the Chengdu Plain Economic Zone, should speed up the formation of a diversified and multi-channel investment system with government investment as the leading factor, corporate investment as the main body, and social investment gathering [15].

6.2 Improve the Construction of the Talent Environment and Enhance the Attractiveness of Talent Development To optimize the construction of the internal and external environment for talents, one is to be guided by talent needs, adhere to the “user thinking”, and actively explore

36

H. Liu et al.

new models that optimize the talent environment and improve service levels [16]. The second is to adhere to the government’s leadership. The third is to improve the social pension and medical security mechanisms for scientific and technological talents [17].

6.3 Breakthrough Administrative Divisions and Build a Talent Sharing Management Mechanism Technological advancement in fields such as Internet of Things, machine learning and deep learning, health informatics [13–16], blockchain and smart contracts [17–19], etc. are key to future sustainability and digitalization of economic fronts [18].

References 1. Song X (2021) Regional cooperation and innovation characteristics and network structure evolution of the Chengdu-Chongqing Double City economic circle [J/OL]. Soft Sci: 1–14. http://kns.cnki.net/kcms/detail/51.1268.g3.20210116.1346.002.html 2. Yang L, Wang Y (2021) Comprehensive evaluation of the operational efficiency of Chinese urban agglomerations. Stat Decis 37(01):69–72 3. Zhang P, Li Y, Jing W, Yang D, Zhang Y, Liu Y, ... & Qin M (2020) Comprehensive assessment of the effect of urban built-up land expansion and climate change on net primary productivity. Complexity, 1–12 4. Shan X, Luo Z (2021) The structural characteristics and evolution logic of collaborative governance of the dual-city economic circle in Chengdu-Chongqing area—a social network analysis based on institutional collective action. J Chongqing Univ (Soc Sci Edn): 1–17. http://kns.cnki. net/kcms/detail/50.1023.c.20201104.1317.003.html 5. Ye W, Chen K (2020) The characteristics of innovation coordination and spatial effect of Chengdu-Chongqing urban agglomeration. Econ Syst Reform 05:65–72 6. Liu H, Zhu Z (2020) Research on the labor market integration and its influencing factors in the dual-city economic circle of Chengyu area. Soft Sci 34(10):90–96 7. Huang X, Peng W, He X (2020) Research on the evolution and influence mechanism of the technological innovation network of the Chengdu-Chongqing double-city economic circle. Econ Syst Reform 04:50–57 8. Zhuang J, Chen J (2020) The status quo of the ecological environment of scientific and technological talents and its impact on the willingness of talents to reside in Shanghai: a case study of Shanghai. Sci Technol Rev 38(10):103–112 9. Wang X (2020) An empirical study on the optimization of talent ecological environment and the improvement of technological innovation capability. Hebei University of Economics and Business 10. Ye C (2019) Research on the optimization of talent ecological environment in Xiangshan County. Ningbo University 11. Cui L (2016) Research on the evaluation and optimization of the ecological environment of scientific and technological talents in Shandong Province. Qufu Normal University 12. Li R (2012) Evaluation and optimization of talent ecological environment in Shandong Peninsula blue economic zone. Ocean University of China

Research on the Ecological Environment Evaluation of Technological …

37

13. Jose A, Azam S, Karim A, Shanmugam B, Faisal F, Islam A, Boer FD, Jonkman M (2020) A framework to address security concerns in three layers of IoT. In: 2020 2nd international conference on electrical, control and instrumentation engineering (ICECIE) 14. Karim A, Azam S, Shanmugam B, Kannoorpatti K (2020) Efficient clustering of emails into spam and ham: the foundational study of a comprehensive unsupervised framework. IEEE Access 8:154759–154788 15. Hassan MM, Peya ZJ, Zaman S, Angon JH, Keya AI, Dulla AU (2020) A machine learning approach to identify the correlation and association among the students’ drug addict behavior. In: 2020 11th international conference on computing, communication and networking technologies (ICCCNT) 16. Rony M, Hassan M, Ahmed E, Karim A, Azam S, Reza D (2021) Identifying long-term deposit customers: a machine learning approach. In: 2021 2nd international informatics and software engineering conference (IISEC). Available https://doi.org/10.1109/iisec54230.2021.9672452. Accessed 20 Aug 2022 17. Wang G, Shi Z, Nixon M, Han S (2019) ChainSplitter: towards blockchain-based industrial IoT architecture for supporting hierarchical storage. In: 2019 IEEE international conference on blockchain (Blockchain) 18. Vokerla RR, Shanmugam B, Azam S, Karim A, Boer FD, Jonkman M, Faisal F (2019) An overview of blockchain applications and attacks. In: 2019 international conference on vision towards emerging trends in communication and networking (ViTECoN) 19. Khan SN, Loukil F, Ghedira-Guegan C, Benkhelifa E, Bani-Hani A (2021) Blockchain smart contracts: applications, challenges, and future trends. Peer-to-peer Networking and Appl 14:2901–2925

Current Landscape and Future Prospects of Community Healthcare Information System: A Conceptual Study W.R.T. Indian Healthcare Priyanka Gandhi and Sonal Pahwa

Abstract The fundamental constituents of a community information system (CIS) are collecting and managing data which leads to robust analysis of services w.r.t. particular program within a community. These services are usually rendered by faithbased organizations, community forums, NGOs and other groups working besides formal services. CIS has a significant impact in achieving developmental and sustainability goals by engaging engage community members and offer them with an option to access health and social services while holding service providers accountable. Many CIS programs have been implemented across the globe in healthcare sector with a mixed response. Indian healthcare sector shows a huge potential of implementing such a program. The focus of this conceptual research paper is to throw light on key success factors of a successful Community Healthcare Information System (CHIS) given the framework of existing understanding about system development and implementation success and failure. In addition to satisfy academic interest, the aim of this research is to provide a framework to develop a strong CHIS model to tackle the diverse healthcare needs of Indian population and to provide important insights for implementing CHIS keeping in mind the unique challenges faced by Indian healthcare landscape. Keywords Community health information systems · Indian healthcare · Success factor · Challenges in healthcare · Hospitals

P. Gandhi (B) · S. Pahwa Jagan Institute of Management Studies, Delhi, India e-mail: [email protected] S. Pahwa e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 K. Chaudhary et al. (eds.), Intelligent Data Analytics in Business, Lecture Notes in Electrical Engineering 1068, https://doi.org/10.1007/978-981-99-5358-5_3

39

40

P. Gandhi and S. Pahwa

1 Contextual Background In terms of economic contribution, the Indian Healthcare has emerged as one of India’s most significant sector. Indian Hospitals, telemedicine, medical tourism, outsourcing services, medical support devices, clinical trials, health insurance services all fall under the healthcare industry umbrella. The Indian healthcare system has seen an exponential growth because of increased accessibility and availability of services, along with increase in expenditure by both public and private enterprises. In the metropolitan cities, the presence of government-run public healthcare system is limited due to the presence of private secondary and tertiary care institutions. The public healthcare system in India majority focuses on providing basic healthcare in rural regions through primary healthcare centres (PHCs). The major secondary, tertiary, and quaternary care facilities are managed by the private sector, primarily focusing on metros and tier I and tier II cities with improved services provided at higher cost. India’s economic and social growth has progressed significantly since independence. India has made significant financial investments in the creation of a comprehensive healthcare system. In comparison to other developing countries, India spends comparatively more on health care. The importance of a healthcare information system as a tool to India’s different developmental operations in the healthcare sector was recognised as early as the Bhore committee report, which was published shortly after independence. The Indian National Health Policy (1983) states that without an effective health information system, appropriate decision-making and programme planning in the health and related fields are impossible, and that a nationwide organisational structure should be established to procure essential health information to support local healthcare management and effective decentralisation of activities. National Health Information Systems provide inputs for regional and global health policy formulation. The need for action to develop the information infrastructure is global, and the World Health Organization’s Division of Information Support convened an inter-regional debate on National Health Information Systems in Costa Rica as early as 1979. Salient Features of Healthcare Industry (Fig. 1).

2 Literature Review Some of prominent research in this domain can be summarised as (Table 1). The current study is undertaken with the objective of understanding current healthcare landscape in India, analysing the country specific challenges and proposing a technology-based CHIS model to fulfil the future requirements of Indian Healthcare system. For this purpose, following objectives have been drafted:

Current Landscape and Future Prospects of Community Healthcare …

Strong Manpower Pool

High Demand

Opportunities

•Healthcare industry in India is projected to reach $372 bn by 2022. •At the sametime, this industry is a key employment genertor also.

•In India, healthcare accounts for about 1.8 percent of GDP. In the Budget, a new scheme called the PM Aatmanirbhar Swasth Bharat Yojana was introduced, with a promise to spend Rs 64,180 crore on it over the next six years, with a focus on three areas: prevention, cure, and well-being.

•India's competitive advantage is its large pool of well-trained medical personnel.

41

Government Intiatives •Indian Govrenment is very much and committed to improve the health infrastucture. •Also Indian Government is planning to introduce credit incentive program to improve the health sector.

Fig. 1 Features of healthcare industry [12, 13, 19, 24] Source Author’s Own Composition Table 1 Prominent research in community health care Paper Title

Author

Year

Key Findings

Prioritising the role of community health workers in the COVID-19 response

Ballard et al. [3]

2020 The poor and disadvantaged are disproportionately affected by COVID-19. Community health workers, especially in countries with less resilient health systems, are in a unique position to help combat the epidemic. This article discusses the specific steps that must be taken at various stages of the pandemic to achieve the following objectives: Based on practitioner knowledge from four WHO areas, the goal is to (1) PROTECT healthcare personnel, (2) INTERRUPT the virus, (3) MAINTAIN existing healthcare services while expanding their capacity, and (4) SHIELD the most vulnerable from socioeconomic shocks

What is “community health”? Examining the meaning of an evolving field in public health

Goodman, 2014 This article looks at community health definition R. A., frameworks and variables that are important in Bunnell, developing the meaning of this term and growing R., & field. Finally, the article proposes a framework for Posner, S. better understanding the meaning, scope, and science F. [14] of community health in order to conceptualise and advance this sector of public health practise

The future role of community health centres in a changing healthcare landscape

Hawkins, 2011 The Affordable Care Act (ACA) provides an D., & opportunity to rethink healthcare delivery by making Groves, D. it more comprehensive, patient-centred, and [17] accessible, with a focus on prevention. This essay examines how CHCs can be made well equipped to meet the Act’s requirements by offering affordable and high-quality care to the population (continued)

42

P. Gandhi and S. Pahwa

Table 1 (continued) Paper Title

Author

Community care of North Carolina: improving care through community health networks

Steiner, B. 2008 Although the USA has the highest healthcare costs in D., the world, it ranks far behind many other Denham, industrialised countries in terms of health outcomes. A. C., It is really challenging to bridge this gap. This article Ashkin, describes the responses of one state to develop E., community health networks in order to meet quality, Newton, utilisation, and cost goals for participants W. P., Wroth, T., & Dobson, L. A. [28]

Year

Key Findings

Health reform and the health of the public: forging community health partnerships

Baker, E. 1994 The concept of health system reform should not be L., restricted to medical care; it should also encompass Melton, R. strengthening public health practises and supporting J., Stange, new systems of integration among all community P. V., organisations whose objectives have an impact on Fields, M. public health L., Koplan, J. P., Guerra, F. A., & Satcher, D. [5]

Source Author’s Own Composition

3 Research Objectives RO1: RO2: RO3: RO4:

To assess the Indian Healthcare System and its complexity. To determine the challenges pertaining to Indian Healthcare System. To study the components of a successful CHIS program. To propose a model to build an effective CHIS model suitable for Indian perspective.

4 Research Methodology This is a conceptual study based on theoretical analysis of secondary data collected via various sources. Many CIS and CHIS programs implemented across the globe have been evaluated critically to identify the critical success factors responsible for a robust CHIS and considering the characteristics and challenges of Indian healthcare sector, a model has been proposed for developing a unique CHIS program suitable for India. The first section of the paper talks about contextual background, research questions and methodology followed. Second section features the Indian healthcare landscape, mainly focusing on the key challenges foreseen for implementing such a

Current Landscape and Future Prospects of Community Healthcare …

43

program at national level. This is followed by the third section on key success factors for implementing a strong CHIS. In the fourth section, researchers have proposed a model for developing and implementing CHIS for India.

5 Indian Healthcare Landscape: Associated Challenges Public Health System in India In India, the healthcare landscape is a kaleidoscope of colours. There is an existence of gleaming steel and glass institutions that provide high-tech medicine to the wellheeled, mostly metropolitan Indians. The shabby outposts in the furthest corners of the “other India,” frantically seeking to live up to their status as health centres, waiting to be transformed into sanctuaries of health and wellness, are on the other extreme of the spectrum. This spectrum is projected to widen further in the future, adding to the complexity, given the current rate of change. Key Challenges in Indian Health Landscape • Lack of Primary Healthcare Services: As the well-developed primary care centres are also providing the basic healthcare services only. • Deficiencies of Resources: Pitiable health administration skills, as well as an absence of suitable hands-on experiences and sympathetic management for health workers, hinder the intended level of health care from being delivered. • Inadequate Funding: In India, the healthcare spending has been continuously low-slung (approximately 1.3 of GDP). However, the govt. is now having no other option just increase it. • Overlapping Jurisdiction: There is no one public health authority with the legal jurisdiction to disseminate strategies and impose health standards strategies and compliances [1]. These challenges in the Indian healthcare sector can also be summarised under: 5 A’s (Fig. 2).

6 Proposed Solution: Community Healthcare Information System A CHIS is a dynamic system which has detailed information on data collection, systematic flow of data with an emphasis on regularly assessing and improving quality of data and usage of information. Likewise, a robust CHIS should include critical information regarding collection and management of relevant data along with assessment of health-related services being provided to the particular communities by facilities outside of formal healthcare structure [10]. The system should have

44

P. Gandhi and S. Pahwa

Awarness

Access

Absence

•Absence of Basic Healthcare Services to majority of poulation. •Some of the main causes are : low education status, low priority for health in the population etc. •Even in places where services are "accessible" there exist hurdles in accesing the financial, organisational, social, and cultural worlds may limit their use. Gulliford.et.al,2002. •We must foster conversation on the determinants of healthcare access as thinkers in the fields of community medicine and public health. • Humanpower crisis in healthcare • It is the need for a health-human-power policy to be established, one that lays out steps to assure that every Indian is cared for by a compassionate, skilled, and capable healthcare professional. •Ethics in healthcare should be a passionately debated topic both within and outside the industry. ( Emanuel)

Affordability

Accountabilty

•There exist a great gap between the cost incured by an individual in the public and private sector of the health care. •Lack of regulation in the health sector, resulting in a wide range of service quality and costs. (Balarajan et. al.)(axena, D., & Joshi, N. (2019)

Fig. 2 Challenges of Indian health care [2, 11, 16, 27]. Source Author’s Own Composition

the capabilities of sharing information between community-based services, health facilities, and governmental setups in order to become more effective. This information should be useful for designing required programs and policies, identifying specific needs of population groups, maintaining the continuity of services along with addressing concerns related to accessibility and accountability. The CHIS should be able to serve the population in a better way. When this information is shared with community members, they can engage more beneficiaries to the program and make health and social systems more accountable, hence lead to sustainability of such programs [18, 26]. The review of CHIS programs reveals the following essential components: 1. Community engagement w.r.t. healthcare needs—Information generated by a CHIS should serve as a vital resource for community members to actively participate in health and social welfare services, and to make service delivery organisations more accountable [20, 22]. This can also help in defining and prioritising community needs, set objectives while planning, implementing and reviewing programs. Community engagement can also aid in outbreak surveillance and containment was observed at the time of Ebola outbreaks [21]. 2. Identifying target population—A well-designed CHIS should involve the expertise of Community Workers as they know their catchment area very well. CWs can play a crucial role in making the CHIS program more successful by identifying, reaching out, and convincing the target population. They can use their regular house visits to track cases and reinforce follow-up. A good CHIS also has tools and mechanisms to communicate client specific needs across the levels of the health system [6].

Current Landscape and Future Prospects of Community Healthcare …

45

3. Comprehensive case management and care coordination—To be able to provide holistic information and cater to an individual’s health needs CHIS should be able to bridge the gap between community and facility-based health and social welfare services [4, 7, 23]. 4. Ensuring accountability—An effective CHIS should have scope for using mechanism such as tracking of public expenditure, data-driven journalism and providing key information regarding services delivered to the community via community meetings and participation. In this way, service providers can be held accountable and real impact of efforts can be assessed. This also helps the decision-makers to take timely corrective actions without investing too much resources.

7 Key Success Factors for CHIS The factors responsible for success or failure of systems development projects have received substantial focus in the academic literature. Major flaws of IT based projects across sectors are inability to adequately fulfil program objectives, unreasonable costs and failure to meet schedule and timelines in different phases of project. The key strengths of these programs are thorough testing before implementation, reviewing the best practices and adapting them to country / project specific requirements, high involvements of senior management and empowerment at user level. CHIS program should ensure complete integration with national health plan and information system currently in place in order to make sure smooth transition. Management of data should be decentralised for effective implementation of program. The technology selected for data collection and analysis should be user friendly especially at ground zero level. And most importantly, program should be action oriented and capable of providing all the necessary information for decision-making. Finer details like developing standard guidelines for operating the program, data collection forms and analysis technique ensure that system runs consistently across various locations and quality of data is maintained. Providing requisite skill training to community workers and personnel at every level is crucial for successful implementation of the program.

8 Suggested Model The proposed CHIS model has been designed keeping in mind the developmental and implementation phase of a national level CHIS program. The model has three major segments, i.e. enabling environment, information generation, and system performance. The first segment entails the critical requirements for creating an enabling environment crucial for a successful CHIS. This segment incorporates the role of design, leadership, governance, and management of the system. Second segment

46

P. Gandhi and S. Pahwa

Enabling Environment

•System Design •Leadership and Governance •System Management

Information Generation

•Data Sources •Data Managment •Information Dissemination

System Perfromance

•Data Quality •Data Usage

Fig. 3 CHIS model. Source Author’s Own Composition

focuses on information generation and talks about various sources of data, its management, and dissemination of information. The third segment of the model consists of system performance and focuses on usage and quality of data [25]. All these are explained in detail in following section (Fig. 3). A. Enabling Environment 1. System Design—Importance of a fool proof system design cannot be over emphasised for any type of system (paper-based, electronic, or hybrid). A strong design is capable of producing the desired information and aids decision-making process. Following points should be considered at the development phase: • Main objective of the CHIS and its corresponding data requirements • Current workflows in community health or social welfare programs and roles and responsibilities of key stakeholders • Interaction of community workers with beneficiaries, their constraints, and factors which can improve their performance • The best available technology suitable for CHIS having potential for future upgradation. 2. Leadership and Governance—Commitment from leadership at national level is essential for developing a strategic vision and governance structure for CHIS. The national level efforts at senior leadership positions can spearhead such initiatives in the best possible way and can ensure that the benefits of such programs reach to every deserving candidate. Strong leadership and governance are crucial for: • Better coordination among all stakeholders • Allocation of appropriate budget and uninterrupted financing of important schemes • Removing delicacies in data collection and standardising CHIS indicators and reporting mechanism • Formulating policies for use of technology while ensuring confidentiality of data

Current Landscape and Future Prospects of Community Healthcare …

47

• Developing and implementation of training strategy to ensure uniformity across all regions. 3. System Management—This entails the overall management of processes and functioning of CHIS. The detailed analysis of financial and human resource requirement is critical for smooth implementation of the program [15]. The important points to be considered are • Funds required at initial stage for developing the system and provide operational training to staff. • Supervision during implementation phase • Ensuring proper infrastructure in terms of required equipment and their maintenance, ensuring stable internet connectivity and access to transport and tolls required by community workers • HR management for proper training and resolving operational constraints. • Upgrading skillset of existing staff for use of technology. B. Information Generation 1. Data Sources—Data-based information for designing CHIS comes mainly from routinely collected medical information such as disease patterns, case management, women and child health cards as well as non-routine population-level information generated from household surveys. Ideally, the information from non-routine sources should be analysed along with routine information for trend analysis and accurate predictions. 2. Data Management—An exhaustive and well-documented data management process is must for producing reliable information for decision-making. The system should incorporate guidelines regarding access rights, data protection, storage, retrieval and confidentiality. Additionally, data management must consider the process of resolving operational issues. 3. Information Dissemination—Once the information is generated and analysed, it can be used at multiple levels by various stakeholders. The CHIS should be able to share the relevant data according to specific requirements at different levels in the form of statistical reports, dashboards, scorecards, etc. [8]. C. System Performance 1. Data Quality—An effective CHIS program will surely yield a good-quality data. If there are lacunas at the designing stage, data elements are not well defined and standardised, then it will not give a clear picture of ground reality which will result in poor decision-making. Data quality also gets impacted by training and workload of community workers along with data verification at household level. 2. Data Usage—The main objective of an all-round CHIS program should be to seamlessly provide important information to all stakeholders and aid the decision-making process. It should be a proactive and interactive process which considers data requirements during planning, monitoring, review and

48

P. Gandhi and S. Pahwa

improvement stage of project. This denotes the smooth access of information by concerned ministries in order to run the program effectively. Also, this information can be used for performance management of ground level staff [9].

9 Conclusion Socio-economic development of a country and its healthcare system are very closely intertwined, and one can’t be improved without other. Although India has witnessed substantial economic development in last two decades, its healthcare system still stands at a crossroad. There is a need to develop healthcare reforms unique to Indian circumstances. Indian healthcare sector is quite complex with diverse needs, multiple products and different beneficiaries. The challenges faced by healthcare sector in the recent pandemic have also highlighted the immediate implementation of a Community Healthcare Information System. The solution lies in exploring the options supported by latest advancements in technology in order to achieve the desired goals of making healthcare accessible and affordable. New age health system should be built keeping in mind the responsiveness to community needs especially accessibility to remote areas. This requires a strong political will supported by administratively difficult decision with long term impact. The suggested model has tried to incorporate all the essential components of a resilient CHIS model. The next step will involve the development of this theoretical model and testing the same in real-life scenario.

References 1. Agarwal S, Perry HB, Long LA, Labrique AB (2015) Evidence on feasibility and effective use of mHealth strategies by frontline health workers in developing countries: systematic review. Tropical Med Int Health 20(8):1003–1014 2. Balarajan Y, Selvaraj S, Subramanian SV (2011) Health care and equity in India. The Lancet 377(9764):505–515 3. Ballard M, Bancroft E, Nesbit J, Johnson A, Holeman I, Foth J, Rogers D, Yang J, Nardella J, Olsen H, Raghavan M (2020) Prioritising the role of community health workers in the COVID-19 response. BMJ Glob Health 5(6):e002550 4. Balatimore MD (2017) How your OVC program can be ready for a site improvement monitoring system (SIMS) assessment. Catholic Relief Serv 5. Baker EL, Melton RJ, Stange PV, Fields ML, Koplan JP, Guerra FA, Satcher D (1994) Health reform and the health of the public: forging community health partnerships. Jama, 272(16):1276–1282 6. Braun R, Catalani C, Wimbush J, Israelski D (2013) Community health workers and mobile technology: a systematic review of the literature. PLoS ONE 8(6):e65772 7. Chewicha K, Azim T (2013) Community health information system for family centered health care: scale-up in Southern Nations Nationalities and People’s Region. Ethiopia Ministry Health Q Health Bull 5(1):49–51

Current Landscape and Future Prospects of Community Healthcare …

49

8. Coombs CR, Doherty NF, Loan-Clarke J (1999) Factors affecting the level of success of community information systems. J Manage Med; Walker D (2018) Model of a communitybased information system: essential components and functions. MEASURE Eval. ISBN: 9781-64232-014-5 9. DeRenzi B, Findlater L, Payne J, Birnbaum B, Mangilima J, Parikh T, Borriello G, Lesh N (2012, March). Improving community health worker performance through automated SMS. In: Proceedings of the fifth international conference on information and communication technologies and development, pp 25–34 10. De La Torre C, Unfried K (2014) Monitoring and evaluation at the community level: a strategic review of MEASURE evaluation phase III accomplishments and contributions. MEASURE Evaluation, Chapel Hill, NC 11. Emanuel EJ, Emanuel LL (1996) What is accountability in health care? Ann Intern Med 124(2):229–239 12. Gandhi P, Tandon N (2017) Study to analyze the variables that affect the CRM implementation in the hospitals. Adv Comput Sci Technol 10(5):933–944 13. Gandhi P, Tandon DN (2017) The need for digitalization in healthcare industry and its impact. Int J Innov Res Multidisc Field 3 14. Goodman RA, Bunnell R, Posner SF (2014) What is “community health”? Examining the meaning of an evolving field in public health. Prev Med 67:S58–S61 15. Guenther T, Laínez YB, Oliphant NP, Dale M, Raharison S, Miller L, Namara G, Diaz T (2014) Routine monitoring systems for integrated community case management programs: lessons from 18 countries in sub–Saharan Africa. J Glob Health 4(2) 16. Gulliford M, Figueroa-Munoz J, Morgan M, Hughes D, Gibson B, Beech R, Hudson M (2002) What does ‘access to health care’ mean? J Health Serv Res Policy 7(3):186–188 17. Hawkins D, Groves D (2011) The future role of community health centers in a changing health care landscape. J Ambulatory Care Manage 34(1):90–99 18. Jeremie N, Kaseje D, Olayo R, Akinyi C (2014) Utilization of community-based health information systems in decision making and health action in Nyalenda, Kisumu County, Kenya. Universal J Med Sci 2(4):37–42 19. JIMS, R., & JIMS, K. (2018). Impact of CRM on hospitals: a study conducted to gain view of the practitioners working in various private and Govt. Hospitals in Delhi. Priyanka Gandhi Dr. Neelam Tandon Assistant Professor Professor 20. Kaseje D, Olayo R, Musita C, Oindo CO, Wafula C, Muga R (2010) Evidence-based dialogue with communities for district health systems’ performance improvement. Glob Public Health 5(6):595–610 21. Madan M (2020). Health values and perceived behavioural control as antecedents of millennial’s health consciousness and behaviour intentions towards healthy food choices. Int J Adv Sci Technol 22. Marston C, Renedo A, McGowan CR, Portela A (2013) Effects of community participation on improving uptake of skilled care for maternal and newborn health: a systematic review. PLoS ONE 8(2):e55012 23. MEASURE Evaluation (2012) Clarification regarding usage of the child status index (CSI). Chapel Hill, NC, USA: MEASURE Evaluation, University of North Carolina. Retrieved from https://www.measureevaluation.org/our-work/ovc/clarification-regarding-usage-of-thechild-status-index 24. Gandhi P (2022) A new era in healthcare marketing through digital transformation and CRM. Int J Health Sci 6(S1):13300–13310. https://doi.org/10.53730/ijhs.v6nS1.8608 25. Sanders IT (1968) Health is a community affair: report of the national commission on community health services. Harvard University Press, Cambridge, Mass, 252 p 26. Sabitu K, Iliyasu Z, Hassan SS, Mande AT (2004) Evaluation of a community level nutrition information system for action in a rural community of Zaria, Northern Nigeria 27. Saxena D, Joshi N (2019) Digitally empowered village: case of Akodara in Gujarat, India. South Asian J Bus Manage Cases 8(1):27–31

50

P. Gandhi and S. Pahwa

28. Steiner BD, Denham AC, Ashkin E, Newton WP, Wroth T, Dobson LA (2008) Community care of North Carolina: improving care through community health networks. Ann Family Med 6(4):361–367

Fake News Detection Using Machine Learning Ajay Agarwal, Shashank Mishra, and Sartaj Ahmad

Abstract Fake news means the content without any source. It may be false or true but in absence of trustable sources, it is not a good idea to believe in these contents. Nowadays, many articles are being published on social media which have not any trustable sources. It is not a good idea to believe in such fake articles. In this paper, we review existing research based on fake news classification so that such views can help us to get a new approach/model which can become more robust, efficient, and effective. We have tried to make a model which can help us to differentiate fake news with trustable content accurately.

1 Introduction Many items are being shared on social media these days that have no reliable origins. To take news from social media instead of traditional news organizations as it is inexpensive [1]. Even while social media makes it easier and faster to get news online, much fake news to spread incorrect information is spread on social media for financial and political advantage. By the end of the presidential election in the USA, it was believed that over 1 million tweets were linked to fake news Pizzagate. Fake news was even selected as the term of the year by the Macquarie Dictionary in 2016 after this situation occurred. Fake news’ widespread dissemination has the potential to harm both individuals and society. Fake news must be addressed on a worldwide scale. Also, as a global challenge to get organizations to take action on this issue [2]. Machine learning and artificial intelligence, according to scientists, could be used to combat false news. A. Agarwal · S. Mishra (B) · S. Ahmad Department of Information Technology, KIET Group of Institutions, Ghaziabad, India e-mail: [email protected] A. Agarwal e-mail: [email protected] S. Ahmad e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 K. Chaudhary et al. (eds.), Intelligent Data Analytics in Business, Lecture Notes in Electrical Engineering 1068, https://doi.org/10.1007/978-981-99-5358-5_4

51

52

A. Agarwal et al.

In this paper, we have described some methods for the detection of fake news. We developed our systems for deception detection supported by Support Vector Classifier, KNN (K-Nearest Neighbor), and Random Forest Classifier. The accuracy of the detection achieved by the Support Vector Classifier system was at the top which was around 93% at different test sizes The purpose of the study was to see how various algorithms perform in this problem using a manually labeled news dataset and to support the idea of utilizing machine learning to detect fake news. [5, 7]. We achieved our goal of developing a system for fake news detection with great accuracy. Following are the types of fake news: 1. 2. 3. 4. 5.

Rumors spread by deafeningly deafening Content that is completely unfounded. For the sake of amusement For unrelated stuff, create a false image or headline. Information that has been taken from unauthorized online sources.

2 Literature Review In [1], they encourage platforms to work with independent researchers to assess the scale of the fake news problem, as well as the design and effectiveness of interventions. There is a minimal study on fake news, and no extensive data mechanism to provide a dynamic picture of how ubiquitous fake news distribution platforms is evolving. It’s hard to duplicate Google’s 2010 performance. Even if Google possessed the underlying code, it couldn’t accomplish it since the patterns develop from a complicated interaction between code, content, and people. It is, nevertheless, conceivable to document what the Google of 2018 is up to. In [2], Bradford’s law was utilized to assess the distribution of articles in magazines and how they are classed where they were published. This premise implied that a few core journals included the same or a similar number of articles as would be found clustered in the remaining regions. According to this law, three zones (core, zone 1, and zone 2) have been formed. The core consisted of only 29 journals, each with a similar quantity of articles to those in zones 1 and 2. Zones 1 and 2 had significantly more journals. In [3], it addresses one of the hurdles to measuring the impact of fake news objectively detecting false material by using a unique dataset of fake paid-for articles collected from an SEC probe. Following bogus news, the authors discover an increase in anomalous trade volume and a transitory pricing impact for small businesses; after studying these specific situations and applying a linguistic algorithm to a much bigger set of news material using a sample of known bogus stories to test the system, there was no impact for major corporations. Following the public disclosure of the SEC’s inquiry, they observe a strong spillover impact to news in general, with investors reacting less to all news, even valid news. In [4], 44% of consumers aimed to protect the information proactively, kept up with the news, and saved their favorites, according to the study. A total of 33%

Fake News Detection Using Machine Learning

53

proactively examined the information but did not include it in their top news stories. If the information appeared in the news or the media, 19% of people saw it; 2% of the population was unconcerned to take any more steps to collect information, another 2% refused to participate to hear the bad news about Coronavirus. In [5], the question of how to classify news content into fake and authentic instances is a natural way to frame the fake news problem in NLP. This is especially true when it comes to entire text vs tweets or social media headlines—because the categorization of text is based primarily on the linguistic properties of longer material. Fake news items may be characterized as deceptive text in NLP, and deception identification in the text has significant literature. In [6], linear and SoftMax layers were utilized to divide the data into four multiclasses, WCE was used to compensate for the training deficit. For each class, the corpus data and label distribution were analyzed, this training loss is defined by different weight values, as shown below. To AGR 7.4%, DSG 2.0%, DSC 17.7%, and UNR 72.8%, the FNC-1 dataset is unbalanced. The most often used loss function is cross-entropy (CE). It’s a way of figuring out how much information exists between two probability distributions. In [7], they used Dataset 1, which comprises 800 fake and 800 actual hotel reviews, in their experiment. They discovered that bogus material contains more function and substance words than genuine reviews based on their examination of sample data. In [8], it was discovered, in particular, that fake political news spread further and wider, it spread faster and further than any other sort of misleading information. False political information also spread quicker than other types, spreading more than 22,000 people roughly three times faster than false news, which only reached 8,000 people. When they reintroduced bot traffic to the analysis, they discovered that none of our primary conclusions had changed—false news continued to spread further, quicker, deeper, and, more widely, across all forms of data than the truth. In [9], demonstrate that the resulting technique, dubbed ELMCoxBAR, outperforms some established law L1- or L2-regularized Cox regression, random survival forest with varied splitting criteria, and boosted Cox model are examples of survival prediction approaches, on both synthetic and real-world datasets. In [10], in numerous ways, this back-of-the-envelope assessment is cautious, including the possibility of exaggerating the influence of fake news. They consider how many articles voters read, irrelevant as to whether or not they agree on them. They don’t take into account diminishing returns, which could mitigate the impact of fake news to the point where just a small number of voters are exposed to a large number of tales. Also, this crude calculation ignores the reality that people who are already predisposed to vote for Trump consume a big share of pro-Trump false news—the lesser the influence of fake news on vote shares, the larger the selective exposure. In [11], researchers are interested in studies that integrate blockchain in a database to improve users’ privacy during the learning process, as well as research on how machine learning may be used to improve the computer distribution of resources and crypto investing decisions. Only a few are actual mergers of the two technologies,

54

A. Agarwal et al.

whereas the majority are adaptations of one technology to another. As a result, it’s reasonable to say that present multidisciplinary research is still in its early phases. In [12], the authors are grateful to Peter Duebeen and Elizabeth Barnes for their helpful comments on the paper. The contributions of Dale R. Durran and Jonathan A. Weyn to this research were made possible by the Office of Naval Research’s Grant N00014-17-1-2660 (ONR). Grant N00014-20-1-2387 from the Office of Naval Research also helped Dale R. Durran. Jonathan A. Weyn was additionally supported by a Department of Defense National Defense Science and Engineering Graduate (NDSEG) scholarship (DoD). Microsoft Azure contributed computational resources as part of a grant from Microsoft’s AI for Earth program. Stan Posey from Nvidia graciously donated two V100 GPUs to the University of Washington for this study. In [13], their research demonstrates how meta-learning techniques are being used to address some of the present issues in machine learning, particularly those relating to the selection of the best models for a given problem or the ongoing updating of these models once learned. In [14], the project includes developing a deep convolutional neural network (DCNN) for identifying brain tumors using MR images. There are 253 brain MR images in the dataset used in this investigation, with 155 of them showing malignancies. With an overall accuracy of 96%, our approach can identify MR pictures with malignancies. Test dataset contains, the model surpassed existing conventional approaches for brain tumor diagnosis. Furthermore, the proposed model has a precision-recall score of 0.93, a Cohen’s Kappa of 0.91, and an AUC of 0.95. As a result, the proposed model can assist clinical professionals in determining whether a patient has a brain tumor and, if so, what the consequences are to accelerate the healing process. In [15], the main purpose of this study was to investigate and develop methods for evaluating PV module defects. The information for the study came from three separate places in South Africa. Due to a confidentiality agreement with the data source, the locations of the sites could not be published. A machine learning technique for PV fault identification was developed using thermal infrared imaging. A feature-based technique using SIFT descriptors was combined with a bag of visual words model to create a succinct feature set for analysis. Random Forest and support vector machine classifiers were used to identify defective modules.

3 Methodology The system, which is based on machine learning classifiers, is described in this paper. We tested and trained our model with three different classification algorithms to determine the most appropriate algorithm. We used Python and related libraries as Sci-kit in this paper. Python comes with a large number of extensions and packages that can be used in machine learning. The Sci-kit package is a wonderful source of machine learning algorithms because

Fake News Detection Using Machine Learning

55

it contains practically every type of algorithm that is widely available for Python, making ML algorithm evaluation fast and quick [6].

4 Implementation It involves the following stages.

4.1 Dataset From different sources, we can collect online, such as homepages of news organizations, internet sites, and social networking websites. The work of determining the validity of news can be difficult, and it usually necessitates the use of domain experts who do a meticulous examination of statements and supporting evidence, as well as context and analysis from trustworthy sources. Data gathered must first be preprocessed. This requires cleaning, transforming, and integrating data before it can undergo the training process. The dataset which we used contains three attributes title, text, and label [6, 10].

4.2 Classification Algorithms The classifier is trained in this section. We explored three different ML algorithms named as K-Nearest Neighbor (KNN), Random Forest Classifier (RFC), and Support Vector Classifier (SVC). Brief introduction of algorithms.

4.2.1

K-Nearest Neighbor (KNN)

K-Nearest Neighbors is a fundamental yet important classification technique in machine learning [1]. It is part of the supervised learning area and has a lot of uses in pattern recognition, processing, and intrusion detection. It’s extensively used in real-world circumstances since it’s nonparametric, which means it doesn’t make any assumptions about data distribution (as against other algorithms such as GMM, which assume a normal distribution of the given data).

56

4.2.2

A. Agarwal et al.

Random Forest Classifier (RFC)

Random Forest is a more complex version of the decision tree (DT), as well as a supervised machine learning model. RF is made up of many decision trees that work together to predict the conclusion of a category, with the final projection being based on the most popular category. Because of the poor association among trees, the Random Forest has a low error rate when compared to other models.

4.2.3

Support Vector Classifier (SVC)

The support vector machine algorithm’s goal is to find a support vectors in an Ndimensional region that categorizes data points clearly. An SVM model is ready to classify new text after receiving a set of labeled training data for each category. They have two major benefits over newer algorithms such as neural networks; they are faster and perform better with fewer data (in the hundreds). As a result, the approach is well suited to text categorization problems, where having access to a dataset with a few thousand tagged samples at most are usual.

5 Implementation Steps Step 1: In this section, we trained and used three categorization algorithms. Step 2: In this section, we developed all of the classifiers for predicting the detection of false news. We utilized the sklearn algorithms K-Nearest Neighbor, Random Forest Classifier, and Support Vector Classifier. Step 3: In this step, we compare their Precision, Recall, F1-score, and Accuracy. Figure 1 defines the implementation step(s) involved in model designing and testing phases to find the outcomes.

6 Result Figure 2 at a test size of 20%, compares F1-score, Precision, Recall, and accuracy of several classifier algorithms [K-Nearest Neighbor [KNN, Support Vector Classifier (SVC)], Random Forest Classifier (RFC)]. This demonstrates that SVC has the highest accuracy of all, around 93%. Figure 3 at a test size of 30%, compares F1-score, Precision, Recall, and accuracy of several classifier algorithms [K-Nearest Neighbor (KNN), Support Vector Classifier (SVC), Random Forest Classifier (RFC)]. This demonstrates that SVC has the highest accuracy of all, around 93%.

Fake News Detection Using Machine Learning

57

Fig. 1 Flow diagram

Fig. 2 Comparison at test size 0.2

Table 1 describes different comparison parameters at the test size of 20% for all three algorithms. Table 2 describes different comparison parameters at the test size of 30% for all three algorithms.

58

A. Agarwal et al.

Fig. 3 Comparison at test size 0.3 Table 1 Comparison parameters for test size 20% Test size = 0.2 Random forest classifier Support vector classifier K-neighbors classifier

Precision

Recall

F1-score

Accuracy

Real

0.88

0.82

0.85

0.84

Fake

0.82

0.88

0.85

Real

0.93

0.93

0.93

Fake

0.93

0.93

0.93

Real

1

0.19

0.31

Fake

0.54

1

0.7

Precision

Recall

F1-score

Accuracy

Real

0.88

0.82

0.85

0.85

Fake

0.82

0.89

0.85

Real

0.93

0.93

0.93

Fake

0.92

0.93

0.93

Real

1

0.17

0.29

Fake

0.54

1

0.7

0.92 0.58

Table 2 Comparison parameters for test size 30% Test size = 0.3 Random forest classifier Support vector classifier K-neighbors classifier

0.92 0.57

Fake News Detection Using Machine Learning

59

7 Conclusion Manually categorizing news demands deep expertise in the domain. We employed machine learning techniques to classify fake news in our study. Rather than categorizing political news, we collect datasets from the internet that contain news pieces from many domains to cover most of the news. The research’s main goal is to find textual characteristics that distinguish false news from legitimate news. From the dataset, various textual characteristics were extracted. The learning models were trained, and parameter tweaked to attain optimum accuracy. Some algorithms have a higher level of accuracy than others. We compared the outcomes of each method at various test sizes using a variety of performance indicators.

References 1. Lazer DMJ, Baum MA, Benkler Y et al (2018) The science of fake news. Science 359(6380):1094–1096 2. García SA, García GG, Prieto MS, Guerrero AJM, Jiménez CR (2020) The impact of term fake news on the scientific community scientific performance and mapping in web of science. Soc Sci 9(5) 3. Kogan S, Moskowitz TJ, Niessner M (2019) Fake news: evidence from financial markets 4. Hua J, Shaw R (2020) Coronavirus (covid-19) “infodemic” and emerging issues through a data lens: the case of China. Int J Environ Res Public Health 17(7):2309 5. Asr FT, Taboada M (2019) Misinfotext: a collection of news articles, with false and true labels 6. Jwa H, Oh D, Park K, Kang JM, Lim H (2019) exBAKE: automatic fake news detection model based on bidirectional encoder representations from transformers (Bert). Appl Sci 9(19) 7. Ahmed H, Traore I, Saad S (2018) Detecting opinion spams and fake news using text classification. Secur Privacy 1(1) 8. Vosoughi S, Roy D, Aral S (2018) The spread of true and false news online. Science 359(6380):1146–1151 9. Wang H, Li G (2019) Extreme learning machine cox model for high-dimensional survival analysis 10. Allcott H, Gentzkow M (2017) Social media and fake news in the 2016 election. J Econ Perspect 31(2):211–236 11. Chen F, Wan H, Cai H, Cheng G (2021) Machine learning in/for blockchain: future and challenges. Can J Stat 12. Weyn JA, Durran DR, Caruana R, Cresswell-Clay N (2021) Sub-seasonal forecasting with a large ensemble of deep-learning weather prediction models. J Adv Model Earth Syst 13. Monterio JP, Ramos D, Carneiro D, Duarte F, Fernande JM, Novais P (2021) Meta-learning and the new challenges of machine learning. Int J Intell Syst 14. Gurunathan A, Krishnan B (2020) Detection and diagnosis of brain tumours using deep learning convolutional neural networks. Int J Imag Syst Technol 15. Dunderdale C, Brettenny W, Clohessy C, Ernest Van Dyk E (2019) Photovoltaic defect classification through thermal infrared imaging using a machine learning approach. Progr Photovoltaics

Status of Basic Digital Tools Awareness Among Women Vegetable Vendors and Consequences-Hyderabad Local Markets K. Athhar Shafiyah and Shaik Kamaruddin

Abstract It is a well-known fact that we live in a digital era, where technology has become the backbone of business tasks all over the world. All the countries including India have moved on with the digitalisation of the IT world. But the darker side of India is that it is lagging in the literacy growth rate because most of the population lives in rural areas with huge poverty obstacles as a result woman does not get the opportunity to pursue education and get awareness of the present digital tools function. The study focuses on the awareness of the women vegetable vendors about the digital tools used in performing their business and startups. Primary data was collected through observation and scheduled questionnaire by using the convenience sampling method with a sample of 43 women vegetable vendors at Hyderabad local markets. The results are shown and analysed in terms of descriptive statistics. Hypothesis testing has been done to see the relationship between the variables. The study has concluded with possible suggestions to overcome the challenges for a better profitable world for women vegetable vendors. Keywords Women vegetable vendors · Digitalisation · Business · Literacy · Awareness

1 Introduction Women entrepreneurship is not a new concept but it is age-old terminology. Women doing business is an age-old phenomenon irrespective of religion and caste. The history of the Islam has witnessed women (the first wife of the Chief Prophet Bibi Khadija) being involved in the trade of transferring different goods and metals to other places and selling them with the help of co-workers, as well as the seasonal trade [1]. Which happened way back 14 centuries ago, it concludes that women were active in business tasks and transactions for ages not from the past few years. Usually, K. Athhar Shafiyah (B) · S. Kamaruddin Department of Business Management, Maulana Azad National Urdu University, Hyderabad, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 K. Chaudhary et al. (eds.), Intelligent Data Analytics in Business, Lecture Notes in Electrical Engineering 1068, https://doi.org/10.1007/978-981-99-5358-5_5

61

62

K. Athhar Shafiyah and S. Kamaruddin

people assume that traditional customs are the barriers to the way women create their image in a society which can be by doing any kind of job or business but the reference above highlights that the Parada system (women covering body and face) was not a barrier in doing the business nor in ages-old nor at present. In the twenty-first century, lack of literacy, unawareness of the digital world, and low skills are the most important barriers to doing business. There are several historical references from every caste and religion that women were involved in trade and commerce activities in different forms. Working as a home servant or helper is also a kind of trade where she sells her service and energy to be paid accordingly. Skill is very important to do any kind of business activity and also every single job. Small or big all the jobs involve skill, without which business can’t boom easily. Since the 1970s and 1980s, there has been an increase in interest in researching women entrepreneurs, primarily from the USA and Canada, as a consequence of the rapid increase in the number of businesses founded by women [2]. Taking into consideration the potential of women doing business for the economic growth of the nation, there were quite a few studies were done. The job of the women vegetable vendors is challenging as they have to face different kinds of challenges including environmental, physical, and mental. The efforts of the women vegetable vendors should not go vine. The efforts should be paid off, so the study has focused on analysing the factors causing hurdles and loss of the women vegetable vendors. All of the projections for digital transformation have one commonality that is change. Digitalisation is bringing more benefits than ever before, but it also necessitates a shift in thinking and a willingness to embrace change. To find a position in the evolving digital world, both businesses and individuals must accept the new reality of perpetual change [3]. The fields like mass media, commercial platforms, and the retail industry were considered important platforms to reach customers during the 1990s– 2000s. From 2000 to 2015, because of smartphones and social media, digitalisation showed a drastic sea prevalence in India and the world. In India, the use of digital payments was widespread after demonetisation. Demonetisation occurs whenever there is a change in the national currency and the change was done by the Government of India in 2015. After the demonetisation, the use of digital payment modes for ease of doing business (buying and selling) has shown slow growth in digital payment usage. It was constrained to some classes in the society, but it showed rapid spread over the nation during the first wave of COVID-19 in 2020 where the concept of “contactless delivery and contactless payments” became the first option for all classes. The consequences arise for the women vegetable vendors to sell the vegetables to customers who do not carry cash in physical form. The majority of women vegetable vendors belong to rural India as the profession of their families is agriculture; thus, they get involved in doing the job of the vegetable vendor so most of our targeted population of the study falls under the category of rural women vegetable vendors. In such a scenario, it becomes important to know the digital awareness of the women vegetable vendors, and analyse the consequences of not knowing the use of digital payment tools like Paytm, BHIM UPI, Google Pay, PhonePe, etc. The assessment of the root causes has been included and discussed in the findings making conclusion remarks and suggestions.

Status of Basic Digital Tools Awareness Among Women Vegetable …

63

You can tell the condition of a nation by looking at the status of its women. —Jawaharlal Nehru

1.1 What Is the Rural Sector, and Where Is a Rural Region Defined? According to the most recent census, the rural sector includes any location that fits the following requirements. • The population should be less than 5000. • A population density of fewer than 400 people per square kilometre. • More than 25% of the male population is involved in agricultural pursuits. India’s census report 2011 reports in Fig. 1 that around 70% of the population lives in rural areas. The literacy rate differed much in terms of rural and urban India as per Fig. 2. India has 83.3 crores of rural population and 37.7 crores of the urban population (census report, 2011). So, there is a significant change in the percentage of urban and rural women’s literacy which is not a good sign for rural businesswomen in the world of digital trade and commerce.

2 Literature Review GSMA [4] stated in their detailed study about rural women in India where the study has highlighted women’s mobile usage is hampered by a lack of mobile literacy and digital abilities. In India, there is a significant change in the percentage of digital literacy among men and women. Google and other trusts are helping them to accelerate the digital usage of mobile phones. It was witnessed that the developing countries reach the internet through mobile phones. The barriers are shown in the form of a chart represented as an image. It is widely acknowledged that internet connection has enormous potential to socially, economically, and politically empower women. In the words of UN Women’s Executive Director, “Internet access enhances women’s economic empowerment, political participation and social inclusion through initiatives that support increased productivity and income generation, mobilization and accountability, as well as improved livelihoods and expansion of services.” Rosser [5] highlights that there have been few studies that look at how technological designs, particularly for information technology (IT), may alter based on the gender of the creator and user. In the late 1980s and early 1990s, the word cyberfeminism, which openly combines gender and information technology, appeared. Ali Research [6] reported that women entrepreneurs will have more options in the digital world, and their potential will grow in the future years. Platform technologies will make female entrepreneurship more accessible and decrease the technological

64

K. Athhar Shafiyah and S. Kamaruddin

Fig. 1 Rural population percentage living across India Fig. 2 India’s population and literacy

Rural

Urban

100% 80%

79.92% 69%

58.75%

60% 40%

31%

20% 0%

Population

Literacy

Status of Basic Digital Tools Awareness Among Women Vegetable …

65

barrier. Wajcman [7] states that over the previous quarter-century, feminist technological theories have gone a long way. Women’s identities, needs, and priorities are shaped by digital technology. Women’s ability to generate new, favourable interpretations of artefacts is influenced by their larger economic and social situations. Agarwal [8] highlighted that merchants, particularly small businesses, are concerned about giving away items and then failing to get paid the next day, despite an SMS or paper slip stating payment permission. This is due to two factors: (a) a lack of understanding about how cashless transactions work and what each electronic confirmation implies, and (b) time-consuming and inefficient dispute resolution methods. Digital payment confirmations are often obtained in English via SMS or printed charge slips. Written English comprehension in India is low in general and significantly lower in the local merchant class. Dholakia and Kshetri [9] state that the patterns of the effect of internal and external forces change depending on the stage of adoption. The characteristics that were discovered have a major impact on internet adoption and e-commerce usage. Jitendra et al. [10] state that banks are now carried on mobile phones. Mobile phones may be used to begin transactions such as bill payments, cash transfers, and monthly instalment payments. Furthermore, walletrelated applications are still installed and may be utilised for payment. The primary forces driving mobile-based digital payment include smartphones, provider offers, deep internet penetration, and government backing, which overall creates an impact on the customer and the seller in the Indian context. Rohith [11] stated that the administration has constantly said that encouraging digital payments is a significant policy goal. The Union Budget (2021) allows INR 15 billion for programmes to encourage digital payments. Between February 2020 and October 2020, the number of online payments on the Unified Payments Interface (UPI) platform surged by nearly 58%. (And crossed 2 billion transactions in October 2020). India’s digital payments market is expected to reach USD 1 trillion by 2023. Renu and Gareema [12] recommended that SHGs might assist individuals in the rural region with the push for a digital banking system. Self-help groups would carry out the functions of Bank Mitras to service post offices and banks to propagate the digital wave. In this approach, rural social infrastructures such as Mahila Mandal, Youth Clubs, and Panchayati Raj Institutions should be expanded to support the expansion of the digital rural economy. Line department officials, which include school teachers, village development officers, and health professionals, would, on the other hand, educate residents about the digital economy and financial inclusion (Fig. 3).

3 Statement of the Problem The consequences faced by the woman vegetable vendors have not been in the light of research which is a concerning issue for the healthy economic status of the rural families ultimately contributing to rural development. The rural women are less literate due to which they are not able to use the digital payment mode for their business.

66

K. Athhar Shafiyah and S. Kamaruddin

Fig. 3 Source GSMA-Digital Literacy: Empowering women to use the digital tools (2015)

3.1 Objectives of the Study • To analyse the awareness of digitalisation among women vegetable vendors. • To know the awareness of government schemes among women vegetable vendors. • To examine the relationship between literacy and digital access concerning digital business.

3.2 Hypothesis H0: There is no relationship between Education and Digital usage. H1: There is a relationship between Education and Digital usage.

Status of Basic Digital Tools Awareness Among Women Vegetable …

67

3.3 Methodology of the Study The study is exploratory and qualitative research based on descriptive statistics. Primary data has been collected from the women vegetable vendors at Hyderabad local markets with the help of a scheduled questionnaire, interview, and observation through a convenience sampling method by taking 43 samples. The results have been analysed and presented in the form of descriptive statistics. Hypothesis testing has been done to check the correlation between the variables with the help of SPSS.

3.4 Limitations of the Study • The study is confined to the Hyderabad local market women vegetable vendors. • The biased response of the respondents may affect the results.

4 Data Analysis Table 1 presents that there are 55.8% of the Women’s Vegetable vendors in the taken sample are between 30 and 39 and next to them are between 40 and 49 years old, which indicates that the majority of the women are in their ‘30s and ‘40 s. At this age, women won’t be in a position to learn a new thing as they are surrounded by personal life responsibilities. Table 2 presents that there are 25.6% of women vendors are illiterate, whereas only 9.3% have reached or did incomplete high school education. The government and studies speak of making literacy rates high among women, but the ground reality is quite different because in women vegetable vendors, there are no college-educated women when it is compared with western countries even a waiter has a postgraduate degree. The empirical study has found that 86% of the women vegetable vendors live in rural areas or came from a rural background, and only 14% of the vendors live in the outskirts of the urban area. 67.4% of the women vegetable vendors can speak Urdu/Hindi other than the Telugu language, and 20.9% of them can’t speak any other Table 1 Age of the women vendors

Years

Frequency

Percentile

19–29

7

16.3

30–39

24

55.8

40–49

9

20.9

50–59

3

Total

43

7.0 100.0

68 Table 2 Status of respondent’s literacy

Fig. 4 Smart phone digital functions without a smartphone to a greater extent

K. Athhar Shafiyah and S. Kamaruddin

Literacy scale

Frequency

Percentile

No literacy

11

25.6

Primary

28

65.1

High school

4

9.3

College

0

0.0

Total

43

100

Have smart Phone 72.10%

80.00% 60.00% 40.00%

27.90%

20.00% 0.00%

Yes

No

language apart from the local language (Telugu). It is to be a concern because only 11.6% know the English language, in that case also they can’t speak or understand accurate English, having such a poor command of the technical language how one can learn the contemporary digital business tools which can upgrade their knowledge of the digital business world and make them more profit. The study has explored that 72% of the women vegetable vendors do not have a smartphone. Smartphone plays a major role in performing digital activities whether it is for awareness or knowledge or payments. The common man can’t perform (Fig. 4).

4.1 The Study Has Found the Following Responses The study has found above the percentage of respondents’ responses to the questions asked. Table 3 shows that only 2.3% of the people know about the market value. It is not a good sign of progress neither it is beneficial to the women vegetable vendor, because when they don’t have any idea about the market value, how will they keep an eye on the changes taking place in the market value every day. As they don’t know the current market value of the vegetable which has increased or decreased depending on the demand and supply. The greater consequences that have to be faced by the woman vegetable vendors in both increase or decrease in the market value as it is unknown and sudden to their knowledge. In the case of low market value, they sell the vegetables for the same high price as they are unaware that the market value has fallen and they lose the buyer’s satisfaction and trust. When the market value is high

Status of Basic Digital Tools Awareness Among Women Vegetable …

69

and they sell it for a low price being unaware of the market value, they have to face the loss in the total earnings of the day. From the sample of the study, none of the respondents knows about the GST and the process of the loan, which is not healthy for digital progress. The respondents don’t have any awareness about the Indian government schemes launched for the betterment and empowerment of women in the business sector, but the response got during the fieldwork is of concern that none of them has any knowledge about the schemes neither they know that there are 9 schemes specially meant for the woman entrepreneurship by giving them financial support to make their image and own efforts in the life. The response statement often collected from the respondents is “We don’t know about any schemes dear, and as we do not have a good education so such kind of understanding and process seems difficult to us, so let it be life is going on we are earning the amount for to full fill our essential needs, Governments (state and central) are also not truly concerned about our livelihood, if so then they don’t make the schemes and leave for namesake.” Women vegetable vendors in the Hyderabad local markets of the study are unaware of the Table 4 schemes made for the prosperity of women by the Government of India. It has been found in the study from Table 3 that only 20.9% of the people know about the farms’ law recently made by the Government of India which is under process and they have a very little amount of information about the farms’ law where the fact is that the law is related to the agriculture sector in which the vendors also fall to a larger extent into the category as they come with the background of farming families. It’s their right to have an understanding and awareness of the farms’ law, when they don’t know about what is included in the farms’ law how will they know Table 3 Percentage response of the respondent to various questions Questions posed to the respondent

“Yes” percentile (%) “No” percentile (%)

Know about the market value

2.3

97.7

Know any scheme of the govt specially made for women

0

100

Have any idea about taking a loan

0

100

Know about GST

0

100

Know about farms law

20.9

79.1

Heard of Google pay/Phone pay/Paytm/BHIM

69.8

30.2

Use Google pay/Phone pay/Paytm/BHIM by yourself

16.3

83.7

Have any idea about the online selling market

9.3

90.7

Know how to start your own business

2.3

97.7

Know about the barcode

14

86

Know any source from where you can learn about 60.5 basic digital usage

39.5

Interested in learning about basic digital usage

90.7

9.3

70

K. Athhar Shafiyah and S. Kamaruddin

Table 4 9 government schemes meant for the betterment of the women in the business field S. No.

Name of the scheme Year

Purpose

Central/state

1

Annapurna scheme

2001

Loan to women in the food catering industry for buying equipment (up to 50,000/-)

Central to State

2

Bharatiya Mahila Bank business loan

2013

Loan for retail and service (5,000,000/ Central -) Loan for manufacturing enterprise (1,000,000/-)

3

Mudra Yojna scheme

2015

10 lak loan to non-corporate, non-farm small enterprise

Central

4

Orient Mahila Vikas 2018 Yojna scheme

To start a business and streamline it (10–15 laks)

Oriental Bank of Commerce

5

Dena Shakti scheme 2014

Agriculture, manufacturing and other small business (20 laks)

Dena Banks

6

Udyogini scheme

2022

Loan to agriculture and small business Punjab and (1 lak) Sindh Bank

7

Cent Kalyani scheme

2013

Agriculture, SMEs, and small retail

The central bank of India

8

Mahila Udiyam Nidhi scheme

2021

Purchase of auto rickshaws, beauty parlours, daycare centres, etc. (10 laks)

Punjab National Bank

9

Stree Shakti scheme 2001

Own business (up to 50 laks)

SBI Bank

the pros and cons of the law, this is again the issue linked with the digital world and the literacy rate. The knowledge and use of digital payment tools like Paytm, BHIM UPI, Google Pay, PhonePe, etc. are very essential to promote digitalisation in India and smooth functioning of digital tasks. The study has found that 69.8% of the respondents have heard of digital payment tools, whereas only 16.3% of the vendors know to use the tools by themselves, which is quite a concern issue. It is of concern because if the vendor does not know the usage of digital tools, then they will have to lose the buyer during the study respondents said that “As we don’t know how to use the digital payment tools like Paytm, BHIM UPI, Google Pay, Phone Pay etc. and also don’t have a smartphone, the buyers will return from us with empty hands because at times buyers won’t be having cash in hands and they will buy from any mall or a shopkeeper, so we have to face the loss and consequences.” The study has found that respondents do not have much idea about online selling (9.3%), startups (2.3%), and barcode (14%); these three are important parts of the digitalised business world. Women vegetable vendors said during the fieldwork that they intend to earn bread and full fill basic needs not to have an extraordinary income. But this kind of mindset is not good for a healthy and developing India; this needs to be changed with the help of awareness and counselling camps for a better fruitful future for them as they deserve for the endless hard work, they perform being a women

Status of Basic Digital Tools Awareness Among Women Vegetable …

71

vegetable vendor. The study has explored that 60.5% of the women vegetable vendors have the source from where they can learn the usage of digital tools knowledge. Respondents said that they have the source to learn like their sons and daughters who can teach them, and few said that they can learn through YouTube videos. But only 9.3% of the women vegetable vendors are willing to learn rest of the respondents said “What will we do by learning this, this is not the stage of our life to learn let it be things are going on their way. We are illiterates so won’t be able to learn it properly so it will be waste of time trying to learn, we will tell our daughters to build that code all money we will send to their accounts.” But the money in other family members’ accounts cannot justify the earnings of the women’s vegetable vendor.

5 Hypothesis Testing H0: There is no relationship between Education and Digital usage for business. H1: There is a relationship between Education and Digital usage for business. Null hypothesis (H0) has been rejected at the 0.00 significance level which is less than the significant value of 0.05 at the 95% of the confidence level where educational status as an independent variable and knowledge of using digital tools such as Paytm, BHIM UPI, Google Pay, PhonePe, etc. as a dependent variable. The null hypothesis has been rejected and the alternate hypothesis has been accepted which concludes that there is a significant relationship between education and digital tools usage for business. There is another hypothetical aspect that can be analysed as there is not much of a positive relationship between education and digital tools usage by keeping the observation it can be said that despite having less educational status the digital usage knowledge and awareness can be promoted to a large extent with the help of awareness camps conducted by the government and non-government organisation agencies periodically.

6 Consequences Faced by Women Vegetable Vendors • As a result of poverty, they are not well educated and they are bound to perform household necessities. • In the fight of acquiring basic needs of life, food, water, and shelter they lag in the awareness of the educational opportunities. • They don’t have any kind of computer knowledge nor do they have much smartphone usage knowledge. • They have very poor professional skills in doing business and living. • They don’t have any idea about online sales and shops, as a result, they are unable to utilise the government schemes meant for their better future.

72

K. Athhar Shafiyah and S. Kamaruddin

• As a result of poor digital awareness and lack of literacy, they fear approaching the urban people(buyers). • Rural vendors face network issues in their native places, where 3G/4G/ is not easily accessible. • They don’t have any clue to create a business account or register their business online and deal with android apps on mobile phones with one click. • As a result of poor literacy, they have a digital phobia which keeps them away from the digital business world.

7 Suggestions to Overcome and Highly Take Part in the Digital Business World • The technology phobia should be removed from rural women’s mindsets through regular training and motivational classes. • The young generation of girls should be taught about online business transactions and trade policies to further not face the same issue in the coming years. • Government should send the businesswomen of the digital India team from urban centres to teach rural women vendors about the digitalised business skills and knowledge. • When the scheme is launched, government should take necessary steps to take awareness to every nook and corner that the targeted population get benefited from rather than just creating the scheme and spending crores of rupees on it failing in fulfilling the goals. • There should be early-age schooling facilities in rural areas with digital labs to create the next rural generation so competent to compete with the digitalised business world. • In rural schools, there should be a habit of online learning and access awareness among rural children.

8 Conclusion The study has found that there is a huge gap between women vegetable vendors and digital tools usage awareness. It has been observed that literacy is the root cause, so it is necessary to promote education among girl children so that we don’t have to face such consequences on the way to develop the digital business world in future. Those who have poor literacy have inbuilt technology phobia and feel ineligible to learn as English is the technical language. To overcome the barrier causing consequences, the present government has to concentrate on adult education, training, and awareness camps. It’s the primary responsibility of the government and non-government organisations to periodically conduct the awareness camp related to the schemes, market value, online selling, entrepreneurship initiative, the process

Status of Basic Digital Tools Awareness Among Women Vegetable …

73

of taking a loan, digital tools access, etc. The purpose of making schemes and the steps towards digitalisation can be fruitful in all aspects. It was observed during the data collection that the age of the women vegetable vendors was not a barrier to learning or carrying a smartphone thus the training and awareness camps can boost their awareness of smartphone, internet, and digital tools usage which will ultimately land them in a profit world and a better business world filled with the opportunities. The increase in profit and improvement in the social status of the women vegetable vendors can be considered a good sign of development in India. If the grass-roots level digitalisation and advancement of the business world consistently show positive progress then the day won’t be too far that India will be known to the world as a developed country, not as a developing country. The government has to put a serious concern on the issue and take the necessary “Action plan”. Then only the loss and the greatest consequence on the way to digitalised business can be minimised which is being faced by the rural women vegetable vendors because of poor knowledge of digital usage. Any society that fails to harness the energy and the creativity of its women is at a huge disadvantage in the modern world. —Tian Wei

References 1. Mujahid A (2013) Golden stories of Sayyida Khadijah (R.A). Darussalam 2. Neider L (1987) A preliminary investigation of female entrepreneurs in Florida. J Small Bus Manage 25:22–29 3. Auriga (2016, Dec 12) Digital transformation: history, present, and future trends. AURIGA. https://auriga.com/blog/digital-transformation-history-present-and-future-trends/ 4. Digital Inclusion and Connected Women Teams (2015) Accelerating digital literacy: empowering women to use mobile internet. GSMA mobile for development. https://bit.ly/3bP nsfk 5. Rosser SV (2005) Through the lens of feminist theory: focus on women and information technology. Frontiers 26(1):1–23 6. Ali Research (2019) 2019 global research report of female entrepreneurship & employment. China Women’s University 7. Wajcman J (2009) Feminist theories of technology. Camb J Econ 34(1):143–152 8. Rajeev A (2021, May 03) Challenges to India’s digital payment revolution. BW Business World. https://www.businessworld.in/article/Challenges-To-India-s-Digital-Payment-Revolu tion/03-05-2021-388437/ 9. Dholakia RR, Kshetri N (2004) Factors impacting the adoption of the Internet among SMEs. Small Bus Econ 23(4):311–322 10. Jitendra S et al (2021) Evolution of mobile-based digital payments and challenges: an Indian context. Int J Dig Literacy Dig Competence 12(1). https://www.igi-global.com/article/evalua tion-of-mobile-based-digital-payments-and-challenges/281640 11. Reji R (2021, Aug 26) Challenges of digital payments in India. BWDISRUPT. http://bwdisr upt.businessworld.in/article/Challenges-Of-Digital-Payments-In-India/26-08-2021-401960/ 12. Renu S, Gareema M (2019) Impact of digitalisation on rural banking customer: with reference to payment system. Emerg Econ Stud 5(1):31–41

74

K. Athhar Shafiyah and S. Kamaruddin

13. http://www.chinadaily.com.cn/global/2019-11/12/content_37522218.htm. Retrieved on 20/12/ 2021 14. https://archive.india.gov.in/citizen/graminbharat/graminbharat.php. Retrieved on 25/12/2021 15. https://bit.ly/3hIs36C. Retrieved on 05/12/2021 16. https://censusindia.gov.in/2011-Common/CensusData2011.html. Retrieved on 28/12/2021

Influence of Covid-19 on Punjab Textile Industry Rajni

and Palak Bajaj

Abstract The national economy of any country has badly affected by COVID-19. The financial health of many businesses has also turned down due to COVID-19. The present study has evaluated the influence of the COVID-19 on financial position of Punjab textile companies listed on BSE. Wilcoxon signed rank test has been applied to analyse data related to pre- and post-COVID-19, i.e. 2019 and 2020. It can be concluded that there is decrease in profitability, leverage and activity ratio of Punjab textile companies but liquidity have been increase as mean value of current ratio has been increased. The results have found that there is no significant difference between pre- and post-profitability, leverage and liquidity position of Punjab textile industry. But significant difference in the value of assets turnover ratio during pre- and post-COVID-19 can be seen, which an alarming factor for Punjab textile companies. Keywords Textile · Wilcoxon signed rank test · Financial position · Liquidity · Profitability · Leverage

1 Introduction In 2019–2020, the economy of India has extensively influenced due to COVID-19 has extensively at central as well as at state level [1]. The total income of central and state government has been reduced, but expenditure has been increased. The fiscal deficit of India in 2020–21 was 3.5% of GDP and revenue deficit was 2.7% of GDP [2]. The Government of India has launched a number of policy measures to maintain a balance between supply and aggregate demand in spite of a number of challenges, e.g. repeated wave of corona virus, restrictions on travelling, global inflation and supply chain disruption in economy. To celebrate azadi ka amrit mahotsav Government of India has launched schemes for Amrit Kaal, which would be revival strategies for India. In budget FY 2022–23 total expenditure have been estimated to 39.45 lakh crore and total receipts estimated to 22.84 lakh crore. The fiscal deficit projected Rajni (B) · P. Bajaj I.K. Gujral Punjab Technical University, Kapurthala, Jalandhar, Punjab, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 K. Chaudhary et al. (eds.), Intelligent Data Analytics in Business, Lecture Notes in Electrical Engineering 1068, https://doi.org/10.1007/978-981-99-5358-5_6

75

76

Rajni and P. Bajaj

at 6.8% in FY 2022–23 (Economic Survey 2021–22). After reviewing the fiscal condition of India in present scenario, it is urgent need to study the influence of COVID-19 on Punjab textile industries as this sector is earning child for Government of Punjab. Government of India has announced polices for fiscal consolidation in budget 2022–23. Still it is a road map for the growth of Indian economy as well as for Punjab. In light of this, present study is divided into five parts. In addition to introduction, the second part reviews different studies conducted on COVID-19 influence. The third part explains research methodology related to study. The fourth parts deals with data analysis related to pre- and post-COVID-19 influence. Finally, the last part concludes by explaining strategies adopted by Government of India to overcome the influence of COVID-19 (Alok et al.).

2 Review of Related Studies Devi et al. [3] present study has been conducted to examine influence of COVID-19 on financial position of Indonesia Stock Exchange. Wilcoxon signed ranks have been applied on pre- and post-data of June, 2019 and June, 2020 related to liquidity ratio, leverage ratio, profitability ratio and activity ratio. The result showed that liquidity and profitability position have been decreased, but leverage and activity ratio have been increased during this period. But no significant difference have been found in liquidity and leverage ratio pre- and post-COVID-19 on the other hand profitability and activity ratios have shown significant difference in pre and post COVID-19 period. Liu et al. [4] studied the association between risk and return trade off from 1997 to 2017 through Wilcoxon signed ranks test. The result showed that risk and return have low relation in short term but in long term, both the variables are having positive relationship. In long term, speculation can minimise the risk. Mamahit et al. [5] examined the influence of merger and acquisition on financial position of listed in Indonesia stock exchange. Panel data regression and Wilcoxon test have been applied on pre-post-data related to liquidity and profitability. It have been concluded that financial position after merger has been declined and no significant relation has been found between the variables. Suganda and Sumani [6] evaluated the influence of divestiture on financial position from 2009 to 2018. Wilcoxon signed rank tests have been applied on financial ratios and concluded that total assets turnover ratio, net profit margin to sale, return on assets and price earnings ratio have significant change from pre- and post-period but cash ratio, market to book ratio and return on equity ratio have not shown any significant change. Phi et al. [7] present study has been conducted to examine the relationship among type of ownership and position of 25,247 firms of manufacturing, services and agriculture over the world. Descriptive statistics, correlation, regression and Wilcoxon

Influence of Covid-19 on Punjab Textile Industry

77

signed rank test have been applied to analyse data. It have been concluded that privatization influenced positively on the position firms. Candido et al. [8] investigated the influence of ISO 9001 certification on the position of firm. A sample of 143 Portuguese companies has been taken. Wilcoxon signed rank test has been applied to check the pre- and post-influence of ISO certification. They observed that ISO certification has positive influence on financial position of firms. Aguilar and Torres [9] examined the influence of covid-19 on financial position of companies. A sample banks of five countries (Spain, Germany, France, Italy and Netherlands) have been evaluated with the help of Wilcoxon signed rank test. It has been observed that Italy is most affected by sentiments regarding covid-19. Kumar et al. [10] analysed the influence of Covid-19 of NSE listed 1335 companies from January, 2018 to December, 2020. OLS market model and cross-sectional t-test have been applied to analyse data. It have been concluded that large-scale companies have more negative influence than small scale companies. Erfani and Islam [11] studied the influence of EU membership on financial position of firm. 9 years data from 2001 to 2009 have been evaluated with Wilcoxon test and panel regression. They observed that financial position has been increased of those companies which have started working in EU. Atayah et al. [12] evaluated the financial position of logistics firms of G-20 countries. Data have been collected from 19,608 firms from first quarter 2010 to last quarter 2021. Descriptive statistics, spearman rank correlation, t-test and placebo test have been applied and concluded that there is significant positive change in the financial position of logistics firm of Germany, Korea, Russia, Mexico, Saudi Arabia and UK. Panchal [13] evaluated the association among NPAs, advances and profitability during Covid-19 of banking sector in India. Five years data of SBI, Bank of Baroda, HDFC and Axis bank have been collected and examined with descriptive and correlation test. Results have shown negative influence of Covid-19 on the financial position of banking sector in India. Saldanha and Nitin [14] examined the influence of Covid-19 on preference of banking sector customers. Primary data have been collected through google form and analysed by using percentage and charts. They have found that services quality hasn’t improved and customers prefer digital banking. Government of India has also launched digital money in budget 2022–23. Kaur [15] examined the influence of Covid-19 on textile industry of Punjab. Primary data from 123 textile companies have been collected and evaluated through factor and regression analyses. The study has found that Covid-19 influence financial position, market potential, health issues of employees and availability of product negatively. Moreover, stress level of entrepreneurs has also increased due to less financial support by the government.

78

Rajni and P. Bajaj

3 Research Methodology The study has been conducted in the state of Punjab in India. The main aim of the study is to analyse influence of COVID-19 on the financial position of Punjab textile companies listed in Bombay stock exchange. The sampling unit of the study was all listed textile companies of Punjab on Bombay Stock Exchange under unit division 13 and 14 under NIC-2008. In order to study the influence of COVID-19 pandemic on financial position of Punjab Textile industry, secondary data has also been collected from money control, Economic Survey, Budget 2022–23. Descriptive statistics and Wilcoxon signed rank test have been applied to check influence of COVID-19 on financial position of Punjab textile industry in SPSS-24 version. Hypothesis H1: The COVID-19 pandemic affects liquidity of Punjab textile industry with proxy variable current ratio. H2: The COVID-19 pandemic affects leverage of Punjab textile industry with proxy variable debt–equity ratio. H3: The COVID-19 pandemic affects activity of Punjab textile industry with proxy variable asset turnover ratio. H4: The COVID-19 pandemic affects profitability of Punjab textile industry with proxy variable return on assets ratio.

4 Analysis and Interpretation of Growth and Contribution of Textile Industry in Punjab The data has been analysed with descriptive analysis related to financial position of Punjab textile industry between pre and post COVID-19 pandemic. Firstly, normality test has been conducted to check normality of data. The basis requirement to apply parametric test is normality. The output of descriptive analysis shown that mean value of return on assets has declined during post-COVID-19 as compared to pre-COVID19 of Punjab textile companies. The mean value of return on assets during preCOVID-19 was − 5.1847 and post-COVID-19 was − 8.5847. This indicates negative influence of COVID-19 on financial position of Punjab textile industry. The mean value of debt–equity ratio has declined during post-COVID-19 as compared to preCOVID-19 of Punjab textile companies. The mean value of debt–equity ratio during pre-COVID-19 was 1.008 and post-COVID-19 was 0.984. This doesn’t indicate negative influence of COVID-19 on financial position of Punjab textile industry. The mean value of assets turnover ratio has declined during post-COVID-19 as compared to pre-COVID-19 of Punjab textile companies. The mean value of assets turnover ratio during pre-COVID-19 was 99.5633 and post-COVID-19 was 86.3547. This indicates negative influence of COVID-19 on financial position of Punjab textile industry. The mean value of current ratio has increase during post-COVID-19 as compared to pre-COVID-19 of Punjab textile companies. The mean value of return

Influence of Covid-19 on Punjab Textile Industry

79

on assets during pre-COVID-19 was 1.508 and post-COVID-19 was 1.6327. This doesn’t indicate negative influence of COVID-19 on financial position of Punjab textile industry (Table 1). As all significant values in Table 2 are not greater than 0.05, level of significance, which indicates that data is not normal distributed. So, nonparametric test can be applied to check influence of COVID-19 on financial position of Punjab textile industry in Punjab. Therefore, data have been analysed with nonparametric Wilcoxon signed rank test. Table 3 shows that the values of ties for return on assets, debt–equity ratio and assets turnover ratio were 0, meaning that there were no same value of return on assets, debt–equity ratio and assets turnover ratio during pre- and post-COVID19. At the same time, current ratio has a ties value of 2, meaning that two textile companies had same current ratio during pre- and post-COVID-19. There were 8 textile companies experienced decrease in return on assets and debt–equity ratio and 7 textile companies experienced increase in return on assets and debt–equity ratio during pre- and post-COVID-19 pandemic as shown by negative rank at the N value of 8 textile companies and the positive rank at the N value of 7 textile companies of return on assets and debt–equity ratio respectively. Table 3 also shows that 12 textile companies experienced decrease in assets turnover ratio and 3 textile companies experienced increase in assets turnover ratio during pre- and post-COVID19 pandemic as shown by negative rank at the N value of 12 textile companies and the positive rank at the N value of 3 textile companies. Values of ties for return on assets, debt–equity ratio and assets turnover ratio were 0, meaning that there were no textile company with same value of return on assets, debt–equity ratio and assets turnover ratio during pre- and post-COVID-19. At the same time, current ratio has a ties value of 2, meaning that two textile companies had same current ratio during pre- and post-COVID-19 (Table 4). The result of Wilcoxon signed rank test shows that asymp. Sig. (2-tailed) values of return on assets, debt–equity ratio and current ratios are greater than 5% level of significance, i.e. 0.05. It can be concluded that there is no significant difference between pre- and post-financial position of Punjab textile industry. So, hypotheses H1, H2 and H4 are rejected. But significant difference in the value of assets turnover ratio during pre- and post-COVID-19 can be seen from assumption Sig. (2-tailed) less than 0.05. So, hypothesis H3 is accepted.

5 Findings It can be concluded that there is decrease in profitability, leverage and activity ratio of Punjab textile companies, but liquidity has been increase as mean value of current ratio has been increased. It can be concluded that there is no significant difference between pre- and post-profitability, leverage and liquidity position of Punjab textile industry. But significant difference in the value of assets turnover ratio during pre- and post-COVID-19 can be seen. The results of the study can be beneficial for investors

99.5633

86.3547

1.508

15

15

15

Assets turnover before covid

Assets turnover after 15 covid

15

Debt equity after covid

Current ratio before covid

Current ratio after covid

Source Compiled by researcher using SPSS

1.6327

0.984

1.008

15

Debt equity before covid

1.63679

1.23303

46.94505

57.58176

2.68677

3.43464

43.61149

− 8.5847

15

Return on assets after covid

15.4978

− 5.1847

15

Std. deviation

Mean

Return on assets before covid

N

Table 1 Descriptive analysis

7.84

− 1.68

0.03

0.08

12.64

5.52

4.3

181.44

250.35

12.36

− 2.21

11.12

32.94

8.31

Maximum

− 159.27

− 40.87

Minimum

0.28

0.51

55.78

59.46

− 0.95

− 1.14

− 5.26

− 11.03

25th

Percentiles

1.18

1.14

81.83

85.52

0.35

0.37

1.73

2

50th (Median)

2.37

2.43

1.21E + 02

1.29E + 02

1.32

1.26

6.25

4.42

75th

80 Rajni and P. Bajaj

Influence of Covid-19 on Punjab Textile Industry

81

Table 2 Normality test Normality test Kolmogorov-Smirnova

Shapiro–Wilk

Statistic

df

Sig.

Statistic

df

Sig.

Return on assets before covid

0.268

15

0.005

0.781

15

0.002

Return on assets after covid

0.33

15

0

0.56

15

0

Debt–equity before 0.318 covid

15

0

0.671

15

0

Debt–equity after covid

0.302

15

0.001

0.766

15

0.001

Assets turnover before covid

0.133

15

0.200*

0.929

15

0.261

Assets turnover after covid

0.135

15

0.200*

0.978

15

0.956

Current ratio before 0.173 covid

15

0.200*

0.923

15

0.211

Current ratio after covid

15

0.113

0.846

15

0.015

0.199

Source Compiled by researcher using SPSS

or potential investors while making investment decisions during the crisis arises due to COVID-19 pandemic. The government of Punjab can also use these results for deciding tax incentives for textile industry working in Punjab. The main limitation of the study is that it is limited to Punjab region only. Further studies can be conducted on the basis of 2022 financial reports to see present status of COVID-19 influence on Punjab textile industry.

82

Rajni and P. Bajaj

Table 3 Wilcoxon signed rank test

Return on assets after covid—return on assets before covid

Debt–equity after covid—Debt–equity before covid

Assets turnover after covid—assets turnover before covid

Current ratio after covid—current ratio before covid

N

Mean Rank

Sum of Ranks

Negative ranks

8a

6.62

53

Positive ranks

7b

9.57

67

Ties

0c

Total

15

Negative ranks

8a

7.19

57.5

Positive ranks

7b

8.93

62.5

Ties

0c

Total

15

Negative ranks

12a

7.92

95

Positive ranks

3b

8.33

25

Ties

0c

Total

15

Negative ranks

8a

5.75

46

Positive ranks

5b

9

45

Ties

2c

Total

15

Source Compiled by researcher using SPSS Table 4 T-statistics Post-covid return on assets—pre-covid return on assets

Post-covid debt–equity ratio—pre-covid debt–equity ratio

Post-covid assets turnover ratio—pre-covid asset turnover ratio

Post-covid current ratio—pre-covid current ratio

Z

− 0.398a

− 0.142a

− 1.988b

− 0.035b

Asymp. sig. (2-tailed)

0.691

0.887

0.047

0.972

Source Compiled by researcher using SPSS a Based on negative ranks b Based on positive ranks c Wilcoxon signed ranks test

Influence of Covid-19 on Punjab Textile Industry

83

References 1. Deb P, Xu TT (2021). State level health and economic influence of COVID-19 in India. International Monetary Fund. https://www.imf.org/en/Publications/WP/Issues/2021/11/19/StateLevel-Health-and-Economic-Influence-of-COVID-19-in-India-509673 2. Alok VN, Jatoliya M, Pareek A (2021) Governance and economic influence of covid-19 in Indian Federation. Indian J Publ Administration 67(3):452–469. https://doi.org/10.1177/001 95561211045099 3. Devi S, Warasniasih N, Sindy M, Masdiantini PR, Musmini LS (2020) The Influence of covid19 pandemic on the financial position of firms on the Indonesia stock exchange. J Econ Bus Account Ventura 23(2):226–242 4. Liu C, Shi H, Wu L, Guo M (2020) The short term and long term trade off between risk and return: Choas vs rationality. J Bus Manag 21(1):23–43. https://doi.org/10.3846/jbem.2019. 11349 5. Mamahit BLA, Pangemanan SS, Tulung JE (2019). The effect of merger and acquisition on financial position of companies listed in Indonesia stock exchange. J EMBA 7(4):5903–5913 6. Suganda B, Sumani (2019) Divestiture and company’s financial position: an empirical study on companies listed on Indonesia stock exchange. Adv Econ Bus Manage 100:87–91 7. Minh PNT, Taghizadeh HF, Anh TC, Naoyuki Y, Ju KC (2019) Position differential between private and state owned enterprises: an analysis of profitability and leverage. ADBI working paper series, 950, 1–19 8. Candido JFC, Coelho LMS, Peixinho RMT (2016) The financial influence of a withdrawn ISO 9001 certificate. Int J Oper Prod Manage 36(1):23–41 9. Alicia A, Diego T (2021) The influence of covid-19 on analysts’ sentiment about the banking sector. Banco De Espana Publication 2124:1–45 10. Rahul K, Prince B, Deeksha G (2021) The influence of the covid-19 outbreak on the Indian stock market—a sectoral analysis. Investment Manage Fin Innov 18(3):334–346 11. Erfani GR, Islam T (2012) The influence of EU membership on the financial position business firms in central and Eastern Europe. International Journal of Economic Behaviour. 2:82–90 12. Atayah OF, Dhiaf MM, Najaf K, Frederico GF (2021) Influence of covid-19 on financial position of logistics firms: evidence from G-20 countries. J Glob Oper Strategic Sourc: 2398– 5364. https://doi.org/10.1108/JGOSS-03-2021-0028 13. Panchal N (2021) Influence of covid-19 on banking in India—an empirical analysis. Toward Excellence 13(2):446–459 14. Saldanha A, Nitin K (2021) Influence of the covid-19 pandemic on the Indian retail banking sector. Ushus J Bus Manage 20(2):1–15. https://doi.org/10.12725/ujbm.55.1 15. Kaur K (2021) The early influence of covid-19 on textile industry: an empirical analysis. Manag Labour Stud 46(3):235–247 16. https://www.indiabudget.gov.in/economicsurvey/

A New Transformation for Manufacturing Industries with Big Data Analytics and Industry 4.0 Nabeela Hasan and Mansaf Alam

Abstract This efficient technology, as Industrial Revolution 4.0, came into existence in 2011 as a modern and efficient technique given by German experts. Numerous businesses are utilizing Industry 4.0 to increase their production. Manufacturing businesses, in particular, employ this technology to monitor production and associated organizational processes to take appropriate actions to manage their supply chain operations. This paper brings together a diverse research group to discuss the most recent advances in core theoretical and experimental elements of learning ability and its implementation in production and manufacturing mechanisms.

1 Introduction The economic business climate, mass customization, and globalization are compelling “traditional” sectors to embrace new business opportunities and transition to Industry 4.0 [1, 2]. This technology may be seen as an efficient idea in production to maximize efficiency and output while using the fewest resources possible [3]. Industry 4.0 innovations have ushered in new product development, emphasizing resource efficiency and output maximization. “Smart manufacturing” or “digital manufacturing” may be thought of as the backbone of this technology since it enables businesses to conduct flexible production processes with mass customization [4]. According to Zhong et al. [5], this 4.0 technology was a German-based project focused on transforming and upgrading industrial technologies. Manufacturing protocols in Industry 4.0 may monitor physical operations and build a so-called digital twin of physical items [6]. It enables industrial firms to build real-time connectivity efficient decision-making. These technologies have revolutionized companies’

N. Hasan (B) · M. Alam Jamia Millia Islamia, New Delhi 110025, India e-mail: [email protected] M. Alam e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 K. Chaudhary et al. (eds.), Intelligent Data Analytics in Business, Lecture Notes in Electrical Engineering 1068, https://doi.org/10.1007/978-981-99-5358-5_7

85

86

N. Hasan and M. Alam

Fig. 1 Various facets of Industry 4.0

old business models by combining smart manufacturing with incorporated production systems [7]. As a result, traditional business models have transformed. The IoT, ML, cloud computing, virtual and augmented reality, and digital twins may all be regarded as critical enablers of Industry 4.0 techniques shown in Fig. 1. Industry 4.0 technology, it is claimed, will contribute to companies’ economic stability [3]. Enhancing resource efficiency offers an excellent foundation for sustained value formation through the environmental extent of sustainability [8]. Reviewing pertinent research material may assist researchers in acquiring an indepth understanding of their field of study. Prior to commencing data collection and other crucial research activities, it is crucial to do a comprehensive literature review of relevant research. The purpose of this research is to evaluate the importance of Industry 4.0 in the industrial sector. In order to get a deeper understanding of the impact of Industry 4.0 on the services and manufacturing sectors, relevant research material such as books, articles, and journals has been assessed. Furthermore, the study material has helped to appreciate the benefits and downsides of applying Industry 4.0 in manufacturing organizations. This article highlights the key findings of the reviewed literature.

2 A New Era: Industry 4.0 Industry 4.0 is generally called the “Fourth Industrial Revolution,” an idea created during Germany’s 2011 Hanover show [6, 9]. The technology Industry 4.0 was born in 2014 with the help of the German Government, and the aim was to help and manage organizations with this new efficient technology [4]. Industry 4.0 refers to the current

A New Transformation for Manufacturing Industries with Big Data …

87

data interchange and automation developments among industrial technologies or processes. Industry 4.0 is centered on creating “smart” factories that adapt to dynamic production situations, management objectives, and intelligent business concepts [2]. The primary enabler technologies include cyber-physical systems (CPS), block chain technology, augmented and virtual reality, additive manufacturing, machine and deep learning, manufacturing execution systems, design flexibility, and other innovative technology [10, 11]. When coupled with IoT, CPS may link physical items, enabling human–machine interaction, architecture, operations, and other system processes [12]. By transmitting data collected via sensing devices and generated during manufacturing, IoT and CPS allow accessible communication among the digital world and physical entities [13]. However, some Industry 4.0 technologies are facilitators of Industry 4.0-enabled business activities.

2.1 Industry 4.0 Enabling Technologies This technology is not a standalone system but a collection of disparate technologies that have been de facto consolidated by technical leaders, critical users, system administrators, and public policymakers. It is unmistakably a complex architecture, distinguished by the coexistence of old and modern technology, all of which are linked through a cloud-based network. The technologies concerning Industry 4.0 are shown in Table 1.

3 The Manufacturing Industry with Industry 4.0 This technology shares monitoring of real-time data for the complete production cycle. By tracking the complete value chain and production operations, a manufacturing organization may establish new ways for controlling the supply chain by increasing the production rate of the industry [14]. Controlling the allocation of resources and commodities enables the successful management of manufacturing operations. As an output, various researches have demonstrated that efficient adoption of this technology helps in improving industrial businesses and also improves their efficiency. A large amount of productivity can also help an organization fulfill every client’s order. On the other hand, dangers associated with excess output and wastage associated with surplus production may be avoided by executing this technology [15]. Industry 4.0 techniques are a bit critical for maintaining an end-to-end network. Another critical element of Industry 4.0 is a complex reality. According to [16], significant organizations also utilize mixed reality equipment such as glasses and helmets to construct smart factories as the logic is to incorporate mixed reality an efficient manufacturing method that would improve employee interaction. Additionally, the display

88

N. Hasan and M. Alam

Table 1 Industry 4.0 enabling technologies Technology

Description

Internet of Things The devices based on the Internet of Things (IoT) can self-identify (location, status diagnostics, data collection, analysis and deployment) and are linked through structured communication channels Big data analytics Collecting and analyzing vast amounts of accessible data through a succession of approaches for filtering, capturing, and reporting insights, where data is analyzed in larger quantities, at faster speeds, and with more diversity Cloud manufacturing

Cloud manufacturing uses cloud technology to manufacture, enabling quick and easy access to IT services-infrastructure, framework, or implementation assist manufacturing operations and supply chain. Cloud manufacturing encompasses everything from digitalizing physical resources required for industrial machinery to cloud-hosted application processes and data across various platforms, implementation and collaborating tools

Artificial intelligence

It is concerned with the methods and techniques established to make machines “intelligent,” capable of functioning effectively in their environment via anticipation. Industrial AI refers to computer science-based capabilities that, when combined with machine learning, enable the creation of intelligent sensors, smart manufacturing and edge computing systems

Additive manufacturing

AM is generally said as 3D printing. Additive manufacturing is used in prototyping (to aid in product innovation, static modeling, wind tunnel testing), manufacturing (straightforward manufacturing), repair and maintenance, and modeling stages. The United States International Standard Organization classifies additive manufacturing methods

of contextual data might boost worker productivity in this situation. According to surveys, maintenance people in various industrial businesses employ mixed reality equipment and technology [17]. The essential components of Industry 4.0 are robotics [18].

4 Evolution for Manufacturing Industry Automation technologies’ role in manufacturing continues to grow due to the twentyfirst century’s digital revolution. Large industrial enterprises have shown an interest in automating production technology. These small scale business organizations are always difficult to prove a sufficient level of optimality in digital technology. After 2019, the epidemic of Covid-19 paved the way for the digitalization of all company activities [19]. The production activities of many organizations have been adversely impacted since most firms have been compelled to run their operations in the workplaces during lockdowns aimed at containing the corona viruses spreading. Numerous small firms’ output has been reduced during lockdown times [20]. Consequently, it has become critical for manufacturers to use automation technology to control manufacturing costs so that the demands are met across many segments of the market.

A New Transformation for Manufacturing Industries with Big Data …

89

Industry 4.0 has great potential for industrial business to establish and enhance e-commerce and supply chain management. Additionally, Industry 4.0 deployment aids in monitoring and controlling the distribution of manufactured items in various specialized markets. Automation technology, particularly robots, may assist industrial firms in successfully performing repetitive activities. The organization’s labor cost is reduced as a result of this procedure, and the organization’s productivity is increased as well [21]. Additionally, it becomes simpler to regulate production rates with fewer manufacturing personnel. According to [22], developing these things in large quantities has become critical to deploy Industry 4.0, which enables manufacturing enterprises to correctly fulfill client orders by managing product supply in several marketplaces. A significant number of firms have embraced Industry 4.0 in order to eliminate human mistakes in industrial processes [23]. Additionally, efficient information exchange through cloud-based technologies may assist increase worker productivity and promote successful strategic management for value development.

5 Automation Using Industry 4.0 and IoT for the Manufacturing Division Since the beginning of the twenty-first century, numerous industrial enterprises have shown an interest in implementing Industry 4.0. However, in the aftermath of the Covid-19 epidemic, industrial businesses realized the critical need to use automated technology to control their production rates (Fig. 2). The statistic indicates that the market for intelligent factories has been growing fast since 2019, after the Covid-19 epidemic. The market focusing on efficient and smart factories in 2019 was worth around 153.7 billion and is predicted to reach 244.8 billion by 2024. Small and medium enterprises (SMEs) in many nations are also interested in using IoT-based technology and intelligent devices to help them meet their production objectives [24]. However, SMEs confront a variety of problems while implementing the 4.0 technology. The most typical barrier to SMEs are the adoption of modern technology, which is a shortage due to less technical skills and a lack of resources and funding to apply such automated systems. As a result, SMEs are missing out on the full advantages of Industry 4.0 deployment. On the other hand, many small businesses are unfamiliar with Industry 4.0. [20]. As a result of these factors, Industry 4.0 adoption has accelerated among global manufacturing industry leaders. Nonetheless, its popularity is lower in the world’s SME sectors.

90

N. Hasan and M. Alam

Fig. 2 Growth of smart factories

6 An Amalgamation of Industry 4.0 and Big Data Analytics for the Manufacturing Sector The manufacturing sector has gained many benefits with the application of Big Data analytics in respect of production supervision and real-time utilization, all of which promotes sustainability. Future research might create novel extensive data analytics methodologies for sustainable industrial development. It is essential to review and analyzes large amounts of data produced by industrial activities at different stages. In future research, analysis and design approaches may be used with data analytics to create a deeper insight into shop floor modeling approaches. Additionally, there are very few studies on the evolution of IT-enabled big data analytics. Future studies in this field may be undertaken by taking into account multi-component-based systems, which is a significant drawback of most studies. Table 2 summarizes the study difficulties and challenges associated with big data-enabled industrial sustainability strategies. Furthermore, data analytics may be utilized to accelerate development in the essential fields in the manufacturing industry as presented below: 1. Leverage analytics to increase production effectiveness. 2. Enhancement of the consumer experience, including personalization and identifying unique value offerings. 3. Inventories management via.

A New Transformation for Manufacturing Industries with Big Data … Table 2 Research challenges in industrial sustainability strategies

91

Research challenges

References

Data quality management

[6, 25]

Data acquisition

[26, 27]

Cloud-based technology

[28]

Architectural issues

[28, 29]

Model and algorithm development

[30–32]

Energy consumption

[33]

Data integration

[30, 31]

a. Real-time monitoring and insight into inventory across supplier chains. b. Optimization of delivery routes. 4. Minimize damage associated with damaged, delayed, or lost products in transit, as well as provide real-time asset monitoring via the use of real-time notifications. 5. To minimize mistakes and retractions throughout product development and to enhance the quality of products and packaging by. a. Utilize analytically based solutions. b. Product design. 6. Predictive maintenance contributes to asset longevity via the following: a. b. c. d.

Capital management. Increasing the availability of assets. Defect and fault diagnosis. Preventing unscheduled downtime.

7. Enhance supply chain transparency with actionable insights from location-based IoT services.

7 Advancements of Industry 4.0 This technology has proven critical for manufacturing companies to manage their production rates. A well-managed production rate and logistics chain may assist a business in meeting clients’ requests in various market sectors. On the other side, implementing Industry 4.0 may assist in avoiding the problem of excess output. Excessive production entails the squandering of precious resources and materials [22]. Effective production management and distribution methods enable a business to maintain a competitive edge in extremely competing manufacturing sectors.

92

N. Hasan and M. Alam

7.1 Increased Productivity According to research, implementing Industry 4.0 may assist industrial businesses to increase their efficiency. Proper information exchange through the Internet of Things and cloud-based technologies enables a firm to examine each stage of the supply chain properly. This technique enables firms to build strategies for managing their supply chains and manufacturing operations [24]. Intelligent machines may also undertake organizations’ repetitive duties. Consequently, more things can be produced in a specific amount of time, and the organization’s production rate rises.

7.2 Cost-Efficient Technology Industry 4.0 can provide cost-efficient solutions. The motive of Industry 4.0 technology is to digitize the process of manufacturing. Intelligent technology may be utilized to automate tedious processes [24]. Human error is also reduced in this approach. Also, the corporations gain the ability to regulate their manufacturing costs with a restricted number of personnel on-site. As a consequence, the company’s labor costs reduce as well.

7.3 Collaborative Work Technology 4.0 is dependent on the Internet of Things (IoT) and cloud-based data exchange technologies that facilitate the exchange of pertinent information across various departments in order for them to handle their tasks effectively. Efficient information flow across departments also helps improve team cooperation, making it simpler for the business to fulfill its goals. For instance, information on the logistics system, rate of production, and revenue may assist the sales and production departments in collaborating on plans to improvise the features for the product and revenue of the firm.

7.4 Increased Customer Satisfaction The use of automation technologies and intelligent machines in manufacturing may come up with an efficient quality of product and can be done by reducing human errors in manufacturing. Additionally, the likelihood of defective items is reduced, and also a considerable amount of products may be created in the defined time. This enhancement in product quality contributes to the increase in satisfaction among customers. Additionally, the increment in the cost of production allows a business to

A New Transformation for Manufacturing Industries with Big Data …

93

manage the availability of products around various marketplaces and achieve the goal of ensuring that clients would not get any difficulty while purchasing these items.

7.5 Resilience and Flexibility Industry 4.0 also contributes to the industrial system’s flexibility and responsiveness. It becomes simpler to adjust operations up and down or add new product lines when Industry 4.0 is implemented [22]. Additionally, Industry 4.0 creates prospects for one-off and high-mix production systems.

8 Challenges While Adopting Industry 4.0 This adoption of IoT technology and cloud-oriented technologies might result in cyber-security concerns. Cyber-attacks on cloud-based databases have a high probability. However, cyber-attacks on businesses’ computers and databases may result in data loss and manipulation [34]. Additionally, private information about businesses might be disclosed due to cyber-attacks on the company’s servers (Fig. 3). Industry 4.0 includes advanced cyber-physical equipment, Internet of Things (IoT) solutions, cloud-based databases for data interchange, and mixed reality technological developments. Lack of maintenance of these systems or ineffective machine management could culminate technical issues. These technical defects may also Fig. 3 Challenges of Industry 4.0

94

N. Hasan and M. Alam

result in operational issues. Due to technical obstacles, it may be difficult for manufacturing company management to make decisions on production line or distribution network modifications [35]. Therefore, technological issues must always be addressed and remedied as soon as possible with the aid of qualified professionals. Subsequently, in the realization of Industry 4.0 technology, it is crucial for manufacturing companies to instruct their employees in the proper use of digital technology and intelligent machinery. Without enough training, they will be unable to handle these technologies appropriately, resulting in technical issues. Even after implementing Industry 4.0, it may be hard to boost the organization’s productivity without proper worker training [36]. However, businesses cannot always afford to spend much in employee training. As a result, they are unable to collect all characteristics of Industry 4.0 adoption.

9 Conclusion The research study summarizes adopting 4.0 by analyzing various literature reviews. The result of the various literature reviews shows that this 4.0 technology assists manufacturing organizations in improving their effectiveness. However, the large installation price, ongoing maintenance expenses and training costs are significant impediments to Industry 4.0 adoption. However, if businesses can educate workers about the advantages of modern innovations and convince them that they can utilize them simply, it would be simpler for enterprises to deploy Industry 4.0 effectively.

References 1. Aiello G, Giallanza A, Vacante S, Fasoli S, Mascarella G (2020) Propulsion monitoring system for digitized ship management: preliminary results from a case study. Procedia Manuf 42:16–23 2. Oztemel E, Gursev S (2020) Literature review of Industry 4.0 and related technologies. J Intell Manuf 31:127–182 3. Stock T, Seliger G (2016) Opportunities of sustainable manufacturing in Industry 4.0, vol 40. In: Seliger G, Malon J, Kohl H (eds) Procedia CIRP, proceedings of the 13th global conference on sustainable manufacturing—decoupling growth from resource use, Binh Duong, Vietnam, 16–18 Sept 2016. Elsevier BV, Amsterdam, The Netherlands, pp 536–541 4. Machado CG, Winroth MP, Ribeiro da Silva EHD (2020) Sustainable manufacturing in Industry 4.0: an emerging research agenda. Int J Prod Res 58:1462–1484 5. Zhong RY, Xu X, Klotz E, Newman ST (2017) Intelligent manufacturing in the context of Industry 4.0: a review. Engineering 3:616–630 6. Kim JH (2017) A review of cyber-physical system research relevant to the emerging IT trends: Industry 4.0, IoT, big data, and cloud computing. J Ind Integr Manag 2:1750011 7. Ejsmont K, Gladysz B, Kluczek A (2020) Impact of Industry 4.0 on sustainability-bibliometric literature review. Sustainability 12:5650 8. De Sousa Jabbour ABL, Jabbour CJC, Foropon C, Filho MG (2018) When titans meet—can Industry 4.0 revolutionize the environmentally-sustainable manufacturing wave? The role of critical success factors. Technol Forecast Soc Change 132:18–25

A New Transformation for Manufacturing Industries with Big Data …

95

9. Lee J, Azamfar M, Singh J (2019) A block chain enabled cyber-physical system architecture for Industry 4.0 manufacturing systems. Manuf Lett 20:34–39 10. Thramboulidis K, Vachtsevanou DC, Kontou I (2019) CPuS-IoT: a cyber-physical microservice and IoT-based framework for manufacturing assembly systems. Annu Rev Control 47:237–248 11. Ghobakhloo M, Fathi M (2019) Corporate survival in Industry 4.0 era: the enabling role of lean-digitized manufacturing. J Manuf Technol Manage 12. Butt J (2020) A strategic roadmap for the manufacturing industry to implement industry 4.0. Designs 4(2):11. https://doi.org/10.3390/designs4020011 13. Moktadir MA, Ali SM, Kusi-Sarpong S, Shaikh MAA (2018) Assessing challenges for implementing Industry 4.0: implications for process safety and environmental protection. Process Saf Environ Prot 117:730–741 14. Machado CG, Winroth MP, Ribeiro da Silva EHD (2020) Sustainable manufacturing in Industry 4.0: an emerging research agenda. Int J Prod Res 58(5):1462–1484 15. Sharma M, Kamble S, Mani V, Sehrawat R, Belhadi A, Sharma V (2021) Industry 4.0 adoption for sustainability in multi-tier manufacturing supply chain in emerging economies. J Clean Prod 281:125013 16. Strandhagen JW, Alfnes E, Strandhagen JO, Vallandingham LR (2017) The fit of Industry 4.0 applications in manufacturing logistics: a multiple case study. Adv Manuf 5(4):344–358 17. Fatorachian H, Kazemi H (2018) A critical investigation of Industry 4.0 in manufacturing: theoretical operationalization framework. Prod Plann Control 29(8):633–644 18. Zheng T, Ardolino M, Bacchetti A, Perona M, Zanardini M (2019) The impacts of Industry 4.0: a descriptive survey in the Italian manufacturing sector. J Manuf Technol Manage 19. Kolla S, Minufekr M, Plapper P (2019) Deriving essential components of lean and industry 4.0 assessment models for manufacturing SMEs. Procedia Cirp 81:753–758 20. Luthra S, Mangla SK (2018) Evaluating challenges to Industry 4.0 initiatives for supply chain sustainability in emerging economies. Process Saf Environ Prot 117:168–179 21. Bibby L, Dehe B (2018) Defining and assessing industry 4.0 maturity levels-case of the defence sector. Prod Plann Control 29(12):1030–1043 22. Mohamed M (2018) Challenges and benefits of Industry 4.0: an overview. Int J Supply Oper Manage 5(3):256–265 23. Dean M, Spoehr J (2018) The fourth industrial revolution and the future of manufacturing work in Australia: Challenges and opportunities. Labour Ind 28(3):166–181 24. Bellandi M, De Propris L, Santini E (2019) Industry 4.0+ challenges to local productive systems and place based integrated industrial policies. In: Transforming industrial policy for the digital age. Edward Elgar Publishing 25. Wang L, Wang G (2016) Big data in cyber-physical systems, digital manufacturing and Industry 4.0. Int J Eng Manuf IJEM 6:1–8 26. Hack-Polay D, Rahman M, Billah MM, Al-Sabbahy HZ (2020) Big data analytics and sustainable textile manufacturing: decision-making about the applications of biotechnologies in developing countries. Manag Decis 27. Lu Y, Xu X (2019) Cloud-based manufacturing equipment and big data analytics to enable on-demand manufacturing services. Robot Comput Integr Manuf 57:92–102 28. Li D, Tang H, Wang S, Liu C (2017) A big data enabled load-balancing control for smart manufacturing of Industry 4.0. Clust Comput 20:1855–1864 29. Collins K (2020) Cyber-physical production networks, real-time big data analytics, and cognitive automation in sustainable smart manufacturing. J Self-Gov Manag Econ 8:21–27 30. Plumpton D (2019) Cyber-physical systems, internet of things, and big data in Industry 4.0: digital manufacturing technologies, business process optimization, and sustainable organizational performance. Econ Manag Financ Mark 14:23–29 31. Giallanza A, Aiello G, Marannano G, Nigrelli V (2020) Industry 4.0: smart test bench for shipbuilding industry. Int J Interact Des Manuf (IJIDeM) 14:1525–1533 32. Borregan-Alvarado J, Alvarez-Meaza I, Cilleruelo-Carrasco E, Garechana-Anacabe GA (2020) Bibliometric analysis in Industry 4.0 and advanced manufacturing: what about the sustainable supply chain? Sustainability 12:7840

96

N. Hasan and M. Alam

33. Giallanza A, Aiello G, Marannano G (2021) Industry 4.0: advanced digital solutions implemented on a close power loop test bench. Procedia Comput Sci 180:93–101 34. Chalmeta R, Santos-de León NJ (2020) Sustainable supply chain in the era of Industry 4.0 and big data: a systematic analysis of literature and research. Sustainability 12:4108 35. Addo-Tenkorang R, Helo PT (2016) Big data applications in operations/supply-chain management: a literature review. Comput Ind Eng 101:528–543 36. Dubey R, Gunasekaran A, Childe SJ, Wamba SF, Papadopoulos T (2016) The impact of big data on world-class sustainable manufacturing. Int J Adv Manuf Technol 84:631–645

Analysis for Detection in MANETs: Security Perspective Taran Singh Bharati

Abstract In the emergency situations like war, natural calamity, disaster, etc., the existing networks may be demolished. Therefore in order to rehabilitate and perform the basic operations, a network is set up temporarily for time being is called ad hoc network. If its nodes are mobile, it is called a mobile adhoc network. Since these networks can pass every crucial and confidential information, they may suffer from many problems, e.g., security, routing, connectivity coverage, mobility of nodes, power back-up, etc. This paper lets you see into the security issues, specially securing it from intrusions into the network. There are many ways to secure these networks, i.e., encryption, game theory, genetic algorithms, SVM, mobile agents and hybrid way. Keywords Attacks · Intrusions · Detection system · Key management

1 Introduction There are some networks which are formed with the help of no additional infrastructure, and they are much suitable in emergency situations such as natural calamity, war, disaster, etc. Nodes in these networks have to do all works cooperatively by themselves like routing, managing, arbitrations, etc. Since these are wireless networks and made for time being therefore there so many vulnerabilities, security, privacy, topology, and energy issues. This network is susceptible to security attacks. Some security attacks are less dangerous and some are more disastrous because they are not only capable to see the contents of the messages but modify also. In order to detect and handle these attacks some specialized software tools are designed which collect the network operations’ data to analyze through intrusion detection engine. It has rules and threshold on the basis of various parameters which decide threshold. If threshold is beyond the set limit, an alarming section of intrusion detection system (IDS) declares the intrusion attack into the network by T. S. Bharati (B) Department of Computer Science, Jamia Millia Islamia, New Delhi, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 K. Chaudhary et al. (eds.), Intelligent Data Analytics in Business, Lecture Notes in Electrical Engineering 1068, https://doi.org/10.1007/978-981-99-5358-5_8

97

98

T. S. Bharati

sending the alarming messages. Intrusion Detection System is installed in router/ gateway to inspect all traffic passing through network. Depending on the installations positions, IDSs are categorized as host based, network based, etc. [1, 2]. Intrusion detection systems can be thought as statistical-based, anomaly-based, rulebased intrusion detections. To collect that data from all nodes into network a global data collector is used and different intrusion detection strategies are applied on the data to detect the intrusions [3–6]. Mobile ad hoc networks can be made secure in various ways, e.g., through data forwarding, key management schemes, routing, mobile agents, etc. [7–15]. There is some detection mechanism which uses the signatures of the previous intrusions to detect whether there is an intrusion attack or not. But the signature-based detection is not powerful enough to catch the new attacks whose signature patterns are not already stored in the database. Statistical-Based— the behavior pattern of the user is modeled and a threshold values is fixed to check the normality in the behavior, if the threshold is within the fixed limit, the user is assumed to be normal, abnormal otherwise. Agents-Based Intrusions—they are the mobile malicious codes which themselves install, collect, and report the activates of the host to the controllers. Game Theoretical-Based Intrusions—in order to detect the intrusions in network, these models can be used. Genetic algorithms—are ways to filter the more fit users from the population, other learning models, e.g., instance-based learning, neural networks, etc. Data Mining and Machine Learning Based—Analysis based on states—a system is modeled as a collection of states and system moves from one state to another. A system is supposed to reach the safe state after performing a task.

2 IDS-Based Agents in MANETs A local intrusion detection system (LIDS) from the management of information base (MIB), collects the information regarding the local activities performed. Agents have the properties that they are independent, resistant to failures, more, scalable. They also cause the network traffic reduction which is good for the restarting and offers the solution for the complex tasks [1, 7, 16–20]. Agents keep the information regarding the traffics flow and audit activities of the users. These activities are fed to detection engine of the IDS to detect the intrusion if any locally with the help if a database which stores the signature patterns of the intrusion attacks known so far. Anomaly Detection Module works along with other ADMs to the anomaly detection. To check that attack has the same signature pattern which is stored in the database, it is done by misuses detection module. Module for Secure Communication—this is used to setup the secure communicate to the LID, MIB, ADM, and MDM; Stationary Secure Database (SSD)—it is referred to ensure that attack is a new attack launched. There can be reactive, autonomous, commutative, temporary continuous, and cooperative [8]. Agents are self-adjustable, cooperative which send environmental data are called reactive, the agents who send senses every activity, some make dialogues to others, and some are wise which help in decision making.

Analysis for Detection in MANETs: Security Perspective

99

3 Architecture of Agents (i) Layered Architecture: Four-layered architecture reactive, notification, decision making, surveillance, are available [9]. (ii) Mobile Agents Distributed Architecture: Three types of architectures of the agents are cited for monitoring the activities at host level, to help in decision making and resolve the issue of detection if any. (iii) Agents’ Architecture using Watermarking and Fingerprinting: The architecture of agents keeps the modules of monitoring the user patterns, for generating the reports on the intrusion attacks, and detection. These agents need to be controlled for their operations which are done by transceivers [7].

4 Based IDSs Agents Security There is a protection of agents against platforms and platforms against agents. For mobile agents anomaly-based security classifiers are trained for some high information gain and the things [10, 11]: (i) Home Agent: This is installed before transmitting data to confirm attacks from its neighbors. Nodes share information between the neighbors. It can be understood as; the anomaly’s data is collected for user patterns. This data may not be directly processed because of presence of some unwanted noise, etc. There for preprocessing the collected data is needed. (ii) Analysis for Classifier: Different features are assigned some values and weights, and probabilities. They are arranged in feature vector. Bayesian classification having average probability p is used to classifying the system.

5 Intrusion Detections Various kinds of detections are found for detecting the anomalies to determine the unusual activities, misused-based detections. Sometimes no single method is capable enough; in that case, we have to combine methods to have hybrid kind of detection method. These are activities like congestion, paging, file server failure, broadcast flooding ensure the performance of the system. Some activities capture the system resources and hence are threat to availability. Anomaly detection at network level is in three steps; input data types, proximity measures, and labeling the date, classification methods, and feature identification [21, 22]: (i) State Transition-Based Intrusion Detection Rule-based expert system is used to detect the audit-based penetration in the system [4]. State represents some parameters, state of registers, values of volatile and permanent memory locations. It has initial state of intrusion detection in state

100

T. S. Bharati

diagram. There is a compromised state when, a state after completion of its task reaches to one of the compromised states. NetSTAT addresses the Network-based IDS (NIDS). It offers the security and protection to Network Security Officer (NSO). The NetSTAT’s architecture employs the fact-based and state transition scenario database. The intrusion detection using STAT tool has following [3, 23, 24]: (a) Audit Collection Mechanism: To collect the audit records, preprocessed to filter unnecessary noises or unwanted information. (b) Audit Record Preprocessor: Audit records are transformed into the form that is much suitable. (c) Rule-Based: Rules specifying penetrations are applied. (d) Fact-Based Initializer: Facts are initialized. (e) Fact-Based Updater: If new facts are encountered, fact database is updated. (f) Inference Engine: This applies the algorithms to see the changes in the states. (g) Detection Engine: It controls the design of expert system to make it up-todate without affecting knowledge base. (h) Decision Table SSO Interface: It tells the SSO for the decision whether system has been compromised or not. Genetic algorithms are used to select features to detect the misbehavior. The performance of intrusion detection depends on the quality of the fitness function [6, 25–31]: (i) Routing Security in MANETs: Anomaly detection in MANETs: in the routing, every node participates and every node suffers from the lack of power, etc. messages are moved by store and forward manner. Because of selfishness some nodes do not forward the other’s message ahead for routing. Node reputation, messages sent and received, energy level, packets dropped, residual energy, etc. are used select the neighbor node for routing. Ad hoc routing protocols are focused on anomaly detection model. Selfish nodes’ detection removal is done [32]: (a) On the basis of communication ratio: the percentage of reply messages sent by the destination to the sender. (b) For the routing in sequence, the nodes with high values of communication ration and energy range are chosen. (ii) Key Management in MANETs: Keys are needed for encryption, decryption, session protection, etc. They are managed with the help of trusted third party (TTP), Key Distribution Centre (KDC) and by Certification Authority (CA) in Centralized Key Management, Distributed Key Management, and Decentralized Key Management. Others are symmetric, group, hybrid key management [33]. Based on the chaotic map and heuristic search, an adaptive key generation for privacy preserving works by taking the data from edge computing, plaintext conversion into groups of 16 bits, a optimal key is generated [34].

Analysis for Detection in MANETs: Security Perspective Fig. 1 Analysis steps for intrusions in MANETs

101

Download Datasets Pere-processing the Data Feature Selection Detection Communication

(iii) Group Key Management and Secret Sharing for MANETs: Schemes keep the components; Dealer -assigns shares to all participants; combiner: -which combines the shares received from all participants to reconstruct the shared key. The secret sharing is of several types such as two party sharing and multiparty secret. General Sharing -there is a large integer n and are m people. Threshold Schemes (t, n)—there are n people in the network. For reconstructing the original key, at least t (t ≤ n) number of people is required. People less than t cannot reconstruct the secret key [35].

6 Methodology Validation is done on ns2 and ns3. Machine learning techniques, i.e., classification, clustering, genetic algorithms, etc. are also advised to be used. The same can be implemented in Python programming language also, as (Figs. 1, 2 and 3):

7 Conclusions and Discussions Mobile ad hoc networks are very useful in real life when no other existing network is in position to work. Because of their limitations and nature, its issues like privacy and security are assessed and analyzed. Intrusion detection techniques and their pros

102

T. S. Bharati

Series 1

30 25 20 15 10 5 0

Series 1

Fig. 2 Research papers’ collection and distribution

Machine learning based 24%

Signature based 12%

Anomaly based 16% Misbehaviour based 8% Agent based 24%

State Transition Analysis based 16%

Fig. 3 Analysis of literature

and cons, working and, solutions are proposed. MANETs can be implemented in Python/R/Matlab languages apart from network simulators.

References 1. Mishra A, Nadkarni K, Patcha A (2004) Intrusion detection in wireless ad hoc networks. Wirel Commun IEEE 11(1):48–60 2. Pieprzyk J, Hardjono T, Seberry J (2003) Fundamentals of computer security 3. KoralIligun RA, Kemmere (1995) State transition analysis: a rule-based intrusion detection approach. IEEE Trans Softw Eng XX(Y) 4. Vigna G, Kemmerer RA (1998) NetSTAT: a network-based intrusion detection approach. In: Proceedings of 14th annual computer security applications conference. IEEE, pp 25–34

Analysis for Detection in MANETs: Security Perspective

103

5. Zhang Y, Lee W, Huang YA (2003) Intrusion detection techniques for mobile wireless networks. Wirel Netw 9(5):545–556 6. El-Ghali M, Masri W (2009) Intrusion detection using signatures extracted from execution profiles. In: Proceedings of the 2009 ICSE workshop on software engineering for secure systems. IEEE Computer Society, pp 17–24 7. Paez R, Satizabal C, Forne J (2006) Cooperative itinerant agents (CIA): security scheme for intrusion detection systems. In: ICISP’06 International conference on internet surveillance and protection, 2006. IEEE, pp 26–26 8. Boughaci D, Ider K, Yahiaoui S (2007) Design and implementation of a misused intrusion detection system using autonomous and mobile agents. In: Proceedings of the 2007 Euro American conference on telematics and information systems. ACM, p 12 9. Bernardes MC, Moreira EDS (2000) Implementation of an intrusion detection system based on mobile agents. In: Proceedings of international symposium on software engineering for parallel and distributed systems, 2000. IEEE, pp 158–164 10. Zhou H, Li J, Zhao N, Dai F, Jiang R (2008) An intrusion detection system model for adhoc networks based on the Adjacent Agent. In: International conference on multimedia and information technology, 2008, pp 598–601 11. Esfandi A (2010) Efficient anomaly intrusion detection system in adhoc networks by mobile agents. In: 2010 3rd IEEE international conference on computer science and information technology (ICCSIT), vol 7. IEEE, pp 73–77 12. Ritonga MA, Nakayama M (2008) Manager-based architecture in ad hoc network intrusion detection system for fast detection time. In: International symposium on applications and the internet, 2008. 13. Butun I, Morgera SD, Sankar R (2014) A survey of intrusion detection systems in wireless sensor networks. Commun Surv Tutorials IEEE 16(1):266–282 14. Bharati TS (2018) MANETs and Its’ Security. Int J Comput Netw Wirel Commun (IJCNWC) 8(4):166–171 15. Bharati TS (2019) Trust based security of MANETs. Int J Innovative Technol Exploring Eng 8(8):792–795. ISSN 2278 3075 16. Deng H, Zeng QA, Agrawla DP (2003) SVM-based Intrusion detection system for wireless adhoc networks. IEEE 17. Sterne D, Balagusubramanyam P, Carman D, Wilson B, Talpade R, Ko C, Balupari R et al (2005) A general cooperative intrusion detection architecture for MANETs. In: Proceedings of the third IEEE international workshop on information assurance (IWIA’05) 18. Bharati TS, Kumar R (2015) Secure intrusion detection system for mobile adhoc networks. In: 2015 2nd International conference on computing for sustainable global development (INDIACom), pp 1257–1261, 11–13 Mar 2015, ISSN 0973-7529; ISBN 978-93-80544-14-4 19. Bharati TS (2015) Enhanced intrusion detection system for mobile adhoc networks using mobile agents with no manager. Int J Comput Appl 111(10):33–35 20. Bharati TS (2017) Agents to secure MANETs. Int J Adv Eng Res Dev 4(11):1267–1272. ISSN (o): 2348 4470 21. Bhuyan MH, Bhattacharyya DK, Kalita JK (2014) Network anomaly detection: methods, systems and tools. Commun Surv Tutorials IEEE 16(1):303–336 22. Ayman ELS (2014) a new hierarchical group key management based on clustering scheme for mobile ad Hoc Networks. Int J Adv Comput Sci Appl 5(4) 23. Kemmerer RA (1997) NSTAT: a model-based real-time network intrusion detection system. Computer Science Department, University of California, Santa Barbara, Report TRCS97-18. http://www.cs.ucsb.edu/TRs/TRCS97-18.html 24. Vigna G, Eckmann ST, Kemmerer RA (2000) The STAT tool suite. In: Proceedings of DARPA information survivability conference and exposition, DISCEX’00, vol 2. IEEE, pp 46–55 25. Nallusamy R, Jayarajan K, Duraiswamy K (2009) Intrusion detection in mobile ad hoc networks using GA based feature selection. arXiv preprint arXiv:0912.2843 26. Poonothai T, Duraiswamy K (2014) Cross layer intrusion detection system of mobile adhoc network using feature selection approach. WSEAS Trans Commun 13:71–79

104

T. S. Bharati

27. Chen TM, Venkataramanan V (2005) Dempster-shafer theory for intrusion detection in ad hoc networks. Internet Comput IEEE 9(6):35–41 28. Khalili A (2003) towards secure key distribution in truly ad hoc networks. In: Proceedings of the 2003 symposium on applications and the internet workshops (SAINT‘w03), 2003 29. Capkun S, Buttyán L, Hubaux J-P (2003) Self-organized public-key management for mobile ad hoc networks. IEEE Trans Mob Comput 2(1):52–64 30. Bharati TS, Kumar R (2015) Intrusion detection system for manet using machine learning and state transition analysis. Int J Comput Eng Technol 6(12):1–8 31. Bharati TS, Kumar R (2016) Enhanced key management for mobile adhoc networks. Int J Eng Sci Comput 6(4):4184–4187 32. Ponnusamy M (2021) Detection of selfish nodes through reputation model in mobile adhoc network-MANET. Turk J Comput Math Educ (TURCOMAT) 12(9):2404–2410 33. Wu B, Wu J, Cardei M (2007) A survey of key management in mobile ad hoc networks. In: Handbook of research on wireless security, vol 2, pp 2479–499 34. Pamarthi S, Narmadha R (2022) Adaptive key management-based cryptographic algorithm for privacy preservation in wireless mobile adhoc networks for IoT applications. Wirel Pers Commun 124(1):349–376 35. Huang D, Medhi D (2008) A Secure group key management scheme for hierarchical mobile adhic networks. Sci Direct Ad Hoc Netw 6:560–577

Exploring the Role of Business Intelligence Systems in IoT-Cloud Environment Manzoor Ansari and Mansaf Alam

Abstract In recent decades, due to digital business technology improvements, several internet-based market strategies have been developed. Many assets have increased dramatically in business intelligence, allowing for more automation and interaction with real-world objects. There has been a growing convergence of business and IoT-Cloud operations as most participants leverage IoT data to make decisions. Due to the rapidly developing economic environment and technological advancements, it is critical to have a clear perspective of the business and management significance of the IoT and Cloud ecosystem. Cloud-based IoT ecosystems have been analyzed, and there is rising interest in IoT-enabled smart devices to produce large amounts of data in large-scale production. Consequently, an integrated IoTenabled Cloud-based business analytics system is required to improve the performance and utilization of IoT devices. The IoT-Cloud system has been extensively adopted in numerous business sectors as technology and different Cloud services have been addressed. IoT-based Cloud Computing has gained much attention from several researchers and business analysts in numerous business applications. In this study, IoT-enabled Cloud-based business intelligence system has been proposed to analyze the impact on the business industry. This study concluded the business development using consumer-generated IoT data and highlighted the future scope of the business trends in the IoT-Cloud environment. In addition, statistical analysis reveals that IoTenabled Cloud-based business intelligence model enhances customer experience and business operations in a Cloud environment toward digital transformation. Keywords Internet of Things (IoT) · Cloud Computing · Business Intelligence · Air Pollution · Power BI

M. Ansari (B) · M. Alam Department of Computer Science, Jamia Millia Islamia University, New Delhi, India e-mail: [email protected] M. Alam e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 K. Chaudhary et al. (eds.), Intelligent Data Analytics in Business, Lecture Notes in Electrical Engineering 1068, https://doi.org/10.1007/978-981-99-5358-5_9

105

106

M. Ansari and M. Alam

1 Introduction In the recent Fourth Industrial Revolution, The Internet of Things (IoT) has brought a new era in which a global network of assets and technologies capable of communicating with one another drives digital entrepreneurship and innovation. IoT market for businesses has evolved to be the most significant of all industries in the world [1]. According to a recent report [2], 98% of respondents indicated that most businesses involve enterprise IoT initiatives in their business strategy road maps to continually improve operations, enhance production perceivability, empower business models, and develop new products and services to customers. Reviewing pertinent research material may assist researchers in acquiring an in-depth understanding of their field of study. As a result, it is critical to thoroughly evaluate relevant research literature prior to initiating data collecting and other critical research tasks. This study aims to assess the significance of Industry 4.0 in the industry. As a result, pertinent study literature such as books, papers, and journals have been evaluated to understand better the influence of Industry 4.0 on the manufacturing sector. Additionally, the researched material has aided in comprehending the advantages and drawbacks of implementing Industry 4.0 in manufacturing firms. This article summarizes the essential conclusions from the examined literature. Correspondingly, sophisticated systems for managing e-business need to provide and integrate large volumes of consumer-generated data more rapidly, improve the customer’s experience, and adapt to a more volatile world [3]. On the other hand, Cloud Computing is nothing new development in the IoT ecosystem. It has caused a significant transformation in business philosophy and service approaches. Cloud Computing offers enterprises of any sector the speed and accuracy they require [4]. A Cloud Computing service is a computer service distributed through the network. In addition, distributed computing, parallel processing, network storage, and configuration management are all forms of Cloud Computing. It is also one of the most promising approaches in information technology development [5]. Cloud business intelligence combines Cloud Computing with enterprise resource planning (ERP), leading to efficient visual analytics, prescriptive analytics, and cross statistical modeling [6]. Cloud business intelligence provides a modular approach with substantial capabilities that streamlines operations, reduces costs, and improves customer service [7]. Thus, adopting business intelligence in the IoT-Cloud ecosystem to develop e-commerce management has become a prominent research area.

Exploring the Role of Business Intelligence Systems in IoT-Cloud …

107

2 Motivation for the Business Intelligence Model in IoT-Cloud Environment Numerous conventional IoT-Cloud-enabled models cannot support a diverse set of current IoT data sources and real-time computing techniques to provide decisionmaking modeling with rapid and precise outcomes. In recent research, it has been stated that the deployment of monitoring systems, including communication networks, smartphones, and sensor systems, has resulted in the deterioration of several socio-techno-economic dimensions. Numerous challenges associated with given techniques include the following: • Managing heterogeneous methodologies (such as visualization, implementation, statistics, connectivity, and stability) • Providing reliable systems for business data analysis • Real-time access • Interoperability among resulting enabling technologies • Proposing a decision-making approach • Ensure security and privacy. Additionally, evaluate massive volumes of varied data in real time in response to various intelligent IoT-Cloud-enabled business services. As a result of these limitations, it becomes vital to have a holistic perspective of how many business services are monitored and controlled via the intelligent IoT data. This study aims to develop a comprehensive IoT-Cloud-based business paradigm system that enables real-time monitoring, statistical analysis, and decision-making strategy for intelligent IoT data. The IoT-Cloud business paradigm is widely regarded as a promising approach for fulfilling the growing demand for real-time business activity monitoring and appropriately handling growing volumes of raw sensor data.

3 Backgrounds and Related Work Several researchers have adopted various approaches to realize the benefits of current breakthroughs in Cloud Computing and IoT to develop business intelligence. Numerous efforts have been made to enable remote end-user monitoring and continuous collection of rich information to their physical environment using networked sensors embedded in their Cloud environments. We have shown related work in the domains of several IoT-Cloud-enabled business services. Additionally, this section attempts to describe the benefits, challenges, and quality parameters of IoT-Cloud-enabled business intelligence. In [8], the authors concluded that IoT devices may help businesses grow and emphasize the research’s potential scope. The descriptive approach has been used to evaluate the descriptive and inferential data in this content. Conceptual analysis of data has successfully been able to grasp the problem. This research will also assist corporate executives to comprehend

108

M. Ansari and M. Alam

the advantages and risks of this framework. The primary goal of this study [9] is to design a novel hybrid red deer-bird swarm method (RD-BSA) with improved convergence and less control parameters. The suggested privacy preservation approach is compared to existing privacy preservation approaches on three financial datasets. The algorithm’s efficiency is assessed using statistical, key sensitivity, KPA, and CPA evaluations. The suggested model’s performance is compared to other models in several different ways. In the literature [10], the authors have proposed a Customer Relationship Management (CRM) mechanism in the Cloud environment. CRM is becoming increasingly efficient due to the availability of comprehensive and reliable data on consumer behavior. Storing and analyzing that data is critical for sales and marketing because it facilitates direct sales efforts and can also reflect emerging market trends, new market forces, and ways for businesses to maintain a competitive edge. In contrast, others struggle to develop a mechanism to get a boost on Cloud Computing systems. According to the analysis [11], a Cloud ecosystem prototype model was provided to demonstrate the portals’ key aspects. Based on the value chain structure, there are three portal models for Cloud manufacturing environments. Cloud-based platforms relatively close analytics can develop manufacturing ecosystems where equipment owners, product designers, and consumers may collaborate and interact. This industry research Cloud ecosystem may assist firms in investigating transitioning to cloud manufacturing environments. Economic risk analysis has been implemented in [12]. To improve data quality management efforts is facilitating in the usage of key success indicators that are crucial to the individual’s goals and objectives. The statistical findings of the simulation study demonstrate enhanced performance with a 5 ms lag reaction time and improved revenue analysis with a 29.42% improvement over previous models, demonstrating the dependability of the proposed framework. This study [13] proposes a business model for IoT applications running on Cloud and fog resources. This business model has two components: flow (data and cooperation) and placement (specialized in processing and storage). The placement construct, on the other hand, is about what and how to fragment, where to store, and what constraints exist. The study also covers the design of a system based on deep learning that suggests how alternative flows and positioning should be implemented. The suggestions consider both the technical capabilities of Cloud and fog resources and the network architecture that connects them to objects. Table 1 shows the comparative analysis of related work.

4 The Business Intelligence Model in IoT-Cloud Environment In the last few decades, IoT and the Cloud have played a significant influence in the advancement of business and marketing. Several business intelligence models have been proposed by many researchers in IoT-Cloud integration. In Fig. 1, the Business Intelligence Cloud of Things (BICOT) model has been proposed to analyze

Exploring the Role of Business Intelligence Systems in IoT-Cloud …

109

Table 1 Comparative analysis of related work S. No.

Applications

Refs.

Authors

Year

Key contributions

Results

1

Banking

[14]

Casturi et al.

(2019)

• Focusing on identifying the most significant network and data security vulnerabilities on Cloud systems

• To propose many Cloud Computing applications in banking and e-commerce and security challenges

[15]

Arjun et al.

(2021)

• An analysis of prior • This framework’s work on intelligent potential for decision support models incorporating new for banking from 1970 technologies such as to 2020 is presented big data, IoT, VR, and other undiscovered conceptual associations

[16]

Onasanya et al.

(2017)

• Proposes an IoT-based healthcare system focusing on cancer care and business analytics/ Cloud services

• With this approach and methodology, healthcare practitioners can better understand and diagnose cancer patients using IoT data collected by sensor networks and other smart devices

[17]

Albahri et al.

(2021)

• Proposes an IoT telemedicine categorization taxonomy and examines related work in several disciplines

• The study addresses the uncertainty in IoT-based telemedicine developments

[18]

Weking et al.

(2020)

• Provides a framework for expressing, investigating, and categorizing BMs in Industrial Revolution 4.0. It reveals how Industrial Revolution 4.0 affects ecosystem roles, BMs, and service systems

• The taxonomy helps practitioners assess their current BM’s Industry 4.0 readiness. The patterns also show Industry 4.0 business opportunities

[19]

Rana et al.

(2021)

• Provides an overview of • This study describes the the publishing research IoT dilemma, remote on the Internet of sensor monitoring, and Things-based effective techniques assembling and arranging method in passionate ventures

2

3

Health care

Industry 4.0

(continued)

110

M. Ansari and M. Alam

Table 1 (continued) S. No.

Applications

Refs.

Authors

Year

Key contributions

Results

4

Others

[20]

Tan et al.

(2021)

• An autonomous three-layer framework (Q-Learning) has been developed to aid in the discovery of business processes and the rebuilding of services

• EDQL-BPR outperforms numerous sample business process reconstructions in terms of optimum model convergence, QoS optimality, effectiveness, and efficiency in an unpredictable Cloud context

[14]

Casturi et al.

(2019)

• A hybrid business intelligence model (HBIM) adheres to business and IT architecture

• Proposes an accounting hybrid architecture with best-in-industry powerful modeling, supported by a technical team, minimizing investment risk

the impact of the IoT-Cloud environment from a business perspective. The BICOT model contains different phases: user layer (consumer layer), network, intelligent edge, intelligent Cloud, and business intelligence. The consumer-generated IoT data is collected from various sources and then sent to the Cloud, where it can be managed and shared across enterprise systems, services, and domains. However, several challenges hinder the effective analysis and extraction of information for business and research purposes. To address this, appropriate analytical tools (such as MS Power BI, Tableau, Oracle BI, IBM Cognos Analytics) and algorithms such as time series and computational intelligence can be employed. Predictive analytics can utilise consumer-generated IoT data to assess service quality, vulnerability, statistics, bussiness trends and monitor business activities. By leveraging suitable analytics technologies, such as Cloud Computing, interactions and data transfers between service providers, business facilities, network infrastructure, and reporting solutions can be accelerated. Business intelligence (BI) plays a crucial role in gathering vital statistics from vast amounts of consumer-generated IoT data and transforming it into actionable information. This empowers manufacturers to make informed policy decisions while enhancing productivity and efficiency. From a business perspective, the IoT-Cloud framework is described in the following Layers: User Layer: This layer represents the users of the IoT system, including both individuals and businesses. It encompasses the applications and interfaces through which users interact with the IoT system, enabling them to access and control IoT devices and services. Network Layer: The network layer consists of the physical infrastructure and devices that form the IoT network. It includes sensors, actuators, industrial assets, and other physical entities that collect and transmit data. These devices are responsible for capturing and communicating data from the physical world to the IoT system.

Exploring the Role of Business Intelligence Systems in IoT-Cloud …

111

Intelligence Edge: The intelligence edge layer is responsible for processing and analysing data at the edge of the network. It includes IoT gateways that enable connectivity between IoT devices and the Cloud and peer Cloud services that provide edge computing capabilities. Edge services perform local data processing, filtering, and analysis, allowing for real-time decision-making and reducing latency. Intelligence Cloud: The intelligence Cloud layer represents the Cloud-Based infrastructure and services that support IoT operations. It encompasses various components such as IoT transformations and connectivity, device registry for managing IoT devices, device management for configuration and monitoring, API management for integrating with other systems, application logic for implementing business rules, analytics for data processing and insights generation, and security mechanisms including blockchain and IoT governance for ensuring data privacy, integrity, and compliance. Business Intelligence: The business intelligence layer leverages the data and insights generated by the IoT system to drive business decision-making and optimise operations. It involves integrating IoT data into enterprise applications, monitoring business activities in real-time, performing advanced analytics to uncover patterns and trends, utilising tools like Power BI for data visualisation and reporting, and leveraging time series analysis and exploration techniques to gain deeper insights into IoT data. In essence, the IoT-Cloud framework from a business perspective encompasses the user layer for user interaction, the network layer for physical entities and devices, the intelligence edge layer for edge processing, the intelligence Cloud layer for Cloud-Based services and analytics, and the business intelligence layer for leveraging IoT data for business insights and decision-making. Predictive analytics may consumer-generated IoT data from numerous services to determine service quality and vulnerability, statistics and business trends, and business activity monitoring and its advancement. It will enable business analytics and commercial professionals to maintain current on the latest trends and breakthroughs in service provider and company management via appropriate analytics technologies. Cloud Computing is helpful since it accelerates interactions and consumergenerated IoT data transfers between service and business facilities and network infrastructure and reporting solutions. Business intelligence (BI) gathers essential statistics from a broad range of immense amounts of consumer-generated IoT data. It transforms it into actionable information that allows manufacturers to make knowledgeable policy decisions while growing their productivity and efficiency. Additionally, numerous enterprise techniques, such as Power BI, dashboards, business activity monitoring, Business Analytics, and Time Series Explorer, will be necessary for businesses to leverage the IoT-Cloud ecosystem. Thus, several of the expected advantages for firms have included the following:

112

M. Ansari and M. Alam

Fig. 1 Business intelligence model in IoT-Cloud environment

4.1 Improved Consumer Satisfaction Integrating Cloud with IoT, business intelligence enables organizations to study and exploit consumer-generated IoT data to understand better and predict customer needs. With reliable data on individual consumer preferences, companies can offer highly customized marketing and promotional items that correspond with individual interests, enhancing customer satisfaction and engagement.

4.2 Potential Business Operations The potential for enhancing corporate operations through comprehensive analysis is immense and cross-sectoral. Industries can leverage IoT to improve production and supply chains by using connected devices inside the processes themselves. Similarly, producers may employ connected sensors to monitor agricultural production.

4.3 Enhanced Sustainability Due to the massive volume of information generated by intelligent, connected devices in terms of quickly rising data and sources, enterprises now have the chance to use

Exploring the Role of Business Intelligence Systems in IoT-Cloud …

113

business intelligence to increase profitability. Insights derived from data may be utilized to enhance current products and services.

4.4 Predict Emerging Trends BI enables the investigation of patterns, the prediction of trends, and forecasting of future events with higher certainty than ever before. BI enables access to these patterns throughout a company, ensuring that all relevant consumers and organizations are constantly informed of emerging trends.

5 Use Case Scenario Cloud-Based Power BI for Environment Monitoring Several IoT-Cloud-based BI solutions are available on the market in the digital business world, but Power BI is one of the most user-friendly statistics techniques. It is widely used in marketing, retail, statistics, analytics, human resource management, logistics, environment monitoring, and other industries sector to handle essentially any type of IoT sensing data. Increasingly, air pollution has become a significant concern in many emerging business sectors. In terms of the overall economy, air pollution has a significant impact. Approximately one-third of India’s annual GDP (or USD 95 billion) is supposed to be lost due to air pollution in Indian businesses. In this case study, IoT-enabled air pollution data has been gathered from the Central Pollution Control Board (CPCB) of India and stored onto the Cloud infrastructure [21]. This dataset contains a huge amount of air pollution dataset from the year 2015 to 2020, including the air quality index (AQI), station code, date, and state. It also contains a significant amount of air pollutants, which impact the ecological environment and influence business ethics. Annual average concentrations of five criterion pollutants (PM10 , PM2.5 , SO2 , NO2 , and CO) have been measured at continuous and manual sampling locations across India between 2015 and 2020, as depicted in Fig. 2. The overall trend of air pollution distribution indicates that pollution levels were maximum in 2019. Figure 3 depicts the AQI categories and the frequency with which they arise throughout the year. It is clear evidence that the AQI in the year 2019 (as shown in Fig. 5) is worse than in other years. Figure 3 also depicts the classification of the air quality index (AQI) into six categories: moderate, satisfactory, poor, very poor, good, and severe. The nature of overall AQI across all cities is explained in Fig. 4. The pattern of overall AQI is observed to be declining in nature, but AQI began to rise in Delhi, followed by Bengaluru. Metropolitan areas of India, such as Delhi, Bengaluru, Lucknow, Chennai, and Hyderabad, are considered one of the world’s

114

M. Ansari and M. Alam

Fig. 2 Measure of pollutants (No2 , CO, NH3 , PM10 , PM2.5 , SO2 ) by year

Fig. 3 Air quality index (AQI) by year

most polluted cities, with the standard air quality index being consistently violated on a daily basis.

Exploring the Role of Business Intelligence Systems in IoT-Cloud …

115

Fig. 4 Air quality index (AQI) by city

6 Conclusion and Future Directions Over the last few years, IoT and Cloud Computing have had a significant impact on business world. Cloud computing and the IoT have the potential to generate revenue for a business application due to their adaptability, versatility, and on-demand availability. Consequently, an IoT and its application deployment expand, and enormous volumes of IoT data will be generated, necessitating a wide range of computational aspects (such as business data analysis and statistics algorithm) and their integration into Cloud Computing platforms. Although IoT-Cloud-enabled industrial assets are technically feasible, it will require a new business model to implement the technology further and make our business more efficient. For the establishment of a

116

M. Ansari and M. Alam

Fig. 5 AQI by year (2019)

business model, other factors such as the business rules, regulations, and incentives of potential participants must be considered. This study is concerned with IoTenabled Cloud-based business intelligence models in order to better understand the theoretical aspects of the models. IoT-enabled Cloud-based effective business model system development is significantly affected by business intelligence, including business policy, intellectual assets, business activity monitoring, profitability analysis, and administrative purpose. It is essential to identify the technological and organizational resources of the industry while integrating business intelligence into the

Exploring the Role of Business Intelligence Systems in IoT-Cloud …

117

enterprise information system. Cloud-based business and IoT-enabled time series AI models will be deployed in further research, which will be made publicly available.

References 1. Nagy J, Oláh J, Erdei E, Máté D, Popp J (2018) The role and impact of Industry 4.0 and the Internet of things on the business strategy of the value chain—the case of Hungary. Sustainability 10(10):3491 2. Chui M, Ganesan V, Patel M (2017) Taking the pulse of enterprise IoT. McKinsey Global Institute. 3. Pantano E, Giglio S, Dennis C (2019) Making sense of consumers’ tweets: sentiment outcomes for fast fashion retailers through big data analytics. Int J Retail Distrib Manage 4. Tuli S, Gill SS, Xu M, Garraghan P, Bahsoon R, Dustdar S, Sakellariou R, Rana O, Buyya R, Casale G, Jennings NR (2022) HUNTER: AI based holistic resource management for sustainable cloud computing. J Syst Softw 184:111124 5. Zhang Y, Guan Z, Qian H, Xu L, Liu H, Wen Q, Sun L, Jiang J, Fan L, Ke M (2021) CloudRCA: a root cause analysis framework for cloud computing platforms. In: Proceedings of the 30th ACM international conference on information and knowledge management, pp 4373–4382 6. Rodríguez-Espíndola O, Chowdhury S, Dey PK, Albores P, Emrouznejad A (2022) Analysis of the adoption of emergent technologies for risk management in the era of digital manufacturing. Technol Forecast Soc Change 178:121562 7. Abosuliman SS, Almagrabi AO (2021) Routing and scheduling of intelligent autonomous vehicles in industrial logistics systems. Soft Comput 25(18):11975–11988 8. Vemuri VP, Naik VR, Chaudhary V, RameshBabu K, Mengstie M (2021) Analyzing the use of Internet of things (IoT) in artificial intelligence and its impact on business environment. Mater Today: Proceedings 9. Balashunmugaraja B, Ganeshbabu TR (2022) Privacy preservation of cloud data in business application enabled by multi-objective red deer-bird swarm algorithm. Knowl-Based Syst 236:107748 10. Vemula HL, Khichi T, Murugesan G Valderrama-Plasencia L, Salazar-Gonzales M, Ventayen RJM (2021) ROLE of cloud computing and its impact on consumer behavior in financial sector. Mater Today: Proc 11. Helo P, Hao Y, Toshev R, Boldosova V (2021) Cloud manufacturing ecosystem analysis and design. Robot Comput-Integr Manuf 67:102050 12. Shao C, Yang Y, Juneja S, Seetharam T (2022) IoT data visualization for business intelligence in corporate finance. Inf Process Manage 59(1):102736 13. Maamar Z, Al-Khafajiy M, Dohan M (2021) An IoT application business-model on top of cloud and fog nodes. In: International conference on advanced information networking and applications. Springer, Cham, pp 174–186 14. Casturi R, Sunderraman R (2019) Enterprise framework for business intelligence tools on cloud computing environment for investment platforms. In: Proceedings of the 9th international conference on information systems and technologies, pp 1–7 15. Arjun R, Kuanr A, Suprabha KR (2021) Developing banking intelligence in emerging markets: systematic review and agenda. Int J Inf Manage Data Insights 1(2):100026 16. Onasanya A, Elshakankiri M (2017) IoT implementation for cancer care and business analytics/ cloud services in healthcare systems. In: Proceedings of the10th international conference on utility and cloud computing, pp 205–206 17. Albahri AS, Alwan JK, Taha ZK, Ismail SF, Hamid RA, Zaidan AA, Albahri OS, Zaidan BB, Alamoodi AH, Alsalem, MA (2021) IoT-based telemedicine for disease prevention and health promotion: state-of-the-art. J Netw Comput Appl 173:102873

118

M. Ansari and M. Alam

18. Weking J, Stöcker M, Kowalkiewicz M, Böhm M, Krcmar H (2020) Leveraging industry 4.0—a business model pattern framework. Int J Product Econ 225:107588 19. Rana AK, Sharma S (2021) Industry 4.0 manufacturing based on IoT, cloud computing, and big data: manufacturing purpose scenario. In: Advances in communication and computational technology. Springer, Singapore, pp 1109–1119 20. Tan W, Huang L, Kataev MY, Sun Y, Zhao L, Zhu H, Xie N (2021) Method towards reconstructing collaborative business processes with cloud services using evolutionary deep Q-learning. J Ind Inf Integr 21:100189 21. Central pollution Control Board. Retrieved Feb 24 2022, from https://cpcb.nic.in/air-pol-lut ion/

Leading a Culturally Diverse Team in the Workplace Rajni Bala

Abstract In the competitive era it is important for managers to manage diverse teams in the organizations. It refers the way in which organizations ensure that diverse group members are valued and fairly treated in all areas like hiring, compensation, promotion, performance appraisal, customer service activities, etc. Today people from different cultural are coming together due to increase in merger, joint venture and strategic alliances. In the rapidly changing organizational environment, it has become much more important to understand diversity in the organizational contexts and take progressive strides toward a more inclusive, equitable and representative workforce. Today, managers are more concerned regarding how to manage diversity in organization. The study examines the impact of cultural diversity on teams and organizations and explores the qualities that the leaders should display in order to perfectly lead and manage multicultural teams. The study is exploratory in nature. The study targets the people belonging to different cultural and working as a team in different organizations. The data is collected through questionnaire from 70 respondents belonging to 10 diverse multicultural companies of Gurugram, Noida and Delhi. The study found that there is a positive impact of cultural diversity on teams and organizations. Working in a culturally diverse team in a culturally diverse organization helps in the development of the individual members in the multicultural organization. Leaders have a significant role to play in handling diversity-related matters in the organization and also have an influence over the performance of multicultural teams. Leadership qualities should be given a special attention in the multinational and multicultural organizations. Keywords Cultural diversity · Inclusion · Organizational environment · Identity groups · Workforce

R. Bala (B) Chitkara Business School, Chitkara University, Punjab, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 K. Chaudhary et al. (eds.), Intelligent Data Analytics in Business, Lecture Notes in Electrical Engineering 1068, https://doi.org/10.1007/978-981-99-5358-5_10

119

120

R. Bala

1 Introduction When two or more people of different cultures come together is called cultural diversity or a multicultural team. Culture is something which is shared among a group of people. Culture is organized and systematic. Culture can be learnt. An iceberg can be used as a popular metaphor to define what culture actually is. Dress, language, mannerism and architecture are all the outer facts of a culture. Generally cultural diversity is misunderstood as place of birth or ethnic background. These features portray some aspects of culture, but it is not the real picture. A cultural identity means that you feel a sense of belonging or deep connection with a certain cultural group, so much so that it is part of how you define yourself. MBI Framework for Managing Cultural Diversity in an Organization Mapping: Mapping is referred to as describing the characteristics of people and identifying the similarities and differences in a systematic and objective way. This helps in understanding the differences in the cultures among team members. Bridging: Bridging further involves three different processes which are: 1. Preparing: Preparing involves getting more motivated and confident in communicating with culturally diverse teammates which may be achieved by exploring information about other cultures, communicating with friends and colleagues from different cultures or through observations. 2. Decentering: Decentering refers to the shifting of the center of focus away from our own self while we convey or interpret a message. 3. Recentering: Recentering signifies establishing of a common view of a situation and shared norms for interactions. It is all about agreeing collectively to the available range of norms that can be accepted regarding the way team members interact with each other in an organization. Integrating: Integration involves empowering participation, resolving conflicts and combining the ideas.

2 Literature Review Bhatnagar [1] conducted a research on “A study of attitudes towards women managers in banks”. The data was collected from employees (136 male, 65 female) working at senior, middle, junior and clerical levels in banks. The study produced a portrayal of a lukewarm attitude toward women managers. The study described the resistant nature of men in accepting the worthwhile of women in the organizations as bosses. The reason for the slow entry of women in the corporate sector is the difference between maternal and work roles. Watson et al. [2] conducted a study on “Cultural Diversity’s impact on interaction process and performance comparing homogeneous and diverse groups”. The study

Leading a Culturally Diverse Team in the Workplace

121

was conducted on 173 undergraduates’ students. The study found that the cultural diversity leaving a negative impact on the performance and process in newly formed groups. Kochan et al. (2003) conducted study on “The effects of diversity on business performance: report of the diversity research network”. The study examined that diversity had impact on the group processes, but the nature of the effect depends upon whether the diversity was based on race or on gender of the employees. It was found that gender diversity increased constructive group decisions, and on the other hand, the racial diversity inhibited them. Woodard and Saini (2005) conducted study on “Comparative study of diversity management in USA and Indian organizations”. The study found that there is a wide difference between actual implementation and legal promise. In India, Women’s rights are not clearly defined. A woman faces several kind of discrimination from society, superiors and employees. Abdel Hakim [3] conducted a study on “leading culturally diverse teams in The United Arab Emirates”. That mainly focused on Group Information Technology organization with about 1500 employees. The study was conducted by using interview methods and then assessment of leadership and finally measuring the performance of team members over time in the organization. The study analyzed the advantages and disadvantages of the cultural diversity on the project team and the role of leadership in managing the multicultural teams in the workplace environment, which specified the use of a proper approach by the team leader to solve issues that arose during the research study. Kautish [4] conducted research on “Paradigm of Workforce Cultural Diversity and Human Resource Management”. The study discussed the various principles concerning cultural diversity in work environment and human resource management. This case study explains the cultural conflicts that might be faced by employees after they merge with other companies. This paper portrays the extent to which the managers and employees accept the cultural diversity in the organizations to be relating to the organization strategy. Shaban [5] conducted study on “Managing and Leading a diverse workforce: One of the main challenges in Management”. This research tells us that how diverse teams can be effectively lead and manage by managers. The study tell us that how teams with different age group, gender or different nationalities can be easily managed by managers. In study it was found that through training programs, a manager can easily manage a diverse team. Training programs should be designed in such a way that they can provide different ways that explain how diverse team members can respond to differences. Jacob et al. [6] conducted study on “The role of Cultural Diversity and how they impact work team performance”. The study was conducted on thirty-nine employees belonging to different countries and genders at Abu Dhabi University. The study found the negative and positive correlation between work team performance and cultural diversity. The employees working in Abu Dhabi University were satisfied with the working environment. They were not facing any issue as a part of multicultural team. But some employees were not satisfied with the working environment.

122

R. Bala

They were facing problems and state that diverse cultural clusters are formed in the organization that is hampering other team members’ decisions. Stah and Maznevski [7] conducted study on “Unravelling the effects of cultural diversity in teams: A retrospective of research on multicultural work groups and an agenda for future research”. The research was conducted to find that how cultural diversity can positively and negatively affect the team members. The study found that there is no direct impact of diverse cultural on team performance. But there is an indirect impact like conflict, creativity and cohesion among team members which is generated as a part of diverse cultural team.

3 Research Methodology 3.1 Rational of the Study The study was needed to further explore the concept of cultural diversity in the business environment in North Indian organizations, its effects on individuals, teams and how can it be effectively managed. Despite having already been researched a lot, this topic still lacks empirical researches thus reducing the reliabilities of their correctness in the Indian context. The study aim was fulfilled by reviewing the various available literature closely related to the topic of the research. This was followed by an exploratory research in order to gain more insight into the subject and to develop a smooth framework for further studies on the topic. This research paper aims to provide an empirical research framework approach to be used for the purpose of a deep and reliable study. It attempted to use this empirical method which can be further referred by different organizations as a base for developing different selection and training agendas and also by team leaders to review and revise their existing management styles and developing the relevant skills for the times to come.

3.2 Research Objectives • To explore the concept of cultural diversity in the business environment in North Indian organizations. • To study the impact of cultural diversity on teams and organizations. • To explore the qualities that the leaders should display in order to perfectly lead and manage multicultural teams.

Leading a Culturally Diverse Team in the Workplace

123

Table 1 Research design Sr. No.

Description

Contents

1

Area of population

Gurugram, Delhi and Noida

2

Sample area

10 corporate houses

3

Sample unit

People working as part of multicultural teams

4

Sample size

70 respondents

5

Sample technique

Convenience sampling

6

Research design

Exploratory

7

Data collection instrument

Questionnaire (five points Likert scale)

8

Respondents

Workers and employees

4 Data Analysis and Interpretation See Table 2.

5 Findings The study found that there are various training programs that the organizations provide to the employees to promote diversity and inclusion like Jumpstart Program, parental counseling, leader employee interactions, cultural training, diversity management program, etc. Majority of the people believe that their supervisors in the organizations are good at handling diversity related conflicts that might be a hurdle in the team decisions or dynamics. They also believe that their fellow employees understand their values and beliefs and make them feel involved in the organization. The act of making ethical, gender and religion-based jokes in the organizations is found to be extremely less tolerated which suggests the level of good conducts of the organizations for the employees. Majority of the employees believe that their organization’s policies make them feel valued and are totally inclusion focused for individuals of every cultural background. They are in favor that the promotion of the employees belonging to different cultural aspects in the organizations is fairly on the basis of their individual performances. The environment of the company is well suited to most of the employees for free expression of their ideas and beliefs, and also it makes the employees to easily understand the people of different ethnicities, race and cultures in the organization. A major proportion of the employees in the organizations believe that they feel confident enough to lead and work in a culturally diverse team in the respective organizations. Majority of the employees are of the view that cultural distance rarely affects the team dynamics in a culturally diverse organization, and a very less proportion employees find it to be never affecting team dynamics, and an equal proportion of them believe that cultural distance and relative status always or often affects the dynamics of the diverse team in an organization.

124

R. Bala

Table 2 Data analysis Statements

Strongly agree

Agree

Neutral

Disagree

Strongly disagree

Organization commitement to diversity and inclusion

27.1%

60%

5.7%

2.9%

4.3%

Equal respect to each culture and background

35.7%

51.4%

8.6%

1.4%

2.9%

Good training programs promote diversity and inclusion

27.1%

54.3%

15.7%

–

2.9%

Handling diversity matters perfectly by supervisors

25.7%

54.3%

15.7%

1.4%

2.9%

Colleagues 30% understand cultural values and beliefs

52.9%

14.3%

–

2.9%

Tolerance 35.7% regarding racial, ethnic and gender-based jokes in the firm

37.1%

22.9%

1.4%

2.9%

Employees feel valued and respected in the organization

28.6%

54.3%

14.3%

–

2.9%

Employees promotion is fair and based on performance

25.7%

47.1%

20%

7.1%

–

Firm provides open 24.3% environment to employees, and they can freely express their ideas and beliefs

54.3%

18.6%

–

2.9%

(continued)

Leading a Culturally Diverse Team in the Workplace

125

Table 2 (continued) Statements

Strongly agree

Agree

Neutral

Disagree

Strongly disagree

Organization understands the employee’s ethnic backgrounds

24.3%

52.9%

18.6%

1.4%

2.9%

Statements

Very confident

Fairly confident

Unsure

Not confident

Employees confidence to lead and work in a culturally diverse team

44.3%

41.4%

8.6%

5.7%

Statements

Always

Often

Occasionally

Rarely

Cultural distance or 21.4% relative status affects team dynamics

21.4%

20%

30%

Statements

Extremely helpful

Mildly helpful

Can’t say

Not helpful

Working in culturally diverse teams helps in individual development

54.3%

31.4%

12.9%

1.4%

Statements

Yes

May be

No

Employees having 38.6% experience of working in globally virtual teams

14.3%

47.1%

Leader role in 88.6% handling multicultural teams and their diversity-related issues

4.3%

7.1%

Most of the employees who participated in the research survey have no experiences of working in a globally virtual team, but on the other hand, 38% of the employees have an experience of working in a GVT which signifies that there are people in the organizations who have the knowledge of how global cultural diversity affects the team performance and individual members. People strongly believe that working in a culturally diverse team in a culturally diverse organization extremely helps in the development of the individual members in the teams in the multicultural organization.

126

R. Bala

Leaders play a significant role in handling diversity-related matters in the organization. They have an influence over the performance of multicultural teams, projects and organizations. This suggests that leadership qualities should be given a special attention in the multinational and multicultural organizations. There is no particular leadership quality that must be present in the leader to be successful in the process of leading and managing cultural diversity in any organization. This is because of the involvedness and complexity that are engaged in these kinds of teams. The major qualities that a leader must have are self-motivation, integrity, self-awareness, ethical believes, empathy, keen observer, flexible, unbiased, cooperative, monist, cordial and handle team irrespective of cultural difference.

6 Conclusion Cultural diversity exists in almost all organizations in the modern times, and there are no boundaries to this multiculturalism in the organizations, making it an important component of the business environment to be taken care of. The research brought to notice further surface research relevant topics which would include more concise and in-depth definitions of culture and cultural diversity, along with a standardized scale to measure and assess the success levels of such multicultural teams and development of qualities of team leaders.

References 1. Bhatnagar D (1987) A study of attitudes towards women officers in Banks, IIMA Working Papers, WP1987-04-01_00744, Indian Institute of Management Ahmedabad, Research and Publication Department 2. Watson WE, Kumar K, Michaelsen LK (1993) Cultural diversity’s impact on interaction process and performance: comparing homogeneous and diverse task groups. Acad Manag J 36(3):590– 602 3. Abdel Hakim M (2008) Leading culturally diverse teams in the United Arab Emirates, The British University in Dubai, Faculty of Business, supervised by Dr. Mohammed Dulaimi 4. Kautish P (2012) Paradigm of workforce cultural diversity and human resource management. Indian J Manage 5(1):37–39 5. Shaban A (2016) Managing and leading a diverse workforce: one of the main challenges in management. Procedia-Soc Behav Sci 230:76–84. https://www.researchgate.net/publication/ 308739249_Managing_and_Leading_a_Diverse_Workforce_One_of_the_Main_Challenges_ in_Management 6. Jacob C, Gaikar Vilas B, Paul Raj P (2020) The role of cultural diversity and how they impact work team performance. Int J Mech Eng Technol 11(9):11–22 7. Stahl GK, Maznevski ML (2021) Unraveling the effects of cultural diversity in teams: a retrospective of research on multicultural work groups and an agenda for future research. J Int Bus Stud 52:4–22

An Integrated Secure Blockchain and Deep Neural Network Framework for Better Agricultural Business Outcomes Disha Garg and Mansaf Alam

Abstract Agriculture is vital to the survival of humanity, as it involves manufacturing, security, traceability, and long-term resource management. With resources shrinking, it is more crucial than ever to develop technology that helps agriculture survive. Agriculture will gain greater autonomy and intelligence as a result of the integration of blockchain and deep learning for agricultural applications, which will be more efficient and optimal. Blockchain has various applications for agriculture business: food supply chain, logistics, and crowdsourcing in addition to others. The data imply that using blockchain for agricultural business reasons has a lot of advantages. IoT devices generate a lot of data, and a decentralized data structure is provided by blockchain for storing and retrieving this data, which is shared with a lot of untrustworthy parties. Deep learning is a powerful analytic tool that provides scalable and accurate data analysis. This combination allows for secure data and resource exchange across the network’s numerous devices in agriculture. We propose a secure framework based on deep neural networks and blockchain for securing and enhancing business benefits in agriculture through this research. This system can also reward farmers via smart contracts if they solve an issue that is shared by other farmers in the farmer community. Keywords Blockchain · IoT · Deep neural network · Data analytics · Business

1 Introduction There is a need for new techniques and innovations for current agricultural development in order to make the agriculture industry more transparent and accountable. Blockchain technology is one of the newer tools. In contrast to traditional centralized systems, a decentralized data structure is provided by blockchain to store and retrieve data that is shared among multiple unreliable parties. A malicious user cannot change D. Garg (B) · M. Alam Department of Computer Science, Jamia Millia Islamia, New Delhi, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 K. Chaudhary et al. (eds.), Intelligent Data Analytics in Business, Lecture Notes in Electrical Engineering 1068, https://doi.org/10.1007/978-981-99-5358-5_11

127

128

D. Garg and M. Alam

(or erase) information from a blockchain-based system because information is stored across multiple computers (or nodes), and all information blocks are connected to the previous information block in blockchain network. At the same time, the blockchain helps to securely store and validate sensitive information, which maintains trust in the decentralized network [1, 2]. The hash function, in particular, is employed as a critical element for data authentication because it is an algorithmic approach to generate unique IDs. Hash values can be included into a format of stored chain to maintain data integrity and verify whether the data has been tampered. In stored transactions, digital signatures are employed to validate the genuine identities of data senders and recipients [3]. Agriculture is one underserved industry that blockchain has the ability to completely transform. More importantly, it has an increasing number of challenges that we must address immediately. The agriculture sector can benefit from blockchain technology in a variety of ways, including supply chain management, waste reduction, plant disease image classification, soil and crop image classification, seed image classification, and so on. Existing blockchain platforms can be used to develop these applications, making them simple and quick to develop. Different computational and cryptographic strategies can be integrated into different deployment circumstances of these applications to enable flexibility to fulfill urgent user requirements. Blockchain technology may also be used to consolidate information on seed quality, measure crop growth, and trace the journey once the seed leaves the field. Blockchain can help overcome the issues that IoT systems present. Variety of applications is provided by IoT, i.e., agriculture, health care, energy, and transportation, as well as other industries can get benefit from incorporating IoT. IoT devices are expanding rapidly, yet there are numerous issues [4] associated with them. IoT devices are typically restricted in processing power and prone to security threats [5]. As a result, while building an IoT-based system, security is a critical consideration. The blockchain is one of the most promising technologies for addressing many IoT concerns (trust, adversary attack, and so on) [6]. The combination of blockchain with deep learning (DL) could be very promising for agricultural process automation. The usage of smart contracts is a critical component of connecting these two developments. The smart contracts are a key component of blockchain technology. These contracts are heavily used in the Ethereum system that creates an immutable ledger of data, similar to real-world contracts [7]. A smart contract ensures that money is handled in a secure and unrestricted manner. This is especially beneficial for farmers who are experiencing financial difficulties. Deep learning algorithms outperform shallow machine learning algorithms [4]. Other sectors where blockchain and deep learning have found applications include supply chain, energy management, and the stock market [5, 6, 7]. In this research, we propose a secure system based on blockchain and deep learning for providing solutions to fellow farmers in a farmer community and rewarding them using smart contracts. By exploiting the properties of blockchain, this system provides anonymity and data security. For agriculture data prediction, we used deep neural networks. Data is fed into the model in the form of images captured by IoT devices and fellow farmers. This data can be related to plant disease images, crop

An Integrated Secure Blockchain and Deep Neural Network Framework …

129

and seeds images, food supply chain, weather monitoring, and agriculture finance in addition to others. In this network, all of these images are automatically classified. This network is connected to a smart contract based on the blockchain. This smart contract allows farmers to register and be validated. They can find solutions by sharing faulty images via smart contracts, with a reward going from one farmer to another.

1.1 Motivation There have been various attempts crop disease prediction [8, 9], supply chain traceability [10], and greenhouse prediction [10]. The majority of them rely on mathematical models based on image processing to analyze data obtained in laboratories. Numerous problems have yet to be recognized by such methods, but many people have found answers. In such cases, getting the solutions straight from the farmers who adopted them is the greatest approach to share the answer and help the others suffering similar challenges. This necessitates the creation of a safe, efficient, and a cost-effective framework for resolving agricultural issues. We also discuss the benefits of combining blockchain and deep learning as a potential option for ensuring a secure and trustworthy framework.

2 Related Work In this section, we discuss the previous researches based on machine learning, deep learning, and blockchain.

2.1 Deep Learning (DL) in Agriculture In recent years, the use of deep learning in agriculture has yielded impressive outcomes. The deep convolutional neural network (DCNN) [11] was used to classify the radish as infected or healthy. To recognize plant varieties, Ghazi et al. [11] used a hybridization of transfer learning with well-known CNN architectures such as VGGNet, AlexNet, and GoogLeNet. Jwade et al. [12] developed an autonomous method that was 95.8% accurate in recognizing sheep kinds in a sheep environment. In the field of mowing grass and row crops, Steen et al. [13] can detect a barrier with excellent accuracy. It cannot, however, recognize humans or other distant things. Existing research, on the other hand, is limited by the lack of privacy, low accuracy, centralization, low latency, and a significant amount of data.

130

D. Garg and M. Alam

2.2 Blockchain in Agriculture Blockchain is a distributed ledger that allows untrusted parties in a decentralized network to share transactions or sensitive data. Food safety [14], food security [15], traceability for waste reduction [19] are all examples of blockchain applications in agriculture that benefit small-scale farmers. A contract management system based on a decentralized ledger is being developed in Italy which is described in [9] to give legal protection to agricultural laborers on a temporary basis. Furthermore, under this integrated system, salary payments can be made using cryptocurrency. Plastic bank [16] encourages the collection and recycling of plastic trash by using a blockchainbased token, resulting in a new solution for agricultural land cleanup. The most common application of blockchain technology in agriculture is product supply chain management traceability.

2.3 Integration of Blockchain and Deep Learning in Agriculture Blockchain integration in deep learning has recently received a lot of attention. Much research on deep learning and blockchain has witnessed tremendous growth in the realm of agriculture for numerous applications. Using cutting-edge technologies such as IoT, blockchain, and deep learning, Prince Waqas Khan et al. [17] suggested an efficient supply chain food traceability system for Industry 4.0. It enables the end-users to inspect the source and supply chain of the food before consuming it. Machine learning assimilation in Blockchain-based smart applications was discussed by Sudeep Tanwar et al. [18]. To analyze vulnerabilities on a blockchainbased network, SVMs, clustering, deep learning, and bagging techniques like as convolution neural networks (CNN) and long-short term memory (LSTM) can all be used according to the authors. Ali et al. [10] provide a methodology to predict greenhouse environment conditions for tomato production. The authors presented a method based on a wireless visual sensor network (WVSN), image processing, and machine learning for plant leaves disease detection. Farmers experience a variety of issues relating to agriculture. Farmers can share their difficulties with other farmers in these scenarios and receive solutions to their agricultural problems. Our strategy is secure and offers farmers with rewards for delivering answers. Some of our model highlights are shown here: • Our model addresses agricultural security concerns and limits. • For the farmers, our concept employs a reward-based system. • Our model ensures that as many farmers as possible are involved in the process, resulting in the best possible outcome. • Our model recognizes and suggests solutions to a variety of agricultural issues.

An Integrated Secure Blockchain and Deep Neural Network Framework …

131

3 An Integrated Secure Blockchain and Deep Neural Networks Framework Figure 1 depicts an integrated blockchain and deep learning proposed framework for interacting with other farmers. The suggested framework is divided into three phases: Phase 1: registration, Phase 2: transaction, and Phase 3: validation Phase 1 Registration: The farmer creates an account in order to participate in a network and complete the transaction. Every network transaction is verified. There is a database connected to the smart contract in which the information about the images and remedies suggested by other farmers is stored, allowing us to expand our reach to include new ailments and retrain our model to include more diseases. Farmers use the network to connect with other farmers to find a solution.

Crop and food

Food supply

Weather

Agricultural

production data

chain data

monitoring data

finance data

Data captured from IoT devices

Deep Neural Network

Smart contract Database

Farmers requesting solutions with payments

Farmers submitting solutions

Distributed network of farmer’s community

Fig. 1 Blockchain and deep neural network-based secure framework

132

D. Garg and M. Alam

Phase 2 Transaction: Everyone on the network has access to all of the solutions given by the farmers. The farmer has the choice of selecting which remedy is the most viable for his problem. He can then give the other farmer a fixed fee in exchange for the solution. Any farmer can either request that their problem be detected or contribute to the network by providing a solution to a specific problem. Phase 3 Validation: All of the images that the farmers provide are saved in a database that may be used to expand the reach of the deep learning model and train it. The consensus technique is Proof of Work (PoW) [3], and the model is based on the Ethereum network. In a decentralized situation, it allows all participants to reach a consensus on some occurrence. Proof of Work is the algorithm for confirming transactions and adding new blocks to the chain. Before getting solution from the network concerning the problem, the farmers must pay the needed amount as proof. The public and private keys assigned to each user are used to validate and sign every transaction. Deep neural networks are utilized in our system to classify images into multiple classes. All of the images are required to be identical in order to achieve the best results. The images might be of various sizes and resolutions, as well as different classes. To create the dataset, the images must be rescaled, resized, and color-toned by deep learning model. As a result, our method works well with a variety of image sizes and is then passed on to deep learning model for classification task. Our system directly targets rewards and solutions for agricultural challenges. Farmers can get motivated to become more active on the network and contribute more. For better solutions and rewards, this network would be strengthened and would improve the network’s accuracy and legitimacy.

4 Benefits of Implementing the Blockchain and Deep Learning in Agriculture Business Blockchain technology is an electronic platform that records transactions in real time. When participants in a blockchain network perform transactions, the time, date, nature, and cost of the exchange are all documented. Once all parties have acknowledged the information’s integrity, it is saved and made available to all others in an ongoing and irreversible manner. There are following benefits of implementing blockchain and deep learning in agriculture: Fig. 2 illustrates the benefits of blockchain and deep learning.

4.1 Improved Safety and Quality of Foods Food safety is becoming increasingly important around the world. To address food safety issues from a technical standpoint, people require a trusted food traceability system that can track and monitor the entire lifespan of food production, including

An Integrated Secure Blockchain and Deep Neural Network Framework …

133

Improved safety and quality of foods

Increased supply chain traceability

Increased farmer efficiency

More equitable payments to farmers

Fig. 2 Benefits of implementing blockchain and deep learning in agriculture

the processes of food raw material cultivation/breeding, processing, transporting, warehousing, and selling, among other things. To deliver meaningful insights, deep learning is applied to the data provided by the sensors [19]. Growers, inventors, producers, service providers, and merchants in the agriculture sector should be able to transparently access the data by storing it on the blockchain. Lin et al. [20] presented a blockchain-based and IoT-based open and ecological food traceability system. It is a reliable, self-organizing, open, and ecological environment for smart agriculture.

4.2 Increased Supply Chain Traceability Organizations that use deep learning and blockchain to manage their supply chains can collect and analyze data on how items are created or sourced, what raw materials they contain, how they are maintained, and so on. Every piece of information about a commodity can be kept in a blockchain-based system, making it available to all interested parties [3]. With the purpose of identifying raw material origins without releasing secret business information, Dos Santos et al. [21] developed a prototype for food product certification and traceability.

4.3 Increased Farmer Efficiency To collect data and manage procedures, most farmers presently utilize a combination of various apps built by software development companies, spreadsheets, and notes. However, sending this data to other service providers is not simple and takes a lot of effort. Farmers could use blockchain technology to keep all of their data in one

134

D. Garg and M. Alam

location, where it could be easily accessed by those who need it, simplifying the process and saving time and energy [22]. Zhang [23] proposes a digital voucher or cryptocurrency that may be used by farmers and enterprises to trade waste, energy, fertilizers, or raw materials. By encouraging farmers and businesses to collaborate in the production and commercialization of energy, this method might maximize the use of agricultural waste.

4.4 More Equitable Payments and Benefits to Farmers When a specific, previously specified condition is met, smart contracts based on the blockchain automatically initiate payments without imposing excessive transaction fees. It means that farmers may theoretically be paid for their goods as soon as they are delivered, without sacrificing a significant portion of their income. By allowing farmers to connect directly with retailers, smart contracts would eliminate the need for middlemen. They would be able to negotiate a better deal on their products as a result. Nishant Jha et al. [24] proposed a low-cost, efficient, and low-cost crop insurance system that will ensure that many farmers are insured and get crop insurance payouts on time.

5 Conclusion Blockchain is a new technology that is rapidly gaining popularity in agriculture. Despite the fact that technology has begun to transform several industries, it still has a long way to go. However, it is becoming evident that blockchain technology has applications in agriculture business. Deep neural network is used for problem prediction and analysis. Using the combination of deep neural networks and blockchain network, we proposed a framework for safeguarding and boosting agricultural yield in this article. Farmers who join the network have the option to earn rewards or bitcoin by assisting other farmers and providing solutions. Blockchain can be utilized in the food sector for supply chain traceability in a variety of ways because production goes via processing and final packaging to the customer.

References 1. Srinivas J, Das AK (2020) 9 Lightweight security protocols for blockchain technology. Cyber Defense Mech Secur Priv Challenges 131 2. Prieto J, Das AK, Ferretti S, Pinto A, Corchado JM (2020) Blockchain and applications. Springer, Berlin, Germany 3. Lin W, Huang X, Fang H, Wang V, Hua Y, Wang J, Yau L (2020) Blockchain technology in current agricultural systems: from techniques to applications. IEEE Access 8:143920–143937

An Integrated Secure Blockchain and Deep Neural Network Framework …

135

4. Anand T, Sinha S, Mandal M, Chamola V, Yu FR (2021) AgriSegNet: deep aerial semantic segmentation framework for IOT-assisted precision agriculture. IEEE Sens J 1 5. Hassija V, Chamola V, Garg S, Krishna DNG, Kaddoum G, Jayakody DNK (2020) A blockchain-based framework for lightweight data sharing and energy tradingin v2g network. IEEE Trans Veh Technol 69(6):5799–5812 6. Aggarwal S, Kumar N (2020) Blockchain 2.0: smart contracts. Adv Comput 121:301–322 7. Nasir IM, Khan MA, Armghan A, Javed MY (2020) SCNN: a secure convolutional neural network using blockchain. In: 2020 2nd International conference on computer and information sciences (ICCIS). IEEE, pp 1–5 8. Sa I, Chen Z, Popovi´c M, Khanna R, Liebisch F, Nieto J, Siegwart R (2017) Weednet: dense semantic weed classification using multispectral images and mav for smart farming. IEEE Robot Autom Lett 3(1):588–595 9. Pinna A, Ibba S (2018) A blockchain-based decentralized system for proper handling of temporary employment contracts. In: Proceedings of science information conference on Cham. Springer, Switzerland, 2018, pp 1231–1243 10. Ali A, Hassanein HS (2019) Wireless sensor network and deep learning for prediction greenhouse environments. In: 2019 International conference on smart applications, communications and networking (SmartNets), 2019, pp 1–5 11. Ghazi MM, Yanikoglu B, Aptoula E (2017) Plant identification using deep neural networks via optimization of transfer learning parameters. Neurocomputing 235:228–235 12. Jwade SA, Guzzomi A, Mian A (2019) On farm automatic sheep breed classification using deep learning. Comput Electron Agric 167. Article 105055 13. Steen KA, Christiansen P, Karstoft H, Jorgensen RN (2016) Using deep learning to challenge safety standard for highly autonomous machines in agriculture. J Imaging 2(1). Article no 6 14. Tian F (2017) A supply chain traceability system for food safety based on HACCP, blockchain & Internet of Things. In: Proceedings of international conference on service system service management, 2017, pp 16 15. Ahmed S, Broek NT (2017) Blockchain could boost food security Nature 550(7674):43 16. Katz D (2019) Plastic bank: launching social plastic revolution. Field Actions Sci Rep J Field Actions 19:96–99 17. Khan PW, Byun YC, Park N (2020) IoT-blockchain enabled optimized provenance system for food industry 4.0 using advanced deep learning. Sensors 20(10):2990 18. Tanwar S, Bhatia Q, Patel P, Kumari A, Singh PK, Hong WC (2019) Machine learning adoption in blockchain-based smart applications: the challenges, and a way forward. IEEE Access 8:474– 488 19. Ferrag MA, Shu L, Djallel H, Choo KKR (2021) Deep learning-based intrusion detection for distributed denial of service attack in agriculture 4.0. Electronics 10(11):1257 20. Lin J, Shen Z, Zhang A, Chai Y (2018) Blockchain and IoT based food traceability for smart agriculture. In: Proceedings of the 3rd international conference on crowd science and engineering, pp 1–6 21. dos Santos RB, Torrisi NM, Yamada ERK, Pantoni RP (2019) IGR token-raw material and ingredient certification of recipe based foods using smart contracts. In: Informatics, voll 6, no 1. Multidisciplinary Digital Publishing Institute, p 11 22. Torky M, Hassanein AE (2020) Integrating blockchain and the internet of things in precision agriculture: analysis, opportunities, and challenges. Comput Electron Agric 105476 23. Zhang D (2019) Application of blockchain technology in incentivizing efficient use of rural wastes: a case study on yitong system. Energy Procedia 158:6707–6714 24. Jha N, Prashar D, Khalaf OI, Alotaibi Y, Alsufyani A, Alghamdi S (2021) Blockchain based crop insurance: a decentralized insurance system for modernization of Indian farmers. Sustainability 13(16):8921

ASCII Strip-Based Novel Approach of Information Hiding in Frequency Domain Ravi Saini, Gaurav Kumar, Kamaldeep Mintwal, Rajkumar Yadav, and Rainu Nandal

Abstract Image steganography is used for hiding secret information as well as for copyright protection. A new approach for information hiding or copyright protection is proposed in this paper based on frequency domain and ASCII strip. A new concept of ASCII strip has been used in this paper. ASCII strip can contain secret information or copyright information. This strip is used as a watermark image in the proposed method. In embedding process, firstly the ASCII strip is generated, then host image is ruptured into sub-bands using DWT. After that, DCT is applied to the chosen sub-band, and ASCII strip is embedded. In extraction process, firstly DWT is used to conquer the watermarked image in four sub-bands, then DCT is performed on chosen sub-band to retrieve the strip and then ASCII strip is converted to hidden text information. Experimental results show that combining DWT-DCT with ASCII strip improves the imperceptibility and robustness. It also gives promising results when it was compared with other similar techniques. Keywords Image · Steganography · Discrete cosine transform · Discrete wavelet transform

1 Introduction Steganography is not a new approach to hide the information. It is used from very old times. At that time, physical steganography is used for the secret communication, but nowadays where digital communication is very popular, steganography is also digitized. Digital steganography is a process to camouflage the covert information in a digital file. This digital file can be a multimedia file like audio, video, image, or a text file. Image is considered as the most suitable and popular file to hide the information. Steganography can be used in many useful applications like copyright control of files, embedding individuals’ details in identity cards. It is also used by search engines for enhancing search quality and robustness of the image. Hiding information can be R. Saini (B) · G. Kumar · K. Mintwal · R. Yadav · R. Nandal UIET, MDU, Rohtak, Haryana, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 K. Chaudhary et al. (eds.), Intelligent Data Analytics in Business, Lecture Notes in Electrical Engineering 1068, https://doi.org/10.1007/978-981-99-5358-5_12

137

138

R. Saini et al.

used in secure transfer of furtive data of TV broadcasting agencies, TCP/IP packets, security agencies [1], embedding of checksum [2], and in medical imaging systems [3] where the patient information can be inserted in medical image. It will be very helpful for the patients that their details are embedded in the image. Cheddad [4] gives a method which protects the scanned document from adding fake information. With the help of this method, experts can find out the fake document. Mainly, there are two types of methods: one is spatial domain methods, and other is frequency domain methods. In spatial domain, insertion of the secret data into the image is done in the different bits of the pixel value. In embedding process of LSB, image quality is decreased when embedding is done in the LSB. Embedding at the last bit (1st LSB) cannot be visible to human eyes, but the size of secret data falls down. That is a tradeoff between data size and image quality. LSB embedding is no doubt a big milestone in the steganography, but its low security left researchers to research more, and they put it in the frequency domain. In these types of methods, secret data is embedded in a specific region of the image which makes it more robust. Complexity of these methods is more compared to spatial methods. Discrete Fourier transform (DFT), discrete cosine transform (DCT), and discrete wavelet transform (DWT) methods are used in frequency domain.

2 Related Work Ali Al-Haj [5] uses the DWT and DCT together for digital watermarking system. In this approach, the image is transformed by 2-level DWT transform and block-based DCT. Laskar [6] proposed a new approach which is used for ownership verification of the images. This approach uses 3-level DWT and DCT for watermarking. Results show that gain factor α improves the performance of the algorithm. Potdar [7] used an approach in which image is divided into sub-images. Compressed and encrypted secret data is also divided and embedded into sub-images. This method is very robust against image cropping attack. Lagrange Interpolating Polynomial is used to recover the data with encryption algorithm. Kumar [8] applies the DCT on the secret image to get DCT coefficients and then DWT is applied on both cover and host images. Author compares the proposed algorithm with the LSB method and shows that the proposed method has better PSNR values as compared to LSB method. Joshi [9] describes a watermark technique using DWT. In this method, image is decomposed to four sub-bands, and LL sub-band is used to embed the watermark. Author analyzes the results for different values of strength factor of watermark. DWT and DCT, these two transforms are used in many signal processing applications. A brief introduction about DWT and DCT related to watermarking is given in following subsections.

ASCII Strip-Based Novel Approach of Information Hiding …

139

Fig. 1 Image decomposed into DWT sub-bands

2.1 DWT Discrete wavelet transform is a mathematical tool for processing the images at multiple resolutions. For representing signals, wavelets are used as special functions [10]. Applying DWT on image will divide the image in four sub-bands LL, HL, LH, and HH as shown in Fig. 1. DWT splits the signals to low-frequency and high-frequency sub-bands [9]. DWT is very convenient for finding the region of the image for watermark insertion. Embedding watermark in lower sub-bands decreases the quality of the image but has better robustness. High sub-bands have the details of outlines of the objects of the image, and changes in these details are not visible to human eyes. In many DWT watermarking techniques, embedding is done in midbands HL and LH which improve the imperceptibility and robustness [11, 12].

2.2 DCT Discrete cosine transform transforms an image from spatial to frequency domain, i.e., converts a signal to frequency components [13]. DCT coefficients for output image ‘f ’ with input image ‘g’ are computed by Eq. 1. In the equation, input image has M × N pixels, g(x, y) is pixel’s intensity, and f (i, j) is DCT coefficient in DCT matrix. Image is divided into blocks in block-based DCT and then DCT applied to every block. Each block has three sub-bands, i.e., high-, mid-, and low-frequency sub-bands. Insertion of watermark is done in the midband because low-frequency components contain the main visible details and high-frequency components can be removed through compression. So inserting watermark in midband will not affect the visible quality and cannot be removed by compression or noise [14, 15]. / f (i, j ) =

2 M

/

    M−1 N −1 ∑∑ 2 (2y + 1) j π (2x + 1)i π cos g(x, y) cos αi α j N 2M 2N x=0 y=0 (1)

140

R. Saini et al.

where, √ 1/ 2 for i = 0 αi = 1 otherwise  √ 1/ 2 for j = 0 αj = 1 otherwise 

/ g(x, y) =

2 M

/

(2)

(3)

    M−1 N −1 2 ∑∑ (2y + 1) j π (2x + 1)i π cos αi α j f (i, j) cos N x=0 y=0 2M 2N (4)

3 Proposed Method In this section, ASCII strip-based new method is proposed. ASCII strip is a bitmap image which consists of black and white color pixels. This strip is generated from characters ASCII values. Firstly, the characters of the text information are converted to their corresponding ASCII values, then these values binary equivalents are computed. Finally, the binary equivalents formed the ASCII strip (where all ones represent the white color pixels and all zeros represent the black color pixels). This whole process is called as ASCII strip generation. Strip can be easily converted to text just by applying the generating process in reverse order. This process is called as ASCII strip conversion. This strip can be used as watermark for embedding in the images. An example of ASCII strip generator is given in Fig. 2 where a message ‘UIET MDU ROHTAK’ is converted to image (ASCII strip). ASCII strip shown in the example is used as a watermark for embedding process in this paper. Dimensions of this strip are 15 × 7, and it has 15 characters (including space). From this example, it is clear that this small size strip contains large information. An m × n size strip can contain ‘m’ characters, and ‘n’ is always 7 because ASCII code is of 7 bit. Benefit of using the strip is that any information like patient information, employee details, copyright, or other information can be converted to ASCII strip.

3.1 Embedding Algorithm Step 1: Generate the ASCII Strip (Watermark image) from the text information. Step 2: Perform DWT transform on the cover image to conquer the image in four sub-bands: LL, LH, HL and HH. Step 3: Split the HL Sub band in blocks of size 8 × 8.

ASCII Strip-Based Novel Approach of Information Hiding …

141

Fig. 2 ASCII strip generation of text

Step 4: Perform DCT transform on these blocks in selected sub band HL. Step 5: Create a vector of ones and zeros from the watermark. Step 6: Create two distinct pseudo random sequences that are used for embedding the bit ‘1’ and ‘0’. All elements in both pseudo random sequences should be equal to total elements in midband of the DCT (Discrete cosine transform) modified DWT (Discrete Wavelet transform) sub band. Step 7: Insert these pseudo random sequences PR_zero and PR_one, with α (gain factor), in 8 × 8 DCT block of chosen sub band. Embedding is done in DCT coefficient of the intermediate band. If M represents the matrix of the block for midband coefficient then, If watermark bit is ‘1’ then M’ is given by Eq. 5 else it is shown by Eq. 6. M ' = M + α ∗ PR_one

(5)

M ' = M + α ∗ PR_ zero

(6)

Step 8: Perform Inverse DCT to every DCT block after embedding the bits in the DCT block’s midband. Step 9: Perform Inverse DWT on the modified image which include the updated subband that gives output as watermarked image. The flowchart of the embedding algorithm is given in Fig. 3.

142

R. Saini et al.

Fig. 3 Flowchart of embedding algorithm

3.2 Extraction Algorithm This DWT-DCT based algorithm doesn’t require the host image to retrieve the hidden image or watermark. Hence it is a blind algorithm. The flowchart of extracting algorithm is shown in Fig. 4.

Fig. 4 Flowchart of extraction algorithm

ASCII Strip-Based Novel Approach of Information Hiding …

143

Step 1: Split the watermarked image by performing DWT into four sub bands: LL, LH, HL and HH. Step 2: Create blocks of size 8 × 8 by dividing the HL sub band. Step 3: Perform DCT transform in HL sub band on every block and compute mid band coefficient of every block. Step 4: Generate the pseudo random sequences PR_one and PR_zero (same as in embedding process). Step 5: Find out the relation between the pseudo random sequences PR_one and PR_ zero and the mid band coefficient in every block of HL sub band. If the correlation with sequence PR_one is greater than PR_zero correlation than the watermark bit is taken as ‘1’ otherwise ‘0’. Step 6: Regenerate the Watermark (ASCII Strip) from the extracted bits. Step 7: Generate text information or copyright information from the ASCII Strip.

4 Results and Analysis For experimental results, images used in this paper are Boat, Moon Surface, Lena, and Airplane. All these are 512 × 512 grayscale images. ASCII strip is used as watermark image. There are two main criterion for measuring the performance of a method, imperceptibility and robustness. Imperceptibility means after inlay the strip (watermark) into the cover image, the image should not be distorted. PSNR values are used to measure the imperceptibility of the new method. PSNR value is given by following equation.  PSNR = 10log10

(2b − 1)2 MSE

 (7)

MSE calculates the average of the square of deflection between the original and the stego images. These values are kept low because near to zero values are better. The equation of MSE is given below: MSE =

N ∑ M ∑  2 1 X i j − Yi j . 2 [N × M] i=1 j=1

(8)

Recovered watermark from the noisy watermarked image shows the robustness of the method. Robustness of the proposed method is measured by correlation coefficient given by Eq. 9.

144

R. Saini et al.

∑ ∑  x

Correlation Coefficient = /

∑ ∑  x

y

y

Ox y

  Ox y − O ' Rx y − R ' 2  ∑ ∑    ' 2 − O' x y Rx y − R

(9)

where O is the original watermark, R is the recovered watermark, and O' and R' are the mean of the original and recovered watermark, respectively. Analysis of the new method is done on the basis of visual analysis, peak signal-to-noise ratio (PSNR) values, and correlation coefficient. In Table 1, results of the watermarked images are compared for three values of α. α is used in the DCT block of HL sub-band for the performance comparison of the new method. Comparison between DWT method and proposed method is shown in Table 2 in which proposed method provides better PSNR values. The higher value of PSNR for our proposed method shows that it provides very less degradation in image quality after insertion of ASCII strip. Table 3 shows the similarity between the original and recovered watermark by correlation coefficient. When correlation coefficient is ‘1’, then original and recovered watermark are identical (no error). The experimental results and visual analysis of the watermarked images histograms show that the new method performance is quite satisfactory. From the analysis of these results, it is observed that the suitable value for α is ‘8’ for the proposed algorithm (when no noise added). Values of α above ‘8’ give low PSNR and higher distortion in the image but retrieve the exact watermark (ASCII strip). Values below ‘20’ give higher PSNR and lower distortion but do not retrieve the exact watermark (ASCII strip). After adding noise to the watermarked image, α equals to 20 shows the better results in terms of robustness (Figs. 5, 6, 7 and 8). Table 1 Performance analysis of the proposed method for three distinct values of α Image

α

PSNR

MSE

Boat

8

53.1020

0.31832

Boat

10

51.3978

0.47129

Boat

20

45.4206

1.86640

Moon surface

8

52.9651

0.31820

Moon surface

10

51.2610

0.47120

Moon surface

20

45.2841

1.8660

Airplane

8

52.9300

0.31830

Airplane

10

51.2258

0.47129

Airplane

20

45.2841

1.8660

Lena

8

52.7545

0.31833

Lena

10

51.0503

0.47130

Lena

20

45.0732

1.86642

ASCII Strip-Based Novel Approach of Information Hiding …

145

Table 2 Performance comparison between DWT method and proposed method Image

DWT method PSNR

Proposed method PSNR

Boat

31.3143

53.1020

Moon surface

31.1770

52.9651

Airplane

31.1423

52.9300

Lena

30.9668

52.7545

Table 3 Similarity between original watermark and recovered watermark after adding noise to watermarked image Image Boat

Moon Surface

Lena

Airplane

Noise

Correlation coefficient α = 10

α = 20

No noise

1

1

Gaussian noise (Mean = 0, Variance = 0.01)

0.7760

0.9815

Gaussian noise (Mean = 0.05, Variance = 0.01)

0.7991

1

Poisson

0.9817

1

Salt & pepper (density = 0.01)

0.9638

1

Salt & pepper (density = 0.05)

0.7205

0.9258

No noise

1

1

Gaussian noise (Mean = 0, Variance = 0.01)

0.8135

0.9817

Gaussian noise (Mean = 0.05, Variance = 0.01)

0.8143

1

Poisson

0.9815

1

Salt & pepper (density = 0.01)

0.9258

1

Salt & pepper (density = 0.05)

0.5713

0.9272

No noise

1

1

Gaussian noise (Mean = 0, Variance = 0.01)

0.7386

1

Gaussian noise (Mean = 0.05, Variance = 0.01)

0.8135

0.9815

Poisson

1

1

Salt & pepper (density = 0.01)

0.9452

1

Salt & pepper (density = 0.05)

0.5463

0.9258

No noise

1

1

Gaussian noise (Mean = 0, Variance = 0.01)

0.7951

0.9633

Gaussian noise (Mean = 0.05, Variance = 0.01)

0.7575

0.9815

Poisson

0.9815

1

Salt and pepper (density = 0.01)

0.9452

1

Salt and pepper (density = 0.05)

0.6262

0.9815

146

R. Saini et al.

Fig. 5 a Cover image boat, b histogram of cover image boat, c, d, e are the histograms of watermarked image Boat when alpha is 8, 10, and 20, respectively

Fig. 6 a Cover image moon surface, b histogram of cover image moon surface, c, d, e are the histograms of watermarked image moon surface when alpha is 8, 10, and 20, respectively

ASCII Strip-Based Novel Approach of Information Hiding …

147

Fig. 7 a Cover image Lena, b histogram of cover image Lena, c, d, e are the histograms of watermarked image Lena when alpha is 8, 10, and 20, respectively

Fig. 8 a Cover image airplane, b histogram of cover image airplane, c, d, e are the histograms of watermarked image airplane when α is 8, 10, and 20, respectively

148

R. Saini et al.

5 Conclusion and Future Scope A new DWT-DCT-based approach is presented in this paper for information hiding in digital images. This work concludes that proposed algorithm provides large embedding capacity because a small size ASCII strip contains more information. For example, a 15 × 7 size ASCII strip contains 15 characters. It also provides more security because to retrieve the information, ASCII strip converter is required. Observation shows that suitable value for α is ‘8’ for the proposed algorithm (when no noise added). Suitable value for α provides better imperceptibility. When α is ‘20’, it shows better results in terms of robustness. Comparison of DWT method and proposed method on various images shows that the performance of the new method is good enough. All the analyses are done on the grayscale images. The algorithm can be extended further more by increasing the levels of DWT.

References 1. Johnson N (1998) Jajodia, S: Exploring steganography: seeing the unseen. IEEE Comput 5(1):26–34 2. Bender W, Butera W, Gruhl D, Hwang R, Paiz F, Pogreb S (2000) Applications for data hiding. IBM Syst J 39(3):547–568 3. Katzenbeisser S, Petitolas F (2000) Information hiding techniques for steganography and digital watermarking. EDPACS 28(6):1–2 4. Cheddad A, Condell J, Curran K, Kevitt PM (2009) A secure and improved self-embedding algorithm to combat digital document forgery. Sign Process 89(12):2324–2332 5. Al-Haj A (2007) Combined DWT-DCT Digital Image Watermarking. J Comput Sci 3(9):740– 746 6. Laskar RH, Choudhury M, Chakraborty K, Chakraborty S (2011) A joint DWT-DCT based robust digital watermarking algorithm for ownership verification of digital images: communications in computer and information science computer networks and intelligent computing. 4(9):482–491 7. Potdar VM, Han S, Chang E (2005) Fingerprinted secret sharing steganography for robustness against image cropping attacks. In: Proceedings of IEEE third international conference on industrial informatics (INDIN), pp 717–724 8. Kumar V, Kumar D (2010) Digital image steganography based on combination of DCT and DWT. Inf Commun Technol 13(9):596–601 9. Joshi K, Yadav R, Yadav A (2016) An additive watermarking technique in gray scale images using discrete wavelet transformation and its analysis on watermark strength. Int J Comput Inf Eng 10(7):1428–1433 10. Vetterli V, Martin S, Kovacevic A, Jelena H (1996) Wavelets and subband coding. J Electron Imaging 23(4):203–212 11. Wang S, Lin Y (2004) Wavelet tree quantization for copyright protection watermarking. IEEE Trans Image Process 13(2):154–165 12. Tay R, Havlicek J (2002) Image watermarking using wavelets. MWSCAS 163–169 13. Ranga RK, Yip P (1990) Discrete cosine transform. Boston Academic Press 14. Chu W (2003) DCT-based image watermarking using Subsampling. IEEE Trans Multimedia 5(1):34–38 15. Lin S, Chin FC (2000) A robust DCT-based watermarking for copyright protection. IEEE Trans Consum Electron 46(3):415–421

ASCII Strip-Based Novel Approach of Information Hiding …

149

16. Yadav R, Saini R (2011) Biometric template security using invisible watermarking with minimum degradation in quality of template. Int J Comput Sci Eng 3(1):3656–3668 17. Younus ZS, Hussain MK (2019) Image steganography using exploiting modification direction for compressed encrypted data. J King Saudi Univ Comput Inf Sci 8(1):1–11 18. Rajendiran P, Sekar KR, Ramesh C, Manikandan R (2019) A novel steganography methodology applied in transposition cipher for hiding text message. Int J Emerg Technol 156–158

Investigating the Mediating Effect of eWOM While Exploring a Destination in Uttarakhand: A Tourist Perspective Anuj, Rajesh Kumar Upadhyay, Himanshu Kargeti, Madhur Thapliyal, and Himani Kargeti

Abstract This article is an endeavor to examine the impact of eWOM on travel intentions through traveler involvement and source credibility for the potential tourist. Data was gathered from 293 participants who represented a cross-section of eWOM users and evaluated with SEM, CFA, and reliability. Outcomes depict a significant and positive association amid traveler involvement, source credibility, eWOM, and travel intention. Direct and indirect effects depict that eWOM fully mediates the relation between traveler involvement and source credibility on travel intention. Further conclusion, future research directions, implications, and limitations of the study are discussed. Keywords Traveler involvement · Source credibility · eWOM · Travel intention

1 Introduction Technology and internet services have given conventional markets and customers a new dimension by giving several benefits. The internet has aided customers in receiving accurate information. Consumers may now acquire extreme inclinations with greater ease because to the increasing operating rate and evolution of the internet, which is working in their favor. With the expansion of the internet, electronic word Anuj · H. Kargeti School of Management, Graphic Era Hill University, Dehradun, Uttarakhand, India R. K. Upadhyay (B) College of Business Studies, COER University, Roorkee, India e-mail: [email protected] H. Kargeti Department of Management, St. Andrews College of Technology and Management, Gurgaon, India e-mail: [email protected] M. Thapliyal School of Computing, Graphic Era Hill University, Dehradun, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 K. Chaudhary et al. (eds.), Intelligent Data Analytics in Business, Lecture Notes in Electrical Engineering 1068, https://doi.org/10.1007/978-981-99-5358-5_13

151

152

Anuj et al.

of mouth (eWOM) has become a significant component of the customer feedback and review system [10]. eWOM, which is made up of prepared letters or phrases, symbols, and informal information, is said to be the greatest type of marketing since it gives more accurate and unbiased information than traditional marketing tactics or commercials [3]. If marketers want to establish effective marketing strategies, they must precisely determine the emphasis of traveler involvement. Travel content creators should be paid attention to by online travel marketers since they will influence others and, as a result, have the ability to increase sales [17]. eWOM mainly happens through public and indirect interactions between persons with weak social links, whereas conventional word of mouth (WOM) contacts usually occur among sender and recipient with high bond [18]. As a result, customers may have difficulties determining the credibility of a suggestion source. Credibility is critical for eWOM message appraisal, according to previous study [15, 22]. The intrinsic influence of eWOM on customer behavior intentions might be greater than traditional WOM. For many reasons like its influence on promotion tactics, influential impact on decisionmaking, and influential effect on travel intention, eWOM communication has gotten a lot of attention in recent years [12]. This article is an attempt to examine the relationship between traveler involvement, source credibility on eWOM, and how it affects travel intentions of tourists. The mediating effects of eWOM on travel intention are also explored in this article.

2 Hypothesis Formulation and Conceptual Framework 2.1 Traveler Involvement (TI) and Electronic Word of Mouth (eWOM) The concept of involvement is applicable to a wide range of consumer behaviors and marketing situations. According to [4], involvement is a notion that has been frequently used in many activities like sports, shopping, leisure, internet use, and traveling. The level of commitment a person has to a certain activity or product is measured by involvement [14]. Jamrozy et al. [13] found that highly involved tourists are more likely to share knowledge, because most travelers today seek for tourist destinations on the internet, their interest in visiting a tourist location begins with good tourist information online, and the participation of travelers in transmission of information about tourist excursions is quite beneficial. Travelers’ involvement plays an imperative role while opting for tourism products [4]. Thus, the hypothesis is formulated as: H1: Traveler Involvement (TI) has positive and significant effect on Electronic Word of Mouth (eWOM).

Investigating the Mediating Effect of eWOM While Exploring …

153

2.2 Source Credibility (SC) and Electronic Word of Mouth (eWOM) The persuasiveness and impact of eWOM messages on the receiver can be influenced by the characteristics of the information source. Previous research has looked at the influence of source credibility on information adoption [5], as well as the credibility influence on information acceptance [19]. Source expertise has been proven to alter the impression of the information source in previous research (Pan 2014). Thus, source credibility is a pivotal factor for making decision based on eWOM among travelers, therefore, hypothesis is formulated as: H2: Source Credibility (SC) has positive and significant effect on Electronic Word of Mouth (eWOM).

2.3 Electronic Word of Mouth (eWOM) and Travel Intention (TI) According to [7], the underlying influence of eWOM on customers’ behavioral intents can be highly significant than conventional WOM. This backs with earlier travel and leisure eWOM research that suggests favorable online reviews may enhance users’ travel intention [6]. Positive eWOM reviews have the most impact on the likelihood of repurchasing and travel intentions [9]. Thus, hypothesis is proposed as: H3: Electronic Word of Mouth (eWOM) has positive and substantial effect on Travel Intention (TI).

2.4 Electronic Word of Mouth (eWOM) as Mediator Individuals who are passionate about a product or service have a better comprehension of it, digest product messaging more consciously or deliberately, discuss it with others, help in making better service/product choices, and favor it as a substantial basis of pleasure [4]. Their involvement encourages them to share their views/experiences which influence travel intentions of others. Because a trustworthy reviewer demonstrates a high degree of impartiality and honesty, the recipient cannot doubt the accuracy of the online reviews/comments presented. Thus, eWOM is influenced from both traveler involvement and source credibility, therefore, the following hypotheses are proposed. H4: Electronic Word of Mouth (eWOM) significantly and positively mediates the relationship of Traveler Involvement (TI) and Travel Intention (TI). H5: Electronic Word of Mouth (eWOM) significantly and positively mediates the relationship of Source Credibility (SC) and Travel Intention (TI).

154

Anuj et al.

Fig. 1 Hypothesized model

2.5 Theoretical Model A hypothetical model is developed based on predicted literature in order to find the link between selected variables. To evaluate the expected model, Likert scale (Five points) is utilized, ranging from strong disagreement (1) to strong agreement (5). Traveler involvement was measured using five points adopted from [16]. Source credibility was measured using 4 items adopted from [21]. eWOM was measured using three items taken from Wixom and Todd [20], whereas travel intention is examined with three points adopted from Jalilv and Samiei [12] (Fig. 1).

3 Methodology 3.1 Data Collection and Sampling Data was gathered via a self-directed questionnaire. Data was collected from academicians and postgraduate students at universities and institutes in the Indian state of Uttarakhand. The students who were active on social media completed the questionnaire as technology users. The random sample approach was used in this study, and data was gathered from September 2021 to November 2021 through online mode. Table 1 demonstrates respondent’s demographic profile.

Investigating the Mediating Effect of eWOM While Exploring … Table 1 Demographics profile of respondents Gender Age

Profile

155

Particular

Frequency

Male

168

Female

125

20–25 years

162

26–40 years

64

41–55 years

53

56 and above

14

Academicians

121

Postgraduate students

172

Total

293

AVE—Average variance extracted MSV—Maximum shared variance CR—Composite reliability ***p 0.7, AVE > 0.5, and CR > AVE circumstances. As specified by Hair et al. [11] for discriminant validity, the model also meets the requirements of AVE > MSV and AVE > 0.5.

4.2 Structural Equation Modeling: Hypothesis Testing Due to mediating effects and to evaluate the hypothesis, structural equation modeling is conducted after a confirmatory factor analysis. Table 3 shows the findings of all hypotheses. H1 is that Traveler Involvement (TIT) has a significant and positive impact on Electronic Word of Mouth (eWOM). As TIT shows substantial influence

156

Anuj et al.

Table 2 Reliability and validity and factor loading Constructs

Item

AVE

MSV

Cronbach’s alpha /CR

Factor loading

TIT

TIT1

0.733

0.269

0.932/0.932

0.869

eWOM

SC

TI

t-value

TIT2

0.855

19.427***

TIT3

0.876

20.328***

TIT4

0.832

18.509***

TIT5

0.849

19.199***

eWOM1

0.700

0.443

0.873/0.875

0.789

eWOM2

0.844

15.453***

eWOM3

0.876

16.036***

SC1

0.619

0.359

0.866/0.867

0.811

SC2

0.772

13.868***

SC3

0.8

14.432***

SC4

0.763

13.667***

TI1

0.718

0.443

0.883/0.875

0.852

TI2

0.792

15.757***

TI3

0.895

18.107***

***p