Entrepreneurship and Big Data: The Digital Revolution (Big Data for Industry 4.0) [1 ed.] 0367476738, 9780367476731

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Entrepreneurship and Big Data

Big Data for Industry 4.0: Challenges and Applications Series Editors: Sandhya Makkar, K. Martin Sagayam, and Rohail Hassan Industry 4.0, or the fourth industrial revolution, refers to interconnectivity, automation, and real-time data exchange between machines and processes. There is a tremendous growth in Big Data, from the Internet of Things (IoT) and information services which drives the industry, to the development of new models and distributed tools to handle Big Data. Cutting-edge digital technologies are being harnessed to optimize and automate production including upstream supply-chain processes, warehouse management systems, automated guided vehicles, drones, etc. The ultimate goal of Industry 4.0 is to drive manufacturing or services in a progressive way to be faster, more effective, and more efficient; that can be achieved only by embedding modern-day technology in machines, components, and parts that will transmit realtime data to networked IT systems. These, in turn, apply advanced soft computing paradigms such as machine learning algorithms to run the process automatically without any manual operations. The new book series will provide readers with an overview of the state-of-the-art in the field of Industry 4.0 and related research advancements. The respective books will identify and discuss new dimensions of both risk factors and success factors, along with performance metrics that can be employed in future research work. The series will also discuss a number of real-time issues, problems, and applications with corresponding solutions and suggestions, sharing new theoretical findings, tools, and techniques for Industry 4.0, and covering both theoretical and application-oriented approaches. The book series will offer a valuable asset for newcomers to the field and practicing professionals alike. The focus is to collate the recent advances in the field, so that undergraduate and postgraduate students, researchers, academicians, and industry people can easily understand the implications and applications of the field. Industry 4.0 Interoperability, Analytics, Security, and Case Studies Edited by G. Rajesh, X. Mercilin Raajini, and Hien Dang Big Data and Artificial Intelligence for Healthcare Applications Edited by Ankur Saxena, Nicolas Brault, and Shazia Rashid Machine Learning and Deep Learning Techniques in Wireless and Mobile Networking Systems Edited by K. Suganthi, R. Karthik, G. Rajesh, and Ho Chiung Ching Big Data for Entrepreneurship and Sustainable Development Edited by Mohammed el Amine Abdelli, Wissem Ajili Ben Youssef, Ugur Ozgoker, and Imen Ben Slimene Entrepreneurship and Big Data The Digital Revolution Edited by Meghna Chhabra, Rohail Hassan, and Amjad Shamim Microgrids Design, Challenges, and Prospects Edited by Ghous Bakhsh, Biswa Ranjan Acharya, Ranjit Singh Sarban Singh, and Fatma Newagy For more information on this series, please visit: https://www.routledge.com/BigData-for-Industry-4.0-Challenges-and-Applications/book-series/CRCBDICA

Entrepreneurship and Big Data The Digital Revolution

Edited by

Meghna Chhabra Rohail Hassan Amjad Shamim

First edition published 2022 by CRC Press 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742 and by CRC Press 2 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN CRC Press is an imprint of Taylor & Francis Group, LLC © 2022 Taylor & Francis Group, LLC Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint. Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers. For permission to photocopy or use material electronically from this work, access www.copyright.com or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978750-8400. For works that are not available on CCC please contact [email protected] Trademark notice: Product or corporate names may be trademarks or registered trademarks and are used only for identification and explanation without intent to infringe. ISBN: 978-0-367-47673-1 (hbk) ISBN: 978-0-367-56481-0 (pbk) ISBN: 978-1-003-09794-5 (ebk) DOI: 10.1201/9781003097945 Typeset in Times by SPi Technologies India Pvt Ltd (Straive)

Contents Preface.......................................................................................................................vii Editor Biographies.....................................................................................................ix Contributors...............................................................................................................xi Chapter 1 Ecosystem for Entrepreneurship in a Big Data-Driven Universe.......... 1 Nupur Kashyap and Ankur Kashyap Chapter 2 The Application and Influence of Industrial Internet of Things, Big Data, and Analytics Towards Sustainable Value Creation............ 13 Russell Tatenda Munodawafa and Satirenjit Kaur Johl Chapter 3 Robotic Process Automation (RPA) in Global Business Services (GBS): New Insights for Entrepreneurs................................ 25 Dayana Jalaludin, Siti Nursyahirah Abd Aziz, and Eng Kar Seong Chapter 4 Social Entrepreneurship in an Era of Disruption: Converging Social Change and Sustainability Through Big Data Analysis in a Post-COVID World....................................................................... 39 Manmeet Bali Nag Chapter 5 Big Data: A Boon for Food and Servicepreneurship........................... 55 Kiran Sood, Navneet Seth, Munish Jindal, and Harsh Sadawarti Chapter 6 Adoption of Big Data in Agripreneurship: A Panacea to the Global Food Challenge........................................................................ 71 Uzairu Muhammad Gwadabe and Nalini Arumugam Chapter 7 Anticipating and Avoiding the Pitfalls that Can Sink a Startup.............83 Mahima Birla, Sunita Kishnani, and B Venkat Chapter 8 The Influential Role of Breakthrough Strategies of the Family Business and Its Implication in Entrepreneurship............................... 99 Tanvi Thakkar and Mahima Birla Chapter 9 Crucial Factors for Successful Entrepreneurial Start-ups................. 113 Ghazala Khan and Rohail Hassan

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viContents

Chapter 10 Social Entrepreneurship.................................................................... 137 Sangeeta Sumbly Chapter 11 Terracotta Pottery Catastrophe: Survival Issues and the Road Ahead for Sustainable Enterprise...................................................... 153 Shweta Dahiya, Parveen Siwach, Anupama Panghal, and Shilpa Sindhu Chapter 12 Social Sustainability Through Women Entrepreneurs in India: A Case of Inclusion and Development Through Small Organizations.......................................................................... 167 Amrinder Kaur Chapter 13 Leveraging on Demographics Insights of Online Shoppers for Netpreneurs.................................................................................. 185 Deepak Halan Chapter 14 Study on the Effectiveness of Social Networks in Persuading Entrepreneurial Initiatives with Reference to College Students in Chennai............................................................. 195 V. Jayanthi and S. Subbulakshmi Chapter 15 Curbing Inconsistencies Through Financial Bootstrapping: Study of Indian Startups Ecosystem.................................................. 213 Anju Singla and Prihana Vasishta Chapter 16 Repurposing the Role of Entrepreneurs in the Havoc of COVID-19....... 229 Manpreet Arora and Roshan Lal Sharma Chapter 17 Business and Financial Management Sustainability of a Small-Medium Enterprise: A Case Study of Rohaya and Abu Bakar Enterprise, Penang, Malaysia.......................................... 251 Rohani Yusof, Mohd Hafiz Abdul Halim, Yuslina Abdul Ghani, and Zuhairah Abdul Hadi Chapter 18 Sustainable Enterprise Development: A Review of Training Topologies for Entrepreneurs............................................................ 267 Zahid Hussain Bhat, Riyaz Ahmad Rainayee, and Meghna Chhabra Index....................................................................................................................... 285

Preface The disruptive potential of Big Data has received growing attention in research and practice over the last few years. Industrial Revolution 4.0 has opened our eyes towards a new chapter of our lives. Currently, all industries are undergoing a form of digital transformation. Companies explore the avenues to walk on technologies and seek paths to increase customers’ demand and expectations. The story has moved away from fulfilling customers’ needs to creating an impact on their lives and improving well-being. We wake up in the morning to start our day with data consumption and end the day with data consumption. Big Data is around us, and we consume data to make our lives better. This emergence is making it imperative to scrutinize Big Data in relevance to contemporary entrepreneurship. The entrepreneurial ecosystem is changing due to Big Data, and thus it is intervening an impact on everyone’s life. This book’s theme revolves around how Big Data in the digital transformation impacts the various facets of entrepreneurship development which is essential for improving our well-being. The book contains insightful chapters on the ecosystem themes for entrepreneurship in the Big Data-driven universe, social entrepreneurship in the era of disruption, and sustainability in the digital world. The book is ideally designed for entrepreneurs, researchers, business owners, managers, graduate students, and academics seeking current research on Big Data in contemporary entrepreneurship.

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Editor Biographies Meghna Chhabra is Associate Professor (Entrepreneurship & Finance) and Area Chair Entrepreneurship and Small Business Management at Faculty of Management Studies, Manav Rachna International Institute of Research & Studies, Faridabad, Haryana, India. She is also on the boards of the various university-level committees promoting research and consultancy. Dr Meghna has more than 15 years of experience in industry and academia and has worked closely with students for employability training and job placements. She has been running the E-Cell @ FMS. She has various publications in national and international journals to her credit, including two publications in the Small Enterprises Development, Management and Extension (SEDME) journal promoted by NI-MSME, an organization under the Ministry of the Government of India. She has also conducted the National Science and Technology Entrepreneurship Development Board (NSTEDB), Dept. of Science and Technology, Government of India-sponsored “Entrepreneurship Awareness Camps”. Her research interest lies primarily in studying the gender gap in entrepreneurship, women entrepreneurship, and entrepreneurship education. Currently, she is also working on an Indian Council of Social Science and Research (ICSSR) project on “Capacity Building of Women Entrepreneurs”. She is also a recipient of a faculty grant for conducting the “Faculty Development Program in Entrepreneurship Development” from the All India Council of Technical Education (AICTE). She is also on the editorial board of the ‘International Journal of Technology Transfer and Commercialisation’ (Inderscience Publishers) and the journal ‘Management, Innovation and Entrepreneurial Research’. Rohail Hassan, PhD, is Senior Lecturer of Corporate Governance and Finance at Othman Yeop Abdullah Graduate School of Business (OYAGSB), Universiti Utara Malaysia. His research interests include corporate governance, women empowerment, gender-related issues, diversity and inclusion, gender diversity, Big Data and analytics, firm performance, and strategy. The main research has been published in leading management journals and top-tier peer-reviewed journals ranked by ABS, including Journal of Business Research (3*), Journal of Management and Organization (2*), Journal of Cleaner Production (2*), Journal of Intellectual Capital (2*), Journal of Islamic Accounting and Business Research (1*), Economics Bulletin, Sustainability, Journal of Risk and Financial Management, and International Journal of Financial Studies). He is currently working as a series editor for the work titled Big Data for Industry 4.0: Challenges and Applications from Taylor & Francis Group. Rohail is also serving as an International Editorial Board Member to the ix

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Editor Biographies

Asia-Pacific Journal of Business Administration and the Journal of Risk and Financial Management, and is Editor of Humanities and Social Sciences Reviews and reviewer to several prestigious academic journals such as Journal of Business Research, Journal of Management and Organization (JMO), International Journal of Finance and Economics (IJFE), Applied Economics (incorporating Applied Financial Economics), Sustainability, and International Journal of Environmental Research and Public Health. Amjad Shamim is currently Program Manager for MBA in Energy Management and Senior Lecturer in Marketing at Universiti Teknologi PETRONAS, Malaysia. He is also Core Researcher at Center of Social Innovation, Institute of SelfSustainable Building. He holds PhD, MS and MBA in Marketing. He is the recipient of UTP Brand Advocate Award (2020), and Liam Glynn Research Scholarship Award by Service Special Interest Group (AMA-SERVSIG) sponsored by Arizona State University Center for Services Leadership USA (2019). Dr. Amjad has published over 30 articles in reputed international journals such as Journal of Retailing and Consumer Services, International Journal of Hospitality Management, Journal of Consumer Marketing, International Journal of Retail & Distribution Management, Asia Pacific Journal of Marketing and Logistics, Journal of Islamic Marketing, Journal of Entrepreneurship in Emerging Economies, Frontiers in Psychology, Entertainment Computing, Environmental Science and Pollution Research, and IEEE Access among others. In addition, he has received Seven research grants amounting to USD 84,000 from national and international funding bodies. His research projects are in the areas of value co-creation practice in oil and gas industry, value-in-experience model for retailing, neuromarketing approach for online business and service-dominant logic application in services sector. His research interests are  in the areas of value co-creation, service-dominant logic, service thinking, service ­inclusion, brand experience, and neuromarketing.

Contributors Manpreet Arora School of Commerce and Management Studies Central University of Himachal Pradesh Dharamshala, Himachal Pradesh, India Nalini Arumugam School of Agriculture Sciences and Biotechnology Universiti Sultan Zainal Abidin Terengganu, Malaysia Siti Nursyahirah Abd Aziz School of Management Universiti Sains Malaysia Pulau Pinang, Malaysia Zahid Hussain Bhat AAA Memorial Degree College Bemina, Cluster University, Srinagar, Jammu and Kashmir, India Mahima Birla Faculty of Management Pacific University Udaipur, Rajasthan, India Meghna Chhabra Faculty of Management Studies Manav Rachna International Institute of Research and Studies Faridabad, Haryana, India Shweta Dahiya Department of Food Business Management and Entrepreneurship Development National Institute of Food Technology Entrepreneurship and Management Sonipat, Haryana, India

Yuslina Abdul Ghani Commerce Department Seberang Perai Polytechnic Penang, Malaysia Uzairu Muhammad Gwadabe Faculty of Business and Management Universiti Sultan Zainal Abidin Terengganu, Malaysia Zuhairah Abdul Hadi Commerce Department Seberang Perai Polytechnic Penang, Malaysia Deepak Halan School of Management Sciences Apeejay Stya University Gurugram, India Mohd Hafiz Abdul Halim Commerce Department Seberang Perai Polytechnic Penang, Malaysia Rohail Hassan Othman Yeop Abdullah Graduate School of Business Universiti Utara Malaysia Sintok, Kedah Darul Aman, Malaysia Dayana Jalaludin School of Management Universiti Sains Malaysia, Pulau Pinang, Malaysia V. Jayanthi Vels Institute of Science, Technology & Advanced Studies Chennai, India

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xiiContributors

Munish Jindal HoverRobotix India Satirenjit Kaur Johl Department of Management and Humanities Universiti Teknologi PETRONAS Bandar Seri Iskandar, Malaysia. Ankur Kashyap Bennett University Greater Noida, India Nupur Kashyap GITI (Women), Raebareli, India Amrinder Kaur Pink Guava Consulting Services Gurgaon, India Ghazala Khan Putra Business School Malaysia University Putra Malaysia Malaysia Sunita Kishnani Chief Marketing Officer, Systematix Infotech Pvt. Ltd. Indore, Madhya Pradesh, India Russell Tatenda Munodawafa Department of Management and Humanities Universiti Teknologi PETRONAS Bandar Seri Iskandar, Malaysia Manmeet Bali Nag School of Management Manav Rachna University Faridabad, India

Anupama Panghal Department of Food Business Management and Entrepreneurship Development National Institute of Food Technology Entrepreneurship and Management Sonipat, Haryana, India Riyaz Ahmad Rainayee Department of Commerce University of Kashmir, India Harsh Sadawarti CT University (Pb.) India Eng Kar Seong School of Management Universiti Sains Malaysia Pulau Pinang, Malaysia Navneet Seth Department of Applied Sciences Baba Hira Singh Bhattal Institute of Engineering and Technology Lehragaga, Punjab, India Roshan Lal Sharma Department of English Central University of Himachal Pradesh Dharamshala, Himachal Pradesh, India Shilpa Sindhu School of Management The NorthCap University Gurugram, India Anju Singla Centre of Management and Humanities (CMH) Punjab Engineering College (Deemed to be University) Chandigarh, India

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Contributors

Parveen Siwach Department of Food Business Management and Entrepreneurship Development National Institute of Food Technology Entrepreneurship and Management Sonipat, Haryana, India Kiran Sood Chitkara Business School Chitkara University Punjab, India S. Subbulakshmi Department of Commerce S.D.N.B. Vaishnav College Chennai, India Sangeeta Sumbly School of Management Studies IILM University Gurugram, India

Tanvi Thakkar Welingkar Institute of Management Development and Research Mumbai, India Prihana Vasishta Centre of Management and Humanities (CMH) Punjab Engineering College (Deemed to be University) Chandigarh, India B Venkat AICTE, New Delhi, India Rohani Yusof Commerce Department Seberang Perai Polytechnic Penang, Malaysia

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Ecosystem for Entrepreneurship in a Big Data-Driven Universe Nupur Kashyap GITI (Women), India

Ankur Kashyap Bennett University, India

CONTENTS Introduction���������������������������������������������������������������������������������������������������������������� 1 Role of Big Data�������������������������������������������������������������������������������������������������������� 2 Emerging Technologies���������������������������������������������������������������������������������������������� 2 Research Methodology���������������������������������������������������������������������������������������������� 5 Proposed Framework������������������������������������������������������������������������������������������������� 5 Identifying Challenges����������������������������������������������������������������������������������������������� 6 Conclusion����������������������������������������������������������������������������������������������������������������� 8 References������������������������������������������������������������������������������������������������������������������ 9

INTRODUCTION The term “entrepreneurship” was first used in the Middle Ages, when “the entrepreneur was someone who performs tasks in projects like buildings, construction, and the likes by using all the resources he had”. However, the word entrepreneur gained its present form in the 17th century, as Cantillon described it “as a person responsible for undertaking a business venture” (Entrepreneurship and Big Data, 2019). According to a note by the UNCTAD secretariat (2011), the ability and propensity of an enterprise to innovate not only depends on its access to knowledge from research institutes or technology services centers but also many other factors, including access to finance; access to human resources; adequate basic physical infrastructure; firmlevel capabilities (design, operation, maintenance, managerial); inter-firm linkages and collaboration and partnerships in R&D among academic and commercial entities; general business services; and demand conditions.

DOI: 10.1201/9781003097945-1

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Entrepreneurship and Big Data

ROLE OF BIG DATA Earlier, Big Data was considered a problem, but it became a boon for entrepreneurs with the advent of new technologies. One could find its reach in each corner of the business arena in one way or another, directly or indirectly. Whether it is text messages, pictures, videos, blogs, reports, multimedia content, digital traces, swipes on different sites, reading preferences on mobile, time spent on each page, omitted portions, likes on social media, or anything related produce Big Data and this data is under the scanner of data analysts to generate useful information out of this. Big Data as a lucrative entrepreneurial option has gone beyond the boundaries of internetrelated establishments. In Japan, data generated through geo-coded maps of agricultural fields and the real-time monitoring of every activity from seeding, watering, fertilizing, and, some experts estimate, harvesting to improve yields around USD 100 per acre in increased profit (OECD, 2014). New York-based start-up Muze has introduced a new mobile app on iOS that provides users the facilities, besides adding texts, to pin their pictures or Graphics Interchange Format (GIFs), in and out zooming the text and draw on message board using multiple sizes and colors of pens (Perez, 2020). In business, Google and Facebook are highly data-driven firms that opted for AI at a very early stage and got the market advantage of embracing AI (Prüfer & Prüfer, 2020). “Big Data” is often used as a synonym for customer analytics, real-time analytics, or predictive analytics (Lochy, 2017). Worldwide, Big Data market revenues for software and services are projected to increase from $42 billion in 2018 to $103 billion in 2027, attaining a Compound Annual Growth Rate (CAGR) of 10.48% (Columbus, 2018). According to NewVantage Venture Partners, Big Data delivers the most value to enterprises by decreasing expenses (49.2%) and creating new avenues for innovation and disruption (44.3%) Figure 1.1.

EMERGING TECHNOLOGIES Whereas the human brain can manage two to three dimensions of information, algorithms allow for hundreds of dimensions. Thus, data science could extract meaningful information from the association, classification, and data clusters (Prüfer & Prüfer, 2020). According to blogs, with on an average more than 40,000 searches on Google every second, i.e. 3.5 billion searches per day, 2.5 quintillion bytes of data were created each day in 2018. This pace is only increasing by leaps and bounds with the development of the Internet of Things (IoT) (Marr, 2018). At present, during 2020, 1.7 MB of data is created every second by every person (Bulao, 2020). The need for advanced versions of present time technologies will always be felt for processing and analyzing these ever-increasing data. The Internet of Things (IoT) is the talk of the town nowadays. With smart sensors and their wireless connectivity, much data are gathered and analyzed. The global audit and consulting company Deloitte’s state-of-the-art, IoT-powered office, “The Edge Amsterdam,” is equipped with 28,000 sensors that send data to be analyzed to increase the building’s efficiency (Khvoynitskaya, 2018). The sensors track all of the employees’ movements and activities and indicate to them the unoccupied places in meeting rooms. Another

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49.2%

Decrease expenses

44.31%

New Avenues for innovation

20.19%

36.1%

Launch new products/services

32.8%

Monetize Big Data- increased/new revenue

26.8% 22.0%

31.1%

Accelerate new capabilities deployment Transform business for the future

27.9%

Establish a data-driven culture

27.9%

Started and Benefits

23.4%

33.4% 23.7% 41.5%

Started and no Benefits

Not Started

Big Data business initiatives underway; with successful results Decrease expenses through operational cost efficiencies

Started

Success

72.6%

Establish a data-driven culture

69.4%

Create new avenues for innovation and disruption

64.5%

Accelerate the speed with which new capabilities and services are deployed

64.5%

Launch new product and service offerings

62.9%

Monetize Big Data through increased revenues and new revenue sources

54.8%

49.2% 27.9% 44.3% 31.1% 36.1% 32.8%

Transform and reposition your business for the future

51.6%

27.9%

FIGURE 1.1  Big Data Initiatives and success rate. Source: Big Data Executive Survey (2017), reported by Columbus (2018).

application adjusts the temperature, humidity, and CO2 concentration to maintain a comfortable environment for employees, resulting in increased staff members’ increased efficiency. Another revolutionary breakthrough is Artificial Intelligence (AI) which, according to Jens Prüfer and Patricia Prüfer (2020), “is a concept, in which machines mimic cognitive functions of learning and problem solving”. Unlike the “Industrial Revolution,” when the main drivers were coal and steam, the present “second machine age” is driven by data and artificial intelligence (Obschonka & Audretsch, 2019; Di Vaio et al., 2020). The spread of AI could be assessed with a report of The Economist according to which a factory in Southern England “OCADO” better known as Custom Fulfilment Centre (CFC), uses a unique grid system, “The Hive” where 700 robots assemble customers’ orders. They work on an air traffic control system fulfilling 65,000 customers every day and giving tough competition to leading online grocery outlet Amazon. It was made possible with the help of artificial intelligence and automation. The sources, individuals related to machine-based intelligence, and entrepreneurial research processes should be focused upon for augmentation of Big Data and AI (Obschonka & Audretsch, 2019). An advertisement for HG Hector, the four-wheeler SUV, says, “It is a Human Thing”, the reason being that Alexa with human-like capabilities is incorporated in the vehicle that gives directions and suggestions (just like a living person) throughout the journey. The robotic workforce will soon become the new norm. The importance of robots can be surmised because XYZ Robotics raises $17 million for its

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pick-and-place logistics robots (Heater, 2020). Drones have already started working in the field of delivery. Keeping in view the wide range of the drone’s commercial prospects, a market rise of more than five times is estimated in the next five years (from $5.2 billion in 2016 to $27.1 billion in 2021; Fatbit.com, n.d.). The latest in this series is neurotechnologies, i.e., the use of technology in monitoring body functions. Mind-controlled robots are one of its examples. Virtual and Augmented Reality, wearable technology, Edge computing, Blockchain (a decentralized, distributed, and public digital ledger consisting of records called blocks that is used to record transactions across many computers so that if any involved block is altered, all subsequent blocks would be altered (Wikipedia). No code development platform also falls in this category. Personalized wealth management advice is provided to the customers regarding their policies based on customers’ financial and personal details and status. Besides, there are digital assistants (as with Disha on the IRCTC website of Indian railways), cashless payments (through PayPal, Paytm, MobiKwik, and Bitcoin), hybrid wireless technologies, deep learning, automated software, video search optimization (tracking locations, tracking the uncertainty of nature), and many more technologies that help in using Big Data for the entrepreneurship purposes. A study by Microsoft’s IoT Signals indicated that “one-third of IoT projects are abandoned after the proof of concept stage”. What are the factors that lead to the failure of such projects that are worth discussing? As Big Data is something different from the traditional way of obtaining the final output, the challenges must be treated differently. Researchers have considered many factors that hinder the growth rate of new ventures. Some of the studies citing these challenges are given in Table 1.1.

TABLE 1.1 Challenges faced by entrepreneurs in Big Data universe Challenges Security and privacy, dynamic provisioning, algorithms, Misuse of Big Data, data management Data storage, data transmission, data management, data processing, data visualization, data analysis, integration, architecture, security, privacy, quality Knowledge discovery and computational complexities; scalability and visualization of data; and information security. Data – storage, quality, security and privacy, service delivery and billing, interoperability and portability, reliability and availability, performance and bandwidth cost Big Data professionals, interactiveness, loading, and synchronization, visualization Data challenges, process challenges, management challenges Privacy and security, data access and sharing of information, human resources and manpower, quality of data Data growth, data infrastructure, data governance/policy, data integration, data velocity, data variety, data compliance/regulation, data visualization

Authors Alam et al. (2014) Yang et al. (2017)

Acharjyaa and Ahmed (2016) Balachandran et al. (2017)

Wani and Suraiya (2018) Sivarajah et al. (2017) Satyanarayana (2015) Khan et al. (2014)

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RESEARCH METHODOLOGY This study is exploratory research and used primary and secondary data to analyze the ecosystem for entrepreneurship for Big Data. For designing the research problem, the authors used the existing resources available on the subject matter; afterward, primary research tools are employed to synthesize the proposed model (Figure 1.3). The research was conducted in two phases: identifying the problem, and proposing a conceptual model for understanding the ecosystem based on qualitative analysis of the data gathered through focus group discussions (FGDs) and informal interviews. A structured approach was applied for gathering primary data for the study. To begin with, a two-panel of a total of ten experts was formed for conducting two FGDs. The six academic experts were invited from private and public HEIs having sufficient experience in entrepreneurship. Apart from academic experts, the group had representation from industry also. Two experts from the computing industry at the senior level were part of these FGDs. Two students working on a technology-based start-up at an academic incubation center have also participated actively in the discussions. Both the FGDs were conducted online because of the lockdown imposed by the government to restrict the COVID-19 pandemic. Both authors acted as moderators in each discussion.

PROPOSED FRAMEWORK As the data is huge and complex, the processing of these data is not an easy job, significantly when the tools used for analyzing are not updated. They also increase the cost and time taken to process, thus, always lagging behind the schedule. With the help of emerging newer technologies, this work could be performed better at a fast pace, almost in a real-time process. In the OECD synthesis report 2014, the key players in the Big Data ecosystem are given, as shown in Figure 1.2.

Data driven entrepreneurs and innovators across society (ex. Start-ups, civic entrepreneurs)

Data analytics providers (ex. Analytic software solutions)

Data providers (ex. Consumers, data brokers, government, & the IOT)

IT infrastructure Providers (ex. Database management tools, cloud computing)

Internet Service Providers (eg, Fix and mobile broadband)

FIGURE 1.2  Key players of Big Data ecosystem (OECD, 2014).

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FIGURE 1.3  Entrepreneur Ecosystem in Big Data-Driven Universe.

Based on these factors and expanding this view, a framework is proposed where Big Data and emerging technologies (like the Internet of Things, artificial intelligence, etc.) work hand in hand. These two are the essential ingredients for digital entrepreneurs. The proposed framework presents the entrepreneurial ecosystem and the factors that need to be focused on or improved upon to allow entrepreneurs to achieve their desired goals. Digital entrepreneurship is driven by data providers, data analyzers, internet providers, and IT infrastructure providers. There is a hindrance in the form of a 7-S wall that is to be taken care of before the launch of an innovation. These 7-S are: security lapse of data, sorting difficulty of data, storage hassle of data, scarcity of skilled personnel, shortage of funds, slow internet & connectivity, and shoddy collaboration & cooperation.

IDENTIFYING CHALLENGES According to IoT expert Sandra Khvoynitskaya (2018), the most common IoT challenges are “technical unpreparedness, budget shortages, and skill gap”. These challenges are a part of emerging technologies, and it is hoped they will disappear as soon as the IoT system improves. We have classified the challenges into seven categories (7-S) that are impediments to Big Data implementation. 1. Security lapse of data: Data is the key element in an innovative system. When the data is stored on cloud-based storage systems, one has no direct control over it, and the chances are always there that the data could be leaked. Cloud is a sharing server, so data privacy is a big concern. In the recent past, many cases of data theft were reported. Some firms had to pay the enormous ransom amount to get their data back. With newer security approaches, some of the current techniques are efficient in securing the data channels, such as Datagram Transport Layer Security (DTLS) (Ryan and Watson, 2017). 2. Sorting difficulty of data: Selecting the appropriate data is a cumbersome process. Data science methods – Python programming language, Natural Language Processing (NLP) techniques, NoSQL – are solutions for sorting unstructured

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data. The Deep Neural Network (DNN) comprises several processing layers that are competent for learning hierarchical representations from the input data and modeling complex behaviors of heterogeneous data sets (Saleem et al., 2019). These technologies include large-scale distributed file systems like Hadoop, which can handle enormously large data sets. The firms require highquality data for making suitable operational, strategic, and financial decisions. The latest information technology is required; otherwise, the dependence on secondary or redundant data will lead to less accurate decisions. 3. Scarcity of skilled data personnel: For emerging entrepreneurs, it is a challenge to arrange adequate resources and to attract personnel with skills for data analysis as compared to large organizations and established entrepreneurs (Pappas et al., 2017). OECD (2019) stressed that policymakers should bring schemes that instill digital and entrepreneurial skills in unrepresented groups through education and training programs. These training programs should be data specific, as mere knowledge is not all entrepreneurs needed. A key skill involves explaining Big Data outcomes to executives – in visual displays or verbal narrative (Chhabra et al., 2020; Chieng et al., 2015; Davenport and Dyché, 2013; Del Giudice et al., 2021; Singh et al., 2018) for relatively better decision making. 4. Shortage of funds: One of the reasons for the failure of innovation projects could be the paucity of funds. A study by Thomas Niebel, Fabienne Rasel, and Steffen Viete (2018) suggests that Big Data analytics is the prominent factor for the probable market success of product innovations and applies to both manufacturers and the service sector, depending on the firms’ investment in IT-specific skills. Bryan Ritchie and Nick Swisher (2018) found that although the start-ups may lack political or financial power, their (particularly highpotential) collective contributions to the economy are important, and they experience growth rates substantially higher than other firms, creating a huge impact on the overall economic productivity of the economy. Nevertheless, as money attracts money, this growth is owed to an adequate amount of funds with the entrepreneur. OECD (2019) suggested some means of financial access to entrepreneurs: a. Support and/or promote crowdfunding platforms to improve access to startup financing for the digital entrepreneur. b. Use award programs to provide small grants. 5. Storagehassle of data: As the name implies, Big Data is enormous. With the digitization of each and every thing in this universe, including entrepreneurial activities, large amounts of digital information exist on virtually any topic of interest, in which mobile phones, online shopping, social networks, electronic communication, GPS, and instrumented machinery all produce torrents of data as a by-product of their ordinary operations (McAfee & Brynjolfsson, 2012). One has to prepare an information warehouse that can handle so much data. Nevertheless, this being a costly affair, the companies prefer to store systems on cloud-based platforms. According to Ryan and Watson (2017), to manage IoT databases’ size, newer methods for querying semistructured data, data streaming, continuous sampling data, and data mining are needed. Due to the

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velocity of Big Data, it is difficult to store information with the traditional storage methods as they are unable to scale up rapidly (Yang et al., 2017) 6. Slow internet and connectivity: IoT works on the internet platform and requires high speed and reliability. There is a problem of connectivity, and the network’s erratic behavior causes a problem for IoT. In production or business processes, all the activities need to be connected via the internet to gain portability. The expansion of broadband would enhance pattern recognition by using the connected resources accessed via the internet (Caceres-Diaz et al., 2019). 7. Shoddy collaboration and cooperation: Pappas et al. (2017) stressed the need for co-creation and inclusive growth to stimulate societal innovation taking advantage of Big Data. Similarly, a collaboration between multiple stakeholders of the entrepreneurship system – government, society, and entrepreneur – tends to prove beneficial for innovation’s success rate. Myriad sources drive the source of data in large quantities; thus, balanced cooperation is of utmost importance. Multiscale collaborations require multi-spatiotemporal collaboration across different domains supported by distributed storage (Yang et al., 2017). The cloud environment should allow data scientists and business analysts to explore knowledge acquisition data interactively and collaboratively for further processing (Acharjyaa & Ahmed, 2016; Chung et al., 2014). According to a report of the World Economic Forum (2019) produced in collaboration with McKinsey & Company the classification of data collaborative is as given: • “Data cooperatives: Corporations and other essential data holders group together to link and connect data resources. • Prizes and challenges: Corporations make data available to qualified applicants who compete to develop new apps or discover innovative uses for the data. • Research partnerships: Corporations share data with universities and other academic organizations. • Intelligence products: Shared corporate data is used to build a tool, dashboard, report, app, or another technical device to support a public or humanitarian objective. • Application Programming Interfaces (APIs): APIs allow developers and others to access data for testing, product development, and data analytics. • Trusted intermediary: Corporations share data with a limited number of known partners.”

CONCLUSION Digitalization plays the role of an outcome as well as a source for innovation. At the same time, entrepreneurs and intrapreneurs may be the drivers and the agents of digital transformations (Satalkina & Steiner, 2020; Shahzad et al., 2020). With the advent of the Big Data era, a new job profile will undoubtedly occur, catering to data industries’ demands. Various fields are doing wonders after integrating these technologies with Big Data to make the ecosystem more conducive for entrepreneurship. The framework proposed here is in the primary stage. It is suggested to develop and test

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it for best practices that can further be used to incorporate newer technologies and Big Data for easing the challenges faced by entrepreneurs.

REFERENCES Acharjyaa, D. P., and Ahmed, K. (2016). A Survey on Big Data Analytics: Challenges, Open Research Issues and Tools, (IJACSA) International Journal of Advanced Computer Science and Applications, 7(2) 2016. Alam, J. R., et al. (2014). A Review on the Role of Big Data in Business. International Journal of Computer Science and Mobile Computing, 3(4) (April), 446–453. Balachandran, B. M., et al. (2017). Challenges and Benefits of Deploying Big Data Analytics in the Cloud for Business Intelligence. Procedia Computer Science 112 (2017) 1112–1122. Big Data Executive Survey (2017). Executive Summary of Findings, Big Data Business Impact: Achieving Business Results through Innovation and Disruption, NewVantage Partners LLC. Blockchain. n.d. Wikipedia. https://en.wikipedia.org/wiki/Blockchain Bulao, J. (2020). How Much Data Is Created Every Day in 2020? https://techjury.net/blog/ how-much-data-is-created-every-day/#gref Caceres-Diaz, P., Usero-Sanchez, M. B., Montoro-Sanchez, A. (2019). Digital Infrastructure and Entrepreneurship: The Digital Era’s Enabling Effect, 30th European Conference of the International Telecommunications Society (ITS): “Towards a Connected and Automated Society”, Helsinki, Finland, 16–19 June 2019, International Telecommuni­ cations Society (ITS), Calgary. Chhabra, M., Gera, R., Hassan, R., & Hasan, S. (2020). An exploratory study of cognitive, social and normative dimensions of female entrepreneurship within transition economies: Evidence from India and Vietnam. Pakistan Journal of Commerce and Social Sciences (PJCSS), 14(4), 1012–1042. Chieng, L. B., Singh, M. M., Zaaba, Z. F., & Hassan, R. (2015). Multi-Facet Trust Model for Online Social Network Environment. International Journal of Network Security and Its Applications, 7(1), 1. Chung, S. K., Yee, O. C., Singh, M. M., & Hassan, R. (2014, September). SQL injections attack and session hijacking on e-learning systems. In 2014 International Conference on Computer, Communications, and Control Technology (I4CT) (pp. 338–342). IEEE. Columbus, L. (2018). Ten Charts That Will Change Your Perspective Of Big Data’s Growth, Forbes, 23 May 2018, https://www.forbes.com/sites/louiscolumbus/2018/05/23/10-chartsthat-will-change-your-perspective-of-big-datas-growth/#3a1c122e2926 Davenport, T. H., and Dyché, J. (2013). Big Data in Big Companies, International Institute for Analytics, May 2013.Copyright © Thomas H. Davenport and SAS Institute Inc. Del Giudice, M., Di Vaio, A., Hassan, R., and Palladino, R. (2021). Digitalization and new technologies for sustainable business models at the ship–port interface: a bibliometric analysis. Maritime Policy & Management, 1–37. Di Vaio, A., Palladino, R., Hassan, R., and Escobar, O. (2020). Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review. Journal of Business Research, 121, 283–314. Entrepreneurship and Big Data. (2019). Source Title: Big Data Analytics for Entrepreneurial Success, doi: 10.4018/978-1-5225-7609-9.ch007 Fatbit.com (n.d.) Five Technologies that Will Drive Entrepreneurship in 2019 & Next Few Years, https://www.fatbit.com/fab/technologies-will-drive-entrepreneurship/ Heater, B. (2020). XYZ Robotics raises $17M for its pick-and-place logistics robots. TechCrunch.com, 26 August 2020, https://techcrunch.com/2020/08/25/xyz-roboticsraises-17m-for-its-pick-and-place-logistics-robots/

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Khan, N., Yaqoob, I., Hashem, I. A. T., Inayat, Z., Ali, W. K. M., Alam, M., Shiraz, M., and Gani, A. (2014). Review Article Big Data: Survey, Technologies, Opportunities, and Challenges, Hindawi Publishing Corporation, The Scientific World Journal, Volume 2014, Article ID 712826, 18 pages. doi: 10.1155/2014/712826 Khvoynitskaya, S. (2018). Five Success Stories of Enterprise IoT, Explained. Blog 27-022020, https://www.itransition.com/blog/enterprise-iot Lochy, J. (2017). Big Data in the Financial Services Industry – from data to insights, 9 September 2019, https://www.finextra.com/blogposting/17847/big-data-in-the-financialservices-industry---from-data-to-insights Marr, B. (2018). How Much Data Do We Create Every Day? The Mind-Blowing Stats Everyone Should Read. https://www.forbes.com/sites/bernardmarr/2018/05/21/how-much-datado-we-create-every-day-the-mind-blowing-stats-everyone-should-read/#2ad28f4e60ba McAfee, A., and Brynjolfsson, E. (2012). Big Data: The Management Revolution, Harvard Business Review, 90(10) (October), 60–68. Niebel, T., Rasel, F., & Viete, S. (2018): BIG Data – BIG Gains? Understanding the Link between Big Data Analytics and Innovation, Economics of Innovation and New Technology, 28(3), 296–316. doi: 10.1080/10438599.2018.1493075 Note by the UNCTAD secretariat (2011). TD/B/C.II/13, Key aspects of entrepreneurship and innovation policy frameworks for enhancing local productive capacities. United Nations Conference on Trade and Development, Trade and Development Board, Investment, Enterprise and Development Commission, Thirdsession, Geneva, 2–6 May 2011, TD/B/C.II/131. Obschonka, M., and Audretsch, D. B. (2019). Artificial Intelligence and Big Data in Entrepreneurship: A New Era Has Begun. Small Business Economics. doi: 10.1007/ s11187-019-00202-4 OECD (2014).Data-driven Innovation for Growth and Well-being. Interim synthesis report, October 2014. OECD (2019). What Potential Does Digital Entrepreneurship Have for Being Inclusive? The Missing Entrepreneurs 2019 © OECD/EU 2019 Pappas, I., Jaccheri, L., Mikalef, P., and Giannakos, M. (2017). “Social Innovation and Social Entrepreneurship Through Big Data: Developing a Research Agenda,” MCIS 2017 Proceedings. http://aisel.aisnet.org/mcis2017/122. Perez, S. (2020). Muze redesigns mobile messaging as a free-form canvas for creativity, https://techcrunch.com/2020/08/25/muze-redesigns-mobile-messaging-as-a-free-formcanvas-for-creativity/, 26 August 2020 Prüfer, J., and Prüfer, P. (2020). Data science for entrepreneurship research: studying demand dynamics for entrepreneurial skills in the Netherlands, Small Bus Econ, 55, 651–672. Ritchie, B., and Swisher, N. (2018). “The Big Small: The Economic Benefits of Startups / University of Notre Dame”. IDEA Center, https://ideacenter.nd.edu/news-events/news/ the-bigsmall-the-economic-benefits-of-startups/. Ryan, P. J., and Watson, R. B. (2017). Research Challenges for the Internet of Things: What Role Can OR Play? Review, Systems, 5, 24; doi: 10.3390/systems5010024 Saleem, T. J., et al. (2019). Deep Learning for Internet of Things Data Analytics. Procedia Computer Science, 163(2019), 381–390. Satalkina, L., and Steiner, G. (2020). Digital Entrepreneurship and Its Role in Innovation Systems: A Systematic Literature Review as a Basis for Future Research Avenues for Sustainable Transitions, Sustainability, 12, 2764; doi: 10.3390/su12072764 Satyanarayana, L. V. (2015). A Survey on Challenges and Advantages in Big Data, IJCST, 6(2) (April–June), 6(2), 115–119. Shahzad, A., Hassan, R., Aremu, A. Y., Hussain, A., & Lodhi, R. N. (2020). Effects of COVID19 in E-learning on higher education institution students: the group comparison between male and female. Quality & Quantity, 1–22.

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Singh, M. M., Adzman, K. A. A. K., & Hassan, R. (2018). Near Field Communication (NFC) technology security vulnerabilities and countermeasures. International Journal of Engineering and Technology, 7(4.31), 298–305. Sivarajah, U., Kamal, M. M., Irani, Z., and Weerakkody, V. (2017). Critical Analysis of Big Data Challenges and Analytical Methods, Journal of Business Research, 70, 263–286. Wani, M. A. and Suraiya, J. (2018). Big Data: Issues, Challenges, and Techniques in Business Intelligence. In: V. Aggarwal, V. Bhatnagar, and D. Mishra (eds), Big Data Analytics. Advances in Intelligent Systems and Computing, 654. Springer, Singapore. doi: 10.1007/978-981-10-6620-7_59 World Economic Forum. (2019). Data Collaboration for the Common Good: Enabling Trust and Innovation Through Public-Private Partnerships, Geneva, Switzerland Yang, C., Huang, Q., Li, Z., Liu, K., and Hu, F. (2017). Big Data and Cloud Computing: Innovation Opportunities and Challenges, International Journal of Digital Earth, 10(1), 13–53, doi: 10.1080/17538947.2016.1239771

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The Application and Influence of Industrial Internet of Things, Big Data, and Analytics Towards Sustainable Value Creation Russell Tatenda Munodawafa and Satirenjit Kaur Johl Universiti Teknologi PETRONAS, Malaysia

CONTENTS Introduction�������������������������������������������������������������������������������������������������������������� 13 Literature Review����������������������������������������������������������������������������������������������������� 14 Sustainable Value Creation���������������������������������������������������������������������������������� 14 Industrial Internet of Things (IIoT)�������������������������������������������������������������������������� 15 Big Data and Analytics Infrastructure���������������������������������������������������������������������� 16 The Proposed Sustainable Value Creation Framework�������������������������������������������� 17 Resource������������������������������������������������������������������������������������������������������������������� 18 Capability����������������������������������������������������������������������������������������������������������������� 18 Competitive Advantage�������������������������������������������������������������������������������������������� 19 Conclusion��������������������������������������������������������������������������������������������������������������� 20 References���������������������������������������������������������������������������������������������������������������� 21

INTRODUCTION The industrial revolutions that have taken place throughout human history on Earth have profoundly affected people, the planet, and profit. Beginning with the first industrial revolution, technologies developed in this era enabled more efficient steam power use. These timeframe technologies also enhanced production through mechanization, thus establishing industrial value creation through manufacturing (Nuvolari, 2018). The second revolution introduced electricity to industrial value creation. Technologies that harnessed electricity profoundly impacted the industrial value DOI: 10.1201/9781003097945-2

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creation process through mass production. The introduction of information communication technologies (ICT) and digital electronics marked the third stage of industrialization. The technologies at this stage further enhanced the industrial value creation process through automation of industrial processes (Nuvolari, 2018). Another wave of technological developments that will further enhance industrial value creation has been noted, emanating from the late 2010s. These technological developments are based on the Industrial Internet of Things (IIoT), or Industry 4.0. The IIoT enables a new level of connectivity and real-time information exchange for the industrial value creation process (Müller, Veile, & Voigt, 2020b). The technologies underpinning IIoT, such as Big Data, Cyber-Physical Systems (CPS), Cloud, and the Internet of Things (IoT), promise to further enhance industrial value creation through digitizing, connecting, and autonomizing the value creation chains. This enables manufacturing to be “smart” – leading to an optimized and more efficient value creation process (Müller, Veile, & Voigt, 2020b). IIoT’s promises of efficiency also coincide with the growing global awareness of environmental, economic, and social issues. It is widely accepted that to ensure future sustainability and competitiveness, the industry cannot just pursue profit alone. Growing environmental and social problems require that the industry shift from focusing purely on profit. They must balance the need for profit while protecting the interests of the planet and people (Müller & Voigt, 2018). Hence, the industry’s current and future focus is shifting towards an industrial value creation process that is sustainable on three fronts – economy, environment, and social. Ensuring the protection of the biosphere, which houses both industry and people, will influence current and future competitiveness for industry, as an industry also relies on people (Dantas et al., 2021). As a result of its technical nature, current literature on IIoT has focused extensively on its feasibility and influence from a technical perspective. Studies that focus on the integration and influence of IIoT technologies such as Big Data in addressing environmental issues and subsequent value creation are scant (Müller, Veile, & Voigt, 2020b). This paper, therefore, asks the question: How can IIoT technologies influence sustainable value creation? Focusing on specific IIoT technologies can enhance understanding of the potential effect of IIoT on sustainable value creation. In addition, most studies on IIoT have come from a wide array of approaches. However, technology-specific frameworks upon which to base empirical analyses are still lacking in this emerging research (Müller, Buliga, & Voigt, 2020a). Therefore, the conceptual framework proposed in this chapter can assist in further understanding the potential influence of IIoT towards sustainable value creation.

LITERATURE REVIEW Sustainable Value Creation Sustainable development’s inception is linked to the Brundtland Commission of 1987, in which experts and scientists acknowledged the negative effects anthropogenic activities were having on the biosphere (Olawumi & Chan, 2018). Hence, according to the report, corrective action must be undertaken to ensure the continued

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uninhibited operation of the biosphere, which serves the dual functions of resource provider and anthropogenic emissions sink. Thus, development from that juncture needed to be sustainable, i.e., to focus on satisfying present needs without negatively impacting the future generation’s ability to satisfy their own need (Hoff, Gausset, & Lex, 2019). Hence, economic development should be within the threshold of the environment while addressing societal needs. Sustainable value creation is thus defined as a value creation process that makes a positive contribution to sustainable development by considering the three dimensions (Stock, Obenaus, Kunz, & Kohl, 2018). Incorporating sustainable development dimensions can come through several different means, such as its innovation process – allowing the entrepreneurial firm to have an innovative business model (Stock et al., 2018). A firm’s innovation ability is an essential antecedent in its quest to attain sustainable value creation. A firm’s innovation practices in the present age should be geared towards addressing environmental, economic, and social sustainability challenges in this present age of rapid technological advancement driven by IIoT or Industry 4.0. As sustainable value creation challenges mount in the present era, the realization of sustainable value creation is one of the key aims of IIoT-based technological advancements. Therefore, IIoT and its technologies are expected to play a formidable role in the sustainable value creation process in the ensuing decades (Ranta, AarikkaStenroos, & Väisänen, 2021).

INDUSTRIAL INTERNET OF THINGS (IIOT) IIoT traces its genesis to the German government’s initiative to ensure the country’s manufacturing industry (Kiel, Müller, Arnold, & Voigt, 2017; Müller, Kiel, & Voigt, 2018). The initiative, also referred to as Industry 4.0, leverages advancements in information communication technology development to digitize and integrate the different value chain elements horizontally, vertically, and in end engineering (Vaidya, Ambad, & Bhosle, 2018). Numerous manufacturing regions have drawn inspiration from IIoT (Industry 4.0) and launched their iterations of this initiative. For instance, under its “Made in China 2025” manifesto, China’s iteration looks to capitalize on connectivity technologies to position China as a leading industrial power (Müller & Voigt, 2018). South Korea’s iteration aims to digitize the country’s industries. The European Union’s (EU) version aims to manufacture and remain competitive globally, simultaneously sustainably. Meanwhile, in the United States, this iteration is an industry led by an industry members’ consortium (Müller & Voigt, 2018). IIoT’s ability to usher in unprecedented connectivity and integration of sustainable value creation is facilitated by several core technologies such as CPS, Cloud, and IoT (Frank, Dalenogare, & Ayala, 2019). CPS features electronics such as sensors and data processors that enable firms to capture data in real time. Advances in these electronics technologies, such as light-dependent resistors (LDR), have enabled firms to capture more parameters of data during their industrial value creation process (Sawant, Bondre, Joshi, Tambavekar, & Deshmukh, 2018). This captured data can then be instantaneously fed to the cloud for access across the industrial process’s value chain activities to enhance the monitoring and control of the process (Mhatre & Rai, 2017). IoT also enables machines and other critical components of the value

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Data

Big Data & Analytics

CPS

Data

Cloud

Data

IoT

FIGURE 2.1  Technologies underpinning IIoT.

creation process to communicate vital data to humans or other machines along the industrial value process (Suresh et al., 2020). Hence, it can also become self-aware or “smart”. This constant stream of data from industrial value creation elements in increasingly large volumes, higher velocity, and various formats would require the infrastructure to assimilate this data (Suresh et al., 2020). Not only must the data be assimilated, but it must also be analyzed in real time and utilized to unlock the potential value it may add to the sustainable industrial value creation process (Desai, 2018). The constant data exchange is all linked via the cloud, as data from the IIoT technologies is uploaded to the cloud. Figure 2.1 highlights the interconnectedness of the IIoT technologies, which creates the stream of data. Uploading to the cloud enables the data to be accessible by the other dimensions of IIoT across the entire value creation process. Hence, to use this data for entrepreneurial value creation, firms need to have in place the necessary data and analytics infrastructure (Serrano, 2021).

BIG DATA AND ANALYTICS INFRASTRUCTURE Data exchange is at the heart of IIoT. Various sensors within CPS and IoT increase the parameters of data that can be collected within the organizational value creation process (Suresh et al., 2020). The intensive data being generated by these IIoT technologies requires the necessary data infrastructure to be assimilated, processed, and actioned in a rapid manner (Desai, 2018). Leveraging value from this large stream of data requires analytics to be conducted, and the results are communicated to d­ ecision-making elements in real-time (Lin, Jun, Hongyan, Zhongwei, & Zhanfang, 2018). As this data comes in various forms, the Big Data infrastructure must also be capable of coping with and aggregating the different structures that this data comes in (Campos, Sharma, Gabiria, Jantunen, & Baglee, 2017). For example, different sensors capture different parameters of data that may result in structured, unstructured, or semi-structured data (Campos et al., 2017) as explained by Figure 2.2.

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The Application and Influence of IIoT

Unstructured Data

• Data that is maintained in the same format it was collected • E.g. Audio

Semi-Structured Data

• Data not having a predefined format due to having separators • E.g. Extensible Markup Language (XML)

Structured Data

• Data in a defined format i.e numeric or alphanumeric • E.g. Database

FIGURE 2.2  Summary of the different structures of data.

The capability of firms to ingest, prepare, store, and model this data hinges on their Big Data infrastructure. Some of the infrastructures that enable real-time big data analytics include algorithms, software, hardware for data mining, data processing, and data security (Das & Dash, 2021). These infrastructures ensure compatibility and modularity of the data, enabling firms’ value creation activities to be connected in a consistent manner (Oussous, Benjelloun, Ait Lahcen, & Belfkih, 2018). Furthermore, the advancement of communication network technologies has seen the emergence of the fifth generation of network communication technology that can boost data transfer speeds and effectively reduce data transfer latency. This fifth generation of network technology, also known as “5G”, promises to exponentiate the capacity of firms to capture, process, store, and utilize data, further boosting a firm’s real-time big data analytics capability (Chettri & Bera, 2020).

THE PROPOSED SUSTAINABLE VALUE CREATION FRAMEWORK This chapter proposes a framework that can help understand how IIoT technologies could influence sustainable value creation. According to Barney (1991), the resource-based view (RBV) of the firm postulates that leveraging knowledge from data and information can enhance the firm’s entrepreneurial decision-making process. This can subsequently result in the firm realizing competitive advantage. The RBV, thus, showcases how value (competitive advantage) can be created and sustained by firms’ via resource and capability (Alvarez & Barney, 2017; Barney, 1991). Figure 2.3 gives an illustration of the proposed sustainable value creation network.

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Entrepreneurship and Big Data Entrepreneurial Decision-making

Capability • CPS • IoT • Cloud

• Big Data & Analytics

• Sustainable Value Creation

Competitive Advantage

Resource

Data

FIGURE 2.3  Proposed framework.

RESOURCE Looking at sustainable value creation from the resource-based view theoretical lens, data is a useful resource at the disposal of firms. It can provide useful information upon which knowledge can be built (Sampson Abeeku, Mary, & Divine Quazie, 2020). Data can be sourced from various points along the value creation chain of the firm. Examples of such sources include sensors, actuators, Radio Frequency Identification (RFID), and barcode tags/readers, as well as other Near Field Communication (NFC)-enabled technologies for inventory and tracking purposes (Suresh et al., 2020). Apart from inventory and production lines, other crucial data sources include Supervisory Control and Data Acquisition (SCADA) and Point of Sale (POS) systems (Bosi et al., 2020). In IIoT, CPS and IoT technologies enable firms to acquire data from the aforementioned sources of their value creation process (Ilin, Levina, Borremans, & Kalyazina, 2021). Furthermore, the advent of 5G network technology further exponentiates the size and availability of data that entrepreneurial firms can utilize for value creation (Bärring et al., 2018). However, data, although valuable even in its raw state, is often meaningless if unprocessed. Hence, once generated or ingested by a firm, the data needs to be processed into information so that it can become meaningful and useful in decision-making (Arunkumar & Kannimuthu, 2020).

CAPABILITY The clusters and assets equipped with sensors capture data and communicates in real time to the cloud, where it can be accessed by other assets and clusters in the value chain. This helps keep the assets in an optimized setting, increasing the assets’ productivity (Stock et al., 2018). In addition, data can also be analyzed to help manage the assets and keep decision-making in line with real-time developments. It is the analysis of data that can be the basis of a firm’s capability. This is because firms can

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differentiate their data analytics capabilities based on the algorithms they develop or adopt. Although firms can outsource their data analytics, firms with the capability to develop a unique in-house machine or deep learning algorithms can make better and unique decisions based on their distinct and proprietary algorithm (Cavalcante, Frazzon, Forcellini, & Ivanov, 2019). Examples include analysis of production costs data through machine learning or the use of data derived from production processes or product lines to model a digital replica. The digital replica of the real-life object can be used to run simulations that may not be feasible or practical in the real world due to cost, time, or other organizational constraints (Di Vaio et al., 2020; Gaikwad et al., 2020). Furthermore, the development or attainment of digital capabilities through servitization enables firms to add a digital dimension to their value creation processes. This subsequently enables firms to deliver greater value to their customers by including service provision in value creation and delivery, i.e., firms do not just focus on creating a distinct product. Instead, they enhance the product to include services. This has resulted in a proliferation of the servitized or “as-a-service” business model as firms seek to servitize their value creation process (Hung, 2019). The “as-a-service” approach enables firms to differentiate themselves based on the value created, especially in this current age where economic value derived from manufacturing needs to be coupled with ecological value. Practical application of the “as-a-service” business model can be seen in the emergence of various forms of data servitization capabilities such as Data Analytics-as-a-service (DAaaS) (Unhelkar & Trivikram Rao, 2020), Data Science-as-a-Service (DSaaS) (Elshawi, Sakr, Talia, & Trunfio, 2018), and Data Mining-as-a-Service (DMaS) (Del Giudice et al., 2021; Dong et al., 2020).

COMPETITIVE ADVANTAGE The expected results of Big Data’s real-time analytics capability are reduced downtime, idle time, production errors, and overproduction (Stock et al., 2018). Data can also be available to stakeholders and members of the value chain so that they may be well informed of the impacts of their activities (Bonilla, Silva, Terra da Silva, Franco Gonçalves, & Sacomano, 2018). All these results in reduced energy usage and waste generation whilst concurrently increasing asset efficiency – a sustainable value creation process (Müller et al., 2018). Furthermore, data mined from the various data points still needs to run through analytics systems so that meaningful information can be deduced from it. This is where analytics for Big Data plays a critical role, i.e., deriving information from the data, usually from in-house, open-source and/or proprietary analytics software (Adamu et al., 2021; Bosse, Nahhas, Pohl, & Turowski, 2019). Once the firm’s decision-makers are furnished with meaningful information from the analyzed data, they can make decisions in line with the competitive nature of their business environment. In fact, entrepreneurial firms that can capture, leverage, and perform analytics of the data closer to the point of a collection can make further savings to energy use, further reducing waste generated whilst increasing the efficient utilization of the firm’s assets. Furthermore, the firm’s privacy, data security, and bottom-line performance are all simultaneously improved due to

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Entrepreneurship and Big Data

Stage 1

Stage 2

•Data Extracon

•Data Repository

Stage 3 •Data Exploitaon Tools

Stage 4 •Advanced Analycs Lab

Stage 5 •Connecvity (Ulizaon)

Big Data Advanced Analytics

FIGURE 2.4  Example of data pipeline in commercial application with Repsol. Source: Repsol (2019).

developing a real-time analytics capability (Pääkkönen & Pakkala, 2020; Shahzad et al., 2020a, 2020b). Such is the importance of Big Data and analytics capabilities in securing and sustaining the future that many firms across numerous sectors are embracing or drafting digital strategies. For instance, companies in the energy sector such as Repsol consider obtaining digital capabilities such as data and analytics as being one of the key components in their strategy. The potential of value derived from data and analytics is best illustrated by Repsol’s ability to leverage data from over 6 million customers, i.e., approximately 100 million transactions per year. Development and application of data analytics techniques such as machine learning could help Repsol predict future customer behavior and make entrepreneurial decisions such as optimal pricing of their products and services. However, to also ensure that the transition to sustainability does not just focus on the economic aspect, data models are also utilized and applied to find the optimum blends of crude and process optimization and simulation. The application of data analytics ensures the efficiency of refineries’ operations; it also ensures that the carbon intensity of refinery operations is lowered (Repsol, 2019). Figure 2.4 illustrates the pipeline of data and analytics of Repsol. Hence, firms must develop some form of digital strategy to leverage and capitalize on the big data made available by IIoT technologies. The IIoT technologies data can help firms develop data lakes to analyze and make informed decisions in the value creation process.

CONCLUSION This chapter analyzed the Industrial Internet of Things (IIoT) potential applications, also known as Industry 4.0, and technologies towards sustainable value creation. From this overview of the applicability, data was identified as being at the core of IIoT. Developments in digital electronics have enabled firms to capture more parameters of data. Hence, data is generated and exchanged between the IIoT technologies constantly. The data exchange process is linked to all other IIoT technologies and the rest of the organization via the cloud. This data is in increasing volume and arriving at a higher velocity and in a variety of formats. To cope with this, firms need to have the necessary infrastructure to ingest, assimilate, and process this data. Hence, Big

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Data infrastructure is a necessary component. In addition to coping with the sheer volume of data, firms also need to have the analytics infrastructure to analyze and model this data for decision-making. The analytics needs to be conducted and communicated in real time. Other IIoT technologies and humans involved in the value creation process can make the necessary decisions that increase efficiency and reduce energy usage and wastage. Analytics enable decision making, even in real time, which is a critical capability in value creation. These aspects contribute towards making the process sustainable and ensures that the firm can be competitive. An example of such application is provided for a leading energy sector firm’s application of Big Data and analytics to optimize refinery operations to lower carbon intensity. Future research can have a deeper look at aspects within the sustainable industrial value creation process that IIoT can further enhance. Also, given that IIoT can potentially impact industries by allowing new innovative business models, it would be interesting if future research could investigate the influence of IIoT on firms that focus on ecological innovations. Future research could also take a context-specific approach to applying IIoT technologies towards sustainable value creation. For example, different industries and sectors may utilize IIoT differently to ensure sustainable development. This can be seen in the importance of sustainable value creation in certain sectors, such as energy, especially towards optimizing energy generation and distribution. It would be interesting to note how energy transmission and distribution companies could benefit from analyzing large data sets generated from Internet Protocol (IP)-enabled digital meters for consumers. Lastly, empirical testing would help to validate this proposed framework.

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Robotic Process Automation (RPA) in Global Business Services (GBS) New Insights for Entrepreneurs Dayana Jalaludin, Siti Nursyahirah Abd Aziz, and Eng Kar Seong Universiti Sains Malaysia, Malaysia

CONTENTS Global Business Services – the New Way of Business�������������������������������������������� 26 Robotic Process Automation (RPA) – an Introduction�������������������������������������������� 26 Blockchain����������������������������������������������������������������������������������������������������������27 Artificial Intelligence������������������������������������������������������������������������������������������ 27 Virtual Agent������������������������������������������������������������������������������������������������������� 27 Cybersecurity������������������������������������������������������������������������������������������������������ 27 Robotic Process Automation������������������������������������������������������������������������������� 27 Feasibility of RPA in Supporting Management Functions�������������������������������������� 29 Implementation Pathways of RPA in the Organization������������������������������������������� 31 Establishing Strategic Goals������������������������������������������������������������������������������� 31 Critical Process Assessment�������������������������������������������������������������������������������� 32 Tactical Evaluation���������������������������������������������������������������������������������������������� 32 The RPA Maturity Model������������������������������������������������������������������������������������ 33 Level 1 Pilot�������������������������������������������������������������������������������������������������������� 33 Level 2 Ramp Up������������������������������������������������������������������������������������������������ 34 Level 3 Operations at Scale��������������������������������������������������������������������������������� 34 Level 4 World Class�������������������������������������������������������������������������������������������� 34 Challenges of RPA Implementation������������������������������������������������������������������������� 34 Morale Impact on Employees����������������������������������������������������������������������������� 35 High Cost of Robot Maintenance������������������������������������������������������������������������ 35 Limitation of RPA����������������������������������������������������������������������������������������������� 35 Conclusion��������������������������������������������������������������������������������������������������������������� 35 References���������������������������������������������������������������������������������������������������������������� 36 DOI: 10.1201/9781003097945-3

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GLOBAL BUSINESS SERVICES – THE NEW WAY OF BUSINESS Global Business Services (GBS), sometimes known as Global Business Centre, is a new business model where GBS exists as a one-stop center that provides various functional solutions, often completing the whole beginning-to-end cycle. For instance, a GBS center located in Penang, Malaysia, provides procurement-to-­ payment service for its various business units located in various countries on the Asian and European continents. Here, GBS specializes in providing related financial services for its various wings operating in different parts of the world. In this new business model of a ‘one-stop service center’, work functions are highly centralized, thus integrating work and business processes with governance. The arrangement of this new business model is practical as it allows optimization of processes and lowers costs, where a GBS may focus on managing administrative employees and resources, leaving the business with more space to focus on managing the customers and increasing productivity. GBS differs from traditional shared-services organizations as GBS groups emphasize optimizing their workforce specialists from various functional areas such as IT, human resources, finance, and many more. GBS handles a range of processes that take a system on services from beginning to end and deliver complete functional solutions. Meanwhile, the traditional shared-services organizations would focus only on supporting tasks associated with a single function. Among the main advantages of GBS is its flexible business structure, which allows for responses and solutions being provided in a fast and systematic manner to the customers, thus promoting good internal customer management. GBS employees are located not only in the office, but work may also happen both in the office and at home. Considering the fast development that comes with Industrial Revolution 4.0 (IR 4.0), GBS has become a familiar scene in many global businesses, especially those that foresee the importance of leveraging the new technology as part of their competitive strategy. The Enterprise Resource Planning (ERP) system software, in particular, is used for daily business tasks as it allows for integration and automation of many functions along the administrative chain, including risk management, financial management, customer management, project management, and value chain management. This chapter explores how GBS units may function with the support of emerging technology such as Robotic Process Automation (RPA), a part of the ERP system. It specifically discusses how automation and artificial intelligence support accounting and finance functions and give more room for service efficiency and costsaving, particularly for multinational companies.

ROBOTIC PROCESS AUTOMATION (RPA) – AN INTRODUCTION The technological development that comes together with Industrial Revolution 4.0 has allowed business firms to adopt various techniques to enhance their business operations, mainly when they are huge, with subsidiaries operating in different parts of the globe. Among these technologies are blockchain, artificial intelligence, virtual agent, cybersecurity, and robotic process automation.

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Blockchain The blockchain is a digital ledger of economic transactions that allows the sharing and updates of related information by many users in the network. Here, all users would be alerted and informed whenever there are any network transactions (Ammous, 2016).

Artificial Intelligence On the other hand, artificial intelligence (AI) is an intelligent machine that can mimic human actions when performing tasks. Among the examples of AI would be a selfdriving car and drone. The specialty of AI lies in its ability to learn and recognize the pattern that emerges from a big volume of data, such as language processing, machine learning, and deep learning (Ammous, 2016).

Virtual Agent In today’s world, effective communication must happen fast and go beyond distance and time zone to a world-class business to remain competitive. Computergenerated technology such as the virtual agent plays the role of a customer service representative. It allows for intelligent conversation with the users, enabling adequate responses and behavior by business firms when being approached by their customers (Lun, n.d.).

Cybersecurity Given that the virtual world is now as important as our physical world, cybersecurity is a crucial aspect of today’s business organization to ensure a business firm’s integrity and assurance of confidentiality. Invulnerable defense from threats such as malware and viruses can be put in place only when there is a strong foothold on cybersecurity that encompasses “tools, policies, security concepts, security safeguards, guidelines, risk guidelines, risk management approaches, actions, training, best practices, assurance and technologies that can be used to protect the cyber environment and organization and user's assets” (ITU, 2009; Syed et al., 2020).

Robotic Process Automation Robotic Process Automation (RPA) is a new approach to business processes automation. RPA is a construct of three main elements, i.e., automation, process, and robot (Anagnoste, 2017). A robot means software or machine that can be programmed to resemble a human being and replicate certain human movements and functions. Meanwhile, the process is a series of work done to achieve certain goals, and objective and automation are the changes of work from manual operation to another form of operation (Chacón-Montero, Jiménez-Ramírez & Enríquez, 2019).

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Generally, the utilization of RPA allows humans to focus on more value-added work as it takes over repetitive tasks and produces error-free results. Through RPA, tasks may be replicated and repeated within a system and between different systems (Kappagantula, 2019; Sing, 2019). Figure 3.1 depicts the general uses of RPA in the organization, which include: (1) generation of a report via automated extraction of data; (2) quality assurance via regression testing processes and automated case scenarios; (3) full-fledged, error-free data migration; (4) performing multiple tasks via data processing and manipulation; (5) information validation and auditing as it checks on compliance and cross-verifies data; and (6) technical debt management via custom in-house generated software that aims to reduce the gap between systems. There are several benefits of RPA. Firstly, it can achieve cost savings for GBS as well as for entrepreneurs. With automation of work processes, administrative costs can be reduced by up to 50%. This is because the RPA can automate the manual works. It is expected that GBS has a lot of repetitive processes, while for start-up or small entrepreneurs’ businesses, there are many manual administrative works. Secondly, it is 24 hours and 365 days of productivity. It simply means that by using RPA, productivity can be increased and maximized. Robots can work around the clock, day and night, weekdays and weekends, nonstop. Human labor can work only during working hours, and if needed to work overtime or during weekends, there is additional labor cost on overtime payment. Thirdly, it increases the speed to complete the tasks. RPA can reduce the time spent on labor-intensive or repetitive tasks by 25%. Lastly, accuracy can be assured. Robots don’t get judgment calls, and they do not get tired. It can avoid human mistakes or errors, especially when there is a huge repetitive manual works to be performed (Gupta, 2018).

Automated report generation Technical debt management

Quality Assurance

General use of RPA Information validation and auditing.

Data migraon Perform Multiple Tasks.

FIGURE 3.1  General use of RPA. Source: Kappagantula (2019); Sing (2019).

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There are two ways that GBS can leverage RPA. The first way is through outsourcing. Some GBS are engaging consultation firms like Deloitte, KPMG, or BDO to automate their internal processes using RPA. The second way is to have an internal team that specializes in RPA. This is somewhat more challenging yet sustainable for GBS, and most of the GBS is having an RPA-specialized team internally. One GBS, IBM, acquired a Brazil-based RPA provider to support extreme automation, enabling IBM to have in-house RPA automation capabilities (IBM, 2020). On the other hand, the entrepreneur Michael Dell, who owns the GBS of Dell Technologies in several countries, also started to use RPA to automate his business processes. Not only was Michael Dell an early user of RPA, but his company was one of the pioneer companies to implement the bots for the human resource process by using RPA (Automation Anywhere, 2020a). Some GBS has also formed the RPA Ambassador group to promote the RPA and guide the respective functions in using RPA software. GBS of large multinational companies often use RPA to automate processes. However, for entrepreneurs who are just beginning their start-up journey, usually, RPA is not a priority for them, as they are focused more on product development, sales, and hiring. However, start-up entrepreneurs often encounter the same issues that larger firms or GBS do, although it is just on a smaller scale and could benefit significantly from automation technologies from the start. RPA has become accessible enough, and it is easy for start-ups to benefit from the technology almost from the beginning (Malina Platon, 2020). About 90% of businesses worldwide and 99.9% of companies in the United States fall under the small business category (Saba Mirza, 2020), and they have started to embed RPA into their business. This shows that nowadays, entrepreneurs who want to start up the business are required to focus on sales and automation to achieve competitive advantage and benefit from automation technologies, including time and cost savings in the long term. The RPA can launch and operate other software (Frankenfield, 2018), thus giving it superiority in carrying various types of tasks in specific orders, either high or low in volume, repetitive or recurring on a low scale, and involving a substantial or small amount of data. However, the RPA also has its limitations. It can function only based on the program formatted on the software, and any discrepancies would disable the processing system. In other words, processing and decision-making via RPA can happen only when structured data are accompanying it and the business rules have been explicitly defined. Furthermore, as the RPA is automated based on limited thinking ability, it lacks common sense in making a complex judgment. Any error in the coding or robotic framework will lead to repetitive errors until the flaws are corrected. The GBS uses two common software of RPA, which is Automation Anywhere (AA) and UiPath.

FEASIBILITY OF RPA IN SUPPORTING MANAGEMENT FUNCTIONS The RPA is an easy and convenient technology for GBS firms. These organizations operate beyond borders hence involving communications that cross different countries, time zone, and local cultures. In the GBS business model, tasks need to be performed with a high standardization process involving formalized processes,

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considering there is usually involvement of huge data and long-distance interaction by parties from different parts of the world. In short, the processes’ efficiency can happen only when there is a standardization of inputs, streamlining of processes, and continuous improvement of the system (Deloitte, 2018; Ali et al., 2021). As for startup or small entrepreneurs, RPA can help perform administrative tasks such as open files or web pages, look for specified data, cut and paste data from one application to another, fill out forms, and a lot more. It is something like digital and virtual employees in a digital workforce, working alongside to help entrepreneurs thrive (Automation Anywhere, 2020b). This can help start-up entrepreneurs when they have challenges in hiring the right person in the right position. They do not need to hire a local employee with high wages to perform a high volume of repetitive tasks or hire the high expatriate cost on commission and visa. Start-up entrepreneurs need to develop the bots by RPA on repetitive and manual tasks. It is always easier to adopt the RPA at the beginning stage rather than to adopt it later when all the systems and processes have been developed. Software providers such as Automation Anywhere and UiPath do provide the training for the users. Many resources are also available to help the users to build the bots. The tools and functions of the software are user-friendly and easy to use as it is a drag-and-drop process workflow design. The RPA, when being utilized correctly, would fulfill the requirements mentioned earlier due to its ability to provide continuous and error-free services. Robots can work 24 hours a day year-round and perform tasks strictly according to instructions, thus eliminating the non-value-added work throughout the processes. Additionally, the highly automated work environment provided via RPA implementation does not require any special skills on the part of the end user, allowing smooth deployment of present tasks and easy transformation when there is migration in the systems. Here, all that is required would be software programming changes, and it would not involve re-training employees or hiring new staff. Such conditions give high empowerment on the employees’ side and benefit the GBS firms in terms of time savings, cost reduction, and resource optimization (Nizri, 2017; ACCA 2018). In many aspects of the accounting and finance functions, some of the tasks may be repetitive, while others may need to be conducted via several types of tasks in a specific order. For example, at the logistic part of a GBS firm, the RPA ensures smooth processing of information from the websites of transport companies, forms the pool of orders, and confirms the order execution. Here, the accounting and finance function of the GBS would rely on the efficiency of RPA in supporting the management of bulk orders involving invoice processing, sales execution, and customer services (Frankenfield, 2018). Here, the robots would load information into the accounting system, subsequently starting processing orders by integrating the information with various departments. Using RPA on accounting and finance functions at the beginning stage for start-up entrepreneurs is relatively more straightforward. They can engage external consulting firms to help to automate the accounting and finance function in the first place. After deploying the bots on accounting and finance functions, the enterprise can train their internal accountants to maintain and use the bots to perform daily accounting tasks like matching the invoices with shipping documents or monthly accounting activities such as journal booking and balance sheet reconciliation.

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Another example of RPA implementation is in the area of human resource (HR) management. The robots can detect and manage the large bulk of information involving the thousands of employees in the GBS firms in an error-free and fast manner. In terms of recruitment and staff management, the RPA system permits resumes from various locations, allowing the detection of suitable human talent, thus resulting in better human resource optimization. In any organization, there will always be changes involving HR policies from time to time. Through RPA, there would be smooth coordination between the HR and accounting and finance function, thus allowing easy adaptation in payroll and other related benefits (Frankenfield, 2018).

IMPLEMENTATION PATHWAYS OF RPA IN THE ORGANIZATION The following conceptual model provides a general guideline for how business firms may implement RPA in a structured manner (Santos, Pereira, & Vasconcelos, 2019). This model can be utilized by organizations in implementing RPA for the first time or to improvise their current RPA usage. There are three main steps in using this model, i.e., strategic goals, process assessment, and tactical evaluation (Figure 3.2).

Establishing Strategic Goals The first step in RPA implementation is to establish the alignment between automation and its present goals. Establishing the right strategic goals when automation is put in place is essential to guiding the whole process and motivating the users. During this first step, the advantages of having the RPA and its disadvantages and limitations must be clearly spelled out to ensure all the objectives set would be suitable, achievable, and realistic. Additionally, future challenges that come with the new technology and the long-term planning must also be taken into perspective. GBS or start-up entrepreneurs are all required to establish strategic goals by considering automation. This is because the initial investment and subsequent payment on an RPA license is high. Fifty percent of companies with RPA initiatives have fewer than ten bots in production (IBM, 2020). It shows that half of the companies with RPA are not fully utilized RPA. Thus, businesses must include automation and digitalization in strategic goals. DHL Global Forwarding, Freight (DGFF) is improving its finance and logistics processes across its five GBS. DGFF included automation in its goal: to

FIGURE 3.2  RPA conceptual model. Source: Santos, Pereira, and Vasconcelos (2019).

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create a global process automation hub to remove non-added-value processes within the entire organization (UiPath, 2020). Before using RPA, DGFF had a team of 30 employees complete the same process. Using UiPath, one of the RPA software, 15 of these employees changed their roles to higher-value, more rewarding work. It saves 50% of manpower by using RPA (DHL, n.d.).

Critical Process Assessment Selecting the correct process is vital to ensure that the automation using RPA would meet its intended objectives in an effective, efficient, and cost-saving manner. For instance, a thorough checklist regarding the features that constitute a process would provide some directions to help a firm decide on the suitability of automation for the process chosen. Here, automation’s reliability, especially in generating return on investment as scheduled, would be evaluated to avoid investment wastage. It is also vital to pay some attention to foreseeing the future challenges and consequences that may arise due to the disadvantages of RPA. Such an attitude would also assist in preventing the firm from choosing the wrong processes to automate. For example, the automation of low-volume processes that currently require only a small number of employees may not benefit the firm, particularly in covering its monetary and nonmonetary costs. Both GBS and entrepreneurs must perform a cost-benefit analysis before starting the automation by RPA on the processes. It needs to balance the time spent on automating the process, which depends on the complexity of the process and the time saved. Usually, there is a checklist or automation feasibility assessment template to be filled for each process that is going to be automated by RPA. This is to assess if the process is feasible and suitable to be automated by RPA. The feasibility assessment template consists of two categories. The first category is process input. In this section, the subject matter expert in GBS, or users, or entrepreneurs must check if the process input is an electronic format, scanned format, hard copy, unstructured input, or structured input. If the process input is mostly unstructured input or hard copy and scanned format, it is harder to automate by RPA. The second category is technical feasibility. This section focuses on whether the process involves other software like Citrix, Oracle, or any other software and whether it includes judgmental decision-making such as credit assessment. If it involves judgmental decision-making, the feasibility will be lower (Kopper, Rodrigues, Zomb & Zuccolillo, 2020).

Tactical Evaluation Once the process to be automated has been identified, the next step is to map to accomplish the targeted results. Tactical evaluation is mainly about planning to get the job done, thus achieving the strategic objectives set earlier. Among the factors considered during this stage are time, money, the talent available, and the risks and challenges encountered. The tactical evaluation approach would lead to determining the most efficient way to optimize the resources and achieve quality results. Through tactical evaluation, the judgment regarding the value of the RPA for the selected process would be reported. In this stage, businesses start to plan the timeline to

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automate the process with the necessary resources. For GBS, approval is needed by the management, and the timeline is planned and set by the in-house RPA developer. For entrepreneurs, it is a bit different from GBS, as the entrepreneurs need to ensure the availability of fund and budget, the number of resources to be involved, the targeted timeline to complete, or if is outsourced the process to automate, then the considerations and planning will be more towards the communications with consultation firms in explaining the internal process.

The RPA Maturity Model Once a firm has implemented the RPA, it is essential to know its maturity level to progress further by strategizing for improvement and moving to the next ideal level. There are four RPA maturity advancements, starting with Pilot, Ramp-up, Operations at Scale, and World Class. Here, the maturity level would reflect the capabilities of an organization regarding its RPA implementation (Rosemann & Bruin, 2005). It describes how systems, processes, and firms progress and evolve as defined, implemented, and improved (Clark & Jones, 1999). A consistent definition of the levels is also crucial in making sure the progression of the business is well-tracked (Figure 3.3).

Level 1 Pilot The pilot stage is the starting point of RPA execution. The RPA is initiated in the firm via a small-scale project where the feasibility and achievability of automation would be carried out during this stage before the firm goes all out and continues with the full project implementation. The pilot stage enables the employees to gradually adapt to the changes which would affect their daily routines and tasks. During this stage, the acceleration of learning happens among the users, creating the best practices for the next projects. The pilot stage is essential as it enables a business to control the underlying risks and foreseen negative impacts before committing to a more substantial amount of resources in the future. The setup of the objectives, particularly on the areas that the firm intends to automate, and the setup of the expectations in terms of best- and worst-case scenarios are among the main steps during the pilot stage. A pilot project cannot be too simple, and issues and flaws that arise during this stage must be viewed as a learning process and not a failure. Mistakes may happen during this initial stage, but what is more

Level 1 Pilot

Level 2 Ramp Up

Level 3 Operations at Scale

Level 4 World Class

FIGURE 3.3  RPA maturity level framework. Source: https://www.ibm.com/downloads/cas/ WZRJZR8Q.

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important is making room for improvement before the firm advances its RPA implementation into the ramp-up level.

Level 2 Ramp Up Once the pilot stage is successful, the firm can gradually increase the implementation of RPA and move to the ramp-up level where there would be an expansion of RPA implementation into single or multiple lines of business. Here, greater attention is typically given towards opportunity identification, process design, and build-out of robots. During this stage, the focus would be largely on establishing the best practices to automate the selected process and expanding on the internal automation and expertise of the RPA technology. The ramp-up level is often related to the venture capital aspect of the firm as it gives answers regarding the return of investment achievement.

Level 3 Operations at Scale Scalability is about having enough capacity and capability to operate at scale. It relates to whether a firm has enough funds and expertise to operate the process via RPA at scale. The Enterprise level of RPA is an end-to-end program starting with a vision, strategy, governance, measurement, and operational capabilities. A firm needs to set up a transparent governance model when planning to operate at scale. There needs to be clarity on the person in charge before, during, and after RPA implementation, particularly one who manages the material and processes RPA will handle, and how each employee will be involved with RPA. A strong foundation must be put in place to create highly adaptable robots that work well alongside the engaged human workforce to achieve the desired outcomes. The implementation of RPA at scale requires responsive technological support from the Information Technology side in the firm.

Level 4 World Class World class means scaling up the implementation of RPA beyond the enterprise level. The firm might want to move forward by adopting transformative components into its RPA program, given its successful RPA implementation at the enterprise level. Integration between RPA with other technology such as Artificial Intelligence (AI), Cognitive Machine Reading (CMR), and also Machine Learning (ML) could give more benefit to the firm. For example, the integration of RPA and AI would enable unstructured data to be processed without employers’ intervention, where previously, RPA could work only with structured data.

CHALLENGES OF RPA IMPLEMENTATION Besides its technical related issues (as discussed in the introductory part of the ­chapter), the RPA possessed three main challenges that need to be addressed by the firm wisely, i.e., the moral impact on human employees, the high cost of robot maintenance, and the limitation of RPA.

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Morale Impact on Employees The replacement of humans with robots and the relocation of employees to work on another task may create tension among employees. Some employees may be reluctant to change, while others may see robots as their opponents and started to be demotivated in their work. Thus, talent management between humans and robots is a crucial issue in the RPA environment and needs to be addressed right away during the early stage of the robotic software execution (The Impact of RPA on Employee Experience, 2019).

High Cost of Robot Maintenance The RPA system requires consistent maintenance, which could also be costly to the firm. The recurring changes due to the updates of the user interface and the robots’ reconfiguration can sometimes be time-consuming and disrupt the firm’s productivity. On some prior occasions, the RPA team has not been informed earlier by the software provider about the changes. This created the team's need to continuously observe the robots (Stople, Steinsund, Jon, & Bygstad, 2017).

Limitation of RPA Although RPA brings many benefits to businesses, RPA has its limitations. RPA cannot automate everything. Firstly, handwritten or scanned documents are not able to be automated by using RPA. RPA requires structured data. This limitation prevents some GBS or small business entrepreneurs still using hard-copy documents, such as invoices, shipping documents, payment vouchers, or journal vouchers, from fully utilizing the RPA. Secondly, those non-rule-based judgment calls are not able to be automated by RPA. When the processes require some judgment or decision-making, RPA cannot be automated (Indico, 2019).

CONCLUSION RPA is an advanced technological tool that can help GBS firms boost their productivity and quality as it automates the firms’ processes. However, in choosing what task to be automated, firms should carefully consider the limitation and future challenges of RPA implementation, especially regarding its effect on the tasks and workforce. Although RPA may look simple and the implementation of this technology seems easy. According to Business Today, the failure rate in implementing RPA at the initial project point can be as high as 30 to 50 percent (Das, 2018). Interestingly, this statistic reflects not a technological flop, but mainly the failure to set up a good framework and choose the right process to be automated. The automation of a complex project will contribute to higher risks and the possibility of failure. Hence, any firms that would like to implement RPA should take cautious steps by understanding the ropes of its execution, for instance, as guided by the RPA implementation pathways and progress maturity level. Given that the sky is the limit, we foresee that the future of RPA implementation in the GBS business model would progress towards integration

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between RPA with other advanced technology such as AI, Machine Learning, Natural Languages Processing, and many more.

REFERENCES ACCA. (2018). Embracing robotic automation during the evolution of finance. Retrieved from https://www.accaglobal.com/gb/en/professional-insights/technology/embracingrobotic-automation-during-the-evolution-of-finance.html. Ali, S. E. A., Lai, F. W., Hassan, R., & Shad, M. K. (2021). The long-run impact of information security breach announcements on investors’ confidence: The context of efficient market hypothesis. Sustainability, 13(3), 1066. Ammous, S. (2016). Blockchain technology: What is it good for? Retrieved from https://pdfs. semanticscholar.org/0f75/78d3b14efc547d2bee8b971de1218b5d073f.pdf. Anagnoste, S. (2017). Robotic Automation Process – the next major revolution in terms of backoffice operations improvement. In Proceedings of the International Conference on Business Excellence (Vol. 11, No. 1, pp. 676–686). De Gruyter Open. Automation Anywhere. (2020a). Dell leverages RPA to boost enterprise HR productivity with Workday by up to 85%. Retrieved from https://www.automationanywhere.com/images/ casestudy/Casestudy-Dell.pdf Automation Anywhere. (2020b). RPA for small business: Why and how to start. Retrieved from https://www.automationanywhere.com/company/blog/learn-rpa/rpa-for-smallbusiness-why-and-how-to-start Chacón-Montero, J., Jiménez-Ramírez, A., & Enríquez, J. G. (2019, May). Towards a method for automated testing in robotic process automation projects. In Proceedings ofthe 14th International Workshop on Automation of Software Test (pp. 42–47). IEEE Press. Clark, T., & Jones, R. (1999). Organisational interoperability maturity model for C2. In Proceedings of the 1999 Command and Control Research and Technology Symposium (pp. 1–13). Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.2 00.107&rep=rep1&type=pdf. Das, G., (2018). Robotic process automation failure rate is 30–50%, says EXL CEO Rohit Kapoor. Business Today. Retrieved from https://www.businesstoday.in/sectors/bpo/ robotic-process-automation-failure-rate-indian-bpo-business-process-outsourcingexl-ceo-rohit-kapoor/story/267187.html. Accessed on 20 November 2019. DHL. n.d. Bring on the bots! – how automation can break through bottlenecks and boost business. Retrieved from https://www.dhl.com/global-en/spotlight/digitalization/logisticsautomation.html/. Accessed on 22 November 2020. Frankenfield, J. (2018). Robotic Process Automation. Retrieved from https://www.investopedia.com/terms/r/robotic-process-automation-rpa.asp. Accessed on 1 November 2019. Global RPA Survey 2018: Deloitte. (2018). Retrieved from https://www2.deloitte.com/bg/ en/pages/technology/articles/deloitte-global-rpa-survey-2018.html. Accessed on 18 November 2019. Gupta, M. (2018). Minimize cost, maximize productive output with RPA. Retrieved from http:// www.gridinfocom.com/minimize-cost-maximize-productive-output-with-rpa/. Accessed on 21 November 2020. IBM. (2020). How to get started with extreme automation using AI-driven RPA tools. Retrieved from https://www.ibm.com/cloud/blog/get-started-with-extreme-automation-using-aidriven-rpa-tools Indico (2019). Implementation pathways of RPA in the organization. Retrieved from https:// indico.io/wp-content/uploads/2019/08/Everest-Group-Unstructured-Data-ProcessAutomation.pdf. ITU. (2009) Overview of cybersecurity. Recommendation ITU-T X.1205. Geneva: International Telecommunication Union (ITU). https://www.itu.int/rec/T-REC-X.1205-200804-I/en.

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Kappagantula, S. (2019). Robotic Process Automation (RPA) tutorial – learn to automate tasks in RPA. Retrieved from https://www.edureka.co/blog/rpa-tutorial/. Accessed on 16 November 2019. Kopper, V., Rodrigues, G., Zomb, M., & Zuccolillo, F. (2020). Implementing robotic process automation for internal process optimization. Retrieved from https://web.wpi.edu/Pubs/ E-project/Available/E-project-051920-165454/unrestricted/MQP_Report_Final.pdf Mirza, S., (2020). RPA for small business: why and how to start. Retrieved from https://www. automationanywhere.com/company/blog/learn-rpa/rpa-for-small-business-whyand-how-to-start. Nizri, G (2017). Business benefits of robotic process automation. Retrieved from https://ayehu. com/business-benefits-robotic-process-automation-2/. Platon, M., (2020). Every entrepreneur should befriend a robot. Retrieved from https://www. entrepreneur.com/article/345180. Rosemann, M., & Bruin, T. D. (2005). Towards a business process management maturity model. Retrieved from https://eprints.qut.edu.au/25194/. Santos, F., Pereira, R., & Vasconcelos, J. B. (2019). Toward robotic process automation implementation: an end-to-end perspective. Business Process Management Journal. Stople, A., Steinsund, H., Jon, I., & Bygstad, B. (2017). Lightweight IT and the IT function: experiences from Robotic Process Automation in a Norwegian Bank (Vol 25, No. 1). Retrieved from https://ojs.bibsys.no/index.php/Nokobit/article/view/405 Syed, E., Azhar, A., Fong-Woon, L., & Rohail, H. (2020, November). Socio-economic factors on sector-wide systematic risk of information security breaches: Conceptual framework. In Proceedings of the International Economics and Business Management Conference, Melaka, Malaysia (pp. 2–3). The impact of RPA on employee experience. (2019). Retrieved from https://dfe.org.pl/wpcontent/uploads/2019/04/Forrester_RPA-Impact_Employee-Engagement.pdf UiPath. (2020). Growing shared services capabilities via RPA-enabled human and virtual service centers. Retrieved from https://www.uipath.com/resources/automation-casestudies/dhl-global-forwarding-freight#:~:text=The%20goal%20is%20to%20 use,the%20Division's%20ongoing%20business%20goals/. Accessed on 22 November 2020. Van Lun, E. (n.d.). Virtual Agent, animated intelligent agent for automated chat with human users. Retrieved from https://www.chatbots.org/virtual_agent/. Accessed on 20 November 2019.

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Social Entrepreneurship in an Era of Disruption Converging Social Change and Sustainability Through Big Data Analysis in a Post-COVID World Manmeet Bali Nag Manav Rachna University, India

CONTENTS Introduction: Nature and Essence of Social Entrepreneurship and Its Variants������� 39 Big Data Leveraging Social Entrepreneurial Mechanisms��������������������������������� 40 Current Trends and Futuristic Endeavors of Leveraging Big Data Towards Viable Social and Business Paradigms�������������������������������������������������������������������� 42 Social Entrepreneurship in the COVID-19 Era: Extrapolating Models/Interventions From Past and Present����������������������������������������������������������� 45 Monetizing Social Value Creation Through Data: Yunus Social Business Model��������������������������������������������������������������������������������������������������� 46 Initiatives and Associated Dimensions of Disruption, Data, and Social Innovation������������������������������������������������������������������������������������������ 46 Big Data Analytics and Innovation: Amazon Business Model and Social Cause Alignment������������������������������������������������������������������������������� 47 Implications and Challenges Associated with Viable Business Propositions, in Social Entrepreneurial Perspective���������������������������������������������������������������������� 47 Conclusion��������������������������������������������������������������������������������������������������������������� 50 Bibliography������������������������������������������������������������������������������������������������������������ 51

INTRODUCTION: NATURE AND ESSENCE OF SOCIAL ENTREPRENEURSHIP AND ITS VARIANTS With time, social entrepreneurship is advancing as a prime driver in philanthropy clubbed areas with business ideation. In the pursuit of this endeavor, a societal commitment attains hues of a mission-oriented entrepreneurial venture/model, leading to profits clubbed with a greater good. There has been huge traction in this entrepreneurial DOI: 10.1201/9781003097945-4

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terrain in the last few decades, which has amplified voices to attain social goals in various dimensions and sectors. There is a marked difference between social and conventional entrepreneurship and its market outreach, and venture capitalization go. The expanse of social entrepreneurship goes beyond the optics of profit-making towards the wider horizons of societal enhancement on various benchmarks of liquidating or diluting societal bottlenecks. The arena of social work and social enterprise stands at the crossroads of unprecedented times in history. The interjection of philanthropy in a business model has given a new meaning to employability and entrepreneurship. The capitalist economy, nurtured by social endeavors and models which are a harbinger of more significant rewards towards a bigger humanistic purpose, is the pursuit. Therefore, volunteerism, civic responsibility, and community development are becoming a business-oriented movement wherein social causes intertwine with profits, thereby creating a win-win situation. The transformational progress of systems should address a specific societal loophole genre and plug them in to sustain the entrepreneurial ecosystem. A whole ambit of societal transformation warrants structured channels to divert from routine profitmaking towards community sustainability. This sets the stage for convergence of Big Data and the social entrepreneurial terrain. Contemporary issues about environmental problems, climate change, population, poverty, illiteracy, malnutrition, imbalanced growth, and many huge problems are categories and subcategories of sustainable entrepreneurship’s more enormous social realm. Entrepreneurship is leveraging Big Data for proactive solutions. The convergence of financial stability, profit-centric approach, and social value creation is at the heart of any social entrepreneurial ecosystem. Some famous socially relevant business houses lead by the likes of Azim Premji (Wipro) in India, the Bill and Melinda Gates Foundation, and so on have attributed great relevance to social causes clubbed with corporate profits by leveraging Big Data. Big Data as technology helps synthesize extensive data about population, market insights, competitors, and marketing strategies, to form futuristic strategies (Adamu et al., 2021). Innovative technologies like Artificial Intelligence, augmented reality, blockchain technology, and the Internet of Things have thrown open many mobile applications for data analysis, processing, and inventions (Di Vaio et al., 2020). Large and diverse data sets are being structured, semi-structured, and unstructured with the help of advanced data analytics techniques, thereby facilitating a Big Data Value Ecosystem of analysts, researchers, and businesses (Figure 4.1; Cavanillas et al., 2016).

Big Data Leveraging Social Entrepreneurial Mechanisms Big Data is all prevalent now, and possibilities of its wide applications, for social good, multiply rapidly with the proliferation of ‘tools’ for collecting, collating, and analyzing such data. There is a pertinent need to generate and utilize data and build data systems for policy initiatives in the contemporary pandemic crisis. An emerging trend for applying Big Data for social good and triggering social change is increasingly noticeable. NASA researchers Michael Cox and David Ellsworth coined the term Big Data in 1997 to refer to supercomputers generating massive amounts of information that cannot be processed and visualized (Friedman, 2012).

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FIGURE 4.1  The dimensions of a Big Data Value Ecosystem (adapted from Cavanillas et al. [2016]).

While Big Data drives corporate profit, it also opens the doors to use Big Data in social entrepreneurship to alleviate the world’s most intractable problems like health, environment, strife, hunger, and poverty. Some researchers believe that data analytics is capable of creating a data bourgeoisie (Crawford, 2013). “A new crop of social entrepreneurs and activists see opportunities to improve the state of the world by making sense of the current-day data deluge” (Malik, 2013). Thus, Big Data is likely to emerge as a critical factor for practitioners in social relief by providing empirical tools for problem-solving. Big Data drives the formulation of marketing strategies by monitoring impacts in real time, thereby creating accurate customer profiles and predicting organizations’ futuristic profitability. Diverse data sets have now proliferated data points ranging from public records, point of sales systems, sensor data to intrusive cell phone histories, making it possible to have near accurate correlations with a high degree of precision. Most of the models and innovative techniques emerge from PPP (public-private partnerships) or non-profit-making enterprises. In 2007, Ushahidi (2007) helped spur donations for victims through mapping violence in post-election Kenya through user-generated data. It created a platform called the ‘Swift River’, whereby data transmitted by ordinary individuals through various digital means in a short period. The operational models of organizations like DataKind (n.d.) and Data Without Borders (n.d.) have been operating gratis to pairing data analysts with human rights organizations. Recently, they have been organizing workshops and ‘data dives’ to assist multilateral organizations like the World Bank analysis of multiple data sets for facilitating practices of ethics, honesty, and transparency in governance. Many such collectives or non-profit making enterprises (see Developers for Good, n.d.) considerably similar to them are operating globally and locally by supporting data scientists inclined to social causes. However, as such tools penetrate deeper for societal change, profit motives are also beginning to emerge for Big Data usage for social good.

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A potent model that can be cited is that of DemystData. A Hong Kong-based company uses Big Data to link financial institutions to individuals who are excluded from financial systems. This is achieved by mining data from social media and similar online sources to fill the financial market gaps and bring the marginalized under the radar. ‘Socially minded analytics’ have also been encouraged by open government initiatives, e.g., in the United States, President Obama initiated a model facilitating public by data usage. An executive order was issued, mandating that “All Federal Services would standardize and publicize available datasets, thereby making it easier for citizens to find and analyze government” (Kalil, 2012; Shahzad et al., 2021). The growth of ‘socially-minded data analytics’ is bolstered on the one hand by Artificial Intelligence (AI)-based analysis software and, on the other hand, by the proliferation of digital information affecting social problems. In this direction, IBM initiated a model incorporating data visualization tool in 2008, ‘Many Eyes’, to provide businesses (Del Giudice et al., 2021; Emerging Graphic Tool Gets People Talking, 2008) to draw applications from user-generated insights and innovation. As a result, this was followed by publicly available versus proprietary data and its takeaway. With the growth of data analytics usage in the social sector, a critical challenge remains the reliability of Big Data and the pitfalls of its standardization. “Data analytics for social change requires unflappable standards” (Bernholz, 2012), which would subsequently build on innovative thinking, commitment to openness, and utmost respect to personal privacy rights. Also of particular concern are privacy and data security issues in healthcare and the ethical use of Big Data for social upliftment (Ali et al., 2021). Projects and models in this direction are being launched by some organizations like mHealth Alliance (n.d.) which operate in countries like Bangladesh and South Africa (Gangadharan, 2013). Big Data and GIS are being optimized to limit the spread of COVID-19 in China and Taiwan, wherein real-time location-sharing mobile applications to track patients are being used. Similarly, Big Data is being synthesized into predictive models to isolate high-risk individuals and associated medical staff in Singapore countries. In America, widespread predictive analysis models have emerged to optimize the channels of the supply chain and apply data in enforcement measures. India has made strides by using data trends based on mobility to access the pandemic patterns and evaluate its transmission predictability. “Arogya Setu application is based on such patterns of geo-referencing to predict and accurately find contact tracing, for surveillance in hotspots” (Thehindu.com, 2020). Big Data analytics also utilizes social media as well as crowdsourcing platforms to put government initiatives into action.

CURRENT TRENDS AND FUTURISTIC ENDEAVORS OF LEVERAGING BIG DATA TOWARDS VIABLE SOCIAL AND BUSINESS PARADIGMS Since early 2020, the world has been surprised by the spread of a new coronavirus strain, later named COVID-19. It has severely disrupted the economy and social interactions, besides causing an immense strain on the health system. “While this crisis emerged simultaneously across many nations, it had similarities to natural disasters, which are “acute collectively experienced events with sudden onset, and

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which result in a catastrophic depletion of resources” (Kaniasty & Norris, 1993; Shah, 2012; USAID, 2020). Unlike other natural disasters which occur with sudden ferocity and disappear, the pandemic of COVID-19 is creeping on us slowly. It is impacting the daily routine of the way we live. “Here we are dealing with changes in the way we go to work and go about our daily lives, this kind of disruption is probably going to be happening for weeks or months – and we are not used to that disruption” (Bacq et al., 2020; Shahzad et al., 2020a, 2020b). Thus, “COVID-19 quickly warranted classification as a grand challenge to society, a problem that, like poverty, climate change, and diseases such as cancer, calls for a focused effort by entire disciplines and communities” (George & Howard-Grenville 2016; Hilbert, 1902). As is evident from contemporary sources and the unfolding of events across the world, the rapid spread of the deadly virus COVID-19 poses difficult yet solution-oriented questions to all of us to leap into a post-COVID world with a new perspective. Generally, emergency response teams directly responsible for dealing with it or impacted by it are the first to respond. However, in challenges like COVID-19, unique challenges are unleashed too while the crisis still unravels (Drabek & McEntire, 1993). Pandemics thus need result-oriented disruptive solutions to resolve the unprecedented situation. “While the world operates largely for self-gratification, it has been seen that major catastrophes unleash not the criminal [in society], but the altruistic” (Quarantelli, 1985). “This necessitates pro-social behaviors and the creation of emergent organizations to address human suffering” (Bacq et al., 2020). Thus, it is emerging that COVID-19 is no different from earlier challenges. The world is coming together in collaborative efforts towards alleviating social problems generated by the pandemic. Hence, COVID-19 presents us with opportunities to study extreme case studies to harness the utility of Big Data in the evolution of innovative quick response mechanisms for societal protection, growth and to manage grand challenges presented to society in a “[o]nce in a century epidemic” (Gates, 2020). Such game-changing extreme situations and case studies “often reveal more information because they activate more actors and more basic mechanisms in the situation studied” (Flyvbjerg, 2006). Such attempts of efficacy have been curated to focus on plausible interventions to the social ecosystem around COVID-19. In this context, a credible case study is the “virtual idea blitz.” (Using Idea Contagion to Combat Virus Contagion – “Virtual Idea Blitz” by Kelley School of Business, 2020). The Virtual Idea Blitz had more than 200 people from across the world over seven days. As an example of usage of Big Data for social entrepreneurship, a framework of data resources, concepts, and their impact on handling the COVID-19 situation may be elucidated as follows. (a) Conception and ideation of social compassion (Kelley Business School initiative) Several initiatives have heralded concepts integrated from collective thought and brainstorming (Nager et al., 2011), Hackathons (Briscoe & Mulligan, 2014) and Sprints (Knapp & Zeratsky, 2016) and derived a coherent, integrated response to COVID-19. Virtual Idea Blitz was initiated by the faculty of Kelley School of

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Business, Indiana University, in order to frame compassionate post-crisis organizing in sync with the necessitated goals (Dutton & Worline, 2006; Williams & Shepherd, 2018) as well as with “Social entrepreneurship in the face of grand challenges” (van Tulder & van Mil, 2020). The exercises also represent “Entrepreneurial hustle: Navigating uncertainty and enrolling venture stakeholders through urgent unorthodox action” (Fisher & Stevenson, 2020). The parameters of crisis and collective compassion were mapped, and the best fit model of Social Compassion was enunciated for all practical and futuristic purposes. (b) Models of Sectored Social endeavors towards sustainability and development Workshops and research in the COVID scenario have pointed out remote patient monitoring and AI in healthcare. While innovations are done for emergent situations, their sustainability in the long term is essential for creating a stable ecosystem of harnessing the Big Data tools for the society at large. Solutions are being driven through explosive growth in telehealth (Olson, 2020). There is a huge demand for tele-solutions to provide health solutions through Remote Patient Monitoring (Griggs, 2018) and Artificial Intelligence-based solutions in healthcare. Using machine learning and artificial intelligence (AI) technology, data-driven firms from “Big Tech” to financial services, travel, insurance, retail, and media, make personalized recommendations for what to buy and practice personalized pricing, risk, credit, and the like using the data that they have amassed about their customers. (Theodoros Evgeniou, 2020)

Big Data gathered for other commercial purposes can be utilized to assess an individual’s susceptibility to COVID-19 and his/her financial and physical ability to fight the health challenges and economic outfall in case of an infection. On the one side, Big Data with regard to individuals’ lifestyles can extrapolate their susceptibility to infections, and on the other side, data with regard to their past medication and ­co-morbidities as assessed through their past decisions of availing insurance, medication, and hospitalization can be used to assess the risks and to mitigate them thereby. AI tools are increasingly being deployed during the current pandemic to provide solutions in the short term but would be helpful in sustainable benefits in the long run. Big Data is also being used for efficient and effective distribution of health supplies of masks, beds, PPE kits, medications, and ICU equipment. Optimum distribution of such assets amongst the affected population is of prime importance with data tools. Technology providers are thus providing solutions to fight the pandemic. A Georgia Tech team of five members (MIT, 2020) devised a tracking model focused at a national level, which extracts data from multiple live data sources and facilitates hospitals in predicting personal protected equipment (PPE) burn rate and identification of hospitals in critical need, in order to help suppliers in prioritization of hospital needs, and thereby direct their respective supplies of PPE equipment. Such models are the need of the hour to develop long-term tools for optimizing health services worldwide. Distance monitoring and curing patients through remote video conferencing have gained a new acceptance level during the pandemic. While earlier, there was a

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reluctance of people to adopt video meetings and consultations, the pandemic has led to widespread acceptance of remote consultations between doctors and patients. Many applications (Apps) are increasingly being used for tele-consultation, prescription, and medical supplies in India’s case. These tools and methodologies provide long-term solutions to health-related issues and other social sector interventions of poverty alleviation. (c) Futuristic foresight The startup ecosystem has been coming up with solutions well before the pandemic, but there has been a massive failure of startups due to customer orientation’s slow rate. Business is fundamentally a human endeavor – humans trying to connect to other humans. Products, technology, business models, funding – success in these dimensions result from getting people right. So ultimately startup success comes down to people – the people inside the organization and the people outside it. (Yohn, 2019)

In the future, customer orientation and providing design solutions to solve problems through Big Data would have to be the main objective of startups. Customized and flexible solutions would also be a major dimension of the emerging Big Data ecosystem within privacy and ethics limits. Language challenges through AI Tools that translate queries and answers in real-time communication would gain increased adaptation. Finally, a social entrepreneur’s financial sustainability cannot sustain for long on the largesse of philanthropists. So the period of free tech solutions would have to morph to low-priced solutions that are sustainable through revenue generation from customers and pharmaceutical and medical gadget suppliers. Big Data companies would also need to be given a share of the revenue pie. Otherwise, the entire emerging ecosystem could collapse, and this chance for sustainable change would be lost. One should draw lessons from earlier worldwide events like the two World Wars, the Great Depression, and the Spanish-flu pandemic. Each of these epoch marking events has led to great human imagination leaps, and the same would hold now.

SOCIAL ENTREPRENEURSHIP IN THE COVID-19 ERA: EXTRAPOLATING MODELS/INTERVENTIONS FROM PAST AND PRESENT Social entrepreneurship is of immense value and highly critical during a pandemic like COVID-19, as has been witnessed as a black swan moment in world history since March 2020. The “COVID-19 crisis has quickly become an unprecedented grand challenge that created many social, health, and economic problems on a global scale” (Gates, 2020). “These problems overwhelmed existing healthcare and governmental organizations, which elicited the need for independent social entrepreneurial ventures – organizing efforts centered on opportunities to create social value – to participate in a large-scale response” (Bacq et al., 2020; Chhabra et al., 2020).

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Monetizing Social Value Creation Through Data: Yunus Social Business Model In the realm of plausible models of social business framework encompassing an altruistic dimension, Mohammad Yunus’s (MY) model of social entrepreneurship justifies traditional profit-making business with philanthropy. Social business is a business dedicated to a social goal, basis non-loss, and non-dividend benchmarks. The broader vision of the MY model opines that all levels, from local to international governmental bodies, have the inherent capacity of creating social business funds. Social entrepreneurship involves the process of re-investing the social dividends to develop the business further. Grameen Banks, established under the premise of the MY model in Bangladesh, are prime examples of social business wherein shareholders happen to be the borrowers themselves. Data is garnered via administrative and governance bodies to analyze and evaluate the ecosystem of poverty, unemployment, and illiteracy. “Self-sustainable businesses lead to the reinvestment of funds” (Dees, 2011). Yunus and Lehmann-Ortega (2009) outlined the concept, model details, operations, and performance monitoring aspects of the social business using the Grameen Bank social businesses, leveraging data. Verticals producing Grameen phones, yogurt, and rural drinking water supply projects were incorporated. In the modern-day world, social business needs to be incorporated into the current economic structure and framework. This paves the way for a multidimensional model for sustainable business and investment. The MY model addresses the social problem from a solution perspective and an innovative approach by using data and research tools on a progressive learning curve. “This is a leap forward, enabling betterment of community, corporate and culture due to the unique experience provided by social enterprise” (Esty, 2011). It pioneered in earmarking the dimensions and benchmarks of the Grameen Bank movement, which started from a humble beginning and made strides. The pilot project was scaled up by the MY model on the basic premise of lending money to the poor for self-upliftment. Grameen says collateral disbursed loans and women turned out to be the main beneficiaries of the project.

Initiatives and Associated Dimensions of Disruption, Data, and Social Innovation In the wake of COVID-19, there has been a widespread lay-off scenario in the organized sector and unorganized labor unemployment. “The UN University estimated that the economic fallout could push an estimated half a billion people into poverty and take global development progress back three decades, primarily in emerging economies” (Foundation, 2020). Social enterprises could be curated and implemented with the aid of Big Data, thereby catapulting social impact measurement, scaling up, and financial sustainability. The COVID-19 pandemic poses a grave danger, but simultaneously, there is a heartening surge of innovative entrepreneurs. Hackathons, simulation exercises, business model innovations, volunteering, philanthropy, and such initiatives have

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brought forth the irresistible urge of communities and individuals to re-balance society like never before. This emphasizes the point that the COVID-19 pandemic could prove to be a positive disruption in the social entrepreneurial terrain. For example, “Some heartening examples of Atlanta high school students started an organization delivering free meals to front line hospital workers, to a group of Colombian engineers building low-cost ventilators, innovators creating novel solutions to the problems caused by the pandemic” (Bhaskar, 2020). Religious philanthropy and community service have come to the fore in Khlasa aid, United Sikhs, etc. (Sikhs, 2020). COVID-19-inspired innovations are being actualized into sustainable businesses for humanity. Leveraging Big Data from such initiatives for social entrepreneurship can go a long way in streamlining society at many levels.

Big Data Analytics and Innovation: Amazon Business Model and Social Cause Alignment E-commerce giants like Amazon have innovated and enunciate ease of doing business for MSMEs impacted by COVID-19 by leveraging Big Data. Amazon ushered in Local Shops, Amazon Launchpad, Amazon Saheli, and Amazon Karigar for small and micro sellers to facilitate selling while handholding them in the onboarding process on its platforms (Hussain et al., 2021). The initiative has been leveraged by processing Big Data of startups, women entrepreneurs, artisans, micro-businesses, and weavers, and customer demand has been curated for their products. Alongside a multitude of benefits accrue to them via group health insurance benefit to cover medical expenses due to COVID-19, Hindi mobile apps for convenience, and fee waivers to help sellers “navigate the economic challenges” during COVID. Amazon’s superior model of leveraging Big Data successfully towards such socially viable and profitable initiatives in COVID times can set a precedent for future endeavors. Amazon’s “Personalized Recommendation System” uses a comprehensive, collaborative filtering engine (CFE) to harness behavioral analytics via Big Data. The “Anticipatory Shipping Model” is an anticipatory delivery model which synthesizes huge data information to anticipate a customer’s buying behavior. Data optimization involves “Supply Chain Optimization”, “graph theory”, analysis, and “Price Optimization”, which facilitates small sellers in a big way. “Amazon Go” has facilitated small sellers to use the Amazon cashier-less app, which sets the stage for rudderless small entrepreneurs to assimilate into the bigger e-commerce and digital marketing paradigms. Big Data analysis has ushered this whole ecosystem towards a socially aware and enabling entrepreneurial framework.

IMPLICATIONS AND CHALLENGES ASSOCIATED WITH VIABLE BUSINESS PROPOSITIONS, IN SOCIAL ENTREPRENEURIAL PERSPECTIVE Social entrepreneurs plug the market failures and streamline government functioning by catering to marginalized sections of population needs. In the current scenario, such populations are in the throes of disruption, uprooting, and reverse migration due to risks of COVID-19. The recent urban migration crisis in India wreaked havoc on

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infrastructure, jobs, health, and well-being. The pandemic brought forth the systemic inequalities of a global and national economic system. The “International Labour Organization (ILO) has warned that the steep decline in the ability to work and operate is threatening the livelihoods of 1.6 billion workers in the informal economy” (ILO: As job losses escalate, nearly half of global workforce at risk of losing livelihoods, 2020). This has accelerated the pace of data-based technologies. The governing class is also contemplating leveraging Big Data to do skill-mapping and optimize the employability potential of migrated labor, thereby optimizing their skills towards development employability (Srivastava, 2020). The Schwab Foundation 2020 Impact Report (Schwab Foundation, 2020) demonstrated how the network of 400 leading social innovators and entrepreneurs it supports had improved the lives of more than 622 million people, protecting livelihoods, driving movements for social inclusion and environmental sustainability, and providing improved access to health, sanitation, education, and energy. The collective aspirations of a social entrepreneurial ecosystem are leveraging Big Data to coordinate and accelerate response to pandemic-related disruptions by assessing and highlighting needs across social enterprise networks and by expanding financial support; by coordinating non-financial support by intermediaries, procurement agencies, legal services and technological support ecosystem; and by advancing communication efforts advocating applicable fiscal and policy interventions Some challenges, as evident from research and as synthesized from the deliberations of this chapter, can be encapsulated in the following points: 1. Access and analysis restrictions: Non-profit-making organizations and social enterprises often have limited availability of Big Data in a collated format from the government and private sector. While the private sector typically restricts access to its proprietary data due to data monetization that adds to its market capitalization, the government sector data is restricted in most nations due to a lack of transparency in governance. Right now, we are in a state of evolution, where the private and government sectors slowly open up their huge reservoir of data sets for social entrepreneurs. 2. Insufficient technological intervention: An added challenge is the lack of software personnel in the non-profit sector who have the skill sets for data analysis of Big Data and the leadership that can effectively identify the potential benefits and tools for using Big Data. The potential can be realized after more social entrepreneurs act as bridges between Big Data companies and private companies, building a triad that mutually benefits each other in terms of revenue potential as well as societal benefit. 3. Privacy concerns and data theft: Ethics of privacy concerns of data being collected by big corporates like Google or Facebook, or by government agencies – all of which are resistant to legislative restrictions on end use of data collected by them. Corporates or governments collect rights for the use of data through complexly worded legalese and long documents which the public generally cannot read or comprehend fully. There has been a huge public movement recently against the extent of data being collected and its end usage, and there

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is a significant risk of abuse of Big Data. With cross-country implications of data and lack of legislative control over the transfer of such data, the social enterprises have an added dimension to deal with. Also, there needs to be a realization that access to such Big Data cannot be provided indiscriminately to all without a system of checks and balances (Syed et al., 2020). 4. Insufficiency of data for research and extrapolation: Multilateral organizations like the World Bank are currently using Big Data for many studies regarding human development parameters, which are not easily accessible to social entrepreneurs. The data will take time to come out from academic pursuits for practical applicability for meaningful change. 5. Need for policy interventions: Governments worldwide are increasing transparency of data, e.g., the Right to Information introduced in India has led to a sea change in data availability to the public. The availability of such data has great potential. Developing countries with large mobile penetration rates (e.g., Haiti, which has a mobile spread of over 90%) can map cholera outbreaks and identify areas for quick response teams to tackle it through GPS tracking. Moreover, information available from multilateral organizations or government is at a macro level, wherein the raw data of individual, village, townships, or other levels is not released. Even if released, social entrepreneurs will have to scale up their skill sets to micro-analyze raw Big Data before making sense of it. 6. Geo-political manifestations and fallout: The demographic and geographic spread of Big Data is also a limitation for various reasons. Such reasons may be political like China, which restricts access to international corporates and restricts the release of its data sets, or technical, like telecom coverage and data speeds. 7. Socio-cultural biases and under-representation: There is an inherent bias towards some age groups and income levels in terms of data collected from social media platforms, leaving out a large population section. In the above scenario, the demographic spread of a humongous data set may be very narrow across gender, age, and ethnicity, making it non-suitable for broader usage. Some geographical locations or age groups may be unrepresented in a Big Data set. 8. Redundancy of data collation and analysis techniques: Old-school statistical tools of random sampling present a greater degree of confidence to people involved in social entrepreneurship. The evolution to the usage of Big Data would have to overcome this skew in data coverage eventually. The system of blind trust in Big Data companies’ integrity and its usage is unsustainable in the long run. Once trust and a system of checks and balances to verify that trust is established between data companies and social networks, society can significantly benefit from this data. 9. Streamlining till grassroots level: In times to come, Big Data could be made applicable even in those small hamlets in the forests of Jharkhand or Chhattisgarh, in India, to provide reaping advice to herders, artisans, small businesses, street vendors, or even transporters to kick-start a sustainable local economy and reduce poverty. For example, Ivory Coast academics have recently used a data set of text messages from a mobile telephony company to create a contagion simulation model. Such models can be replicated for a wide range of applications, as listed previously.

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The synergy of Big Data and social enterprise is currently at a preliminary stage. However, the stage is set for exponential growth in the power of this synergy once the challenges with regard to availability, safety, privacy, and coverage of data are addressed along with enhancement in skill sets of the workforce involved in social organizations.

CONCLUSION Social entrepreneurship will undoubtedly be an antidote to our systemic inequalities and market failures in the long run. The COVID pandemic had brought forth the overt and covert underpinnings of our socio-economic landscape worldwide. It has forced us to recalibrate our systems to streamline the gaping biases and inequalities to sustain an egalitarian world. This should set a precursor for balanced ­socio-economic growth in the long run. It sets a new precedent for systems to change leaders, who would herald new beginnings under progressive yet empathetic technological interventions of communities as agents of change. Big Data assuredly remains a primary harbinger to navigate complex institutional arrangements in a dynamic world. This would imply dismantling the old archaic structures and polishing the rough edges. Social entrepreneurial models of running inclusive, sustainable organizations that nurture communities are critical to the COVID-19 response and recovery period. This pool of critical knowledge, experience, and responses can revitalize the sustainable development agenda and build an egalitarian, more resilient, and inclusive future, primarily in healthcare, sanitation, and education. According to the World Economic Forum report (Oddup, 2020), “40 global organizations, including IKEA, SAP, and Salesforce, have pledged to support thousands of social entrepreneurs spread over 190 countries financially”. François Bonnici, (Foundation, 2020) Head of the Schwab Foundation for Social Entrepreneurship, said, Social entrepreneurs and their community partners have been working for years to solve market failures and demonstrate more sustainable and inclusive models. These frontline organizations now face bankruptcy and severe constraints while also innovating and responding to this global pandemic. Through this Alliance, members are committing support for social entrepreneurs to protect decades of work in the impact sector.

This necessitates the utmost importance to build data systems with a robust backing in post-COVID times to accentuate the need for all governments to leverage data purposefully for social good. New age data systems are the need of the hour. As journalist David McCandles (Opinion: Data is new ‘soil’ in times of covid-19 crisis, 2020) puts it, Data needs to be relooked as the new “soil”. For example, the need to mandatorily georeference all possible government datasets cannot be understated. Imagine if census data with population densities, family profiles with members above 50 years, and public health infrastructure are available at a micro-cluster level on an interactive map to the authorities. Such information combined with unclean cooking fuel use and smokers’ presence is already being used in Nigeria to develop geographic risk distribution profile to help prioritize action.

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mHealth Alliance. (n.d.). Retrieved from mHealth Alliance: http://www.mhealthalliance.org/ MIT. (2020). Three Tech Teams Among Winners in MIT COVID-19 Challenge Event. Retrieved from https://www.me.gatech.edu/Georgia-Tech-Teams-Win-MIT-COVID-Challenge Nager, M., Nelsen, C. and Nouyrigat, F. (2011). Startup weekend: How to take a company from concept to creation in 54 hours. New Jersey: John Wiley & Sons. Oddup. (2020, January). Why social entrepreneurship is the need of the hour during Covid-19. Media Wire. Retrieved from https://www.mediawire.in/press-releases/miscellaneoushome/why-social-entrepreneurship-is-the-need-of-the-hour-during-covid19-41279383. html Olson, P. (2020). Telemedicine, Once a Hard Sell, Can’t Keep Up With Demand. Wall Street Journal. Opinion: Data is new ‘soil’ in times of covid-19 crisis. (2020). EconomicTimes. Retrieved from EconomicTimes.indiatimes.com: https://government.economictimes.indiatimes. com/news/governance/opinion-data-is-new-soil-in-times-of-covid-19-crisis/75502320 Quarantelli, E. (1985). What is disaster? The need for clarification in definition and conceptualization in research. Retrieved from University of Delaware: http://udspace.udel.edu/ handle/19716/1119 Seelos, C. and Mair, J. (2005). Social entrepreneurship: Creating new business models to serve the poor. Business Horizons, 48(3), pp. 241–246. Retrieved from Business Horizons: https://www.sciencedirect.com/science/article/pii/S0007681304001314 Shah, R. (2012). Building resilience to recurrent crisis: USAID policy and program guidance. Washington, DC: United States Government. Shahzad, A., Hassan, R., Abdullah, N. I., Hussain, A., and Fareed, M. (2020a). COVID-19 impact on e-commerce usage: An empirical evidence from Malaysian healthcare industry. Humanities & Social Sciences Reviews, 8(3), pp. 599–609. Shahzad, A., Hassan, R., Aremu, A. Y., Hussain, A., and Lodhi, R. N. (2020b). Effects of COVID-19 in e-learning on higher education institution students: the group comparison between male and female. Quality & Quantity, pp. 1–22. Shahzad, A., Yaqub, R. M. S., Di Vaio, A., and Hassan, R. (2021). Antecedents of customer loyalty and performance improvement: Evidence from Pakistan’s telecommunications sector. Utilities Policy, 70, p. 101208. Sikhs, U. (2020, April). United Sikhs Feeds Over 1 Million During Covid-19 Crisis. Retrieved from United Sikhs: https://unitedsikhs.org/united-sikhs-feeds-over-1-million-duringcovid-19-crisis/ Srivastava, P. (2020, June). Yogi govt completes skill-mapping of 23.5 lakh migrant workers, 18 lakh want jobs in UP. The Print. Retrieved from https://theprint.in/india/ yogi-govt-completes-skill-mapping-of-23-5-lakh-migrant-workers-18-lakh-want-jobsin-up/435450/ Syed, E., Azhar, A., Fong-Woon, L., and Rohail, H. (2020, November). Socio-economic factors on sector-wide systematic risk of information security breaches: Conceptual framework. In Proceedings of the International Economics and Business Management Conference, Melaka, Malaysia (pp. 2–3). van Tulder, R., and van Mil, E. (2020). Responsible governance: Broadening the corporate governance discourse to include possible duties and collective action. In Research Handbook of Responsible Management. Edward Elgar Publishing. USAID. (2020, June). Working in crisis and conflict. Retrieved from https://www.usaid.gov/ what-we-do/working-crises-and-conflict Ushahidi. (2007). Empowering citizens through crowdsourcing and digital data collection Retrieved from https://www.ushahidi.com/blog Using Idea Contagion to Combat Virus Contagion – “Virtual Idea Blitz” by Kelley School of  Business. (2020). Retrieved 2020, from Kelly School of Business: https://www .­covid19ideas.org/

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Williams, T.A. and Shepherd, D.A. (2018). To the rescue!? Brokering a rapid, scaled, and customized compassionate response to suffering after disaster. Journal of Management Studies, 55(6), pp. 910–942. Wired. (2000, June). The petabyte age: Because more isn’t just more – more is different.: https://www.wired.com/2008/06/pb-intro/ Yohn, D. L. (2019, May). Why Startups Fail. Forbes. Retrieved from https://www.forbes.com/ sites/deniselyohn/2019/05/01/why-start-ups-fail/#6ff1e2ab28a5 Yunus, M. B., & Lehmann-Ortega, L. (2009). Building social business: Lessons from the Grameen Experience. HEC Paris.

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Big Data A Boon for Food and Servicepreneurship Kiran Sood Chitkara University, India

Navneet Seth Baba Hira Singh Bhattal Institute of Engineering and Technology, India

Munish Jindal HoverRobotix, India

Harsh Sadawarti CT University (Pb.), India

CONTENTS Introduction�������������������������������������������������������������������������������������������������������������� 56 Raw Data to Information: An Absolute Transformation������������������������������������� 56 Evolution of Big Data Since Ancient Times������������������������������������������������������� 56 Categorization of Big Data in the Form of Four V’s������������������������������������������� 57 Development of Framework of Big Data: A Review����������������������������������������������� 58 Methodology������������������������������������������������������������������������������������������������������� 61 Augmentation of Internet Users (1995–2020)��������������������������������������������������������� 61 Big Data Revenue Forecast��������������������������������������������������������������������������������� 61 Big Data Applications into the Various Domains of Service Sectors���������������������� 62 Role of Big Data in Weather Predication������������������������������������������������������������ 62 Role of Big Data in Social Media����������������������������������������������������������������������� 63 Role of Big Data in Healthcare��������������������������������������������������������������������������� 63 Role of Big Data in the Education Sector����������������������������������������������������������� 63 Role of Big Data in Logistics������������������������������������������������������������������������������ 63 Role of Big Data in Travel and Tourism������������������������������������������������������������� 64 Role of Big Data in Government and Law Enforcement������������������������������������ 64 Big Data Applications in Food and Agripreneurship: A Future of Farming������� 64

DOI: 10.1201/9781003097945-5

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Changing the Face of Farming Through Automation����������������������������������������� 65 Challenges Before Agripreneurship�������������������������������������������������������������������� 66 Conclusion��������������������������������������������������������������������������������������������������������������� 67 References���������������������������������������������������������������������������������������������������������������� 67

INTRODUCTION Evolution in technology has brought transformation into various objects we use in our daily lives, from landline phones to smartphones, bulky desktops to floppies, hard disks and now cloud for storing data and similarly now a day’s self-driving cars, smart traffic camera, smart ACs and TVs are some of the Internet of Things devices generating a profuse amount of data (Naresh & Munaswamy, 2019). The Internet of Things comprises two words, the internet and things (devices), a giant network that tracks our daily activities through devices. These devices embedded with sensors gather and share data from all those devices that we come across in our daily lives. This enhancement in technology has completely transformed our lifestyles, from how we react to the way we behave. Every single click converts into your search history, taking the shape of Big Data. Google search, online shopping history, CCTV footage, voice message, Instagram and Facebook posts, tweets, and emails are some of the activities that generate a large amount of data. There are many other aspects because data is evolving and converting into Big Data like banking and finance, transportation, insurance (Sundareswaran, 2018), education, healthcare, retail, and media and entertainment industries. There are now millions of ways in which data is generated each day. How this data has grown exponentially can be seen through its usage over the years, from 100 GB in 1997 to 40,000 GB in 2020. A collection of alphabets, numbers, special symbols, images, audios, and videos stored digitally can be termed as data. Data remains in its raw form until it is processed. Raw data before processing might have different meanings to different persons. Sometimes, data needs to be processed through various stages to get concrete information. So, the processing stage is the utmost requirement to draw meaningful inferences from data.

Raw Data to Information: An Absolute Transformation Using an example, Table 5.1 shows how a numeric value is interpreted uniquely at every stage after being processed. When it comes to interpretation, data comes with a different meaning in different hands until it is processed. Based on this example, it can be said that data needs to be processed through various stages for taking the shape of information.

Evolution of Big Data Since Ancient Times The Land Revenue system of the British in India had three divisions: Permanent Settlement (Zamindari System), Ryotwari System, and Mahelwari System after independence. This created a buffer amount of data-inspired various kingdoms to follow the concept of Big Data in revenue collection throughout generations in the following manner:

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TABLE 5.1 Stages of a transformation of data

Stages Inferences drawn at various stages Interpretation

1st Stage (Raw Data)

2nd Stage (Processing)

3rd Stage (Processing)

130982

13-09-82

13-09-82 (Mr. XYZ)

The numeric value, 130982, has different meanings to different persons. It can be treated as a Roll No, commodity price, loan amount, joining date, manufacturing date, expiry date, relieving period, etc.

After processing, we give meaning to raw data. It could be a date of birth, but not every individual is born with the same date of birth. Hence data needs to be processed further.

Further processing will give more sense in understanding raw data that is the DOB of XYZ. Although many personalities would exist in the universe with the same name and DOB, there remains a possibility of many personalities.

4th stage (Raw data converts into information) 13-09-82 (Mr. XYZ), Fathers Name & Address (Information) It gives greater weightage by adding more quantitative meaningful aspects like the father’s name and address to the raw data. The final stage at which it can be said that there is only one personality, “XYZ,” exists against specified DOB, father’s name and address.

Source: https://www.computerhope.com/issues/ch001996.html.

1. Vedic period: a highly structured form of literature like Upanishads, and Arthasastra can be seen in ancient times during the Vedic period. 2. Medieval period: speaks of that era when initiatives were taken by famous Mughal emperors Alaudin Khilji and Sher Shah Suri to preserve the land records. 3. British period: Britishers extensively used the concept of Big Data in assessing their wartime capacity against opponents, especially during World Wars I and II. 4. Post-independence period: the concept of Big Data was used in the post-­ independence period by a newly formed government for collecting information to abolish the age-old Zamindari system.

Categorization of Big Data in the Form of Four V’s • Volume: it refers to the unbounded size of data produced daily by various organizations. Various parameters like M.B., G.B., T.B., and P.B are being used for measuring the massive amount of data.

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• Variety: nowadays, data has different forms like emails, photos, videos, audios, PDFs. It has three types, structured, unstructured, and semi-structured, explained as follows: o Structured data: the highly organized form of data that can be stored or processed and hence interpreted quickly with no hazels by machines is known as structured data. Salary reports, addresses, Mark sheets, employment rate, etc., are examples of structured data that can be stored in rows and columns (Adamu et al., 2021; Sivarajah et al., 2017). o Unstructured data: this is the most complicated form of data increasing exponentially due to an increase in smartphone usage. Ninety percent of the total data available belongs to an unstructured form, and the remaining 10 percent belongs to a structured form. Videos, images, and audios are some of the best examples of unstructured data. o Semi-structured data: it is a combination of both structured and unstructured data. CSV, XML, Web pages, and JSON are some of the forms of semi-structured data. • Velocity: it refers to the magnitude of fluctuations in the data. A historical crash of the global stock market due to deadly COVID-19 is one of the best examples. • Value: selection of data by organizations that require greater attention for strategic decision making, optimal utilization of resources, understanding of varied customer requirements and maximizing of profits. Table 5.2 outlines the transformation phases of Big Data from the late 1800s to the COVID-19 pandemic.

DEVELOPMENT OF FRAMEWORK OF BIG DATA: A REVIEW Our primary purpose is to examine the past and present trends of Big Data in various sectors of the economy and study the impact and future scope of Big Data on food and servicepreneurship. For the review, there was a need to develop a conceptual framework to support the main objective, i.e., to examine the trends in this study and provide a logical arrangement of subject matter and concepts for analyzing Big Data applications in food and servicepreneurship. Several problems are being faced by the supply chain managers in fulfilling customer demands, so there is an adoption of Big Data techniques, especially in the agricultural sector, to fight against various issues like lack of awareness, scarce and illiterate labor, etc. (Ahearn et al., 2016). The ongoing problems related to behavior and health of livestock were another growing concern for farmers that can be resolved by developing a database. The study clearly stated the benefits of using a database for recording all the observations on health or behavior (Vanderwaal et al., 2017). Agriculture remained a neglected sector in many parts of the world, relying on species and uncertain climate conditions that attracted various researchers’ great attention. An attempt was made to highlight how beneficial the data science is to control the uncertain climatic variables by applying a uniform amount of radiation, water, and fertilizers to boost productivity (Bock & Kirkendall, 2017; Bronson & Knezevic, 2016;

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TABLE 5.2 Transformation phases of Big Data Years 1890 1926 1937 1940s–1950s 1944

1949

1960 1965 1989

1996 1997 1998 1999 2001

2005 2005 2006 2007 2008

2009

2011

2011

Landmarks The tabulating machine was invented in 1890. The machine took on one hand only half a year for processing a large amount of census data of 1880 instead of seven years. Nikola Tesla predicted a pocket-friendly future communication device with a high capacity to analyze a large amount of data. IBM formed a huge database under a successful bookkeeping project. A blessing period of a decade has made jobs more comfortable with the introduction of a high-speed calculation, electronic computing device. A challenging situation for the Wesleyan University librarian was to tackle the decreasing storage capacity of its library. It was an alarming situation to match/fulfill the requirement of approximately 200,000,000 copies by the end of 2040, resulting in a workforce of more than 6000 employees for cataloging. The escalating crisis of storage capacity inspired Claude Shannon, known as the “Father of Information”, to do new research in photography and punch cards. The Library of Congress, with a storage capacity of 100 trillion bits of data, was one of the largest items on his list for doing all researches. The year was dedicated entirely to curtailing the skyrocketing cost of accumulating data. First Data Center is conceived. A revolution came when a computer scientist, Tim Berners-Lee, invented the World Wide Web (WWW) with free access to the entire nation. He brought a storm by the invention of three commands, namely HTML, URL, and HTTP, that has brought the largest explosion of massive data ever in history. A joint effort by R.J.T. Morris and B.J. Truskowski proved successful for minimizing the cost of storing data digitally as compared to physically. The term “Big Data” was coined by David and Michael Cox. Mr. Carlo created a database named NoSQL to fight against an increasing amount of unstructured data. The term Internet of Things (IoT) was first used by Kevin Ashton for his presentation at P&G. Seeing the country grappling with managing data explosion, Gartner introduced the concept of 3V’s in his research under the title of “3D Data Management: Controlling Data Volume, Velocity, and Variety”. O’Reilly Media launched the term “Big Data” in 2005 Hadoop was developed by Doug Cutting and Mike Cafarella to handle the massive amount of unstructured data. Highlighting the growth in data by six times a study was pioneered by International Data Corporation (IDC). North Carolina State University took the grand initiative by establishing an Institute of Advanced Analytics and offering a master’s degree program in analytics. George Glider and Bret predict that in 2015, the massive internet usage of data in the U.S. will increase 50 times more than in 2006, and IP traffic will reach one zettabyte. The Global Information Industry Centre surveyed in 2008 state increasing per day consumption of the internet for obtaining the information. One of the most prominent projects is storing of fingerprints and retina scans of more than 7.8 billion citizens by the Indian Government in the form of ‘Aadhar cards’ made a record in 2009. Taking into consideration the increasing demand of the Big Data industry, a McKinsey report predicts a massive shortage of workforce (analysts) in the U.S. by 2018. Highlighting the issue there will be a deficiency of one and a half million market analysts and executives. IBM created history by analyzing approximately 200 million pages in seconds. (Continued)

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(Continued)

Years 2012 2012

2012

2013 2013

2014

2014

2015 2015

2016 2017

2019

Landmarks Changing the nature of jobs in the Big Data market, Harvard Business Review gave birth to a novel career, “data scientist,” naming it the “sexiest job of the 21st century. Skyrocketing problems related to Big Data prompted the U.S. government to take initiatives by spending approximately $200 million on Big Data research projects. Highlighting the growing issues of the U.S. government related to Big Data, 84 Big Data programs were introduced by President Barack Obama. Dealing with Obama’s 2012 re-election campaign was the most excellent example of this. According to an Information Week report, growing problems related to data storage highlighted the requirement of experts in analytics by 52 percent of Big Data companies. While conducting an annual Digital Universe Study, EMV predicted that by 2020, Big Data will grow exponentially to 44 zettabytes. A study conducted by EMC has shown concern over the decreasing volume of semantic data, further mentioning that semantic data will comprise only 35 percent out of the total available data in 2020. A report by IDC highlighted that the increasing popularity of online business in B2B and B2C would produce a large amount of Big Data. It will soon surpass the daily limit of 450 billion transactions. Based on experiences, Gartner predicts that internet subscribers will soon reach the limit of 25 million in 2020 against 4.9 million in 2014. He further mentioned that by 2020, one-third of data produced digitally would be passed through the cloud. The Giant “Google” in the market of Big Data was titled the largest Big Data company in the world, with a maximum storage capacity of 10 billion gigabytes. Amazon started its operations with approximately 1,400,000 servers in various data centers and producing 1,000,000,000 gigabytes of Big Data from its 152 million customers. The Internet of Things has a substantial impact on Big Data. As per the IDC report, many organizations have started working on IoT-based solutions to the problems. The growing complexity of business has alarmed Gartner, highlighting the application of Machine Learning (ML) in various sectors like healthcare, energy, transportation, distribution, smart cities and agriculture. COVID-19 prompted researchers and scientists to do research work. Research has suggested that Big Data analytics and cell phone tracking helped officials contain the virus’ spread.

Collected from various sources.

Rosenzweig et al., 2014). The studies also suggested the successful use of Big Data applications like Global Gridded Crop Models in solving various technical issues related to temperature control, pest attacks, and deteriorating soil quality. Academicians’ and scientists’ roles have been analyzed to give massive support to agriculture research (Coble et al., 2018). The limited use of fertilizers with their side effects has also been studied (Prasad, 2019). The role of Big Data in various sectors of the economy has also been analyzed (Kitchin, 2014). The research has revealed the positive impact of Big Data in all the activities of routine life, thereby making life easy in all aspects. There is a change in the farmer’s role after adopting the Big Data concept in various farm activities, especially in efficiently managing those activities (Dyer, 2016). The author also favored the concept of additional income of the farmer

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by sharing the data related to his farms with the research organizations. Several issues were raised related to international competition and supply management, connecting national and international markets and requiring a strategic network design (Sonka, 2016). Reducing the gap between producers and sellers, time management, storage facilities, and a key advantage to buyers in terms of velocity and volume has also been discussed. Precision farming has become productive for the farmers in solving day-to-day challenges (Rahate & Khandekar, 2018). The role of wireless connectivity has been lauded in maintaining the valuable data inputs from farming to utilize it for betterment in further planning. Otherwise, these inputs have remained unused or untapped due to limited internet connectivity (Mark et al., 2016).

Methodology The secondary data that has been used for the study has been collected from previous studies and reports. The information regarding the usage of Big Data in the various sectors has been reported in this study. As in the current scenario, Big Data has become the backbone of every aspect for initiating and continuing the business, so every organization is now making a maximum financial contribution towards gathering Big Data from every possible resource. Big Data nowadays is being used in various apps throughout the world by most organizations in the form of database management. The data is being automatically saved with the help of different software and stored for future purposes. Various sectors such as banking and insurance, stock markets, civil aviation, railways, road traffic management, and health departments are using Big Data apps for their routine working. Big Data has also proved to be a boon to fight and tackle the widespread COVID-19 pandemic worldwide. The different nations were able to take daily feedback regarding the increase in the total number of patients being infected by the pandemic.

AUGMENTATION OF INTERNET USERS (1995–2020) Figure 5.1 reflect the number of internet users in millions and the percentage of the world’s population for a period of 26 years commencing from 1995 to 2020. A tremendous amount of growth, approximately 24 percent, can be seen in the form of an increase in the number of internet subscribers from 16 million in 1995, representing 0.4 percent of the world population, to 4,574 million in 2020, or 60.70 percent. Easier accesses to computers and a rise in internet usage in developing countries are two of the reasons behind this enormous growth.

Big Data Revenue Forecast Table 5.3 highlights the global picture of revenue forecast in the top two service sectors, hardware and software, based on past years’ data. The table emphasizes the estimate of the overall growth of 12.57 percent amongst these critical sectors. The software sector has secured the first position by achieving the highest growth rate of 17.24 percent, followed by the service sector and hardware sector with a growth rate

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FIGURE 5.1  Augmentation of internet users (1995– 2020) Source: https://www.internetworldstats.com).

TABLE 5.3 Big Data revenue forecast (in billions of U.S. dollars): a global picture from 2016 to 2027 Segments Service Hardware Software Total

2016 2017 2018 2019 2020 2021 2022 2023 11 9 8 28

14 10 11 35

16 12 14 42

19 14 17 50

21 15 20 56

24 16 24 64

26 18 27 71

27 19 31 77

2024 2025 29 20 34 83

31 22 38 91

2026

2027

CGR

32 23 42 97

33 24 46 103

10.50 9.33 17.24 12.57

Source: https://www.internetworldstats.com.

of 10.5 percent and 9.33 percent, respectively. This growth has made numerous potential companies use data to increase their income.

BIG DATA APPLICATIONS INTO THE VARIOUS DOMAINS OF SERVICE SECTORS Role of Big Data in Weather Predication Big Data is widely used in predicting various natural calamities, viz., a sudden storm, hurricane, global warming, Amazon fire breakout, floods, and earthquakes. For example, predicting a landslide that can cause massive damage to life and property was also impossible without Big Data. The University of Melbourne studied this challenge and developed a tool capable of predicting landslides. The tool predicts the boundary where the landslide is likely to occur two weeks before. This magical tool works on Big Data (Liu et al., 2019) and applied mathematics. Accurate predictions can save lives and can help in relocating the route. This is how Big Data is used in predicting natural calamities across the world.

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Role of Big Data in Social Media This is a massive role, as leveraging Big Data can boost the revenue of many companies. The growing popularity of Facebook, Twitter, and Instagram (Asur & Huberman 2010; Chieng et al., 2015; Chung et al., 2014 Singh et al., 2018) are examples where Big Data plays a vital role in collecting all the emotions of a customer in liking and disliking, posts, messages, and conversation. These emotions are analyzed to conclude.

Role of Big Data in Healthcare Healthcare is one of the most important sectors where Big Data is widely used to save lives. Using Big Data, medical research is done very effectively by analyzing all the previous medical histories. The remarkable role of Big Data in managing COVID-19, beginning in China (Zhou et. al., 2020), is the greatest example. Big Data provides meaningful inputs to doctors, scientists, and researchers to fight against the virus that slowly captured the whole world. Artificial intelligence (AI) was the biggest bet in tracing this virus’s path and predicting its outbreak. The algorithms can read through zettabytes of data collected from the infected areas and give real-time insights. Tech giants like Alibaba and Baidu have been at the forefront of this war by providing their expertise to fight against this virus. AI has slowly helped to close the gap in fighting COVID-19.

Role of Big Data in the Education Sector The massive global effort to achieve universal education is made possible by data. Data show successes and failures. Millions of children are not going to schools; whether to hold them back is one of the many questions that Big Data can answer. A sustainable development goal has addressed this issue to give quality education by 2030. There is no way to compare results on a global scale. Linking different assessment criteria’s Big Data is developing global measures of learning, enabling countries to monitor students’ progress, and strengthen policies to improve. Using Big Data, every student and teacher has a chance to excel (Ali et al., 2021; Del Giudice et al., 2021; Di Vaio et al., 2020; Shahzad et al., 2020b, 2021).

Role of Big Data in Logistics Big Data is used to cut the processing time of transportation and storage of goods from supplier to consumer. To achieve minimum time, sensors within the vehicle analyze the fastest routes. This analysis is based on various data (Ahearn et al., 2016), such as weather, traffic, the list of orders, etc. This helps in reducing the delivery time. Big Data is also applicable to warehousing management efficiently. This analysis, along with tracking sensors, provides information regarding the underutilized space, which results in efficient resource allocation and also reduces the cost.

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Role of Big Data in Travel and Tourism The global tourist market is expected to grow soon, and Big Data is used in various ways in this sector. Looking into past occupancy rates, room tariffs, school holidays, and peak season, the tourism industry can anticipate demand and maximize revenue. Companies use Big Data to analyze information about their competitors, which gives an understanding of what other hotels are offering their customers. The data collected from tourists’ past travel history and likes can receive personalized experiences focused on their needs. Few countries use Big Data to examine tourism flows and discover investment opportunities in their country.

Role of Big Data in Government and Law Enforcement • Maintaining law and order is of utmost importance to any government. Big Data plays an active role by bringing new policies and schemes for the welfare of its citizens. Predictive policing uses Big Data to forecast criminal activity before it happens based on the information given to them through big data analysis. The New York Police Department uses Big Data analytics to protect its citizens. The department identifies and analyzes crime trends, fingerprints, emails, and records from the police investigation and other public databases. • Governments are also using Big Data to tackle the major issue of unemployment. By analyzing the number of students graduating every year and the number of job openings, governments can begin to address unemployment in the country. • Governments use Big Data tools to discover areas that fall under the poverty line and do the needful.

Big Data Applications in Food and Agripreneurship: A Future of Farming A famous saying by Giada De Laurentiis is, “food brings people together on many different levels. It is the nourishment of the soul and body”. However, the world is facing an imminent food crisis, and it faces the challenge of feeding nine million people by 2050. The changing climate, expanding deserts, rising sea levels, and degrading arable land (Bock & Kirkendall 2017) increase life complexities. Pests and diseases are spreading; rainforests are dying, destroying biodiversity. Freshwater is becoming scarce, and the soil is rendered less fertile. Industrial exploitation poisons our seas and rivers; fish stocks are on the verge of collapse. Safely feeding the growing population, keeping in mind that the integrity of the food supply chain cannot be compromised through adulteration or mislabeling of products, is another challenge for the food industry (Bronson & Knezevic, 2016). Hence, there is a need to harness and identify Big Data to ramp up food production with less pressure on our water resources. This large volume of data will change the landscape for various business opportunities into business values for making huge profits. Companies are turning to automation and R&D to develop better-quality products to satisfy customers’ varying needs, the underlying opportunities giving rise to startups in the food sector bridging the employability gap. The paradigm shift has made a tremendous

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contribution towards the growth of the Indian economy since it has provided job opportunities (Coble et al., 2018) to thousands of people, especially at the provincial level, keeping in mind the safety and health-consciousness of customers’ new job roles in food processing, food safety, quality management, food engineering, and food technology. The complex supply chains involve many parties and increase the level of risk. Many governments have not imposed strict safety standards given recent food scandals around the world. The outbreak of COVID-19 has badly affected the food supply across the nation. Using online Foodapps, Zomato, and Swiggy, the government and many organizations are fulfilling CSR activities by supplying all the necessary food items like fruits, vegetables, and grocery items especially to poor people residing in slum areas.

Changing the Face of Farming Through Automation According to Sherrie Silver (Advocate for the Rural Youth International Fund for Agriculture Development), “Farming is often seen as unglamorous, but it is not just about working in a field; it is actually about entrepreneurship as well.” According to the United Nations, by 2050, there will be food, shelter, fiber, and fuel deficits affecting 300 million. There has been a drastic plunge in land under cultivation (Jackson, 2016) from 913 million acres in 2014 to 899 million acres in 2018 in the United States. There is a need to harness the data (Elgendy and Elragal, 2014) to alleviate farmers’ difficulties and increase their incomes. Targeted weather warnings, smarter credit scoring, more inclusive value chains, and precise irrigation alerts (Dyer, 2016) using Big Data can change the fate of farming. Some of the big data applications reforming the entire agricultural economy are described as follows: 1. Agri-tech apps: apps-based services are being offered to African farmers like Wefarm for exchanging group wisdom. Another Kenya-based app, iCow, used in dairy farming in real time, alerts farmers to milk the cow. 2. Data-driven farming: agriculture scientists have been working on this problem, and the most promising approach came in the form of data-driven farming, which can map every farm in the world, for example, to know the moisture level in the soil six inches below the soil, and what the soil nutrient level is throughout the farm. Through a famous Big Data technique, precision agriculture (Mayer-Schonberger & Cukier, 2013), the farmer can apply water and pesticides only where it is needed, instead of applying it uniformly, to improve the agriculture industry’s productivity. Another data-driven technique, phenotyping, can produce using the same seed variety to grow differently in different parts of the farm. 3. Cloud computing technologies: in this digital world, map-produced cloud computing technologies like Hadoop allow for running of calculations across thousands of machines and facilitate solutions to problems. New satellite data, weather data (Prasad, 2019), digitization of the soils, and new sensors on the farm have given us a better understanding of better interactions among crop, weather, and soil. Using geometric tools like satellites, Big Data is revolutionizing the way we farm and feed the world.

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4. IoT-based farming through SAP Leonardo IoT based discovery: strategically placed sensors deliver data that makes farm digital intelligent. Data is collected and fed to a cloud platform where the data is analyzed and can be accessed in real time. In many industries, visual inspection of assets and types of equipment is risky and expensive. Spotting and tackling issues in real time are often not possible. The Field Force Drone Application sees things differently. It is an innovative approach to remote inspection that unlocks new value by applying new technologies; increases inspection capacity (Naresh and Munaswamy, 2019), safety, and efficiency; and reduces issue response time and cost, combining IoT, machine learning, and computer vision to turn images into insights. The Field Force Drone Application can be easily implemented, rapidly scaled, and adapted to many use cases. It can check farming fields for crop diseases and determine the best harvesting time to maximize yield. The possibilities are enormous. 5. Artificial machine and robotic farming: farming is a tough job these days. For example, milking cows is not just the type of work people want to do anymore. Standards of living are rising across the globe; the work is not as attractive as it once was, leading some farmers to consider how robots could fill such a gap as farming becomes more automated from sowing to harvesting with no human intervention. Robotic farming plays an essential role in selecting the harvesting of delicate crops like strawberries, mushrooms, and flowers; such crops can be easily damaged, resulting in financial losses to farmers (Chhabra et al., 2020; Hussain et al., 2020, 2021; Shahzad et al., 2020a; Syed et al., 2020). 6. Vertical farming: this is a well-growing trend in agriculture, where crops are grown using aeroponic technology, uses no soil, no pesticides or herbicides, and 90 percent less water. Crops are grown using aeroponic technology. A reusable cloth medium replacing soil and energy-efficient LED lights substituting the sunlight help crops grow indoors.

Challenges Before Agripreneurship Farming is a complicated business, totally dependent on nature. Considerations like soil and crop types, topography, planting, watching the weather, applying a uniform amount of water, nutrients and pesticides, manually inspecting fields (Rosenzweig et al., 2014), and climate change are significant in agriculture affecting output to a greater extent. There is a shortage of skilled workforce (Aitkenhead et al., 2013) and managerial workforce in the agriculture sector to carry out its routine activities. The sector is underequipped, with infrastructural facilities facing difficulties transporting agricultural produce from cold storage to end consumers. Lack of advanced tools and agricultural equipment, skyrocketing infrastructure (Sonka, 2016), and distribution costs add more fuel to the fire. There is no relief provided for the farmers for crop loss arising out of natural calamities, creating much distress. Various countries face the problem of controlling agricultural production by businesspeople being present at both urban and rural levels. This mainly includes the community of traders, commission agents (Sen & Madhu, 2017), and small financers who often control the prices of goods and commodities in the local market, thereby losing the farmers.

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Due to a lack of youth awareness towards making career opportunities in agriculture, this sector has remained untapped.

CONCLUSION There is a need to accomplish massive increases in efficiency on the farm as farmers face the incredible challenge of increasing food production without getting any more land. Data science is the next big wave of revolution, which can dramatically increase output. Big Data in agriculture is in its pioneering stage in many corners of the world. Major advancements in agriculture throughout the history of farming in both equipment and genetic improvements have taken place. However, a country like India is dominated by small and marginal farms, and farmers need to transform into larger ones. For this transformation, an assessment of various parameters like cultural practices, cropping pattern, and rainfall pattern, management practices, uncertain monsoon patterns, issues with market prices, problems with farmers’ knowledge and skill, issues with approachability, internal infrastructural problems, lack of great capital spending on developing infrastructure for agriculture, loan availability and logistics are of paramount importance. An IoT-based integration of all the operations of the agriculture sector at a macro level is required to solve the problems of this sector. Achieving the goal of smart farming requires success in maintaining a datasheet containing a record of every individual farmer by name or by a farm giving customization solutions to farmers, and this can be possible only by quantifying things using a unique farm code.

REFERENCES Adamu, H., Lutfi, S. L., Malim, N. H. A. H., Hassan, R., Di Vaio, A., & Mohamed, A. S. A. (2021). Framing Twitter Public Sentiment on Nigerian Government COVID-19 Palliatives Distribution Using Machine Learning. Sustainability, 13(6), 3497. Ahearn, M.C., Armbruster, W., & Young, R. (2016). Big Data’s Potential to Improve Food Supply Chain Environmental Sustainability and Food Safety. International Food and Agribusiness Management Review, 19(A), 155–172. Aitkenhead, M., Donnelly, D., Coull, M., & Black, H. (2013, October). E-smart: Environmental Sensing for Monitoring and Advising in Real-Time. In International Symposium on Environmental Software Systems (pp. 129–142). Berlin, Heidelberg: Springer. Ali, S. E. A., Lai, F. W., Hassan, R., & Shad, M. K. (2021). The Long-Run Impact of Information Security Breach Announcements on Investors’ Confidence: The Context of Efficient Market Hypothesis. Sustainability, 13(3), 1066. Asur, S., & Huberman, B.A. (2010). Predicting the Future with Social Media. ACM International Conference on Web Intelligence and Intelligent Agent Technology, l (1), 492–499. Bock, M. E., & Kirkendall, N. J. (2017). Improving Crop Estimates by Integrating Multiple Data Sources. The National Academies Press, Washington DC, United States. Retrieved from https://books.google.co.in/books/about/Improving_Crop_Estimates_by_Integrating.html? id=NqRGDwAAQBAJ&printsec=frontcover&source=kp_read_button&redir_esc= y#v=onepage&q&f=false. Bronson. K., & Knezevic. I. (2016). Big Data in Food and Agriculture. Big Data & Society, 1–5. https://doi.org/10.1177/2053951716648174

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Chhabra, M., Gera, R., Hassan, R., & Hasan, S. (2020). An Exploratory Study of Cognitive, Social and Normative Dimensions of Female Entrepreneurship within Transition Economies: Evidence from India and Vietnam. Pakistan Journal of Commerce and Social Sciences (PJCSS), 14(4), 1012–1042. Chieng, L. B., Singh, M. M., Zaaba, Z. F., & Hassan, R. (2015). Multi-Facet Trust Model for Online Social Network Environment. International Journal of Network Security and Its Applications, 7(1), 1. Chung, S. K., Yee, O. C., Singh, M. M., & Hassan, R. (2014, September). SQL Injections Attack and Session Hijacking on E-learning Systems. In 2014 International Conference on Computer, Communications, and Control Technology (I4CT) (pp. 338–342). IEEE. Coble, K. H., Mishra, A. K., Ferrel, S., & Griffin, T. (2018). Big Data in Agriculture: A Challenge for the Future. Applied Economic Perspectives and Policy. 40(1), 79–96. doi: 10.1093/aepp/ppx056. Del Giudice, M., Di Vaio, A., Hassan, R., & Palladino, R. (2021). Digitalization and new technologies for sustainable business models at the ship–port interface: a bibliometric analysis. Maritime Policy & Management, 1–37. Di Vaio, A., Palladino, R., Hassan, R., & Escobar, O. (2020). Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review. Journal of Business Research, 121, 283–314. Dyer, J. (2016). The Data Farm: An Investigation of the Implications of Collecting Data on the Farm. Nuffield Australia Project No 1506. Rural Finance Corporation, Australia. Elgendy, N., & Elragal, A. (2014). Big Data Analytics: A Literature Review Paper. In: Proceedings of Industrial Conference on Data Mining. Russia, 214–227. Hussain, A., Shahzad, A., & Hassan, R. (2020). Organizational and Environmental Factors with the Mediating Role of E-Commerce and SME Performance. Journal of Open Innovation: Technology, Market, and Complexity, 6(4), 196. Hussain, A., Shahzad, A., Hassan, R., & Doski, S. A. (2021). COVID-19 Impact on B2B E-commerce: A Multi-Group Analysis of Sports and Surgical SMEs. Pakistan Journal of Commerce and Social Sciences (PJCSS), 15(1), 166–195. Jackson, E. (2016). The Value of Big Data in Agriculture: Inputs, Farming and Processing. International Food and Agribusiness Management Review (IFAMR), 19(A), 5–6. Kitchin, R. (2014). The Data Revolution: Big Data, Open Data, Data Infrastructures and their Consequences. SAGE Publications Ltd, United Kingdom. Liu, Y., Zhang, L., Yang, Y., Zhou, L., Ren, L., Wang, F., … & Deen, M. J. (2019). A novel cloud-based framework for the elderly healthcare services using digital twin. IEEE Access, 7, 49088–49101. Mark, T. B., Griffin, T. W., & Whitacre, B. E. (2016). The Role of Wireless Broadband Connectivity on ‘Big Data’ and the Agricultural Industry in the United States and Australia. International Food and Agribusiness Management Review, 19(A), 1–14. Mayer-Schonberger, V. & Cukier, K. (2013). Big Data: A Revolution That Will Transform How We Live, Work and Think. Houghton Mifflin Harcourt Publishing Company, Boston, Massachusetts, United States. Naresh, M., & Munaswamy, P. (2019). Smart agriculture system using IOT technology. International Journal of Recent Technology and Engineering, 7(5), 98–102. Prasad, K. (2019). Big Data in the Bigger World of Agriculture Today. IEEE India Info, 14(3), 154–157. Retrieved from https://i1.rgstatic.net/publication/304190397_Big_Data_in_ food_and_agriculture/links/57edc17c08ae2df364033b1a/largepreview.png. Rahate, O., & Khandekar, Y. (2018). Precision agriculture and its scenario in India. Proceedings of the International Conference on Industrial Engineering and Operations Management, Washington, DC, USA, September 27–29, http://ieomsociety.org/dc2018/ papers/7.pdf

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Adoption of Big Data in Agripreneurship A Panacea to the Global Food Challenge Uzairu Muhammad Gwadabe Universiti Sultan Zainal Abidin, Malaysia

Nalini Arumugam School of Agriculture Sciences and Biotechnology Universiti Sultan Zainal Abidin, Malaysia

CONTENTS Introduction�������������������������������������������������������������������������������������������������������������� 71 The Concept of Big Data����������������������������������������������������������������������������������������� 73 Definition of Big Data���������������������������������������������������������������������������������������������� 73 Big Data Applications in Agriculture����������������������������������������������������������������������� 74 Agripreneurship and Big Data in Malaysia������������������������������������������������������������� 75 Challenges of Big Data Among Agripreneurs��������������������������������������������������������� 77 Conclusion��������������������������������������������������������������������������������������������������������������� 78 Acknowledgments���������������������������������������������������������������������������������������������������� 79 References���������������������������������������������������������������������������������������������������������������� 79

INTRODUCTION The current global food production needs to increase by at least 60% to feed the world’s growing population, headed towards 9.8 billion by the year 2050. The focus is to upsurge food production without wrecking the planet (Khokhar & Kashiwase, 2015; United Nations, 2017). However, one of the critical challenges is climate change, which pressurizes agriculture; the soils, freshwater, and biodiversity are rapidly degrading. Consequently, the agricultural industry is facing the challenge of producing more food on the existing farmland that can feed the earth without destroying forests and polluting the environment through chemicals and fertilizers. In developing countries, most farmers, especially small-scale farmers, continue to use traditional farming practices and limited access to the latest knowledge and technologies (Balana et al., 2020; Mastoi et al., 2014; Shahzad et al., 2020a, 2020b). This practice results in inefficiency and low yields. On the other hand, the demand for agricultural products continuously increases (Schwab, 2018). DOI: 10.1201/9781003097945-6

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In Malaysia, the agricultural sector is critical to the economy. Apart from being the primary food provider, the sector employs more than 14.9 million people and contributes 12% to the national Gross Domestic Product (GDP). However, in the last two decades, the country’s share of the world’s production of crops steadily declined (Department of Statistics Malaysia, 2019). For instance, in the last quarter of 2018, the Malaysian economy recorded better performance; all major sectors reported positive growth except for the agricultural sector, which recorded negative results (Department of Statistics Malaysia, 2019). Also, rice production, which is a staple food in Malaysia, does not satisfy the nation’s needs. Thus, the country imports a significant percentage of agricultural food (Hussain et al., 2020, 2021; Mirimo & Shamsudin, 2018; Syed et al., 2020). To improve the agriculture condition in Malaysia, entrepreneurship in agriculture, known as agripreneurship, has a crucial role to play (Iza et al., 2019; Kahan, 2013; Ndedi & Feussi, 2018). Agripreneurship is the consideration of agriculture as a business (Sharma, 2020). It is the process by which farmers become creative, innovative, determined, and willing to take calculated risks to improve and grow their farming businesses steadily (Global Forum for Rural Advisory Services, 2020). Therefore, agripreneurship would avail small farmers with the capability and mindset to develop innovative ways of doing the routine to grow their agricultural business. Correspondingly, Yusoff, Ahmad, and Halim (2016) express that agripreneurship provides small-scale farmers explicitly with the ability to develop goals and create wealth by applying innovative skills within the agricultural sector. It prepares them with entrepreneurial characteristics that can adapt to environmental dynamism and develop better methods to organize their farms and experiments on crops and cultivars. Therefore, this study focuses on small-scale farmers who will subsequently be referred to as agripreneurs. Although they are weak in terms of resources, capital, and modern agricultural techniques, they contribute to more than 80% of food production in Malaysia. Therefore, being the largest food producer, agripreneurs are the  right category of farmers who can substantially increase the current food production. Agripreneurs can upsurge total crop production by increasing yields and reducing waste on existing farmland through precision agriculture, facilitated by various Internet of Things (IoT) and Information and Communication Technological (ICT) devices for agriculture. The devices collect real-time and historical information in structured and unstructured data sets that accumulate at high speed and become vast and diverse. However, over time, the data accumulates fast and becomes big and complex, so the traditional recording facilities cannot handle it (Cynthia, 2017). Such massive data sets that are usually beyond the capacity of available data processing facilities are called Big Data (BD) (Protopop & Shanoyan, 2016). Similarly, the devices that generate and process such data are called BD devices. In agriculture, BD devices increase efficiency and productivity and reduce the costs of agricultural inputs. The devices ensure that the crops and soil receive the exact amount of inputs they need for optimum health and productivity (Hedley, 2015). The insight gained from BD devices for agriculture leads to scientific and rational decisions that can provide solutions to agricultural problems (Carolan, 2017).

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By understanding how to engage with BD devices for agriculture, agripreneurs will better appreciate their farmland by improving agricultural productivity while conserving and enhancing natural resources and gaining insight into how to handle the farmlands. The application of BD devices has shown development in some key sectors, especially in commercial agriculture. The concept can also be an essential tool for developing agripreneurship (Protopop & Shanoyan, 2016). Thus, it recently attracts a growing interest among stakeholders. However, there is limited literature and evidence on how revolutionary BD devices help agripreneurs increase their productivity and supply chain efficiency. Therefore, this chapter aims to provide a synthesized and integrated overview of the current state of heart on agripreneurship in relation to the application of BD devices in agriculture in Malaysia. The study also highlights the benefit agripreneurs might derive from the adoption of BD solutions for agriculture in their daily farming routine to maximize yield on their existing farmland.

THE CONCEPT OF BIG DATA Though the concept of BD is in its development stage, its roots go back to the 1960s and 1970s. However, in recent times, the world of data began in 2005 to create the first world data centers (Dontha, 2017). However, the first person who uses the term in its modern context was Roger Mougalas in 2005. Mougalas used the word to mean a large data set that is practically impossible to store, process, and analyze using traditional methods. Mougalas coined the term to mean the amount of data collected from different sources. Later on, an open-source software utility company, Hadoop, was established in 2016, purposely created to store and analyze a large and complex data set, followed by other database management companies (Adamu et al., 2021; Hashem et al., 2015; Khan et al., 2014). The establishment of Hadoop and other similar companies like Spark has smoothed the evolution of BD. Large data size continued to be generated by direct activities of humans and advancement in ICT, IoT, and machine learning. Such developments contribute to the accumulation of data by connecting smart devices to the internet to collect valuable information in agriculture like weather conditions, water, humidity, soil moisture, and pests and diseases (Oracle, 2019; Rijmenam, 2019; Soni, 2018).

DEFINITION OF BIG DATA According to Gartner, “big data is high-velocity, high-volume and/or high-variety information assets that need to be innovative and cost-effective forms of information processing that enable enhanced insight, decision making, and process automation” (Gartner, 2020). Gartner views BD from three perspectives, the volume, velocity, and variability of the data, which rise beyond the capacity of the traditional data handling technique or facilities. In a different definition, BD is “data sets characterized by huge amounts (volume) which is frequently updated (velocity) in different formats, like textual, numeric, images or videos (variety)” (Kaplan & Haenlein, 2019). Therefore, BD deals with innovative technology integration to uncover insights from complex, diverse, broad-based data sets (Hashem et al., 2015). Similarly, Khan et al.

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(2014) defined BD as information assets that are large, fast, and diverse; a data set that requires unique analytical techniques and methods to decode the valuable information it contains. Therefore, considering the concept of BD is still developing, it is difficult to provide its single definition, but its theme refers to a data set that is large or complex to be collected, stored, retrieved, processed, or interpreted by the traditional data handling facilities. Thus, the prerequisites for BD applications are to have the ability and infrastructure to generate, process, store, and derive valuable information from the accumulated information. The main concern about BD is the size, diversity, or variability of the data and the benefits derived from it. The usefulness of BD is the ability to analyze and draw conclusions that may help to improve the routine activities, such as cost or time optimization, or outcome that can facilitate the development of a new product or process, or anything that may lead to smart decisions. Efficient analyses of BD can provide solutions to critical survival-related issues like identifying the causes of a failure, problems, and defects associated with any activity or venture. Application of BD can help predict a customer’s transactional habits or preferences, calculation of risk portfolios, and ability to detect a potential hazard before it affects the venture (Gandomi & Haider, 2015; Strong, 2015).

BIG DATA APPLICATIONS IN AGRICULTURE In recent years, global digital transformation increased opportunities in the agricultural sector. BD applications in agriculture are receiving increased attention from various players in the food and agribusiness sectors. Smart devices and technologies like mobile phones and social media enable agripreneurs to interact with society. Rapid ICT advancements through IoT and cloud computing drive smart farming (Sundmaeker et al., 2016). The general advancement in ICT has led to widespread mobile phones for virtually all rural farmers, and that has significantly impacted farmers’ lives. The information generated by mobile phones enhances the efficiency of trade in the agricultural sector. For instance, the introduction to mobile money platforms has also expanded financial inclusion that provides agripreneurs with the ability to use banking services to carry on transactions, borrow funds, and save money. That has saved time and reduced operations costs for agripreneurs (Ali et al., 2021; Protopop & Shanoyan, 2016). Thus, BD devices in agriculture are blending the traditional agricultural processes with contemporary data science. Farmers can handle large numbers of cases containing large amounts of data and better understand their farming business (Foodtank, 2019; Hedley, 2015). The BD solution is creating new approaches that can help farmers analyze their farm operations and ultimately make farming more efficient, profitable, and sustainable (Foodtank, 2019). In that regard, all agripreneurs must get to the heart of the challenge by migrating from traditional farming techniques to modern, sophisticated agriculture aided by BD devices. The agricultural devices can provide farmers with information that can help them increase crop yields without any negative impact on the environment and increase the amount of farmland (Peter, 2019). Agripreneurs need those smart devices to

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efficiently determine how various crops can perform in different ways and different seasons of the year. For example, BD devices for agriculture can help agripreneurs understand micronutrients, minerals, and biotics in farming (Dutta et al., 2018; Peter, 2019). Studies proved that information derived from soil and weather conditions could be a solution to tackle issues like delayed planting or weeding, and lack of appropriate land preparation and harvesting techniques, which are some of the problems that affect small farmers’ productivity. Thus, a solution to those inefficient agricultural processes will increase general crop production around the globe (Bunge, 2014; Del Giudice et al., 2021; Di Vaio et al., 2020; Kshetri, 2014; Oluoch-Kosura, 2010; Shahzad et al., 2021). Therefore, many agencies around the globe pay attention to BD-based solutions. For instance, the United States Department of Agriculture is focused on supporting sustainable land use and world food security. It has launched the first two mobile applications that can link farmers worldwide and equipped them with detailed information on ways to improve farmland productivity. The two applications, LandInfo and LandCover, were tested and issued under the five-year project of the department (Jan, 2015). LandInfo users can collect and exchange soil and land cover information as well as access global climate information. LandCover simplifies data collection for use in the surveillance of inventories and land cover. The programs generate critical indicators for these types of covers on the phone and store data on servers that users can access via the internet. This kind of knowledge exchange is essential for agripreneurs to avoid many problems that might have happened. Consequently, productivity can be increased to meet the need for feeding the growing global population (Jan, 2015). Similarly, Elsheikh et al. (2013) designed a Land Suitability Evaluator to determine the best crop for a given piece of land. This technology can help farmers identify and decide on the crops to plant for the best yield. This development could be a solution to the problem highlighted by Jaabi (2017) that some crops are planted at inappropriate farmlands; the land might be more suitable for different plantations.

AGRIPRENEURSHIP AND BIG DATA IN MALAYSIA Ownership of agricultural land in Malaysia is generally divided into small-scale farms (agripreneurs), an average farmland size of one to two hectares, and the ­plantation-based estate sector with a farm size of over 500 hectares (Bakar, 2009). Agripreneurs produce up to 80% of Malaysia’s food supply and contribute significantly to the economic well-being of the country (Casey, 2016). To date, agripreneurs in Malaysia have not fully adopted modern farming techniques; hence, they exercise traditional farming practices coupled with mechanized title techniques (Haris et al., 2018). That includes small farms’ size in remote areas with restricted access to the latest information and technologies. As such, agripreneurs are vulnerable and exposed to a wide range of uncertainties, like attacks from pests and diseases, market and price fluctuations, and climate change, among other issues. Therefore, the agripreneurs always worry about equipment and labor costs and what, when, and how to plant crops. The optimal level of inputs, monitoring of pests and diseases, quality of the soil and water, timing, and price of the sale also need technological solutions.

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The Malaysian government is seriously committed to developing and modernizing agripreneurship, especially in its efforts to attract more young graduates to embrace agriculture. For instance, the Agro Youth Entrepreneur Incubator Programme (IUBT) of the Ministry of Agriculture aims to develop agripreneurship among young people and university graduates through practical training in food crops production. Also, the government supports agripreneurship through various educational programs and training. Also, the government has been allocating billions of Ringgit in support of various agripreneurship programs such as the ‘Young Agro entrepreneurs’, ‘My Kampung My Future’ programs, and so on (Yusoff, Ahmad, & Halim, 2016). The focus is to strategically change the mind of the youths to accept agriculture and make it a business. In this regard, the Malaysian Ministry of Agriculture and the Agro-based Industry has implemented a program which is termed as Pertanian Adalah Perniagaan, which means “Agriculture is a Business”. The policy calls on agripreneurs to migrate from traditional to modern agriculture (Protopop & Shanoyan, 2016). Correspondingly, Annamalah et al. (2016) argued that the solution to a significant increase in food production is that Malaysian agricultural entrepreneurs should focus on diversifying and modernizing the agricultural process to create businesses that can increase income and thus allow farmers to live comfortably. Since agriculture is one of the important sectors in Malaysia, smart farming devices will facilitate the understanding of real-time information generated on the farm. As the government makes efforts to enhance small-scale farming by convincing the youths to embrace agriculture, the policy would be more attractive when the sector is modernized and equipped with ICT infrastructure. Also, that can help agripreneurs reduce operating costs and human errors, optimizing decision-making, increasing production, and improving the supply chain’s efficiency. Many studies suggest that agripreneurs can use smartphones to access information that can improve farming procedures. Smartphones with internet connection in Malaysia have grown at a remarkably rapid rate. This can result in mass mobilization and accumulation of information at the small farming stage. Therefore, to create a conducive environment in managing the accumulating information, the application of BD devices for agriculture could be a solution (Protopop & Shanoyan, 2016). Further, more BD devices have emerged to help store and make sense of these data sets. Some of the software is free and can be installed on tablets and mobile phones. This development shows how easily these technologies can penetrate the industry. Nowadays, social media plays a vital role in information, communication, and marketing (Protopop & Shanoyan, 2016). BD devices for agriculture are practical solutions to agripreneurship. For example, a system called Web Paddy GIS, developed by Norasma et al. (2013), supports paddy farming in Malaysia. The system has specific features that help the fertilizer application on the farm; it can provide information on water scheduling, pest control, and farm yield reports. The system is another form of BD device. Although initially there was concern about whether the semi-learned farmers could use the technology, the project was successfully implemented as the schoolgoing children of the farmers helped their parents to assimilate and use the technology. However, issues like access to a reliable internet connection are a challenge.

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In a study on multi-spectral bands onboard the earth observation satellite (SPOT satellite), Jayaselan et al. (2018) determined the quantum of nitrogen that needs to be added to palm oil trees to improve productivity. The project is underway to introduce bands onboard the Unmanned Aerial Vehicle (UAV) and promises to be successful. The service is an advantage for agripreneurs, as they can use such technology at low prices. The National Applied Research and Development Centre (MIMOS) is Malaysia’s leading ICT, Industrial Electronics and Nano-Semiconductor. The center launched Mi-Trace in Mid-2016 for the end-to-end tracking and tracing process of premium products.

CHALLENGES OF BIG DATA AMONG AGRIPRENEURS The rapid growth rate in the amount of data is of great concern. Though the advanced technology in data mining and storage facilities provides a solution for storage and data analysis, there is concern about the diversity, transfer speed, and data security (Khan et al., 2014). Therefore, agripreneurs need to understand BD and the way it works. Studies revealed that agripreneurs and large-scale farmers also face difficulties at the early stage of using BD-based solutions (Michaud, 2018). Correspondingly, in a study by Gartner, only 15% of ventures it studied were able to accomplish their BD projects successfully and effectively (Cynthia, 2017). The NewVantage Partners conducted a five-year study and found that 51% of BD-related projects it studied did not achieve measurable results (Cynthia, 2017). Thus, it becomes evident that agripreneurs find it challenging to acquaint themselves with BD-based solutions. According to Shirkhorshidi et al. (2014) and Adamu et al. (2021), the challenges of BD can be traced from its significant features: Volume: agriculture is a sector that deals with massive data sets. Data accumulation starts from determining the best seeds for different soil and weather types up to the harvesting stage. So, there is no way agripreneurs cannot deal with a large volume of data if they want to optimize the quality and yield of the crop. The volume of data is one of the distinguishing characteristics of data that agripreneurs need to consider while handling data. A typical example is how agripreneurs extract and analyze relevant and valuable information systematically from the vast amount of data that flows every day through the farming routine (Kshetri, 2014) Variability: due to limited access to modern technology, agripreneurs find it challenging to handle inconsistent data sets. The irregularity of the data becomes a challenge in managing information efficiently (Shirkhorshidi et al., 2014). For instance, in farmland, the amount of manure needed may differ based on weather changes. Therefore, the amount of data needed at different points of time and in different parts of the farmland may vary. That makes it challenging for agripreneurs to collect, process, and analyze a data set in a different format (Hashem et al., 2015). Variety: this refers to the heterogeneous nature and sources of data, whether structured or unstructured. In previous days, databases and spreadsheets were the main formats compatible with almost all the data processing software. However, data can be in different formats like pictures, audios, PDF, emails, monitoring devices, videos, and files. Therefore, this diversity of unstructured data results in specific problems of

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extraction, storage, and analysis (Shirkhorshidi et al., 2014). Thus, the discrepancy in the flow of the data is a challenge to handle by agripreneurs. Velocity: in this context, velocity refers to the speed at which farmers can generate the necessary information. The real potentiality of data depends on the speed at which it can be generated and processed to obtain the required information. Data flows at high speed and must be processed at a reasonable period (Khan et al., 2014). However, responding to the rapid accumulation of BD is one of the main challenges for agripreneurs. Complexity: data comes in different structures and sources. Therefore, it is imperative to correlate and or create relevant relationships among various sets of data. Otherwise, the data can look meaningless (Hashem et al., 2015). That aspect is another obstacle to agripreneurs since the traditional grouping methods cannot handle some data set complexity.

CONCLUSION Agripreneurship, which is the inculcation of entrepreneurship in agriculture, is a factor that can provide small farmers with optimism towards the adoption of BD devices for agriculture. The use of BD devices in agriculture is a promising approach to increase food production despite environmental challenges. The devices will allow for precision agriculture, a technique that maps soils and plants to determine their needs. This will improve the yield, reduce the planting cost, and be better for the environment. BD devices in agriculture will benefit agripreneurs through real-time measurement, feedback, forecasting, and future planning. For example, the devices can provide realtime data on the need for fertilizers and chemicals by different plants in the same farmland. This type of information will increase the productivity of the existing farmland without harming the environment. This is because the devices ensure that crops and soils receive the amount of input they need to achieve optimum health and productivity. The proliferation of smartphones and internet connectivity will facilitate the adoption of BD devices among agripreneurs. Social media is also widespread, even among rural farmers. The medium can be used to connect agripreneurs with experts on BD devices to discuss technical aspects and challenges they may encounter. Moving forward, the government, through various agencies, farmer associations, and value chain stakeholders, will have to make a significant effort to overcome the obstacles that agripreneurs may face in the early stage of adoption. The government, agripreneurs, and all stakeholders need to increase the attention given to BD devices in agriculture to increase agricultural productivity with relatively fewer resources and address the task of increasing food production to feed the growing population. The BD-based solution for agriculture will help the Malaysian government realize its ambition to modernize the agricultural sector, youth empowerment, and rural development. To achieve successful adoption, this study recommends a collaborative outreach awareness program for agripreneurs. The awareness should be initiated through various agencies of the Ministry of Agriculture. With the growing awareness of the

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potential benefits of BD applications to agripreneurs, there could be an increasing understanding of the benefits of using the devices in agriculture. Finally, more research is needed to gain insights into the drivers and obstacles of BD-based solutions in agriculture. It is imperative to conduct a study to develop a framework that may lead to the successful adoption of BD-based agriculture solutions.

ACKNOWLEDGMENTS This research is a part of the Fundamental Research Grant Scheme (FRGS) RR261. The researchers would like to thank the Malaysian Ministry of Higher Education and Universiti Sultan Zainal Abidin, Malaysia, for supporting this study.

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Anticipating and Avoiding the Pitfalls that Can Sink a Startup Mahima Birla Pacific University, India

Sunita Kishnani Systematix Infotech Pvt. Ltd., India

B Venkat AICTE, India

CONTENTS Introduction�������������������������������������������������������������������������������������������������������������� 84 Valuable Lessons from Successful Startups������������������������������������������������������������� 84 Cockroach Labs��������������������������������������������������������������������������������������������������� 84 Okera Inc.������������������������������������������������������������������������������������������������������������ 85 Cazoo Limited����������������������������������������������������������������������������������������������������� 85 Observe.AI���������������������������������������������������������������������������������������������������������� 85 Get the Basics Right for a Successful Startup��������������������������������������������������������� 86 Understanding Unit Economics������������������������������������������������������������������������������� 89 What Is CAC?���������������������������������������������������������������������������������������������������������� 89 What Is Churn Rate?������������������������������������������������������������������������������������������������ 89 What Is LTV?����������������������������������������������������������������������������������������������������������� 90 LTV to CAC Ratio��������������������������������������������������������������������������������������������������� 90 The Payback Period on CAC����������������������������������������������������������������������������������� 90 How Unit Economics Helps You Remain Sustainable and Profitable��������������������� 91 Understanding Customer Satisfaction Metrics�������������������������������������������������������� 91 What Is CSAT?�������������������������������������������������������������������������������������������������������� 91 What Is NPS?����������������������������������������������������������������������������������������������������������� 91 Key Financial Metrics���������������������������������������������������������������������������������������������� 94 Burn Rate������������������������������������������������������������������������������������������������������������ 94 Cost of Human Capital���������������������������������������������������������������������������������������� 94 Annual Recurring Revenue��������������������������������������������������������������������������������� 94 Inventories����������������������������������������������������������������������������������������������������������� 94 Summary������������������������������������������������������������������������������������������������������������������ 95 Bibliography������������������������������������������������������������������������������������������������������������ 95 DOI: 10.1201/9781003097945-7

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INTRODUCTION Even the most valuable companies of today were once humble startups. With ample finance opportunities and advanced technologies, it has become easier these days to establish a startup. Today, pioneering innovations can take shape in a college, home, or garage. As a result, there is huge growth in startups. An estimated 50 million startups are opening every year worldwide (The Ultimate Startup Failure Rate Report [2020], 2020). Each of them is hoping to be a unicorn (startup valued at a billion dollars or more) or be acquired by a bigger company. However, for every single hugely successful startup, thousands fail. The top 20 reasons for these failures are seen in the infographic in “The Top 20 Reasons Startups Fail” (2019). Bringing an idea into reality is lucrative yet challenging. The chapter further unfolds success stories of some startups, helping you to understand aspects that need the most attention and overcoming the challenges within. Your startup becomes a unicorn, and your growth story is written in golden words.

VALUABLE LESSONS FROM SUCCESSFUL STARTUPS The best way to start an entrepreneurship journey is to read more about real entrepreneurs and their real businesses that hire real people to conceive real products and services for real customers. Therefore, let us read about a few startups and analyze what made them successful.

Cockroach Labs Cockroach Labs, a big data industry company, is amongst “The 10 Hottest Big Data Startups of 2020” (Whiting, 2020). CockroachDB is considered the most outstanding innovation in the open-source software movement to disrupt the database and data management tools. CockroachDB is the world’s first cloud-native, distributed relational database that combines the rich functionality of SQL with the horizontal scalability common to NoSQL. It has outstanding rebuild capabilities for disaster recovery and is exceptionally reliable – therefore, the name Cockroach. Due to a strong focus on R&D, CockroachDB is a globally spanning database that retains data close to a customer’s legal jurisdiction, promises data privacy, and renders data quickly. Legacy players like Oracle and Microsoft SQL Server have long dominated the database market. Globally, stay-at-home orders after the COVID-19 pandemic radically increased pressure on the enterprise’s tech stack. At the same time, economic insecurity is pushing businesses to increase efficiency and reduce costs. Therefore, the switch from legacy, closed-source data platforms to modern, distributed, cloudnative platforms and tools is seen as much faster than expected. Cockroach Labs has so far received $195 million in funding. YOY customer growth has been 295% since inception. Existing customers’ usage has doubled, and customer retention is over 90%. Cockroach Labs is a classic example of understanding market transition and finding an opportunity to build a successful product and company.

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Okera Inc. Okera is named as a “Cool Vendor” in DataOps 2020 (“Okera Named a Cool Vendor by Gartner”, 2020), is amongst “The 10 Hottest Big Data Startups of 2020” (Whiting, 2020) and also has been honored with Datanami Editors’ Choice Award for Best Big Data Security Product or Technology (“2020 Datanami Readers’ Choice Awards”, 2020). A US-based data governance company, Okera provides enterprises with secure data access for modern analytic platforms. Okera Active Data Access Platform (ODAP) and Okera Policy Builder deliver peace of mind to share data confidently. They enable detailed data access control policies and governance compliant with privacy regulations such as GDPR and CCPA and reduce data security breaches. It provides native support across Big Data cloud applications for data-driven initiatives in enterprises. Data is used more effectively as the same data can be used in various projects without creating multiple copies or custom plumbing. Okera has so far raised $29.6 million in funding. According to Gartner, 36% of the data administrator’s time is spent on data preparation and data integration, which is more than any other data management task (Okera Named a Cool Vendor by Gartner, 2020). Okera spotted the market gap and worked on a product that addresses this. The result is a successful startup that has the trust of large enterprise clients and investors.

Cazoo Limited UK-based used-car startup Cazoo has so far raised $239 million in funding. Used-car businesses have always been there; what made Cazoo, then, the fastest British business to achieve unicorn status? Cazoo transformed how 8 million used cars are bought annually by launching a digital platform and home delivery much like any other product. Cazoo brought in the comfort and trust factor for a used-car buyer. Cazoo reconditions each car, and it passes through more than 150 inspection points. A seven-day money-back guarantee replacing the traditional seven-minute test drive, recent MOTs, full-service history, seven-day free insurance, a free 90-day warranty, and RAC roadside assistance build customer trust. The key differentiation behind Cazoo’s success can be attributed to reinventing the used car buying experience and the flawless execution of the idea.

Observe.AI Founded in May 2017, Observe.AI is a speech analytics and AI platform for contact centers that provide agents with real-time feedback on customer sentiment and suggest the following best action during a live customer call. When 50% of customers are unsatisfied with their call center experience, Observe.AI knew there was a huge opportunity for their product. Observe.AI figured out the market opportunity first and then launched the product. The product-market fit helped them become a successful startup with $35 million in total funding, 100+ clients, and multi-million revenue.

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GET THE BASICS RIGHT FOR A SUCCESSFUL STARTUP The first and foremost question for a startup should be a perfect alignment of operations that lead to its survival and successful scaling. Following are the various aspects that a startup should think through for a thriving foundation. 1. Right Founding Team The game can only be won with the right combination of team players. Founders and their team are the single biggest factor behind the success or failure of a startup, more so than the product/service itself. Later is the outcome of the intellect that the exceptional founding team brings to turn an idea into a marketable product/service. Therefore, from idea to establishing a founding team is a journey that requires careful considerations. Some of these are listed as follows: • Solopreneur, pair or more. Nothing is wrong with choosing to be a solopreneur, but realize that the startup turf is tough to play on. The right companion brings complementing skills, congruence to take quick decisions, and most importantly, support each other on the bumpy startup ride. Having a co-founder also signals confidence to investors regarding both the success of the idea and the co-founder’s diverse skills to make a startup successful. • Choice of cofounder. Spouse, friend, relative, or colleague – you have many options for selecting a co-founder. Many startups fail due to interpersonal issues; therefore, make a prudent choice. Choose a co-founder known to you for a reasonably long time, because you can assess whether he/she complements your temperament, skills, and expertise and is a leader and a self-­ sufficient, persuasive, emotionally strong personality. • Selection of founding team. The founding team must be lean. Therefore, select members that bring the necessary skills and abilities. Some of the traits in the founding team should be the subject knowledge, passion for building, and multi-tasking. Do not hire super talent because the startup may not afford it, but it should be the team you are confident of nurturing to managerial or leadership roles going forward. • Deciding key roles. Correct distribution of the key roles among the founding team is a crucial decision in the success of a startup. When the equity is equal among founders, then who would head becomes a sensitive subject. Take a thorough decision by analyzing prior experience and key competencies while allocating roles. For instance, a founder with experience in sales and marketing should take the role of a CEO as sales, marketing, and investor relations are the prime focus for any startup. The one with technical background should take the role of Product Engineering Customer Service Head. Wasserman (2014) noted that “65% of high-potential startups fail as a result of conflict among co-founders.” The saying “Two is a company, and three is a crowd” stands true for a startup as well. More than two founders mean more internal discussions, task delays, low trust, politics, and ego clash. Here is a real-world example:

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An NBFC in India was founded by four friends with equal investment. As the roles were not clearly defined, two of them never took interest and continued focus on their existing business, while the other two were full-time. Again, one friend offered investment for the equity and joined, becoming the fifth founder. Only one founder was full-time to look after everything – new business, collections, accounts, compliance, etc. Despite five experienced industry founders, the startup never saw growth because of the informal arrangement. The company had been profitable all through, but after eight years, when their NBFC license was canceled due to some minor non-compliance, all partners started blaming the one who was managing the business. Unclear roles, too many founders without active involvement, no growth plan or strategy all led to the end of an eight-year-old profitable startup. 2. Follow the 30/70 Concept Many entrepreneurs think that idea will lead to success, but execution plays a major role. Even the best idea fails as a business if not executed rightly. In contrast, even if the idea is not original, it still achieves success with the right execution. Let’s refer to this as a 30/70 concept. Having an excellent idea that can be worked upon, improvised, and stretched is important. However, 70% of the focus should be planning and execution. Only with implementation can one distinguish whether the idea was good or bad, amazing or pathetic, useful or not. A few who worked upon an existing idea and still are successful include Facebook through MySpace, and Instagram, though Hipstamatic was the first. The point is that a great idea can be improvised to have a new and fresh feel, making it more relevant with the appropriate execution. In the beginning, we have seen Cazoo as the successful execution of an idea in an existing industry. Byju’s, an EdTech unicorn startup from India, is another example. Byju’s disrupted the entrance examination coaching industry. Byju’s improvised classroom coaching through a digital platform making appropriate use of technology and content right when the internet and mobile penetration was increasing in India. Besides, he ensured that the digital medium is complemented with the personal touch of the teachers. Therefore, not the idea but the execution made Byju’s distinct and reached to masses (Soni, 2018). The execution plan lays down the strategy for a startup. It provides a framework with clear milestones, tasks, and responsibilities for managing and reviewing the business, communicating critical goals, timelines, and success parameters. The following is the guideline for creating an execution plan: • Create a list of functions and goals in all these functions. • Identify tasks to achieve goals. • Prioritize tasks, assign resources, set completion timeline, and allocate budget. • Create a training plan and processes. Train the team and explain processes. • Review progress and adherence to processes. Identify road blockers and realign.

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A well-defined execution plan helps in preparing a finance plan as well. Only founders have a vision of what and how they want to achieve their goals in a startup. Thus, simple delegation does not work. The founder’s direct involvement is essential for the training and guiding team, followed by a continuous review mechanism and process alignment. 3. Plan Minimum Viable Product (MVP) and Start Early The product plan is another area where many startups fail. A startup has limited resources in terms of money and manpower. Therefore, plan a product with minimum features that can be quickly built and launched to prove the value that it adds to the customer’s life and evaluate growth prospects based on its state and adoption speed. The approach is called “Minimum Viable Product (MVP)” (Minimum Viable Product [MVP], 2020) and is essential for tech startups. Planning an MVP means considering a full-scale product – each offering feature, functionality, and execution of only those that are most essential from a user’s perspective The MVP approach reduces the probability of failure as it surfaces learnings and an agile way of adjusting the product as per the customer’s need. It also helps to legally safeguard the original idea, product, or service at an early stage. Following is the guideline for planning MVP: • Plan one or two key features that address the biggest pain point of your customers. • Evolve the product by listening to your customers to upgrade existing features and functionality and gathering feedback on the features to be incorporated later. • Track metrics, and improve and release upgraded products. The frequency of initial releases can be monthly. Do not lose focus on the main problem statement by incorporating overly wide features or associated offerings. How not considering MVP killed a big idea can be seen in the following real-world example. Internet pharmacy was an untouched territory in India until 2013. A startup decided to foray into this space as early as 2012. Their idea was to address the pain point of diabetic, hypertensive, and other lifestyle-­ diseased patients who visit the physical pharmacy store every month to buy the same medication. As they worked on the idea, they realized that pharmacies make 50% of their revenue from non-pharmacy products. They decided to include them in the product catalog. Next, they thought, there are not many organized pharmacy retail chains, so let’s venture into this area as well. Then, they thought, there is no portal to manage personal health records (PHR), so let’s build that. As soon as an online pharmacy and retail stores were launched in two cities, they also decided to add a location-based doctor’s list and functionality to book a doctor’s appointment. The simple idea of ordering and home delivery of medicines was complicated with many addons during the founding years. Soon, other online pharmacy startups were seen in the market. By 2014, three other online pharmacy startups offering medicine ordering and home delivery, that too in few cities, started getting huge traction, and this startup was still struggling with whether to focus on online, offline, or the health portal

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market. As per the EY report “E-pharma: Delivering Healthier Outcomes’, e-pharma players are expected to attain a combined market size of US$2.7 billion by 2023 from about US$360 million currently. But this startup is nowhere amongst the top players despite being a pioneer in the field. 4. Assess Viability of Business Model Start simple, execute smart, build on scale. For a startup, it is important to have a viable business model; therefore, ensure that enthusiasm does not drive you away from Unit Economics and Customer Satisfaction. Keep evaluating and analyzing how quickly you will scale up and how profitable your venture is likely to be with the help of the Unit Economics and Customer Satisfaction (CSAT) Score. Remember, calculating these is simple, and you do not need a financial analyst for this.

UNDERSTANDING UNIT ECONOMICS Unit economics helps to know whether the business will be sustainable and scalable. It measures profitability on a per-unit (per-customer) basis. Essential metrics for unit economics are: • • • • •

Cost of Customer Acquisition (CAC) Churn Rate Lifetime Value (LTV) LTV to CAC Ratio Payback period on CAC

WHAT IS CAC? CAC is the cost that is spent on convincing a potential customer to purchase a ­product/service. The formula and an example follow. CAC =



Cost of sales and marketing ( including salaries and related expenses) for a given period Number of customers acquired in that period

CAC =

$1, 000, 000 in Q1 10, 000

CAC = $100

WHAT IS CHURN RATE? Churn rate is the number of customers that drop your services in a given period. This metric is important, as a cost is involved in acquiring a new customer (CAC). If the customer continues, then not only is CAC saved but this also increases LTV. To

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reduce the churn rate, build a responsive customer support mechanism. Ask customers to rate, find out the main separation reasons, and address them. Measure support on the following metrics to figure out improvement areas. • • • •

Response time Resolution time and rate Number of interactions-to-resolution Escalation rate

WHAT IS LTV? LTV is projected gross margin from a customer over the lifetime of his relationship with the business. Gross margin also considers any support, installation, and servicing costs (Karnes, 2020). LTV helps forecasting cash flow and the number of customers to be acquired and retained to achieve desired profitability. Example The average sale for a B2B medical-supplies portal is $1000. On average, their customers buy 8 times a year, and the retention period is 2 years. Their gross profit margin is 20% after deducting the cost of goods sold (COGS) and all expenses, overheads, sales, and marketing costs. Customer LTV will be: LTV = Average sale value per transaction × No. of Transactions × Retention Period $16000 = $1000 × 8 × 2 Customer LTV = LTV × Gross Margin $3200 = ($1000 × 8 × 2) × 20%

In a subscription model (SaaS), Customer LTV takes into consideration the monthly churn rate as well. Therefore, LTV will be the gross margin earned on monthly recurring revenue per customer and dividing that by the monthly churn rate.

LTV TO CAC RATIO LTV to CAC ratio is for unit economics analysis. The ideal is 3:1, where you get three times the value of acquisition from each new customer. If the ratio is low, say 1:1, then refine sales, customer acquisition, and pricing models. If it is high, say 6:1, it means you are missing out on valuable growth opportunities as each customer is paying more than the cost of acquiring them. If so, allocate more time and budget to acquire new customers and scale fast.

THE PAYBACK PERIOD ON CAC The payback period is the amount of time a startup takes to start earning from each customer. The average payback for a startup is 15 months. The shorter the payback period, the faster will be the growth and less working capital requirement.

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HOW UNIT ECONOMICS HELPS YOU REMAIN SUSTAINABLE AND PROFITABLE Startup founders are highly confident about their idea and the business. However, “let’s start, and we will get traction and customers” never works, and it is the biggest reason for failure. A startup needs both a healthy growth rate as well as profitability to sustain. A startup will be profitable when LTV > CAC, i.e., 3:1 to cover all costs such as infrastructure, product development, and logistics. Unit economics enables one to predict revenue curve and make long-term financial projections accurately. That is why nowadays, investors focus more on unit economics than Gross Merchandise Value (GMV) (Pilcher, 2020).

UNDERSTANDING CUSTOMER SATISFACTION METRICS Customer satisfaction assessment aligns product-market fit, marketing, promotion, and pricing strategy to reduce customer churn rate. It is also a metric to assess brand loyalty.

WHAT IS CSAT? CSAT score indicates customers’ satisfaction for a product, service, transaction, interaction etc., usually on a scale of 1–5 (Highly Dissatisfied [1], Dissatisfied [2], Somewhat Satisfied [3], Satisfied [4], and Highly Satisfied [5]). It provides insight as to what is working or failing. CSAT Score % 

Sum of Satisfied and Highly Satisfied Responses 100 Total Number of Responses

WHAT IS NPS? Net Promoters Score (NPS) predicts how likely your customers are to promote your product/service or brand. NPS is usually an assessment of overall experience with the brand and is generally asked as “Considering your overall experience, how likely are you to recommend us?” The scale used is 0–10, where “0” is lowest and “10” is highest. The customer ratings are classified as follows: o Promoters – 9–10 o Passive –7–8 o Detractors – below 7 NPS = Percentage of Promotors − Percentage of Detractors NPS is expressed as a number in the range of −100 to 100. The score is negative when a startup has more detractors than promoters, and positive when it has more promoters. Along with the NPS Survey, ask a few questions to understand the good and bad aspects of the customer’s overall experience. This helps to focus and improve upon the weak points.

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Refer to the same online pharmacy startup and see how a complicated business model and execution barred it from achieving its growth potential. Instead of starting with home delivery of medicines in one or a few cities, they chose to ship medication all over India. This means enabling the logistics function from day one to cater to the whole country. Further, their retail pharmacies were also in four different states, with multiple stores in one city. Their portal was also over-featured as against the MVP approach. Because of this, sales and marketing expenses increased manyfold. The average sale value per transaction for pharmacy in India is shallow. As we just studied, if the average value per transaction is low, customer LTV will not suffice to recover CAC and other business costs. In contrast, their competitors focused on setting up operations in one city only. Therefore, their CAC was significantly low. Now you can realize that instead of widening and getting too thin, their competitor decided to focus on the major pain point, learn with a simpler model, and scale fast after the success. Their competitor is now among the top players in India. A startup should work in an agile model. Let the model be simple and easy to execute, learn from the errors, and fix quickly to focus on the unit economics and customer satisfaction index. Once established, scaling is easy and error-free. 5. Decide the right time for market launch Simply having a great product does not lead to success. A startup needs to market their product to succeed. Marketing here means public launch after the test marketing is done among limited customers who have tried your product/service. Test marketing and timing of market launch both are important. Remember, great marketing can get you customers, but a terrible product will take them away. Test marketing is more crucial for a tech startup because even with a highly qualified and capable product team, bugs are inescapable. Therefore, the thought process for test marketing should start right from the moment you start working on your idea and developing a product/service. As the product is built, discuss it informally and share the prototype with the ­family/friends representing your target audience. After a few of these discussions, assess whether the sales benefits that you presented are convincing and easily understood or not. As product development progresses, expand this group to include people you are connected to but do not have a personal relationship. Share the initial versions with this group for beta testing to determine whether it is easy to navigate and use. If multiple users give the same feedback about a feature or functionality, it is worth addressing. Use this feedback to enhance the product and make it ready for launch. In the third phase, create a larger group. At this stage, gather specific terms that the target audience uses for search, their major locations, and popularity/trends for your type of product/service. Too early or late go-to-market kills possibilities of going big with the idea. Do not wait for a matured product to start marketing; instead, go ahead with a Minimum Viable Product (MVP). They are launching a marketing campaign before MVP risks success because the venture will get good coverage but no traction. Now, to decide the right time for the go-to-market, ask yourself the following questions:

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What is my product/service disrupting? What is the bottom-line benefit to the customers? Am I done with the test marketing? Do I know the early adopters? What is the best way to reach out to the customers? What marketing communication would strike my audience?

If you have clear answers to these questions, then strategize the timeline for announcing the launch. Also, decide whether you would like the information to spread slowly or through one-to-one marketing, big announcement, launch city by city or consider an entire target geography, etc. The strategy will also include what you want to happen next after the market launch. How an untimely market launch proves fatal for a startup is seen in Cuil’s case. Launched in 2008, Cuil was considered to be a solid contender to dent the Google search engine (Sullivan, 2008). Cuil claimed to have indexed 120 billion webpages, larger than Google’s index at that time, and the founders, being former Google and IBM employees, knew search. Yet, it failed miserably. The reason is that the product could not perform the core functionality of finding relevant search results. Cuil created huge traction by using the terms “pioneering”, “significant breakthroughs”, “ideal search engine”, “complete user privacy”, “next-generation approach to search” in their marketing communication. Soon after the launch, negative reviews – “it is buggy”, “slow”, “hand-tweaked”, “requires exact spelling” – started appearing (PCMagStaff, 2008). If these were the claims in a launch, then Cuil should have had a good product to back that up, but such was not the case. The result was that Cuil suffered an early launch with a load of buzz and negative reaction. Once a promising search startup, Cuil ended its journey in 2010, having raised $33 million between its founding and shutdown (O’Dell, 2012). Like an early launch, a delayed launch also kills a startup. The same online pharmacy in India that we discussed earlier also suffered on delayed launch and missed the boat. When this online pharmacy started in 2014, there were no major competitors. However, as they were busy making an over-featured product and extended offering, they did not market their MVP in a timely manner. The MVP for them was the website and mobile app that lets a buyer order medicines online and utilize a home delivery service. As a result, many other players such as 1mg, Pharmeasy, and Netmeds, who started during that time and focused only on online medicine ordering and home delivery, are today successful online pharmacies in India. 6. Manage cash flow and adhere to legal compliances About half of all establishments survive five years or longer (U.S. Small Business Administration. Office of Advocacy. Frequently Asked Questions, 2016). About onethird of establishments survive ten years or longer (U.S. Small Business Administration. Office of Advocacy. Frequently Asked Questions, 2016) Poor cash flow management, or poor understanding of cash flow, contributes to the failure of a business; therefore, it is important to focus on sustainability. Sustainability is accomplished with a balanced focus on both healthy growth and

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efficient operations management. Startups tend to focus more on developing the product, infrastructure, acquiring customers, and increasing revenue. Equal focus is required on operations, i.e., cost of goods sold or product development, inventory, salaries, cash flow, and other financial issues. With an imbalanced focus, you may incur losses or penalties for non-compliance. Negligence in finance management puts a business straight on the path to failure.

KEY FINANCIAL METRICS Burn Rate A startup requires sufficient cash flow for smooth operations. Burn rate refers to the speed at which funds are utilized to clear business obligations. A startup must know its burn rate and how it can be reduced or managed well so that the operations are not hampered. Some ways to address this are reducing overhead, bargaining better supplier margins and credit period, encouraging cash sales, and achieving better customer margins.

Cost of Human Capital Unless founders themselves are skilled and experienced enough to build MVP and do sales and marketing, they need to hire a team. Salaries are generally the biggest expense head in tech companies. Therefore, do not hire the best; rather, hire the right talent. Some ways to manage are no inflated salaries, hiring of revenue-earning team members first, and pay packages comprising fixed, variable pay, and stock options. Keep track of the salary bill and ensure that it is as per the budget.

Annual Recurring Revenue Calculate the Annual Recurring Revenue (ARR) by multiplying the immediate previous month’s revenue (called Revenue Run Rate) by 12. Revenue run rate is a simple metric to indicate how your startup is scaling. Revenue run rate derived after identifying trends and patterns in sales helps in aligning sales strategy.

Inventories If your startup is into product manufacturing or trading, then ensure you are stocking the right products in the right quantities. Measure effectiveness with the stock turnover ratio that indicates how fast a business sells inventory. A low turnover suggests poor sales and overstocking. 7. Be ready to pivot In the startup journey, you may not get the expected response and growth. Circumstances might also change quickly. Therefore, be ready to change the course and find a viable, scalable business model. The Lean Startup approach calls this

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change “Pivot”. A pivot aims to test a fundamental hypothesis about a product or service. It is a strategic decision that leads a startup to change one or more significantly, but not all, of its basic components: product, founding team, business model, or growth strategy (Bajwa, 2017). Understanding pivot help startups avoid setbacks and keep investors on board. Some startups that pivoted and became more successful include Twitter, which originally was a podcast service, and Flickr, which offered an online multiplayer role-playing game. Pivot is basically in response to the user’s reaction to your product/service – whether they think that you are solving the right problem. Another major reason for the pivot is internal when you realize that the business model is flawed. Groupon is one such startup that pivoted because of a flawed business model.

SUMMARY To summarize, the factors that have a clear impact on the success of a startup are: • • • • • •

An original business idea or a high degree of improvisation in an existing idea Founder’s skills and a reliable team A robust execution plan for product and marketing A viable business model Cash for seed fund and bootstrapping Understanding signs for pivoting

But the most crucial factor above all is the timing of your business. Airbnb was launched in October 2007, just two months prior to the great recession in the United States. At that time, people were looking for ways to earn additional income, including sharing their living space (Kalita, 2012). Similarly, Andrew Mason founded Groupon in 2008, a website that offered deals on various products and services to consumers. They saw immediate success because they were able to offer performance-based marketing to both the brands and consumers (Conklin, 2020). Startup founders should answer the question, “Why is now the right time?” with the data points that establish the solid reasons that there is a market need for working on an idea outside of their motives. The success lies in perfect timing and something unique to your startup coupled with passion and perseverance.

BIBLIOGRAPHY 2020 Datanami Readers’ Choice Awards. (2020). Retrieved from https://www.datanami.com: https://www.datanami.com/2020-datanami-readers-choice-awards/ Bajwa, S., Wang, X., Nguyen Duc, A., Chanin, R., Prikladnicki, R., Pompermaier, L. & Bajwa, S. (2017, May). Start-ups must be ready to pivot. IEEE Software, 34, 18–22. Retrieved from https://www.researchgate.net/publication/316945271_Start-Ups_Must_Be_ Ready_to_Pivot Cockroach Labs caps two years of exceptional growth by raising $86.6M of new investment. (2020, May 05). Retrieved from https://www.prnewswire.com/news-releases/cockroach-labs-caps-two-years-of-exceptional-growth-by-raising-86-6m-of-new-investment-301052418.html

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Conklin, A. (2020, March 29). 10 successful startups founded during 2008 Great Recession. Fox Business. Retrieved from https://www.foxbusiness.com/markets/startups-greatrecession Data management startup Okera emerges from stealth with $14.6 million in funding. (2018, May 22). Retrieved from https://www.dbta.com: https://www.dbta.com/Editorial/NewsFlashes/Data-Management-Startup-Okera-Emerges-from-Stealth-with-146-Million-inFunding-125193.aspx Deutscher, M. (2020, May 05). Database startup Cockroach Labs reels in $86.6M funding round. SiliconANGLE. Retrieved from https://siliconangle.com: https://siliconangle. com/2020/05/05/database-startup-cockroach-labs-reels-86-6m-funding-round/ ETHealthWorld. (2019, 05). Indian e-pharma market poised to touch US$2.7 billion by 2023: EY. Retrieved from https://health.economictimes.indiatimes.com/news/pharma/indiane-pharma-market-poised-to-touch-us2-7-billion-by-2023-ey/69557064 Flint, M. (2019, 11 8). Poor cash flow – How to recognize it and how you can make sure it won’t be a problem. Preferred CFO. Retrieved from https://www.preferredcfo.com/ cash-flow-reason-small-businesses-fail/ Flockett, A. (2020). Cazoo reaches unicorn status is record time. Startups Magazine. Retrieved from https://startupsmagazine.co.uk/article-cazoo-reaches-unicorn-status-record-time# Kalita, S. M. (2012, October 20). An economic recovery will kill Airbnb. Quartz. Retrieved from https://qz.com/17766/an-economic-recovery-will-kill-airbnb/ Karnes, K. (2020, January 28). Customer Life-time Value: What is it and how to calculate. CleverTap. Retrieved from https://clevertap.com/blog/customer-lifetime-value/ Lunden, I. (2020, March 23). Cazoo, the used car sales portal, raises another 116m. TechCrunch. Retrieved from https://techcrunch.com/2020/03/23/cazoo-the-used-carsales-portal-raises-another-116m/ McLaughlin, K. (2019, June 4). Cockroach Labs stands up to Amazon’s Open Source offensive. The Information. Retrieved from https://www.theinformation.com/articles/ cockroach-labs-stands-up-to-amazons-open-source-offensive Miller, R. (2020, May 05). Cockroach Labs scores $86.6m Series D as scalable database resonates. TechCrunch. Retrieved from https://techcrunch.com/2020/05/05/cockroachlabs-scores-86-6m-series-d-as-scalable-database-resonates/ Minimum Viable Product (MVP). (2020, March 30). Retrieved from https://www.techopedia. com/: https://www.techopedia.com/definition/27809/minimum-viable-product-mvp O’Dell, J. (2012, February 21). Google buys what’s left of defunct search startup Cuil. Reuters. Retrieved from https://www.reuters.com: https://www.reuters.com/article/ idUS60851776220120220 Okera named a cool vendor by Gartner. (2020, November 3). Retrieved from https://www. prnewswire.com/news-releases/okera-named-a-cool-vendor-by-gartner-301165268. html Pagan Research. (2019, November 18). The 40 Most Promising Big Data Startups In The World! Retrieved from https://paganresearch.io/blog-details/the-40-most-promisingbig-data-startups-in-the-world PCMagStaff. (2008, July 28). The new Cuil search engine sucks. PCMag. Retrieved from https://www.pcmag.com/archive/the-new-cuil-search-engine-sucks-230287 Pilcher, R. (2020, February 20). What are unit economics and why are they important in early stage startups? Ligher Capital. Retrieved from https://www.lightercapital.com/blog/ what-are-unit-economics/ Punamiya, V. (2020, January 20). Observe.AI – this AI-powered agent will take your operational efficiency to next level. Startup Talky. Retrieved from https://startuptalky.com/ observe-ai-success-story/# Putrevu, S. (2020, January 28). Why San Francisco-based voice AI platform Observe.ai found a target market first and then built a product. YourStory. Retrieved from https://yourstory.com/2019/12/san-francisco-observe-ai-voice-customer-experience-product

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Rastogi, V. (2020, May 14). Micro, small, and medium enterprises in India – an explainer. India Briefing. Retrieved from https://www.india-briefing.com/: https://www.indiabriefing.com/news/micro-small-medium-enterprises-india-explainer-17887.html/ Ryan Gavin, C. L. (2020, April 30). The B2B digital inflection point: How sales have changed during COVID-19. McKinsey.com. Retrieved from https://www.mckinsey.com/: https:// www.mckinsey.com/business-functions/marketing-and-sales/our-insights/ the-b2b-digital-inflection-point-how-sales-have-changed-during-covid-19 Sajid, A. (2020, August 6). 70 best startups you need to watch out for in 2020. Cloudways. Retrieved from https://www.cloudways.com/blog/best-startups-watch-out/#ai Shimel, A. (2019, October 31). DevOps chat: Scaling data without complexity with Cockroach Labs. DevOps.com. Retrieved from https://devops.com/devops-chat-scaling-datawithout-complexity-with-cockroach-labs/ Silicon Canals Editorial Team. (2020, June 24). How this European unicorn is trying to make buying cars online as easy as smartphones. Silicon Canals. Retrieved from https://­ siliconcanals.com/news/startups/travel-mobility/european-startup-cazoo-becomesunicorn/ Sobti, R. (2020, April 12). Covid-19 lockdown could be a good time for MSMEs to log-in | Opinion. Retrieved from https://www.hindustantimes.com/: https://www.hindustantimes.com/opinion/covid-19-lockdown-could-be-a-good-time-for-msmes-to-log-inopinion/story-QDmpw4vV1LLxL4BsbzOn6L.html Soni, S. (2018, March). Decoding Byju’s Journey From Start-up to Unicorn. Retrieved from https://www.entrepreneur.com: https://www.entrepreneur.com/article/310403 Sullivan, D. (2008, July 28). Cuil Launches – Can This Search Start-Up Really Best Google? Retrieved from https://searchengineland.com/: https://searchengineland.com/cuillaunches-can-this-search-start-up-really-best-google-14459 The top 20 reasons startups fail. (2019, 11). Retrieved from https://www.cbinsights.com/ research/startup-failure-reasons-top/ The ultimate startup failure rate report [2020]. (2020). Retrieved from https://www.failory. com/blog/startup-failure-rate U.S. Small Business Administration. Office of Advocacy. Frequently asked questions. (2016, June). Retrieved from https://www.sba.gov/sites/default/files/advocacy/SB-FAQ-2016_ WEB.pdf Used car website Cazoo launches. (2019, December 02). Fleet News. Retrieved from https:// www.fleetnews.co.uk/news/fleet-industry-news/2019/12/02/used-car-websitecazoo-launches Wasserman, N. (2014, 02). Fighting co-founders doom startups. CNN Money. Retrieved from https://money.cnn.com/2014/02/24/smallbusiness/startups-entrepreneur-cofounder/ Whiting, R. (2019, August 21). Lifeguard on duty: Startup Okera offers tools for governing access to data lakes. CRN. Retrieved from https://www.crn.com: https://www.crn.com/ news/applications-os/lifeguard-on-duty-startup-okera-offers-tools-for-governingaccess-to-data-lakes Whiting, R. (2020, June 19). The 10 hottest Big Data startups of 2020 (so far). CRN. Retrieved from https://www.crn.com/slide-shows/applications-os/the-10-hottest-big-data-startupsof-2020-so-far-/4 Woodie, A. (2018, May 23). Okera emerges from stealth with Big Data fabric. Datanami. Retrieved from https://www.datanami.com: https://www.datanami.com/2018/05/23/ okera-emerges-from-stealth-with-big-data-fabric/

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The Influential Role of Breakthrough Strategies of the Family Business and Its Implication in Entrepreneurship Tanvi Thakkar Welingkar Institute of Management Development and Research, India

Mahima Birla Pacific University, India

CONTENTS Background������������������������������������������������������������������������������������������������������������ 100 TEAM Work����������������������������������������������������������������������������������������������������������� 100 Goal and Accomplishment������������������������������������������������������������������������������������� 100 Learning Process���������������������������������������������������������������������������������������������������� 101 Nothing Worth Having Comes Easy���������������������������������������������������������������������� 101 Education Plays a Key Role����������������������������������������������������������������������������������� 102 Setting a Goal��������������������������������������������������������������������������������������������������������� 102 Dream Big, Start Small, Act Now�������������������������������������������������������������������������� 104 Introduction of Pitambari Powder�������������������������������������������������������������������������� 104 The Ladder of Success Is Never Crowded at the Top�������������������������������������������� 104 At First, They Will Ask, Why Are You Doing It? Later They Will Ask, How Did You Do It?����������������������������������������������������������������������������������������������� 105 Big Data����������������������������������������������������������������������������������������������������������������� 107 Positive Reinforcement������������������������������������������������������������������������������������������ 108 Installation of RFID����������������������������������������������������������������������������������������������� 109 Finding a Solution�������������������������������������������������������������������������������������������������� 110 Conclusion������������������������������������������������������������������������������������������������������������� 110 Bibliography���������������������������������������������������������������������������������������������������������� 110 Primary Source�������������������������������������������������������������������������������������������������� 110 Secondary Sources�������������������������������������������������������������������������������������������� 111 Other References����������������������������������������������������������������������������������������������� 111 DOI: 10.1201/9781003097945-8

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BACKGROUND It was a day of celebration, 3 February 2017 – the Golden Jubilee of Pitambari Group. Mr. Parikshit Prabhudesai, son of Mr. Ravindra Prabhudesai, the founder of Pitambari, was hosting the event. Many guests were invited. Traders, retailers who have been in association with them for so many years, were all excited to hear the story of the inception of Pitambari. A huge photograph of the late Mr. Vamanrao Prabhudesai, co-founder of Pitambari, was put up on stage. Mr. Ravindra Prabhudesai ignited the lamp and took the microphone to begin. Looking at the crowd, it was like a dream come true to him. After graduating with a BSc (Chemistry), he was a small-time manufacturer of blue flame liquids, mainly used to heat food and usually found in five-star restaurants. After that, he manufactured Copshine powder which was used to clean water tanks. He has witnessed many milestones and is a self-made man. He has a profound personality, and the credit goes to his father, who was very strict and disciplined.

TEAM WORK Mr. Ravindra Prabhudesai is of the view that Together Everyone Achieves More – TEAM work. He began his speech for the day in remembrance of his father; he worked as Union Leader at Central Railways in those days. Not that he was good at handling union problems, but he disliked conflicts. Hence, railway recruiters found him as the most suitable person. It was a secured job. He was also the founder of Thane SahakariJanta Bank and played a key role at RSS Shakha. He introduced the concept of Sampark at RSS, where the motto was networking. People were encouraged to socialize by visiting each other’s houses before or after the meeting and greeting their families. He thought this would bring a feeling of unity. Approximately 5000 people joined hands together in that group.

GOAL AND ACCOMPLISHMENT Mr. Vamanrao believed that discipline is the bridge between goal and accomplishment, and he was very aggressive when things went wrong. However, he attacked the behavior and not the person. Hence, people were happy working with him. Lately, he realized that to grow in life, he had to leave this secured job, get out of his comfort zone and work independently. He left the job in 1967, and with the desire to start something on his own, he purchased a rickshaw. For years he was riding rickshaws where he was earning well for survival. One day he met Mr. Gogte, who now runs his own company; they had worked together at Central Railway. Knowing about Vamanrao’s personality as a leader, Mr. Gogte decided to give him a contract with his company POSCO, a tin-sheet making company. His company was facing many theft issues and avoiding pilferages; Mr. Gogte thought Mr. Vamanrao would be the best person to appoint for transportation. It was an excellent opportunity for Mr. Vamanrao to grow his business. He immediately applied for a loan and purchased ten rickshaws. The transportation business was a success story, and references started taking place. He got a new contract with a very reputed tile making company. Years passed by, and

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his son, Mr. Ravindra, also joined in the business. After a few years, business got stagnant. Mr. Ravindra said, “I do not wish to continue [in] this business”. Glaring at the inquisitive audience, he continued: “My father was building a villa; he called me in person and said, “This bungalow needs maintenance of Rs 15000 per month; can you afford it once I am no more?’ With sheer tone, I said, I shall do it. The confidence was appreciative. However, he continued, “I do not wish to continue [in the] transport business. I shall do something else”. Mr. Vamanrao was disappointed as he raised his volume to ask him back, “What?” Mr. Ravindra said, “anything but not transport, as I do not see myself interested and will not grow it”. After deep thoughts and discussion with mates, Mr. Vamanrao took over a sick unit of the tile manufacturing company and handed it over to his son. Mr. Ravindra was pleased as this was his first venture, and his father trusted his caliber.

LEARNING PROCESS Mr. Ravindra did not know the basics of the tile manufacturing process, but he thought that every practice is a learning process. Hence, he was highly dependent on supervisors and operators. They guided him through the process and material requirements. The process of tile manufacturing requires the steering of hard sand. It was a difficult and time-consuming process for laborers. To increase productivity, supervisors demanded soft sand, which would benefit in steering. For the convenience of laborers, Mr. Ravindra ordered soft sand this time. He started measuring the results of productivity. The results were positive, and the staff was also delighted. Mass production was done and delivered on a large scale to all the builders. Everything was running smoothly. One day, there was a call, “Can I talk to Mr. Prabhudesai?” The caller’s tone seemed very angry, and the receptionist replied politely, “I will transfer the call, please be online.” She briefly described the situation to Mr. Ravindra and transferred the call. Mr. Ravindra, on the other end of the line, began, “May I know who is on the call?” The same tone continued, “I am Mr. Kadam from Kadam Construction. I had purchased tiles from your company three months ago”. Ravindra intervened; “Oh, yes sir, I know you, please tell me how can I help you? Do you need more tiles of the same variety?” “NOOOOO …” Mr. Kadam shouted over the phone and said, “Your tiles are of so poor quality that pitting started taking place. Please take all your tiles back and give me my money back”. Mr. Ravindra was in complete silence for a while, not knowing what to say but then apologized and promised a replacement. Hearing this, the angry man said, “12 days; not more than that I can give you” and hung the phone.

NOTHING WORTH HAVING COMES EASY With this distress situation, Mr. Ravindra went to his team and asked them to start working on the Kadam construction re-work immediately. He was guilty of supplying poor quality. That day he went home early and discussed the same with his father. Mr. Vamanrao consoled him and said, “It’s business, and things may go wrong sometimes; make sure the same mistake is not repeated”. The next morning, Mr. Ravindra

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was on his way to work; his marketing agent called him and requested that he come to the office urgently; he sounded low, which made for a tense moment. After a while, he was in his office. Mr. Ravindra hurriedly ran to his cabin and asked for a meeting. The marketing team started with complaint reports of a delivery done in the last two months. The manager said, “Sir, there are too many pitting complaints in the tile, and customers are very unhappy about it”. Mr. Vamanrao entered the meeting, as he had followed Ravindra to his office having been worried for his son, who was upset about the pitting problem. He heard the situation and decided to do an analysis. Reports indicated tiles that were delivered only after using soft sand called for a replacement. Mr. Ravindra realized his mistake, and there was a turning point in his life. He understood the importance of education in being an entrepreneur, the importance of change in management, etc. Had he known all this, he would not have depended completely on his co-workers for their guidance.

EDUCATION PLAYS A KEY ROLE Mr. Ravindra Prabhudesai thought to himself that it was going to get harder before it gets easier, and hence he started studying diploma management at Bedekar College in Thane. He studied only 90 minutes a day which, he had mentioned to KYT in Thane Vaibhav newspaper (Figure 8.1), was an eye-opener for him. He came across many new concepts which he felt he was deprived of for so many years. Also, he realized the mistakes he had been making so far, and there was nobody to correct him. Something that excited him was a lecture by Prof. Bandhekar, a visiting lecturer at the college who had been working with Colgate toothpaste. For the first time, Mr. Ravindra came across the term Fast Moving Consumer Goods (FMCG) from Prof. Bandhekar. Mr. Ravindra started studying FMCG in depth and decided to do some product manufacturing in this field. Brainstorming was held at home along with family members. RSS Shakha meetings were still part of the Prabhudesai family, as Sampark proved to be an opportunity for studying FMCG very closely. Mr. Ravindra realized that most FMCG are found mostly in the kitchen. He visited every kitchen of the guest and made small notes of it during Sampark. He visited approximately 40 houses in three months. His course was completed, and he was certified with a diploma in management.

SETTING A GOAL Setting a goal is the first step in turning the invisible into visible. Mr. Ravindra was now ready to take up some business professionally. He began with his small research, which he had collected in that three months. The research was about kitchen utensils; ladies used lime and tamarind to wash utensils. There were no branded products yet in this industry. He expressed his thoughts to his father about making products for cleaning vessels of brass and copper. Mr. Vamanrao showed confidence in his son as always. He knew his son had manufactured blue flame liquid, and it was a success. Now knowing the importance of branding and intellectual property, Mr. Ravindra wanted to have a brand name that was easy to pronounce and had an association. All the family members were excited about the product name, and Mr. Ravindra had in

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FIGURE 8.1  Entrepreneurial journey of Pitambari (Source: Clipping provided by the Pitambari group; permission granted to reproduce).

mind to give feminism a name. Since it was used for brass and copper vessels, Pita and Tamba, which in Marathi mean brass and copper respectively, was suggested; all of them liked it and came up with “Pitambar”. This name did not sound sufficiently feminist, so it became “Pitambari”. The name was finalized, and they next turned to manufacture.

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DREAM BIG, START SMALL, ACT NOW Manufacturing started from home. There was no factory unit designated initially, unlike now. Pitambari powder for washing brass and copper utensils was made out of good-quality granules and manufactured at home. Then it was time to market the product. Mr. Ravindra himself went to explain the product to retailers and educate them about its importance and usage. However, this channel of communication did not work. His father suggested door-to-door selling by giving agents or traders 50% commission on the sale of per product. A young and energetic team was appointed and was sent into the field. With a 50% commission higher than any other commission, these agents worked harder for Pitambari powder than for any other product. It was affordable for Prabhudesai because the supply chain was very short – i.e., Manufacturer to Trader to Customer. All products were sold, hard work was celebrated, and Pitambari powder was well accepted in the market by customers.

INTRODUCTION OF PITAMBARI POWDER Pitambari powder did not need any briefing anymore to sell in the market. Reverse marketing started taking place. Retailers were curious to know from the agents what product they were selling as customers went to retail shops asking for Pitambari powder. For a long period, agents became traders of Pitambari powder and started selling in retail stores. Many other retailers tried reaching them directly. It was a huge success, as the same quality was maintained. For more than a decade, Pitambari powder enjoyed a monopoly. Many competitors came and went but could not beat the quality. Also, competitors came up with economical costs which fit the local pocket, but they were also knocked out with innovative marketing strategies by Pitambari. The proper structure was built up as follows: Manufacturing of Pitambari powder → Super Stockiest → Agent → Retailers → Customers

THE LADDER OF SUCCESS IS NEVER CROWDED AT THE TOP Progressing with brass and copper vessels, Pitambari launched two additional products: Rooperi, used to wash silver vessels; and Sanitall, which is used for cleaning toilets. In 2012, after elaborate research, Pitambari started their healthcare division. Fulfilling a need for Ayurvedic medicine which can cure diseases without any side effects was their vision. The Gomutra Plus capsule was their first experiment in Ayurveda. Research on the study of 21 obese patients diagnosed that highly significant relief was found in weight reduction and body mass index in both trial and control groups by consuming Gomutra in liquid form. The trial group’s results were not as good as those of the control group. A recent study in Udaipur on 30 patients was evident that obesity has become a major concern for the country because of junk food, and Gomutra has proved effective in obesity management in reducing weight and BMI. Further to this study, the Gomutra Plus capsule was made up of pure, fresh Gomutra procured from healthy Indian breed cows only. Gomutra Plus capsule

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contains Gomutra powder and Triphalachurna, which is equivalent to two teaspoons of Gomutra. Once the collected Gomutra has gone through strict quality testing, it is accepted only if the Gomutra obtained is found to be as per specifications set by them. It is nothing but a solid, concentrated form of Gomutra, which can be taken without experiencing unpleasant odor and undesired taste. For the first time in Ayurveda, an innovation that takes into consideration the environment and society could see a way forward. The product was well accepted in the market and started running successfully. Pitambari’s next unique product was Gopiyush. Piyush, cow colostrum, contains more immunoglobulin, lactose, protein, and fat than normal cow’s milk, which helps Vrishan Karma of the human body release action on immunity. It is a potent immunomodulation. Gopiyush capsule contains 400 mg of Gopiyush, i.e., Indian cow colostrum powder. It is a proven immune system modulator. Hence, it can be used therapeutically in various diseases. It brings about early & efficient recovery in treating various diseases if used as an adjuvant. Prevention of Rotavirus infection by oral administration of cow colostrum experimented on seven infants from 13 in an orphanage, resulting in Rota colostrum prevented diarrhea outbreak but did not act on rotavirus infection were no side-effects of Rota colostrum. As it is processed from colostrum, Milk derived from cows belongs to the Indian breed; the product is free from A1 Beta casein protein which is quite hazardous to our psychological and physical health. Being processed under vacuum and low-temperature immunity modifying components remains preserved in the Gopiyush capsule. Pitambari was now a brand of trust and loyalty. People were confident of the brand and its association because of its constant efforts in supplying good quality. Mr. Vamanrao was always of the opinion, “Never cheat your customer. Educate them about your product, and you will succeed with no accident, but with the hard work of what you are doing”. Pitambari produces products across seven divisions, namely home care, healthcare, agricare, food care, Agarbatti, farm, and agro-tourism (Figure 8.2). The company manufactures more than 40 products across these divisions, with homecare being the very first division and Pitambari Shining Powder being the first product to be launched.

AT FIRST, THEY WILL ASK, WHY ARE YOU DOING IT? LATER THEY WILL ASK, HOW DID YOU DO IT? There was a round of applause for a good 40 seconds, and tears rolled out from all beloved families, where Mr. Parikshit Prabhudesai took the microphone and thanked his father for his valuable speech. Parikshit continued, My father is my role model; he explained to me the importance of education in the journey of entrepreneurship. As in today’s era to break the traditional circle was important. It can only happen with the help of innovation, education, and technology.

Everybody in the audience was eager to hear from their young COO, who had joined  the organization in 2014 after completing his post-graduate program in

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FIGURE 8.2  Pitambari Products (Source: Clipping provided by the Pitambari group; permission granted to reproduce).

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entrepreneurship management from Welingkar Institute of Management Development and Research. Parikshit Prabhudesai joined the business as Production Manager; He was a BMS (Bachelor of Management Studies) graduate and had a high interest and intellect in marketing. His interest also lies in research and development. He joined the company when Saptashakti Sesame Oil was about to launch. One of the studies done by Parikshit in his post-graduate program revealed that sesame oil is a healthy food that is resistant to oxidative stress in the human body and protects against injury in the animal body. In the beginning, the product was not wholly accepted in the market. Parikshit tried finding the reasons for the lack of acceptance by going to retail shops, taking feedback from customers, and analyzing competitors’ products. This was the first thing that he had learned from his post-graduate program – when things go wrong, go to GEMBA, i.e., where the actual work takes place. He concluded that visibility of color was the main reason for customers not buying the product, and that is why they could not relate to those customers. As competitors had a different color, a lovely aroma of the oil, and a little preservative, consumers choose their product over Saptashakti Sesame Oil. The required changes took place, and today Saptashakti Sesame Oil is the highest manufacturer of sesame oil in the country. Later, Parikshit shifted his interest in the Research and Development Department. He understood that today’s era moves towards Big Data, machine learning, artificial intelligence, and product sustainability to the environment. In 2015, the Prabhudesai family decided to give tribute to their ancestors to make environment-friendly products. They purchased land in Maharashtra and decided to plant flowers, unlike other farmers who planted cashew and mangoes. Two hundred Champa trees were planted in Dakoli, which yield 5000 flowers a day. These flowers were sold in the market. Gradually, supply of flowers in Dakoli exceeded demand. Flowers were distributed outside the Dakoli market but did not get a good response. Somebody from the family suggested that Dadar in Mumbai is the biggest market of flowers, and one can supply there. Transportation arrangements were made to sell flowers in the Dadar market. This marketing plan failed miserably, as flowers would get damaged in transportation and distance was an issue. Also, flowers did not remain fresh till the time they reached customers.

BIG DATA Mr. Parikshit and his father are Saturday Club members (Figure 8.3), where entrepreneurs meet and exchange ideas. One of the businessmen suggested that innovation is the game of new business and should sell extract flowers to make perfumes. They thought the idea was bright; hence, Mr. Prabhudesai called his team, and with the help of big data, he started with his experiment. Around 120,000 kg flowers gave an extract of 1 kg Concrete, and 300,000 kg flowers give an extract of 1 kg Absolute. Essential oils extracted from the lavender plant study show that it is an essential source for making perfumes that can avoid side effects, unlike other penetrated perfumes in the market. This short-time experiment would not have been possible without data, unlike the old times, where data collection and analysis took most of the research time. With the help of technology, aromatic essential oils were extracted from perfume-generating

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FIGURE 8.3  Strategic Saturdays (Source: Clipping provided by the Pitambari group; permission granted to reproduce).

plants. Marketing of the product was started, and the response was prolonged. Still, demand was less than supply. Parikshit decided to cut the trees and plant cashew trees with a frustrated mind like what the other Dakoli farmers did. He requested that his father discontinue the flower business, explaining that planting flower trees was purely a loss business. His father replied, “I think we should wait for two months and wait for the market to respond on extract”. Parikshit was disappointed, as he thought it was a waste of time to wait for two months. More than one month into the wait, the response was the same. Mr. Ravindra Prabhudesai was worried, and his heart was heavy in destroying those trees which yield so many beautiful flowers.

POSITIVE REINFORCEMENT Mistakes are proof that you are trying. Finally, two months were over, and the day had come where the junior Prabhudesai was waiting for a “yes” from his father to cut the flower trees, which had become a liability. Mr. Ravindra Prabhudesai was in his room worshiping god with incense stick; he murmured, “I wish to protect these trees from destruction, please help me.” He stopped with the incense stick and tried putting it in an extract made out of flower; as expected, it smelled good. He immediately ran to his son and asked him to experiment the same. Three days of the experiment took place, with positive results. The extract of the flowers helped make the incense stick, which is the first and only Agarbatti in India to be made out of natural extracts. There was a ray of hope, and the probability of success was very high. Sample products were made and distributed amongst employees for research and feedback. Internal

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feedback was very positive, and now was time for external research. With machine learning, different age group members were taken as samples from RSS Shakha and the Saturday Club. People who worship god temples regularly buy incense sticks, and only those audiences were targeted. Suggestions were noted and worked upon. The product was now ready with all positive reinforcement. It was named “Devbhakti Agarbatti”

INSTALLATION OF RFID Marketing was difficult, as retailers refused to keep new products. Many “me too” products were available in the market. One of the drawbacks of Pitambari was that all the product sales were handled by one person only. There were no separate teams for selling different products. While marketing the product, the salesperson enters a particular shop for pitching Pitambari Powder, then he pitches about sesame oil, followed by medicine, and lastly Agarbatti. Parikshit observed this problem closely and analyzed that, by the time marketing agents pitch about Agarbatti, retailers have lost interest because they had to pay attention to walk-in customers at their shops. Parikshit decided to set up a different marketing team for “Devbhakti Agarbatti” and accordingly for the rest of the products. He assisted Tab to his entire sales team with software installed, which showcased Pitambari products used in and around the vicinity in a radius of 200 meters. This was done with the help of the introduction of RFID installed at the retailers’ end. He made 40 calls a day and personally visited those shops with his team members. Also, the breakthrough strategy was built. A Pitambari Home care marketing agent was placed everywhere before Agarbatti Marketing employees would reach. The benefit of this was that Homecare Marketing agents would light incense sticks, and it would last till the time the Agarbatti marketing agent would arrive. This gives a demo and aroma of natural flowers extract to customers as well. Hence, pitching becomes easy. The strategy worked, and the Mumbai market accepted Devbhakti Agarbatti. But a major problem was occurring that caused the distributor to lose interest in continuing the product: the incense sticks would break before reaching the retailers. Hard cardboard was made to overcome this, but the problem continued. It was decided to hire a consultant to analyze the problem breakage of Agarbatti. Various consultants across the country were asked to give quotations, and the best were shortlisted. Consulting fees seemed to be very high for Parikshit. He said, “This is the beginning of the business, and to incur Rs 4 lakhs in consultation without any earning is not a good idea”. The manager replied, “Sir, the market is already to purchase; if this consultant helps solve our problem, we might end up covering this cost in a short while”. Parikshit thought about what his manager said but was not convinced. “Saving is also earning, and what if we apply differential diagnosis?” It is a technique learned at Welingkar Institute of Management from Dr. Shrinivas Gondhaekar (Dr. G). Parikshit and the manager had tried differential diagnosis before on Saptashakti Sesame Oil, and it had worked successfully. However, the manager was clueless as to how would Parikshit work this out in incense sticks.

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FINDING A SOLUTION Parikshit called for a meeting and began with his analysis skills: “Let us begin with which product is the problem”. The team answered, “Sir, it is only in Agarbatti”. Parikshit then asked, “Does the breakage take place at godown?” Someone from the team replied, “No, breakage takes place only after reaching the Super Stockiest”. “Does a Super Stockiest complain about any other Agarbatti manufacturer besides us?” There was silence for a while, as the team did not encounter this question with the Super Stockiest. After a pause, Parikshit called his Super Stockiest himself and asked him the same question. As expected, he replied “no” and named various competitors whose boxes never had breakage. Mr. Parikshit smiled and remained on the phone. He said, “Get 20 boxes of these competitors tomorrow at 9:00 am”. Everyone started talking amongst themselves, how is it possible to discover why our Agarbatti break into pieces by observing other boxes which are unbreakable?” It was 9:00 in the morning; everyone was in the boardroom, and the competitor’s boxes were lying on the table. Parikshit entered the room with his product box, “Devbhakti” Agarbatti. He first opened all the competitors’ boxes and his box, for everyone it was no different as any boxes had breakage. Parikshit pointed to them about packaging; all the competitors’ boxes were placed vertically in packing while Devbhakti boxes were placed horizontally in packaging, which caused weightage while stacking and breakage of Agarbatti. A minor change in the stacking of boxes has helped them to prevent breakage in the product during transit across India.

CONCLUSION Every business which is passed from one generation to another needs trust, acceptance of change, innovation, and technology. In this case, Mr. Vamanrao Pabhudesai always trusted his son’s caliber and stood by him in his career. Following the legacy, Mr. Ravindra Prabhudesai, founder of Pitambari Products, accepted and welcomed the new technology initiated by his son Mr. Parikshit Prabhudesai. This case has witnessed how education plays a crucial role in the field of entrepreneurship. The old saying that graduation or post-graduation is only for those who want a job and business can be learned only by going on the shop floor is also a subject of debate now. This case showcases how trust, persistence, education, innovation, machine learning, Big Data, and RFID are some of the breakthrough strategies of entrepreneurs.

BIBLIOGRAPHY Primary Source The authors have retrieved primary data by visiting the founder’s office in Thane, Mumbai, namely Mr. Ravindra Prabhudesai, and his son Mr. Parikshit Prabhudesai on 20 August 2018. The formal and informal interview took place over four days, where Mr. Prabhudesai helped us meet his junior staff, executive staff that was part of his journey from the foundation day. The staffs were very co-operative in providing information about the steppingstones of Pitambari and their challenges. We were also privileged to meet their other family members, who were very kind and humble to us in providing information.

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Secondary Sources Dandin, S. V., & Mench, R. (2015). Development and implementation of Kaizen activities in an automobile manufacturing firm. Development, 2(6), 60–65. Dhanavade, B. V., & Pandya, A. (2017). A comprehensive review on Vrishan Karma (aphrodisiac action) by Piyush (cow colostrum). Journal of Ayurveda and Integrated Medical Sciences (ISSN 2456-3110), 2(2), 139–142. Ebina, T., Sato, A., Umezu, K., Ishida, N., Ohyama, S., Oizumi, A., … & Kitaoka, S. (1985). Prevention of rotavirus infection by oral administration of cow colostrum containing antihumanrotavirus antibody. Medical Microbiology and Immunology, 174(4), 177–185. Gujarathi, R. A., Dwivedi, R., & Vyas, M. K. (2014). An observational pilot study on the effect of GomutraHaritaki, diet control and exercise in the management of Sthaulya (obesity). Ayu, 35(2), 129. Kaviani, M., Darjani, Z., Tomovska, J., Mazandarani, Z., & Shariati, M. A. (2015). Comparing different extraction methods of sesame oil. International Journal of Pharmaceutical Research and Allied Sciences, 4(2), 22–25. Martel, J. P. (1981). U.S. Patent No. 4,257,945. Washington, DC: U.S. Patent and Trademark Office. Periasamy, S., Chien, S. P., Chang, P. C., Hsu, D. Z., & Liu, M. Y. (2014). Sesame oil mitigates nutritional steatohepatitis via attenuation of oxidative stress and inflammation: a tale of two-hit hypothesis. The Journal of Nutritional Biochemistry, 25(2), 232–240. Saini, N. K. (2016). Clinical trial of gomutra (cows urine) in obesity management. International Journal of Ayurveda and Pharma Research, 10, 54–57. Tiwari, P. (2017). Assessment of the practices and challenges of Kaizen implementation in micro and small enterprises: The case of manufacturing enterprises. International Journal of Engineering and Management Research (IJEMR), 7(4), 313–322. Waithaka, P. N., Gathuru, E. M., Githaiga, B. M., & Kwoko, J. M. (2016). Making of perfumes from essential oils extracted from lavender plant collected from Egerton University, Main Campus Njoro, Kenya. Pyrex Journal of Biomedical Research, 2(6), 35–40.

Other References http://pitambari.com/Healthcare/Gomutra-Plus/index.html http://pitambari.com/Healthcare/Gopiyush-Capsules/index.html

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Crucial Factors for Successful Entrepreneurial Start-ups Ghazala Khan University Putra Malaysia, Malaysia

Rohail Hassan Universiti Utara Malaysia, Malaysia

CONTENTS Introduction������������������������������������������������������������������������������������������������������������ 114 Literature Review��������������������������������������������������������������������������������������������������� 115 Entrepreneur������������������������������������������������������������������������������������������������������ 116 Entrepreneurial Competencies�������������������������������������������������������������������������� 117 Strategic Competency����������������������������������������������������������������������������������� 117 Personal Competency����������������������������������������������������������������������������������� 117 Conceptual Competency������������������������������������������������������������������������������� 117 Ethical Competency������������������������������������������������������������������������������������� 118 Opportunity Competency����������������������������������������������������������������������������� 118 Learning Competency���������������������������������������������������������������������������������� 118 Familism������������������������������������������������������������������������������������������������������� 118 Entrepreneurial Innovativeness������������������������������������������������������������������������� 119 Network Competence���������������������������������������������������������������������������������������� 119 Environmental Turbulence�������������������������������������������������������������������������������� 119 Government Support����������������������������������������������������������������������������������������� 119 Business Success����������������������������������������������������������������������������������������������� 119 Theories to Support Research Work���������������������������������������������������������������������� 120 Survey: an Illustration�������������������������������������������������������������������������������������������� 121 Demographic and Descriptive Data������������������������������������������������������������������ 122 Discussion on Descriptive Statistics���������������������������������������������������������������������� 123 Inferential Statistics������������������������������������������������������������������������������������������� 126 Importance Performance Map Analysis (IPMA)�����������������������������������������126 Theoretical and Practical Contribution������������������������������������������������������������� 128 Limitations������������������������������������������������������������������������������������������������������������� 129 Future Commendations������������������������������������������������������������������������������������������ 130 References�������������������������������������������������������������������������������������������������������������� 131 DOI: 10.1201/9781003097945-9

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INTRODUCTION In Malaysia, 76.5% of small and medium enterprises (SMEs) fall into the microenterprises category; 21.2% are categorized as small, while 2.3% are under medium enterprises (SME Corporation Malaysia, 2018). Globally, SMEs constitute 90% to 95% of the whole business and can produce 60% to 90% of employment (Tehseen, 2017). Over a few decades, SMEs in Malaysia have advanced in business performance. These enterprises’ contribution to the GDP is still low compared to the counterparts in emerging economies. Malaysian SME’s contribution to the GDP was 36.6% in 2017 (SME Annual Report, 2016/17), and at present it is 32% (Tahir, Razak, & Rentah, 2018). Business survival in Malaysia is the topmost challenge faced by organizations today with increasing intensity (Tehseen, Qureshi, Johara, & Ramayah, 2019b). Moreover, the downfall in the global innovation ranking of Malaysian SMEs is another alarming factor for this research. Similarly, by studying their business and networking methods, the government can assess the influence of essential aptitudes on Malaysian venture performance, particularly the service sector. Thus, the most critical issue is the greater failure/less survival rate of SMEs, and in the service industry, it is prevalent, especially in Malaysia (Abdullah, Ahmad, Zainudin, & Rus, 2016). Globally, the survival rate is 42.9% in the service industry (Canadian Centre for Data Development & Economic Research, 2020). Nevertheless, survival and success are the main goals for every business (Khalique, Shah, & Hina, 2018). Thus, business survival is a more prominent issue than Malaysia’s profitable business (Rahman, Yaacob, & Radzi, 2016). Strategies guide entrepreneurs to achieve their desired goal. Any firm that survives in three years can provide employment and have the active turnover to be considered a surviving and successful business (Rahman et al., 2016). In contrast, the results show that 80% of Malaysian SMEs do not survive (Yusoff et al., 2018). Secondly, a prominent issue for the growth and survival of Malaysian SMEs is entrepreneurial innovativeness. Due to previous studies’ conflicting results, some researchers proposed to inspect the protagonist of innovation in business success (Kheng et al., 2013; Mohsin, Halim, & Farhana, 2017; Tajuddin et al., 2015). Similarly, network competence is another potential medium toward a successful business. It helps attain valuable resources in the shape of updated information, skills, and knowledge (Ahmad et al., 2018; Centobelli, Cerchione, & Esposito, 2018a, 2018b; Tehseen et al., 2019a). Various studies have mentioned that the majority of successful Malaysian SMEs have managed and controlled with the concept of Guanxi (way of networking or relationships) as the critical success factor in their businesses (Hassan, Yaacob, & Abdullatiff, 2014; Kheng & Minai, 2016; Luo & Child, 2015). This network style is pivotal to business success, but these characteristics are still not widely realized among Malaysian SMEs for venture success (Kheng & Minai, 2016). The need of the hour is to conduct a comprehensive single research study to under­ stand the influence of entrepreneurial competencies (Karmarkar et al., 2014), entrepre­ neurial innovativeness, and network competence on SMEs’ success in the Malaysian service industry. Hence, entrepreneurial competencies are conceptualized with their seven dimensions (strategic, learning, conceptual, opportunity, personal, ethical, and

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familism). At the same time, entrepreneurial innovativeness and network competence are operationalized as individual single dimension variables (Chhabra, 2018). This specific research aims to investigate the crucial factors, which are most important for successful start-ups. The main objective is to highlight the essential skills and, moreover, to categorize these skills based on their importance and performance by utilizing the most advanced PLS-SEM technique called IMPA. These skills are known as soft skills. Furthermore, Malaysian SMEs face a highly competitive environment in terms of entrepreneurial innovation (Chhabra & Karmarkar, 2016b; Halim et al., 2015). Besides, Malaysian SMEs lack competencies that can discriminate the services and products (Mohsin et al., 2017). The rest of the chapter is designed as follows. The next section reviews the literature and definitions of the terms used in research. This is followed by a discussion of theories, then an illustration of the survey, the data collection procedure and the methodology. Next, in contrast, inferential statistics and the analytical approach are described. This is followed by the theoretical and practical contribution, a discussion on descriptive statistics, implications, limitation, and future recommendation.

LITERATURE REVIEW The service sector remains the prime industry all over the world (North & Varvakis, 2016). The service business has a vital part in their homegrown community and economy (Arham, 2014). However, the service industry is declining and losing human resources due to a turbulent environment, and now entrepreneurs strive to figure out success factors which can enhance business success (Didonet et al., 2020; Singh & Chhabra, 2020). Previous studies focused on the quality of services for improving service industry performance (Arham, 2014). Few studies examined the impact of leadership, entrepreneurial competencies, entrepreneurial innovativeness, and network competence for business success (Tehseen, 2017) within the Malaysian context, especially the service sector (Arham, 2014). A review of numerous research studies on SMEs in the service industry reveals research work done on the Malaysian enterprises is restricted only to a limited population; for instance, work done by Aman et al. (2011) on crucial victory factor covers only the foodservice sector in Melaka. The current research aims to recognize government assistance for business success. Data is collected from 60 respondents based on entrepreneurial quality, human resources, pricing, delivery, and services as the internal factor for business success. However, the study fails to deliver the statistics on the entrepreneurs’ entrepreneurial competencies (Aman et al., 2011). Work is done by Arham (2014) on leadership behavior in the service industry. One hundred ninety-three business owners and managers participated in the survey. Only transformational and transactional leadership attitudes were studied. Similarly, Anuar et al., (2016) examine the influence of skills, quality management, and innovation in the service sector and covers 50 respondents in Klang valley. This study has endorsed the positive influence of innovation and quality management on Malaysian SMEs’ venture performance. Furthermore, this study declares that entrepreneurial competencies have no significant influence on business success; therefore, the study result cannot be

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simplified over the entire population due to the small sample (Ali & Iskandar, 2016). Therefore, this study used a large sample size. Moreover, Idris et al. (2013) conducted a study on operational flexibility dimensions in the service sector; 254 respondents participated in the survey. Mohamad Radzi et al. (2017) conducted a study and examined entrepreneurial competency as a single dimension and mentioned it as a small business owner’s knowledge. Conversely, Ahmad, Ramayah, Wilson, and Kummerow (2010) directed research using 212 Malaysian entrepreneurs as a sample to measure the impression of crucial competencies on the success of Malaysian SMEs’ venture and to introduce environmental turbulence as a moderator. The outcomes revealed that ECOMP are robust interpreters for business success. The researchers declare that the connotation between competencies and success of venture are intensely apparent in an aggressive and dynamic business environment compared to benevolent and sound surroundings. Self-reporting used for this study receives much criticism due to the complexities of the independent evaluation of variables (Ahmad et al., 2010). SMEs’ failure rate in the service industry is quite common, especially in Malaysia (Abdullah et al., 2016). Therefore, the company’s success as a dependent variable requires more elaboration to understand the reasons behind the failures (Hashim et al., 2018). Similarly, Jalali, Jaafar, and Ramayah (2014) have argued that additional research work is mandatory to frame better distinct aspects of entrepreneurial success by using suitable performance measures. Consequently, this research intends to distinguish the dominant factors which are crucial for SMEs’ survival. This is also consistent with the research work of Man and Lau (2000), Ahmad (2007), and Tehseen (2017). It is justified that objective measurement is difficult to evaluate in Malaysian SMEs. The literature suggests that subjective measurement is appropriate for measuring performance when there is a lack of objective data availability. The influence of competencies on the performance of the firm is an issue of great interest (Chhabra et al., 2020). Competencies required by entrepreneurs and managers differ. Entrepreneurs require more complex competencies. The researchers summarize that entrepreneurial competencies required by entrepreneurs are different from managerial competencies (Ahmad, 2007). Recently, several studies have been conducted on this topic, for instance, the significance of skills/competencies for achieving venture success (Aisha, Sudirman, & Siswanto 2016). The positive influence of personal, ethical, opportunity, strategic, learning, conceptual competence, and familism on innovation has been supported by Mohammadkazemi, Rasekh, and Navid (2016) in the case study of the Isfahan sports club. Furthermore, they practiced the standardized investigation that was investigated by Ahmad (2007) to gauge the magnitude of the entrepreneur’s skills. The most important terms used in the article are defined as follows to understand the topic entirely.

Entrepreneur There are many definitions for the term entrepreneur: Scholars and researchers still do not agree on a single definition. In general, an entrepreneur is a businessperson who can start a new venture and can sense an opportunity to manage all obstructions

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during a new business creation process (Chhabra & Karmarkar, 2016a). An entrepreneur should have the risk-taking ability, capability to survive in bad situations, and the competency to convert raw ideas into practical shape (Baron, 1998). In other words, an entrepreneur is a person who sets up a small firm, acts as the manager, and leads the company toward success in available resources, situation, and a best conceivable way (Gilman & Edwards, 2008).

Entrepreneurial Competencies Entrepreneurial competencies are fundamental qualities or behaviors of an individual, for instance, knowledge for specific work, motives for innovative tasks, traits, skills, and a strong personality to run a business (Chhabra & Goyal, 2019). These abilities originate speculation, support the existence, and lead toward development. The influence of competencies on the firm’s performance is an issue of great interest. Recently, several studies have been conducted on this topic; for instance, the significance of skills/competencies for achieving venture success (Aisha et al., 2016). The seven competencies/skills are as follows: (1) strategic, (2) personal, (3) conceptual, (4) ethical, (5) opportunity, (6) learning, and (7) familism Strategic Competency Strategic competency (SC) is the persons’ aptitude to judge, assess, and act accordingly to impose schemes planned out by the firm (Rahman, Amran, Ahmad, & Taghizadeh, 2014). It also enables an entrepreneur to get a clear and broad vision for the business. Moreover, strategic competency helps an entrepreneur to strategize long-term visions (Goldman & Scott, 2016). Entrepreneurs must have a clear mindset, enabling them to how and where to compete and achieve their goals (Rahman, Taghizadeh, Ramayah, & Ahmad, 2015). Furthermore, strategic skills enable business owners to utilize better organizational competencies (Gümüsay & Bohné, 2018). Personal Competency Qualities present in the businessperson’s personality lead to personal competency (PC), enhancing a person’s effectiveness. According to the dynamism of a business nature (Rahman, Hanafi, Mukhtar, & Ahmad, 2014), PC consistently grows. Similarly, Ahmad et al. (2010) points toward some behaviors such as sharp thinking ability and keeping in mind all angles while making a decision. Also, recognizing and improving one’s own shortfalls, constructive response to handle criticism, motivating staff and co-workers, establishing positive and high-energy environments in an organization, and keeping an enthusiastic approach toward responsibilities are attributes of personal competency (Ahmad et al., 2010). Conceptual Competency Conceptual competency (CC) is responsible for entrepreneurs’ critical thinking and enables them to deal with uncertain situations (Mohsin et al., 2017). It also aids the entrepreneur in handling business successfully (Ahmad et al., 2010). This area of competency constructs a range of diverse natural abilities that reflect an entrepreneur’s

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attitude during the decision-making process (Man & Lau, 2000; Man, Lau, & Chan, 2002). CC enhances the entrepreneur’s decision-making skills, observation, risktaking aptitude, and innovative thinking (Man et al., 2002). Ethical Competency Ethical competency (EC) is an asset for an entrepreneur. Ethics are part of individual personal attributes, which reflect on the person as trustworthy, loyal, honest, and responsible for all his acts and decisions. Orme and Ashton (2003) report that ethics is the center of every competency-based framework and is the leading brick for a solid foundation for any deal. Kaur and Bains (2013) explain that ethical competency is an elevated level of ethics and awareness. Thus, the entrepreneur can understand and overcome the worst situation of ethical difficulties (Jiang, Hu, Hong, Liao, & Liu, 2016). Entrepreneurs feel more courageous and competitive. They can converse at an organizational level with confidence and run the business more effectively. Opportunity Competency An entrepreneur’s opportunity competency (OC) has to do with ability to understand and recognize different opportunities. It indicates a businessperson’s expertise to seize the right market opportunity for business success (Rahman et al., 2014). OC depicts the entrepreneur’s ability to explore, identify, acquire, and appraise all chances available in the market (Man & Lau, 2000). Furthermore, this competency enables entrepreneurs to identify accurate opportunities and evade potential risks (Ahmad et al., 2010). Moreover, opportunity recognition permits the entrepreneur to know the customer’s needs and fulfill these unanticipated demands (Rahman, Taghizadeh, Ramayah, & Ahmad, 2015). Therefore, Man (2001) classifies three main clusters of this competency: exploring opportunities, identifying the best choice, and finally, its assessment. Learning Competency Entrepreneurial learning competency (LC) is a meeting point for organizational education and entrepreneurship. LC represents the entrepreneur’s knowledge, abilities, behavior, and openness to learning. It is a lifetime process (Aldrich & Yang, 2014). Gaining and improving experience throughout life is very important for achievement in business. Therefore, learning competency creates new entrepreneurial acquaintances from different means (Xiu-Qing & Li, 2013). As a result, competent entrepreneurs generate knowledge and develop skills based on their experience, retain knowledge, and practice it for their business success (Argote & Miron-Spektor, 2011). Familism Familism is a new term and variable introduced by a fresh stream of research in entrepreneurship; it deals with business development and organization (Kuada, 2015). Familism is a symbol of attachment to the family. It is a feeling of faithfulness, a sense of responsibility, harmony, and unity among business and family members

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(Zeiders, Updegraff, Umaña-Taylor, McHale, & Padilla, 2016). Moreover, it can be explained as support for each other, close bonds among family members, and taking care of inconvenient situations.

Entrepreneurial Innovativeness Entrepreneurial innovativeness reflects the eagerness to display creativity and trailing to introduce new products or services, technological leadership, and research and development (R&D) in the process of the new venture (Lumpkin & Dess, 1996). Entrepreneurial innovativeness is a continuous process of creating, generating, and developing innovative ideas and converting them into new business opportunities (Maciariello, 2009).

Network Competence Network competence is the entrepreneur’s ability to develop skillful new relationships and manage them professionally with their customers, competitors, and suppliers. It can effectively manage dealings with external parties (Ritter, Wilkinson, & Johnston, 2002).

Environmental Turbulence Environmental turbulence can be defined as unpredictable change, uncertainty, complexity, vitality, vigor, and dynamism in the organizational environment (Smith, Sinha, Lancioni, & Forman, 1999). Besides, this research explains environmental turbulence as an uncertain behavior and change in clients’ demands and behavior, the customer, and unpredictable acts of competitive firms to win the business game in the context of Malaysian SMEs (Ahmad, 2007).

Government Support For this study, government support supports Malaysian SMEs’ supportive mechanism in training programs to enhance innovative skills, financial assistance, and training to polish entrepreneurial competencies and establish successful networking by getting network competence provided by the government through related agencies.

Business Success For this specific research, business accomplishment has been operationalized with four precise magnitudes: financial performance, non-financial performance, business growth, and performance relative to competitors, as defined by Ahmad (2007) in the Malaysian context. The success of a firm is identified through its performance, which can be measured as financial parameters, i.e., a profit of the company (Buttner & Moore, 1997), and non-financial parameter, which is the growth rate of sales and employees that are in the company operation for more than three years (Chandler &

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Hanks, 1994). Business success represents the survival and growth of a venture for this study (Headd, 2003).

THEORIES TO SUPPORT RESEARCH WORK Institutional theory (IT), strategic contingency theory (SCT), and resource-based view (RBV) are the suggested theories to support the conceptual study model. These three theories are most relevant to this research study, especially to define the relationship of competencies, network competence, and innovation (independent variable) concerning business success (dependent variable). RBV insists that managers envisage the significance of the available resources for the future and must possess complete charge of reserves (Lockett, Thompson, & Morgenstern, 2009). While few of them claim that this theory explains the sustainable competitive advantage (SCA), success can be achieved with the help of a perfect combination of resources and competencies (Hoopes, Madsen, & Walker, 2003). Hence, SCT states that the concept of “fit” refers to alignment and matching between environment and organizational resources to cope with pressures and prospects. Therefore, the strategies could be amended by balancing the funds with organizational competencies and other organization components with the organizational prospects and intimidations (Venkatraman & Camillus, 1984). Moreover, institutional theory describes how organizations can increase their survival abilities in a competitive environment by satisfying their stakeholders. It has different interpretations and strands, but institutions are equipped with practices and rules that shape this theory meaning. Institutions are composed of rules and incentives; per designed institutional structure, members are supposed to react and respond to the essential components. This theory has immense potential for entrepreneurship and management. Institutional theory has been constructive in utilizing a theoretical lens for entrepreneurship (Amenta & Ramsey, 2010). The following hypothesis has been derived in light of the previously discussed essential factors for business success. H1a: Entrepreneurial competencies have a positive relationship with SMEs’ business success in the Malaysian service industry. H1b: Network competence positively influences venture success in the Malaysian SMEs’ service industry. H1c: Entrepreneurial innovativeness has a positive influence on SMEs’ venture success in the Malaysian service sector. H2a: High turbulent environment significantly moderates the relationship between entrepreneurial competencies and business success in the Malaysian SME service sector. Thus, the relationship between entrepreneurial competencies and business success will be stronger when environmental turbulence is high. H2b: High turbulent environment significantly moderates the relationship between network competence and business success in the Malaysian SME

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service sector. Thus, the relationship between network competence and business success will be stronger when environmental turbulence is high. H2c: High turbulent environment significantly moderates the relationship between entrepreneurial innovativeness and business success in the Malaysian SME service sector. Thus, the relationship between entrepreneurial innovativeness and business success will be stronger when environmental turbulence is high. H3a: High government support significantly moderates the relationship between entrepreneurial competencies and business success in the Malaysian SME service sector. Thus, the relationship between entrepreneurial competencies and business success will be stronger when government support is high. H3b: High government support significantly moderates the relationship between network competence and business success in the Malaysian SME service sector. Thus, the relationship between network competence and business success will be stronger when government support is high. H3c: High government support significantly moderates the relationship between entrepreneurial innovativeness and business success in the Malaysian SME service sector. Thus, the relationship between entrepreneurial innovativeness and business success will be stronger when government support is high.

SURVEY: AN ILLUSTRATION This research positions entrepreneurial competencies, network competence, and entrepreneurial innovativeness as independent variables, while environmental turbulence and government support are moderators. It is assumed that government support may enable the entrepreneur to create a positive relationship between entrepreneurial skills and venture performance. These independent variables are hypothesized to investigate the relationship significance with business success, which acts as a dependent variable (DV). This study investigates environmental turbulence and government support, which is believed to moderate the relationship between IV and DV. The entrepreneurial competencies consist of seven domains (strategic, personal, ethical, conceptual, opportunity, learning, and familism). Business success has four domains (financial performance, non-financial performance, business growth, and performance relative to competitors). The respondents for this proposed study are Malay, Chinese, and Indian entrepreneurs who own SMEs in Malaysia from the central region, including Selangor, Putrajaya, and Kuala Lumpur. Fifteen hundred questionnaires were distributed in the federal territories of Kuala Lumpur, Putrajaya, and the state of Selangor. The data collection period was December 2018 to March 2019. In the first approach, the researcher identified some walk-in events arranged by agencies, for instance, Event Brite, and advertised on

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Facebook, such as the Lean in Malaysia, Institute of Entrepreneurship in Kuala Lumpur, and Malaysian Global Innovation & Creativity Centre (Magic). They arrange workshops and seminars for entrepreneurs. There, entrepreneurs from the service sector are invited to attend workshops, and the researcher requested that those who fulfill the criteria mentioned in the questionnaire fill out the survey. This technique is cost-effective. The standardized questionnaires’ face-to-face strategy is more appropriate for this research to acquire the utmost responsibility and decrease non-response bias. This strategy also ensures the maximum response from the targeted population (Memon et al., 2017). In the second approach, the researcher identified SMEs related to the service sector in Taipan Business Centre USJ9 and USJ10 and visited their shops and offices to meet them in person and collected the questionnaires on the spot. Before collection, the researcher made sure that all questionnaires are complete and properly filled. The completed questionnaire was collected on the spot through an in-person face-to-face strategy. A third approach researcher used Snowball Sampling and distributed 900 questionnaires in the federal tertiary, Putrajaya, Cyberjaya, and Selangor, with known networks’ assistance. A drop and pick strategy/mail survey was exercised, but the response rate was not satisfactory due to difficulty accessing Malaysian entrepreneurs’ email (Ahmad, 2007, p. 183). Four hundred thirty-five (435) questionnaires were incomplete; 361 questionnaires sent by mail were not received, as respondents were unwilling to participate; and 104 questionnaires did not fulfill the inclusion criteria. Therefore, those 900 questionnaires were not included in the study. Altogether, 600 complete questionnaires were collected, and the response rate was 40%.

Demographic and Descriptive Data This survey involves 600 entrepreneurs as the primary informants to provide information by using diverse sources, as it is believed that they are a true reflection of their business successes or failures. Moreover, they are mainly involved in enlightening the understudy variables by identifying behaviors to achieve venture success (Baer & Frese, 2003). The researcher considers the entrepreneurs who have established firms and are actively managing the business, getting government support, and being involved in innovative practices. The final statistics are collected from the population with the mean of the standardized questionnaire. The researcher exercises an inperson gathering/meetings strategy. Education, innovative practices, and government support condition of the respondents are considered before data collection. The minimum education requirement for respondents is SPM; subsequently, they can comprehend the survey, and they must have the ability to converse in English. The study focused on educated entrepreneurs to verify the aforementioned previous research work of Ahmad (2007) and Tehseen (2017) and compare it with the new research work of Idris et al. (2020). The survey is done at one point in time, and the participants respond once only. The reason for exercising the cross-sectional study is that it can measure prevalence for all factors under investigation simultaneously (Creswell & Clark, 2017). Inferential statistics support generalizing what is occurring in the world based on gathered data. The PLS-SEM technique is employed for inferential data

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analysis. The suggested research model is assessed using a two-step approach in Smart-PLS, which includes testing the measurement model and examining the structural model (Hair et al., 2014). Furthermore, the advanced technique of PLS-SEM (IPMA) has been used to illustrate the managerial implications of this research study.

DISCUSSION ON DESCRIPTIVE STATISTICS As multiple races live in Malaysia, and the data was randomly collected, there was no bias decision to include or exclude a specific racial community. Table 9.1 depicts the demographic profile of the respondents and their firms. Six hundred (600) business owners/business partners took part in the survey. The survey participants who responded include 181 Malay entrepreneurs, 240 Chinese business owners, and 179 Indian entrepreneurs. Of the respondents, 132 (22%) are aged between 26 and 30 years old. Interestingly, 468 (78%) of the respondents are above 30 years old. Overall, 336 males and 264 females participated in the survey. Regarding the education level, 384 respondents were undergraduate, and 216 were postgraduate or highly qualified. The results verify that the education level positively impacts business success (Fahmi Idris et al., 2020). Table 9.2 discloses the demographic profile of the SMEs from the service sector. As per the nature of the business, 96 businesses (16%) fall under the category of wholesale service, while 204 businesses (34%) relate to the retail service, and 300 (50%) are involved directly in providing services to the customer. The data shows that 528 are small business enterprises, while 72 fall under the medium enterprise category. The details show that 96 (16%) of the companies

TABLE 9.1 Demographic profile of respondents Frequency (n = 600)

Demographic Age Gender Race

Level of Education

26–30 30 and above Male Female Malay Chinese Indians Undergraduate Postgraduate degree or higher

132 468 336 264 181 240 179 384 216

Percentage % 22.0 78.0 56.0 44.0 30.2 40.0 29.8 64.0 36.0

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TABLE 9.2 Demographic profile Demographic

Frequency (n = 600)

Percentage %

Current position in the business Business owner Business partner

252 348

42.0 58.0

Year of company establishment 3–5 6–10 11–15 More than 15

168 275 145 12

28.0 45.8 24.2 2.0

Number of employees 5–30 30–75

528 72

88.0 12.0

Nature of business of your organization Wholesale Retail Service

96 204 300

16.0 34.0 50.0

96

16.0

144 24 24 48

24.0 4.0 4.0 8.0

12 24 37 24 35 12 96 24

2.0 4.0 6.2 4.0 5.8 2.0 16.0 4.0

Ownership structure Sole proprietor Private limited company

288

48.0

312

52.0

Are your business assets growing? Yes No

552

92.0

48

8.0

What would you consider the current stage of your business? Growth stage Maturity stage Decline stage

204

34.0

348 48

58.0 8.0

Annual sales of your firm RM300,000 to less than RM3 million RM3 million to not exceeding RM20 million

528

88.0

72

12.0

311 289

51.8 48.2

Type of industry Pharmaceutical, medical, and surgical goods Textile/clothing and dress making Courier/cargo Travel services/tourism Household appliances/equipment maintenance/dry cleaning Security services Sports and recreational goods/fitness Software and information service industry Home delivery services Financial services Consultancy Information transmission/education and training Hotel/restaurants/hospitality Real estate business

Firm location Kuala Lumpur Selangor

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provide pharmaceutical, medical, and surgical goods; 144 (24%) are involved in textile/clothing and dressmaking business, 24 (4%) are dealing in courier/cargo services, 24 (4%) in travel services/tourism, 48 (8%) in household appliances/ equipment maintenance/dry cleaning, and 12 (2%) deal with security services. In addition, 12 (2%) belonged to sports and recreational goods/fitness, while 24 (4%) deal in the software and information service industry. Moreover, 37 businesses (6.2%) provide home delivery services, 24 (4%) are involved in financial services, 35 (5.8%) are in consultancy services, 12 (2%) are related to information transmission/education and training, 24 (4%) provide hotel/restaurant/hospitality services, and 24 (4%) deal in the real estate business. The ownership structure depicts that 288 (48%) of the participants are sole proprietors, and 312 (52%) own private limited companies. During the survey, 552 businesses (92%) admitted that their business assets are growing, while 48 (8%) were not satisfied with their business asset growth. Consequently, 204 entrepreneurs (34%) agreed that their business was at the growth stage. In comparison, 348 venture owners (58%) thought that their business was at the maturity stage, and 48 of them (8%) admitted that their business was in the declining stage. Thus, it is verified that education positively impacts business growth and success (Ahmad, 2007; Fahmi Idris et al., 2020). The firm’s annual sales depict that 528 firms (88%) manage to reach an annual sale of RM300,000 to less than RM3 million, while 72 firms (12%) enjoy annual sales of RM3 million but not exceeding RM20 million. In terms of location, 311 firms (51.8%) are in the federal territories of Kuala Lumpur, while 289 firms (48.2%) are situated in Selangor (Table 9.3).

TABLE 9.3 Descriptive statistics of constructs Construct Strategic competency (SC) Conceptual competency (CC) Opportunity competency (OC) Learning competency (LC) Personal competency (PC) Ethical (EC) Familism (FC) Performance relative to competitors (CP) Nonfinancial performance (NFP) Financial performance (FP) Business growth (BG) Network competence (NC) Environmental turbulence (ET) Government support (GS) Entrepreneurial innovativeness (EI)

Skewness −1.312 −1.543 −1.111 −1.621 −1.375 −1.278 −0.867 −2.766 −.732 −.739 −1.206 −.816 −.356 −.638 −.349

Std. Error of Skewness 0.100 0.100 0.100 0.100 0.100 0.100 0.100 0.100 0.100 0.100 0.100 0.100 0.100 0.100 0.100

Kurtosis 0.637 1.510 0.316 1.881 1.623 1.108 −.044 9.241 .292 −.664 .794 .308 −1.059 −.060 −1.477

Std Error of Kurtosis 0.199 0.199 0.199 0.199 0.199 0.199 0.199 0.199 0.199 0.199 0.199 0.199 0.199 0.199 0.199

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Inferential Statistics Importance Performance Map Analysis (IPMA) IPMA is a matrix analysis, and it describes managerial implications well (Ramayah et al., 2016). “It is a valuable tool that extends the PLS-SEM estimations’ results by contrasting the total constructs effect on some target variable with the average val­ ues of the constructs’ scores” (Hair et al., 2017). The total effect specifies the exog­ enous construct’s significance for symbolizing the precise exogenous constructs, although the average construct scores depict their performance. The foremost reason is recognizing the endogenous latent variable’s significant exogenous construct (Hair et al., 2017). Thus, this research study’s analysis offers precious information on managerial implications to spotlight and benefit performance. IPMA is incredibly significant in providing supplementary conclusions with the combination of the performance analysis and the important dimensions in PLS-SEM’s practical applications. In consequence, it demonstrates which construct needs improvement in terms of performance. Table 9.4 discloses the findings of index values. It can be examined by the network competence (NC) as the vital factor and has a high importance level, so the network competence has too much potential for improving the performance level of the construct. Environmental turbulence (ET) is the second most important variable for determining Malaysian SMEs’ venture success (see Figure 9.1), and its importance level is exceptionally high. To work in a turbulent environment, entrepreneurs and managers need a high level of competencies, improved innovativeness, and extensive communication networks with the government to acquire resources. They must have risk-taking ability and proactiveness to handle the high turbulent environment with strategies. Strategic competency theory explains the concept of fit. Thus, there must be proper alignment between internal and external resources, and it must fit the environment and performance. For that reason, the highest significant values are compared with other predictors (see Table 9.4). Entrepreneurial competencies (ECOMP) are the third main influential factor for Malaysian business success due to their importance and performance level. EI is equally crucial due to its importance and performance level. Entrepreneurs and managers must utilize entrepreneurial innovativeness throughout the fiscal year. It is

TABLE 9.4 Total effects and index values for BS Constructs ECOMP EI ET GS NC

Total Effect of a construct (importance) 0.212 −0.363 0.58 −0.677 1.439

Performances 79.339 63.199 60.749 69.87 75.756

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FIGURE 9.1  IPMA for business success.

essential to be innovative in services, processes, and products. Entrepreneurs ought to keep the business ready for any dynamic change in demand, market, or environment. Government support is the least important factor for BS. The government introduced several schemes to support the entrepreneurs, but there is a lack of proper resource distribution. The institutional theory explains how organizations can investigate to channel resources, and how they can recognize, evaluate, exploit, and process the available opportunities for business survival in a competitive environment, even though the performance of NC is less compared to ECOMP. Collectively, to get venture success in a highly turbulent environment, networking needs the management’s attention. The current research judges ECOMP and BS as a reflective-formative type II second-order construct and utilized the combination of repeated indicator approach and two-stage approach. The outcome of the calculations designated that the PLS model has significant reliability and validity. Also, discriminant validity validates the PLS model. The structural model analysis depicted the significance and non-significance test of the proposition. Moreover, the analysis of moderators is explained, and momentous interaction influences are described. Moreover, the MLMV technique of CLC is also implemented. Consequently, the results are compared. Nonetheless, there is no noteworthy difference in the findings after the comparison. It is confirmed that CMV does not influence the findings of this research. For that reason, the researcher confidently concludes the findings. Additionally, PLS analysis findings are extended through IMPMA, and managerial implications are also suggested based on IPMA results (Tables 9.5 and 9.6).

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TABLE 9.5 Comparison of t-values between CLC and original PLS estimates Original PLS Estimation Relationship H1a: ECOMP → BS H1b: NC → BS H1c: EI →BS H2a: ECOMP*ET → BS H2b: NC*ET → BS H2c: EI*ET → BS H3a: ECOMP*GS → BS H3b: NC*GS → BS H3c: EI*GS → BS

CLC Estimation

Beta

t-value

Beta

t-value

Decision

0.339 0.719 −0.151 −0.185 −0.087 −0.083 0.132 −0.135 0.095

***8.522 ***5.829 ***6.032 ***4.566 **2.164 **2.232 ***3.629 ***3.412 *1.689

0.348 0.763 −0.118 −0.173 −0.116 −0.078 0.122 −0.114 0.147

***8.394 ***5.91 ***4.364 ***4.343 ***3.399 **2.65 ***3.424 **2.151 ***3.465

Supported Supported Not Supported Not Supported Not Supported Not Supported Supported Not Supported Supported

Note: critical t values: *1.65 (significance level 10%) ***1.96 (significance level 5%), ***2.57 (significance level 1%)

TABLE 9.6 Comparison of R2 values between CLC estimates and original PLS estimates Original PLS Estimation Construct

Before Interaction

After Interaction

BS

R R Adjusted 0.747 0.745

R R Adjusted 0.807 0.803

2

2

2

2

CLC Estimation Before Interaction R 0.744 2

R Adjusted 0.742 2

After Interaction R2 0.813

R2 Adjusted 0.809

THEORETICAL AND PRACTICAL CONTRIBUTION • The first theoretical addition of the research findings is associated with entrepreneurial competencies’ independent variable by assimilating the organization’s major theory: the resource-based view (RBV). This research encompasses the theory by investigating, identifying, and testing the impact of seven dimensions of entrepreneurial competencies and confirming them as vital and specific resources for SMEs’ business success in the service sector. This research confirms entrepreneurial competencies as the third most important factor for small and medium enterprises in Malaysia. Moreover, as the second contribution, this study breaks the ground in investigating the direct effect of network competence in SMEs in the service sector. • Secondly, this study links network competence with RBV and SCT. The prominent contribution is the declaration of network competence as the most crucial external resource and vital factor for venture success. The concept of Guanxi is highlighted for future research as a new contribution. Network competence is explored as an independent variable. Internet-based advanced communication

Crucial Factors for Successful Entrepreneurial

•

•

•

•

129

technologies, business networks, and social networks are explained under network competence. The third academic input of the research relates to entrepreneurial innovativeness as an independent variable with the support of RBV. The findings suggest a direct effect of entrepreneurial innovativeness as the least essential factor because it is a risk-taking activity. Entrepreneurial innovativeness can be a significant factor for business success only with government support. Moreover, entrepreneurs ought to innovate services and products with strategies and proactively with the concept of fit factor. The fourth theoretical contribution of this research work is to inspect the moderating impact of government support with institutional theory assistance. The researcher introduces government support as a moderator in this research as a new contribution. The fifth and final theoretical contribution of the research work is that it investigates the influence of a turbulent environment as a moderator with strategic contingency theory (SCT). Results confirmed the negative moderating influence of high environmental turbulence on entrepreneurial competencies, entrepreneurial innovativeness, and network competence. Furthermore, the study’s findings recommend that the service sector has the abilities that should be considered specific resources and should be appropriately aligned with government support and the business environment. This study confirms that the competencies influence Malaysian SMEs in the service sectors. The researcher verifies that network competence, seven dimensions of entrepreneurial competencies, and entrepreneurial innovativeness are the crucial resources for survival and excellent performance of the service sector under the moderating influence of low environmental turbulence and high government support. Thus, a further understanding of these variables’ relationship complements new information to the literature of entrepreneurship and leadership in the Malaysian SME service sector context.

LIMITATIONS Regardless of numerous inferences, there are a few limitations to consider while understanding the research study’s findings. • Quantitative methodology and positivism are multifaceted truths. It is believed that immense investigation is applicable only to subjective and computable data. The company is the element for investigation, and collected data is selfreported. Thus, the participants provide an assessment of their entrepreneurial competencies and firms’ performance. “This may also raise issues that whether a single informant can present the whole company in the right way” (Al-Ansari, 2014). The absence of multiple respondents and scale might be a possible limitation. • Second, the research investigates the Malaysian SMEs in the service sectors, which generally handle the various services and products. Thus, this study did not explore the definite merchandise in the service industry. Moreover, the

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findings of the service industry may not be generalized to any products industry. • Third, the study collects data from only two states. The urban area is mostly located in the west region of Malaysia. Selangor is the busiest area for SMEs’ business activities, which is 19.8%, and WP Kuala Lumpur is the second biggest business hub with a ratio of 14.7%. So, the results might not be generalized for the whole population. • Fourth, this study explores the firm performance subjective measures based on business owners’ perceptions and could not be confirmed by lacking objective measurement for performance. Moreover, subjective measurement is widely practiced for multiple reasons, like access to accounting data (Ahmad, 2007). • Furthermore, self-reported facts are collected for the measurement of variables. “This approach is necessary due to the difficulties related to the independent assessment of these variables” (Ahmad, 2007). Self-reported data is quite common for measuring entrepreneurial competencies (Ahmad, 2007; Chandler & Hanks, 1994; Man & Lau, 2000; Tehseen, 2017). Additionally, the researcher selects three competencies to examine business success; however, some other competencies might impact venture performance.

FUTURE COMMENDATIONS • Alternate methods could be used for upcoming research, as could action research and case studies to get core information on competencies, innovativeness, and network competence from executives, managers, and entrepreneurs regarding their perspective. • Second, future studies must examine the service sector for specific services or products to understand better the influence of competencies and innovativeness, government support, and the business environment. As this research reveals the direct impact of entrepreneurial innovativeness, it does not support business success, but the introduction of government support as a moderator to support business success has a positive influence on business success. Therefore, future studies must study the moderating and mediating influence of government support. • Third, the competencies and innovative approach may differ from one product or service to another. Therefore, the impact of the market and networking can vary across diverse services. • Fourth, imminent research should explore some aptitudes and issues regarding SME’s business performance. For example, advertising competencies, technological, and quality. Besides this, extending the research work model, diverse internal and external aspects as moderators might be studied. • Furthermore, entrepreneurial competencies and innovativeness are changing representations to the demand of customer and market environment. Thus, it may need more time to study the situation. Additionally, future studies should include the firms which could not survive to gain in-depth knowledge of the factors which contribute to the failure of the business.

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• This study can be replicated in other Asian countries like Indonesia, Pakistan, Bangladesh, and other countries that deal with the service sector. The same study can be conducted in large enterprises. A proportional study can be conducted between SMEs and large firms in the service sector. Comparative studies among managers, entrepreneurs, business partners, and owners would propose valuable insight into their perspective competencies, which are mandatory for venture performance. Another interesting study is to compare competencies based on gender to determine the differences in innovative practices and networking. • Moreover, a significant proportion of females are involved in the service industry as compared to the other industries, and they are only 19.7% of the total Malaysian SMEs. It is suggested that men and women manage to deal with their business differently (Ahmad, 2007), and they may differ in competencies and innovative practices. • Future studies could do comparative studies between firms that are operated internationally and domestically to examine the diverse proficiencies and creative practices. Competencies could be compared during the three stages of the firm. Therefore, the forthcoming studies could involve a mixed methodology. Similarities in culture in different Malaysian states ought to be investigated.

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Social Entrepreneurship Sangeeta Sumbly IILM University, India

CONTENTS Comparison of Social Entrepreneurships with Commercial Entrepreneurships��������������������������������������������������������������������������������������������� 139 Similarities Between Commercial Entrepreneurship and Social Entrepreneurship����������������������������������������������������������������������������������������������� 139 Entrepreneurial Process�������������������������������������������������������������������������������� 139 Impact-Oriented Mindset����������������������������������������������������������������������������� 139 Difference Between the Two Types of Entrepreneurship���������������������������������� 140 Mission-Driven and Revenue-Driven����������������������������������������������������������� 140 Measurement of Outcomes��������������������������������������������������������������������������� 140 Different Approach to Entrepreneurship���������������������������������������������������������������� 140 Models of Social Entrepreneurship������������������������������������������������������������������������ 142 Support to Entrepreneurs����������������������������������������������������������������������������������143 Providing Intermediary or Linkage to the Market�������������������������������������������� 144 Employing the Economically Poor, Marginalized Communities���������������������� 144 The Beneficiary Population as Customers�������������������������������������������������������� 145 The Co-operative Model of Social Entrepreneurship��������������������������������������� 145 Model of Support or Subsidy���������������������������������������������������������������������������� 146 Measuring Social Impact��������������������������������������������������������������������������������������� 146 Importance of Measuring the Social Impact����������������������������������������������������� 147 Key Challenges in Measuring Social Impact���������������������������������������������������� 148 Lack of Maturity in the Measurement of Impact����������������������������������������� 148 No Consensus on the Usage of Cost-Related Impact Data�������������������������� 149 Methodology to Measure Social Impact����������������������������������������������������������� 149 Defining the Social Value Proposition (SVP)����������������������������������������������� 149 Quantify the Venture’s Social Value������������������������������������������������������������� 149 Monetize the Social Value���������������������������������������������������������������������������� 149 Conclusion and Discussion������������������������������������������������������������������������������������ 150 References�������������������������������������������������������������������������������������������������������������� 151 UN data estimates of 2019 show that the world’s population surpasses 7.7 billion people (World Population Prospects, 2019). The UN report – The Sustainable Development Goals Report, 2019 – highlighted that in 2018, close to 8 percent of the world’s workers survived on less than US$1.90 per person per day. The report further details that approximately 10 percent of the world population live in deplorable DOI: 10.1201/9781003097945-10

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conditions and are trying to fulfill their basic needs like food, shelter, medical facilities, and sanitation and water access. High levels of employment and illiteracy worsen their situation (Chhabra, 2018a). The scarcity of resources, the needs of the growing population, and the growing burden on the environment have made people conscious that governments alone cannot take care of these challenges. Organizations need to step in to fulfill the customer needs of the marginalized and underserved communities. Many enterprising people see opportunity in these challenges and are willing to take risks to create innovative and transformative products and services for the benefit of society (Chhabra et al., 2020). They mobilize the resources, ideas, and technology to create a greater impact. They create organizations that can fulfill society’s needs and at the same time be financially viable. These entrepreneurs, who pursue a social goal while creating economically sustainable organizations, are recognized for their efforts and are grouped as social entrepreneurs. Thake and Zadek (1997) said, The desire for social justice drives social entrepreneurs. They seek a direct link between their actions and an improvement in the quality of life for the people they work with and those they seek to serve. They aim to produce solutions which are sustainable financially, organizationally, socially, and environmentally.

Social entrepreneurship is more prevalent in low- to middle-income markets and is most active in Ethiopia, Nigeria, India, Mexico, Brazil, and South Africa. Social entrepreneurs try to solve the humongous social challenges existing in such countries by “creating business models revolving around innovative and effective products and services that resolve community problems” (Maingi et al., 2020). Social enterprises create new, socially and financially sustainable solutions to the many challenges facing the economically backward classes of the society. There are many examples of entrepreneurs creating successful business models in the social sector while working towards the 17 Sustainable Development Goals identified by the United Nations. Case 1: Aravind Eye Hospital in Hyderabad, India. In 1976, Dr. Govindappa Venkataswamy started the first Aravind Eye Hospital under the GOVIL Trust. There are 13 eye hospitals under the trust that provide world-class eye care, with 50% of their patients receiving eye treatments free or at highly subsidized charges (Makhlouf, 2011). Economies of scale, high operational efficiency, and use of the latest technology have allowed Aravind to reduce treatment costs for its patients. The treatment cost of the poor is offset by the profit earned from its paying patients, who constitute 40–50% of the total patients. Using this business model, the healthcare chain has provided treatment to more than 60 million outpatients and performed more than 7.8 million surgeries. The quality of the care provided ranks with the best despite its being in a business with social betterment at its core. Ophthalmology students from institutions like Harvard and Johns Hopkins regularly are trained at Aravind Eye Hospital. Indeed, the hospital’s model showcases how “High volume, high quality, and affordable costs” can enable the poor to get better health facilities while servicing the economically privileged

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class alongside, and builds a good case for entrepreneurship in the social sector (Makhlouf, 2011). One of the critical reasons why Aravind Hospitals have been able to get operational efficiencies and the higher success rate is due to their ability to break differentiated processes into small units and re-integrating them with precision. The analysis of huge patient data has helped them improve their success rate and make headway in the area of research of new solutions in eye care. They continue collecting large aggregate data in registries and near real-time analysis, thereby rapidly identifying solutions to the clinical questions. They adopt progressive technologies, such as the program with Google to create an artificial intelligence algorithm for early detection of the onset of blindness, to find innovative solutions to prevent sight impairment (Basu, 2018).

COMPARISON OF SOCIAL ENTREPRENEURSHIPS WITH COMMERCIAL ENTREPRENEURSHIPS Traditionally, entrepreneurship is all about identifying new opportunities and capitalizing on them for revenue generation (Chhabra & Karmarkar, 2016b). It is about creating value and monetizing the same, despite constraints of resources. Entrepreneurs identify need gaps in the markets and use innovation to fulfill the same to maximize wealth (Chhabra, 2018b). When the scope of innovation in an organization, be it product or process, is extended to fulfill a greater need of society in a financially viable manner for society’s benefit, it is called a social venture. However, when the focus of the entrepreneur is the creation of innovative goods and services with the objective of maximization of profits while fulfilling a need gap in the market, it is called commercial entrepreneurship, or simply entrepreneurship.

Similarities Between Commercial Entrepreneurship and Social Entrepreneurship Entrepreneurial Process The entrepreneurial process is the same for social and commercial entrepreneurship. Both types of entrepreneurs focus on vision and opportunity. These enterprising individuals identify an unfulfilled customer need and are willing to invest time and effort and take risks to fulfill the customer’s need through innovative resources. Their process of identifying customer pain points, using design thinking for creating out-ofthe-box, cost-effective, and sustainable solutions is similar. Impact-Oriented Mindset Both social and commercial entrepreneurs seek innovative solutions that are both impactful and quantifiable. Both aim to maximize return on investment and efforts, though the definition of return varies. While one seeks a financial return on investment, the social entrepreneur evaluates her achievement by calculating the social impact created by her.

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Difference Between the Two Types of Entrepreneurship Mission-Driven and Revenue-Driven The biggest difference between these entrepreneurs is the nature of the immediate return each tends to seek. Commercial entrepreneurs are market-focused and profitoriented. The objective of achieving the social impact that translates into fulfilling a solid organizational mission is the motivation for a social entrepreneur. Profitability and economic value are important to them more for ensuring that their venture is sustainable and fulfills their social mission. Furthermore, social (impact) entrepreneurs reinvest the majority of their profit in increasing their social impact rather than distributing it among stakeholders. By contrast, commercial entrepreneurs seek to maximize profits and market share, among other financial parameters. Commercial entrepreneurs have to operate within the dynamics of the markets. If commercial entrepreneurs are unable to optimize financial value, they could be out of the business because of competition and financial viability. Their focus is wealth creation for their shareholders and themselves, and they often change their business model and their product/service to maximize that. Measurement of Outcomes The success of commercial entrepreneurship can be determined by measuring the organization’s profitability, market share, revenue earned, and other financial parameters. These are easily measurable in tangible and quantifiable values. Therefore, the success of such an enterprise is dependent on the degree of social change caused by it in society and communities. Due to the intangible, multifactorial nature and temporal dimensions of the outcome, it becomes challenging to measure social entrepreneurship. Case 2: Glasswing – leveraging community support to increase social impact. Glasswing International, a Skoll awardee in 2020 for social entrepreneurship, was launched in 2007 to leverage community resources to reduce the level of violence amongst youth in Central America. It adopted a community school approach. The organization conducts life skill sessions and mental health interventions after school hours for marginalized communities. These sessions increase the youths’ ability to cope with their life challenges and increase their interest in education and creating careers for themselves. This led to transforming communities by offering viable youth alternatives to crime and violence. Today the program covers over 100 schools in El Salvador. They have improved one million-plus lives, mobilized over 137,274 volunteers to improve the education of over 398,166 participants and the health of 313,081 participants. Their model of social impact has been replicated in ten other countries.

DIFFERENT APPROACH TO ENTREPRENEURSHIP The approach to social entrepreneurship is exceptionally different from commercial entrepreneurship. Resources such as employees are managed differently. A profitseeking entrepreneurial venture has to be competitive and agile to penetrate markets and achieve larger customer bases. Hence, it has to pay a premium to attract promising

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and competitive employees having a good skill set, knowledge, and attitude. Compensation is a tool to attract employees in commercial, entrepreneurial ventures, reward them, and recognize their contribution. On the other hand, social entrepreneurs have limited full-time dedicated staff and are dependent on volunteers and part-time employees to fulfill their human resource needs. Due to the non-capitalistic nature of their organization, they are unable to pay their employees handsomely, preferring to channel their revenues toward increasing the scope of their impact. They attract those employees who place greater value on the non-financial benefits remuneration. Due to the nature of their mission to maximize social impact, these entrepreneurs tend to reinvest their profits back into the venture. Case 3: SELCO – changing lives by making solar energy accessible to the poor. SELCO Solar Light Private Limited is the brainchild of a social entrepreneur who decided to use his doctorate in rural electrification from the University of Massachusetts, USA, to benefit financially challenged people in India. Dr. Harish Hande, the social venture’s founder, chose to provide solar power solutions to the poor so that they could break from the cycle of poverty, exacerbated by unreliable access to power. In India, 57 million people do not have access to electricity, or they get it only on an intermittent basis. The absence of electricity has a crippling effect on the lives of these people, limiting their ability to generate income or educate their children, thereby stifling them in a cycle of poverty. While many locations still do not have access to electricity, the poor people cannot fund their basic energy needs in the locations having access to electricity. As per Dr. Harish Hande, “We set up SELCO to bust three myths – the poor people cannot afford technology, the poor people cannot maintain technology, and it is not possible to run a commercial venture that fulfills a social objective” (Mukherji & Jose, 2010). SELCO fulfilled the need gap of the rural poor in a very innovative manner. The team at SELCO, under Dr. Hande, understood the root cause of the problem, i.e., affordability and accessibility of power. Instead of fitting a standard solution to a problem, they customize energy solutions for their consumers. They create, develop, and use custom-made solutions by understanding the needs of their customers. Their solutions factor in the terrain of their customers’ location, income, and prevailing. By creating innovative solar products and pricing packages that suit their customers’ affordability, ranging from daily income earners to regular, salaried lower-income families, SELCO provides eco-friendly power solutions to each household’s cottage industries’ needs. Unlike the commercial players in the markets, who upfront-load the cost of the solar panel onto the customers, SELCO focuses on controlling inflation on the supply side and making the solar power accessible through rental schemes and financing schemes. In the initial days, SELCO also facilitated the development of financing schemes by state-owned rural banks that were better suited to their poor customers’ needs. These schemes match the repayment installment structure with the additional revenues generated by their customers from increased productivity arising out of solar solutions. SELCO has ensured widespread adoption of energy solutions by creating conducive market conditions by working on the five essential aspects of the ecosystem. Economic inclusion (financing schemes), human capital development

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(training and employing local talent), policies promoting sustainable energy (by educating government agencies and getting their support), strengthening supply chain (nurturing local vendors), and introducing innovative products (that cater to customer needs) have allowed SELCO to provide energy services to households, health and education institutions, cottage industries, and other rural organizations. SELCO has been rigorously mapping the change caused by electrification to society in their areas of operations. The data collected and analyzed on their customers has helped them measure impact and receive support from important stakeholders like the government, financial institutions, and international organizations like Skoll Foundation. Further, SELCO has scaled its operations and provides power solutions to people living in Tamil Naidu, Maharashtra, Karnataka, Bihar, and Kerala. Since its launch in 1995, SELCO has changed the lives of over 1 million people and delivered many innovative modern energy solutions. It has 67 energy centers, 510 employees, and a turnover of USD$3 million (Ashoka, n.d.). In addition to SELCO Solar Light Private Limited, Harish Hande has created social impact through the SELCO Foundation (which includes the SELCO Incubation Centre) and the SELCO Energy Access Fund.

MODELS OF SOCIAL ENTREPRENEURSHIP The term social entrepreneurship became popular in the 1950s. However, it received visibility and recognition much later due to social entrepreneurs’ efforts like Akhter Hameed Khan and Muhammad Yunus. Their ventures and solutions’ success story has increased interest in this format of entrepreneurship (Gandhi & Raina, 2018). The 15-year period from 2004 to 2020 saw an increase of over 60% in the number of social ventures launched. Today, social entrepreneurs are highly motivated, socially responsible people who try to help the world eliminate ills like poverty, hunger, illiteracy, discrimination, lack of water and proper sanitation, unemployment, and a host of similar problems. However, dealing with such a large scale is impossible without adequate funds, nor can they rely entirely on philanthropy. Thus, social entrepreneurs need to adopt sustainable business models that would allow their ventures to contribute to the social good for a long time. There are many explanations and definitions of what constitutes the business model. A business model is a statement that shares the processes adopted by an organization to generate revenues over time. The statement reflects revenue-generating and profit-making opportunities created by the various initiatives of the entrepreneurial venture. It also reflects the set of assumptions made by the entrepreneur. It helps in organizing and synergizing the various components of the strategy and execution of the business. Social enterprises explain the relationship between various components connecting the organization with the beneficiary and the consumer. The framework of the social business model consists of the following components: Offer: The customer value proposition, the benefit offered by the organization to the customer through its products and services.

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Market: The customer base being targeted by the social entrepreneurship venture, which includes the geographies, customer segments, the distribution channel, and the communication strategy along with the pricing strategy. Governance: The model of governance and the set of processes or laws define the working relationships with multiple stakeholders to fulfill the organization’s social mission. It also includes the legalities and the rules and regulations set by the government and other institutions for promoting and managing social ventures. Social entrepreneurial ecosystem: This refers to the partner networks, social impact investing funds, non-government organizations already operating in that space, consumer groups, government agencies, and all other players in the environment. Surplus: This is how the organization manages the revenue surplus, especially the distribution of profits among the shareholders and reinvestment of the same into the business to maximize the social impact. Economic Profit Equation: This describes the profitability arising out of the costs incurred and revenue generated by the organization. Social Value: It identifies the social impact the organization is making and the risks and benefits associated with the activity based on its initiatives. Based on the combinations of the aforementioned components, there are six social entrepreneurship models, which are popular (Müller, 2012). Each of these has a different form of articulation depending on how they generate revenues, their markets, their product or services, and the social impact.

Support to Entrepreneurs The entrepreneur support model focuses on creating entrepreneurs at the grassroots level and providing support either in guidance, training, better-quality raw materials, or even financing, thereby ensuring that they become self-sustaining (Chhabra & Goyal, 2019). An example of this model is the Grameen Bank of Bangladesh. Muhammad Yunus, the founder of Grameen Bank and Nobel Peace Prize Awardee, launched the social entrepreneurial venture in 1976 to provide financial support to poor rural entrepreneurs and reduce their high-interest burden under predatory lending. It provided credit without collateral to impoverished people in rural Bangladesh. It was still able to recover the principal amount and interest from this segment of the population. After seeing success on a smaller scale, Grameen Bank (GB) adopted this unconventional banking practice across regions. It used mutual trust, creativity, participation, and accountability to empower the poor. For the past few decades, the bank has been providing microcredits and other financial services to women (who make up 97% of their borrowers) struggling in poverty and vulnerability, thereby helping them sustain and increase their business incomes

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(Rahim & Wisuttisak, 2013). Today it has over 9.6 million members with a presence in 81,678 villages of Bangladesh. The GB model is accepted and adopted in 68 other countries around the world.

Providing Intermediary or Linkage to the Market The intermediary model aims to connect the vulnerable segment to new market and customer segments to provide them a higher and fairer value for their product or service. The social entrepreneur guides to support and provides access to quality raw material, technology, and financing to the small entrepreneurs and different markets for their products to customers willing to pay a more suitable price. Take the example of Fruandes, a social venture initiated in 2002 in Colombia. It encourages small-scale, organic fruit producers by helping them market their products to international markets such as the Netherlands, Sweden, Italy, Switzerland, Canada, France, Japan, and the USA, where they get higher and fairer prices (Fruandes, 2020). Fruit growers from different countries and regions supply organic pineapple, banana, sugarcane, mango, golden berries, dragon fruit, and cacao nibs to Fruandes. Fruandes takes care of the logistics involved in gathering the fruits and other products from small producers and supplying them to the international markets, tasks that small-time producers find challenging to manage (Fruandes, 2020). Further, Fruandes has developed a “Grower’s Service Model” that provides the growers with technical guidance, training, and organizational support (Michael, 2017). Producers also get support while applying for need-based funding from financial institutions. This type of support helps them increase their scale of operations and revenue margins, thereby helping them earn better and rise above their poverty. Fraundes’s network of fruit producers has certification for organic farming for over 137 hectares. Their revenue is over US$2 million (Michael, 2017).

Employing the Economically Poor, Marginalized Communities This social venture model focuses on improving lives by employing economically challenged people who are vulnerable or are marginalized on account of any other reason. The social entrepreneurs employ the people from the underserved and underincome communities, providing them with income to uplift their families from poverty. This leads to a social change in the community, leading to better health and better literacy levels. Yellow Leaf is a social enterprise that has created high-wage employment for women residing in the rural areas of Thailand. The husband-andwife duo of Joe Demin and Rachel Connors, who are the founders of the enterprise, decided to monetize the artisans’ potential and their weaving skills. They founded a social enterprise, Yellow Leaf, through which they sell uniquely designed hammocks that are incredibly soft and handwoven by the artisans. The team’s goal is to empower the artisans to lift their families from poverty by presenting their work to the world. Through design innovations, the use of new technology, and the production of quality

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products, they have been able to create markets, get impact investment, and improve the quality of lives of the artisans (Smith, 2011).

The Beneficiary Population as Customers Under this model, the social entrepreneur designs an innovative product or service to benefit underserved markets or the people categorized at the “Bottom of the Income Pyramid”, a phrase coined by C.K. Prahalad for people who earn less than $2.50 per day (Prahalad, 2005). The social venture creates products and services that are affordable and accessible to poor people. Many social enterprises use this model, wherein through technology and innovation, they provide affordable and relevant products and services designed for the underserved community. VisionSpring, a social enterprise, has improved the lives of lower-income customers by providing them with affordable eye tests and glasses. The enterprise has been creating change in more than 43 countries, including Bangladesh, El Salvador, India, and South Africa (Stephens, 2020). Their glasses are competitively priced and designed to be affordable for a person whose income is as low as $4 per day. Through these stores and association with large NGOs/health organizations, the company distributes eyeglasses and vision testing at very reasonable costs. They maintain potential manufacturers and distribution partners’ databases to ensure they continue to create innovative and costeffective eye care solutions for their underserved customers (Manaus, 2013). They have distributed 6.8 million corrective pairs of eyeglasses, increased productivity by 22–32%, and created wealth of $1.4 billion (Vision Spring, 2020).

The Co-operative Model of Social Entrepreneurship This model is based on a group of vendors/producers joining hands together by creating co-operative societies to get better economies of scale, access to technology, and better leverage in distribution channels. Social ventures that follow the co-operative model organize their members in a democratic structure. The management of such enterprises is in consultation with all members. These co-operatives address common challenges like unemployment, lack of resources, and suitable market structures to connect the producers with the consumers. The members share resources, skill sets, and technology to strengthen the organization. Case 4: Amul story – empowering small-time farmers. An early example of this model of entrepreneurship is Amul (Anand Milk Union Limited). Co-owned by 3.6 million milk producers in Gujarat, the co-operative is a pioneer in the Indian dairy industry (Varma & Ravi, 2017). The formation of the Kaira District Co-operative Milk Producers Union and Amul Dairy in 1946 and 1950 has provided the dairy farmers with access to a wide range of domestic markets and spurred India’s milk revolution. Some of the reasons that led to Amul’s success include its robust and seamless supply chain, hierarchical network of co-operatives, a strong connection between small and large suppliers and management by professionals (Varma & Ravi, 2017). It  has

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empowered the small-time farmers, providing them with a steady income as well as participation in profits. The co-operative has ensured the development of a strong supply chain and provided access to technology and innovation. The company depicted how by eliminating intermediaries and adopting professional management systems and milk sourcing processes, one can ensure that low-income farmers generate better incomes and rise above their poverty. Today Amul is an over $6.6 billion business owned by members (farmers) who get to share in the profits (Amul, 2019).

Model of Support or Subsidy In the subsidy model, the entrepreneur uses profits earned through its commercial venture to benefit the economically challenged customer segment. The social enterprise develops products or services for different customer segments, wherein the profit from one fund subsidizes the cost of the other initiatives. Such social ventures create two organizational structures – one is for the activities relating to maximizing social impact, while the other is designed for maximizing economic value. Aravind Eye Hospitals in India follow this model of creating social impact. The hospitals provide treatments, using the latest technology, to their patients. Their world-class eye treatments are provided at differential rates to different segments of society. The profits generated from the services provided to people in the upper-income segment subsidize the services provided to the underserved population. This allows them to treat at least 50% of the patients for free or at very subsidized rates. In addition to the aforementioned models of social entrepreneurship, many others are evolving. One such is the hybrid model, which many social entrepreneurs extensively adopt. The hybrid model is when an organization adopts multiple models to increase its social impact (Dao & Martin, 2017). For example, SELCO Solar Lights Pvt Ltd provides solar power to the poor rural population through innovative products and pricing. It also offers employment to the local young after training them with technical skills. Besides, it has encouraged entrepreneurship at the grassroots level by encouraging locals to become its vendors or service partners. The SELCO Foundation is funding and supporting innovation in the space of sustainable energy. Since the passion and motivation of social entrepreneurs are to maximize social impact, they are willing to use hybrid models to benefit society.

MEASURING SOCIAL IMPACT One of the most challenging and frustrating aspects of a social entrepreneur’s work involves measuring and communicating the positive social impact of his/her organization (Kramer, 2005; Kramer et al. 2007). To calculate the good work done by an entrepreneur is difficult since it has far-reaching implications. First, knowing what “good” means is hard; second, because it is extremely tough to equate the excellent work done to a monetary value, it is difficult to understand the impact made by the work done clearly. Most entrepreneurs struggle to attribute a monetary value to the social impact made on society, community, and the environment (Chhabra & Karmarkar, 2016a). For example, it is almost impossible to calculate accurately the value of education

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given to underprivileged girls from low-income families. How does one measure the impact of this education that empowers them to become financially independent or improve their family members’ health? As per a UNICEF report, an educated woman gets access to information and knowledge, develops skills, and has self-confidence, making her a better parent, a better income-generator, and a good citizen (UNICEF, n.d.). Educated girls grow into women who positively impact their families, the economy, and the next generation. Therefore, there are humongous benefits for society as a whole (UNICEF, n.d.). However, it is difficult to measure the change in society resulting from educating the girl child.

Importance of Measuring the Social Impact Perhaps the main benefit of learning how to calculate a firm’s social impact lies in the entrepreneur being able to measure the results of the organization’s work (Merchant & Vander Stede, 2007). Measuring impact allows social entrepreneurs to allocate resources to programs that make a bigger difference to society or move closer to the fulfillment of their missions. When they can measure their firm’s social impact, they can identify themselves and their organization with the industry. Another advantage of accurate measurement is being able to impress multiple stakeholders, including investors, and show that the organization is achieving its goal. This becomes important in view of the size of the impact investing industry. According to the Global Impact Investors Network’s report in 2020, the impact investing industry has grown to $715 billion. Today, with such large funds, all stakeholders, including investors, employees, government, customers, and other institutions, wish to understand the efficiency and social result of the investments made. A social entrepreneurial venture to attract investments and support from various government and other organizations has to have absolute clarity about its vision and financial goals (PWC, 2014). Only when it can quantify and highlight the value creation can it expand and cause a larger change in society, environment, or any other social mission. To share an example, Skoll Foundation is a California-based private foundation that is focused on driving large-scale social change by “investing in, connecting, and celebrating social entrepreneurs and other innovators focused on solving the world’s social problems” (Skoll, 2020). The foundation awards $1.5 million in core support investments to five social entrepreneurs each year to increase their work scale. The awarded social entrepreneurs gain recognition and leverage in the global community of visionary leaders and innovators, which further helps them with funding and associations with other players from the social entrepreneurship ecosystem. In June 2020, one of the winners of the award was Dr. Aparna Hegde, founder of ARMMAN, a social venture operating in India. Since 2008, she has consistently worked toward improving the lives of pregnant women, mothers, and children in India. She used technology to increase the number of lives she could improve. Her ability to quantify the efforts of ARMMAN enabled her to be a worthy partner to the Government of India and jointly manage a voice helpline platform called Kilkari (Skoll, 2020). Case 5: ARMMAN – using technology and data to increase the scale of social impact. ARMMAN, launched in 2008, has adopted a “tech plus touch” model,

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leveraging technology to ensure access to health-related information for pregnant women, mothers, and children. ARMMAN’s initial innovation was the launch of a free mobile voice call service called mMitra. Through this service, ARMMAN sends preventive health care information weekly to mothers during pregnancy and infancy, thereby reducing mothers’ mortality rate and increasing the birth of healthier children. It has also created health entrepreneurs who go to the doorsteps of these women to ensure that they have access to health facilities and can take action promptly. Additionally, in 2019, they partnered with the Government of India to jointly manage a similar voice helpline platform, Kilkari, that delivers health care information to 16  million subscribers in five languages in 13 states. Their partnership with the Indian government also includes managing a Mobile Academy that trains frontline government health workers (ASHAs), helping them refresh their knowledge on health safety and preventive techniques and further sensitizing them to have better engagement with pregnant women, mothers, and children providers. The organization has been able to garner 2.2 million subscribers in its mMitra program, supported and trained health workers in 100 government hospitals, worked with 43 community NGOs, and impacted the lives of 16 million women. The organization also collects huge amounts of data on medical history, health parameters, and other details of pregnant women, mothers, and children and uses these details to pre-empt their health issues and provide interventions to reduce mortality rates (Skoll, 2020). Today, they associate with the Google Research Centre in the “AI for Social Good” Program. The Google Research team, in association with IIT Madras professors, use artificial intelligence to predict the risk of expectant mothers dropping out of healthcare programs. This enables them to improve timing of targeted interventions and increase positive healthcare outcomes for mothers and their babies (Google India, 2020). Their ability to quantify the impact of their services has helped them get more funding for their initiatives, have an association with the Government of India by proving their experience, as well as get more support from high tech organizations like Google which further augments the efficiency of their program (Google India, 2020).

Key Challenges in Measuring Social Impact In the present scenario, social entrepreneurs need not rely on individual stories or anecdotes alone to convey a sense of moving in the right direction or achieving their vision and mission; instead, they can support the impact of their effort with complex data. However, social entrepreneurs face two critical challenges when they try to measure the impact of their work. Lack of Maturity in the Measurement of Impact Even though social entrepreneurship ventures have existed for a long time, only recently have they become a recognized sector of study. Therefore, a systematic format of measurement of social impact is still under development. Additionally, the social impact is visible over a longer period. This makes monetizing the value of the full range of benefits or social impact even more complicated, leading to multiple

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errors in the calculation. These challenges ensure that a standard framework like the multiple financial parameters used for commercial, profit-making entrepreneurial ventures, e.g., return on investment (ROI) (Kickul & Lyons, 2020), does not exist for social ventures. No Consensus on the Usage of Cost-Related Impact Data Impact analysis is required for multiple purposes, including funding. There are two schools of thought here. One set of practitioners suggests that the cost and impact data of social entrepreneurial ventures can be compared across different program areas, even for making funding allocation decisions, while the other set feels that the data can be used only to evaluate similar programs.

Methodology to Measure Social Impact One of the key benefits of calculating a firm’s social impact is to get a clear picture of the quantifiable results of the organization’s output (Merchant & Van der Stede, 2007). This allows entrepreneurs to evaluate which of their social development programs are working and which are not (Karmarkar et al., 2014; Kramer, 2005; Tuan, 2008). Measuring social impact allows entrepreneurs to allocate resources to areas that are generating maximum impact. Another benefit is that it allows impact investors to see the social value being created by the organization. Finally, measuring social impact allows the entrepreneurs and their organization to compare and evaluate their efforts with other social enterprises, ensuring bigger outcomes and creating further social change (Kickul & Lyons, 2020). Defining the Social Value Proposition (SVP) The SVP is the vision and the purpose for the organization, as well as its aimed impact on society, individuals, and the environment. The firm needs to create an SVP that explains the value the organization plans to create and why it exists. The SVP can be created post discussions with all the relevant stakeholders. Quantify the Venture’s Social Value To ensure objectivity and measurement of social value, it must be quantifiable. Post discussion with all stakeholders, a social entrepreneur can identify a few social indicators representing the change created by the social entrepreneurial venture. The social indicators have to be tracked over a period to measure social impact. Monetize the Social Value The last step that helps measure social impact is the monetization of the social value of the relevant indicators. Monetizing the impact enhances the credibility of the social venture and its mission. It establishes metrics used to measure the effectiveness of the venture’s programs to achieve the desired social impact (London, 2009; Scholten, 2006). Monetizing ensures that all stakeholders, whether customers, vendors, or government organizations, can comprehend the impact being created. While there is no perfect methodology, Melinda T. Tuan (2008) has identified eight integrated approaches that can be used to estimate social impact. These include

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cost-effectiveness analysis, cost-benefit analysis, REDF’s Social Return on Investment (SROI), the Robin Hood Foundation’s Benefit-Cost Ratio, the Acumen Fund’s Best Available Charitable Option (BACO) Ratio, the William and Flora Hewlett Foundation’s Expected Return (ER), the Center for High Impact Philanthropy’s (CHIP) Cost per Impact, and the Foundation Investment Bubble Chart (Kickul & Lyons, 2020).

CONCLUSION AND DISCUSSION Social entrepreneurs are enterprising visionaries trying to balance society by redistribution of resources, wealth, and education between the haves and the have-nots, the large and the small, the local and the global. Social entrepreneurs offer solutions to several problems in society. Reduction of poverty, providing basic healthcare, improving education levels, and promoting safety and economic growth are important goals that governments cannot achieve alone. Through social innovation, the entrepreneurs can generate new social, economic, and institutional structures that improve society and fulfill its needs. Social entrepreneurs identify their mission and attempt to use their entrepreneurial ventures and social enterprises to maximize social impact in different social sectors. Social entrepreneurs adopt different models to sustain and grow their social ventures to increase their social impact scale and scope. The driving factor in these ventures is to reinvest profits for social enterprises’ growth and social change. They can choose to create change in society by adopting one or a combination of the six models of social entrepreneurship. Creating and delivering innovative, cost-effective products and services for the underserved section of society; employing the poor, marginalized communities; facilitating financial and non-financial support to entrepreneurs at grassroots levels; developing the co-operative model of business to empower many; providing support or subsidy to different beneficiaries on a need basis; and organizing linkages to the intermediaries are some of the ways the entrepreneurial ventures create social impact. Additionally, many social entrepreneurs have achieved scale and increased impact by understanding their environment and beneficiaries better. Their increased focus on data collection and analysis allows the possibility of further research. Big Data and new technologies like artificial intelligence can help them increase their scope of impact. Measuring social impact is critical for the growth, funding, and recognition of social enterprises. The value of the social changes caused by a venture needs to be measured and highlighted in quantifiable terms for entrepreneurs and other ecosystem members to evaluate deliverables and comparison effectively. Several methodologies are adopted for measuring and monetizing social value. However, the methodologies currently in use have some limitations (Lazzarini, 2018). Further, the research and documentation in this area of entrepreneurship are still fragmented, limiting the interest of other stakeholders who could contribute to the development of this sector. As the sector becomes better organized, focuses further on data collection and adoption of new technologies, and receives regular funding, it will evolve to attract larger numbers of social change-makers.

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REFERENCES Amul. (2011). Note on the achievement of the dairy co-operative. https://www.amul. com/m/a-note-on-the-achievements-of-the-dairy-cooperatives Amul. (2019). PTI (2019, May 28). Amul aims to achieve a business turnover of Rs 50000 crores by 2021. Economic Times. https://economictimes.indiatimes.com/industry/consproducts/fmcg/amul-aims-to-achieve-a-business-turnover-of-rs-50000-crore-by-2021/ articleshow/69546342.cms?from=mdr Ashoka. (n.d.). Harish Hande. https://www.ashoka.org/en-in/fellow/harish-hande Basu, S. (2018, November 12). A Madurai-based hospital and Google are working together to stop early blindness. The Hindu. https://www.thehindu.com/sci-tech/technology/an-aifor-an-eye/article25476723.ece Chhabra, M. (2018a). Ecopreneurship as a solution to environmental issues and challenges: State-of-the-art and future perspective. In Dahiya, S. (Ed.), Sustainability and digitalization: Present reality and future prospects, Sudha Enterprises, Rewari (Haryana), 35–46. Chhabra, M. (2018b). Gender gap in “success factors” among entrepreneurs: A study of micro and small enterprises. SEDME (Small Enterprises Development, Management & Extension Journal), 45(2), 1–17. Chhabra, M., Gera, R., Hassan, R., & Hasan, S. (2020). An exploratory study of cognitive, social and normative dimensions of female entrepreneurship within transition economies: Evidence from India and Vietnam. Pakistan Journal of Commerce and Social Sciences (PJCSS), 14(4), 1012–42. Chhabra, M., & Goyal, A. P. (2019). Education and entrepreneurial experience WRT female entrepreneurs. International Journal of Research in Humanities, Arts and Literature, 7, 95–110. Chhabra, M., & Karmarkar, Y. (2016a). Effect of gender on inception stage of entrepreneurs: Evidence from small and micro enterprises in Indore. SEDME (Small Enterprises Development, Management & Extension Journal), 43(3), 1–16. Chhabra, M., & Karmarkar, Y. (2016b). Gender gap in entrepreneurship a Study of Small and Micro Enterprises. ZENITH International Journal of Multidisciplinary Research, 6(8), 82–99. Dao, H. C., & Martin, B. C. (2017). Hybrid social enterprise business model synergy: Creation of a measure. In Hybrid ventures. Emerald Publishing Limited. 95–7. Fruandes. (2020). Impact report. https://fruandes.com/assets/FRUANDES-INFORMEIMPACTO.pdf Gandhi, T., & Raina, R. (2018). Social entrepreneurship: the need, relevance, facets and constraints. Journal of Global Entrepreneurship Research, 8(1), 9. Google India. (2020, February 13). Applying AI to big problems—six research projects we’re supporting in public health, education, disaster prevention, and conservation. https:// india.googleblog.com/2020/02/applying-ai-to-big-problemssix-research.html Karmarkar, Y., Chabra, M., & Deshpande, A. (2014). Entrepreneurial leadership style(s): A taxonomic review. Annual Research Journal of Symbiosis Centre for Management Studies, 2(1), 156–189. Kickul, J., & Lyons, T. S. (2020). Understanding social entrepreneurship: The relentless pursuit of mission in an everchanging world. Oxfordshire: Routledge. Kramer, M. R., & Foundation Strategy Group. (2005). Measuring innovation: Evaluation in the field of social entrepreneurship. https://community-wealth.org/sites/clone.community-wealth.org/files/downloads/report-kramer.pdf Kramer, M., Graves, R., Hirschhorn, J., & Fiske, L. (2007). From insight to action: New directions in foundation evaluation. FSG Social Impact Advisors.

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Terracotta Pottery Catastrophe Survival Issues and the Road Ahead for Sustainable Enterprise Shweta Dahiya, Parveen Siwach and Anupama Panghal National Institute of Food Technology Entrepreneurship and Management, India

Shilpa Sindhu The NorthCap University, India

CONTENTS Introduction������������������������������������������������������������������������������������������������������������ 154 Research Methodology������������������������������������������������������������������������������������������ 156 Objectives���������������������������������������������������������������������������������������������������������� 156 Data Collection and Sampling Design�������������������������������������������������������������� 156 ISM Methodology��������������������������������������������������������������������������������������������� 157 Results and Discussion������������������������������������������������������������������������������������������ 159 MICMAC Analysis������������������������������������������������������������������������������������������� 159 Formation of the ISM model (Diagraph) and Interpretations��������������������������� 160 Experiences of the Entrepreneurs���������������������������������������������������������������������162 Entrepreneur 1���������������������������������������������������������������������������������������������� 162 Entrepreneur 2���������������������������������������������������������������������������������������������� 163 Entrepreneur 3���������������������������������������������������������������������������������������������� 163 Entrepreneur 4���������������������������������������������������������������������������������������������� 163 Conclusion������������������������������������������������������������������������������������������������������������� 163 Acknowledgment��������������������������������������������������������������������������������������������������� 164 Notes���������������������������������������������������������������������������������������������������������������������� 164 References�������������������������������������������������������������������������������������������������������������� 164

DOI: 10.1201/9781003097945-11

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INTRODUCTION Terracotta is a complex, semi-consistent, moisture-free clay, usually burnt for making pottery (Gangopadhyay & Sen, 2019). Terracotta pottery is an ancient craft. The Terracotta figures indicate the existence of pottery crafted from Roman times (Burr, 1933). Various handmade artifacts like bowls and vessels in different colors such as brown, red, or orange were traced from the Neolithic age (Mazzocchin, Agnoli, & Colpo, 2003). Few pottery specimens painted during the Harappan period include a neck sherd small jar with a comb-like motif (Dangi & Uesugi, 2013). The concept of sustainability relates deeply to Terracotta pottery. Clay, the primary raw material of pottery, is an abundantly available natural resource (Akpang & Esege, 2014; Echeta & Esege, 2014; Menon & Varma, 2010; Rice, 2015). Also, adopting earthenware items like clay plates for food, cups, and kulhad for tea and coffee reduces the entire supply chain’s carbon footprint. Due to increasing consumer awareness and demand for sustainable products (Chhabra, 2018a), Terracotta is increasing its product domain from traditional products to modern-day requirements like designer lights, Terracotta coolers, ovenware, non-stick tawas, and other cookware made of clay. Pottery craft is part of the cultural heritage of India(Sundaram & Bhattacharya, 2013). Madhya Pradesh, Gujarat, Haryana, New Delhi, Orissa, and Tamilnadu are the significant places for Terracotta pottery in India. Terracotta pottery is a perfect blend of sustainability with cultural creativity. Prajapati colony, or Kumhar Gram, which was chosen as the study unit for this study, is one of India’s most significant potters’ colonies in Uttam Nagar Delhi,1 set up in 1970. There are presently around 700 families of artisans in the Prajapati community of Uttam Nagar. They make products ranging from a humble teacup or Terracotta planter to more sophisticated items of modern life. The Terracotta pottery-making process involves a few articulated steps. There are various methods of preparing, forming, dyeing, and decorating the pottery. The process requires precision, and artisans have developed an expertise in this traditional art, generation by generation. Maintaining product standardization in an entirely manual production process is incredible about this art of pottery making. The product ranges from Kulhad, a humble teacup, or Terracotta planter to more sophisticated modern life items. A few images of the Terracotta products are shown in Figure 11.1. Though there is a growing appreciation for a Terracotta product in the country, it is still limited to a few market segments. With growing commercialization and availability of cheap alternative products in the market, Terracotta pottery is facing fierce competition. The traditional potters are leaving their profession, as it is not enough to support their livelihood. Recently, National Green Tribunal (NGT), Delhi, has ordered that all pottery kilns in Uttam Nagar must shut down their operations.2 The definitive aim of potters is to present their art through various colors, techniques, and grazing. The grazing and ceramic firing process consumes high energy and releases high levels of carbon dioxide into the environment (Lu et al., 2019). Using traditional sources in pottery making creates a high level of air pollution and requires sustainable methods to solve this problem. According to NGT’s scanner, these potters are responsible for contributing to air pollution in Delhi. As a result,

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FIGURE 11.1  A few of the pottery products produced by the Prajapati Community (Source: Author).

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these urban potters are forced to stop firing their clay works in traditional kilns. They are left with the option of either switching to modern fuel like LPG or leaving this occupation. The only source of income for artisans is Terracotta pottery. The community has no alternate occupation and solution to the problem. The association of potters claimed that the material they used for firing, like wood, sawdust, and cow dung cake, is not as harmful as other sources of pollution like diesel or petrol. Their primary raw material is clay, which is 100% organic. Through this study, an attempt has been made to understand and model the challenges perceived by Prajapati community artisans for sustaining their traditional art of work. Modeling challenges helps the stakeholders take prudent decisions to protect the art of Terracotta pottery and support the artisans. The rest of the chapter is structured in sections. First, the methods and approach adopted for this study are discussed. This is followed by a presentation of the results and discussion, and then the conclusion.

RESEARCH METHODOLOGY This chapter evaluates the traditional methods used by the Prajapati community Terracotta potters and their survival under strict environmental guidelines of the government.

Objectives • To understand the present state of Prajapati Community artisans • To identify the challenges faced by Prajapati community artisans for sustaining their traditional art of work. • To model the identified challenges for a suggestive framework.

Data Collection and Sampling Design An exploratory qualitative research design was chosen for the study. Such study design helped researchers understand the artisan respondents and get appropriate responses from them in a systematic and focused manner (Chhabra, 2018b; Chhabra & Karmarkar, 2016). To ascertain the information reliability and concept contextualization, discussions with multiple artisans were carried out. For the qualitative study, a representative sample was chosen from the entire community. The respondents were interviewed with the help of a set of open-ended interview questions. The questions focused on personal information about the artisan, and then whatever information he/ she wanted to share about his/her entrepreneurial journey or his/her viewpoint presently. To approach and interview the respondents, help was provided by a senior academician who has actively been involved in working with and training the artisans for years. Interviews were taken in person, and responses were recorded on the semistructured questionnaire. In all, personal interviews were conducted with 23 artisans. The interviews were conducted in October 2019 over three days. One of the interview questions was related to the challenges perceived by artisans given recent regulatory restrictions and changing consumer demand. The artisans were asked to respond to the question by giving a comparative rating to seven different variables (challenges) in the order of their perceived priority. The seven

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identified challenges were then modeled with a qualitative tool – interpretive structural modeling (ISM), which is discussed further in this section.

ISM Methodology Interpretive structural modeling (ISM) is a step-by-step approach of modeling the variables into a meaningful relationship (Chander, Jain, & Shankar, 2013; Sage, 1977; Singh, Shankar, Narain, & Agarwal, 2003; Warfield, 1974). ISM is a wellestablished modeling technique adopted by researchers in studies related to different domains of entrepreneurship, a few examples of which are: analysis of barriers to women entrepreneurship in Indian MSMEs (Tripathi and Singh, 2018); interaction among the barriers to entrepreneurship (Raeesi et al., 2013); barriers to green entrepreneurship (Makki et al., 2020); and main barriers of Portuguese entrepreneurship ecosystem (Banha et al., 2017). Step 1: identification of variables. The variables under study, in this case, are the challenges which artisans need to face in the event of a ban on traditional furnaces and a changing market scenario. For identification of significant and most critical challenges, a three-step process was followed. The first step was to identify the challenges from previous studies through reviewing the related literature; secondly, the identified challenges were verified from artisans, and information was collected about any other relevant challenge through a questionnaire; finally, a focus group discussion was carried with the experts in the field for confirming the challenges. The experts included for this purpose were the four faculty members from different institutes, the head of the Prajapati community, and two active researchers. Consequently, seven significant challenges were identified for further modeling and are described in Table 11.1. Step 2: identification of contextual relationship amongst the variables. All the identified variables are related to each other in one way, so to identify the relationship between these variables, a structural self-interaction matrix (SSIM) was developed, as shown in Table 11.2. SSIM was framed using the four symbols proposed in ISM, to represent the direction of relationship amongst the variables, with the following rule (Chander, Jain, & Shankar, 2013; Singh, Shankar, Narain, & Agarwal, 2003): V = Variable i will lead to variable j; A = Variable j will lead to variable i; X = Variables i and j will lead to each other; and O = Variables i and j are unrelated Step 3: development of Reachability Matrix and Final Reachability Matrix after including transitivity. SSIM is transformed into a binary matrix by replacing each entry of V, A, X, O by 1and 0, by following the rule that if (i, j) entry in SSIM is V, X, then (i, j) entry in reachability matrix will be 1 and (j, i) will be 0; if (i, j) entry in SSIM is A, O, then (i, j) entry in reachability matrix will be 0 and (j, i) will be 1 (Chander et al., 2013; Singh et al., 2003). The final Reachability matrix is then framed after including transitivities in the initial reachability matrix, in the form of 1* and is shown in Table 11.3. The thumb

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TABLE 11.1 Literature support for the identified variables (challenges) S.No

Challenge

Literature Support

1. 2.

Increased costs Lack of technical expertise for the new equipment Uncertainty of raw material and equipment sourcing Product quality will be different Uncertain demand Seems non-sustainable in the long run Uncertain government policies

Bodke, 2016; Mohapatra et al., 2016 Gupta, 1988; McKitterick, Quinn, McAdam, & Dunn, 2016; Middleton, 2007; Panda et al., 2019 McKitterick et al., 2016; Middleton, 2007

3. 4. 5. 6. 7.

Echeta & Esege, 2014; Sarma, 2018; Wierenga, 2019 Bodke, 2016; Panda et al., 2019; Sankaran, 2018 Datta & Chan, 2016; Lewis, 2008; Panda et al., 2019; Toledo-López et al., 2012 Anjum, 2013; Barrutia, Aguado, & Echebarria, 2007; Echebarria, Barrutia, & Aguado, 2009; Jan, Marimuthu, Hassan, & Mehreen, 2019

TABLE 11.2 SSIM j

i

Variables 1 2 3 4 5 6 7

7 A O A O A A

6 V V V V V

5 0 O O V

4 O V V

3 A O

2 A

TABLE 11.3 Final Reachability Matrix (transitivity) Variables

1

2

3

4

5

6

7

Driving Power

1 2 3 4 5 6 7 Dependence

1 1 1 1 1 0 1 6

0 1 1 1* 1 0 1 5

0 0 1 1* 1 0 1 4

0 1 1 1 1* 0 1 5

0 1* 1* 1 1 0 1 5

1 1 1 1 1 1 1 7

0 0 0 0 0 0 1 1

2 5 6 6 6 1 7

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TABLE 11.4 Consolidated level of variables Variable 1 2 3 4 5 6 7

Reachability Set

Antecedent set

Intersection set

Level

1,6 1,2,4,5,6 1,2,3,4,5,6 1,2,3,4,5,6 1,2,3,4,5,6 6 1,2,3,4,5,6,7

1,2,3,4,5,7 2,3,4,5,7 3,4,5,7 2,3,4,5,7 2,3,4,5,7 1,2,3,4,5,6,7 7

1 2,4,5 3,4,5 2,3,4,5 2,3,4,5 6 7

II III IV III III I V

rule for transitivity is that if A = B and B = C, then it should be that A = C. So, wherever A is not equal to C, then it is done in the form of 1* (Singh et al., 2003). Step 4: level partitioning of the Reachability Matrix. Reachability (horizontal; the variable itself and those it will lead to) and antecedent (vertical; the variable itself and the variables which leads it) sets were obtained by partitioning from the final reachability matrix. The intersections were then carried out for each variable for its reachability and antecedent sets. Level 1 was given to those variables whose intersection set covers the reachability set entirely. Then, subsequent levels were given to further variables. As soon as any variable became level, it was excluded from the system (Singh et al., 2003). In the present study, it took a total of five iterations to get levels for each variable. The consolidated level partitions for all the variables are shown in Table 11.4.

RESULTS AND DISCUSSION Based on the variables’ dependence and driving power (Table 11.3) and the levels achieved by each variable (Table 11.4), MICMAC clustering of the variables and ISM model development are discussed in this section. Further, brief experiences of few pottery artisans are also a part of this section.

MICMAC Analysis The dependence and driving power of all the variables were identified from the final reachability matrix (Table 11.3), and accordingly, MICMAC was developed and shown in Figure 11.2. As per the MICMAC analysis, all the variables are categorized into four clusters, which are discussed here (Chander et al., 2013). Cluster I (Autonomous Variables). The variables unrelated to other variables, with low driving power and low dependence, are categorized as autonomous variables. In this study, no variable emerged as an autonomous variable. Cluster II (Dependent Variables). Variables in this cluster have high dependence and low driving power. These variables are very significant for the system. In this study, variable 1 (increased costs) and variable 6 (non-sustainability in the long run) emerged as dependent variables.

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7

Linkage

6

3

4,5

5

Independent

2

Variables Driving Power

Variables 4

Dependent Variables

3 Autonomous

2

1

Variables 1

6 1

2

3

4

5

6

7

Dependence

FIGURE 11.2  MICMAC.

Cluster III (Linkage Variables). The variables in this cluster are unstable, with high dependence and high driving power. The impact of any type of change to these variables is reflected on other variables too. In this study, the variables which emerged as linkage variables are variable 2 (lack of technical expertise for the new equipment), variable 4(product quality will be different), and variable 5 (uncertain demand). Cluster IV (Independent Variables). The variables in this cluster have very high driving power. They are the most strategic variables. In this study, variable 3(uncertainty of raw material and equipment sourcing); and variable 7(uncertain government policies) emerged as independent variables.

Formation of the ISM model (Diagraph) and Interpretations Based on the outcome of the ISM model, the diagraph was prepared, which modeled the factors based on their dependence and driving powers. This is shown in Figure 11.3. The ISM model showed that “uncertain government policies” (variable 7) is one of the most significant challenges for artisans. This challenge revealed the highest driving power as per the ISM model, and therefore it leads to most of the other challenges for the pottery artisans. From the interview responses of artisans, it was realized that the majority of artisans feel that sudden change in government policies and regulations affects their business decisions and models to a great extent. Stable government policies help the artisans to decide about their suppliers, customers, and product type. Accordingly, they can plan to update their technical skills as well. Similarly, “uncertainty of raw material and equipment sourcing” (variable 3) is also a challenge that has high driving power. Interviewing the artisans also revealed that they are facing this as a significant challenge to explore from where to buy the new proposed equipment (electric/gas furnace). Similarly, for carrying out production with the latest technology, there would be a change in a few of the raw materials as well, for which artisans were unaware and uncertain. They had a set of suppliers with long-term contracts. Still, many of those suppliers will not supply the new types of raw material and modern furnaces, which

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FIGURE 11.3  ISM model (diagraph).

may need changes because of changed regulations, for which artisans are not prepared. Further, “lack of technical expertise for the new equipment”(variable 2); “product quality will be different” (variable 4), and “uncertain demand” (variable 5) emerged as the linkage variables in the study. They support other dependent challenges to be met, provided driving forces support them. It was observed by the authors that artisans completely lack the knowledge and technical know-how for using modern-day furnaces for pottery production. They are much accustomed and experienced with the traditional methods and equipment, and this sudden change has left them disturbed and directionless. Lack of know-how is, therefore, leading to other challenges to a great extent. Similarly, artisans were worried about the effect of a government decision and changing consumer requirements on product quality and price, leading to uncertain demand patterns from the consumer side. Uncertainty in demand is the factor that creates a disturbance in their business to a great extent and leads to an increase in business costs as well. “Increased costs” (variable 1) has therefore emerged as one of the significant dependent variables, which reflects that if other underlying challenges can be corrected, then an increase in costs will be restricted. Notably, the increase in production costs is the challenge for artisans, which is emerging because of the higher costs of modern furnaces and variable costs like electricity charges and higher labor charges associated with running these furnaces. Artisans and their business models are not prepared for those increased production costs, which creates more uncertainties. Furthermore, the most substantial dependent variable which emerged from the study is “seems non-sustainable in the long run” (variable 6). Artisans state that because of change in the product production process, replacing traditional furnaces with electric/gas ones will impact the quality of the product. The

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type of product output may or may not be liked by consumers. Because of such uncertainties, artisans doubt the sustainability of such practices in the long run. Therefore, running the business with modern furnaces and as per new customer expectations can sustain for long, only if the underlying challenges mentioned in the study are adequately addressed.

Experiences of the Entrepreneurs As discussed in previous sections of this chapter, the sustainability of Prajapati community artisans and their art is facing several challenges: declining demand, rising competition, and, most significantly, the government’s concern over the generation of pollution from their traditional furnaces. As per the discussion of authors with artisans, they feel that shifting to any other fuel base for the kilns is costly, and it is challenging for most of the artisans to convert to gas-fired or electric furnaces. The experiences and opinions of a few of the respondent artisans(as shared with the authors through their interviews with these artisans) are reiterated as follows: Entrepreneur 1 Mr. Harkishan Prajapati is a 63-year-old male, with education up to class 10. Since childhood, he has been engaged in Terracotta pottery. Mr. Harkishan Prajapati is one of the most prominent National Award Winner Potter artisans and is Pradhan (Head) of the village from the Prajapati Community of Uttam Nagar. He has also received the title of Master Craftsman. He hails from Mandoti village of Jhajjar, Haryana. About his journey as a potter artisan, he shared that he wanted to study further. However, due to the financial condition of his family, he had to leave his studies and help the family in making Terracotta pottery. He came to the Prajapati colony in 1976 and worked for another potter for a few months. Within three months, he started his separate work and took a house on rent. Initially, he worked for 18 hours a day with his elder brother, wife, and niece and used to make 125 pots a day. To avoid disturbing his neighbors, he used to do work that produces noise in the daytime and soundless work at night. Even today, he gets the clay from his native place Jhajjar, Haryana. He is skilled in Terracotta pottery and glazed earthenware. He sells his products in craft exhibitions and craft fairs, or directly to consumers. He wishes that the new generations will continue the profession of pottery and that the culture will survive. His daughters have also been trained to do Terracotta pottery artwork with clay. When asked about the ban of traditional kilns, he said it would not be suitable for the community, as gas-fired furnaces are expensive and people cannot afford them. Due to the lack of other skills and lack of education, it is difficult for artisans to change their profession. He also mentioned that the material they used for firing, like wood, sawdust, and cow dung cake, is not as harmful as other pollution sources like diesel or petrol. His opinion on the ban of traditional kilns is that artisans do not have sufficient skills to change their ways, and the cost is high for gas-fired furnaces. According to him, all the artisans are ready to relocate if they are provided with land and proper infrastructure.

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Entrepreneur 2 Mr. Ram Prasad Prajapati is an 85-year-old male and is uneducated. He has been engaged in Terracotta pottery for over 65 years. Mr. Prasad Prajapati, who hails from Alwar, came to the Prajapati colony at Uttam Nagar 60 years back. He purchased land from the local Jaat community. Initially, he faced insufficient water, issues with clay transport, and losses due to natural calamities. Though he wants the next generation to continue the traditional pottery profession, he is doubtful about the financial prospects. He said, “it would be better for the new generation to study and try out other professions to survive”. Regarding the ban of traditional kilns, he believes that 70% of artisans in the village survive on this occupation, and the ban would therefore present a situation of crisis for them. He wishes that this traditional art would be preserved. As quoted by Mr. Prasad Prajapati, “Clay is needed in every stage of life from the moment he is born and also when he dies.” Entrepreneur 3 Mr. Gagan Prajapati is a 33-year-old male, with an education till class 10. He has been in the business of Terracotta pottery since childhood, as a part of the family occupation. Mr. Gagan Prajapati started the Terracotta pottery by helping his father in a small production unit. Being a dynamic potter, he changed his business plan a couple of years back and started buying ready-made craft from different parts of the country, which he sells as a wholesaler from his shop at Uttam Nagar. However, he said that the profit margin is not very high, and that at times payment is delayed and money gets stuck. As a wholesaler, he buys products from Kolkata, Rajasthan, Banaras, Agra, and Haryana. He believes that the craft will become extinct if the situation remains the same, and challenges for artisans are not addressed adequately. Therefore, to keep Indian craftsmanship in Terracotta alive, the government should support and provide artisans facilities. Entrepreneur 4 Mr. Naveen Prajapati is a 20-year-old male. He is pursuing a bachelor’s degree and has five years’ experience in the Terracotta pottery business. Mr. Naveen Prajapati is a third-generation potter. He is born and brought up in Uttam Nagar, Delhi. He helps his father run the pottery production unit and is also studying to find new professions. Though he would love to continue his old pottery profession, he would also like to consider a more economically viable profession. He is doing a course in spoken English and computers to assimilate with modern urban society. This would also help to reach more people and extend his family business.

CONCLUSION The Prajapati community has retained its rich culture of pottery making over many generations. Due to the changing business landscape and customer expectations, the pottery craft is losing its shine. Therefore, the next generation of the Prajapati community is finding it difficult to retain this rich culture and tradition. Artisans are facing uncertainty as the government policies and the subsequent impact on

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availability of raw materials pose a big challenge. Artisans, therefore, seek government support to provide them with financial aid for a new pottery startup with gas-fired/ electric kilns, as the cost of new equipment is too high for them. With growing concern for the misbalance in the ecosystem, there is a strong need to understand and preserve the Terracotta craft. There are Terracotta refrigerators, Terracotta coolers, and many other lifestyle products to achieve conservation of energy and sustainability. Artisans are concerned about uncertainty in demand for the type of products developed through advanced kilns. Moreover, there is a lack of confidence amongst the artisans in the technical expertise required for new pottery production methods. The community expects the government to safeguard their skill sets and their profession. Efforts need to be made by all the stakeholders to revive the Terracotta pottery craft and provide sustainable livelihood to the artisans.

ACKNOWLEDGMENT The authors would like to thank Mr. Harkishan Prajapati, Head of the Kumhar Gram (Potters Village) in Uttam Nagar, Delhi, and other potters for sharing their knowledge and experience in the practice of traditional Terracotta pottery. The authors also thank Ms. Ela Mukherjee, a ceramic artist, for her help in approaching the respondents and sharing her knowledge and experience in Terracotta pottery.

NOTES 1 . https://www.whatsuplife.in/delhi/blog/kumhar-colony-potters-village-uttam-nagar-delhi/ 2. https://www.newslaundry.com/2019/08/05/potters-delhi-pollution-national-greentribunal; https://scroll.in/article/911098/pollution-check-potters-in-delhi-scramble-for-solutionsafter-court-order-shuts-wood-fired-kilns

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Social Sustainability Through Women Entrepreneurs in India A Case of Inclusion and Development Through Small Organizations Amrinder Kaur Pink Guava Consulting Services, India

CONTENTS Introduction ����������������������������������������������������������������������������������������������������������� 167 Literature Review�������������������������������������������������������������������������������������������������� 169 Women Entrepreneurship in India������������������������������������������������������������������� 169 Women Entrepreneurs�������������������������������������������������������������������������������������� 170 Social Sustainability and GRI�������������������������������������������������������������������������� 171 Research Methodology����������������������������������������������������������������������������������������� 172 Discussion ������������������������������������������������������������������������������������������������������������� 178 Conclusion������������������������������������������������������������������������������������������������������������ 179 Limitations of Research ���������������������������������������������������������������������������������������� 180 Acknowledgment�������������������������������������������������������������������������������������������������� 180 References������������������������������������������������������������������������������������������������������������� 180

INTRODUCTION Social sustainability is an essential part of sustainable entrepreneurship and is built on the triple bottom line concept of people, profit, and plant (Elkington, 1999; Muñoz and Cohen, 2017; WCED, 1987). Social sustainability is a development that includes the communities, people resources through practices, decisions leading to greater economic success and better utilization of resources (Ambepitiya, 2016; Kaur and Sharma, 2017; WCED, 1987). Thus, decisions and practices for social sustainability take place through responsible usage of resources, community involvement, employ­ ment generation, care, and other goals for the prosperity and sustenance of people and planet while gaining profits (Muñoz and Cohen, 2017; Orobia, Tusiime Mwesigwa, DOI: 10.1201/9781003097945-12

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Ssekiziyivu, 2020). Small organizations are essential stakeholders to achieve social sustainability goals, as they utilize local resources and people to create value for themselves and others involved (Kaur and Sharma, 2016). Small organizations support economies worldwide, as they not only provide an opportunity for employment and finances but also hedge on for innovation, competitiveness, and agility to support the supply chain and larger firms (Akehurst, Simarro, and Mas-Tur, 2012; Anderson, Li, Harrison and Robson, 2003; Audrestch, 2001; Kaur and Sharma, 2016; Muske et al., 2007). Small organizations run by women play an essential intrinsic role in attaining social sustainability goals of inclusive development while taking care of resources (Chhabra, 2018a; Yadav and Unni, 2016). Women entrepreneurs are becoming essential and are garnering much interest from an economic and research perspective, along with social sustainability needs (Chhabra et al., 2020; Lepeley, Pizarro and Mandakovic, 2015; Yadav and Unni, 2016). Women present half of the working population and thus are a significant part of supporting development (Chhabra and Karmarkar, 2016b), enhancing the economy, and contributing to GDP (Gundry, Miriam, and Posig, 2002). Women entrepreneurs contribute by creating better living standards and are also a source of contributions to society in terms of education (Chhabra and Goyal, 2019), gender diversity, greater living standards, and just an equitable world where opportunities do not distinguish and thus hold (Ambepitiya, 2016; Yadav and Unni, 2016). Women entrepreneurship started gaining greater visibility and interest across the globe with globalization and enhanced literacy rates. Enterprises/businesses set by women entrepreneurs are due to multiple motivations varying from necessity at one end to ambition at the other, with a potential to contribute to uplifting the whole society, including communities and their families (Brush and Cooper, 2012; De Vita, Mari, and Poggesi, 2014; Kickul, Gundry, and Sampson, 2007). Women entrepreneurs in India are an encouraging story and have endless potential and heights to achieve (Chhabra and Karmarkar, 2016a). The work is still in progress if compared to their global counterparts. With changing times and a startup ecosystem in India, more and more women are donning the entrepreneurial hat (Chhabra, 2018b). Moreover, despite the support prevalent, women entrepreneurs in India continue to work at a smaller scale. Nevertheless, there are inspiring stories, and more support is prevalent for other women entrepreneurs (Ambepitiya, 2016; Ashwini, 2020). Women entrepreneurship has another significant outcome in terms of social sustainability, which involves equitable utilization of resources (WCED, 1987) and enhancing quality of life (Karmarkar et al., 2014). Social sustainability needs economic prosperity. Moreover, for this, women entrepreneurs need access to capital, talent, and resources, including advisory, to grow and establish their ventures (Brush and Cooper, 2012; Kickul, Gundry, and Sampson, 2007). Thus, their success depends on how well they manage and leverage the support groups, communities in which they operate to create a sustainable venture that adds value to the founders (Kumar and Chhabra, 2021) and the other stakeholders, including customers, while also utilizing the resources and talent available. This chapter thus attempts the following: • To understand how sustainable small-scale enterprises founded and driven by women entrepreneurs are.

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• To understand how women entrepreneurs are contributing to society. • To understand the reason women entrepreneurs choose entrepreneurship. Women entrepreneurs are significant as women are half of the population and thus are the much-needed drivers for social sustainability to support the economy and growth for a prosperous, poverty-less world where local communities are involved and skilled in their work. It is a development where customers, employees, and vendors are a focus and product responsible for equitable resources distribution (Sreekumar et al., 2018). The journey has started, and the chapter provides evidence through qualitative research involving 10 women entrepreneurs in India. Looking ahead in the manuscript, the literature review is next, followed by research methodology and discussion. The conclusion is then discussed, with the Limitation of Research in the final section to support further research and researchers.

LITERATURE REVIEW Women Entrepreneurship in India A country, its socio-cultural way, and thus the ecosystem present play a significant part in shaping women entrepreneurs’ entrepreneurial pursuits. Researchers also highlight that certain specific characteristics are evident in societies that influence women to flourish and choose entrepreneurship, such as religious inclination in certain parts of Asia or support groups and networking in Eastern Europe (De Vita, Mari, and Poggesi, 2014). India has been a traditional society with some orthodox mindset, where girls ear­ lier were not inspired to take risks and pursue entrepreneurial ambitions. However, as is evident worldwide, women are coming out of their comfort zones with more access to education and better living standards. In India, too, women have started setting up their enterprises (Ashwini, 2020; Das, 2000; Prasad et al., 2013; Sharma, 2013). Growth and success for an enterprise are dependent on both human and social factors, including the entrepreneur or women entrepreneur’s own experience, skills, capabilities, and support from social aspects to firm up her success. In India, women entrepreneurs, along with their own motivation and intention, are also limited by social and ecosystem support to take risks and inspire a larger scale. They usually limit themselves on a smaller scale to comply and not affect their family and social order. Researchers (Gundry, Kickul, and Iakovleva, 2014; Prasad et al., 2013; Sehgal and Khandelwal, 2020) also enlist through their research about the social context and preference for the family in Indian women entrepreneurs. Accordingly, it is an essential factor for women’s entrepreneurial growth in countries like India. Women entrepreneurs in India prefer to grow up to a point and then prefer to work within their priorities (De Vita, Mari, and Poggesi, 2014). Smaller organizations thus are a norm. Ashwini (2020) reports that women entrepreneurs in India usually start early and are in the 20–30 age bracket, with the majority being solopreneurs (more than 50%). Education also has a significant role for women entrepreneurs in India as it deter­ mines their aspirations and chances to connect to critical resources; however, it is not the limiting factor. Women entrepreneurs in the family business and having a prior corporate experience significantly affect business venture success and growth.

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Women Entrepreneurs The motivation for women entrepreneurs to venture into entrepreneurship usually range across numerous factors, including greater autonomy, independence, flexibility, and job satisfaction, just like their male counterparts (Akehurst, Simarro, and MasTur, 2012; Brush, 1992; Das, 2000; Verheul, Stel, and Thurik, 2006). However, the instance of women entrepreneurs embracing entrepreneurship in terms of overall percentage is lower than their male counterparts. This trend is the same whether in a developing country or the developed world (Prasad et al., 2013; Sharma, 2013). Women venture into entrepreneurship due to aspirations for utilizing opportunities or out of necessity (Jennings and Brush, 2013) including childcare and eldercare responsibilities. An entrepreneur who embraces entrepreneurship irrespective of gender brings their individual characteristics, including experience, formal educa­ tion, aspirations, and other trait characteristics and resources. The further trajectory and charter of the enterprises differ between the two genders, with women limiting themselves to smaller scales than the male counterparts (Brush et al., 2010) due to several factors. Compared with male counterparts, women entrepreneurs do not have the same access to resources, including financial, human, and network, including family. Researchers also highlight that the likelihood of women embracing entrepreneurship due to human capital, including knowledge, competency, and skills to produce goods/ services, is higher when compared to their male counterparts. In contrast, financial capital as a factor does not have much difference for both genders. Access to a net­ work, including family, increased the chances of women embracing entrepreneurship in larger families, further necessitating the availability of needed support, including child care (Brush and Cooper, 2012) (see Table 12.1). Compared to male counterparts, women can have certain disadvantages concern­ ing experience in handling and managing organizations. This affects their chances of scaling up their ventures successfully. Women entrepreneurs also prefer to be in smaller setups or work at a smaller scale to create a better work-life balance for themselves (Fischer, Reuber, and Dyke, 1993; Kickul, Gundry, and Sampson 2007). Researchers also highlight that representation of women in varied roles across different corporate organizations, particularly in leadership roles, is skewed towards men. Various reasons contribute to it, including the societal perception of women being communal and caregivers and men being decisive, dominating, and aggressive, which is needed to excel in leadership positions (Narayan, 2018). It affects women’s escalation across rank and contributes to a lower representation of women. It also considers that women leaders rise to leadership roles with comparative difficulty and deal with biased perceptions. It usually takes more effort in breaking the image of giving success and ROI in the hierarchy for the women to rise (Castrilion, 2019). Research also provides evidence that there is a correlation between the successes of organizations with women in leadership roles, as their behavior traits as nurturers with a focus on community and the team can lead to sustainable organizational success, and this is valid for entrepreneurship as well (Yadav and Unni, 2016). Women venturing into entrepreneurship many times is due to the necessities cre­ ated by the corporate world wherein breaking stereotypes becomes difficult. Women

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TABLE 12.1 Some factors that support women entrepreneurship as per the literature Factors that support women entrepreneurship Access to capital, mentoring, and advisory support Access to resources, including right vendors and supplier Advisory to support while making the crucial decisions Access to customers and building references Role models to learn and emulate Risk-taking ability Networking and support from informal systems Responsibilities of families and children More experience and confidence Growth aspiration and technology orientation

Reference Brush et al., (2010); Brush and Cooper, 2012; Orobia, Tusiime, Mwesigwa, and Ssekiziyivu (2020); Sharma (2013); Brush and Cooper, 2012; Jamali (2009); Prasad et al. (2013); Fischer, Reuber, and Dyke (1993); Orobia, Tusiime, Mwesigwa, and Ssekiziyivu (2020); Prasad et al. (2013); Brush and Cooper, 2012; Holienka, Jančovičová, and Kovačičová (2016). Prasad et al. (2013) Jennings and Brush (2013) Farny (2016); Prasad et al. (2013) Brush and Cooper, 2012; Holienka, Jančovičová, and Kovačičová (2016). Kickul, Gundry, and Sampson (2007) Prasad et al. (2013) Jamali (2009); Prasad et al. (2013) Brush et al. (2010)

explore entrepreneurship either with the necessity to earn a living, or the desire to prove themselves, or to create a better work-life balance, or to bring in a change for a purpose. Moreover, the main difference between men and women entrepreneurship to create more scaled-up and successful enterprises are dependent on growth aspira­ tions. Researchers also highlight that along with aspirations, experience, technologi­ cal orientation, network, and an entrepreneur’s attitude is the charter for a successful way to create a broader, more sustainable organization. Bringing support through network, incubation, training, and apprenticeship can make a great deal of difference with women entrepreneurship (Brush et al., 2010; Prasad et al., 2013; Yadav and Unni, 2016; Castrilion, 2019).

Social Sustainability and GRI The Brundtland Commission Report (WCED, 1987) emphasized that sustainable development is the economic prosperity envisaged through environmental stewardship and social inclusion. Thus, the resources can be saved for future generations to focus on the right practices and decisions (Ambepitiya, 2016). Social sustainability is an equitable distribution of resources, including opportuni­ ties, income, and development of skills for the communities involved, that is fair and just for everyone to flourish and prosper (Vallance, Perkins, and Dixon, 2011). The involvement of communities, stakeholders, and customers is a prerequisite for social sustainability, and women are an intrinsic part of it. Ambepitiya (2016) further explains that women predominantly give back their earnings, learn to support their

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families for better education and health facilities, and, thus, support social sustainability for a more equitable future. Women can have ideas and innovation to support and solve immediate problems that communities are facing. Usually, it gets limited, particularly in the developing world, as the ideas due to lack of support do not go up through the community to the policy levels (Ramaniac et al., 2013). Women entrepreneurs predominantly are creating opportunities through formal and informal sectors (Kickul, Gundry, and Sampson, 2007). Women entrepreneurs develop networks through their employees and vendors, wherein they share knowledge, information, and vision, which also support them further to enhance their network (Brush et al., 2010). Researchers also highlight that an organization gets its competitive advantage through its social resources that include people, customers, and partners with a unique propositioning that determines how effectively it is collaborating with its customer, employees, and people (Kaur and Bhardwaj, 2019; Prasad et al., 2013). Stakeholder engagement is an important proposition for the long-term survival and growth of any organization. Elkington (1999) further elaborates this as a triple bottom line including people, profit, and the planet, which is further one of the guiding principles for the global organization GRI (2019). GRI (2019) is a global practice for reporting sustainability initiatives by organizations. The framework comprises economic, environmental, and social impacts by organizations and their activities. GRI is a collaboration between Environmentally Responsible Economies (CERES, 2014) and the United Nations Environment Program (UNEP) and focuses on sustainable development and reporting of impacts by organizations to further improve and reflect upon. One of the “Sustainable Development Goals”, SDG-5, collaborates with gender equality and empowerment of women to achieve the cumulative 17 “Sustainable Development Goals”, and involving women is a way to support the initiatives for further holistic, sustainable development (SDG 5: Achieve gender equality and empower all women and girls, 2017). Women entrepreneurs are uniquely placed explicitly, as they are also caregivers and nurturers to support and include communities. This achieves sustainable development and its goals both in developing and developing economies, including economic, environmental, and social aspects (Ambepitiya, 2016; Castrilion, 2019).

RESEARCH METHODOLOGY Qualitative research has been used to understand social sustainability through smallscale enterprises founded and driven by women entrepreneurs. Qualitative research is used in social sciences to understand different phenomena, mainly to understand the behaviors, perceptions, and experiences in their natural setting without necessarily putting rigid boundaries for research (Kaur and Bhardwaj, 2019; Thomas and Magilvy, 2011). Researchers emphasize that qualitative research is an effective method, mainly when the data is unstructured. Thus, research needs an inquiry process to understand emotions, behaviors, and intentions. Qualitative research is also appropriate in cases with a small number of samples for more significant inquiry or depth. It involves focus groups, as well as in-person interviews using semi-structured or open-ended

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questionnaires. The intent is to understand the phenomena compared to quantitative research, which works to understand the relationship between variables. Qualitative research can help identify the reasons for a relationship and how it affects the entire phenomenon. Hence, it is a useful technique to support researchers in descriptive research for building theories and understanding the experiences, behavior, and intentions in social sciences (Bricki and Green, 2009; Crossman, 2020; Thomas and Magilvy, 2011). The research involves understanding social sustainability on how women entre­ preneurs in India are working for it while moving through choppier waters of entre­ preneurship to create value for themselves and society at large. Qualitative research involves in-depth and in-person interviews of women entrepreneurs using a semistructured questionnaire. The semi-structured questionnaire involved social factors from GRI (2019). The research intends to understand and provide evidence for social sustainability through women entrepreneurs in India for a more equitable and inclu­ sive society. The social sustainability factors as envisaged by GRI (2019) are further summarized in Table 12.2. The semi-structured questionnaire was also tested to capture data as per the objec­ tives through expert opinion from two academicians (professor) and one corporate professional (director). The research sample size involves ten small organizations run by women entre­ preneurs, wherein the women have a greater than 50% stake and are involved in numerous businesses from manufacturing to food. Ten is considered an adequate sample size for qualitative research with a homogenous group (Boddy, 2016). The women entrepreneurs were chosen after a detailed search in social media by under­ standing their work and enterprises. They were connected for the research through a

TABLE 12.2 Social sustainability factors as per GRI (2019) Social-fair labor practices

Social-human right practices

Employment in the supply chain Labor/management relations in the supply chain Occupational health and safety in the supply chain Training and education in the supply chain Diversity and equal opportunity in the supply chain

Investment in the supply chain Non-discrimination in the supply chain Freedom of association and collective bargaining in the supply chain Child labor in the supply chain Forced/ compulsory labor in the supply chain. Security practices in the supply chain.

Social-Society Local communities Anti-corruption practices Public policy Anti-competitive behavior Compliance of laws Supplier assessment for impacts on society and grievance mechanisms for impacts on society

Social-product responsibility For product responsibility related to customer’s health and safety Product and service labeling Marketing communication and customer privacy

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social media engagement channel, LinkedIn, and were interviewed through video chatting options, including Zoom and Skype. Around 25 women entrepreneurs were contacted for the research, of whom 23 responded, and 14 had been chosen for the interview as they had greater than a 50% stake in their ventures. Ten in-person interviews with women entrepreneurs were conducted in 2020, starting from March and culminating by June. The women entre­ preneurs were primarily based in India. Six were based in Delhi/NCR, two in Mumbai, and two in Bangalore, respectively, in India. Before the interview, a brief about the research was shared with them. The inter­ view with a semi-structured questionnaire involving GRI social sustainability factors also worked to understand the women’s journey to becoming an entrepreneur. This includes the challenges that women entrepreneurs faced to adopt social sustainability and an understanding of what keeps driving their work and passion. The average interview lasted for an hour. The author took a written transcript of each interview in the semi-structured ques­ tionnaire during the interview through video chatting options, including Zoom and Skype. For the credibility of qualitative research and to reduce the bias through inter­ views, data captured was shared again with the women entrepreneurs to make sure the discussions had been interpreted correctly. Any mismatch was rectified and con­ sequently verified again from the respective woman entrepreneur. The process of written transcript and consequent sharing was done with 10 women entrepreneurs (Tables 12.3 and 12.4).

TABLE 12.3 Demographic profiling of the ten women entrepreneurs Industry Manufacturing Food Digital Consulting Travel HR Consulting Healthcare

No. of Responses 3 2 2 1 1 1

TABLE 12.4 Company profiling of the ten women entrepreneurs (as per the turnover) Turnover annually (in INR) Up to 5 Lakhs Up to 20 Lakhs Up to 40 Lakhs

Women Entrepreneurs 3 4 3

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The sample had five women who were the sole proprietor of their firm, and the remaining five had been in private limited and limited liability companies with a stake (ownership) of more than 50%. Three of the women only had mentors, advisors in their companies to support their business growth and success, and some relied on their family to support their ambitions. On average, the women entrepreneurs had around 20 stakeholders working with them that included employees and vendors. Most of the women had women represen­ tation in stakeholders ranging from an average of 40% up to 60% being women and the rest being men. Around 60% of women preferred working with women as they believe it is easier for them to understand and work further with women. Further, all the women entrepreneurs had a decent level of education (Table 12.5). The ten women entrepreneurs have an average work experience of 12 years and had multiple tenures of being involved in corporate, NGO, and academic work. Four of the women entrepreneurs had an opportunistic ambition of starting their enter­ prises, while the remaining six chose to become entrepreneurs due to need-based and personal reasons, including better work-life balance to balance out other priorities in life. The responses are summarized in Table 12.6.

TABLE 12.5 Education profiling of the ten women entrepreneurs Education

Women Entrepreneurs

Graduate Post Graduate

2 8

TABLE 12.6 Data collected from ten women entrepreneurs Variable What is social sustainability for you?

Summary of Responses - It is the belief created in the company and the culture to create an inclusive way of working. Thus it means practices to include vendors, suppliers, and above all, the customers. - Not validating anybody’s qualification and supporting through skill development and feedback, especially when working with labor. - Encouraging stakeholders to become more honest and love their work. - Supporting to create leaders and imparting training to support. - Fair compensation for all irrespective of the work they do. - Not choosing a particular gender and having a fair process to assess for opportunities. - Inclusivity also means grassroots-level changes. - Simplifying the complicated jargon for supporting skill development. (Continued)

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TABLE 12.6  (Continued ) Variable

Summary of Responses

What do you think are your challenges with respect to social sustainability?

- Quality of service. - Different priorities and different needs of each team member. It is challenging to create cohesiveness for inclusivity. - Mindset and priorities are not aligned and are indeed a challenge while managing different stakeholders, customers, and employees. - Not much clarity about social sustainability and steps/policies measures to be taken at a small organization level. - The cost of sustainability increases the overall cost of business and can be difficult to manage at times. - Lack of awareness at the grassroots level and among different stakeholders. Priority is always for economic sustainability. - For a small organization building, self-sufficiency can be a challenge. And bringing a change can be tough. - Creating and building a strong supply chain with social sustainability embedded in it can be a challenge. - Need for more investment to tackle the challenges specifically concerning scaling up.

Social fair labor practices including - Employment in the supply chain. - Labor and management relations. - Occupational health and safety training and education. - Diversity and equal opportunity.

Employment - Team-appropriate decision involving all and relevant stakeholders. - Supporting individual goals of different people on the team. - A leader entails creating an inclusive culture with policy support. - Measuring and monitoring progress in improving and supporting development as and when needed. Labor and Management Relations - Culture of inclusiveness through coaching, motivation, and encouragement. - Being available as a founder and leader to lead by example and support stakeholders. - Regular engagement to not let complacency set in. - Eight women entrepreneurs agreed that they do not have written policy or SOP but have a common knowledge with which they work to be socially sustainable. The founder and the leading team are the torchbearers of social sustainability in the organization. - Only three women entrepreneurs emphasized minimal SOPs to support workflow and culture, but majorly with all the women entrepreneurs, there is no policy to support the decision-making. Occupational Health and Safety - A healthy and engaging work atmosphere with suitable training to up-skill through in-house and online learning avenues. - Working with empathy to understand, learn, and engage. - Women entrepreneurs emphasized their focus on women (nine) friendly and disable friendly (four) policies to create structures, processes for an inclusive workplace. Training and education, Diversity and equal opportunity. - Policy framework for fair compensation, incentives that include a focus on gender diversity and respect for all. - Equal opportunity to all the stakeholders that includes customers, employees, and vendors. - Training, education, and working to create a basic SOP level at all levels of engagement for greater clarity. (Continued)

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TABLE 12.6  (Continued ) Variable Social-human rights practices Investment Nondiscrimination Freedom of association and collective bargaining Child labor in the supply chain, forced/ compulsory labor Security practices

Summary of Responses Investment - Almost all the women entrepreneurs accepted that investment in their enterprises is their own. And raising investment solely for women enterprises is tough. - Only three women entrepreneurs had wanted to raise capital for their plans. Non-discrimination - Nurturing youth, women, and creating opportunities based on merit has been the focus of all women entrepreneurs. Freedom of association and collective bargaining - Collaboration and synergies are explored for more meaningful work. - Building a network for oneself and to support others with greater synergies. - Network and mentors support to understand when and how to utilize strategy or utilize someone’s skill to enhance value addition Child labor in the supply chain, forced/compulsory labor - Child labor/forced labor is something all women entrepreneurs felt strongly against. - Two of the women entrepreneurs also shared their initiatives wherein they created child care centers in their offices/factories to support women working there. Security practices - Security practices are through fairness and creating a culture wherein feedback and complaints are taken seriously by the founder entrepreneurs. - They have clauses and complete awareness for women’s safety, including escalation for misconduct and a grievance mechanism.

Social-Society Society related to local communities Anti-corruption practices Public policy, Anti-competitive behavior, and compliance of laws Supplier assessment for impacts on society, Grievance mechanisms for impacts on society

Society related to local communities - Women entrepreneurs in the sample had been working in a small business set-up and, hence, involved local communities like employees and vendors. Supplier assessment for impacts on society - Due diligence is for supplier assessments as per the product/service need. Six of the women entrepreneurs agreed on this, and the rest had no structured process for supplier assessment. - Equal opportunity irrespective of gender is worked on with vendors and suppliers. Eight of the women entrepreneurs agreed on this and stressed long-term relationships with their vendors with due engagement strategy. Anti-competitive behavior, and compliance of laws, - All women entrepreneurs agreed that they work as per the law of the land and follow healthier practices by focusing on their customers to manage the competition. Compliance with law, including financial, is very important for them. - Two of the women entrepreneurs emphasized that they do not bribe as a matter of policy or principle. At times, it is difficult for them, but this is how they work. Grievance mechanisms for impacts on society - The grievance mechanism followed is customer complaints about internal and external customers. - A systemized feedback process is utilized by only two women entrepreneurs. Others did not have much information or process and usually followed a reactive way to handle grievances. (Continued)

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TABLE 12.6  (Continued ) Variable Social-product responsibility For product responsibility related to customer’s health and safety Product and service labeling Marketing communication Customer privacy

Summary of Responses For product responsibility related to customer’s health and safety - Social product responsibility for two of the women entrepreneurs means trying to include people with different abilities like handicapped, blind, etc. Creating products with specific designs to increase customer trust as per need. The rest did not have many thoughts on product responsibility, but its quality and services were essential to all of them. Product and service labeling - Product and service labeling includes maintaining the quality of products and customer experience. Six of the women entrepreneurs emphasized this. Marketing communication - Marketing communication works to bring transparency to let internal/external customers know about the product/service and what to expect from the company at different times. Six of the women entrepreneurs were aware and did emphasize the same in their organizations. Customer Privacy - Only two of the women entrepreneurs emphasized the importance of GDPR and its importance, including not collecting data that is not required. - Confidentially and maintaining client information is vital to all women entrepreneurs. So no customer information is shared. NDAs are signed in consulting-based work.

DISCUSSION Social sustainability for women entrepreneurs in India is a valuable addition to stakeholders, including employees, vendors, and customers. Equal opportunity, fair labor practices, customer privacy, and product responsibility are essential for most respondents and are taken care of through decisions and various practices. Women entrepreneurs in the research are working through small organizations, and they guide their organizations through their leadership qualities, sense of respon­ sible behavior with prior experience, and education. The majority of women in the study (6 out of 10) chose entrepreneurship due to need-based and personal reasons, including better work-life balance to balance other life priorities. So small organiza­ tions are a norm, and also their needs; intention limits their ambition to further scale up. Four of the women entrepreneurs had an opportunistic ambition for setting up their enterprises. Most women entrepreneurs emphasize quality and enhance their employees’ skills and focus on training to up-skill through in-house and online learning avenues. Women entrepreneurs emphasized their focus on women (9 of 10) friendly and disable friendly (4 of 10) policies to create structures and processes for an inclusive workplace. Most women entrepreneurs did not have any concrete SOP, and lack of concrete policies hinders the clarity they seek to progress and create more avenues for growth. Also, Women have to deal with prevalent prejudices with stakeholders and society. Many of the women entrepreneurs (7 of 10) do not have mentors and advisory to firm up their growth and sustenance plans.

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Personal and prior experience, network, and exposure do affect women entrepre­ neurs’ decisions and hence practices. For instance, Ms. Romita Ghosh of Medsamaan had been a survivor of a terminal disease, which led her to start up and contribute to the community through her organization. Through an AI (artificial intelligence) plat­ form, her organization enables access to innovative, cost-effective products to health­ care providers. Her policies and social sustainability in her organization are through her own experience of access to decent education and the support she received from others. In her words: We work to make all our employees leaders and give the training to support. For instance – one person is deaf. He is having just 20% hearing ability and uses gestures to communicate. His work profile is in technical things, and we are always helping him, so he gets better in his work. He has been given work as per his skills, and he is learning. And it is satisfying to me.

Women entrepreneurs are giving jobs to the people, thus involving the local com­ munity, marginalized people, and supporting them by enhancing their skills. Women entrepreneurs focus on quality and customer interest to make sure that their ventures are adding value to their stakeholders. The research also elaborates that lesser women are aware of GDPR basis compliance. However, they value their customer interest and privacy and ensure the same through the organizations they are working with. Marketing communication is also an arena where the majority of the women entre­ preneurs (6 of 10) had been focused. They use it to bring transparency to let internal/ external customers know about the product/service and what to expect from the com­ pany at different times. It is a promising avenue to assure engagement with stake­ holders needed for growth. Women entrepreneurs also asserted that the challenges they face in their organiza­ tions for more growth and success are a lot to people’s mindset; often, the customers, vendors, and other stakeholders find it difficult to accept the change the women entrepreneurs aspire for. So building a self-sufficient organization and efficient sup­ ply chain is a challenge for them. Women entrepreneurs also asserted for lack of clarity on objectives, policies, and the social cost of business to enhance social sus­ tainability. They acknowledge that there is a lack of awareness at the grassroots level. Furthermore, amidst different stakeholders, priority is always for economic sustain­ ability. Processes and support through mentors, advisors including financial support too are a challenge for women entrepreneurs.

CONCLUSION Women entrepreneurs support the social fabric of a country and contribute to the economy and GDP through additional opportunities and livelihood. Women entre­ preneurs support social sustainability through initiatives that give equal opportunities based on merit, thus involving the local communities for a better tomorrow. In India, women work with dual responsibilities of managing their homes along with work. Being the caretakers and nurturers, they have the unique ability to

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empathize with people and, through their work, transform their own lives and many others, including their employees, customers, and all other stakeholders. Women entrepreneurs with good education, access to networks, family support, and peer groups are creating a difference through their enterprises. Many women have given up their corporate life to follow their passion. Others have taken up entre­ preneurship for a better work-life balance, including catering to the needs of children and elderly healthcare. The women entrepreneurs are torchbearers of social sustain­ ability in a small-scale setup where they also guide their ship out of entrepreneur­ ship’s choppier waters. Their enterprises are creating opportunities for them and many others. Skill development and creating an unbiased workplace is an outcome of women entrepreneurship. Women entrepreneurs are giving employment to the local communities following greater product responsibility practices, customer intimacy with fair business practices involving local communities. Women entrepreneurs’ endeavors are working for improved skills and living stan­ dards of the various stakeholders. However, they still have a long way to go in terms of profit, people, and processes, especially for small businesses in India. Through their work and in their organizations, these women entrepreneurs are creating cul­ ture, but it is usually founder-driven. Most of these smaller enterprises lack welldefined processes to create scale and growth for more opportunities for people. Nevertheless, this small niche of woman entrepreneurs in businesses creates socially sustainable development through their focus on greater gender diversity, fair labor practices, human rights, and product responsibilities, leading to inclusive and respon­ sible development.

LIMITATIONS OF RESEARCH The sample size limits this research. It is qualitative research to provide evidence for social sustainability through woman entrepreneurs in India. It can be validated through a more comprehensive follow-up quantitative research that also considers the 360-degree point of view of all the stakeholders along with a larger sample.

ACKNOWLEDGMENT I am thankful to all the respondents in the research who shared their practices, deci­ sions, and journey with me. I am especially indebted to Ms. Romita Ghosh of Medsamaan for her kind gesture to let me quote her.

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Leveraging on Demographics Insights of Online Shoppers for Netpreneurs Deepak Halan Apeejay Stya University, India

CONTENTS Introduction������������������������������������������������������������������������������������������������������������ 185 Literature Review��������������������������������������������������������������������������������������������������� 187 Method������������������������������������������������������������������������������������������������������������������� 188 Data Collection and Analysis���������������������������������������������������������������������������� 188 Findings and Discussion���������������������������������������������������������������������������������������� 188 Respondent Profile�������������������������������������������������������������������������������������������� 188 Nonparametric Test Analysis Results���������������������������������������������������������������� 189 Gender-Wise Significant Differences��������������������������������������������������������������������� 189 Age-wise Significant Differences�������������������������������������������������������������������������� 190 Recommendations and Managerial Implications��������������������������������������������������� 192 Ease of Transaction Related������������������������������������������������������������������������������ 192 Trust Related����������������������������������������������������������������������������������������������������� 192 Subjective Norms Related��������������������������������������������������������������������������������� 193 References�������������������������������������������������������������������������������������������������������������� 193

INTRODUCTION This study aims to provide focused insights to enable netpreneurs to understand their current and potential customers demographically to retain and attract them, respectively. In 2018, sales volume of e-commerce entrepreneurs, or “netpreneurs”, touched 2.93 trillion dollars, accounting for 12.2% of the global retail sales volume (Lipsman, 2019). Online retailing has been growing rapidly across the globe. Further, COVID-19 increased online shopping usage globally, given the need for social distancing. It also led to a considerable rise in the number of first-time users, i.e., those who had never ever shopped online before due to certain inhibiting factors, complete ignorance about online shopping, or lack of a device or data plan. In India, most Indians continue to have more faith in the neighborhood brick-and-mortar stores for shopping.

DOI: 10.1201/9781003097945-13

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They prefer touching and feeling the products and negotiating discounts over the counter before buying (Halan, 2020; Sreekumar et al., 2018). With fierce competition in today’s B2C e-commerce market, an increasing number of netpreneurs face operating profitability issues. E-tailers have resorted to “deep discounting” and have been subject to various governments’ investigations to find out whether their pricing is predatory. For example, the Indian government has notified new rules for e-commerce companies in July 2020 wherein e-tailers are not allowed to “manipulate the price” of the goods and services offered on their platforms to gain unreasonable profit. Moreover, many Indian shoppers known to be cost-conscious and conservative as a part of their value system are generally not attracted to making quick decisions based on promotions and advertisements (Halan, 2020). Therefore e-tailers are now working harder on improving customer experience to achieve higher loyalty levels. They depend less on discounts and trying to enhance experience basis product variety, personalized services, convenience in order fulfillment, post-purchase engagement, and more. For example, Jabong, a key fashion e-tailer in India, collects huge volumes of shoppers’ data such as which products they browse, what they drop out of their cart, what they finally buy, etc., from the thousands of customers visiting its site every day. Slicing and dicing this data enables e-tailers to push what customers are buying instead of firing in all directions. This is important, given the very high number of SKUs (Stock Keeping Units) and brands typically associated with e-marketplaces. Paytm has developed several virtual brand stores and online flea markets. Lamoda, the Russia-based fashion e-tailer, has trained its delivery staff to provide fashion consultancy and even wait for 15 minutes after delivery, giving time for customers to experience the clothes and accessories and return them at the doorstep if found unsuitable. iPhone users can walk into Apple stores and pay via an app on their own, avoiding payment counter queues simply by scanning the barcodes of the products they wish to buy. Given this context, it becomes even more critical for e-tailers to gain data and sharp insights to better address shoppers’ requirements and expectations, reposition themselves to increase loyalty, and attract potential customers. Moreover, with fierce competition in today’s B2C e-commerce market, more and more e-tailers encounter issues in running a profitable business. Netpreneurs have been turning to “Big Data”, or data about almost all facets of consumers that would help them in predictive analytics, i.e., predicting consumer behavior – what they are doing and how they are likely to behave in the future. Big Data comprises online text, videos, useful data, social media data, website analytics, transaction data, etc. On the other hand, “Small Big Data” consists of multiple survey datasets to enable richer data analyses (Warshaw, 2016). Depending on the nature of the research questions asked, the best method to get the answers consists of combining the two types of data. Big Data can help us gauge behaviors and tell us the “what”, while surveys can measure attitudes and opinions and throw light on the “why”. Surveys such as the one that insights provided in this chapter are based on can be used to check the quality of Big Data and vice versa. The current buzzword is “rich data” – “to emphasize the importance of a mindset that focuses on not the sheer size of data but their substance and utility” (Callegaro and Yang, 2018). Big Data and survey research are best used together to provide richer data. This chapter details a survey undertaken to gain insights to

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understand shoppers’ requirements and expectations better so that e-tailers can reposition themselves to increase loyalty and attract potential customers.

LITERATURE REVIEW The existing literature was studied to understand the studies already carried out on consumer demographics’ influence on online shopping intention and to build further upon them. As per Wu (2003), attitude towards online shopping had a significant relationship with consumer demographics such as gender, age, occupation, education, and income. Richa (2012) concluded that shopping preferences are considerably influenced by demographic factors such as age, income, marital status, number of family members, and gender. Lubis (2018) argued that online shopping preferences are affected by “the demographic and proprietary aspects of an online shopping app”. Seock and Bailey (2008) investigated the relationships between shopping orientations and searches for information about and purchases of apparel products online and the differences by gender. Mpinganjira (2014) found that females’ motives are more hedonic during online shopping, while males’ motives were more utilitarian and task-oriented. Gerrard et al. (2006) opined that in online shopping, since the IT (information technology) knowledge of younger buyers is generally higher than that of older ones, the younger buyers are more likely to perceive the threat as low and have higher adoption likelihood. With the outbreak of COVID-19, many senior citizens shifted to online shopping safety that they never considered before (Pantano et al., 2020). Lian and Yen (2014) concluded that older people’s use of the Internet is growing at a considerable rate; however, past studies have focused mainly on the youth. To bridge the gap, they studied the accelerating and inhibiting factors influencing older consumers’ intention to shop online by integrating the Unified Theory of Acceptance and Use of Technology (UTAUT) and innovation resistance theory. Gong et al. (2013) studied online shopping intentions based on Fishbein and Ajzen’s theory of reasoned action (TRA), which propounds that beliefs affect attitudes, which lead to intentions, and ultimately to behaviors. The independent variables included demographics and perceived risk. Some studies (Chang and Chen, 2009; Park and Kim, 2003) reported that customer interface quality positively influenced satisfaction, which affected loyalty. Verhoef and Langerak (2001) concluded that relative benefit in terms of physical effort or perceived compatibility in terms of time pressure relates positively to intention to adopt grocery shopping via websites. Chang and Chen (2009) and Park and Kim (2003) investigated that the perceived security of the e-tailer’s website leads to satisfaction and acts as a switching barrier. Khalifa and Liu (2007) suggested that though the effect of “security, convenience, and cost savings are comparatively small”, it is significant. Both Khalifa and Liu (2007) and Bhattacherjee (2001) reported that perceived usefulness and “online shopping satisfaction have significant positive effects on online repurchase intention”. Chang and Chen (2009) and Chang and Wang (2011) found that online shopping service or “interface quality” impacts repeat purchase through satisfaction. Khalifa and Liu (2007) debated that “online shopping habit and experience”, two different constructs, “positively moderate the relationship between satisfaction and repurchase intention”.

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METHOD Data Collection and Analysis A quantitative survey was undertaken to gain insights into online shopping behavior. The instrument was developed based on exploratory and secondary research. It was closely examined by experts as well as piloted amongst diverse target respondents. Scale items for “trust” and “perceived ease of use” were adapted from Chang and Chen (2009) and Park and Kim (2003), and subjective norm and behavioral intention from Lin (2007). The adaptation was based on the Indian environment (Chhabra, 2018a) and the influencers of behavior in it. The constructs for “easy & fast returns”, “perceived usefulness (money saved)”, and “perceived usefulness (time saved)” was based on desk research, exploratory qualitative research, and expert opinion. The target audience was defined as those who were 18 years or older, had shopped online, off, and on or frequently in at least the last six months to purchase a tangible product. Convenience sampling was used to select online shoppers largely via social media contacts. Extensive secondary research was conducted by referring to several national and international academic journals, newspaper articles, books, websites, and reports. The final set of variables to measure online shopping behavior was shortlisted based on qualitative exploratory surveys and expert opinion insights. The survey instrument consisted of questions related to respondents’ demographics, general online shopping behavior, attitude, subjective norm, trust, “perceived service quality, perceived usefulness, ease of use”, and behavioral intentions using nominal and interval scales (“a 7 point scale ranging from strongly disagree to strongly agree”). Respondents were asked to think about the specific online shopping site they had visited most in the last six months. The majority of the questions were based on shopping experience on “this site”. The target sample size of 500 was achieved after thousands of questionnaires were distributed. A total of 578 questionnaires were returned, out of which 500 were usable valid responses. After data checking and cleaning, the data were analyzed quantitatively using the SPSS software package. Univariate analysis was followed by nonparametric test analysis. The Mann-Whitney U test was applied to compare differences if there were only two independent groups, i.e., to find a significant gender difference. In the case of independent variables (such as age and occupation) that were divided into more than two groups, the “KruskalWallis H test” was used, and a “post hoc analysis” was performed to see which pairs of groups differ significantly.

FINDINGS AND DISCUSSION Respondent Profile Out of the 500 respondents surveyed, both genders were almost in equal proportion. Nearly 58% of the respondents were in the range of 18 to 29 years of age, as evident from Table 13.1. Nearly half of them were students, with the next highest occupation segment being of those in service at about 27%. As inferred from Table 13.2, Amazon emerged as the most visited site for almost half the respondents in the last six months.

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TABLE 13.1 Profile of online shoppers by age 18–21 22–29 30–39 40 & above Total

Sample

%

132 156 94 118 500

26.4 31.2 18.8 23.6 100

TABLE 13.2 Profile of online shoppers by most visited site in last six months Amazon Paytm Flipkart Myntra Others Total

Sample

%

246 40 99 63 52 500

49.2 8.0 19.8 12.6 10.4 100

Clothes and apparel emerged as the most-purchased product category (34%) followed by computers, mobile, and accessories at 15.8%.

Nonparametric Test Analysis Results In all, there were almost 50 parameters, and nonparametric tests were conducted on each to investigate significant difference by gender and age.

GENDER-WISE SIGNIFICANT DIFFERENCES Findings as shown in Table 13.3 highlight that in comparison with males, the agreement that their most visited sites’ customer care is helpful, and that the site cares for and invests in its customers, is higher amongst females. This can be attributed to females finding it easier to locate information and hence depending less on customer care. This finding is supported by studies conducted by Van Slyke et al. (2002), who suggested that women go more by trustworthiness while men tend to focus more on the value gained, and by Seock and Bailey (2008), who found that women tend to visit several sites but end up shopping from one they are emotionally more attached to. Rodgers and Harris (2003) confirmed that women being more cynical about e-commerce vis-à-vis men were emotionally less satisfied with online shopping. As evident from the data, the higher satisfaction levels amongst females translate into higher loyalty and advocacy (Chhabra, 2018b).

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TABLE 13.3 Gender-wise significant differences – for most visited site Variable 1 .  Customer care is very helpful 2. Information required for shopping is difficult to locate 3. **Family feels this is not the right site for online shopping 4. **Mass media reports have not influenced me to try this site 5.  Cares and invests on its customers 6.  **Delivers spurious or fake products 7.  Plan to shop on this site again 8. Intend to use this site within the near future 9.  Will recommend this site to others

Mean – Males (N = 249)

Mean – Females (N = 251)

Significance

4.92 4.38

5.21 4.89

.030 .003

4.71

5.36

.002

2.94

3.25

.008

5.38 5.43 5.86 5.89

5.67 5.64 6.19 6.09

.016 .000 .001 .036

5.86

6.08

.015

** Has been reverse coded.

AGE-WISE SIGNIFICANT DIFFERENCES In terms of perception on the suitability of payment options offered, there is a significant difference between 18- to 21-year-olds and the other three age groups, i.e., 22to 29-year-olds, 30- to 39-year-olds, and 40 years and older, as shown in Table 13.4. Perhaps the youngest group, being more technology savvy, has higher awareness levels of various payment options such as PayPal, Samsung Pay, Google Pay, etc. The payment options are limited to debit/credit/gift card, COD, EMI, and net banking on most sites. While more payment options mean a higher processing fee, sites offer a wider range of payment options to enhance the shopping experience. The 18- to 21-year-old group’s agreement level on their “most visited site”, saving them time vis-à-vis other sites, is also lower than the other age groups. Perhaps this can be attributed to a more negative perception amongst the 18- to 21-year-olds on interaction speed and ease of locating information. The need for speed is known to generally decrease as age increases, while attention spans are generally known to increase with age. Schiffman and Kanuk (2003) argued that “young age is more sensitive to innovation”, so the youth are more active in using online shopping innovation. While younger users are more likely to embrace new technological tools and find them easier to use (Liébana-Cabanillas et al., 2014), given that today most consumers have a smartphone and are online, “trial and error in everyday use as well as learning that occurs every time makes age no longer a limitation in the use of technology” (Lubis, 2018). There is a significant difference between the oldest and three age groups on whether their most visited site offers useful and genuine customer reviews. The latter has a more favorable opinion. Younger shoppers are known to search for more products, browse more sites, and are better positioned to establish credibility of reviews.

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TABLE 13.4 Age-wise significant differences – for most visited site

Variable 1. Offers suitable payment options 2. Ordered items will arrive safely 3. Saves me time as compared to other sites 4. Rewards program that results in considerable savings 5.  High interaction speed 6. “People who are important to me” encourage me to use this site 7. Classmates and friends feel this is the right site for online shopping 8. After watching the advertisements on various media, I was tempted to try this site 9. Is free from errors and contains accurate, current and complete information 10. Offers useful and genuine customer reviews 11. **Information required for shopping is difficult to locate 12. **Family feels this is not the right site for online shopping 13. **Delivers spurious or fake products

Mean 18–21 (N = 132)

Mean 22–29 (N = 156)

Mean 30–39 (N = 94)

Mean 40 and above (N = 118)

Significance

6.11

6.36

6.35

6.47

.005

5.70

5.96

6.16

5.87

.010

5.11

5.37

5.65

5.55

.020

4.34

4.27

3.47

3.61

.001

5.07 4.90

5.22 4.78

5.77 3.89

5.73 4.50

.004 .002

5.43

5.02

4.80

4.47

.011

4.93

4.78

4.20

4.44

.008

5.06

5.17

5.67

5.31

.010

5.43

5.46

5.61

4.78

.000

4.40

4.35

4.67

5.24

.002

4.79

4.60

5.31

5.68

.001

5.45

5.35

5.62

5.81

.010

** Has been reverse coded.

In comparison, older shoppers are more likely to trust reviews only if they have been independently verified and are recent. As consumers grow older, their cumulative understanding makes their shopping more focused on certain brands. It makes them more confident, which can “reduce product risk and the need for conducting prepurchase information searches” (Zhou et al., 2007). However, Wu (2003) found that significantly different attitudes with respect to online shopping existed between the

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lower and higher age groups. Shoppers in the 21–25 and 36–40 age groups had a more optimistic attitude toward buying online vis-à-vis the older groups.

RECOMMENDATIONS AND MANAGERIAL IMPLICATIONS E-commerce has emerged as a significant marketing and sales channel complementing the traditional brick-and-mortar channels. The findings from this study will help e-tailers to understand their shoppers’ characteristics better and use them to meet consumers’ needs and expectations more effectively. The following are the key recommendations and managerial implications, based on the insights gained:

Ease of Transaction Related • Ease of use is an important aspect that directly shapes consumer attitude, impacting loyalty and advocacy. If using the online shopping site is troublefree and straightforward, the shoppers need not expend time and energy struggling with complex systems. They end up enjoying the shopping experience more. For online shoppers, ease of use means the online retail websites can be steered through effortlessly, all the information for shopping on a given site is sufficient, and the interaction speed is high. E-tailers who have young adults (more so the 18- to 21-year-olds) as target audience must ensure high interaction speed and ease in locating information, as the need for speed is relatively higher in this age group. On the other hand, attention spans are lower. • Value for money (VFM) could become an essential factor for preferring online retailers vis-à-vis brick-and-mortar retailers. E-tailers need to provide attractive offers and deals from time to time. This will create a more positive attitude and incentivize online shoppers to revisit the site and recommend others. Since women go more by trustworthiness and assurance aspects while men tend to focus more on the value gained through the purchase, e-tailers who are selling more to men need to lay even greater emphasis on VFM.

Trust Related • Trust is an important antecedent of loyalty and advocacy, and it also influences attitude. The higher the security for transmitting sensitive information, the greater the freedom from errors, inaccuracies, and incomplete on-site information, and the greater the authenticity of customer reviews, the higher the trust levels. Therefore, e-tailers must ensure that they use the latest cybersecurity technologies, keep information on their site updated 24/7, and ensure that customer reviews are not forged. Older shoppers are more likely to doubt the usefulness and credibility of customer reviews. Hence, netpreneurs with a considerable number of older shoppers would need independent verification of the reviews to raise trust levels in their site. Female shoppers can be convinced more by trustworthiness and assurance aspects, vis-à-vis male shoppers.

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Subjective Norms Related • This study also suggests that improving the site’s image perception via the influence of media and the influence of people/reference groups, i.e., relatives, classmates, and friends, is likely to increase site loyalty and advocacy indirectly. As age increases, agreement with classmates and friends decreases; while on the other hand, family opinion begins to matter more. E-tailers need to take initiatives, have unique selling propositions, and good public relations skills to ensure news reports and positive word of mouth, indicating that their site is suitable for online shopping. Both Big Data, such as transactional data and surveys, complement each other in enabling netpreneurs to gain a deeper understanding of their shoppers’ characteristics and demographics. Big Data can help us gauge behaviors and tell us the “what”, while surveys can measure attitudes and opinions and throw light on the “why”. For example, web analytics and transaction data, which are key sources of Big Data, may tell us that, on average, females spend less time per online shopping transaction visà-vis males on a given site. On the other hand, a research survey, such as the one discussed in this chapter, provides a focused insight that females find it easier to locate information while shopping on the same given site, vis-à-vis males, and hence depend less on customer care, which then translates into a more positive perception, in comparison to males, that the site cares about the customer support they are being given. Insights from this survey provide focused recommendations to enable netpreneurs to improve the shopping experience, retain existing customers, and attract potential ones. Surveys can be used to check the quality of Big Data and vice versa and are best used together with big data to provide “rich data”.

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Khalifa, M., & Liu, V. (2007). Online consumer retention: contingent effects of online shopping habit and online shopping experience. European Journal of Information Systems, 16(6), 780–792. Lian, J. W., & Yen, D. C. (2014). Online shopping drivers and barriers for older adults: Age and gender differences. Computers in Human Behavior, 37, 133–143. Liébana-Cabanillas, F., Sánchez-Fernández, J., & Muñoz-Leiva, F. (2014). Antecedents of the adoption of the new mobile payment systems: The moderating effect of age. Computers in Human Behavior, 35, 464–478. Lin, H. F. (2007). Predicting consumer intentions to shop online: An empirical test of competing theories. Electronic Commerce Research and Applications, 6(4), 433–442. Lipsman, A., (2019). Global Ecommerce 2019: Ecommerce Continues Strong Gains Amid Global Economic Uncertainty. eMarketer. Saatavissa (viitattu 8.4.2020): https://www. emarketer.com/content/global-ecommerce-2019. Lubis, A. N. (2018). Evaluating the customer preferences of online shopping: demographic factors and online shop application issue. Academy of Strategic Management Journal, 17(2). Mpinganjira, M. (2014). An investigation of gender differences in shopping orientation: Implications for online retailing. Readings Book, 379. Pantano, E., Pizzi, G., Scarpi, D., & Dennis, C. (2020). Competing during a pandemic? Retailers’ ups and downs during the COVID-19 outbreak. Journal of Business Research. doi: 10.1016/j.jbusres.2020.05.036. Park, C. H., & Kim, Y. G. (2003). Identifying key factors affecting consumer purchase behavior in an online shopping context. International Journal of Retail & Distribution Management, 31(1), 16–29. Richa, D. (2012). Impact of demographic factors of consumers on online shopping behavior: A study of consumers in India. International Journal of Engineering and Management Sciences, 3(1), 43–52. Rodgers, S., & Harris, M. A. (2003). Gender and e-commerce: An exploratory study. Journal of Advertising Research, 43(3), 322–329. Schiffman, L.G., & Kanuk, L.L. (2003). Consumer behavior. New Jersey: Prentice Hall. Seock, Y. K., & Bailey, L. R. (2008). The influence of college students’ shopping orientations and gender differences on online information searches and purchase behaviours. International Journal of Consumer Studies, 32(2), 113–121. Singh, D., & Chhabra, M. (2020). Attainment of customer’s satisfaction in digital food apps industry through result-oriented management. International Journal of Management (IJM), 11(12), 2527–2543. Sreekumar, M. D., Chhabra, M., & Yadav, R. (2018). Productivity in manufacturing industries. International Journal of Innovative Science and Research Technology (IJISRT), 3(10), 634–639. Tripathi, S. K., Chhabra, M., & Pandey, R. K. (2018). Taming tsunami of data by principles of inventory management. Journal of Business and Management (IOSR-JBM), 20, 1–12. Van Slyke, C., Comunale, C. L., & Belanger, F. (2002). Gender differences in perceptions of web-based shopping. Communications of the ACM, 45(8), 82–86. Verhoef, P. C., & Langerak, F. (2001). Possible determinants of consumers’ adoption of electronic grocery shopping in the Netherlands. Journal of Retailing and Consumer Services, 8(5), 275–285. Warshaw, C. (2016). The application of Big Data in surveys to the study of elections, public opinion, and representation. Wu, S. I. (2003). The relationship between consumer characteristics and attitude toward online shopping. Marketing Intelligence & Planning. Zhou, L., Dai, L., & Zhang, D. (2007). Online shopping acceptance model-A critical survey of consumer factors in online shopping. Journal of Electronic Commerce Research, 8(1), 41–62.

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Study on the Effectiveness of Social Networks in Persuading Entrepreneurial Initiatives with Reference to College Students in Chennai V. Jayanthi Vels Institute of Science, Technology & Advanced Studies, India

S. Subbulakshmi S.D.N.B. Vaishnav College, India

CONTENTS Introduction������������������������������������������������������������������������������������������������������������ 196 Objectives���������������������������������������������������������������������������������������������������������� 197 Review of Literature............................................................................................... 197 Hypotheses������������������������������������������������������������������������������������������������������������� 199 Research Methodology������������������������������������������������������������������������������������������ 199 Conceptual Framework������������������������������������������������������������������������������������������ 199 Entrepreneurial Potential Model (EPM)����������������������������������������������������������� 200 Technology Acceptance Model (TAM)������������������������������������������������������������� 200 Techno Entrepreneurial Model (TEM)�������������������������������������������������������������� 200 Research Model������������������������������������������������������������������������������������������������� 201 Analysis and Interpretations���������������������������������������������������������������������������������� 204 General Findings����������������������������������������������������������������������������������������������� 204 Percentage Analysis�������������������������������������������������������������������������������������� 204 Structural Equation Model�������������������������������������������������������������������������������� 204 Measurement Development������������������������������������������������������������������������������� 204 Descriptive Statistics Correlation and Reliability Coefficient���������������������� 204 DOI: 10.1201/9781003097945-14

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Confirmatory Factor Analysis (CFA)���������������������������������������������������������������� 206 Model Validity��������������������������������������������������������������������������������������������������� 207 Implications of the Study��������������������������������������������������������������������������������������� 210 Suggestions������������������������������������������������������������������������������������������������������������ 210 Conclusion������������������������������������������������������������������������������������������������������������� 210 Acknowledgment��������������������������������������������������������������������������������������������������� 210 References�������������������������������������������������������������������������������������������������������������� 211

INTRODUCTION We live in a world of Internet technology (Tripathi et al., 2018). Social networks have changed the way we think, act, and even eat. The acceptance of IT-driven services was studied from various perspectives (Tripathi et al., 2019). Many types of research were done to study the influence of technology on entrepreneurial intention in various countries. Not much such investigation was done in India. We, therefore, indulge in the exploration of the influence of social media in pursuing the person with entrepreneurial desire and capability into the entrepreneurial intention. Entrepreneurs are considered the spine of any economy (Karmarkar et al., 2014). As to the present situation, the doused economic condition can be lifted only by the endeavors of small-scale entrepreneurs (Chhabra, 2018). The students are the looming business magnets. The students in the present situation are anxious to start a new venture as there are fewer job openings in the country due to the global pandemic. The academic segment has a vital role in bringing more start-ups, patents, and licenses into the business world to haul the drenched economy. The increase in university licensing, patenting, and start-up creation in the US has also been observed in many countries in Europe and Asia and in Australia, Canada, and Israel (Grimaldi et al., 2011). Entrepreneurship is considered an emerging field among researchers and has created interest in academia and among policymakers (Shane & Venkataraman, 2000). A cross-cultural study was conducted in four countries and found that university students’ attitude differs significantly as to their perception of entrepreneurship (Heuer & Kolvereid, 2014). A study was conducted to determine whether entrepreneurial education influences entrepreneurship intention and found that personal attitude and entrepreneurial capacity are the significant influencers (Anwar & Saleem, 2018). The trends in business have undergone numerous modifications over the decades (Chhabra & Karmarkar, 2016). The recent change in the business trend is that social media usage has the power to change the phenomena and also lasts long. Social media undoubtedly opened uploads of prospects to the youngsters, especially college students (Singh & Chhabra, 2020). Entrepreneurs regard social media as a helpful tool to tap opportunities from unknown horizons which were not accessible in the pre-social media era (Park & Sung, 2017). The burying behavior of new-generation buyers, especially women, has changed due to social media usage and is taken advantage of by the innovative techno sellers. It is the paramount obligation of the government to provide all the necessary facilities and motivate them. The government has to abridge the measures to persuade the students to bring out their flair into reality. A cell has to be introduced predominantly for students to sway their proposal.

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TABLE 14.1 Social networking sites Social Networking Sites

Year Established

1.

Facebook

2004

2.

Twitter

2006

3.

YouTube

2005

4.

Instagram

2010

5.

LinkedIn

2003

S.NO.

Usage Purpose Connecting people all over the world both for business and non-business. Used to release news, answer queries, and reach the targeted audience. Most popular video-based social media website. Mainly utilized to promote travel, fashion, food, art, etc. Used for professional networking for business.

Account Holders as of 30 April 2020 1.59 billion users

320 million users

1 billion website visitors 400 million active users. 400 million registered users.

Source:  Hampton et al. (2011).

The main purpose of this study is to bring out the perceptions of the student community in the present context and evaluate the effect and role of social media in persuading the entrepreneurial intention of students from various strata. A new model is proposed to test the consequence of social media usage on potential entrepreneurs (Table 14.1). The social media sites listed in Table 14.1 are top-rated among the youth population, which catch the major hours in a day. Some of the young adults are even addicted to social media. Some of the adolescent innovators use the same social media to launch and promote their novel ideas. This chapter examines the impact of social media on the entrepreneurial initiative.

Objectives • To craft a suitable model to study the impact of social media on entrepreneurial traits. • To suggest ways and means to improve students’ entrepreneurial initiatives through social networking sites in colleges in Chennai.

REVIEW OF LITERATURE • Davis (1989) introduced the Technology Acceptance Model (TAM) in 1989. Davis developed and validated a new scale and new variables, namely perceived usefulness and perceived ease of use, which are put forward to be the determinants of user acceptance of Internet services. Definitions of these two variables were used to develop scale items that were pre-tested for content

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•

•

•

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validity and then tested for reliability and construct validity in two studies involving a total of 152 users and four application programs. The reliability of usefulness was .98, and the ease of use was .94. The scale exhibited high discrimination and factorial validity. When compared to the ease of use, usefulness had a greater correlation with user behavior. Venkatesh et al. (2001) developed an extension of the TAM, which explained perceived usefulness and usage intention in terms of social influence and cognitive instrumental processes. The TAM 2 studied the implications of voluntary usage and mandatory usage. The extended model was strongly supported for all organizations, concluding that both social influence processes and cognitive instrumental processes significantly influenced user acceptance. Shang Gao et a1. (2011) have studied the user perception on mobile service acceptance. The authors have developed a survey instrument based on existing scales that might fit the construct definitions. A pilot study was also conducted by distributing the survey to 25 users of a mobile service called Mobile Student Information Systems (MSIS). TAM constructs and predicts user adoption of mobile services through context, trust, personal initiatives, and characteristics. Furthermore, the researchers have validated their instrument by using reliability assessment techniques and confirmatory factor analysis. The findings of this study provide insight for mobile service providers and help them in assessing customer reactions to mobile services. This study showed that the reliabilities of all the scales in this survey instrument were above the target acceptance level. Sušanj et al. (2015) examined the impact of perceived entrepreneurial desirability and perceived entrepreneurial self-efficacy on entrepreneurial intention. The study is based mainly on Krueger and Brazeal’s (1994) study. Data were collected from undergraduate students of business and non-business orientation to compare the various entrepreneurship characteristics within SEM. This study shows that the mediating effect was stronger for the business-oriented groups than the non-business orientation group. The amount of explained variance of all constructs (except entrepreneurial propensity) is also larger in business students than non-business students. This survey model assumes and concludes that entrepreneurship is focused on a planned activity. Hence, intention to become an entrepreneur is its most significant predictor (Chhabra & Goyal, 2019). Rahman et al. (2017) examined the influence of the Technology Readiness Index (TRI) 2.0’s four indicators (optimism, innovativeness, discomfort, and insecurity) on entrepreneurs’ Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) of technology in Bangladesh. The study intends to state that micro-entrepreneurs from the marginalized group are optimistic about the technology. This study has its focus on the marginalized who can handle technology. This study concludes and contributes towards better understanding of how technology figures the market and its impact on societal development. This research study wraps up that the marginalized are ready to use technology; using technology, they can change their future and their living standard.

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• Anwar and Saleem (2018) analyzed characteristics of the entrepreneurially inclined and not inclined students in India. The authors studied the entrepreneurial characteristics, namely risk-taking, innovativeness, locus of control, need for achievement, self-efficacy, and tolerance for ambiguity. The t-test conducted proved that students with entrepreneurial inclination have a higher risk-taking propensity, innovativeness, locus of control need for achievement, and tolerance for ambiguity. There is not much difference as far as self-efficacy is concerned between students with or without inclination (Table 14.2).

HYPOTHESES TABLE 14.2 Regression analysis S.No. 1

2

3

4

Null Hypothesis

Alternate Hypothesis

Perceived desirability does not mediate the Entrepreneurial Capacity towards Entrepreneurial Intention (H01). Perceived desirability does not mediate the Entrepreneurial Attraction towards Entrepreneurial Intention (H02). Perceived feasibility does not mediate the Entrepreneurial Capacity towards Entrepreneurial Intention (H03). Perceived feasibility does not mediate the Entrepreneurial Attraction towards Entrepreneurial Intention (H04).

Perceived desirability mediates the Entrepreneurial Capacity towards Entrepreneurial Intention (H11). Perceived desirability mediates the Entrepreneurial Attraction towards Entrepreneurial Intention (H12). Perceived feasibility mediates the Entrepreneurial Capacity towards Entrepreneurial Intention (H13). Perceived feasibility mediates the Entrepreneurial Attraction towards Entrepreneurial Intention (H14).

RESEARCH METHODOLOGY The study attempts to explore social media impact on entrepreneurial traits. A wellstructured questionnaire with a five-point Likert scale ranging from strongly disagree to strongly agree. The convenient sampling method was adopted by way of collecting primary data through WhatsApp and mail. The questionnaire had two sections. The first part focused on the respondents’ background. The second part covered four questions on perceived desirability, entrepreneurial attraction, and entrepreneurial intention – three questions each for perceived feasibility and entrepreneurial capacity. Due to lockdown, the authors could reach out to students only through an online survey, WhatsApp, and mail. The Cronbach’s alpha was more than 0.9, conforming to internal consistency.

CONCEPTUAL FRAMEWORK The study is based mainly on a combination of two models, namely Krueger and Brazeal’s (1994) Entrepreneurial Potential Model and the Extended Technology Acceptance Model. The first model assumes that entrepreneurship is a planned

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activity, so intention to become an entrepreneur is its most significant predictor. The study is intended to evaluate the direct and indirect effects of entrepreneurial characteristics such as Entrepreneurial Capacity (EC) and Entrepreneurial Attraction (EA) on Entrepreneurial Intention (EI). The second model is an extension of TAM by including a mediating factor, namely attitude. Here, the attitude of students’ Perceived Desirability and Perceived Feasibility towards social media usage for their perspective business is taken for SWOT. Perceived Desirability (PD) and Perceived Feasibility (FS) are scrutinized as mediating variables.

Entrepreneurial Potential Model (EPM) Entrepreneurial capacity refers to having practical knowledge of starting a firm which the person would have acquired through working knowledge or by inducement by others such as family and friends. Entrepreneurial attraction refers to the degree to which the person is attracted to starting a business independently. It has the foundation of internal standards of attraction, non-lucrative career options, social pressures, or shore up given by the government to the self-employed people. The plain Entrepreneurial Capacity and Entrepreneurial Attraction do not become active unless it is intended. Entrepreneurial intention is the summit to be achieved.

Technology Acceptance Model (TAM) TAM is one of the most influential models widely used in the studies of the determinants’ IT acceptance. It is not easy to directly measure IT, contributor, because of its hidden and intangible benefits (Mao & Palvia, 2001) researchers have developed other measures, such as technology acceptance, which directly relates to IT usage. Therefore, it is important for the implementers to fully understand the determinants of IT acceptance as they need to plan effectively for it. TAM (Davis, 1989) was derived from the Theory of Reasoned Action (TRA) (Fishbein & Ajzen, 1975) a powerful explanation for user acceptance and usage behavior of information technology.

Techno Entrepreneurial Model (TEM) The proposed model is a combination of TAM and EPM, which verifies the effectiveness of social media usage in persuading entrepreneurial capacity and entrepreneurial attraction towards entrepreneurial intention. The variables, namely Perceived Desirability and Perceived Feasibility relating to social media usage, are initiated. The Perceived Desirability in this study relegated to the one’s aspiration to use social networking in the impending business to fetch slacken atmosphere in the trade. The Perceived Feasibility in this study is consigned to one’s own placate and practicality in using social media in the imminent business. It examines how the pinnacle is achieved through gathering all the acquaintances and using all the resources that are accessible in the high-tech world (Tables 14.3, 14.4, and 14.5; Figures 14.1 and 14.2).

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TABLE 14.3 Researches on acceptance of Internet-based IT applications using TAM and entrepreneurial potentials Authors

Sample Unit

Predictor/ Study

Jahangir and Begum (2008)

Individual

Electronic banking usage

Nath et al. (2013)

Bank employee

Electronic banking adoption

Liñán and Chen (2006)

Individuals in Spain and Taiwan

Entrepreneurial potential

Ajjan et al. (2015)

Students in Egypt and United States

Social media usage to support entrepreneurship

Mueller et al. (2002)

Students from nine countries

Role of culture in persuading entrepreneurship.

Result Perceived usefulness, perceived case of use. Security and privacy have a direct relationship to customer adaption mediated by customer attitude. Social influence enhances perceived usefulness. Computer self-efficacy and technology facility influence the perceived case of use that inrush influence perceived usefulness, which has a significant effect on behavior intention and usage behavior.

Personal Attraction and Perceived Social Norms promote entrepreneurship. Social media self-efficacy is a new factor that acts as a predictor of entrepreneurial intention. Some cultures are conducive for entrepreneurship.

Research Model TABLE 14.4 Summary of theoretical models Model Techno Entrepreneurial Model

Revised Techno Entrepreneurial Model

Theoretical Model Paths from Entrepreneurial Capacity and Entrepreneurial Attraction to Perceived Desirability and Perceived Feasibility. Perceived Desirability and Perceived Feasibility to Entrepreneurial Intention. Paths from Entrepreneurial Capacity and Entrepreneurial Attraction to Perceived Desirability, Perceived Feasibility, and Entrepreneurial Intention. Perceived Desirability and Perceived Feasibility to Entrepreneurial Intention.

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TABLE 14.5 Theoretical framework Factor Perceived Desirability

Variable PD1

PD3

PD3

PD4

Perceived Feasibility

PF1 PF2

PF3

Entrepreneurial Attraction

EA1

EA2

EA3

EA4

Entrepreneurial Intention

EI1 EI2 EI3 EI4

Entrepreneurial Capacity

EC1 EC2 EC3

Using social media networking for my business is much more desirable for me. I would enjoy personal satisfaction in using social media network innovation in my business Using social networking would increase the quality of work in my business. Using social networking would result in a more relaxed working environment in my business. I feel comfortable using social networking for my business. It would be very feasible for me to use social networking in my business. It would be practical for me to use social networking for my business. Being an entrepreneur implies more advantages than disadvantages to me. If I had opportunities and resources, I would like to start a firm. Being an entrepreneur would entail great satisfaction for me. Among various options, I would rather be an entrepreneur. I am ready to make anything to be an entrepreneur. My professional goal is to become an entrepreneur. I am determined to create a firm in the future. I have a strong intention to start a firm someday. I am prepared to start a new firm. I know the necessary practical details to start a firm. I know how to develop an entrepreneurial project.

Source Dissanayake (2013)

Dissanayake (2013)

Liñán and Chen (2006)

Liñán and Chen (2006)

Liñán and Chen (2006)

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FIGURE 14.1  Techno Entrepreneurial Model (without mediation). Source: Krueger and Brazeal (1994); Davis (1989).

FIGURE 14.2  Revised Techno Entrepreneurial Model (with mediation). Source: Krueger and Brazeal (1994).

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ANALYSIS AND INTERPRETATIONS The data analysis was done using MS Excel, IBM SPSS 23, and IBM AMOS.

General Findings Percentage Analysis • 56% are male, and 44% are female respondents. • 37% of the respondents are from a business family. • 31% of the respondents belong to the group with less than Rs.20,000 income, and 24% belong to more than the Rs 50,000 group. • 39% belong to an urban area, 36% to semi-urban, and 25% belong to a rural area.

Structural Equation Model SEM is a statistical technique for testing and estimating causal relations using statistical data and qualitative causal assumptions. SEM can examine a series of dependence relationships simultaneously. It is particularly useful in testing theories equations that contain multiple equations involving dependence relationships.

Measurement Development The measurement items used in this research are adopted based on a literature review. A five-point Likert scale ranging from (1) strongly disagrees to (5) strongly agree was used to measure each construct, given as follows. Descriptive Statistics Correlation and Reliability Coefficient The mean, standard deviation, and reliability coefficients for the different constructs are computed. Reliability is a measure of the degree to which a set of indicators of a latent construct is internally consistent. Correlation analysis was conducted on all variables to explore the relationship between the variables (Table 14.6). Descriptive statistics and Alpha values of Entrepreneurial Capacity, Entrepreneurial Attraction, Perceived Desirability, Perceived Feasibility, and Entrepreneurial Intention are programmed in Table 14.6. The mean score of EC1, EC2, and EC3 is above 3.50, which indicates that their capability to become entrepreneurs through the SD is high. The mean goodness score of EA1, EA2, EA3, and EA4 are all above 3.6, although the standard deviation indicates that students have the Attraction towards Entrepreneurship. The mean goodness score of PD1, PD2, PD3, and PD4 are all above 3.7, and the standard deviation is not high, indicating that students desire to use the social network for their upcoming business. The mean goodness score of PF1, PF2, and PF3are all above 3.8, and the standard deviation is within the range, which signifies the students’ acceptance of the  feasibility of social network usage for their business. The mean goodness score of EI1, EI2, EI3, and EI4 are all above 3.6; although the standard deviation is high, this indicates that students have the intention to start a business of their own. The Cronbach’s Coefficient Alpha values of all the constructs exceed .75, which proves the internal consistency of the items (Table 14.7).

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TABLE 14.6 Descriptive statistics and Cronbach’s Coefficient Alpha Constructs Entrepreneurial Capacity (EC)

Entrepreneurial Attraction (EA)

Perceived Desirability (PD)

Perceived Feasibility (PF)

Entrepreneurial Intention (EI)

Items

Mean

EC1 EC2 EC3 EA1 EA2 EA3 EA4 PD1 PD2 PD3 PD4 PF1 PF2 PF3 EI1 EI2 EI3 EI4

3.50 3.82 3.81 3.81 3.89 3.63 3.79 3.74 3.82 3.88 3.80 3.85 3.88 3.80 3.81 3.89 3.63 3.79

Standard Deviation

Alpha

1.003 .942 .962 .982 .938 .836 .974 .958 .801 .766 .994 .751 .821 .844 1.047 1.074 .924 1.000

.750

.895

.856

.876

.859

Source:  Computed data.

TABLE 14.7 Correlation matrix Variables

Entrepreneurial Capacity

Entrepreneurial Capacity Entrepreneurial Attraction Perceived Desirability Perceived Feasibility Entrepreneurial Intention Source:  Computed data.

Entrepreneurial Attraction

Perceived Desirability

Perceived Feasibility

Entrepreneurial Intention

–

.966

.734

.697

.256

–

–

.833

.814

.357

.927

.155 .187

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The Entrepreneurial Capacity, Entrepreneurial Attraction, Perceived Desirability, Perceived Feasibility, and Entrepreneurial Intention are all positively correlated.

Confirmatory Factor Analysis (CFA) Confirmatory Factor Analysis is a multivariate technique used to provide a series of relationships such as Convergent Validity and Discriminant Validity that suggest how the measured variables represent a latent construct that is not measured directly (Table 14.8). Table 14.8 displays the standardized loadings are ranging from .46 to .97. The estimates are nearer to and above the .5 and evidence the convergent validity. The Composite Reliability of Entrepreneurial Capacity, Entrepreneurial Attraction, Perceived Desirability, Perceived Feasibility, and Entrepreneurial Intention are .75,.895, 856, .876, and .859 are all in the acceptable range (Hair et al. 2016). The AVE estimates range from 52% to 72%. The Average Variable Extracted is all above the thumb rule, which is .50 (50%) (Hair et al., 2016) and also less than the value of Composite Reliability (CR>AVE). The above evidence supports the construct validity of the model.

TABLE 14.8 Factor Loadings, Composite Reliability, Average Variance Extracted, Maximum Shared Variance, and Average Shared Variance Construct

Item

Entrepreneurial Capacity (EC)

EC1 EC2 EC3 EA1 EA2 EA3 EA4 PD1 PD2 PD3 PD4 PF1 PF2 PF3 EI1 EI2 EI3 EI4

Entrepreneurial Attraction (EA)

Perceived Desirability (PD)

Perceived Feasibility (PF)

Entrepreneurial Intention (EI)

Factor Loadings .465 .801 .848 .849 .978 .657 .835 .867 .853 .810 .668 .918 .689 .920 .664 .827 .818 .812

CR

AVE

MSV

ASV

.750

.525

.421

.279

.895

.701

.697

.474

.856

.645

.573

.398

.876

.721

.710

.507

.859

.613

.471

.373

Source:  Computed data. Note: CR – Composite Reliability, AVE – Average Variable Extracted, MSV – Maximum Shared Variance, and ASV – Average Shared Variance.

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The MSV values ranged from .42 to .71, which are all less than the AVE of that particular construct. The ASV values ranged between .27 and.50, which are all less than the AVE of the construct (MSV < AVE > ASV). These tests indicate that the discriminant validity is upheld for the measurement model.

Model Validity The model validity is assessed through chi-square value, normed chi-square value, and p-value. The other fit indexes such as Comparative Fit Index (CFI), Goodness of Fit Index (GFI), Normed Fit Index (NFI), Standard Root Mean Square Residual (SRMR), and Root Mean Square Error of Approximation (RMSEA) are also used in judging the model Fit (Table 14.9). The Techno Entrepreneurial Model examines whether the Perceived Desirability and Perceived feasibility mediate the relationship between the Entrepreneurial Capacity and Entrepreneurial Attraction to Entrepreneurial Intention (ᵡ2= 170.876, df. = 112, and p > .05). The CFI, GFI, and NFI are all above .80, and SRMR (.048) and RMSEA value (.06) are all in the acceptable range (Hair et al., 2016). The Techno Entrepreneurial Model is then compared to the Revised Techno Entrepreneurial Model, which studies both the direct and mediating relationship between the Entrepreneurial Capacity and Entrepreneurial Attraction to Entrepreneurial Intention (ᵡ2 = 170.424, df. = 110, and p < .05). The CFI, GFI, and NFI are all above .80, with SRMR at .038 and RMSEA value at .063. (Hair et al., 2016).

TABLE 14.9 Model fit index Absolute Measures Chi-Square Df Chi-Square/df P-value CFI SRMR GFI NFI RMSEA

Techno Entrepreneurial Model 170.876 112 1.526 .149 .971 .048 .888 .922 .06

Revised Techno Entrepreneurial Model 170.424 110 1.54 .000 .970 .038 .888 .922 .063

Source:  Computed data Note: Df– degrees of freedom; p-value – probability; CFI– Comparative Fit Index; SRMR– Standard Root Mean Square Residual; GFI– Goodness Fit Index; NFI– Normed Fit Index; RMSEA– Root Mean Square Error of Approximation.

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TABLE 14.10 Path analysis (standardized coefficients) Path Entrepreneurial Capacity → Perceived Desirability Entrepreneurial Capacity → Perceived Feasibility Entrepreneurial Attraction → Perceived Desirability Entrepreneurial Attraction → Perceived Feasibility Perceived Desirability → Entrepreneurial Intention Perceived Feasibility → Entrepreneurial Intention Entrepreneurial Capacity -→ Entrepreneurial Intention Entrepreneurial Attraction → Entrepreneurial Intention

Techno Entrepreneurial Model

Revised Techno Entrepreneurial Model

.654

.294

.856

.439

.115

.472

−.127

.131

.414

.277

.317

.185 .081 .259

Source:  Computed data.

The data fits well with both models and, on the comparison, fits the Techno Entrepreneurial Model far better (p > .05 and Chi Sq/df = 1.52) (Table 14.10). From Table 14.10, given the pattern of impact for the parameter estimates within the Techno Entrepreneurial Model, the regression weight of path analysis from Entrepreneurial Attraction to Entrepreneurial Intention is found to be negative, and all other path coefficients are in the hypothesized (positive) direction. In the Revised Techno Entrepreneurial Model, the regression weights of path analysis are all in the hypothesized (positive) direction. The path coefficients in Techno Entrepreneurial Model are found to be more significant than in the Revised Techno Entrepreneurial Model. Entrepreneurial Attraction to Perceived Feasibility was improved in the Revised Techno Entrepreneurial Model from negative (−.127) to positive (.131), which implies that perceived feasibility is not mediating the Entrepreneurial Attraction to Entrepreneurial Intention. The Perceived Feasibility acts only as a suppressant on Entrepreneurial Attraction. Entrepreneurial Attraction to Perceived Desirability has been improved in the Revised Techno Entrepreneurial Model. Henceforth, the Perceived desirability partially mediates both the Entrepreneurial Capacity and the Entrepreneurial Attraction to Entrepreneurial Intention (Figure 14.3 and Table 14.11).

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FIGURE 14.3  Techno Entrepreneurial Model.

TABLE 14.11 Result of hypotheses S.No.

Null Hypothesis

1

Perceived desirability does not mediate the Entrepreneurial Capacity towards Entrepreneurial Intention. (H01)

2

Perceived desirability does not mediate the Entrepreneurial Attraction towards Entrepreneurial Intention. (H02)

3

Perceived feasibility does not mediate the Entrepreneurial Capacity towards Entrepreneurial Intention. (H03) Perceived feasibility does not mediate the Entrepreneurial Attraction towards Entrepreneurial Intention. (H04)

4

Alternate Hypothesis Perceived desirability mediates the Entrepreneurial Capacity towards Entrepreneurial Intention. (H11) Perceived desirability mediates the Entrepreneurial Attraction towards Entrepreneurial Intention. (H12) Perceived feasibility mediates the Entrepreneurial Capacity towards Entrepreneurial Intention. (H13) Perceived feasibility mediates the Entrepreneurial Attraction towards Entrepreneurial Intention. (H14)

Result Alternate hypothesis is accepted

Alternate hypothesis is accepted

Alternate hypothesis is accepted Null hypothesis is accepted

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IMPLICATIONS OF THE STUDY • The mere Entrepreneurial Attraction does not lead to Entrepreneurial Initiative. • The Entrepreneurial Capacity tied with the desire and feasibility of social media usage shows the way to Entrepreneurial Initiative. • The Perceived Desirability acts as a catalyst more than the Perceived Feasibility in bringing the pinnacle of the business.

SUGGESTIONS • • • • • • •

Entrepreneurial education should be initiated from the school level. Compulsory skill education is to be introduced in colleges. Cultural change is to be kicked off to encourage the entrepreneurs. The students have to be trained to use social media constructively. The cybersecurity guidance has to be issued to the students. The girls have to be specially educated on cybersecurity. The students with innovative designs are to be identified by the higher education institutions and encouraged further. • The education program should be framed in such a way as to boost self-esteem or self-respect.

CONCLUSION This chapter attempted to bring out students’ perceptions of entrepreneurial initiatives and their aspiration to use social media to develop their business. Many such aspects that encourage the students to fire up a new business inspiration are to be researched and brought to light. The authors are hopeful that the study has thrown light on college students’ perceptions of entrepreneurship and social media usage for promoting various business activities. The Techno Entrepreneurial Model will certainly be supportive in investigating the young and budding entrepreneurs. It is the policymakers’ sense of duty to encourage the young entrepreneurs to come with creative proposals and facilitate them financially and morally.

ACKNOWLEDGMENT First and foremost, I bow my head and thank the Almighty for showering his blessings throughout the journey and giving us enough strength and team spirit to complete the work successfully. An honorable mention is made herein that our families and friends have rendered wholehearted support in completing this work. But for their laudable encouragement, this work would not have been completed successfully. We express our deep sense of gratitude to our management (VISTAS and SDNB Vaishnav College) who encouraged us to get this chapter writing done. We take this opportunity to place on record our thanks to the students (respondents) of various colleges for their overwhelming support to this chapter. And our sincere thanks to the Editors.

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REFERENCES Ajjan, H., Fabian, F., Tomczyk, D. & Hattab, H. (2015). Social media use to supportentrepreneurship. Journal of Developmental Entrepreneurship, 20 (3), 1–28. Anwar, I., & Saleem, I. (2018). Effects of entrepreneurial education on entrepreneurial intention of Indian. International Journal of Research, 5 (12), 2306–2316. Chhabra, M. (2018). Gender gap in “success factors” among entrepreneurs: A study of micro and small enterprises. SEDME (Small Enterprises Development, Management & Extension Journal), 45 (2), 1–17. Chhabra, M., & Goyal, A. P. (2019). Education and entrepreneurial experience WRT female entrepreneurs. International Journal of Research in Humanities, Arts and Literature, 7, 95–110. Chhabra, M., & Karmarkar, Y. (2016). Gender gap in entrepreneurship – a study of small and micro enterprises. ZENITH International Journal of Multidisciplinary Research, 6(8), 82–99. Davis, F. D. (1989). Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Qtr, 13 (3), 319–340. Dissanayake, D. (2013). The impact of perceived desirability and perceived feasibility on entrepreneurial intention among undergraduate students in Sri Lanka: An extended model. The Kelaniya Journal of Management, 2, 39–57. Fishbein, N. & Ajzen, I. (1975). Belief, Attitude Intention and Behavior.Reading, MA: Addison-Wesley. Gao, S., Krogstie, J. & Siau, K (2011). Developing an instrument to measure the adoption of mobile services. Mobile Information Systems, 7, 45–67. Grimaldi, R., Kenney, M., Siegel, D. S. & Wright, M.(2011). 30 years after Bayh-Dole reassessing academic entrepreneurship. Research Policy, 40, 1045–1057. Hair, J. F. et al.(2016). Multivariate Data Analysis.Noida: Pearson. Hampton, K. N., Goulet, L. S., Rainie, L., & Purcell, K. (2011). Social Networking Sites and Our Lives (Vol. 1). Washington, DC: Pew Internet & American Life Project. Heuer, A. & Kolvereid, L. (2014). Education in entrepreneurship and theory of planned behaviour.European Journal of Training and Development38 (6), 506–523. Jahangir, N. & Begum, N. (2008). The role of perceived usefulness, perceived ease of use, security and privacy and customer attitude to engender customer adapatation in the context of electronic banking. African Journal of Business Management, 2 (1), 32–40. Karmarkar, Y., Chabra, M., & Deshpande, A. (2014). Entrepreneurial leadership style(s): A taxonomic review. Annual Research Journal of Symbiosis Centre for Management Studies, 2(1), 156–189. Krueger, N. F. Jr., & Brazeal, D. V. (1994). Entrepreneurial potential and potential entrepreneurs. Entrepreneurship Theory and Practice, 91–104. Liñán, F.& Chen, Y. W. (2006). Testing the entrepreneurial intention model on a two-country sample. Document de Treball Universitat Autònoma de Barcelona: Departament d’Economia de l’Empresa, 6–7. Mao, E. & Palvia, P. (2001). Information Technology Acceptance: How much do we know? Seventh Americas Conference Information System. Boston, USA. Mueller, S. L., Thomas, A. S., & Jaeger, A. M. (2002). National entrepreneurial potential: The role of culture, economic development, and political history. Advances in Comparative International Management, 14, 221–257. Nath, R., Bhal, K. T. & Kapoor, G. T. (2013). Factors influencing it adoption by bank employees: An extended tam approach. Vikalpa, 38 (4), 84–96. Park, J. Y., & Sung, C. S. (2017). Does social media use influence entrepreneurial opportunity? A review of its moderating role.Sustainability, 9 (9), 1593. Retrieved from doi:10.3390/su9091593

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Rahman, S. A., Taghizadeh, S. K., Ramayah, T., & Alam, M. (2017). Technology acceptance among micro-entrepreneurs in a marginalized social strata: The case of social innovation in Bangladesh. Technological Forecasting and Social Changes, 118, 1–10. Shane, S. & Venkataraman, S. (2000). The promise of entrepreneurship as a field of research. Academy of Management Review, 25 (1), 217–226. Singh, D., & Chhabra, M. (2020). Attainment of customer’s satisfaction in digital food apps industry through result-oriented management. International Journal of Management (IJM), 11 (12), 2527–2543. Sušanj, Z., Jakopec, A. & Miljković Krečar, I. (2015). Verifying the model predicting the entrepreneurial intention among the students of business and non business orientation. Management, 20 (2), 49–69. Tripathi, S. K., Chhabra, M., & Pandey, R. K. (2018). Taming tsunami of data by principles of inventory management. Journal of Business and Management (IOSR-JBM), 20, 1–12. Tripathi, S. K., Kaur, D. A., Pandey, D. R., & Chhabra, D. M. (2019). Time to modify operating software (Os), databases (Db) and TCP/IP protocols for data trash elimination, based on user defined shelf life of data. International Journal of Computer Engineering and Technology, 10(1). Venkatesh, V., Morris, M.G., Davis, G.B. & Davis, F.D., (2001). User acceptance of information technology: Toward a unified view. MIS QuARTERLY, 27 (3), 425–478. Wright, D. A. (2015). Academic entrepreneurship: time for a rethink? Enterprise Research Centre.

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Curbing Inconsistencies Through Financial Bootstrapping Study of Indian Startups Ecosystem Anju Singla and Prihana Vasishta Punjab Engineering College (Deemed to be University), India

CONTENTS Introduction������������������������������������������������������������������������������������������������������������ 214 Review of Literature���������������������������������������������������������������������������������������������� 214 Bootstrapping Finance�������������������������������������������������������������������������������������� 214 Bootstrapping Techniques��������������������������������������������������������������������������������� 215 Need and Objectives���������������������������������������������������������������������������������������������� 216 Methodology���������������������������������������������������������������������������������������������������������� 217 Result and Discussion�������������������������������������������������������������������������������������������� 217 Advanced States������������������������������������������������������������������������������������������������ 217 Intermediate States�������������������������������������������������������������������������������������������� 220 Beginner States�������������������������������������������������������������������������������������������������� 220 Overview: Seven-Pillar Framework����������������������������������������������������������������������� 220 Startup Policy and Implementation������������������������������������������������������������������������ 220 Incubation Support������������������������������������������������������������������������������������������������� 220 Seed Funding Support�������������������������������������������������������������������������������������������� 221 Funding Support, Angel and Venture Funding������������������������������������������������������� 221 Simplified Regulations������������������������������������������������������������������������������������������� 221 Easing Public Procurement������������������������������������������������������������������������������������ 221 Awareness and Outreach���������������������������������������������������������������������������������������� 222 Inconsistencies in Distribution of Startups Across India��������������������������������������� 222 Type I Inconsistencies��������������������������������������������������������������������������������������� 222 Type II Inconsistencies�������������������������������������������������������������������������������������� 223 Northeastern States�������������������������������������������������������������������������������������������223 Challenges and Chances���������������������������������������������������������������������������������������� 224 Payments That Have to Be Made to the Suppliers�������������������������������������������� 224 Finding a Suitable Business Model������������������������������������������������������������������� 224 DOI: 10.1201/9781003097945-15

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Acquisition of Capital Equipment��������������������������������������������������������������������� 224 The Requirement of Funds for One-Time Startup Expense������������������������������ 225 Acquiring Office Deposit, Equipment, and Furnishing������������������������������������ 225 Recommendations and Implications���������������������������������������������������������������������� 225 For Policymakers����������������������������������������������������������������������������������������������� 225 For Entrepreneurs���������������������������������������������������������������������������������������������� 226 Conclusion������������������������������������������������������������������������������������������������������������� 226 References�������������������������������������������������������������������������������������������������������������� 226

INTRODUCTION In developing economies where job openings are insufficient and several urban residents are growing, entrepreneurial economic activities contribute significantly to employment generation and poverty reduction (Khan & Quaddus, 2017; Williams & Shahid, 2016). Due to the tremendous growth of startups in India, companies created 3.9–4.3 lakh direct jobs and over 1.5–1.6 lakh indirect jobs annually from 2009 to 2019 (NASSCOM, 2019). The report states India is the world’s third-largest startup hub. The Indian government has also initiated various schemes and reforms to promote and grow entrepreneurship and skill development, such as the Prime  Minister’s Employment Generation Programme (PMEGP), Credit Support Scheme, Start-up India, and Make in India, etc. (Chhabra, 2018). According to the Global Entrepreneurship Report, India’s Global Entrepreneurship Index ranking improved from 104 in 2014–15 to 68 in 2017–18 (Chhabra & Karmarkar, 2016b). Despite such considerable growth and the benefits provided by the Indian market, almost 50% of startups die before the seed-stage investment (NASSCOM, 2017). One of the many reasons for the failure is entrepreneurs’ reluctance to finance their innovations or use follow-on funding at later stages (Majumdar, 2020). Any access to funding (internal or external) alleviates financial constraints for both the nascent entrepreneurs’ startup investment and their business’s subsequent growth. Even though the financial resource constraint remains constant everywhere, research regarding the methods or techniques through which entrepreneurs can transform these resource constraints into opportunities is not well researched. The question is, why do some firms grow and others do not? Why are a tiny proportion of them able to convert themselves into operational ventures among all nascent entrepreneurial ventures? Furthermore, what can be done about it? While raising money for the startup, bootstrap financing is one of the most inexpensive routes for an entrepreneur. Some of the startups, such as GrabOn, Gxpress, Wingify, FusionCharts, Zerodha, and Appointy, have been on the bootstrap road for many years and have managed to scale excellent heights.

REVIEW OF LITERATURE Bootstrapping Finance Bhide (1992) defined bootstrapping finance as “launching new ventures with modest personal funds.” As he highlighted the potential of financial bootstrapping of new and small businesses, the definition provided a narrow scope as only personal funds

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were considered. While researches in the 1990s revealed that there were other motives apart from lack of capital for the use of bootstrapping techniques, soon enough, this deduction was criticized, where the results showed that bootstrapping methods were meant to be employed only when external finance was not available (Bhide, 1992; Van Auken et al., 1998). As the business environment kept advancing, the definition of bootstrapping also kept evolving. Van Osnabrugge and Robinson (2000) referred to it as “the highly creative acquisition and use of resources without raising equity from traditional sources.” Similarly, it was described by Winborg and Landstrom (2001) as “methods for meeting the need for resources without relying on long-term external finance from the debt holder and new owners.” This reduces the fixed liability of interest on the debt and lowers the burden of paying debt within a specified period. Ebben and Johnson (2006) stated bootstrapping as a combination of methods that reduce overall capital requirements, improve cash flow, and take advantage of personal financing sources. All researchers focused on one common thing: the generation of capital and cutting of cost through bootstrapping techniques for the sustainability of the startups. In the late 2000s, the researchers’ attention turned to the different bootstrapping techniques applied by entrepreneurs in their day-to-day business, their motive, and the success rate behind applying those bootstrapping techniques. Winborg (2009) found that where there is a shortage of funding, nascent startups use financial bootstrapping techniques to reduce costs.

Bootstrapping Techniques Entrepreneurs have improved and enlarged the effective use of their resources regularly by exercising various bootstrapping techniques, which have been beneficial at any stage of company development. Researches have shown a bootstrapping technique as simple as personal resources provide a business with long-term working capital in personal savings that have supported many young companies (Bhide, 1992; Van Auken & Neeley, 1996). Bill Gates started his business by funding himself through personal savings in the starting years and came up with Microsoft. Use of a personal credit card of the owner or a personal loan for business use, directly incorporating personal properties into company operations and forgiving any personal salary, especially during the earlier stages of the startup, all come under the scope of personal resources as a bootstrapping technique (Schofield, 2015; Van Auken & Neeley, 1996; Whittemore, 1993; Winborg & Landstrom, 2001). Friends, families, and colleagues as a bootstrapping technique have always been a significant source of entrepreneurial funding, psychological and emotional encouragement, and inspiration, particularly when the situation did not seem very promising (Vesper, 1990). Michael Dell, the founder of Dell Technologies, received a thousand dollars from his family to start up his business, which presently has turned into billions. This highly practiced bootstrapping technique confirms that more than 50% of entrepreneurs are estimated to receive financing from at least one of these three sources (Daniels et al., 2016). Similar research in India showed that SMEs’ primary bootstrapping technique is loans from friends and relatives (Shripria et al., 2020). It has also been observed that startups in their growth phase are strongly dependent on cash management

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techniques, mainly trade credits (Allen et al., 2006). Fundamentally, cash management techniques have also been considered essential bootstrapping techniques. It leads to productive use of businesses’ assets by delayed cash outflows, rapid cash inflows, and improved efficiency of assets (Neeley, 2003). Leasing is another technique to get maximum productivity and use the machinery, facilities, and vehicles without any cost of purchasing those properties (Winborg & Landstrom, 2001). A study by Shripria et al. (2020) on Indian SMEs analyzed that leasing the equipment instead of purchasing it stands at ninth place out of the 25 bootstrap financing techniques. According to Helleboogh et al. (2010), outsourcing is an essential aspect of joint utilization of resources. In the case of nascent startups, production or manufacturing, sub-assembly, distribution, and customer support are outsourced by entrepreneurs. WeWork, an Indian company, follows a coworking business model providing a shared workspace with printers, private phone booths, 24/7 building access, mail and package handling, internet, and IT support. Recently, Alvarado and Mora-Esquivel (2020) performed exploratory research among small businesses in Costa Rica on financial bootstrapping. The study identified financial bootstrapping techniques primarily related to the customers and owners who facilitate the joint utilization of facilities and other assets widely used by startups. The findings of this study indicate that Costa Rican small business managers are researching and using a wide variety of different approaches to access financial capital, rather than merely using what entrepreneurs are familiar with (e.g., credit from family or friends). The reviewed literature reveals that financial bootstrapping techniques play a catalyst role in the growth of the economy through supporting the budding startups and ensuring their survival.

NEED AND OBJECTIVES Despite the favorable startup ecosystem in India, approximately 90% of startups fail within the first five years (Chhabra & Goyal, 2019; Chhabra & Karmarkar, 2016a). About 65% of the startups fail due to insufficient funds (The IBM Institute for Business Value, 2016). Regardless of having high profitability and interest-paying potential, younger SMEs in India have less accessibility to debt (Kumar & Rao, 2016). RBI Report (2019) shows the availability of institutional funds (SIDBI, NBFC, NABARD) for startups is negligible. In contrast, Friends and Family (42.9%) remains the only primary finance source in the current Indian entrepreneurial scenario (Figure 15.1). Allen et al. (2006) also found that the major bootstrap financing techniques tapped by Indian companies were borrowing from family and friends, using internally generated funds and trade credits. Given the limitations of access to institutional finance for nascent startups, bootstrapping techniques need to be explored as a way of accessing capital apart from just borrowing from friends and family. Hence, the present study attempts: • To analyze the seven-pillar framework for identifying the inconsistencies in the Indian startup ecosystem. • To suggest the best possible bootstrapping techniques in regard to various challenges faced by Indian entrepreneurs.

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50 45 40 35 30 25 20 15 10 5 0

42.9

17 11.3 9.7

7 3.5

2.2

1.7

1.7

0.6

0.5

0.5

FIGURE 15.1  Source of funds (percent share of the total number of participating firms). Source: RBI Report (2019).

METHODOLOGY To find the inconsistencies, the seven-pillar framework was studied for 34 states and union territories. The State Startup Ranking Framework was introduced by the Department of Industrial Policy and Promotion (DIPP) in February 2018. The framework covered broad categories of seven startup support pillars, viz., Startup Policy and Implementation, Incubation Support, Seed Funding Support, Funding Support, Simplified Regulations, Easing Public Procurement Awareness and Outreach. The report provided the list of states having exemplary performance specific to each pillar. The various states’ scoring concerning each pillar was done dichotomously (state present in the exemplary list was given a one score, otherwise, a zero). States’ aggregate score out of seven was determined by the number of pillars in which the state has shown exemplary performance. Further, states were classified into customized clusters based on their aggregate scores, and inconsistencies were marked by generating and comparing the heat map of the number of startups in India with the heat map of startup support being provided by the Government based on the seven pillars.

RESULT AND DISCUSSION Table 15.1 shows the dichotomous scoring of states and UTs associated with each pillar. The mapping of seven pillars based on exemplary performance brings us to the aggregate points of each state. On basis scoring, the states were classified into three clusters.

Advanced States The cluster includes states with an aggregate score of 4 and above: Andhra Pradesh, Chhattisgarh, Gujarat, Karnataka, Kerala, and Odisha.

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TABLE 15.1 Mapping seven pillars of startup ecosystem based on exemplary performance of each state

State

× × × × × × ◽ × ◽ ◽ × × ◽ ◽ × × × × × × ×

Incubation support × ◽ ◽ × × × × × × ◽ ◽ × × ◽ × × × × × × ×

Seed funding support × × × × ◽ × ◽ × × × × ◽ × ◽ × × × × × × ×

Funding support angel and venture funding × × × × × × ◽ × ◽ × × × ◽ × × × × × × × ×

Simplified regulations × × ◽ ◽ × × × × ◽ × × × ◽ ◽ × × × × × × ×

Easing public procurement × × × × × × × × ◽ × × × × ◽ × × × × × × ×

Awareness and outreach × × × × × × × × ◽ × × × × ◽ × × × × × × ×

Total Score (out of 7) 0 1 2 1 1 0 3 0 6 2 1 1 3 6 0 0 0 0 0 0 0 (Continued)

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Chandigarh Delhi Haryana Himachal Pradesh Jammu Kashmir Punjab Rajasthan Uttarakhand Chhattisgarh Madhya Pradesh Uttar Pradesh Bihar Jharkhand Odisha West Bengal Arunachal Pradesh Assam Manipur Meghalaya Mizoram Nagaland

Startup policy and implementation

× × × ◽ × ×

× × × ◽ × ×

× × × ◽ × ×

× × × ◽ × ×

× × × ◽ × ×

× × × ◽ × ×

× × × ◽ × ×

0 0 0 7 0 0

◽ ◽ ◽ × × × ×

◽ ◽ ◽ × × × ◽

× ◽ ◽ × × ◽ ×

× ◽ ◽ × × × ×

◽ ◽ × × × × ×

× × × × × × ×

◽ ◽ ◽ × × × ◽

4 6 5 0 0 1 2

Source:  Compiled by authors from State Startup Ranking Report (2018).

Curbing Inconsistencies Through Financial Bootstrapping

Sikkim Tripura Goa Gujarat Maharashtra Andaman & Nicobar Islands Andhra Pradesh Karnataka Kerala Puducherry Lakshadweep Tamil Nadu Telangana

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Intermediate States The cluster takes account of states with an aggregate score between 0 and 4: Telangana, Jammu Kashmir, Himachal Pradesh, Delhi, Jharkhand, West Bengal, Tamil Nadu, Uttar Pradesh, Rajasthan, Bihar, Haryana, and Madhya Pradesh form a part of this cluster.

Beginner States This cluster includes states which did not make it to the exemplary lists even once. The states are Chandigarh, Manipur, Mizoram, Nagaland, Puducherry, Sikkim, Tripura, Maharashtra, Uttarakhand, Goa, Assam, and Punjab.

OVERVIEW: SEVEN-PILLAR FRAMEWORK All elements of the seven-pillar framework are critical factors that bring out the differences in the states’ startup ecosystem. The overview of the differences would help identify good practices and encourage states to learn from one another.

STARTUP POLICY AND IMPLEMENTATION Several national startup policies and initiatives are making a significant impact on the ground (Chhabra et al., 2020; Kumar and Chhabra, 2021). Small Industries Development Bank of India (SIDBI) has provided funding support for innovationdriven startups and has backed 75 startups (NASSCOM, 2018). Karnataka collaborated with companies and service providers to develop a startup, Karnataka Booster Kitto, that motivates young startups through a range of tech tools and services like mentorship, virtual telephony, and cloud computing at an affordable price. Telangana established an extensive network of over 100 Startup mentors from business, academia, and the government. States like Andhra Pradesh, Gujarat, and Maharashtra established innovation societies responsible for overseeing state-based startup initiatives. Improved online registration platform and query resolution system would result in effective implementation of startup policies.

INCUBATION SUPPORT The Government of India launched the Atal Incubation policy to provide INR 1.5 crore financial support to set up incubation centers wherein, for a cumulative duration of five years, the mission would include a grant-in-aid to Rs. 10 crores to cover the capital and financial costs for creating Atal Incubation Cell (State Startup Ranking Report, 2018). As of 2019, there were 13 central government ministries and departments that were supporting incubators. Growing incubation support encourages young entrepreneurs with creative ideas to create a prototype at an affordable cost (Majumdar, 2020).

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SEED FUNDING SUPPORT The startup policy (2015–20) of Karnataka provides seed funding under the “Idea2PoC” scheme. Similarly, Rajasthan Startup Policy 2015 includes seed funding in the form of monthly sustenance allowance under the “Assistance for Startup at Idea or prototype stage.” Gujarat provides seed funding in the form of sustenance allowance, marketing assistance, and product development assistance. Also, J&K has introduced the Seed Capital Fund Scheme under which seed money is given to eligible prospective entrepreneurs to kick-start their business. Despite these initiatives, seed-stage funding fell by 21%, and the number of seed-stage deals declined by 18% in the first eight months of the year even though overall annual funding growth was 108% year over year (NASSCOM, 2018). Service-based startups like Flipkart, Quikr, Sulekha, Ola, Yatra, and POPxo are ruling the market compared to the product-based ones. In the recent past, some states decided to lower the seed funding provided to the ventures in the service sector and focus more on the manufacturing sector. The government should develop policies to provide enough seed funding to ventures in the manufacturing sector to succeed in Make in India and Atmanirbhar Bharat.

FUNDING SUPPORT, ANGEL AND VENTURE FUNDING When a venture hits a particular maturity stage, the entrepreneurs are willing to obtain additional capital to keep the company operational. To supplement this, the Chhattisgarh government approved 36 Angel Fund (36AF) for early-stage funding in startups. The Venture Park Incubation Centre in Patna was founded by Bihar Industries Association (BIA) in partnership with the Indian Angel Network. To provide sustained support to the startups, the Government of Gujarat, Karnataka, UP, Bihar, Kerala, and Rajasthan has operationalized funds for investing in venture funds that invest in startups in various sectors. Apart from funding, the investors also provide feedback based on their own experience, which adds more expertise to the ecosystem and actively helps this evolution (Majumdar, 2020).

SIMPLIFIED REGULATIONS Simplified regulations ensure the growth of the Indian startup ecosystem and its smooth functioning. The Startup India website (https://www.startupindia.gov.in/) shows that 28 states in India established an online portal for promoting self-certification or certification by third parties in compliance with all relevant state labor laws. Chhattisgarh, Jharkhand, Odisha, Karnataka, and Gujarat focus on supporting startups working in new or disruptive areas or technologies.

EASING PUBLIC PROCUREMENT The Ministry of Commerce and Industry initiated the Government eMarketplace (GeM), an online marketplace to support various startups. The platform exempts startups from the requirements of any turnover, previous experience, and earnest money deposit submission, thereby giving young entrepreneurs equal opportunities.

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Better implementation structure of the GeM portal and making it accessible and easy to handle are the need of the hour.

AWARENESS AND OUTREACH Various states like Chhattisgarh, Punjab, and Gujarat regularly organize and participate in events to give startups a forum to showcase their innovations, products, and services. Gujarat sponsored large-scale summits such as Vibrant Gujarat Startup Summit 2016 to provide a platform for startups to put forward their viable and creative solutions. The Government of India has set up MHRD Innovation Cell (MIC) and Institution’s Innovation Councils (IIC) under this cell in many higher education institutions (HEIs) throughout the country to promote innovations and convert those innovations into startups. The main objective of the seven pillars of the startup support system is the survival of businesses in India. The discussion of the seven pillars points out that state governments should work on all the elements. As literature shows that most startups fail due to lack of funds, the need of the hour is to create and upgrade the startup ecosystem. Moreover, that raises a need to study the inconsistencies prevailing in the Indian startup ecosystem.

INCONSISTENCIES IN DISTRIBUTION OF STARTUPS ACROSS INDIA As India’s startup culture is growing, the government is coming up with various incentives, subsidies, and policies. DIPP across the country examined a total of 14,565 startups. The largest number of startups is in Maharashtra (2,787), followed by Karnataka (2,107), Delhi (1,949), Uttar Pradesh (1,201), Haryana (765), and Gujarat (764) (State Startup Ranking Report, 2018). State mapping shows that states like Maharashtra, Delhi, Uttar Pradesh, and Haryana, with a huge number of startups, are not covered in the advanced level states. In contrast, the advanced level states like Chhattisgarh and Odisha are not performing well with respect to the overall startup ecosystem. The heat maps in Figure 15.2 were generated to study the various inconsistencies in the number of startups in India (Figure 15.2a) and startup support being provided by the government based on the seven pillars (Figure 15.2b).

Type I Inconsistencies Type I inconsistency can be seen in Maharashtra, Haryana, and Chennai, where states have many startups being formed but still are not considered on exemplary performance lists. The inconsistencies in these states exist because startups’ growth is in only one or two major cities and not in the overall state. Significant startup activity takes place at Mumbai (Maharashtra), Gurgaon (Haryana), and Chennai (Tamil Nadu). Although these cities have the maximum number of startups, their respective states do not have a place in the exemplary lists. The respective states need to formulate a startup environment, focusing beyond Mumbai, Pune, Gurugram, and Chennai to encourage entrepreneurship.

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FIGURE 15.2  Heat map showing distribution of startups across India (State Startup Ranking Report, 2018) vs. heat map generated from the startup ecosystem scoring.

Type II Inconsistencies Type II inconsistencies can be seen in Rajasthan, Jharkhand, Odisha, and Chattisgarh. Despite the number of startups being less, these states find a position in the exemplary performance lists. Various institutional voids, lack of education, traditional culture, and social attitudes result in lower social acceptance for entrepreneurial activities. Hence, there is a need to create the importance of entrepreneurial activities and the creation of industrial bases like technology parks, special economic zones, and industrial hubs in various fields.

Northeastern States Apart from these inconsistencies, the heat map shows that both the parameters are not up to the mark in northeastern states. At the same time, the Indian government has prioritized infrastructure projects to connect the Northeast to the rest of the country, a disconnect remains. Budding entrepreneurs cannot register their companies in Sikkim. The Ministry of Corporate Affairs has taken up with Sikkim’s government on the applicability of the Companies Act 2013 in Sikkim. Digital connectivity remains an issue of vital importance in many parts of the region. Even though there are various institutional voids, potential opportunities for northeastern states can be seen through several institutes in the region, such as IIT Guwahati and IIM Shillong that have set up their incubation centers by providing mentors and accelerators. There is a need to remove the inconsistencies in various states for a better entrepreneurial ecosystem. As bootstrapping finance allows the entrepreneurs to make

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their own choices and expand their enterprise on their terms, it is one of the best solutions to fill the gap of the inconsistencies that various entrepreneurs face in different states.

CHALLENGES AND CHANCES Through reviewed literature, significant entrepreneurial challenges faced in the Indian startup ecosystem were extracted out. A bootstrap financing cluster, as given by Winborg and Landstrom (2001), has been assigned to each problem to resolve the obstacle. Furthermore, the recommendations are mapped to curb these challenges specifically in context to the Indian startups.

Payments That Have to Be Made to the Suppliers Bootstrap cluster: delaying payments In short-term payments, suppliers usually extend credit for 30 to 90 days to trustworthy and regular customers. A successful approach may entail negotiating with suppliers on specific terms, such as extending the average payment period (Schinck & Sarkar, 2012). Factoring as a bootstrapping technique, growing at a moderate pace, is another primary technique in case the startup deals with long-term receivables. Factoring provides immediate cash injection, which further improves the company’s working capital.

Finding a Suitable Business Model Bootstrap cluster: minimizing accounts receivable The business model lays down guidelines if potential customers have to pay for it in advance in the form of a pre-launch booking of the product/service. Techniques to cover up these early payments include speeding up the invoicing, interest rates on outstanding transactions, or termination of contractual agreements with late payers to ensure the company’s best interest (Winborg & Landstrom, 2001). Incorporating proper guidelines regarding minimizing account receivables forms a major part of a good business model of the startup.

Acquisition of Capital Equipment Bootstrap cluster: minimizing investments For entrepreneurs who seek efficiency with minimum resources in hand, in the Indian scenario, lease financing is one of the best bootstrapping solutions in this case. Other approaches involve purchasing used equipment at a reduced cost, requesting a substantial cash discount, or even recruiting temporary employees (Schinck & Sarkar, 2012). All these techniques would help an entrepreneur to acquire the required resources at minimum cost.

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The Requirement of Funds for One-Time Startup Expense Bootstrap cluster: private-owner financing Bootstrapping techniques under private-owner financing clusters strengthen the ability of the entrepreneurs to raise capital. Expenses have to be met out of an entrepreneur’s pocket or by admitting a co-founder who can share the financial risk if the entrepreneur is concerned that his business idea would not attract crowdfunding if it is not novel. An entrepreneur may go for the FFF route (Family, Friends, and Fools) to work with low or no salary or providing free work to reduce costs (Schinck & Sarkar, 2012).

Acquiring Office Deposit, Equipment, and Furnishing Bootstrap cluster: sharing resources with other businesses Sharing of resources are either linked to physical space or to a wide variety of products and facilities, often even sharing of human resources (Neeley, 2003). Partnerships, collaborations, and coworking space are major bootstrapping solutions and emerging trends in India to curb this challenge. Sharing resources with other businesses would help the nascent startups who have insufficient resources initially. Vanacker et al. (2011) reported a positive impact on value-added approaches relating to utilizing funds from owners, government incentives, temporary staff recruiting, minimization of accounts receivable, running the business from home, informal investments from family or friends, joint purchases, sharing premises, minimizing expenditures, and delaying payments. Although different bootstrapping strategies have been provided as solutions to unique challenges, employing a mix of various techniques with different magnitude would be the most effective way of achieving success and minimizing the risk.

RECOMMENDATIONS AND IMPLICATIONS For Policymakers The study’s findings provide insights to policymakers for creating awareness among the budding and existing entrepreneurs about the various bootstrapping techniques for their sustainability. Financial literacy would contribute a lot in this direction. Separate bootstrap financing modules and their implication should be introduced for entrepreneurs. Discussion from the seven-pillar review also points out some essential policy implementation steps that state governments should take to strengthen a particular pillar individually. Further, through the analysis, apart from the kind of support defined in the seven pillars, mentorship is also an essential aspect for budding entrepreneurs, which is not easily accessible in the present scenario. Mentorship should be made available to entrepreneurs continuously. Evolving the seven pillars of the startup ecosystem by proper policy implementation would make the entrepreneurship environment proactive by giving entrepreneurs trust and nurturing their development. The success rate of startups would increase with bootstrap financing, which would further increase self-employability in society.

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For Entrepreneurs The study suggests several real-world policy setting implementations to raise awareness about how entrepreneurs should develop entrepreneurial finance and capital by being more familiar with the different bootstrapping approaches. Nascent startups who still believe in keeping an entire stake in the enterprise and growing organically for survival should focus on the latest innovative bootstrapping techniques emerging in recent times, such as accelerators, angel investors, crowdfunding, and peer-to-peer lending. Since numerous financial bootstrapping techniques reduce resource constraints and improve firm performance, choosing the best bootstrapping method related to better firm performance is essential for young entrepreneurs. Using a mixture of different bootstrapping techniques with efficacy can further improve the ease of doing business standards. The chapter also provides insight and motivates the budding and existing entrepreneurs to use various bootstrapping techniques to remove obstacles and gain a higher growth level.

CONCLUSION Using original and innovative bootstrapping methods leads to minimizing the amount of external finance (financial market transactions or outside financers), and it also allows entrepreneurs to access resources that are not self-owned at little or no cost. There have been many policies to encourage a startup culture at all levels of government, business, and education. However, there are still a lot of challenges startups have to face every day. The paper identified various inconsistencies in the Indian startup ecosystem. The reviewed literature shows that entrepreneurs’ challenges due to these inconsistencies would be curbed out through various financial bootstrapping techniques. The government and the entrepreneurs should consider these bootstrapping techniques in India as they prove to provide a genuinely profitable boost for the economy.

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Repurposing the Role of Entrepreneurs in the Havoc of COVID-19 Manpreet Arora and Roshan Lal Sharma Central University of Himachal Pradesh, India

CONTENTS Introduction������������������������������������������������������������������������������������������������������������ 229 Methodology and Research Questions������������������������������������������������������������������� 232 Entrepreneurship: Literature Review Concerning Its Meaning and Relevance����� 233 Role of Entrepreneurs�������������������������������������������������������������������������������������������� 236 Entrepreneurial Innovations During Pandemics���������������������������������������������������� 239 Use of Big Data in Entrepreneurial Ventures��������������������������������������������������������� 240 Dealing with the Situation and the Way Forward�������������������������������������������������� 241 Concluding Remarks���������������������������������������������������������������������������������������������� 245 References�������������������������������������������������������������������������������������������������������������� 245 Additional Readings������������������������������������������������������������������������������������������ 249

INTRODUCTION Over the last decade, the corporate world has witnessed a paradigm shift in various sectors in almost every world economy (Chhabra & Karmarkar, 2016a). From the highest pressure of inflation in some developing nations to depression in economies like the United States, the whole world has just survived through ups and downs in global economic trends. Added to the economic shocks, the Indian economy experienced an unimaginable decision of demonetization, followed by a major shift in taxation policy, as the traditional taxation system gave way to GST (Chhabra et al., 2020). Simultaneously, China also emerged as a superpower, and different countries of the world started looking forward to doing business with this giant economic player. While many of the economies were in the process of devising ways to come to terms with economic shocks, no one had thought of the spread of COVID-19, which was later termed as a pandemic. The world has faced many infectious diseases in the past, but it becomes the worst-case scenario when a pandemic occurs. According to the experts’ definitions, ‘when any disease spreads beyond the borders of a country, it is termed as a pandemic’. Humanity has been a witness to several such diseases where the consequences had been devastating. Diseases like malaria, tuberculosis, leprosy, influenza, plague, cholera, measles, typhoid fever, smallpox, AIDS, the Russian, Spanish and Asian flus, HIV-AIDS, and then SARS, MERS, and finally COVID-19, DOI: 10.1201/9781003097945-16

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all have changed humanity’s total approach to life. On March 11, 2020, WHO officially announced that the COVID-19 virus was a pandemic, and it had spread to 114 countries in a matter of a few months. The first case was reported in China on November 17, 2019 (WHO). By mid-March, it spread globally to more than 163 countries. Global economic activities came to a standstill. Whether medium or large scale, most of the businesses were forced to reduce operations, and some of them had to face the worst shutdown scenarios. A rapid increase in the number of people was also seen who lost their jobs. The International Monetary Fund termed it to be a ‘Great Lockdown’. The chief economist of the IMF termed it to be a ‘Truly Global Crisis’. According to the US Bureau of Labor Statistics, more than 26 million jobs were lost from February 2020 to April 2020. The unemployment rate of China, as per the National Bureau of Statistics of China, was 5.9% in May 2020. The Australian Bureau of Stats revealed an unemployment rate of 5.2%, while Statistics Korea revealed the unemployment rate of 3.8%. Money experts believe that the situation can become worse in many of these economics. The two largest economies globally, i.e., the United States and China, where the service industry is a major growth source, witnessed a sharp downward trend in retail sales as COVID-19 spread. The US Census Bureau reported a negative rate of −6.2% in the sale of all consumer goods. Simultaneously, the National Bureau of Statistics of China showed a negative trend of −15.8% of sales in all consumer goods. As there was a greater spread of coronavirus, the countries across the globe imposed the lockdown measures, and a large number of manufacturing firms were miscued. Many big industrial manufacturing hubs were forced to close down temporarily. This was not enough; a drastic fall in demand for goods across the global markets has worsened manufacturers’ scenarios. The WTO has predicted that across the globe, the economies were expected to receive a setback of double-digit decline in exports and imports in 2020. OECD forecast that ‘annual global GDP growth is projected to drop 24% in 2020 as a whole, from an already weak 2.9% in 2019’ (OECD, Interim Economic Assessment, 2020 Report, Table 16.1). The OECD interim report also states that there has been an adverse effect of the pandemic on the financial, tourism, and travel sectors. However, it has also left a deep impact on everybody’s confidence. The supply chains have been disrupted. Those economies that were connected with/dependent on China have witnessed a drastic fall in the production and supply chain activities. The OECD has already stated that the Global Economic Prospects are quite subdued and remain very uncertain due to the coronavirus outbreak. COVID-19 has brought much human suffering and has led to high economic disruption across the globe. Not only a sizeable contraction in production activities was done by the manufacturers due to the outbreak; the situation was further worsened due to border closures and cancellation of flights and sea routes for opting the containment measures. OECD projected that economic growth may pick up in the coming years if the effects of coronavirus started to fade. At that point, output will gradually recover. Countries will have to adopt the policy of low-interest rates as a cushion for demand. A wise thought of fiscal policies will also help Asian economies to

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TABLE 16.1 OECD Interim Economic Assessment Coronavirus: The world economy at risk, March 2, 2020 OECD Interim Economic Outlook Forecasts, 2 March 2020 Real GDP growth Year-on-year % change 2019

2020 Interim EO projections

World G 201,2 Australia Canada Euro Area Germany France Italy Japan Korea Mexico Turkey United Kingdom United states Argentina Brazil China India Indonesia Russia Saudi Arabia South Africa

2.9 3.1 1.7 1.6 1.2 0.6 1.3 0.2 0.7 2.0 −0.1 0.9 1.4 2.3 −2.7 1.1 6.1 4.9 5.0 1.0 0.0 0.3

2.4 2.7 1.8 1.3 0.8 0.3 0.9 0.0 0.2 2.0 0.7 2.7 0.8 1.9 −2.0 1.7 4.9 5.1 4.8 1.2 1.4 0.6

2021

Difference from November EO −0.5 −0.5 −0.5 −0.3 −0.3 −0.1 −0.3 −0.4 −0.4 −0.3 −0.5 −0.3 −0.2 −0.1 −0.3 0.0 −0.8 −1.1 −0.2 −0.4 0.0 −0.6

Interim EO projections 3.3 3.5 2.6 1.9 1.2 0.9 1.4 0.5 0.7 2.3 1.4 3.3 0.8 2.1 0.7 1.8 6.4 5.6 5.1 1.3 1.9 1.0

Difference from November EO 0.3 0.2 0.3 0.2 0.0 0.0 0.2 0.0 0.0 0.0 −0.2 0.1 −0.4 0.1 0.0 0.0 0.9 −0.8 0.0 −0.1 0.5 −0.3

Note: Projection based on information available up to February 28. Difference from November 2019 Economic outlook in percentage points, based on rounded figures. 1. Aggregate using moving nominal GDP weights at purchasing power parities. 2. The European Union is a full member of the G20, but the G20 aggregate only includes countries that are also members in their own right. 3. Fiscal years, starting in April. Source:  Assessment, O. I. E. (2020).

create a balance between demand and supply. The impacts of this world recession have been deep and alarming. In such a scenario when the unemployment rate has increased a lot in many countries, production of many products has come to a standstill, and the economies need a revival strategy. Many countries have opened lockdowns, and efforts worldwide are

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going on to set things back on the right track. The solution to many factors in this crisis lies in entrepreneurial momentum, though carefully planned and well thought over. Entrepreneurship is regarded as a key driver of bringing structural changes in an economy. Various aspects of growth are associated with entrepreneurship which can ultimately become a driving force to help people get rid of poverty and inequality. Entrepreneurship has the potential to contribute towards development (Naudé, 2010). Literature suggests that a relationship exists in the ‘entrepreneurial environment’ and the different stages/phases of the development of an economy (MartínezFierro, Biedma-Ferrer, & Ruiz-Navarro, 2016). Authors have identified certain variables for a group of countries where they act as ‘facilitators’ or ‘obstacles’ for an economy at a different development level. Therefore, every country has its obstacles and facilitators in dealing with a pandemic or any crises. COVID-19 has posed certain grave challenges which vary from nation to nation. Some economies are ‘factor driven’ whereas others are ‘efficiency driven’, and some are ‘innovation driven’ as well (Acs, Desai, & Hessels, 2008). Various types of government/institutional arrangements affect entrepreneurial activities positively or negatively. In such a situation, innovation-driven countries can use entrepreneurs’ potential to face the crises for which their role needs to be reassessed, redefined, and repurposed. Economies like Russia relied on the SME sector while following systematic transition (Chepurenko, 2010). Though the ‘entrepreneurship and SME policy in Russia remained reactive than proactive’, ‘productive entrepreneurship’ is the need of the hour that the economies’ governments should support. At this point, when countries across the globe, one way or another, are going through a transition, they must focus on sustainable practices of entrepreneurship. Developing countries have received a considerable shock due to the pandemic, and the socio-economic impacts on economies like Africa have been considerable (Olayide, 2020). ‘Poor data systems and insufficient social safety’ are some of the key characteristics causing lockdowns to be inefficient to control the pandemic in such countries. Olayide further observes that investment in ‘infrastructure’ and ‘pharmaceuticals’ can help tackle the problems and thereby indicate the need for entrepreneurial interventions. Brannen, Ahmed, and Newton (2020), analyzed the macro trends of the medium to long term in the COVID-19 era and found it highly disruptive. According to the authors, the ‘unevenness’ and ‘unpredictability’ in the scenario due to the pandemic ‘will define the challenges ahead’. The author clearly states thus: ‘Covid-19 marks the start of an era of continuous, rapid change’. Caballero-Anthony, Teng, and Montesclaros (2020) consider the pandemic to a ‘once in a lifetime crisis’ that has affected the well-being and security of people drastically and can have ‘long lasting’ consequences. The authors also foresee food crises in Asia if there is a lack of policy and planning at governments’ levels. This indicates challenges as well as opportunities for the entrepreneurs.

METHODOLOGY AND RESEARCH QUESTIONS This chapter seeks to employ qualitative methodology to conceptually argue as to how entrepreneurs’ crucial role becomes in time of pandemics such as COVID-19. To structure the chapter better, firstly, various dimensions of entrepreneurship have

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been discussed with the help of an exhaustive literature review followed by a conceptual overview of the role of entrepreneurs, entrepreneurial innovation, technological advancement, Big Data, and analytics to eventually prove how crucial innovative entrepreneurial endeavors are in coming to terms with today’s crisis-ridden world. The following questions will be explored in the course of the discussion of this chapter. (a) Do crippling and devastating pandemics like COVID-19 offer any scope of recovery for the suffering humanity? (b) How does the notion of entrepreneurship become extremely important in such devastating scenarios, and how do the entrepreneurs, with a remarkable capacity to lead and persuade the suffering masses to become extraordinarily relevant, give a new direction to the world thrown out of gear? (c) How do we repurpose and redefine the role of entrepreneurs to deal with the crisis-ridden situation with the help of innovative entrepreneurship, use of technological advancement, and Big Data to overcome a feeling of despair, anguish, and negativity caused by the crumbling of economic and psychological edifice across the globe today?

ENTREPRENEURSHIP: LITERATURE REVIEW CONCERNING ITS MEANING AND RELEVANCE Entrepreneurship is essential to all economies (Chhabra & Karmarkar, 2016b; Kshetri, 2009; Lazonick, 2008; Ovaska & Sobel, 2005; Schramm, 2004), and there is considerable literature corroborating this fact. It is regarded as an important activity for every type of economy, be it developing or developed (Adenutsi, 2009; Toma, Grigore, & Marinescu, 2014). The empirical evidence proves that through entrepreneurship, the standard of living increases (Dhaliwal, 2016), per capita income increases (Bruton, Ahlstrom, & Si, 2015), and it helps create wealth (Quadrini, 1999) and thus solves multiple problems in an economy (Estrin, Meyer, & Bytchkova, 2006; Karmarkar et al., 2014). Entrepreneurs can drive innovation (Okpara, 2007) and develop new products or services for the masses (Copeland & Savoia, 2011). They can make a meaningful contribution to society. They can add to the most dynamic, vibrant, and static economies with novel ideas and innovative abilities (Bessant & Tidd 2007). Researchers across the world regard entrepreneurship as an indispensable engine for promoting and boosting growth (Acs & Audretsch, 2005). It helps promote innovation, which contributes to creating newer opportunities for the population in the form of ‘employment opportunities’ (Momani, 2017) and reduction of poverty. Many governments across the globe regard entrepreneurship to be an important activity for women empowerment (Ramadani, Gërguri-Rashiti, & Fayolle, 2015), for reduction of inequalities (Pathak & Muralidharan, 2018), to promote productivity (McMillan, 2004), and to solve problems like sustainable goals (Youssef, Boubaker, & Omri, 2018), climate control (Green, 2017), promoting information channels (Chell & Baines, 2000) and solving even Big Data issues (Obschonka & Audretsch, 2019). It is through entrepreneurial intentions only that we get solutions to the problems of the economy as these intentions promote entrepreneurial activities (Vuorio, Puumalainen, & Fellnhofer, 2018). In the present era of the COVID-19 pandemic, where global trade is at a halt, imports and exports are negligible as many countries have imposed lockdown

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measures to stop the spread of infectious disease, and the demand of the economy is dependent on the entrepreneurial activities within the country. The most formidable challenges to the world’s problems are solved through entrepreneurship (Fadaee & Abd Alzahrh, 2014). The motivations, ambitions, and national conditions of countries like India are pushing the entrepreneurial activities in the country. All around the world, the concept of entrepreneurship has gained the spotlight. Governments are emphasizing the policy frameworks relating to entrepreneurial activities and are getting prime attention. Various mechanisms like promoting entrepreneurial education, giving thrust to local produce, providing subsidies and other incentives to start-ups, and providing microfinance are important measures that ultimately lead to entrepreneurial activities, whether big or small. The entrepreneurial ecosystem and studying its interplay with the factors around it are essential aspects that lead to the creation and analysis of information used to promote entrepreneurial activities to promote growth and development in a country. Great recessions have resulted in many business closings and foreclosures, and they always lead to a decrease in potential business income and wealth. Moreover, recessions restrict the opportunities in the wage sector, leading to ambiguities in business ventures. The literature also suggests that local labor market conditions play a determinant role in deciding about entrepreneurial ventures (Karra, Phillips, & Tracey, 2008). Recessions or depressions may negatively affect business start-ups but may show some negative trends in demand and supply. Thus, it is ultimately the entrepreneurs who take up the task of a revival of an economy. After the phase of recession, a rapid rise in the number of unemployment cases and layoffs, a boost in entrepreneurial activity is the only solution to get out of the trap of economic imbalances. It is termed as necessity entrepreneurship which can respond to the challenges thrown by the situation close at hand. Though the motivations of such business ventures are different, with the efforts of entrepreneurs, the success of these ventures can lead a particular economy to rise up from a drastically abysmal situation to a positive one with a promise of a better future. The literature provides evidence that entrepreneurs develop certain leadership qualities, especially in crises (Brouwer, 2000). Otherwise, also, they exhibit leadership qualities and skills in different arenas of life. Ulvenblad and Cederholm Björklund (2018) conducted a study on the agricultural entrepreneurs of Sweden and found that if leadership development programs are offered to entrepreneurs, they benefit a lot from that. Further leadership competencies are complementary to entrepreneurs as knowledge transfer and dissemination of information can have a beneficial effect on industry leadership. It leads the way to manage larger units better, apply innovative techniques, and respond positively to dynamic business environment situations. Bruce, Malcolm, and O’Neill (2017) believe that creative industries help drive the economic growth in a country. Various organizations these days contribute to the development of the country by successfully contributing to the industry. The growing use of digital content, the rapid pace of development of new digital platforms, and emerging digital technologies are making the digital industry an important catalyst in the growth of an economy. Undoubtedly, the role of entrepreneurs in this industry to manage and use Big Data cannot be undermined. Many structured or unstructured

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data where the velocity and volume are relatively high needs to be managed and associated with Big Data. Suppose such data is used effectively in any sector of the economy. In that case, it can provide helpful insights to get a forward-looking approach, especially in times of rapid change and uncertainty. Si, Ahlstrom, Wei, and Cullen (2020) attribute entrepreneurial activities to be the essential pillars for alleviating poverty through entrepreneurship and innovation. The widespread economic growth enables people to get over extreme poverty by providing them with employment opportunities and new venture creation. The initiatives of new venture creation, start-ups, and businesses’ development are the core entrepreneurial activities that lead to any economic development. New technology development and the creation of new techniques solve an economy’s central problems and contribute towards technological independence and strengthening the pace of economic growth. Entrepreneurial intentions can lead to dramatic progress in understanding the key factors associated with the alleviation of poverty. Fitzgerald and Muske (2016) analyze the family businesses which contribute to the economies. They regard such family businesses as crucial for any economy and term them as small businesses or entrepreneurial ventures. Their empirical studies indicate that there is a huge importance of such entrepreneurs in economic development as they make important contributions to the long-term sustainability of the community and their economic sector. Ulvenblad and Cederholm Björklund (2018) studied the concept of leadership with agricultural entrepreneurs. Their findings suggest that the leadership competencies of the entrepreneurs can be enhanced by proper training. Role transformation can help them contribute to society and emerge as catalysts in the development of economies. Fortunato and Alter (2015) have tried to establish a relationship between entrepreneurship and community development. According to them, there can be complex and varied ways by which entrepreneurs can benefit their communities. According to them, the community entrepreneurship development program as a strategy can produce benefits that go beyond economic growth. Lubberink, Blok, Ophem, Velde, and Omta (2018) propagate in their study that social entrepreneurs have an inherent capacity to develop important innovative solutions for every complex societal problem/issue. They explore diverse approaches followed by social entrepreneurs for developing certain innovations which are beneficial for society. They can assume the problem-solving role for the society, which fosters innovation and brings about change in the society. Planko, Cramer, Hekkert, and Chappin (2017) believe that technological sustainability is important for the growth of every economy. The technological innovation systems can provide us with valuable insights that can stimulate the entrepreneur to choose the path of development by adopting various innovative technologies based on sustainability. Societies constantly change and transition towards more sustainable practices. The entrepreneur can adopt sustainable practices by taking care of various problems in society. Unsustainable technologies can lead to drastic situations; therefore, every entrepreneur should try to focus on various technological innovations that fulfill sustainable goals over a period of time. Driving forces of innovative actions are often those entrepreneurs who try to implement various sustainable technology practices in their processes of venture creation and new product

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development. Entrepreneurs can trigger various social changes, and with various research collaborations of product development, they can achieve growth for the society by creating employment opportunities, reduction of unemployment, and, finally, the optimum utilization of resources (Kressel & Lento, 2012; Okpara, 2007; Soete & Stephan, 2004; Kerr, 2013). They must adopt sustainable strategic actions and policies for constructing a supportive information system around the technology they can utilize to increase the chances of successful implementation of technology for the betterment of society. An entrepreneur is regarded as someone who specializes in taking responsibilities and who can take judgmental decisions to the effect to start new start-ups and contribute immensely towards the achievement of economic goals of a country (Kefela, 2011). The economic goals and various socio-cultural issues are also followed and well pursued by the entrepreneur in this direction.

ROLE OF ENTREPRENEURS We have considerable literature that shows how crucial the entrepreneurs’ role can be in varied, challenging/difficult situations. McMillan and Woodruff (2002) opine that in transition economies, entrepreneurs are indispensable and that in economic transition, entrepreneurs act as reformers. The authors have propounded that in transition economies, entrepreneurs try to find new ways of doing business and are responsible for creating various types of jobs in the economies. In transition economies, creating jobs has been the most important activity that an entrepreneur can undertake in welfare. According to Baumol, Strom, and Sheshinski (2007), innovation-driven entrepreneurs play a very dominant role in transforming industrial sectors. There can be so many types of entrepreneurs who can have a deep impact on the development of an economy. For example, innovative entrepreneurs account for a tiny portion of the entire population but still have an extraordinary economic impact on the country. They are as much responsible for developing new technology as for creating new jobs and can revitalize the territories of a country. The basic premise of any entrepreneurial activity in innovation-driven entrepreneurship is the spillover of knowledge which can contribute to a great extent to the innovation and economic development of a country. Therefore, every economy should try to invest in knowledge-driven activities. The role played by an innovative entrepreneur can be very tiny. However, it is quite an influential segment of the entrepreneurs, which is crucial for the economic development of a country (González-Pernía, Jung, & Peña, 2015). Liguori and Pittz (2020) contend that COVID-19 has started affecting small businesses worldwide, and they express a concern that the pandemic has proven the worst for small businesses and their employees. For combating the negative effects of the pandemic, small business owners should try to opt for certain practical, tactical strategies to deal with uncertainty and risk. The authors suggest that to minimize the financial and psychological effects of a pandemic, small business owners should try to enhance the collaborative efforts so that the innovative mindset of the business takes the lead. They should try to maintain a keen eye on the needs and desires of the customer to sustain in the market. The entrepreneurs can respond to the customers’ needs very quickly and have shown that new opportunities can be exploited in a concise period. Small business owners can create numerous possibilities of engaging

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with customers so their efforts are market-driven, especially during a pandemic. It can be positively utilized to connect with the customer base and create certain stronger community ties that can help them improve their skill sets and ultimately lead them towards innovation. The authors also suggest that value creation and resource utilization for long-term goals are the essence of entrepreneurship. They should use their tactics for small businesses to take care of the current environment caused by COVID-19. Ratten (2020) believes that entrepreneurship is dependent upon cultural, social, and lifestyle factors. Therefore, in order to make any societal change, we need to understand entrepreneurship holistically. Culture, lifestyle, and social entrepreneurship are the factors that need to be focused on exploring various problems associated with the economy. We need to focus more on various social changes that appear in the economy, thereby affecting the entrepreneurs. The solution for them lies in focusing on certain social demographics and lifestyle changes coming into the lives of people. Entrepreneurship is regarded as a novel recombination of various ‘products, services and processes’. Thus, to understand the concept of entrepreneurship, certain cultural and lifestyle factors should also be explored to understand the nature of entrepreneurial activity surfacing during the present COVID-19 times. Cultural entrepreneurship is often associated with storytelling, and hence to understand customers’ mentality during the pandemic, the study of cultural factors can be of immense help. The narratives given by the survivors and the problem-facing people in the market can help the entrepreneurs understand market opportunities. The entrepreneurs’ various products and services are influenced by the cultural heritage of people at international levels. Innovation is the key to the novelty offered in products in the marketplace, and that is why entrepreneurs are the drivers. The authors’ interesting aspect is that culture can be related to ‘technology trends too’. Highlighting the fact that the lockdowns during the COVID-19 pandemic resulted in a significant need to work from home as social distancing requirements became inevitable, the author regards culture as a driving force for technology use. In such a scenario, the need for databases, management information systems, Big Data, and advancement in technology gains tremendous significance. Quite obviously, cultural shifts are based on the cultural needs of people, and as a result of these, certain environmental changes take place. Another aspect related to culture affecting entrepreneurial activity is lifestyle entrepreneurship. Lifestyle entrepreneurs are not only interested in financial gains from entrepreneurial ventures. However, they are more interested in turning their ideas or hobbies into business ventures so that they can pursue their hobbies as entrepreneurial opportunities. The main purpose of the business activity is to gain a competitive advantage by exploiting the market opportunity. Lifestyle entrepreneurs are very much different from conventional/traditional entrepreneurs as they focus mainly on personal situations and try to hit the opportunity available through the economic activity that is available/tappable in the market scenario. Lifestyle entrepreneurs try to bridge the gap between social and economic goals by pursuing their hobbies. This type of entrepreneurship is also affected by cultural conditions prevalent in a particular society, economy, or life situation. During pandemics in many countries, it has also been seen that many people turn out to be entrepreneurs by pursuing their

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hobbies like cooking and delivering home food. They integrate the resources from multiple resources, including their family and friends, and try to get the most from the opportunity available in the situation. If the venture becomes successful later on, they try to seek help through government subsidies or plans. The sole aim of lifestyle entrepreneurs is to focus on the service, which is how they ultimately help in value creation. As Tudy (2021) observed, while many people worldwide lost their jobs during the pandemic, some people in the Philippines paid great attention to freelancing. The authors tried to analyze the experiences of professionals who turned into freelancers and focused during the pandemic on working from home. Many of the professionals left their previous jobs to be freelancers, and some of them opted to be freelancers voluntarily who were not professionals. The study revealed certain challenges faced by these freelancers, like ‘distractions at home’, ‘recognition’, etc. However, there were advantages, such as freedom, more time with the family, and flexibility. Freelancing has helped the economy to create alternative employment opportunities, whether the people are professionals or not. This phenomenon of online freelancers has been of enormous help in tackling unemployment in the Philippines. Gerritsen and colleagues (2020), in their study, noted that in New Zealand, grocery shopping was severely hit, thereby affecting economic activity badly. Unhealthy diet patterns were observed during the lockdown. A drastic effect on shopping, cooking, and eating behaviors was noticed, indicating that the pandemic had a negative effect on all kinds of businesses. Liguori and Bendickson (2020) draw our attention towards solving the Sustainable Development Goals proposed by the United Nations and stimulating economies. The most important driving force in achieving these goals is entrepreneurial ecosystems. The key force in entrepreneurship (Liguori & Bendickson, 2020) can ultimately help in value creation via realizing the entrepreneurs’ full potential. The power of development is associated directly with entrepreneurship. During the pandemic, many small businesses were forced to use digital technologies. The evidence proves that technology can help gain a competitive advantage over rivals even during the lockdown. Simultaneously, it may prove to be a means for survival for many businesses. Many technological aspects that were earlier considered to be good to have in business became critical during the pandemic for some businesses to survive. The adaptation of technology in businesses can help small and medium-scale businesses overcome the challenges posed by the pandemic (Akpan, Soopramanien, & Kwak, 2020a). Akpan, Udoh, and Adebisi (2020b) opine that small and medium-scale enterprises face a lot of hindrances in adopting digital technologies in developing economies. Developing economies still have a long way to succeed in terms of adopting advanced technologies to improve various kinds of operations and processes. Those technologies and innovations which appear to be new in developing nations were used by developed economies long ago. To quote the author, ‘Most state-of-the-art technologies, including cloud computing, “big data”, and predictive analytics that can improve operations and strategic decisions, are yet to make inroads in most EMDEs (Emerging Markets and Developing Economies)’. The small and emerging markets have still not  become self-sufficient to use advanced technologies like ‘state-of-the-art

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technologies’, cloud computing, Big Data, and other advanced software that can prove helpful in operational and strategic decisions of the organizations. The author emphasizes that in the new normal imposed by COVID-19 to face global competition, advanced technologies like Big Data are extremely important.

ENTREPRENEURIAL INNOVATIONS DURING PANDEMICS COVID-19 drastically affected the whole world and posed several grave challenges to the governments of various countries and entrepreneurs. However, on the brighter side, there are always hidden opportunities in such situations too. Various types of innovations have already been launched to deal with various challenges posed by this pandemic. COVID-19 has given entrepreneurs ample opportunities for innovation which are quite varied. It has allowed them to bring about certain badly needed changes in the society. Now the organizations focus on survival and need-based entrepreneurs; therefore, enterprises have mushroomed. Individuals are forced to adopt entrepreneurship as their profession in order to deal with their unemployment situation. Earlier, an entrepreneur’s work tended to be less lucrative than jobs in multinational businesses because of the risk factor and level of uncertainty involved. Now, the pandemic has taught us to survive in an environment where nobody thinks about taking a risk or opting to grow; it is about bare survival and existence in the hope to excel in the future. Innovations are always hard to predict, and we have come across various kinds of innovations during this pandemic. It is only now, after COVID-19 struck the world, that with the help of technology, everything is being done through online mode/s. Digital players like Zoom, Webex, GoToMeeting, and Google Apps have benefited a lot from the current situation. Entrepreneurs have been termed as catalysts of innovation driving economic growth. Only entrepreneurs can take up the global challenges and deal with the situation with the combination of ambition, investment, creativity, and innovation. Any entrepreneur can contribute immensely to solve the issues prevalent in the social and economic environment of a country. Undoubtedly, the pandemic has increased the importance of entrepreneurship as well as innovation. Every crisis brings various challenges as well as threats to the entrepreneurs and their organizations. Whether a crisis is caused by natural disaster or any economic breakdown has certain consequences for the nations that require strategic planning and action. It is the entrepreneur only who has to come up and solve the problems. The entrepreneurs can strive and look forward in times of uncertainties, disasters, and disruptions. COVID-19 drastically influenced the self-employed, small-scale entrepreneurs in a developing country like India. Some of them had to temporarily close their businesses due to various restrictions imposed by the government. Nevertheless, that does not imply that restrictions will remain forever, as now, including in India, various world economies are unlocking their economic and business activities. It is the need of the hour to bring the economy back on track and find certain innovative solutions in all aspects of the various entrepreneurial ventures to deal with the disruption caused by the pandemic. Whenever a crisis occurs, it takes a little time to think about how that situation can be dealt with innovatively. It may so happen that for some

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time, the pandemic influences the traditional entrepreneurial decision-making processes. However, the well-being of society depends mainly on entrepreneurial intentions, their ability to take risks and come up with innovative solutions to problems. Entrepreneurs help to build the society in times of crises. During this pandemic, we have learned how advanced healthcare systems are needed throughout the world in which technology plays a vital role. Thus, technological innovations are the need of the hour, as technology plays a major role in the well-being of humanity. During the COVID-19 crisis, entrepreneurs were expected to rise to the occasion via facing challenges of diverse sorts in economic, social, and educational fields, and the health sector. Businesses will undoubtedly play a key role in helping society come out of the economic crisis and creating various innovations that would shape it after the crisis is over. To a considerable extent, the damage was caused to the entrepreneurial ventures because of restrictions on people’s movement, but this phase was temporary. We have already witnessed a gradual shift in various traditional practices to the modern applications during this pandemic, like the use of virtual forms of communication to fulfill the desire for personal and professional communication. The closure of various businesses, especially the ones where public gatherings were more critical, has been a temporary phase. These are short-term impacts that can cause a little disruption in society. The long-term impact cannot be associated with postponing certain activities up to a certain point in time. Consumers have been at one point postponing the decision to buy something, but the demand for various products has to increase shortly undoubtedly.

USE OF BIG DATA IN ENTREPRENEURIAL VENTURES During the current pandemic, artificial intelligence has considerably increased, as now, with the help of machine learning, various socio-economic problems can be addressed by analyzing large datasets. Various applications using Big Data in the environment, agriculture, climate control, etc., can be tackled by analyzing large datasets. These fields have implications for entrepreneurial opportunities and can be successfully utilized to push entrepreneurial activity to a new level and remove the economy’s central problems. Hansen (2019), in his empirical study on Beijing-based entrepreneurs, ‘explores digital entrepreneurship and the impact of digitalization on the entrepreneurial environment in Beijing’. The author highlights that various government initiatives and digitalization have re-energized entrepreneurial activity in China, which has helped provide greater prospects for economic actions. Rowley (2020)found that various companies are now using Big Data and related techniques in the field of data analytics. These techniques are being extensively used in companies to handle various challenges posed by a dynamic business environment. The use of Big Data and analytics is helping entrepreneurs to develop the capabilities of their organizations. Big Data and various flourishing analytic techniques can help entrepreneurs in making ‘intelligent decisions’. The authors strongly assert that during the present phase of human existence, where Big Data and analytical techniques are extensively used, they have ‘stimulated entrepreneurship’. It has also given rise to data entrepreneurs, resulting in drastic changes in entrepreneurial activities amongst firms.

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For instance, Obschonka and Audretsch (2019) reflect on the role that ‘Artificial Intelligence and Big data’ play in the area of entrepreneurship. According to von Bloh, Broekel, Özgun, and Sternberg (2020), there is a high potential for Big Data approaches to be used in news reporting and entrepreneurial activity in the region. In the past, evidence in the literature has shown that entrepreneurs do not assume ‘social, environmental and sustainable responsibilities’ as their vital responsibility. Various social as well as environmental issues sometimes remained unresolved concerning entrepreneurship. Zeng (2018), through his study, concluded that the primary motivation for traditional entrepreneurship lies only in economic achievements. The author contends that ecologically sustainable entrepreneurship, with Big Data’s help, can focus on dealing with the negative issues. With the help of Big Data network systems, the path of ecologically sustainable entrepreneurship can be fostered.

DEALING WITH THE SITUATION AND THE WAY FORWARD In view of the previous discussion, how well we can deal with this temporary situation and how entrepreneurs can be prepared to deal with its long-term dimensions are important aspects to be reflected upon. All over the world, we have observed how various firms have initially responded to the crisis caused by the COVID-19 pandemic by cutting their cost, as some of them have also started engaging with new entrepreneurial activities to increase the cash generation activity to a maximum possible extent. Various companies can be seen now adding to their product lines hand sanitizers and other disinfectants that can be used for the well-being of human beings across the globe. Fashion Hubs like Zara have come up with various innovative solutions like protective clothes, gowns, face masks, and other supplies for individuals and hospitals. Many other organizations that have been dealing with different businesses, such as aviation and the hotel industry, are lending support to hospitals by providing their workforce to them in order not only to assist the employees but also to keep them engaged in various activities. Companies like Philips have added ventilators to their product lines because it has allowed them to cater to the market. Ford has started to produce life-saving medical equipment by using innovative technology solutions in the automobile industry. Examples of these industries prove that entrepreneurs can bring out innovative solutions at the time of crisis also. They are earning profits out of it and helping in creating employment opportunities in society and thereby responding creatively to challenges posed by the pandemic and thus being socially responsible as entrepreneurs. Such actions during the crisis can also help them to create their brand image to a positive forefront. By and large, entrepreneurs tend to come out of their comfort zones as they face external pressures and eventually become creative problem-solvers. Many big business houses have come up with major donations in various sectors. Development of hospitals, providing medical equipment, and contributing to society in various philanthropic activities needed at the time of crisis are all done by entrepreneurs. Consumers always remember the contribution made by the companies towards society in times of crisis. For their actions, they are always remembered, and when the economy returns to normalcy, they benefit at that time. Even more, those companies

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that treated their employees well at the time of crisis will undoubtedly get more loyal managers who can help them attract talent and build up a force of loyal employees. This pandemic has allowed various sectors of the economy, which are likely to grow at a relatively faster pace. Dynamic new technologies have been providing endless opportunities because innovations tend to transform the effects of the crisis in a positive way. Service businesses require even more innovation so that the services could be provided at the doorstep of the consumers taking care of their health and other issues caused by the pandemic. Online businesses have again received a boost, and local entrepreneurs are getting the opportunity to sell their products on such platforms, helping them reach their customers quickly. Platforms like Amazon have gained a lot during this pandemic. Recently, Amazon admitted that many entrepreneurs became millionaires through their Three-Day Prime Sale. The healthcare sector is the one that is going to give abundant opportunities to various entrepreneurial ventures. Health-related smartphone apps are growing at a rapid pace. The concept of artificial intelligence is being used on various platforms, especially in hospitals, to meet the pandemic demand. Earlier, artificial intelligence was thought to be a source of technology for entertainment only. However, now the role has shifted, becoming an important aspect of the healthcare industry. It is also going to establish its roots in aviation as well as the hotel industry. People with technical knowledge and expertise have a greater opportunity to emerge as entrepreneurs with their innovative solutions to the problems around the crisis. The area of technical training and maintenance is also being swept over by artificial intelligence these days. The use of Big Data and bringing out insights for taking various strategic decisions in entrepreneurship will play a major role in times to come. Only those companies will be treated as competent who will have the ability to move quickly during this time of crisis and visualize the future better than their competitors. Companies can gain a strategic advantage over their competitors at the time of the pandemic only if they work upon various innovative solutions at a lower cost. Artificial intelligence and the ability to build up reliable and up-to-date logistic infrastructure for providing the goods at the doorstep of consumers can play an important role in this regard. Almost everybody, be it businesses or individuals, is becoming accustomed to this ‘new normal’ where things are safer to buy/procure online. The use of technology thus has gained momentum to a great extent. The consumers’ established habits have now been broken as normal life stands disrupted, and a change in attitude and expectation can be seen around us. Earlier, many people were uncomfortable using technology or digital platforms for either doing financial activities or using online websites for ordering stuff for home delivery or using interactive modes through video meetings/conferencing in place of face-to-face meetings. Now there is a gradual shift in the attitudinal tendencies of consumers. This ‘new normal’ has shown people that online and offline modes of doing business will be the new future. It is more likely that the use of technology in our future will be quite high. The post-pandemic future will be different as it will create a long-lasting effect on human minds and the way of living life. Various entrepreneurial initiatives presently have already shown us the signs of change where we can expect a better future due to the entrepreneurs’ risk-taking abilities in various sectors of the economy.

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A new form of entrepreneurs is expected to emerge during and after this crisis: the digital entrepreneurs. They are the ones who will help us adopt the ‘new normal’ of life, and it would be based on digital technology and Big Data. It is evident and indispensable for us to adopt technology in our day-to-day lives. Therefore, Big Data solutions will play a dominant role in making various strategic decisions concerning businesses. These entrepreneurs can leverage the benefits of technology for their organization and society at large. Leveraging digital tools has become indispensable for entrepreneurs to survive the ongoing crisis. Interestingly, every society’s digital transformation has gained momentum, which was never expected earlier. Only digital tools may give us a ray of hope to survive this crisis. Historical evidence indicates that in a time of crisis, businesses may have to suspend operations for the short term. However, they always come up with long-lasting, durable, sustainable, and feasible solutions to society’s problems. Ten years back, nobody would have ever thought of drastic use of technology in day-to-day life, but now, even small children are being taught using digital modes. Learning to use gadgets, get an education, and create contact with teachers is a new normal in the education sector. This, too, has given entrepreneurs opportunities to innovate. It is a transformative moment for every type of industry in the world. Transformations are bound to occur, and they are meant to happen; the thing is that their pace has become a little faster. The unprecedented public health crisis has forced us to adopt new ways of life, new ways of dealing with situations and of doing things, and ultimately the newer options have also started giving us hope. The concept of shared value creation is emerging in the time of crisis. Entrepreneurs across the globe are facing challenges, and many entrepreneurs have changed their perspectives of doing business. Large multinational companies are adopting small and medium-scale enterprises. Big giants like Alibaba have come up with various innovative solutions where contactless delivery options are being given to their consumers. Many IT solution companies with online collaborations and new communication-enabled practices have launched certain features to enable their millions of workers to work from home. The current pandemic laid quite a severe impact on various economies of the world. Simultaneously, the digital world has proven to be instrumental and essential in the battle against COVID-19. Those people who already embraced technology benefited greatly; nevertheless, they were able to fast-track the business in a very easy and modest manner in a time of crisis. Therefore, to survive in this new normal, the digital tools and effective use of Big Data models are becoming survival tools. Dealing with digital technology and having the acumen to better leverage the digital economy is coming up as solutions to the community’s problems to survive the crisis. Companies like Infosys, Tata, and Reliance have already started efforts to combat the issues caused by the COVID-19 pandemic. In times of pandemic, the urgent need to be digital has accelerated digital transformation in every sector of the economy. There has been a gradual shift in consumers’ attitudes and entrepreneurs and can certainly be perceived as long-lasting. No business can survive without leveraging digital tools and innovation during this pandemic. New consumer behavior can be analyzed properly for strategic decision-making only by the use of Big Data solutions. The entrepreneurial ecosystem and its variables are now being redefined. We have seen a clear departure from the traditional business models to the focus on shared

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value creation in every field. Online business apps, cashless modes of payments, and new ways of doing business have replaced the traditional forms of activities done by the consumers and business houses. The entrepreneurial ecosystem is adopting the variables like information technology, digital technology, and Big Data in a big way. It surely is a sign of positive change for the future of businesses and humanity. During this pandemic, government has to play a very important role in developing opportunities for entrepreneurs. Investments should be made in ‘workforce training and capacity building for jobs’, especially in the ‘manufacturing and services sectors’ (De Ávila, Miranda, Bozmoski, Sadurní, Bolaños, and Paiz, 2020). There is an urgent need to take care of global demands, and partnering with the private sector is equally important for combating the pandemic’s pressures. So, whatever the recovery and resilience efforts there are from an economy, it should not be detrimental to action institutions or policies. The global economic order has significantly changed the ‘trade policies’ and ‘technology adoption’. The ‘adoption strategies’ should be well thought of, planned, and appropriately executed to get the best benefits from ‘recovery and resilience’ (Bansal, Burman, Chaudhuri, Prabhakar, Raghavan, & Rai, 2020). In recovery and resilience strategies across the globe, ‘access to internet’ has been identified as an important challenge (Revel, 2020). Gerstel and Goodman (2020) believe that the COVID-19 pandemic is so devastating that it has caused ‘economic disruptions and uncertainty’ which are not seen since the Great Depression. Thereby, a strong need is seen to rethink, revise, and reconsider the role of the public and private sectors. Government has to play an important role in fostering and promoting innovation, especially in the field of technology. Center et al. (2020) aptly say that ‘Periods of disorder like today naturally give rise to questions about how past orders came about, why they broke down, and most importantly, how they are replaced’. Undoubtedly, entrepreneurial activity has to bounce back to save the economies from the crises. However, the challenges are many, in addition to manufacturing delays, falling demand, human resource allocations, etc. Further, the activities are hampered due to ‘logistical constraints as well as constraints in suppliers’ production and reliability’ (Meester & Ooijens, 2020). Despite all these challenges, the pandemic has given certain countries opportunities to cut down on oil and gas prices. Some countries like Turkey could recoup their balance of payment position due to a fall in prices. Further, sectors like energy can attract more entrepreneurial opportunities (Miel, 2020). To foster economic growth during and after the pandemic, countries will have to consider their regulatory framework deeply. To boost entrepreneurial activity, the ease in the process of registration of the firm, tax rates and tax structure, labor-related laws, and ease in acquiring technology will play an important role in boosting the entrepreneurial activity in economies (Lewis, 2019). The literature also suggests the crucial role of social entrepreneurs in facing the challenges posed by epidemics in the past (Kahn, 2016). In a nutshell, competition and consumer protection authorities will have to redefine their role to protect the rights of consumers, which can help address the short-term challenges posed by the pandemic. A repurposed role of entrepreneurs is surely going to play an important part in developing sustainable recovery practices across the globe, which can positively affect economies’ long-term development goals across the world (Tyrie, 2020).

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CONCLUDING REMARKS To sum up, it can be indubitably claimed that entrepreneurs are the lifelines to resuscitate the economy of a country that has slid down to the extent that its revival seems impossible. Entrepreneurs are entrepreneurs because self-motivation, self-activation, self-propulsion, and self-reconstruction run in their veins. They are the ones who do not believe in getting stuck with the seamier side of life or any situation. They can utilize their potential in the best possible manner, even under hostile and daunting circumstances. For instance, the COVID-19 pandemic has caused a situation characterized by hopelessness, despair, negativity, pessimism, and lack of spirit to reconstruct and rebuild the socio-cultural, economic, political, and psychological fabric worldwide. In this sense, the situation during the pandemic has been excessively demanding. In such a challenging scenario, entrepreneurs can become harbingers of transformation in the way we think about the new normal caused by the coronavirus. Entrepreneurs are the real leaders who, besides ensuring growth in the economic arena, can also pave the way for lifting the sagging morale of a whole range of businesses. They know how a situation is to be dealt with, i.e., financially, technologically, and from the viewpoint of stirring the market. The result is that the production sector may boom, and demand for the goods may rise. They also know how to address issues concerning strengthening the already-existing infrastructure by rebuilding it should the need arise. Moreover, they also have the expertise as well as the resources to build infrastructure anew wherever required. They also have profound awareness about how they have to connect with the community and society at large. That is why true entrepreneurs take particular care in connecting with localities and communities, keeping in mind the considerations about CSR. In the crises-ridden world of today, because of the COVID-19 pandemic, entrepreneurs need to reassess, reevaluate, repurpose, and redefine their role so that the world around us, which seems to have crumbled because of the COVID-19 pandemic, maybe reconstructed to provide succor to the suffering masses.

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Additional Readings Babah Daouda, F., Ingenbleek, P. T., & van Trijp, H. C. (2016). Step-change: Microentrepreneurs’ entry into the middle-class market. Journal of African Business, 17(2), 129–147. Babajide, A., Lawal, A., Asaleye, A., Okafor, T., & Osuma, G. (2020). Financial stability and entrepreneurship development in sub-Sahara Africa: Implications for sustainable development goals. Cogent Social Sciences, 6(1), 1798330. https://doi.org/10.1080/23311886. 2020.1798330 Bass, A. E. (2017). Identity discovery and verification in artist-entrepreneurs: An active learning exercise. Organization Management Journal, 14(2), 90–103. Chandra, Y., & Paras, A. (2020). Social entrepreneurship in the context of disaster recovery: Organizing for public value creation. Public Management Review, 1–22. https://doi.org /10.1080/14719037.2020.1775282 Choi, Y., & Chang, S. (2020). The effect of social entrepreneurs’ human capital on and firm performance: The moderating role of specific human capital. Cogent Business & Management, 7(1), 1785779. https://doi.org/10.1080/23311975.2020.1785779 Cinnamon, J. (2020). Data inequalities and why they matter for development. Information Technology for Development, 26(2), 214–233. Duffin, E. (2020, June 26). Topic: COVID-19: Impact on the global economy. Retrieved August 13, 2020, from https://www.statista.com/topics/6139/covid-19-impact-on-theglobal-economy/ Ferdousi, F. (2015). Impact of microfinance on sustainable entrepreneurship development. Development Studies Research, 2(1), 51–63. Grillitsch, M. (2019). Following or breaking regional development paths: on the role and capability of the innovative entrepreneur. Regional Studies, 53(5), 681–691. Grüner, H., & Neuberger, L. (2006). Entrepreneurs’ education: Critical areas for the pedagogic-didactic agenda and beyond. Journal of Business Economics and Management, 7(4), 163–170.

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Horst, S. O., Järventie-Thesleff, R., & Perez-Latre, F. J. (2020). Entrepreneurial identity development through digital media. Journal of Media Business Studies, 17(2), 87–112. Jones, L., Palumbo, D., & Brown, D. (2020, June 30). Coronavirus: A visual guide to the economic impact. Retrieved August 13, 2020, from https://www.bbc.com/news/business51706225#:%7E:text=Risk%20of%20recession&text=But%20the%20IMF%20 says%20that,Great%20Depression%20of%20the%201930s.&text=Recovery%20 in%20big%2C%20services%2Dreliant,to%20be%20a%20slow%20process. Jurlina Alibegović, D., Rašić Bakarić, I., & Slijepčević, S. (2019). Impact assessment of entrepreneurial zones on local economic outcomes. Economic research-Ekonomskaistraživanja, 32(1), 3112–3127. Kay, C. (2009). Development strategies and rural development: exploring synergies, eradicating poverty. The Journal of Peasant Studies, 36(1), 103–137. Kearney, C., & Hisrich, R. D. (2014). Entrepreneurship in developing economies: Transformation, barriers and infrastructure. In Necessity Entrepreneurs. Edward Elgar Publishing. Khazami, N., Nefzi, A., & Jaoudi, M. (2020). The effect of social capital on the development of the social identity of agritourist entrepreneur: A qualitative approach. Cogent Social Sciences, 6(1), 1787680. Korent, D., Vuković, K., & Brčić, R. (2015). Entrepreneurial activity and regional development. Economic research-Ekonomskaistraživanja, 28(1), 939–958. Mason, T. W. (1984). Entrepreneurs, high technology and economic development new uses for assessment tools. Impact Assessment, 3(4), 47–55. Qureshi, S. (2020). Why data matters for development? Exploring data justice, micro-entrepreneurship, mobile money and financial inclusion. Information Technology for Development, 26(2). Shen, Y., Shen, M., & Chen, Q. (2016). Measurement of the new economy in China: Big data approach. China Economic Journal, 9(3), 304–316. Staniewski, M., & Awruk, K. (2016). Start-up intentions of potential entrepreneurs–the contribution of hope to success. Economic research-Ekonomskaistraživanja, 29(1), 233–249. Willson, M., & Leaver, T. (2015). Zynga’s FarmVille, social games, and the ethics of big data mining. Communication Research and Practice, 1(2), 147–158. Yun, J. J., Won, D., Park, K., Yang, J., & Zhao, X. (2017). Growth of a platform business model as an entrepreneurial ecosystem and its effects on regional development. European Planning Studies, 25(5), 805–826.

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Business and Financial Management Sustainability of a Small-Medium Enterprise A Case Study of Rohaya and Abu Bakar Enterprise, Penang, Malaysia Rohani Yusof, Mohd Hafiz Abdul Halim, Yuslina Abdul Ghani, and Zuhairah Abdul Hadi Seberang Perai Polytechnic, Malaysia

CONTENTS Business History and Milestone����������������������������������������������������������������������������� 252 Organizational Structure of Rohaya & Abu Bakar Enterprise������������������������������� 252 Performance Development of Rohaya & Abu Bakar Enterprise��������������������������� 252 Entrepreneurship Characteristics and Business Sustainability������������������������������ 254 Company Financial Management�������������������������������������������������������������������������� 256 Challenges�������������������������������������������������������������������������������������������������������������� 258 Learning Strategies������������������������������������������������������������������������������������������������ 258 Case Study Questions: Trigger������������������������������������������������������������������������������260 Part A: Introduction������������������������������������������������������������������������������������������� 260 Part B: Analyzing Problems������������������������������������������������������������������������������ 260 Part C: Decision Making����������������������������������������������������������������������������������� 260 Part D: Conclusions������������������������������������������������������������������������������������������� 260 Bibliography���������������������������������������������������������������������������������������������������������� 260 Appendix A: Rohaya & Abu Bakar Enterprise Profile������������������������������������������ 262 Appendix B: Rohaya & Abu Bakar Business Catalogue��������������������������������������� 263 Appendix C: Rohaya & Abu Bakar Enterprise Organizational Chart�������������������264 Appendix D: Rohaya & Abu Bakar Enterprise SWOT Analysis............................ 264 Appendix E: Rohaya & Abu Bakar Enterprise Financial Statement��������������������� 265 Appendix F: Rohaya & Abu Bakar Enterprise Balance Sheet������������������������������� 266 DOI: 10.1201/9781003097945-17

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BUSINESS HISTORY AND MILESTONE In 1985, with capital of RM50 (±USD200), Mr. Abu Bakar bin Ayub initiated a home-production chili sauce business, registered under the name Perniagaan Abu Bakar bin Ayob (Abu Bakar bin Ayob Enterprise). He later changed his business name in 1991 to Perniagaan Rohaya dan Abu Bakar (Rohaya & Abu Bakar Enterprise) using his name and his wife’s, which has been used since (refer to Appendix A shows the profile of Rohaya & Abu Bakar Enterprise). In the early days of his involvement in the chili sauce manufacturing industry, he and his wife cooked and manufactured their chili sauce only at their home in Permatang, Batu, Penang. Using only regular kitchen appliances and with no help from anyone else, they managed to produce a capacity of only three dozen bottles per day. The increase in customers’ demand from day to day resulted in the growth of his business operations. It has become a well-known sauce manufacturer with the brands ‘Rohaya’ and ‘Munirah’, especially in Penang and other states in Peninsular Malaysia. Additional capital obtained as a loan from some financial institutions and support from government agencies for research and development (R&D) purposes has increased the sales volume in line with its market growth. The product line includes chili sauce, oyster sauce, black pepper sauce, tomato sauce, burger sauce, soy sauce, and many others (refer to Appendix B for the business catalog), prepared not only for retail but also for retail for wholesale consumers as well.

ORGANIZATIONAL STRUCTURE OF ROHAYA & ABU BAKAR ENTERPRISE Rohaya & Abu Bakar Enterprise is owned by Mr. Abu Bakarbin Ayub. It is currently managed by the Administration and Accounting Manager, Mr. Muhamad Zaki bin Rafiee, who also performs duties as Business Development Manager. Mr. Muhamad Zaki’s job scope included monitoring quality assurance, human resources, accounting, and procurement. As for the operation department, Mrs. Rapeesa Binti Yoube was appointed as Production Manager; her job scope includes supervising and monitoring the Kitchen Operation, Filling Operation, and Packaging Operation. The Marketing and Logistics Division is managed by Mr. Kamaruddin bin Abdul Wahab, who monitors the retail and wholesale markets and engages in customer service. To date, 25 employees help further to strengthen the operations of Rohaya & Abu Bakar Enterprise. Refer to Appendix C to see the organizational charts of Rohaya & Abu Bakar Enterprise.

PERFORMANCE DEVELOPMENT OF ROHAYA & ABU BAKAR ENTERPRISE Rohaya & Abu Bakar Enterprise have received awards and recognitions from various agencies (Table 17.1 shows the list of recognition and collaboration certificates received by the company). The company is committed to providing the best quality of products for consumers from the success of R&D by the company’s owner. To ensure the quality of the products produced, the company has collaborated with

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TABLE 17.1 Rohaya & Abu Bakar Enterprise’s awards, recognition, collaboration, and courses No

Award / Recognition / Collaboration / Course

Agency

1.

Penang Bumiputera Entrepreneur Award 2008 – Second Place for Agro-based Industry Category (Anugerah Usahawan Bumiputera Pulau Pinang 2008 – Kedua Kategori Industri Asas Tani) Halal Certification Appreciation at SME Bank Complex Seberang Prai 1 (Penghargaan Pensijilan Halal di Kompleks SME Bank Cawangan Seberang Prai 1) Development program for young entrepreneurs of logistics services (Program pembangunan usahawan muda berasaskan perkhidmatan logistic) Development Program for young entrepreneurs (Program pembangunan usahawan muda) MAHA 2012 Agricultural Production (Processing) Competition – Second Place (Pertandingan Hasil Pertanian (Pemprosesan) MAHA 2012 – Kedua)

PERDA – Penang Regional Development Authority

2.

3.

4. 5.

6.

7.

8.

9.

10. 11.

12. 13.

14.

15.

Registration No P03P1141020-011378 under the Food Act 1983 (No Pendaftaran P03P1141020-011378 dibawah Akta Makanan 1983) Halal Certification Certificate. Reference Number JAKIM/(S)/ (22.00)/492/2/1011-07/2008 (Sijil Pengesahan Halal. Nombor Rujukan JAKIM/(S)/(22.00)/492/2/1011-07/2008) Registration under Section 21 of the Goods and Services Tax Act. No. CBP 000652288000 (Pendaftaran dibawah Seksyen 21 Akta Cukai Barang dan Perkhidmatan. No. CBP 000652288000) MARDI Guidance Entrepreneur (Syarikat Usahawan Anak Angkat MARDI) Financial Statement Interpretation Course (Kursus Interprestasi Penyata Kewangan) Seberang Perai Municipal Council (MPSP) License. Account Number 5105154420 (Lesen Majlis Perbandaran Seberang Perai. Nombor Akaun 5105154420) Certificate of Exemption from Licensing (Sijil Pengecualian Daripada Pelesenan) Scheduled Controlled Goods Permit under the Goods Supply Control Regulations 1974 (Permit Barang Kawalan Berjadual dibawah Peraturan – Peraturan Kawalan Bekalan 1974) Verification of Renewal of Registration under the Business Registration Act 1956. Registration Number PG0186134-K (Perakuan Pembaharuan Pendaftaran dibawah Akta Pendaftaran Perniagaan 1956. Nombor Pendaftaran PG0186134-K) Participants in Food Industry Seminar of SKB 2007 (Seminar Industri Makanan Peserta SKB 2007)

SME Bank – Small Medium Enterprise Development Bank Malaysia Berhad Seberang Perai Politechnic

PERDA High Skills Institute (PERDA-TECH) MAHA 2012 – Malaysian Agriculture, Horticulture and Agrotourism Show 2012 Ministry of Health of Malaysia Department of Malaysia Islamic Development (JAKIM) Royal Malaysian Customs

MARDI – Malaysian Agricultural Research and Development Institute SME Bank MPSP

Royal Malaysian Customs& Excise Department Ministry of Domestic Trade and Consumers Affairs

Companies Commission of Malaysia

SME Bank (Continued)

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TABLE 17.1  (Continued) No

Award / Recognition / Collaboration / Course

16.

Business Matching Session Between Burger Business Operators and Burger Product Manufacturers (Sesi Pemadanan Perniagaan Antara Peniaga-peniaga Burger dan Pengeluar-pengeluar Produk Burger) Credit & Debt Management Course (Kursus Pengurusan Kredit & Hutang)

17.

Agency PERDA

SME Bank

various government agencies and industries and attended various courses and seminars to strengthen its employees’ technical skills. It has always complied with all the regulations and the related Acts by providing full cooperation in registering the business with various parties to continue the business. The full cooperation of the company’s staff is very evident in the preparation of the documents and information required by the government departments such as the Companies Commission of Malaysia or Suruhanjaya Syarikat Malaysia (SSM), Royal Malaysian Customs or Kastam DiRaja Malaysia, Ministry of Health Malaysia or Kementerian Kesihatan Malaysia, Seberang Perai City Council or Majlis Perbandaran Seberang Perai (MPSP), Ministry of Domestic Trade and Consumers Affairs or Kementerian Perdagangan Dalam Negeri, Koperasi dan Kepenggunaan (KPDNKK), Malaysian Agricultural Research and Development Institute or Institut Penyelidikan dan Pembangunan Pertanian Malaysia (MARDI), and SME banks. The company is also acting as a mentor to several educational institutions by sharing its knowledge and expertise in entrepreneurship. Rohaya & Abu Bakar Enterprise strongly encourage academic visit programs or field trips from any Malaysian institution that intends to take a closer look at the company’s operations and management. An interview with Mr. Mohamad Zaki revealed the entrepreneurial aspects and financial management techniques the company has been practicing in order to remain in the industry, despite the growing competition from major local and international brands in the market (e.g., Maggi, Kimball, Heinz, and Life). To maintain the quality of its products in the market, the company worked on obtaining food quality assurance from the Ministry of Health Malaysia and a Halal certificate from the Department of Islamic Development Malaysia or Jabatan Kemajuan Islam Malaysia (JAKIM). Since Islam is the most dominant religion in Malaysia, the Halal certificate obtained has indirectly become the number one contributing factor to the increased of consumers’/customers’ demands for this brand, alongside customers’ satisfaction.

ENTREPRENEURSHIP CHARACTERISTICS AND BUSINESS SUSTAINABILITY A good entrepreneur is proactive, creative, innovative, dynamic, and willing to take  risks in carrying on the business while working tirelessly to create new

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markets (Chhabra, 2018; Chhabra et al., 2020; Karmarkar et al., 2014). In general, Mr. Abu Bakar holds these characteristics. He is the type who is willing to risk and face future business uncertainties. However, he suggests that the risks willing to be taken must not be random; careful thought and research must be carried out in advance to prevent entrepreneurs from taking unnecessary risks. Mr. Abu Bakar is always sensitive to business opportunities in an environment where others may not be aware. He also believes that to be successful, one must have an in-depth knowledge of the business they plan to venture. A person must strive to obtain all the necessary information about the business they plan to start. Selecting a business to start is not about following what others have done or the trends only. An entrepreneur should also be willing to put himself or herself in charge of everything that should be done in the business and take the initiative to solve any problems. Such is the attitude and practices of Mr. Abu Bakar and his management team to succeed in this highly competitive business field. The success of an entrepreneur also depends on the commitment given to running his or her business. A successful entrepreneur will be fully committed to his or her business and there should not be any conflict between his or her personal interest and business interest. Mr. Abu Bakar and his legacy are the results of high perseverance, never giving up, and always thinking of ways and techniques to grow his business. An entrepreneur not only benefits himself or herself or his or her family but also contributes to the economic development of the local community and the country at large (Chhabra and Karmarkar, 2016a, 2016b). The same is true for Mr. Abu Bakar. He has created employment opportunities for the society, thus indirectly reducing social problems and improving both the local economy and the standard of living. From another bigger perspective, the business has helped meet the needs and increased the market selection. Society has needs and wants that should be met. Entrepreneurs produce a variety of products or offer services that meet the community’s needs and wants – producing sufficient products and services by optimizing the country’s raw materials reduces the outflow of the country’s currency, and the country’s inflation rate. Thus, in this regard, Mr. Abu Bakar has performed entrepreneurial activities that meet the people’s need of obtaining local goods, and this indirectly provides a positive impact on local and national economic environment. Rohaya & Abu Bakar Enterprise’s existence can also contribute to the country’s income through import, export, and sales taxes. Ultimately, if all small and medium enterprises (SMEs) work this way, it will help to stabilize the country’s economy. It is incredible how Rohaya & Abu Bakar Enterprise has survived for almost 30 years since its inception and is still strong in the market, despite numerous competitors in the industry, both from well-known international and local brands. Today, Rohaya & Abu Bakar Enterprise have increased their product line from chili sauce to oyster sauce, black pepper, tomato sauce, burger sauce, and soy sauce. To increase penetration of its product into the market, a new sauce with the brand of ‘Mr. Burn’ was introduced on top of ‘Rohaya’ and ‘Munirah’ brands. The recent development in the food industry (Singh & Chhabra, 2020) has given rise to the changes in the operational processes that directly impact Rohaya & Abu Bakar Enterprise’s financial viability. The impact of these changes is managed by

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Mr. Muhamad Zaki bin Rafiee, the Administration and Accounting Manager, who also serves as the Business Development Manager. He constantly monitors every change in manufacturing technology, especially processing machines used to make various types of sauces (from raw materials to end product) and product packaging activities. This ensures the efficiency of the business operations in terms of resource utilization and the effectiveness in meeting consumers’ needs and maintaining business sustainability. Complying with the government policies is a must to this company. For example, on April 1, 2015, the Goods and Service Tax (GST) was introduced by the Malaysian government. With this new policy, all businesses registered under the Royal Malaysian Customs must change their accounting system to a GST-friendly system. For this reason, the management of Rohaya & Abu Bakar decided to change its accounting system to a computer-based GST friendly system named the MYOB Accounting System. Rohaya & Abu Bakar Enterprise’s management department uses the SWOT analysis technique for change management. It assists the company in the development of business strategies for Rohaya & Abu Bakar Enterprise. Moreover, it facilitates the management department in handling the company’s risk management in the operating system, risk control, internal audit, external audit, corporate governance, ethics, and information technology. The development and success of a business organization depend largely on the positive attitude and the employees’ high morality (Chhabra & Goyal, 2019). However, successful work management must start with the entrepreneur himself or herself. The beliefs and philosophies held by the entrepreneur are key to the success of a business. A business’s viability refers to the business model and the basis for making decisions related to the finance environment and social concerns.

COMPANY FINANCIAL MANAGEMENT Financial management is a key element that must be tightly controlled within a company. Financial leakage, especially for small companies, often happens. It can weaken the companies’ operations to the point that some local companies have to be closed down due to financial leakage. Therefore, good financial control and management are necessary to maintain a business’s ability to compete with other businesses in the market, as every business operation requires financial capital to remain in the industry. This includes the purchase of main raw materials, machines needed for product operations, payroll and wages, loan repayment, and other related expenses. For local small businesses, financial problems are often a key issue in business sustainability. The GST system practices that began on April 1, 2015, had slightly affected the company’s sales in 2015, with a slight drop in its monthly sales revenue. This was due to a change in demands from local customers who feared the speculation of rising costs of goods after implementing the GST system. The existing

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customers, especially the entrepreneurs/merchants, had taken some drastic steps in reducing product purchases and adopted a resilient strategy to align their businesses with the new system implemented in Malaysia. This is because the GST system required businesses to purchase new equipment and recording systems (incur costs to the businesses) to meet the government’s requirement to implement the GST system. Nonetheless, the consumers’ and customers’ confidence in the company’s products steadily rose, reflecting a growing demand pattern after the company entered its third term in 2015. A slight change in increasing the company’s cost has had a significant effect on its revenue and the business’s financial structure. This situation has opened the minds of entrepreneurs to continue analyzing market conditions and devising strategies for their greater sustainability in the industry. The GST system had also affected the business system and the recording of business accounts. This enterprise has changed its financial recording system to MYOB, a computer-based, GST-friendly accounting system to cope with this need. The system helps to ease the issuing of purchase orders, invoices, inventory management, sales receipts, and payroll of its employees, which complied with GST requirements and the related institutions. The MYOB-integrated accounting software facilitated the work of updating and maintaining financial records. It also helped the company enhance the effectiveness of management and services for its existing customers and people interested in the company’s products. It is undeniable that in an era of rapidly evolving and ever-changing information technology, it has also impacted the accounting scenario. In the era of information technology and globalization, the use of MYOB’s effective accounting software system to process data and financial information is a key requirement of all parties seeking to maintain their businesses in line with the government’s requirements. Mr. Muhamad Zaki attended a series of briefings and courses related to business development. From the knowledge gained, he applied SWOT analysis as the company’s change management technique. Having previously worked with multinational companies, he had developed his experience and strategy-building skills before joining Rohaya & Abu Bakar Enterprise, and he applied those skills to strengthen the company’s position in the market. He had seen how the company’s growth and strength could be influenced by the company’s internal and external circumstances. He also oversees the company’s management in operating systems, risk control, internal audit, external audit, corporate governance and business ethics, and information technology changes. In addition, he devised a number of strategies including changing the operating location to a better location with more modern and sophisticated facilities and equipment. Appendix D shows the SWOT analysis of Rohaya & Abu Bakar Enterprise. However, a more in-depth analysis of the return on investment and the repayment period (Payback Period) is essential to do before deciding on a course of action, as it can affect many aspects, especially financial costs, employee welfare, and logistics. To maintain the company’s finances, the management department of the company needs to be more careful in the decision-making process that involves key information in the project initial outlay. The management must be wise in evaluating a

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proposal that involves incurring a huge cost for the company. To survive in the market is not easy. It is a challenge for the company to evaluate whether or not the factory change strategy is the best for its financial sustainability (refer to Appendix E and Appendix F that shows the Income Statement and Balance Sheet respectively of the company). Mr. Muhamad Zaki has constantly been monitoring the growth of the food industry sector, especially today’s sauce producers, who have made changes to the operating processes that directly impact the business’s financial viability. He also oversees every change in manufacturing technology, especially processing machines that produce various types of sauces, from raw material processing to product packaging and product distribution. This oversight ensures the business operations’ efficiency in terms of resource utilization and the effectiveness of meeting customers’ needs. At the same time, in order to succeed in keeping with the current globalization era, companies use accounting software systems that are in line with the current requirements to process financial data and information more efficiently.

CHALLENGES In the context of this chapter, the researchers are trying to study how a local company involved in the manufacturing and production of various sauces can survive for more than 30 years in the industry despite the intense competition from other brands. Researchers can also identify the strengths and competencies, deficiencies or weaknesses, opportunities, and threats to Rohaya & Abu Bakar Enterprise (Malaysian Bumiputera entrepreneurs). In teaching and learning, students can use this case in calculating a company’s financial ratios to determine the sustainability of a company to survive in the market. It can be used in the classroom for any course related to finance. The researchers were eager to discover to what extent financial management efficiency can enhance a local company’s competitiveness in the market. Meanwhile, students can try to develop a new strategy for the company to stay competitive in the market by considering the costs involved in making a decision.

LEARNING STRATEGIES The following sections are the guideline for teachers when using this case study in the teaching and learning process. Refer to Table 17.2 for the strategic learning for this case study. Part A consists of the questions developed to trigger the students’ thinking, followed by Part B, which lists down the questions to guide students in analyzing problems. Part C is used to guide students in the decision-making process, and finally, Part D is the conclusion.

Level 1

2

3 4

5

6

7

PBL Process

Action

Document and Record

Group Distribution (1 hour)

Introduction to students about the Problem Based Learning (PBL) process. Provide students with a course guide. Define roles as facilitators and students. Introduce group members. Maximum three people in a group. Assignment of group work. Establishment of basic group rules. Students are given a case study ✓ Propose problems ✓ Identify and explain the problems ✓ Describe the problems Students design a plan ✓ Produce thoughtful ideas (problem-solving) ✓ Determine what needs to be learned to understand or solve problems ✓ Generate learning issues and action plans ✓ Identify resources Obtain research resources ✓ Obtain and formulate relevant information ✓ Produce a self-assessment checklist of criteria on what, why, and how the problems are not solved Discuss, develop, and justify solutions and explanations: ✓ Information sharing ✓ Evaluate sources of information for credibility and validity (sources of criticism) ✓ Apply knowledge related to this problem ✓ Develop more learning issues, if necessary Apply and submit case study reports Individual problem-solving processes learned knowledge, solutions, and facilitators ✓ Comprehensive explanation and/or demonstration of real-world problems Students raise questions about what they want to know more about Feedback from facilitator Resubmit the reports after receiving feedback from the facilitator Conduct feedback with group mates

Semester teaching plan Course outline PBL process Minutes of the first meeting Organizational chart, directory and group rules

Trigger (see the question following this table) (2 hours) Brainstorming (1 hour) Learning issues (1 hour) Self-learning (4 hours) Synthesis and application (2 hours) Reflection and feedback (2 hours)

Task Facilitator Facilitator Facilitator Group learning

Learning objectives

FILA Facilitator and group learning Learning aids

Business and Financial Management Sustainability

TABLE 17.2 Strategic learning

Minutes of the second meeting MS PowerPoint Facilitator

Question-and-answer session Reflection MS PowerPoint Minutes of the third meeting

Facilitator and group learning Group learning

259

Presentation of rubric form

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CASE STUDY QUESTIONS: TRIGGER Part A: Introduction

1. Define entrepreneur. 2. What entrepreneurial traits does Mr. Abu Bakar bin Ayub have? 3. What are the contributions of entrepreneurship to society and to the country? 4. State the type of business owned by Mr. Abu Bakar bin Ayub. Discuss the features of that ownership. 5. What are the main challenges faced by the type of business operation that Mr. Abu Bakar bin Ayub runs? 6. Based on your answer in Question 4, how can financial constraints affect that type of business entity’s growth?

Part B: Analyzing Problems 1. What are the problems faced by Rohaya & Abu Bakar Enterprise? 2. Identify the factors that influence the sustainability of the financial management of Rohaya & Abu Bakar Enterprise. 3. Calculate the financial ratios for Rohaya & Abu Bakar Enterprise for two years (2013 and 2014) and analyze the significant ratio changes for those two years. 4. Identify the business risks of Rohaya & Abu Bakar Enterprise.

Part C: Decision Making 1. Say you are the new Finance Manager for Rohaya & Abu Bakar Business Enterprise. Provide strategic recommendations for the sustainability of the company’s financial position. 2. The owners Rohaya & Abu Bakar Business Enterprise also require that you provide preventive measures that need to be taken immediately by the top management to ensure the sustainability of financial management in the future.

Part D: Conclusions Based on the current financial management practiced by Rohaya & Abu Bakar Enterprise, give your opinion by justifying whether or not Rohaya & Abu Bakar Enterprise can achieve financial sustainability.

BIBLIOGRAPHY Ahmad, A., Isa, A. S., Nazeri, N. A., Ishak, N. F., Yusof, R., Rosli, R. H., & Ghani, Y. A. (2015). Entrepreneurship: A Handbook. Kuala Lumpur: Pearson Malaysia Sdn Bhd. Ariffin, S., & Hambali, I. A. (2013). Fundamentals of Entrepreneurship. Selangor: Oxford Fajar Sdn Bhd. Chhabra, M. (2018). Gender Gap in ‘Success Factors’ among Entrepreneurs: A Study of Micro and Small Enterprises. SEDME (Small Enterprises Development, Management & Extension Journal), 45(2), 1–17.

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Chhabra, M., Gera, R., Hassan, R., & Hasan, S. (2020). An Exploratory Study of Cognitive, Social and Normative Dimensions of Female Entrepreneurship within Transition Economies: Evidence from India and Vietnam. Pakistan Journal of Commerce and Social Sciences (PJCSS), 14(4), 1012–1042. Chhabra, M., & Goyal, A. P. (2019). Education and entrepreneurial experience WRT female entrepreneurs. International Journal of Research in Humanities, Arts and Literature, 7, 95–110. Chhabra, M., & Karmarkar, Y. (2016a). Effect of Gender on Inception Stage of Entrepreneurs: Evidence from Small and Micro Enterprises in Indore. SEDME (Small Enterprises Development, Management & Extension Journal), 43(3), 1–16. Chhabra, M., & Karmarkar, Y. (2016b). Gender Gap in Entrepreneurship – a Study of Small and Micro Enterprises. ZENITH International Journal of Multidisciplinary Research, 6(8), 82–99. Fincham, R., & Rhodes, P. S. (2005). Principles of Organizational Behaviour, 4th Edition. New York: Oxford University Press. Flamholtz, E. G. (1996). Effective Management Control: Theory and Practice. Boston/ London/Dordrecht: Kluwer Academic Publisher. Longenecker, J., Petty, J., Palich, L., & Hoy, F. (2011). Small Business Management: Launching and Growing Entrepreneurial Ventures, 17th Edition. Andover, Hampshire: Cengage Learning. Palich, L., Hoy, F., Longenecker, J. G., & Petty, J. (2013). Small Business Management. Florence: Cengage Learning, Inc. Patterson, C. (2010). Business Briefs: Business Theory Made Simple, 1st Edition. Coleman Patterson & Ventus Publishing ApS. Bookboon.cm Quinn, S. (2010). Management Basics. Susan Quinn & Ventus Publishing. Bookboon.com Robbins, S. P., & Coulter, M. A. (2016). Management, 13th Edition, Pearson. Shah, S. H., & Ali, A. R. (2010). Enterpreneurship, 2nd Edition. Selangor: Oxford Fajar. Singh, D., & Chhabra, M. (2020). Attainment of Customer’s Satisfaction in Digital Food Apps Industry Through Result-Oriented Management. International Journal of Management (IJM), 11(12), 2527–2543. Strydom, J., Jerome Beer, A. D., Holtzhausen, M., Steenkamp, R., Rudansky-Kloppers, S., & Kara, M. (2012). Principles of Business Management, 2nd Edition. Goodwood, South Africa: Oxford University Press. Weihrich, H., & Koontz, H. (2005). Management: A Global Perspective. Singapore: McGraw-Hill. Wren, D. A. (1994). The Evolution of Management Thought, 4th Edition. New York: John Wiley & Sons, Inc. Yusof, A. A., Perumal, S., & Pangil, F. (2005). Principles of Entrepreneurship. Petaling Jaya, Selangor Malaysia: Pearson/Prentice Hall. Zimmerer, T. W., & Scarborough, N. M. (2005). Essentials of Entrepreneurship and Small Business Management, 4th Edition. USA: Pearson Education.

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APPENDIX A: ROHAYA & ABU BAKAR ENTERPRISE PROFILE

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APPENDIX B: ROHAYA & ABU BAKAR BUSINESS CATALOGUE

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APPENDIX C: ROHAYA & ABU BAKAR ENTERPRISE ORGANIZATIONAL CHART MANAGING DIRECTOR Abu Bakar Ayob

PRODUCTION MANAGER Rapeesa Yoube

ADMINISTRATION, ACCOUNT&BUSINESS DEVELOPMENT MANAGER Muhamad Zaki Rafiee

RETAIL MARKET

KITCHEN OPERATION FILLING OPERATION

MARKETING & LOGISTIC MANAGER Kamaruddin Wahab

HUMAN RESOURCE

QUALITY

WHOLESALE OPERATION

ACCOUNT PACKING OPERATION

PROCUREMENT

CUSTOMER SERVICE

APPENDIX D: ROHAYA & ABU BAKAR ENTERPRISE SWOT ANALYSIS SWOT ANALYSIS Strategic planning: Planning for opening a new factory in Kubang Menerong. STRENGTHS ✓ Easy to get Halal certificate and MESTI ✓ Goods are guaranteed safe ✓ Easy to wash ✓ New system is better ✓ New image for company OPPORTUNITIES ✓ Easy for promotion of place ✓ Dare to bargain OEM ✓ Will be frequently visited by government employees

WEAKNESSES ✓ High capital – initial investment, wiring costs, and PBA ✓ Need new employees ✓ Does not fit for all machines and stores ✓ Monthly rent THREATS ✓ Cash flow – weak ✓ No skilled and trustworthy employees ✓ Who wants to manage? ✓ PERDA reserves the right to take back the premise ✓ Monthly rent is increased

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APPENDIX E: ROHAYA & ABU BAKAR ENTERPRISE FINANCIAL STATEMENT ROHAYA & ABU BAKAR ENTERPRISE (Company No. PG0186134-K) INCOME STATEMEN FOR THE YEAR ENDED DECEMBER 31 2014

2014 RM

2013 RM

3,369,412

3,822,674

Opening Stock Purchase – Raw Material Packing Expenses Less: Closing stock

80,000 2,821,380 52,330 (77,220) 2,876,490

75,000 3,244,015 57,295 (80,000) 3,296,310

GROSS PROFIT

492,922

526,365

OTHER INCOME-Hibah

682 492,922

658 527,022

Bank charges Interest on leasing Interest on hire purchase Term loan interest –SME Bank Donation Depreciation EPF & SOCSO Insurance License fee Medical expenses Petrol & toll Transportation Printing & stationery Inspection fee-motor vehicle Sundry expenses Telephone charges Upkeep of office Upkeep of vehicle Repair & maintenance machine Road tax & insurance Utilities charges Salary, wages & allowance

204 1,753 9,278 800 50,605 16,660 2,450 500 4,460 55,558 61,770 5,350 1,400 1,776 1,646 2,778 2,660 5,710 2,075 29,081 205,730

18 1,258 3,822 25,520 650 50,605 16,211 2,820 500 3,590 60,990 60,671 6,170 1,400 2,244 6,020 3,803 3,598 7,376 2,280 30,045 185,271

TOTAL OPERATING COST

462,244

474,862

NET PROFIT

31,360

52,160

INCOME: Sales LESS: COST OF GOODS SOLD

LESS: OPERATING COST

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APPENDIX F: ROHAYA & ABU BAKAR ENTERPRISE BALANCE SHEET ROHAYA & ABU BAKAR ENTERPRISE (Company No. PG0186134-K) BALANCE SHEET AS AT DECEMBER 31 2014

Note

2014 RM

2013 RM

1

79,450

130,055

77,220 567,760 90,350 54,034 10,150 799,514

80,000 692,729 75,450 7,947 14,165 870,291

14,747 96,556 9,998 121,301 678,213 757,663

13,456 105,050 8,880 127,386 872,960 872,960

746,044 31,360 777,404 (128,623) 648,781

693,884 52,160 746,044 11,953 757,997

93,048 15,834 108,882 757,663

83,770 31,193 114,962 872,960

NON-CURRENT ASSET Property, plant and equipment CURRENT ASSETS Inventories Investment Trade Debtors Other Debtors & Deposit Bank Balance-Affin Bank Berhad Cash in hand CURRENT LIABILITIES Hire Purchase Payable Trade Creditors Other Creditors and Accruals Net Current Assets/ (Liabilities)

FINANCED BY:Owner Equity Current Year Earnings Less: Drawings LONG TERM LIABILITY Term Loan: SME Bank Hire Purchase Payable

18

Sustainable Enterprise Development A Review of Training Topologies for Entrepreneurs Zahid Hussain Bhat Cluster University, India

Riyaz Ahmad Rainayee University of Kashmir, India

Meghna Chhabra Manav Rachna International Institute of Research and Studies, India

CONTENTS Introduction������������������������������������������������������������������������������������������������������������ 267 Role of Small and Medium-Sized Enterprises (SMEs)����������������������������������������� 268 Sustainable Enterprise Development��������������������������������������������������������������������� 269 The Rationale of the Study������������������������������������������������������������������������������������ 270 Objectives��������������������������������������������������������������������������������������������������������������� 272 HR as a Competitive Advantage����������������������������������������������������������������������� 272 The Importance of Training and Skill Development in Small Business����������� 274 Training Methods for SMEs���������������������������������������������������������������������������������� 276 Conclusions������������������������������������������������������������������������������������������������������������ 279 References�������������������������������������������������������������������������������������������������������������� 281

INTRODUCTION The term entrepreneurship refers to an individual’s ability to put into action an idea that combines such attributes as ingenuity, creativity, risk-taking, innovation, and the ability to organize and carry out the activities to achieve the objectives proposed (Chhabra et al., 2020). The field of entrepreneurship is a complex one that faces considerable barriers that challenge its reliability as a distinct field of study (Cassell et al. 2002; Chhabra & Goyal, 2019). Acs et al.(2008) offered a potentially defining criterion of entrepreneurship, ‘attainment of beginnings’, in line with the early definition of ‘entrepreneurship’ by Schumpeter (1951). This simply consists of doing things not DOI: 10.1201/9781003097945-18

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usually done in the course of the usual business routine. The conventional concept of entrepreneurship focuses on the entrepreneur’s characteristics and functions by considering entrepreneurs, their inspiration, and their entrepreneurial qualities (Karmarkar et al., 2014). An entrepreneur is an economic agent, in the modern sense of market economy, assuming artistic and assertive behavior by actively taking economic risks to create new ventures. Entrepreneurs are businesspeople who create, develop, and manage a company for this purpose, sacrificing time, energy, and resources (Chhabra, 2018a). Academics are of the opinion that entrepreneurship is essential for the economy because it provides a platform for creativity, employment generation, and economic prosperity (Van Stel, 2006). Having an innate potential for economic growth, entrepreneurship is essential as an efficient means to fight unemployment and poverty in developing countries like India (Chhabra, 2018b). Accordingly, there is an expanding body of literature documenting entrepreneurship’s potential contribution from generating employment to reducing poverty to creativity; entrepreneurship is associated with several pressing worldwide economic imperatives (van Praag & Versloot, 2007; Bandiera et al., 2012; Miranda & Miranda, 2018). Researchers have begun exploring growth in the economy and its possible links to alleviating poverty through entrepreneurship and advancing new technologies and new methods in recent years (Bloom et al., 2016; McCloskey, 2017; Sutter, Bruton, & Chen, 2019). Scientists are becoming increasingly conscious that entrepreneurship will provide a large fraction of the worldwide answer to poverty (Miranda & Miranda, 2018; Sutter et al., 2019; Steven et al., 2020).

ROLE OF SMALL AND MEDIUM-SIZED ENTERPRISES (SMES) In almost all countries, the importance of enterprise as the key driver of growth and employment is central to the cycle of economic activity and development process (Chhabra & Karmarkar, 2016a). Enterprises, driven by the quest for profit, are innovating, investing, and generating employment and wage revenue. Small and mediumsized enterprises (SMEs) are major players in the current economic development (Chhabra & Karmarkar, 2016b). SMEs are becoming increasingly visible as essential players in the economy today. SMEs are essential to all communities, whether they seek to expand from low-income status to transform or seek to compete in highly innovative globalized markets as diversified modern economies. Enterprises and entrepreneurship is a critical catalyst to growth and change everywhere by ensuring that markets remain diverse and competitive. SMEs are considered a panacea for various economic problems, such as unemployment, income deprivation, inequality of income, and economic disparities for a capital-constrained developing country like India (Kumar & Chhabra, 2021). The SME sector has often considered the economy’s bulwark as it contributes 30 percent of GDP and 48 percent of India’s exports (https://msme.gov.in/). SMEs have played a major role in maintaining democratic objectives such as income equality and balancing regional development as planners expected shortly after independence. Compared to smaller firms, where the effects of economic development are more evident, large companies’ trickle-down effects are minimal (Chhabra & Goyal,

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TABLE 18.1 Present ceilings on investment and turnover Classification Micro Small Medium

Investment Up to ₹1 Crore Up to ₹10 Crore Up to ₹20 Crore

Turnover Up to ₹5 Crore Up to ₹50 Crore Up to ₹100 Crore

Source:  Economic Relief Package, Atmanirbhar Bharat Abhiyaan.

2019). Although big businesses have mainly produced wealth islands in the oceans of deprivation, smaller businesses have achieved the socialist goals of providing equitable growth and balanced economic development considerably. SMEs have helped industrialize urban and rural areas, thereby reducing regional imbalances and ensuring a fairer distribution of national earnings. The industrialized urban area had approximately 8.57 lac enterprises account for 54.77 percent of the total working enterprises in the SME sector. In contrast, in the rural areas, 7.07 lac enterprises account for 45.23 percent of the total working enterprises (https://msme. gov.in/). Based on investments in plant and machinery, the Micro, Small and Medium Enterprises (MSME) Development Act 2006 (https://msme.gov.in/) categorizes enterprises into micro, small, and medium enterprises. As a part of the relief measures under Atmanirbhar Bharat Abhiyaan (https://static.pib.gov.in/), the definition of micro, small, and medium enterprises has been altered to provide them greater support and benefits. The earlier definition of MSMEs was based on investments in plants and machinery or equipment. In contrast, MSMEs are now defined in a composite manner, taking both investment and turnover into account. The current definition has taken away the existing difference between manufacturing and services sector units. The present ceiling on investment and turnover to be classified as micro, small, or medium enterprises is as appears in Table 18.1.

SUSTAINABLE ENTERPRISE DEVELOPMENT SMEs have made a vital contribution to the country’s economic development and have provided a solid foundation for India’s industrial development. However, it is increasingly becoming difficult to ensure their sustainable growth in upholding their strategic advantage and adaptability to the complexities of flexibility and promptness of individual businesses. The modern state of the economy’s downturn and the tight constraints of the competitive climate compel firms to hunt for permanent methods to promote a transition to contemporary problems and find means to support enterprises’ stability and development. In view of the challenges faced by domestic businesses, research in the area of sustainable growth is gaining in importance. Sustainable development study at the business level has also drawn interest because of entrepreneurs’ ambitions to work effectively in the long term.

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The most accepted concept of sustainable development is that suggested by the United Nations World Commission on Environment and Development (UNWCED): ‘Sustainable development is a development that meets the needs of the present without compromising the ability of future generations to meet their own needs’ (Imperatives, 1987). Defining sustainable enterprise development implies that its long-term development strategy can ensure a balanced chord of financial, environmental, and social aspects. Sustainable businesses recognize both the businessrelated economic and social interests of individuals and the effect on the environment (Tolstykh et al., 2020). The definition of sustainable enterprise development has various approaches (Zu, 2014; Wirtenberg et al., 2018). Modern researchers’ key positions appear to align with:

a. Provision of innovation and project orientation; b. Balance of economic and environmental indicators; c. Principles of social responsibility to employees and society; d. Natural ecological integrity through the recovery of maintenance costs and the measurement of the company’s effect on the environment’s health.

The COVID-19 pandemic has generated enormous uncertainty around the world (Baker et al. 2020). The pandemic scenario, which caused many businesses to reconstruct their target growth vectors, considering their employees and their clients’ welfare, has increased the social element of the business development strategy. New problems only for small companies or those still in the crisis stage are impossible to reflect. In addition to new business models, SMEs need to be modernized for competition and economic development under new world trade laws and faster technical developments, including the broader use of information technology. Several programs, including better support initiatives and budgetary schemes, have been introduced by the government from time to time to support and promote SMEs. Under the emerging foreign trade regime, technological advances are becoming increasingly critical of SMEs’ sustainability and survival. International business policies need to be implemented by SMEs following the goals and strategies and the global operations management of transnational companies and big firms. The fruitful synthesis of ideas and the potential to incorporate them successfully exposes the core of management skills and is the key factor in achieving sustainable business growth. In my view, this can be accomplished by developing core management abilities, which have been evident because of expertise acquired from their experience through the application of knowledge and personnel skills. Summarizing scholars’ roles, this analysis covers the sustainable growth of an individual enterprise as a balanced development, where goals are integrated and synchronized in compliance with the principle of a triad: technical, environmental, and social.

THE RATIONALE OF THE STUDY The theory of gaining a competitive edge in small and medium-sized enterprises through human resources (HR) is based on the assumption that competitive advantage is the cornerstone of a business plan involving HR expertise, resources, and

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decisions that allow an organization to capitalize on marketplace opportunities. As businesses expand, the loose and informal management forms that led them to success at the start-up stage become ineffective, the original structures become strained, and profits decrease. Smaller companies’ lack of growth and desire to participate in externally funded training programs are at the root of the policy challenge. Although policymakers acknowledge the fundamental role of SMEs in economic development, they also recognize their weaknesses. Training has a prominent role among the many steps for SME development that have been undertaken, particularly the role in fostering entrepreneurial growth. Research findings show that SMEs are not aware of their training needs, nor do they respond adequately to the training initiatives and offerings provided by the universities or the government, even in developed countries. A large number of academic studies concerning SME training have been carried out. Research studies examining government-sponsored training programs were conducted, and the results show that most of them struggle to achieve the desired goals for which the training program was administered (Reid & Harris, 2002; Zhou & Ma, 2018; Steven et al., 2020). The studies carried out in India to understand HR problems in the SME sector have centered on analyzing the different training practices of the several organizations involved in delivering entrepreneurial development programs and creating entrepreneurial development programs’ (EDP) curricula. From SMEs’ viewpoint or those of the owners/managers, no comprehensive examination of their training and educational needs has been conducted. Recent studies (Kachkar, 2019; Zhou & Ma, 2018) suggest that small and micro-enterprises tend to be more vulnerable to external shocks relative to medium and large firms. Lack of attention given to creating a comprehensive business plan, targets, objectives, planning, and budgeting for the new enterprise, and growth of people’s assets, are foremost factors leading to the failure of many SMEs. Moreover, since small businesses face constant change, most often undefined and difficult to predict, learning how to cope with such change using training is crucial to the small business owner. The cost of services, the seeming uselessness of training programs, and the overconfidence of entrepreneurs resulting from past success and their reluctance to abandon their companies to participate in training programs are among the numerous reasons entrepreneurs lack interest in SME training programs. Obviously, the previous achievements endow the entrepreneur with the most justifiable excuse not to undergo any training. Practical evidence, however, indicates that most owners are not fully prepared to evaluate the competitive market conditions and lack the expertise to handle human and financial capital to optimize their financial performance. Another challenge for smaller companies is that the training offered appears to be ad hoc, is misconceived, and happens during daily activities in the absence of a training-needs review, and owner/managers use different forms of subjective evaluations to informally determine the importance of the workplace training (Jones et al., 2013). Therefore, the change to on-the-job training for managerial employees is consistent with the change in ownermanagers’ role from the management of operations to managers. However, there is a fundamental research discrepancy relating to teaching, more broadly within the SMEs’ context. The current research thesis focuses on this lacuna and aims to create linkages between training and performance. Despite the constraints imposed on SMEs, a large part of the research conducted does not examine how smaller

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companies can grow and benefit from a comprehensive training approach (Glaub & Frese, 2011; Sutter, Bruton, & Chen, 2019). Therefore, a growing concern in exploring and addressing the in-house factors that hamper SME growth like education and training is essential. In other terms, the point is that efficient development and use of a firm’s in-house and external experts will create a competitive advantage by developing competencies that are hard to imitate. Consequently, approaches to improve the company’s intrinsic capabilities are assumed to prove more durable and successful.

OBJECTIVES This study’s main objective is to describe and appraise previous works related to sustainable enterprise development training. These are:

1. To improve understanding of the concerns of a small business about their decisions about training. 2. To examine the training approaches and methods and the benefits of training and skill development in small businesses as reported in other studies. 3. To examine the human resources (HR) role as a source of competitive advantage in small businesses’ pursuit for sustainability.

HR as a Competitive Advantage Organizational researchers have come to acknowledge the value of human resources knowledge, expertise, competencies, and skills as the strategic competitive advantage source (Zhou & Ma, 2018; Steven et al., 2020). Sustainable enterprises recognize individuals as strategic assets and view their employees as assets and change agents. Human resource management can be as much, or more, a source of competitive advantage for emerging businesses as it is technology or money. Although rivals quickly introduce development, and capital is abundant for developing companies, sound HR systems are hard to find and/or imitate. Researchers have argued that human resources’ capability is the key to competitive advantage because it is rooted in the members’ (inimitable) collective experience built over a (rare) period. According to them, businesses obtain vital human capital and then develop HR programs to boost inimitable resources. This approach considers staff as a vital asset efficiently managed from the strategic viewpoint and contributes significantly to the organization’s success. It is, thus, seen as more of a competitive edge for organizations (Jennings & Beaver, 1997; Sutter, Bruton, & Chen, 2019). The creation of a critical mass of professional or experienced workers can be a possible source of competitive advantage to them (Sutter, Bruton, & Chen, 2019). HR is more likely to produce a competitive advantage among a firm’s intangible resources because they are often really rare, which can be more difficult for competitors to copy (Glaub & Frese, 2011). The expertise and knowledge of the employees are, in reality, the most valuable assets of small businesses. In SMEs, the intangibles of human capital tend to be those tools. The interest in a company’s HR practices has grown primarily due to the eroding of many of the conventional sources of competitive advantage that businesses have relied on, such as patents, access to capital, and market expansion. Such assets do not differentiate companies as they once did. As a competitive

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advantage source, businesses are increasingly dependent on their human assets – the expertise, skills, and capabilities of the workforce (Jennings & Beaver, 1997). SMEs will have to continuously update their digital technology to address complex challenges like the one faced during the COVID-19 pandemic. Especially as times seem normal, and social distance is still the primary rule, human resource development (HRD) practitioners’ core task remains to make it possible for workers to participate in more effective work. The goal here is that the workers must be prepared and accustomed to any unprecedented situation. Besides, it is similarly necessary to focus on sufficient workforce protection to keep the company going. Businesses make it easier for their staff to show transparency, promote connectivity, and show compassion by deeper interaction with them across digital channels. Employers not only encourage but also facilitate virtual learning and development (Hasan, 2020). In the wake of this pandemic, companies also have launched guidance portals and assistance services to remain linked with their employees (Ravichandar, 2020). Both of these programs that employers take specifically demonstrate their concern for their workers; they need to boost their confidence and stand by them to respond to the new normal. It was the confidence of the workers in the organizations and the assistance provided by Indian employers which helped them to remain strong and display productivity. The roles of management, compassion, technology, training, and development are crucial and will profoundly contribute to overcoming this crisis. In addition, India’s response to COVID-19 from many sectors is equally important facing identical or unfavorable conditions. For instance, sectors such as SMEs, tourism and hospitality, transportation, automation, and real estate are among those badly impacted. Under these conditions, while the reaction was swift, the consequences that followed, including emphasizing human resources, need to be discussed. At this point, HRD practitioners in companies need to be mindful of employee contact with other workers (face-to-face or virtual) and to reconsider their approaches to training and development as they may be an acceptable match to currently fulfill organizational requirements but may require adjustments. It is therefore essential to revisit competencies and capabilities. To meet the complexities of the evolving situation in the middle of the crisis, upskilling and reskilling are critical in today’s context. In order to become the right match for the enterprise, SMEs need to familiarize their staff with the need for upskilling and supply them with training and development programs. In reality, enterprises now have to embrace e-learning, virtual education, webcasts, virtual training and development services, and certifications. The innovation processes that can be applied to build solutions for the workforce to remain socially linked, reduce burnout, achieve work-life balance, etc., need to be identified by HRD practitioners. This condition has created an option to reconsider job aspects from different backgrounds, including individuals and enterprises. It has essentially been an eye-opener when learning about the pandemic, as organizations now need to recognize various emergency scenarios to ensure that their employees are fully aware of and trained to find effective approaches. The decisions related to training, infrastructural assistance, and workplace culture must also be balanced with the emergency context, so there is a connection between workers and other policymakers and paves the way for enhanced learning to sustain development.

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The Importance of Training and Skill Development in Small Business The fundamental means to attract the interest of small businesses in training could be in providing them with ample proof of the association between training and an upsurge in workforce skills and increased organizational performance. While it is a fact that business owners want their enterprise to be successful and regularly strive towards that aim, the biggest irony is that most small business owners are inadequately prepared to cope with the ongoing and unremitting problems facing business today. Although much of what happens appears to be role-specific, preparation is a significant factor for both competitive performance and business strategy. Small companies, regardless of the scale of operations or services rendered, are gradually using training to strengthen their operational capacities (Sullivan, 2000). Usually, training has helped facilitate effective strategy execution by acquainting the workforce with the necessary knowledge and skills needed to carry out their jobs. The rapid changes in the marketplace, particularly the dynamic information economy, require learning new knowledge for small businesses to remain advanced and retain the possible competitive advantage (Jennings & Beaver, 1997). Aragon-Sanchez et  al. (2003) noted that an enterprise’s workforce has its own specific set of skills, knowledge, attitudes, and organizational knowledge that can potentially be employed to allow competitiveness to be achieved. Thus, human resource training is necessary to have adequately competent, versatile, equipped, and enthused staff. A learned and trained workforce is indispensable for SMEs’ growth and success and vital for attaining a global competitive advantage. For some time, training has also been known as a successful way of minimizing small businesses’ failure (Sutter, Bruton, & Chen, 2019). In addition, education and training in entrepreneurship are useful opportunities to improve small business owners’ management skills, which are considered necessary for SMEs’ development and success (Zainol et  al., 2017; Sutter, Bruton, & Chen, 2019). Kachkar (2019) opined that training would assist small and medium-sized businesses in their efforts to solve problems, reduce costs, increase productivity, and improve. Also, enterprises investing in the workforce’s training and performance appraisal are more likely to benefit from low employee turnover. Cassell et  al. (2002) indicated that training should be recommended to present a strategic approach to the challenges of a firm, while Steven et al. (2020) suggested that the demand for training is associated with an improvement in  the operations of a business, thereby reducing costs and improving protocol knowledge. Prior research has established affirmative effects on business performance from training through e-learning, in-house training, and training at the workplace (Jones et al., 2013; McCloskey, 2017; Mustafa et al., 2018). It is generally acknowledged that micro-enterprise growth initiatives are regarded as playing a major role in growing entrepreneurial skills and success in the enterprise. Such systems are developed primarily to help micro-entrepreneurs perform business tasks better (Al Mamun et al., 2019). Accordingly, an empirical study by Zainol et al. (2017) on women entrepreneurs in Malaysia established that entrepreneurs who attended training programs showed an increase in their entrepreneurial skills. Recent research involving small entrepreneurs from Kelantan (Mustafa et al., 2018) found that the duration of time

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spent on training programs had a positive impact on domestic income and lowered their financial susceptibility. Mainly with regards to micro-enterprises, the duration of training sessions, training hours received, and attendance in debates showed a noteworthy influence on the enterprise (Mustafa et al., 2018). Throughout the times, training has evolved and extended its aims and features to respond to the changes in organizations, job climate, and individual needs, providing not only a means of improving individual skills but a tactical resource for controlling outcomes of the enterprise (Reid & Harris, 2002; Kraiger, 2014; Bell et al., 2017). Therefore, it can be seen that a wide literature upholds the claim that training has a positive impact on enterprise efficiency through improved performance and increased economic returns. Employee training is also widely recognized as a tool to enhance SMEs’ efficiency through increased competitiveness and productivity, organizational performance and capabilities, company survival and development (Kotey & Folker, 2007; Zainol et al., 2017; Zhou & Ma, 2018). Methods of technological training keep changing. One significant shift in organizational training is that job training is becoming more interactive and essential to workers. Employees may take advantage of a range of learning strategies that theoretically fit their learning preferences through interactive training, access to classroom learning, and more simulation or role-playing options. In particular, young workers want training that involves them and helps them learn more, and technological advances offer a training environment that can perfectly fit their learning style. Employers can better engage their employees through evolving social media technology, VR and artificial intelligence platforms, and new sensitive online applications. This form of training will increase employee engagement, upon induction or regularly. SMEs are using emerging tools to deal with the effects of the COVID-19 pandemic. For example, mobile and collaborative technologies and the Internet of Things with telecommunications networks of the next generation, Big Data analytics, machine learning, and blockchain network. The literature shows that adequate strategic adoption of information technology will improve competitiveness, performance, and productivity (Bruque & Moyano, 2007; Kleis et  al., 2012). Around the same point, SMEs will be able to explore and comprehend the impact of emerging technologies on current and future business operations and models with the necessary skills, culture, and expertise in the company (Kane et al., 2015). In the present business ecosystem, most global entrepreneurs adopt cutting-edge technologies like artificial intelligence (AI), machine learning (ML), data-enabled systems, internet-based businesses, etc., to become more efficient and trustworthy consumers, and stand high over the competition. However, there is still a need more for Indian SMEs to transform their traditional and conventional methods businesses into updated and contemporary businesses. Many SME entrepreneurs are harnessing technology to innovate their products and services from small towns to metro cities, thereby boosting their business and encouraging the slow MSME sector to grow faster. Though MSMEs may have been slow to technological adoption, they are now actively embracing technology to revamp old businesses and even start technology-based businesses such as e-commerce and online services. It enhances MSMEs’ efficiency, reduces costs, and expands market reach at domestic and international levels.

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To widen the scope of information technology, the Ministry of MSME has been actively working towards ways to facilitate MSMEs and assist them through all the business cycle stages. For ease of doing business, the Ministry has launched a robust Information and Communications Technology (ICT)-based Internet Grievances Monitoring System portal called ‘Champions’ (www.champions.gov.in). The ‘Champions’ stands for Creation and Harmonious Application of Modern Processes for Increasing the Output and National Strength. The ‘Champions’ portal is a combination of technologies formed to help, guide, empower, ease, and support the country’s MSME sector (https://msme.gov.in/).

TRAINING METHODS FOR SMES Training in SMEs has been characterized as casual, relaxed, and on-the-job while having little to no managerial development provisioning. Training is focused primarily on textbook guidelines that involve introducing structured management practices that are more suitable for big organizations. Training is often perceived as an unaffordable privilege in SMEs involving course fees along with an unproductive labor cost. Entrepreneurs have to choose between different options, from implementing procedures to handling people and working in new environments, lacking the expertise and the tools required to anticipate the consequences of their business decisions and activities. Worldwide, scholars, professionals, and governments are beginning to recognize the role of training in providing support for entrepreneurs. From an entrepreneurial perspective, the most important aspects of training are learning the necessary business skills and enhancing acumen to plan, set up, and operate their businesses. As companies move through the different phases of their growth, organizational needs change, and the training that is required changes along with them. The skills required by entrepreneurs develop and change over time. Entrepreneur training programs are goal-based and depend on guidance from mentors, counselors and coaches, and networking, all of which have been shown to have direct, positive results. Therefore different training methods are needed which take into account different and evolving needs. Various topologies for entrepreneurs are presented in this chapter which is discussed as follows: 1. In-House Training Kotey and Folker (2007) observed that the prevalent form of training in the SME sector was workplace-based training, defined as firms’ training to their workforce at their workplace. The majority of published research papers indicate the relentless demand to improve workplace capability levels to keep up with rapid technological developments and the role of in-house training programs playing an essential role in this context (Konings & Vanormelingen, 2015). In this regard, the literature for developed economies typically shows positive impacts of in-house training on employee efficiency and real wages (Konings & Vanormelingen, 2015). In-house training could also be viewed as versatile, informal, appropriate, and easy and offered the advantage of being of a low-cost value. Aragon-Sanchez et al. (2003) reported positive impacts from in-house training measured as having high productivity, high

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quality, low labor turnover, and financial outcomes. Chi et al. (2008) indicated that the training criteria should be performed professionally. Nevertheless, if SMEs cannot adequately finance the training, it should be imported from outside. Therefore, more and more in-house training firms will be visible internally, offering appropriate and specialized training. 2. Mentoring Mentoring differs from other similar types of specific help such as teaching and coaching; in the case of mentoring, the mentor positions the mentees’ interest as a complete priority, not as part of a collection of goals (Sullivan, 2000; Gibson, 2005). Within the context of entrepreneurship, mentoring seeks to increase the success rate of entrepreneurs by providing them with a deeper knowledge and insight into how they view their inspiration to become an entrepreneur, what their emphasis is on entrepreneurship, what they find to be the driving forces for success, and what the main strengths are of their enterprise. As such, the mentored entrepreneur develops its own expertise in making and implementing relevant business decisions. The mentoring objectives and expected outcomes are typically set at the outset by both the mentor and the entrepreneur and then updated according to changing needs. While the learning process in mentoring is formal and less controlled, the partners’ trusting relationship and mutual attractiveness guarantee their success (Sullivan, 2000; Reid & Harris, 2002). The main element of mentoring is that it is a comprehensive and long-range learning process that is continuous, is based on the individual being mentored, is versatile in learning methods and topics, involves the key elements of a process of total quality, is strongly linked to progress, and requires shared respect for the people involved (Sutter, Bruton, & Chen, 2019; Steven et al., 2020) 3. Coaching Coaching in entrepreneurship means tutoring and providing guidance to enhance efficiency in the business. Business entrepreneurs tend to focus on the daily issues they encounter and have to deal with immediately and inevitably lose sight of broader, more distant matters. Via coaching, the problems of today’s and tomorrow’s vision are established, allowing realistic and achievable targets to be set while not losing sight of the end goals. Coaching can thus help the entrepreneur strike a balance between today’s demands and the needs of tomorrow. The main aim of coaching is to recognize the entrepreneurs’ needs and talents and match them with the right tools and strategies, i.e., the ones that are most likely to contribute to growth and success. Coaching will enable customer entrepreneurs to identify and address real issues, handle their major business issues with confidence, and make the necessary changes to survival and profitability (Frese, 2009; Steven et al., 2020) 4. Counseling and Guidance Counseling is a domain-specific training tool for an entrepreneur or group of entrepreneurs in a particular field; it discusses entrepreneurs’ managerial and behavioral competencies in order to assist them in establishing competitive advantages, generating

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profit, and generally improving their companies through their performance (Bechard & Toulouse, 1998). Counseling offers multiple methods to develop a business and aims to provide entrepreneurs’ means to success. Counselors are typically qualified and specialists in the particular fields in which they are counseling; thus, entrepreneurs needing counseling should specify the exact field, e.g., economic efficiency, strategic vision, optimization, HR performance, etc., where they want counseling. There are various types of counseling: a. One-on-one basis – a one-on-one specialist training and advice in a strong relation to the client’s needs (such as how to start a business, expand an existing business, or write a business plan). b. Orientation programs – courses that assist a select group of entrepreneurs assembled for the same purpose: assembling a detailed business solution. Such services seek to familiarize clients with the management skills required to drive company development or get a business off the ground. c. Seminars – stand-alone colloquiums or discussion groups for various entrepreneurs to help them cope with predefined stumbling blocks or advise them on particular business-related matters. d. Workshops – expert-led interactive workshops on various business concerns that include marketing, finance, sales, and more (Bechard & Toulouse, 1998). 5. Learning Based on Technology The use of technology for SME training is a potential offering to overcome the difficulties faced while imparting training such as access to training, training in inaccessible and far-flung areas, and medium of training; while, above all, e-learning has the prospect to offer customized training (Frese & Keith, 2015; Steven et  al., 2020). What constitutes learning through technology depends on the various ways the materials are transmitted through. Online materials are predominantly online, while other methods include webinars, e-content, online discussion forums, etc. With the introduction of information technology in the 1990s, a significant effect on business operation mode was noted. The sooner SMEs realize the flexibility and purpose of technology for operational jobs, strategic and tactical decisions, creation of skill, and imparting training, the better productive use of information technology that can be made (Cassell et al., 2002; Bell et al., 2017). Technology-based training offers SMEs a considerable benefit of learning anytime, anywhere (McCloskey, 2017). Using technology to impart training does not include the use of the internet or ‘distance learning’; instead, it is an approach to organizations and individuals to share knowledge and understand. This includes a long array of learning technologies and strategies and includes benefits that might appeal to an SME owner to attend a training program while on the job with time and workload constraints. The ability to deliver training can be a distribution method ideal for SMEs. It may add the advantages of motivating the enterprise to invest more in training, while the training is cost-effective for the trainer and timeindependent for the owner (Birchall & Giambona, 2007; Frese & Keith, 2015; Kachkar, 2019).

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Since the trainer’s cost covers almost all training methods, those that do not have a trainer do have the cost of the equipment to recognize (i.e., programmed instruction and simulation). Similarly, the expense of the learning facility may be involved for off-the-job and head-on training methods. In contrast, correspondence training methods may not demand premises but may require equipment. Irrespective of how money gets spent, it is usually essential to use strategies that can reduce training costs. Mentoring is one such strategy in which the trainer can train an entrepreneur at a minimal or zero cost, and in which the entrepreneur gets the opportunity to work in the position they are training to fill; therefore, the content of the training matches to their future responsibility for the job. Mentoring is important for circumstances where the training content is ideally suited to the learning method of doing and where learners gain the comprehensive knowledge required to resolve future assignments. This method is also best suitable for a situation where the entrepreneur’s financial expenditure in training is limited in opting for extraordinarily time-consuming and more formal training programs.

CONCLUSIONS The learning and training practices of entrepreneurs are essential factors in an organization’s future success at any level. Entrepreneurs should actively seek practical support and psychological support to build confidence in transforming their vision into a business. Learning and learning activities in terms of sustainability, productivity, creativity, and business processes such as strategic management, decision-making, and recruiting processes are beneficial for both the company outcomes. Entrepreneurs can also agree on the ‘position’ of the ‘guide’ (e.g., tutor, coach, and advisor) they wish to engage, based on their preferences and the company’s needs. Then, entrepreneurs can ‘use’ training and learning tools to make their vision a reality. Assistance in selecting an entrepreneurial path, reorganizing thoughts and ideas on how to start and manage a business, or encouraging entrepreneurs by providing positive reinforcement and feedback to many entrepreneurs can also be of great importance to them. Nevertheless, by relying too heavily on preconceived notions and developed habits created by experiential learning, entrepreneurs can fall into ‘learning traps’. They tend to rely on set situational ‘truths’ rather than be versatile. To reduce the risk of being dependent on a mentor, coach, or consultant, entrepreneurs need to evaluate the training activities they are participating in and choose the methods to define and work in a versatile, open, and competitive environment. This chapter has highlighted the essential role of training in small and medium enterprises by examining key training methods. Owners who resort to a proactive approach of imparting training to their workforce will benefit in the form of a competitive position in the market and a flexible staff well acquainted to deal with the changing and unpredictable factors in India’s environment. The literature focuses on key training methods in SMEs and illustrates the importance of training for firm performance. Suppose SMEs are to optimize their potential for human capital. In that case, such organizational knowledge requires producing through the spectrum of human resource management techniques, such as training, and undoubtedly linking to the organizational objectives.

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Entrepreneurship can provide a positive force to stimulate economic development directly and, in particular, to provide fertile ground for such growth to take root in ensuring its sustainability. The number of people working in micro-enterprises offers a compelling case for integrating such enterprises as a single body in the formation and policy implementation. It guides support for separate initiatives to help existing enterprises in a well-balanced course of action for new enterprises, small and medium-sized businesses, and larger enterprises pursuing the integrated policy goals of labor force growth and productivity improvements. Entrepreneurship training also needs to concentrate on manufacturing and economic growth-related areas. Providing knowledge and strategies for resolving the death-valley era is suggested by analyzing different strategic directions according to the venture’s development curve. Therefore, the trainer/educator/counselor has to arrange the course to represent the program to fit the evolving economic climate and the entrepreneur’s needs. Furthermore, considering the industry’s competitive nature, entrepreneurs need experts who will be able to address their problems globally. Global knowledge and experience are required to pursue creative thinking, gaining and sharing capital, cross-cultural skills, and innovative approaches in the face of problems. As for microenterprise owners working in the present dynamic environment, the current study implies seeking ways to find training programs for growth and development, including developing entrepreneurial skills to cope with the dynamism. This review offers a much-needed substantiating capability of adequate training in delivering positive results for SMEs. Appropriate systems and support personnel should be in place at the SME level to ensure that equipment remains available to ensure the continuity of all business operations (within the digital platforms of the SMEs used). Post-pandemic, SMEs would have to rethink how and when to reshape their policies by integrating crises and contingency planning while leveraging appropriate distribution channels to boost revenues. Practitioners should also explore how emerging technology is shifting the management styles of SMEs and see the consequences of this transition for user data security and privacy. Practitioners and managers need to look for lessons learned from SMEs’ use of new technologies in promoting business continuity during COVID-19. The lessons learned from the review may complement our current knowledge and create a richer understanding of SME training and organizational outcomes across various contexts. This chapter contributes to a structure that is validated with systematic methods of research. Given that entrepreneurship training is essential for the generation of employment and the development of countries, it is surprising that there have been few evaluation studies. The current research emphasizes the call to evaluate entrepreneurial training programs. Future research will also explore a broader range of organizational and financial success metrics due to the training programs. Moreover, to ensure business continuity and growth, the implementation of digital technologies by SMEs involves a strategic reshaping of their business processes. The effects of digital technology implementation on training, innovations, and productivity will be essential for scholars to consider. Aligning this with SMEs’ business strategy in dealing with the repercussions of COVID-19 is another area for research.

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Index A absolute transformation, 55–56 adoption, 58, 71, 73, 75, 78–79, 88, 141, 150, 187, 198, 244, 275 agribusiness, 74 agripreneurship, 64, 66, 71–73, 75–78 Angel Fund, 221 artificial intelligence, 3, 6, 25–27, 34, 40, 42, 44, 63, 107, 139, 148, 150, 179, 240–242, 275 automation, 3, 14, 25–35, 56, 64–65, 73, 273 Automation Anywhere (AA), 29

B B2C, 60, 186 Big data analytics, 7, 17, 42, 60–64, 275 Big data applications, 58, 60, 62, 64–65, 74 blockchain, 4, 26–27, 40, 275 bootstrap cluster, 224–225 bootstrapping, 95, 214–216, 223–226

C capability, 17–21, 34, 72, 117, 196, 204, 276, 280 cloud computing, 5, 65, 74, 220, 238–239 competitive advantage, 17–19, 29, 120, 172, 237–238, 270, 272, 274, 277 complexity, 32, 60, 78, 119 creative industries, 234 critical process assessment, 32 customer support, 90, 193, 216 cyber-physical systems (CPS), 14 cybersecurity, 26–27, 192, 210

D data-driven farming, 65 Data Mining-as-a-Service (DMaaS), 19 Data Science-as-a-Service (DSaaS), 19 demographics, 187–188, 193, 277 digital connectivity, 223 disruption, 41, 43, 46–48

E e-commerce, 47, 185–186, 189, 192, 275 economic growth, 50, 150, 230, 234–235, 244, 268, 280 ecosystem, 5–6, 8, 40–48, 141, 143, 147, 150, 157, 164, 168–169, 216, 218, 220–226, 238, 243–244, 275

education sector, 63, 243 emerging technologies, 2, 6, 275 employment opportunities, 233, 235–236, 238, 241, 255 Enterprise Resource Planning (ERP), 26 entrepreneur ecosystem, 6 entrepreneurial capacity, 196, 200, 204, 206–210 entrepreneurial education, 196, 210, 234 entrepreneurial initiatives, 197, 210, 242 entrepreneurial intention, 196–200, 204–208, 233, 235, 240 entrepreneurial potential model, 199–200 entrepreneurial ventures, 45, 141, 149–150, 214, 229, 235, 237, 239–240, 242 e-tailers, 186–187, 192–193 Extended Technology Acceptance Model, 199

F family businesses, 235 farming, 61, 64–67, 71–77, 144 financial management sustainability, 251 fiscal policies, 230 food industry, 64, 255, 258 futuristic foresight, 45

G Global Business Services (GBS), 26 global crisis, 230 global economic prospects, 230 goods and service tax, 256

H healthcare, 56, 60, 63, 104–105, 138, 148, 150, 174, 179–180, 240, 242 home-production, 252 human labor, 28

I incubation, 5, 142, 171, 217, 220 Industrial Internet of Things (IIoT), 14–15, 20 industrial revolution 4.0, 26 Industry 4.0, 14–15, 20 innovative entrepreneurship, 233 institutional finance, 216 International Labour Organization (ILO), 48 Internet of Things, 2, 6, 14–15, 20, 40, 56, 59–60, 72, 275

285

286Index

O

seven-pillar framework, 216–217, 220 shopping experience, 188, 190, 192–193 small-medium enterprise, 251 small-scale farming, 76 social business, 46, 142 social compassion, 43–44 social endeavors, 40, 44 social entrepreneurial mechanisms, 40 social entrepreneurship, 44–47, 49–50, 138–148, 150, 237 social innovation, 46, 150 social media, 196–197, 199–202, 210 social networks, 7, 49, 129, 196 social value creation, 40, 46 startup ecosystem, 45, 168, 220–222, 224–226 Start-up India, 214 strategic planning, 239 structured data, 16–17, 29, 34–35, 58 sustainable value creation, 14–15, 17–21

online shoppers, 188, 192 online shopping innovation, 190

T

L leadership qualities, 178, 234 leveraging Big Data, 40, 42, 47–48, 63 lifestyle entrepreneurship, 237 light-dependent resistors (LDR), 15

M machine learning, 19–20, 27, 34, 36, 44, 60, 66, 73, 107, 109–110, 240, 275 Make in India, 214, 224

N natural languages, 36 netpreneurs, 185–186, 192–193

P payback period, 89–90, 257 perceived desirability, 199–200, 204, 206–208, 210 performance development, 252 post-COVID world, 43 potentia customers, 185–187, 224 pre-social media era, 196 public procurement, 217, 221

R raw data, 49, 56–57 remote inspection, 66 resource-based view, 17–18, 120, 128 resource utilization, 237, 256, 258 return on investment, 32, 139, 149–150, 257 risk-taking, 117–118, 126, 129, 199, 242, 267 Robotic Process Automation (RPA), 26–27

S seed funding, 217, 221 servicepreneurship, 58

tactical evaluation, 31–32 techno entrepreneurial model, 207–208, 210 technological intervention, 48 Technology Acceptance Model, 197, 199–200 Technology Readiness Index, 198 transaction data, 193 transformational, 40, 115 travel and tourism, 64

U unstructured data, 17, 34, 58–59, 72, 77

V value for money, 192 variability, 73–74, 77 velocity, 4, 8, 16, 20, 58–59, 61, 73, 235 venture funding, 221 viable social and business paradigms, 42 virtual agent, 26–27

W web analytics, 193