Global Trends in Technology Startup Project Development and Management: From Innovation to Startup Creation (Innovation, Technology, and Knowledge Management) 3031403231, 9783031403231

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Innovation, Technology, and Knowledge Management

Srikanta Patnaik Vincenzo Pallotta Kayhan Tajeddini   Editors

Global Trends in Technology Startup Project Development and Management From Innovation to Startup Creation

Innovation, Technology, and Knowledge Management Series Editor Elias G. Carayannis, George Washington University Washington, DC, USA

This series highlights emerging research and practice at the dynamic intersection of innovation, technology, and knowledge management, where individuals, organizations, industries, regions, and nations are harnessing creativity and invention to achieve and sustain growth. Volumes in the series explore the impact of innovation at the "macro" (economies, markets), "meso" (industries, firms), and "micro" levels (teams, individuals), drawing from such related disciplines as finance, organizational psychology, R&D, science policy, information systems, and strategy, with the underlying theme that in order for innovation to be useful it must involve the sharing and application of knowledge. This book series is indexed in Scopus.

Srikanta Patnaik  •  Vincenzo Pallotta Kayhan Tajeddini Editors

Global Trends in Technology Startup Project Development and Management From Innovation to Startup Creation

Editors Srikanta Patnaik Interscience Institute of Management & Technology Bhubaneswar, Odisha, India Kayhan Tajeddini Institute for International Strategy Tokyo Institute of Technology Kawagoe, Saitama, Japan

Vincenzo Pallotta School of Business and Engineering of Canton Vaud (HEIG-VD) University of Applied Sciences of Western Switzerland (HES-SO) Yverdon-les-bains, Switzerland

ISSN 2197-5698     ISSN 2197-5701 (electronic) Innovation, Technology, and Knowledge Management ISBN 978-3-031-40323-1    ISBN 978-3-031-40324-8 (eBook) https://doi.org/10.1007/978-3-031-40324-8 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Paper in this product is recyclable.

Series Foreword

The Springer book series Innovation, Technology, and Knowledge Management was launched in March 2008 as a forum and intellectual, scholarly “podium” for global/ local, transdisciplinary, transsectoral, public-private, and leading/“bleeding”-edge ideas, theories, and perspectives on these topics. The book series is accompanied by the Springer Journal of the Knowledge Economy, which was launched in 2009 with the same editorial leadership. The series showcases provocative views that diverge from the current “conventional wisdom,” that are properly grounded in theory and practice, and that consider the concepts of robust competitiveness,1 sustainable entrepreneurship,2 and democratic capitalism,3 central to its philosophy and objectives. More specifically, the aim of this series is to highlight emerging research and practice at the dynamic intersection of these fields, where individuals, organizations, industries, regions, and nations are harnessing creativity and invention to achieve and sustain growth.  We define sustainable entrepreneurship as the creation of viable, profitable, and scalable firms. Such firms engender the formation of self-replicating and mutually enhancing innovation networks and knowledge clusters (innovation ecosystems), leading toward robust competitiveness (E.G.  Carayannis, International Journal of Innovation and Regional Development 1(3), 235-254, 2009). 2  We understand robust competitiveness to be a state of economic being and becoming that avails systematic and defensible “unfair advantages” to the entities that are part of the economy. Such competitiveness is built on mutually complementary and reinforcing tow-, medium- and high-­ technology and public and private sector entities (government agencies. private firms. universities. and nongovernmental organizations) (E.G. Carayannis, International Journal of Innovation and Regional Development 1(3). 235-254. 2009). 3  The concepts of robust competitiveness and sustainable entrepreneurship are pillars of a regime that we call “democratic capitalisn” (as opposed to “popular or casino capitalism’). in which real opportunities for education and economic prosperity are available to all. especially  — but not only — younger people. These are the direct derivative of a collection of top-down policies as well as bottom-up initiatives (including strong research and development policies and funding. but going beyond these to include the development of innovation networks and knowledge clusters across regions and sectors) (E.G. Carayannis and A. Kaloudis. Japan Economic Currents, p. 6-10 January 2009). 1

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Series Foreword

Books that are part of the series explore the impact of innovation at the “macro” (economies, markets), “meso” (industries, firms), and “micro” levels (teams, individuals), drawing from such related disciplines as finance, organizational psychology, research and development, science policy, information systems, and strategy, with the underlying theme that for innovation to be useful it must involve the sharing and application of knowledge. Some of the key anchoring concepts of the series are outlined in the figure below and the definitions that follow (all definitions are from E.G.  Carayannis and D.FJ. Campbell, International Journal of Technology Management, 46, 3-4, 2009).

Conceptual profile of the series Innovation, Technology, and Knowledge Management • The “Mode 3” Systems Approach for Knowledge Creation, Diffusion, and Use: “Mode 3” is a multilateral, multinodal, multimodal, and multilevel systems approach to the conceptualization, design, and management of real and virtual, “knowledge-stock” and “knowledge-flow,” modalities that catalyze, accelerate, and support the creation, diffusion, sharing, absorption, and use of cospecialized knowledge assets. “Mode 3” is based on a system-theoretic perspective of socioeconomic, political, technological, and cultural trends and conditions that shape the coevolution of knowledge with the “knowledge-based and knowledge-driven, global/local economy and society.” • Quadruple Helix: Quadruple helix, in this context, means to add to the triple helix of government, university, and industry a “fourth helix” that we identify as the “media-based and culture-based public.” This fourth helix associates with “media,” “creative industries,”29 66 “culture,” me “values,” “life styles,” 29 66“art,” and per-haps also the notion of the “‘creative class.”

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• Innovation Networks: Innovation networks are real and virtual infrastructures and infratechnologies that serve to nurture creativity, trigger invention, and catalyze innovation in a public and/or private domain context (for instance, government—university—industrypublic-private research and technology development coopet-itive partnerships). • Knowledge Clusters: Knowledge clusters are agglomerations of cospecial-ized, mutually complementary, and reinforcing knowledge assets in the form of “knowledge stocks” and “knowledge flows” that exhibit self-organizing, learning-­driven, dynamically adaptive competences and trends in the context of an open systems perspective. • Twenty-First Century Innovation Ecosystem: A twenty-first century innovation ecosystem is a multilevel, multimodal, multinodal, and multiagent system of sys-­ tems. The constituent systems consist of innovation metanetworks (networks of innovation networks and knowledge clusters) and knowledge metaclusters (clus-­ ters of innovation networks and knowledge clusters) as building blocks and orga-­ nized in a self-referential or chaotic fractal knowledge and innovation archi-tecture (Carayannis 2001), which in turn constitute agglomerations of human, social, intellectual, and financial capital stocks and flows as well as cultural and technological artifacts and modalities, continually coevolving, cospecializ-­ing, and cooperating. These innovation networks and knowledge clusters also form, reform, and dissolve within diverse institutional, political, technological, and socioeconomic domains, including government, university, industry, and nongovernmental organizations and involving information and communication technologies, biotechnologies, advanced materials, nanotechnologies, and next-Generation energy technologies. Who is this book series published for? The book series addresses a diversity of audiences in different settings: 1. Academic communities: Academic communities worldwide represent a core group of readers. This follows from the theoretical/conceptual interest of the book series to influence academic discourses in the fields of knowledge, also carried by the claim of a certain saturation of academia with the current concepts and the postulate of a window of opportunity for new or at least additional concepts. Thus, it represents a key challenge for the series to exercise a certain impact on discourses in academia. In principle, all academic communities that are interested in knowledge (knowledge and innovation) could be tackled by the book series. The interdisciplinary (transdisciplinary) nature of the book series underscores that the scope of the book series is not limited a priori to a specific basket of disciplines. From a radical viewpoint, one could create the hypothesis that there is no discipline where knowledge is of no importance. 2. Decision makers — private/academic entrepreneurs and public (governmen-tal, subgovernmental) actors: Two different groups of decision makers are being addressed simultaneously: (1) private entrepreneurs (firms, commercial firms, academic firms) and academic entrepreneurs (universities), interested in optimizing knowledge management and in developing heterogeneously composed

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knowledge-based research networks; and (2) public (governmental, subgovernmental) actors that are interested in optimizing and further developing their policies and policy strategies that target knowledge and innovation. One purpose of public knowledge and innovation policy is to enhance the performance and competitiveness of advanced economics. 3. Decision makers in general: Decision makers are systematically being supplied with crucial information, for how to optimize knowledge-referring and knowledge-­enhancing decision-making. The nature of this “crucial information” is conceptual as well as empirical (case-study-based). Empirical information highlights practical examples and points toward practical solutions (perhaps remedies), conceptual information offers the advantage of further-driving and further-carrying tools of understanding. Different groups of addressed decision makers could be decision makers in private firms and multinational corporations, responsible for the knowledge portfolio of companies; knowledge and knowledge management consultants; globalization experts, focusing on the internationalization of research and development, science and technology, and innovation; experts in university/business research networks; and political scientists, economists, and business professionals. 4. Interested global readership: Finally, the Springer book series addresses a whole global readership, composed of members who are generally interested in knowledge and innovation. The global readership could partially coincide with the communities as described above (“academic communities,” “decision makers”), but could also refer to other constituencies and groups. Elias G. Carayannis Series Editor

Editorial

The present day business world is witnessing advancement in technology as well as a surge in the start-up initiation. The objective of technological innovation is not only limited to the intellectual dimension, rather it encompasses the maturity of the opportunity that solve various challenges and issues of the socity. The book on "From Technology Innovation to Creating Startups" encompasses how the innovative ideas and technological advancements are transformed into viable business ventures. It involves identifying a problem or an opportunity in the market, creating a unique solution using cutting-edge technology, and turning it into a sustainable business model. The journey starts with technological innovation, which can come from various sources such as research and development, academic institutions, or individual inventors. The innovative idea needs to be validated and tested in the market to ensure its potential for commercialization. Once the idea has been validated, the next step is to create a startup. This involves developing a business plan, finding investors or funding sources, and building a team to execute the plan. The startup may go through various stages of development, from early-stage seed funding to later-stage funding rounds as it grows and scales. Creating a successful startup requires a combination of factors such as a strong team, a unique value proposition, a viable business model, effective marketing strategies, and a deep understanding of the target market. It also requires a willingness to adapt and pivot based on customer feedback and changing market conditions. Overall, "From Technology Innovation to Creating Startups" is a dynamic and challenging process that can lead to significant rewards for entrepreneurs who are willing to take risks and pursue their visions with passion and persistence. The first chapter by Mukundan K V is titled as “From Technological Innovation to Innovative Business Model Design” explains what technological innovations require to increase their odds of commercial success. Also this chapter describes the types of business models in vogue today, the characteristics that make business models disruptive, the challenges incumbent firms face in responding to rivals backed by disruptive business models and how practicing managers and entrepreneurs can leverage this knowledge to increase their odds of commercial success.

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The author of the second chapter Deepika Gupta has titled her chapter as “Open Innovation and International Entrepreneurship Ecosystem”. This chapter provides a critical review and expands the understanding of the concepts of open innovation model and the international entrepreneurial ecosystem leading to community benefits globally. The next chapter entitled “Technology Innovation & New Age Business Models” by Divya Sharma describes the research done on technology and business model innovation, and drawing on examples from diverse industries, also they discuss the emerging business models in greater detail, and identify their enablers, benefits, and challenges. Andrew Maxwell in his chapter “Enhancing the commercialization of University Research” discussed the activities of the opportunities for improving commercialization success rates across universities and also highlighted the process for enhancing individual and venture success rates to building an expert community that learns from both success and failure. The author’s of the chapter “Mapping Information Systems Flexibility with Organization’s Manufacturing Strategy”, Somen Dey and R. R. K. Sharma done an extensive literature review and it reveals that the concept of flexibility cannot be considered complete unless it is simultaneously studied and implemented for all major functional areas within a manufacturing organization. The chapter entitled “Role of Human Resource Management Practices and HR analytics in Start-Ups” authored by Malabika Sahoo which highlights the role of different human resource practices and HR analytics in bolstering the performance and creativity of employees in start-ups. Javad Tajdini and Omid Tajeddini in their chapter “Corporate entrepreneurship and its effect on business performance: Case study Digikala” explored the perception of an intrapreneur seeking to provide insights into an e-commerce website as the firm’s marketing manager. “Corporate Governance and Firm Performance: Evidence from Microfinance Institutions (MFIs) in Ghana” is the chapter authored by David Boohene and Rosemond Dentaah Agyepong investigates the connection between Ghanaian microfinance institutions' (MFIs') performance and corporate governance practices using exploratory and descriptive statistics from eight (8) MFIs. The chapter by Javad Tajdini and Omid Tajeddini entitled as “Exploring the effects of Instagram and firm website on corporate performance: Evidence from a cosmetics firm” explore the effectiveness of the Instagram platform alongside a corporate’s website to promote multi-level marketing products. The next chapter “Social Networks, Social Media, Social Innovation and Technology for Society” by Duque et  al provides theoretical content on global development trends by considering start-ups as engines of competitiveness based on social technology and demonstrating that influencers are people who have a positive impact on the successful transaction through social media. The chapter by Archana Parashar entitled “Social Media Challenges Encountered by Business Ventures” highlights the beneficial social media opportunities before exploring the challenges entrepreneurs and startups encounter in navigating social

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media platforms. Some of the business strategies aimed at maximizing the potential of social media is also discussed to allow business ventures to employ social media to their advantage. Last but not the least, the chapter entitled “Metaverse Innovation for Startup Creation” by Pragyan Nanda and Srikanta Patnaik introduced of the current state of the Metaverse, the opportunities for innovation and entrepreneurship and discussed some case studies of selected Metaverse-based startups and the factors contributing to their success and failure. The chapter also covred the challenges and opportunities identified for Metaverse-based startup creation, including technical barriers, legal and regulatory challenges, security risks, standardization and interoperability issues, and user adoption and retention. We are thankful to Prof. Elias G. Carayannis, Editor-in-Chief of Springer Book Series Innovation, Technology and Knowledge Management (ITKM) for the kind support to bring out this volume on "From Technology Innovation to Creating Startups - Global Trends in Ecosystem Development and Management". We are also equally thankful to Dr. Nitza Jones, Publishing Editor for her constant support and guidance during the publication of this volume. It would not have been possible without the help of anonymous reviewers, our colleagues and scholars whose support is highly appreciated. Lastly but not the least, We are thankful to all the paper contributors, for their contributions. We are sure that the readers shall get new ideas and knowledge from this volume. Prof. Srikanta Patnaik Prof. Vincenzo Pallotta Prof. Kayhan Tajeddini

Contents

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 From Technological Innovation to Innovative Business Model Design��������������������������������������������������������������������������������������������������������    1 K. V. Mukundhan

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 Open Innovation and International Entrepreneurship Ecosystem ����   17 Deepika Gupta

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 Tech Innovation and New Age Business Models ����������������������������������   37 Divya Sharma

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 Enhancing the Commercialization of University Research ����������������   57 Andrew Maxwell

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 Mapping Information Systems Flexibility with Organization’s Manufacturing Strategy��������������������������������������������������������������������������   79 Somen Dey and R. R. K. Sharma

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Role of Human Resource Management Practices and HR Analytics in Start-Ups ��������������������������������������������������������������  109 Malabika Sahoo

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Corporate Entrepreneurship and Its Effect on Business Performance: Evidence from Digikala��������������������������������������������������  119 Omid Tajeddini and Javad Tajdini

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 Corporate Governance and Firm Performance: Evidence from Microfinance Institutions in Ghana ������������������������������������������������������  127 David Boohene and Rosemond Dentaah Agyepong

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Exploring the Effects of Instagram and Firm Website on Corporate Performance: A Case Study of Cosmetics Firm������������  143 Javad Tajdini and Omid Tajeddini

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10 Social  Networks, Social Media, Social Innovation and Technology for Society����������������������������������������������������������������������  155 Alba Guzmán-Duque, Ismael Ibáñez-Peñuela, and Hermenegildo Gil-Gómez 11 Social  Media Challenges Encountered by Business Ventures��������������  171 Archana Parashar 12 Metaverse  Innovation for Start-up Creation����������������������������������������  185 Pragyan Nanda and Srikanta Patnaik

Chapter 1

From Technological Innovation to Innovative Business Model Design K. V. Mukundhan

1.1 Introduction Technological innovation is “the economic function through which new technologies are introduced into production and consumption” (Scherer, 2001, pp. 7530). The technological innovation process involves recognizing new opportunities and identifying, acquiring, and organizing financial and human resources to transform the opportunities into product/service offerings that satisfy customer needs. Technological innovation has a profound impact on firm performance across the financial, market, and reputational dimensions (Evanschitzky et al., 2012; Zaheer & Bell, 2005; Hauser et al., 2006). While technological innovation has driven human and economic progress since time immemorial, the pace of innovation ushered in by the fourth industrial revolution and the emergence of digital businesses has rapidly changed societal patterns, industry structures, and firm value creation and capture modes. The 2021 annual Boston Consulting Group’s Value Creator report features 19 technology firms among the top 50 value creators that collectively account for a market capitalization of $7 trillion (BCG, 2021). These leading technology firms have returned an average of 40% in returns to their shareholders in the observation period of the report. In the financial services industry, venture capitalists have invested $30.8 billion in disruptive fintech start-ups that challenge some of the well-­ entrenched incumbents in the industry (McKinsey, 2018). Airbnb, the poster child of the tech-enabled disruption, has added 7 million hotel rooms in 220 countries, employing 3% of the workforce and less than 10% of the capital expenditure of leading hotel chains. The phenomenal success of these companies, as evidenced by their ability to generate superior revenues in a short period, and at a fraction of the K. V. Mukundhan (*) Indian Institute of Management Tiruchirappalli, Trichy, Tamil Nadu, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Patnaik et al. (eds.), Global Trends in Technology Startup Project Development and Management, Innovation, Technology, and Knowledge Management, https://doi.org/10.1007/978-3-031-40324-8_1

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investment of incumbents, stands testimony to how technological innovation enables firms to grow and scale businesses quickly and cost-effectively, while addressing unique customer needs that are beyond the reach of incumbent business models.

1.2 Technological Innovations and their Classification Technological innovations differ in the underlying knowledge required to bring them about and vary in their impact on customers and competitors. The literature on innovation broadly classifies technological innovations along four dimensions: “Product vs. Process Innovation,” “Radical vs. Incremental Innovation,” “Competence Enhancing vs. Competence Destroying Innovations,” and “Architectural vs. Component Innovations” (Schilling, 2008). Product innovations are incorporated into the outputs of the organization, such as goods and services. For example, Coca Cola’s development of a new energy drink is product innovation. Process innovations improve the techniques for producing and marketing goods and services by reducing defects, shortening delivery times, reducing waste, etc. For example, industry 4.0 techniques have been widely adopted by the manufacturing industry to improve the predictive maintenance of machines and improve productivity. Radical innovation differs from existing innovations in an industry in terms of its novelty (newness) and differentiated features. For example, when cellular phones were first introduced into the market, they radically differed from wired telephones (existing technology) in terms of their novelty and function. Incremental Innovation refers to an offering that involves a minor change or adjustment from the industry’s existing offerings. For example, Android 13, while offering unique features, represents an incremental innovation over the previous version of the operating system. Innovations are competence-enhancing when they build on a firm’s existing capabilities and knowledge base. On the other hand, competence-destroying innovations either do not draw from a firm’s current capabilities and knowledge base or render them obsolete. For example, when print-based companies such as Encyclopaedia Brittanica offered their information on CDs and DVDs, the change was competence-enhancing because they could reach out to new customers that desired portability and a condensed version of the information. When Wikipedia crowdsourced contributions from volunteers and built a free and credible online alternative to print encyclopedias over time, the existing capabilities and knowledge base of incumbents became obsolete. Technological innovations can result in changes to an entire architecture of a system or its components. Architectural Innovation involves either changing the system’s overall design or how individual components interact. For example, a hybrid or electric vehicle has a different design when compared to an internal combustion vehicle and thus qualifies as an architectural innovation over the incumbent products. An innovation is termed component or modular if a change occurs to one or more components without affecting the overall system. For example, battery

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technology improvements may improve an electric vehicle’s driving range without affecting its overall design and architecture. When studying the impact of technological innovation on industries, we usually come across a combination of these innovation types. When firms with disruptive, new-age business models upstage incumbents in many industries, they are typically backed by technological innovations that are radical, competence-destroying, or architectural. The combination of innovations a firm employs in each instance is strategic and is determined by the advantage it seeks over competitors and substitutes along the dimensions of customer value, price, and cost.

1.3 From Technology to Value Propositions Research in science and technology produces outcomes that solve technical problems and improve the efficiency of existing solutions. However, for technological research to translate to innovations that find wider adoption, it needs to solve visible customer problems and deliver economic value to customers. According to Friedel and Israel (2010), no technological innovation has captured the technical and economic impact of innovation in the last 200 years more than the electric light bulb. The invention of the electric bulb paved the way for transforming the industrial world from iron, coal, and steam to a post-industrial world characterized by electricity, petroleum, light metals and their alloys, and internal combustion engines. In their detailed account, the authors explain various steps Edison took in making the electric light a practical and economical technology, indicating that the technological progress was not complete until the innovation could be commercially available to customers at an affordable price. Similarly, Gladwell (2001) chronicles 70 years of evolution in the technology underlying disposable baby diapers and points to how supermarket retailers with limited shelf space drove innovators to make disposable diapers smaller and more effective. Innovators in the United States and Europe experimented with various diaper materials ranging from cloth, paper, wood pulp, and cellulose to the now commonly used super absorbent polymer. The technological progress in the disposable diaper industry would never be complete until diapers became smaller, absorbed more liquid, and were commercially affordable. Equally, the technology innovation graveyard is filled with numerous examples, such as Microsoft’s portable audio player Zune, Sony’s Betamax, and Tata’s Nano passenger car that could not quite transform the promise of technology into a commercially viable product that would interest most of the customers. The key difference between successful and less successful technological innovators, then, is the capability of firms to situate technological breakthroughs in the context of viable customer problems. To understand how firms can translate technological opportunities into value propositions that solve a customer problem, let us take the example of “Saregama Carvaan”  – a portable audio device that plays a careful selection of yesteryear

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Indian songs. Saregama India Limited is an Indian corporation that earned 85% of its revenues by licensing its library of copyrighted songs to music streaming platforms, video streaming platforms, broadcast platforms, social media platforms, and music societies. While the number of streams of its copyrighted music grew over time, the revenues from licensing were constantly shrinking. In 2017, the company decided to shift its focus to consumer markets and launched the “Carvaan” – a “transistor radio” like device that contained a carefully curated selection of popular songs from its copyrighted music library. The company manufactured the device through third-party contractors in China and established a market presence for the device by tying up with large electronic retail and e-commerce stores. Within two years of the product launch, the consumer markets fetched the company 54% in revenues, up from a mere 1% in 2016. In technological innovation terms, the Carvaan device represented an architectural innovation. It was an FM Radio, MP3 player, and a Bluetooth speaker packaged into one device. Its uncomplicated architecture included a flash memory card with pre-loaded songs enclosed in a transistor-like structure with an LCD display and limited operating buttons. As a device, it demonstrated no significant novelty over prevailing incumbents. There were alternatives available in the market that performed the same functions as that of the Carvaan: smartphone applications, streaming websites, portable music devices, and Bluetooth speakers that doubled up as MP3 players. The story behind how this unsophisticated device changed the identity of Saregama from a music licensing (B2B) company to a product (B2C) company lies in how Saregama’s actions demonstrated the tenets of a good value proposition design. The core consumers of Saregama’s library of music were individuals above 45 years of age who were not comfortable with listening to the music of their choice through smartphones or streaming applications. They preferred to listen to music with limited interaction with the device – like they used to do with the radio transistors of their time. Second, listening to music through a radio transistor evoked a sense of nostalgia in them – something that is absent in the present-day smartphones and video streaming applications. Further, this consumer segment also considered spending on entertainment a wasteful expenditure, despite the value they derived from consuming it. Faced with these unique challenges, the company designed the “Carvaan” to satisfy these very pain points of the consumers. First, they carefully curated the most popular songs from their library for the device based on customer insights gained from the streaming channels. Second, they designed the “Carvaan” device to look exactly like a transistor radio, making it compatible with how the consumer segment perceived a music device. Third, they targeted the children of such consumers and positioned the product as a gift they could give to their parents, thereby circumventing the target consumers’ reluctance to pay for this indulgence. Further, the product also twinned as a portable Bluetooth speaker and an FM radio, ensuring that the device had utility for other household members in addition to the primary target audience. Another example of a firm that leveraged technological innovation to satisfy a unique customer need is the London-headquartered Prodigy Finance. Students from

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developing economies seeking an MBA degree from top international business schools had to overcome several challenges before securing an educational loan from global banks and financial institutions. Lenders in the students’ home country were unwilling to extend credit for overseas education because their risk assessment models did not have the scope to estimate the risk of default. Lenders in the country of their education required students to establish financial self-sufficiency by pledging collateral proportional to the loan size. This requirement was impossible for international students to meet, as they could not always identify guarantors in their country of education to assume the risk underlying such loans. Further, the scholarships students received from the schools were insufficient to meet their tuition requirements. While an international MBA from a top-ranked business school was a certain path to a secure financial future, lenders were conservative in lending to this segment because international students lacked the credit information required for underwriting and risk assessment. Prodigy addressed this problem by designing a lending platform that connected lenders with international students seeking education loans. To start with, Prodigy restricted their customer segment to international students looking to pursue MBA education from the “Financial Times Top 100” ranked institutions. Prodigy then turned to the alumni of these institutions to lend to the students with admission offers through their platform. Alumni were willing to lend to students through Prodigy for two reasons. First, lending to aspirant students allowed them an opportunity to contribute to their alma mater. Second, they were convinced that the risk of non-payment did not exist, given the school’s global reputation and its alumni’s success. On the other hand, the borrowers, too, were keen to repay the loan as per the contract terms because the institution’s reputation was at stake. This dynamic allowed Prodigy to improve financial access to underserved and unserved customers. It also allowed them to circumvent the adverse selection problem plaguing traditional lenders, where higher-risk borrowers typically are extended credit at competitive interest rates due to the lack of sufficient credit information. Despite the lower risk of non-payment from its customers, Prodigy still had to address the issue of legal enforceability of its loan contracts. They managed this by including an arbitration clause in their loan contracts that provided them enforceability in close to 150 countries through their network of lawyers and legal staff. As of date, Prodigy Finance has extended credit worth $1.5 billion to more than 28,000 international students from 150 countries to pursue their education in more than 850 schools around the world. The firm has also started providing a platform for conventional banks and financial institutions to extend credit to international students seeking educational loans. Retail banks have traditionally acted as two-sided platforms in the sense that they raise money from investors on the one hand and lend them to borrowers on the other hand. However, Prodigy’s digital innovation ensured that they could perform the same role as that of traditional lenders at a much lower risk and at a fraction of their operating cost. Given the scalability and cost-efficiency a digital platform offers, Prodigy was able to expand its global footprint and add more educational loan

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accounts at a comparatively lower cost to traditional lenders, whose business models required physical presence in these markets. The examples of Saregama Carvaan and Prodigy illustrate how firms that leverage technology to craft novel value propositions, solve customer problems, and improve the functioning of their business models achieve superior financial performance. Both firms have designed successful offerings, which, according to Christensen et  al. (2016), got the customers’ jobs done. The organized approach firms can employ to translate technological opportunities into bankable value propositions is discussed next.

1.3.1 “Jobs to be Done” Thinking “Jobs to be Done” is an approach firms can follow to design compelling value propositions for their customers. The framework underlying the approach allows firms to design products/services that customers “hire” to satisfy their needs. The framework rests on the philosophical assumption that successful products/services are those that help a customer get a job done. Such products demonstrate good problem-­ solution fit with the customer’s requirements. The framework is also helpful when firms seek to synthesize product concepts from customer market research data. We apply the framework developed by the authors to the examples of Carvaan and Prodigy in Table 1.1 to illustrate how the firms transformed technological opportunities into successful value propositions:

Table 1.1  Applying “Jobs to be Done” thinking for Saregama Carvaan and Prodigy Finance Questions from Christensen et al. (2016) framework Do you have a (customer) job that needs to be done? Where do you see non-consumption?

What workarounds have customers invented? What tasks do customers want to avoid?

How Carvaan answered the question Customers desire a hassle-free option of listening to their favorite yesteryear songs, while relaxing, and reliving the nostalgia A product that is priced unreasonably will not be consumed because the customer segment considers this indulgence a wasteful expenditure With help from their children, customers resort to downloading pirated music and loading them onto their smartphones/music devices Customers would prefer to listen to their favorite music continuously with limited interaction with the device

How Prodigy answered the question International students need access to educational loans at competitive interest rates Given the scholarships available are insufficient, the students might not pursue the education opportunity if the loan is unavailable Customers typically collateralized the assets of their extended family or friends to secure the loan Customers prefer to avoid requesting others to offer collateral or guarantee their loan.

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When the above four questions are rigorously asked and pursued, firms can identify the appropriate solution for addressing the customer’s job. Firms can also group similar customer jobs and address them through a common feature in the solution. For instance, Carvaan delivered on “nostalgia” by designing the device like a transistor radio and curating a popular collection of the song library. Firms can also use the framework to make design/feature improvements. For instance, the key customers of the Carvaan thought spending on the device was an indulgence. The inclusion of the Bluetooth and FM radio option made the device multi-purpose for all in the household and increased the value the core customers perceived in the device.

1.4 From Value Propositions to Business Models Technological skills represent valuable, rare, and inimitable capabilities that firms leverage to create value for their customers. However, the inherent value of the technology is not realized until the firm commercially exploits it. To capture value from their technological innovations, firms must do much more than craft value propositions that satisfy a visible customer need. They must embed their value propositions in profitable business models that exploit their core technological capabilities. For instance, a biotechnology firm specializing in drug discovery (core capability) requires the complementary capabilities of low-cost manufacturing, and distribution channel reach to capture value from its R&D investments. Firms capture value when they exploit their capabilities through a venture driven by a specific business model (Chesbrough & Rosenbloom, 2002). Business models are essentially mechanisms firms employ to create value for their customers and capture value from the industry participants. Johnson et  al. (2008) identify four components that every business model contains: 1. Customer Value Proposition, which articulates a specific customer job that is not addressed (well) by competitor offerings. 2. Profit Formula that generates returns for the company through factors such as revenue model, cost structure, margins, and inventory turnover. 3. Key Resources, such as people, technology, products, facilities, equipment, brand, and partnerships required to create and deliver the value proposition to the customers. 4. Key Processes, such as training, manufacturing, and services, leverage key resources to create and deliver the value proposition to the customers. Being a platform business, Airbnb deals with two sets of customers – the hosts and the guests. The Airbnb business model creates value for hosts by enabling them to let out unoccupied rooms in their properties to guests for a fair price. The business model creates value for guests by providing them with a unique stay experience (“experience the city like a local”) in contrast to the standardized experience of a hotel room. Since adding new rooms requires Airbnb to tie up with more hosts and not build “physical” rooms, the capital requirements to add room capacity to their

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platform are marginal. Further, since the hosts are tasked with maintaining the properties, the investments that Airbnb requires to manage hospitality operations are also minimal. Traditional hotels have also had to manage Food and Beverage (F&B) as part of their hospitality, which adds to their operating cost and affects the customer experience. By unbundling F&B from the stay experience, Airbnb reduced its oversight cost and eliminated the potential of bad F&B ruining customer experience. By connecting hosts with guests through a platform business model, Airbnb has managed to scale up quickly in a costless manner and earn superior profits compared to some of the established incumbents in the industry. Although hotel chains have responded to the threat of Airbnb by reducing listing prices and adding room capacity through acquisitions, the operating cost of their business models is still high compared to Airbnb. The success of Airbnb has been in its ability to satisfy the latent needs of its customers through a profitable business model relative to the incumbents in the hospitality industry. The Airbnb business model in the choice-consequence representation (Ricart & Casadesus-Masanell, 2011) is presented in Fig. 1.1. By offering favorable payment terms to hosts (Key Process), Airbnb can attract more hosts and increase the inventory of rooms available (Key Resources) on its platform. An increase in room inventory increases the selection of rooms available on Airbnb, as each property is

Great Selection of Homes Amazing Travel Experience for Guests More Hosts on Airbnb

Lowers Customer Acquisition Costs

Increases Customer WTP

Better Payment Terms for Hosts More Bookings

Profits Revenues for Airbnb Fig. 1.1  The Airbnb business model

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situated at a different location and comes with different amenities and features. A good selection of rooms on the platform provides traveling guests with many options to “live life like a local” in their budget (Customer Value Proposition). The presence of multiple room options increases the customer’s Willingness to Pay (WTP) for the service and makes Airbnb the preferred platform for their travel accommodation needs. Customer WTP translates to more bookings on the platform, leading to revenues and profits for Airbnb through commissions earned for every booking (Profit Formula). When customers repeatedly book on the Airbnb platform, the cost incurred by the company to acquire and retain customers decreases considerably. Airbnb uses the excess profits to sign up more hosts to improve the selection of homes on its platform.

1.4.1 Designing the Business Model Business model design is an iterative process and seeks to demonstrate the right balance between commitment to business choices and the flexibility of modifying them depending on the emerging need. In every industry, firms refine their business models periodically in line with changing customer requirements and the evolution of the value creation process. Over time, firms perfect their business models to achieve a delicate balance between improving customer willingness to pay and lowering the costs incurred in serving customer needs. Airbnb has consistently managed to add properties that offer a range of amenities to cater to the diverse needs of its customer segments. They have also launched “Airbnb for Business” to target the captive and the most attractive customer segment of traditional hotel chains – the business travelers. To cater to the unique requirements of business travelers, Airbnb leveraged feedback left by hosts and guests on its platform to curate high-ranked properties that can deliver superior customer experience. Similarly, Saregama has reimagined the Carvaan as a platform with the launch of the Carvaan 2.0, which comes with the added functionality of access to 300+ podcast stations, song playlists, kids’ rhymes, and religious books, among other features. The design of business models can also allow firms to profit from substitutes and complements. For example, by positioning the “Carvaan” device as a gift that children can buy for their parents, Saregama could also collect revenues from other industries selling gifting products, such as apparel and jewelry, that come outside the scope of the industry that manufactures and sells music devices. Uber’s business model allows it to gain customers at the expense of taxi cabs and car rental firms while also offering  an alternative to private car ownership. In the lending space, fintech companies such as Prodigy both substitute for banks (when sourcing credit from alumni for current students) and complement banks’ business (when providing a platform to reach customers and assuming the risk of default on behalf of the bank).

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1.5 Types of Business Models Business models differ in how firms collect revenues from customers and how they are organized to optimize the costs incurred in operations. Most new-age business models convert the fixed costs required to operate a business into variable costs charged to customers per transaction basis. For example, owning a typical passenger car entails annual fixed costs in the form of maintenance, insurance, and parking expenses. The Uber business model converts these fixed costs into variable costs that each rider pays incrementally for the time they use the vehicle. Similarly, Airbnb apportions the fixed costs involved in maintaining a property over several guests that occupy the rooms on a time-sharing basis. Asset sharing ensures that the hosts achieve better utilization of their properties and earn higher income when compared to the traditional leasing/renting model. The nature of the business model employed also varies from one industry to another. The subscription model is popular in software services, where firms allow customers access to a product/service in exchange for a subscription fee. For example, Over the Top (OTT) streaming services such as Netflix and Amazon Prime Video allow users access to a large collection of movies, TV shows, and originals for a subscription fee. Customers see superior value in this “all-you-can-eat” subscription model over purchasing movie DVDs, going to cinemas, or subscribing to multiple TV channels in which the shows are broadcast. Similarly, companies like Microsoft are experimenting with the subscription model for their Office suite of software (titled Microsoft 365) as an alternative to outright sales of the software license. The subscription allows customers to pay for the software for the time they need it instead of purchasing a license for a lifetime. The pay-as-you-go or fees-for-service model has recently become popular in Automotive and Property insurance. By attaching an Internet of Things (IoT)-based sensor to the vehicle dashboard, insurance companies monitor customer driving behavior and charge an insurance premium proportional to the miles driven. Customers are happy to allow the companies to monitor their driving behavior because of the prospect of reduced premiums. Insurance firms benefit from monitoring driver behavior as it allows them to capture original driving data (as opposed to drawing from unreliable past driving records), improve their risk prediction models, lower the likelihood of insurance claims, and pass on the operational savings to customers in the form of reduced premiums. Two business models that have become popular in electronic commerce are the inventory and marketplace models. In the inventory model, e-commerce firms keep stock of all the goods they sell on their platform and fulfill sales directly or through third-party logistics providers. In the marketplace model, firms connect a large fragmented base of buyers and sellers to transact with one another on the platform while guaranteeing trust, transaction efficiency, and transparency. Since marketplaces only enable transactions between buyers and sellers, they can scale up very quickly to become “winner-takes-all” businesses. However, growth in the marketplace model comes at the expense of high upfront customer acquisition costs and the cost

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of establishing network effects. On the other hand, an inventory model allows firms to take control of the product and enable a superior purchase and post-purchase customer experience. However, firms following the inventory model forgo capital efficiency in the process, as they require working capital to buy and stock inventory from vendors before a sales transaction can be completed. Firms also use different types of revenue models in their business model designs (Bryce et al., 2011). In upselling, firms provide the “basic” version of a product free to gain widespread adoption and then charge for a premium version. For example, Skype offers free computer-to-computer calls but charges for computer-to-phone calls. In cross-selling, firms also sell other products that are not directly tied to their core product. For example, Airlines offer cheaper seats and charge for add-on services such as reservation, priority boarding, in-flight meals & entertainment. In charging affiliates or third parties, firms provide their offerings to users for free or at subsidized prices and charge a third party for access. For example, Google provides its search engine free for users but charges advertisers to advertise to its millions of users. In bundling, firms combine their core and complementary offerings and sell them as one package. For example, retail banks bundle trading and investment accounts with savings account for certain customers. Pursuing each of these revenue models has different implications for the profit margins earned by firms and the number of customers required to make profits.

1.6 When Do Business Models Become Disruptive? Disruption is defined as the “means for broadening and developing new markets and providing new functionality that, in turn, disrupts existing market linkages” (Hang et al., 2011). Disruptive innovations typically originate from low-end (inferior on attributes that mainstream customers value) or new market footholds (such as by converting non-customers to customers). They offer novel value propositions to attract new customer segments (or the price-sensitive mainstream), are initially sold at a lower price, and penetrate the market from niche to mainstream (Christensen et al., 2013). Firms leverage disruptive innovations to achieve lower cost structures, provide superior customer experience, and break down incumbent defenses and barriers to entry. Despite their impact, technological innovations, per se, have not been entirely responsible for the structural transformation we see in many industries. Industries get disrupted when firms backed by technological capabilities design business models that have transformative potential. Kavadias et al. (2016) identify six elements that characterize a transformative business model: (1) A more personalized product or service, (2) A Closed-Loop process, (3) Asset Sharing, (4) Usage-based Pricing, (5) A Collaborative Ecosystem, and (6) An agile and adaptive organization. Firms with business models that have three or more of these six characteristics are transformative. That is, they will replace the dominant incumbent business models in the industry.

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Firms have leveraged technology and customer data to design personalized products and services that satisfy customer needs better than incumbents. For example, Amazon tracks user purchases on its platform and recommends products customers are more likely to buy together. Because customers receive personalized recommendations, they will be willing to pay for the recommended products and services. In contrast to the linear process, a closed-loop process is one in which materials are recycled and reused in production. Reuse allows firms to reduce their dependency on virgin resources and bring down supply costs. For example, Apple satisfied 59% of its aluminum requirements from recycled sources in 2021. Using recycled inputs reduces the supply cost and the firm’s environmental impact. Asset sharing involves sharing of costly assets between two customers. In the Airbnb example, we explained how underutilized properties (costly assets) are shared by the hosts with guests on a time-sharing basis, thereby creating value for both sets of stakeholders. Usage-based pricing or pay-as-you-go pricing allows customers to pay only for their usage of the products or services without having to purchase them outright for consumption. For example, car rentals allow customers to pay rentals for the duration they have hired the vehicle. A collaborative ecosystem is one in which risk, at least theoretically, is equally allocated among all the participants. The ecosystem of the ride-sharing industry includes drivers, riders, and the transportation network companies such as Uber and Lyft. Uber follows a feedback system that penalizes bad drivers and riders to make their platform attractive to drivers and riders. The customer also rates Uber on service quality, which directly reflects their experience with the service. An agile and adaptive organization can quickly refine its business model choices in response to market feedback. IKEA, the furniture retailer, has demonstrated supreme agility in sourcing wood from conservation forests, the employment of child labor in their supply chain, and handling defective products and product returns. In addition to the six elements mentioned above, start-ups that have become billion-­dollar enterprises share some common characteristics. First, they are more responsive to changing customer preferences in the industry than their peers. Aimed with access to superior customer data, they challenge the dominant template of the industry by creating and capturing value differently. Second, these firms design business models that typically avoid confrontation with those of established incumbents during the initial stages. This approach allows them to sneak under the radar of incumbents and provides them with the time required to resolve the initial kinks in their value creation and capture templates. Third, they employ creative solutions to common industry problems and infuse technology appropriately in the value chain to ensure superior value delivery at a fraction of incumbent costs. Fourth, they typically go after the trapped value in the industry that is beyond the reach of incumbent business models. After establishing their initial foothold in this niche, they leverage technology to improve performance on parameters where the incumbents have traditionally excelled.

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1.7 Why Can’t Incumbent Firms Respond to New-Age Business Models? Five interrelated fault lines indicate when the firm’s position in the industry is unstable and when to reinvent business models. These include changing customer needs, incorrect performance metrics employed by the organization, the emergence of new players in the industry’s periphery, the firm’s current business model, and the lack of talent and capabilities within the organization to recognize and make successful business model transitions (Bertolini et al., 2015). Firms that detect these fault lines early can better prepare and adapt their business models to the changing eventualities. Research suggests that while 80% of executives at large organizations recognize the need for transformation, only about a third are confident that they can get the job done in 5–10 years (Bertolini et al., 2015). Changing the core business model is a difficult decision confronting leadership in incumbent firms. Business model changes typically result in customers getting confused with the firm’s value proposition, employees feeling threatened about job security, and investors unwilling to reward unproven strategies. Business model changes are also associated with high risk, with very few firms able to make successful pivots. On the other hand, if a firm decides not to change its business model, it runs the risk of being upstaged by the disruptor, which has the cost and price advantage to change how value is created for customers in the industry. Even though incumbents see disruption early, the abovementioned challenges prevent them from successfully mounting a strategic response. Changing the firm in responding to disruption requires modifying the organizational structure, rethinking how performance is defined and measured, and building new capabilities as part of the competitive response. Firms generally end up stumbling in this journey of change management.

1.8 Implications for Managers and Entrepreneurs While the knowledge of different technological innovation types is helpful, managers must think in terms of combining these types for maximizing competitive impact. For instance, technological innovations that demonstrate architectural, competence-destroying, or radical characteristics often provide firms with long-­ term success in the competitive environment. However, since technology development is endogenous and demonstrates path dependence to choices made in the past, firms may not always be able to play combinations with innovation types in their industry. In such cases, firms can turn to value proposition design and innovative business models to maximize the impact of their technological competencies. If anything, the examples of Saregama Carvaan (architectural innovation) and Airbnb (platform innovation) demonstrate that incremental technological innovations can

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also spawn successful enterprises when combined with jobs to be done thinking and effective business model design. The second important takeaway for managers is the need to look at technological initiatives through the lens of customer value. It would be pertinent for managers to ask the questions “Which customer problem does this technological solution address?” and “Why would a customer pay for the superior technological outcome arising from my R&D effort?” before committing to major investment outlays. This way, firms can orient their technological initiatives to providing features and options desired by the customer and improve the chances of product acceptance. A single-­ minded focus on customer value also helps firms to leverage technology to address those elements of value that are missing in competitor offerings. For instance, hotels across the world were providing tourists a “standard” hospitality experience. By connecting tourists with host properties through its platform, Airbnb provided a “localized” experience for its customers. It is this focus on customer value that allowed Airbnb to look beyond the traditional approach of building and managing properties and direct its efforts to improving the hospitality experience. The third and the most important takeaway for managers is how business model design can be leveraged for competitive success. The examples provided in this chapter are of companies that have changed the rules of the game in their respective industries. Saregama bucked the trend of its key resources (copyrighted songs) losing value in the licensing business model by deploying them in the device business model. Prodigy initially bypassed banks when raising funds from alumni networks to offer loans to unbanked customers (platform competition) and eventually ended up becoming a partner to banks (platform collaboration) to improve the latter’s risk management and access challenges. Airbnb demonstrated that growth in the hospitality industry can follow an asset-light approach (through platform-mediated room sharing) instead of the asset-heavy approach (building and managing properties) of the leading players. Managers can benefit from the understanding of how business models evolve in an industry, and how experimenting with different types of business models allows them to profit from their technological innovations differently from how incumbents do. The knowledge of business model fault lines can allow entrepreneurs to target frailties in incumbent business models and design business models that are consistent with changing customer preferences in their industry.

References BCG. (2021). Value creation in a decarbonizing economy: The BCG value creator’s report 2021. https://www.bcg.com/publications/2021/value-­creation-­toward-­a-­decarbonized-­economy. Accessed 01 Oct 2022. Bertolini, M., Duncan, D., & Waldeck, A. (2015). Knowing when to reinvent. Harvard Business Review, 93(12), 90–101. Bryce, D.  J., Dyer, J.  H., & Hatch, N.  W. (2011). Competing against free. Harvard Business Review, 89(6), 104–111.

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Chesbrough, H., & Rosenbloom, R.  S. (2002). The role of the business model in capturing value from innovation: Evidence from Xerox Corporation’s technology spin-off companies. Industrial and Corporate Change, 11(3), 529–555. Christensen, C., Raynor, M.  E., & McDonald, R. (2013). Disruptive innovation. Harvard Business Review. Christensen, C. M., Hall, T., Dillon, K., & Duncan, D. S. (2016). Know your customers’ jobs to be done. Harvard Business Review, 94(9), 54–62. Evanschitzky, H., Eisend, M., Calantone, R. J., & Jiang, Y. (2012). Success factors of product innovation: An updated meta-analysis. Journal of Product Innovation Management, 29(S1), 21–37. Friedel, R., & Israel, P. B. (2010). Edison’s electric light: The art of invention. JHU Press. Gladwell, M. (2001). Smaller: The disposable diaper and the meaning of progress. The New Yorker, 74–79. Hang, C.  C., Chen, J., & Yu, D. (2011). An assessment framework for disruptive innovation. Foresight, 13(5), 4–13. Hauser, J., Tellis, G. J., & Griffin, A. (2006). Research on innovation: A review and agenda for marketing science. Marketing Science, 25(6), 687–717. Johnson, M. W., Christensen, C. M., & Kagermann, H. (2008). Reinventing your business model. Harvard Business Review, 86(12), 57–68. Kavadias, S., Ladas, K., & Loch, C. (2016). The transformative business model. Harvard Business Review, 94(10), 91–98. McKinsey. (2018). Synergy and disruption: Ten trends shaping fintech. https://www.mckinsey. com/industries/financial-­services/our-­insights/synergy-­and-­disruption-­ten-­trends-­shaping-­ fintech, Accessed 01 Oct 2022. Ricart, J., & Casadesus-Masanell, R. (2011). How to design a winning business model? Harvard Business Review, 89(1–2), 100–107. Scherer, F.  M. (2001). The economics of innovation and technological change. In J. S. Neil & B. B. Paul (Eds.), International encyclopaedia of the social & behavioural sciences (pp. 7530–7536). Pergamon. Schilling, M.  A. (2008). Strategic management of technological innovation (2nd ed.). McGraw Hill Education. Zaheer, A., & Bell, G. G. (2005). Benefiting from network position: Firm capabilities, structural holes, and performance. Strategic Management Journal, 26(9), 809–825.

Chapter 2

Open Innovation and International Entrepreneurship Ecosystem Deepika Gupta

2.1 Innovation Innovation is one of the important buzzwords that has suddenly been doing rounds across individuals, firms, entrepreneurs, ventures, and even nations. Everyone is realizing and caring about their ability to innovate, on which their future allegedly depends (Christensen & Raynor, 2003), and are thereby busy improving their innovation performance. At a national level, governments are also caring about innovation: how to design policies that stimulate innovation. We are well aware that innovation helps in competitive advantage and long-term financial success. Today, innovation is becoming a part of our culture and embedded into our languages. So, what exactly is innovation? Can it be managed? How has the literature of innovation developed and where are the research gaps?

2.1.1 Defining Innovation Innovation is comprehensively defined by Myers and Marquis (1969) as “Innovation is not a single action but a total process of interrelated sub processes. It is not just the conception of a new idea, nor the invention of a new device, nor the development of a new market. The process is all these things acting in an integrated fashion.” Rogers and Shoemaker (1972) clarify the term “new” as “It matters little, as far as human behaviour is concerned, whether or not an idea is ‘objectively’ new as measured by the lapse of time since its first use or discovery. .. If the idea seems new and D. Gupta (*) Indian Institute of Management, Visakhapatnam, Visakhapatnam, Andhra Pradesh, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Patnaik et al. (eds.), Global Trends in Technology Startup Project Development and Management, Innovation, Technology, and Knowledge Management, https://doi.org/10.1007/978-3-031-40324-8_2

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different to the individual, it is an innovation.” Christopher Freeman (1982) in his famous study of the economics of innovation wrote, “… not to innovate is to die.” Today, ability to innovate is demonstrated by a highly diverse set of industry, whether e-commerce, pharmaceuticals, retail, automobile makers, or computers. In addition to academia, consulting firms too try to understand successful innovation. There can be multiple ideas but what leads to innovation that can achieve commercial success for the organization? The Boston Consulting Group (BCG) highlights three things to have a successful innovation, that is, making innovation a priority (about 75% surveyed companies reporting so), committing investments and talent to it (with major focus on leadership and teaming), and being ready to transform investment into results (driving value creation and resilience). These firms are delivering impressive growth and/or return to their shareholders (see Table 2.1). The ranks are derived based on the above three criteria and emphasize on how different companies across different industries are promoting innovation through various means and methods. Table 2.1 highlights domination of technology companies like Apple, Microsoft, Alphabet, and Meta in the top-ranked innovative companies as innovation is a continuous game changer in this industry. With growing digitization, technological advancements drive innovations in these companies. Companies like Samsung, Huawei, Sony, IBM, Dell, LG, Oracle, Siemens, HP, Lenovo, and others that belong to computing industry also believe and push for technology-driven innovations. Table 2.1  World’s most innovative companies 2022 Ranks Ranks 1–10 Company 11–20 Company 1 Apple 11 Meta (earlier Facebook) 2 Microsoft 12 Nike

Ranks Ranks Ranks 21–30 Company 31–40 Company 41–50 Company 21 Toyota 31 Xiaomi 41 Tencent

22

Alibaba

32

eBay

42

3 4

Amazon 13 Alphabet 14

Walmart Dell

23 24

HP Lenovo

33 34

43 44

5 6

Tesla 15 Samsung 16

Nvidiaa LG

25 26

Zalandoa Bosch

35 36

45 46

ByteDancea Panasonica

7

Moderna

17

Target

27

37

47

Philips

8 9

Huawei Sony

18 19

Pfizer Oracle

28 29

38 39

PepsiCo Hitachib

48 49

Mitsubishi Nestleb

10

IBM

20

Siemens

30

Johnson & Johnson Cisco General Electric Jingdongb

Hyundai Procter & gamble Adidas Coca-­ Cola 3 Mb

General motorsb Fordb Intelb

40

SAP

50

Unileverb

Source: https://www.bcg.com/publications/2022/innovation-­in-­climate-­and-­sustainability-­will-­ lead-­to-­green-­growth, BCG Most Innovative Companies (MIC) Report, 2022 a New entrant b Returnee company

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Manufacturing companies like Tesla, Hyundai, General Motors, and Ford, pharmaceutical companies like Pfizer, Johnson & Johnson, and others are also shifting focus from traditional methods of manufacturing to innovative methods to save costs and meet growing demands of customers, environment, and sustainability. Table  2.1 also highlights the rise of companies like Walmart, Target, PepsiCo, Adidas, SAP, and many others as innovative companies in the survey conducted and thus emphasizes that innovation is not restricted to select few companies but is now becoming an essential tool for strengthening competitive advantages and becoming an essential agenda item for the top management of any organization.

2.1.2 Historical Developments and Literature Review Innovation had its own traces of historical path making it an engine of growth. Importance of innovation has been discussed and debated for hundreds of years. First economists like Schumpeter (1934, 1939, 1942) emphasized on the importance of new products for economic growth and then Marx (1972) suggested how innovations can be associated with waves of economic growth. Thereafter, there had been huge interest in research for innovation though largely focused for military and industry. Various works by Simon (1957), Cyert and March (1963), and others revealed how firms place more emphasis on their internal activities and resources as key influence on innovation. Studies looked at cross-discipline approach incorporating economics, organizational behavior, finance, business, and management. As the twentieth century drew near, more debates existed on understanding the area of innovation management. Studies by Chandler (1962), Cohen and Levinthal (1990), Nelson and Winter (1982), and many others looked at what contributes to innovation performance. Then, studies by Christensen (2003) and Hamel and Prahalad (1994) suggested the importance of discovering and satisfying the needs of customers by playing an active role in the new product development process. However, listening to your customers that actually stifles technological innovation can be detrimental to long-term business success because firms may pursue innovations that are not demanded by their current customers. Therefore, Christensen (2003) distinguished between “disruptive innovations” and “sustaining innovations” (radical or incremental innovations) wherein sustaining innovations appealed to existing customers, since they provided improvements to established products while disruptive innovations tend to provide improvements greater than those demanded and also intended to create new markets, which may eventually capture the existing market. Thereby, innovation management encompasses in itself various debates and arguments in defining itself, judging commercial success, need for flexible organizational structures, and others. There are diverse range of definitions for innovation with very board concepts. In simple terms, innovation can be understood as summing up of theoretical conception, technical invention, and commercial exploitation (Trott, 2017). In recent times, innovation relates to product, process, or business

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Table 2.2  Historical development of innovation models Innovation Years model 1950s/60s Technology – Push 1970s Market-pull 1970s

1980s

Dominant design

Coupling model 1980s/90s Interactive model 1990s Architectural model 1990s Network model 2000s Open innovation

Major features Simple linear sequential process; emphasis on R&D, the market is a recipient of the fruits of R&D Simple linear sequential process; emphasis on marketing, the market is the source for directing R&D, R&D has a reactive role Abernathy and Utterback (1978) illustrate that an innovation system goes through three stages before a dominant design emerges Emphasis on integrating R&D and marketing Combinations of push and pull Recognition of the role of firm-embedded knowledge in influencing innovation Emphasis on knowledge accumulation and external linkages Chesbrough’s (2003) emphasis on further externalization of the innovation process in terms of linkages with knowledge inputs and collaboration to exploit knowledge outputs

model. These are linked to progress made by human and technological capabilities including collaboration, digitization, harnessing data, building artificial intelligence, developing requisite human skills, and implementing cross-functional teams and agile ways of working. Table  2.2 provides the historical development of the dominant models of these innovation processes. As evident from Table 2.2, innovation model has grown from traditional internal to firm approach to information-creation process arising out of social interaction. The innovation process has now shifted from closed systems to new open systems mode involving various players across the supply chain of any firm. Multiple innovation models trace how innovation has historically developed chronologically. These models take into account new technologies, which allow immediate and extensive interaction with many collaborators throughout the process from conception to commercialization. Arguments in innovation have traditionally been centered on social deterministic school (where innovations were the result of combination of external social factors and influences such as demographic changes, economic influences, and cultural changes) or individualistic school (where innovations were the result of unique individual talents). However, now the literature divides innovation into two schools of thoughts which are market-based view (where the market conditions provide the context that facilitates or constrains the extent of firm innovation activity) and resource-based view (where the firm’s resources, capabilities, and skills help to achieve sustainable competitive advantage). The open innovation model is the latest on the growth and development of innovation across the globe. We are now moving from within the firms to beyond the nation’s boundaries in terms of open innovation.

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2.1.3 Research Gaps Innovation has been in existence historically but was never an essential strategic decision-making tool by the top management of any organization. The history and literature review brings out the growth of innovation from being internal to the firm to moving beyond the boundaries not only beyond the firm but also beyond national borders. The innovation now is not limited to department decisions at the firm but has become integrated with functional areas of management like finance, marketing, operations, supply chains, human resources, organizational structures, and even entrepreneurship. With every decade, the innovation models (Crawford & Benedetto, 2014; Eisenhardt & Martin, 2000; Grant, 1996; Galbraith, 1982) have witnessed exponential growth from being simple and linear to being more complex in terms of knowledge creation and transforming organizations into learning modes. This externalization can be achieved through collaborative efforts not just within firms but beyond firms at local, regional, national, and even international levels. The literature guides us into the new innovation model of open innovation that is being experienced in the current twentieth century. This chapter looks at this new concept of innovation and what the future holds for it in terms of its contribution to economic growth and development at national and global levels. Further, we try to integrate open innovation concepts with entrepreneurial ecosystem at international levels in terms of how technology transfer can be infused and thereby absorptive capacity for the firm can be enhanced. We try to fit in the importance of open innovation with global entrepreneurial ecosystems by bridging the gap of how institutional, legal, and regulatory frameworks are essential parts of the ecosystem in addition to various other elements of culture, trust, and cooperation across firms and nations. The legal framework plays an important role as it needs intervention of intellectual property (IP) rights at various levels once the innovation moves beyond firm and national boundaries. The chapter extends into how important the concept of knowledge sharing and building has evolved due to open innovation across nations where the earlier knowledge was restricted at firm level in a nation only. Further, the chapter contributes to the gap on how such tuning benefits collaborative efforts, even with competitors, in the form of strategic alliances, partnerships, joint ventures, etc., across the globe. This requires the intervention of governments with effective governance and system change to improve social outcomes at the core. Here lie the challenges before open innovation to discern and integrate a vision for business and social outcomes to radically improve the response to pandemic, climate emergency, and other terrific challenges. Looking beyond competition and profitability to the firms across the world, the chapter connects economic growth and development across borders through 17 Sustainable Development Goals (SDGs) brought out by the United Nations community. We connect how borders become non-existent due to these twined approaches of open innovation model with international entrepreneurial ecosystem to find solutions to global social and economic challenges and thereby use and allocate resources and capabilities by aligning and sustaining goals and targets of various economies on the planet.

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2.1.4 Objectives of the Study The main objective of the chapter is to provide a critical review and expand the understanding of the concepts of open innovation model and the international entrepreneurial ecosystem leading to community benefits globally. The international entrepreneurial ecosystem should interweave itself with every nation’s legal framework to foster open innovation move beyond geographical boundaries. These two domains interact and integrate with each other to create products and services for economic growth and development. Further, the aim of the chapter is to look at the differences, relationships, and interactions of open innovation with international entrepreneurial ecosystem. In addition, the chapter tries to decipher how knowledge sharing and learning through resources and capabilities in such ecosystems can be deployed to break trade-offs to create social and economic values at national and global levels. Though there is no scientific formula for an ecosystem creation, the chapter looks at how nations or triple helix support or affect innovation including open innovation and entrepreneurial growth and development. Here is the importance stressed on various collaborative efforts, even if done with competitors, in the form of strategic alliances, partnerships, joint ventures, etc. The chapter focused on how external process in the form of open innovation pushes the internal process of technology transfer and absorptive capacity within the firms, whether new or existing, to assimilate and apply technology to commercial ends and thus contribute to economic growth and development. The chapter thereby looks at how the open innovation model integrates externally with international entrepreneurship ecosystem wherein learnings and knowledge rebound back to the firm for internal efficiencies in production, process, or business model. The outlook of the entire chapter is therefore an integration of inside-out and outside-in thought processes that has a huge impact not only at firm level but also at global level. However, these interactions should be viewed with cautious trade-offs given the growing challenges of pandemics, climate changes, and other grand challenges to the world. For this understanding, we look at 17 Sustainable Development Goals (SDGs) brought out by the United Nations community that have made global boundaries fungible to achieve environmental and sustainability challenges.

2.2 Open Innovation The open innovation concept was originally developed by Chesbrough in 2003. This openness concept was emphasized as an alternative to vertical integration. This idea, drawn from business strategy perspective, was radical at a time when proprietary intellectual property rights were sacrosanct. The concept emphasized that companies gain benefit by bringing in ideas not only from outside the organization’s

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boundaries but also from disseminating ideas outside those boundaries (McGahan et al., 2021; Asakawa et al., 2010). Vertical integration was largely implied to be internal to the firm, especially with R&D division. This radical concept introduced competition between an internal R&D with external sources of knowledge and thus supplemented knowledge-sharing between each other. This helped to invite generativity, the emergent process to discover and deploy new combinations of knowledge, because individuals in the team drive this process and there are high chances of being stuck up with process of knowledge creation. Chesbrough and Bogers (2014), say “Open innovation refers to a distributed innovation model that involves purposively managed inflows and outflows of knowledge across organizational boundaries, for pecuniary and non-pecuniary reasons, in line with the organization’s business model.” Thus, Chesbrough’s (2003) emphasis on the new knowledge-based economy is the backbone to the concept of open innovation. In particular, it is the use of cheap and instant information flows that places even more emphasis on the linkages and relationships of firms to fully capture and utilize ideas. Such openness benefits firm financially by creating opportunities in the short run. The idea of open innovation spurted emergence of various studies (Chiaroni et al., 2009; West et al., 2014) for further development of this idea and further integrating them from suppliers to firms as an alternative to vertical integration (Bogers et al., 2010). The open innovation was not restricted to any specific organization (Schmidthuber et al., 2019; Vrande et al., 2009; Perkmann & Walsh, 2007) but was for all types of organizations where innovation collaborations worked across various neighborhoods and ecosystems. The beauty of this idea was the centrality of duality (Enkel et al., 2009) of directions across organizational boundaries. This burgeoned research and practice into the innovation ecosystem of partnerships, alliances, strategic joint ventures, and others. Open innovation became central to addressing the societal challenges. The complexity of open innovation has evolved over time with growing technological and legal complexities. As openness is intensely social, it is also a matter of degree but does not provide a level-playing field. It is dependent on how partners gain experience in sharing knowledge across boundaries. Thus, it depends on the relationships among actors who are asymmetric in ways that make the collaboration fruitful, but that also introduce competition, power, communication, and coordination challenges (Holgersson et al., 2018). The aim should be to reduce trade-offs that need rethinking about the purpose of organizations and their stakeholders. These include large multinational corporations, small entrepreneurial organizations, state-owned enterprises, proprietorships, investment firms, hedge funds, currency funds, national governments, international agencies, state governments, cities, non-governmental organizations, religious organizations, educational institutions, and all others  – must take stock of the resources and capabilities they have, and of how those resources and capabilities can be deployed to break trade-offs to create social value and to address various societal challenges at national and global levels. The objective of innovation should be to create value for society and to assure a sustainable distribution of that value across contributors to its creation. The conceptualization of open innovation makes the organization to be relevant to the

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innovation agenda. The stakes are raised on entry into an innovation ecosystem: Organizations and other actors that are admitted into the innovation ecosystem should be committed to overcome the trade-offs through creative and generative deployment of resources and capabilities. An organization’s claim on the value that is created is an artifact of its contribution to value creation, thereby aligning the open innovation with stakeholder theory (Barney, 2018). The argument in this theory is that stakeholders with valuable resources and capabilities must find organizational arrangements that make their collaboration maximally effective. Such collaborations can occur within or across organizational boundaries. Primacy is placed on enabling joint value creation by enabling and encouraging co-­specialization and by enhancing knowledge exchange. The goal is not whether the organizations themselves survive, but rather whether value is created in the face of a compellingly important global challenge. The most motivating part is the collaboration among critical actors to get important things done. The profits follow when the achievements are significant.

2.3 International Entrepreneurship Ecosystem A key component of any innovation process is an individual. Within organizations, it is individuals (in the role of managers) who define problems, have ideas, and perform creative linkages and associations that lead to inventions. This has led to the development of so-called key individuals in the innovation process, such as inventor, entrepreneur, business sponsor, etc. According to Schumpeter (1934), entrepreneurs keep the gale of creative destruction blowing because they can move resources to areas to use more productively and break away from the familiar lines of businesses. Entrepreneurship is described with such terms as innovative, flexible, dynamic, risk-taking, creative, and growth-oriented. The thoughts on entrepreneurs to be economic agents who transformed demand into supply for profits were laid by Adam Smith (1776). Then, John Stuart Mill (1848) described entrepreneurship as the founding of a private enterprise. This encompassed the risk-takers, the decision-makers, and the individuals who desire wealth by managing limited resources to create new business ventures. Thereafter, Schumpeter clarified the linkage between the terms innovation and entrepreneurship that was considered as influencing growth in the economy. It is something that disrupts the market equilibrium, or “circular flow.” Its essence is “innovation.” According to him, “the carrying out of new combinations we call enterprise; the individuals whose function is to carry them out we call entrepreneurs” (1934: 74). Thereby, entrepreneurship, a creative activity, can be described as a process of action that an entrepreneur undertakes to create, build, and establish an enterprise out of nothing. It is an attitude of mind to seek opportunities, take calculated risks, and derive benefits by setting up a venture, where others see chaos, contradiction, and confusion. An entrepreneur is a person who starts such an enterprise. There are a wide variety of definitions for an entrepreneur – economists view him as a

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fourth factor of production along with land labor and capital. Sociologists feel that certain communities and cultures promote entrepreneurship. According to Peter Drucker (1985), “innovation is the specific function of entrepreneurship,” and “entrepreneurship is the means by which the entrepreneur either creates new wealth-producing resources or endows existing resources with enhanced potential for creating wealth.” Thus, entrepreneurship embedded into itself both individual and the society, including global communities. Various studies contributed to the integration of entrepreneurship and entrepreneurial innovation with the attributes of national innovation systems. It was evident globally that innovation is key to competitiveness and growth and that entrepreneurial dynamism is key to economic renewal and growth. Different countries advocated innovation (largely drawing from science and technology-related policies of the government) and entrepreneurship (evolved from small and medium enterprise policies) to boost the generation of new knowledge and make government investments in these domains more effective. These policies enhanced diffusion of knowledge and technology (network interaction effects) and established right incentives to stimulate private sector innovation to transform knowledge into commercial success. Further, efforts were made to encourage R&D collaboration through local networking and technology transfer, such as working with larger partners or universities. The government made attempts to concentrate on developing an environment and support system to foster the emergence of new entrepreneurs and the start-up and early-stage growth of new firms. Many economies embraced the concept of “acorn to oak tree” (Trott, 2017) as they put in place numerous measures to help small and medium enterprises (SMEs) grow. In India, the Government of India under the leadership of Prime Minister Shri Narendra Modi has policies like Make-up India, Skill India, Atal Innovation Schemes, Start-up India, Micro and Small and Medium Enterprises Scheme (MSMEs) to make the country self-resilient and self-reliance (concept of Aatamnirbhar Bharat). In order to support and understand the process of innovation and entrepreneurship, national economies interrelate with global economies as well. The need for international entrepreneurship rises on the basis of government policies for idea generation process in the form of intellectual property rights to promote knowledge generation, macro-economic, technological or market uncertainties, creation of complementary assets, cooperation and governance in the form of environmental, safety, legal and human rights, political relations across nations, leading to solutions to various societal and global challenges. The role of the nation can be highlighted in Fig. 2.1. The above figure draws inspiration from Michael Porter’s (1987, 1998) national diamond framework and develops the role the nation can play in relation to innovation. It underlines a firm’s relationship with the buyers, factor conditions (e.g., labor, capital, raw materials), related and supporting industries (e.g., technology providers, input providers, etc.), and other institutions that help facilitate strategic orientation and innovative capabilities. These, along with firm’s inner strengths will determine the firm’s opportunities, that is, its strategy-making capabilities and structural features.

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Competition regulations

Factor conditions Buyers and rights Institutional setting

Information, decision centre and political stability

Innovative firm

Customers

Suppliers and other supporting industries

Macro-economic conditions

Environment and safety regulations

Infrastructure building

Fig. 2.1  Role of nation in building innovation. (Source: Trott, 2017)

In order to provide strategic directions toward critical industries and encourage entrepreneurial spirit, the nation becomes a major buyer and also plays a vital role by financing innovation. The government moves funds to universities, research centers and laboratories, and other centrally funded research centers. Government can also support in the form of tax exemptions, subsidies, loan guarantees, export credits, and forms of protection. As a major buyer, the nation can also reduce uncertainty and create favorable cash flows for firms by its willingness to pay higher prices for early ideas and models. Public procurement is seen increasingly as an important potential instrument of innovation policy. Through regulations, education, information dissemination, governance, and other societal actions, the nation can impact upon the way the society perceives discoveries and adapts new technologies. Such interdependency between the government and society helps create a favorable national culture, which welcomes scientific development and removes the potential for various conflicts and barriers across sectors. This helps to create cohesion in the society and promote strategic interventions. This imbibes the nation to set in motion an overall vision and dynamism in the society and also for industries. An entrepreneurial ecosystem was being built with the concept of “national systems of innovation” (Lundvall, 1988). Major discoveries emanating from academic and/or publicly funded research have had enormous global economic and social impacts that are obvious but difficult to predict and quantify (e.g., Google, the World Wide Web, nanotechnologies, etc.). Figure 2.2 brings out the Triple Helix of university-government-industry relationships that drives innovation. As in Fig.  2.2, the interactions and relationships among university-industry-­ government at national level drive innovation. Etzkowitz and Leydesdorff (1995) initiated the concept of Triple Helix for this relationship thereby shifting the

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Develops entrepreneurs Creates technology Technical training and education Creates partnership Delivers educated people to economy

Universities -students -researchers -professors R&D funding Capital Market knowledge Technical training and education Creates partnerships

Industry -start-ups -venture capital -MNCs, etc

Government -nation -regional local

R&D funding Research grants Centrally funded programmes Quality of life Ease of doing business Regulations, infrastructure

Fig. 2.2  Triple Helix of university-government-industry relationships that drives innovation. (Source: Trott, 2017)

dominating government dyad in the existing entrepreneurial ecosystem to a growing triadic relationship in the knowledge society. The aim was knowledge base can be produced, transferred, and applied through knowledge society playing a prominent role. Such activities contributed to modern economies through increased productivity of applied R&D in industry due to university-developed new knowledge and technical know-how, provision of highly valued human capital embodied in staff and students, development of equipment and instrumentation used by industry in production and research and creation of concepts and prototypes for new products and processes, which may have some unexpected and large social and economic impacts. Thereby, the entrepreneurial ecosystems have evolved with either the nations or the universities taking the center stage. However, the Triple Helix did not take into account the informal economy and non-educational institutions wherein the entrepreneurial ecosystem advocated by Isenberg (2010, 2011) focused more on institutional forces and economic development. The aim of both the ecosystems is to promote entrepreneurial thinking and action to support networking with relevant internal and external stakeholders (Igwe et al., 2020). Though no formula for an ecosystem creation exists, it is based on combination of external elements that supports or affects entrepreneurial growth (Maroufkhani et al., 2018; Stam & Spigel, 2017). Accordingly, the entrepreneurial ecosystem is a six-dimensional model that includes policy, finance, culture, support, human capital, and markets (Isenberg, 2011). As innovation moved geographically across local, regional, national, and even across international borders, entrepreneurship too moved accordingly. Different

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economies reacted differently depending on the level of industrialization and government support to entrepreneurial ecosystems Ianioglo (2022). The importance of nation-building pushed entrepreneurial innovation to move across global value chains thereby requiring proper legal and institutional frameworks, social code, business values, and ethics to respond to increased competition and globalization. It therefore became essential for various economies to be most competitive and dynamic knowledge-based economy in the world. The strategy was to have economic convergence and to coordinate “open method” of developing policies to create new skills, knowledge, and innovation. Various governments attempted to develop innovation toolkits and scorecards to try to help firms in their own countries to become more innovative. One such initiative is the open innovation model integrating into the entrepreneurial ecosystem globally.

2.4 Connecting Open Innovation and International Entrepreneurship Ecosystem Innovation is viewed as a management process wherein it encompasses within itself internal processes and external linkages resulting in a cyclic model (Berkhout et al., 2010). The cyclic nature of innovation shows how the firm gathers information over time, how it uses technical and societal knowledge, and how it develops an attractive proposition through developing linkages and partnerships with those having the necessary capabilities (“open innovation”). In addition, the entrepreneur is positioned at the center. This provides a cross-disciplinary view of change processes which are iterative in nature (and their interactions) as they take place in an open innovation arena. Entrepreneurship plays a central role by integrating behavioral sciences and engineering as well as natural sciences and markets are brought together in a coherent system of processes with four principal nodes that function as roundabouts. The message is that without the drive of entrepreneurs there is no innovation, and without innovation there is no new business. Under the new open innovation model, many of the old traditional approaches to management need to change and new approaches need to be adopted. Often complex management relationships need to be developed because organizations are becoming virtual and are trying to produce complex products and services and do so across geographic boundaries. Certain nations and locations have acquired various capabilities over time, for innovation relies upon the accumulation and development of a wide variety of relevant knowledge (Dicken, 1998). The role of entrepreneur assumes great importance in such innovation model where the network environment is dynamic in nature linking with linear and non-linear thinking in the ecosystem. Entrepreneurs around the globe blend new technologies and next-generation thinking, building radically new kinds of organizations adapted to a flat and crowded world (Salkowitz, 2010). Figure  2.3 shows that the combination of change and entrepreneurship is the basis of any new business, venture, or entrepreneurial activity.

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Create technical capabilities

Scientific exploration

Create social insights

Technological research

Entrepreneurship

Market transitions

29 Create technical functions

Product creation

Create customer values

Fig. 2.3 Cyclic model of innovation integrated with entrepreneurship. (Source: Berkhout et al., 2010)

The above model architectures a circle that is innovations build on innovations. Ideas create new concepts, successes create new challenges, and failures create new insights. The new ideas may start anywhere in the circle, causing a wave that propagates clockwise and anti-clockwise through the circle. In an innovative society, businesses are transparent and the speed of propagation along the circle is high, resulting in minimum travel time along the innovation path. Today, time is a crucial factor in innovation. The central position in this circle is frequently adopted by entrepreneur who enhances the innovation process. Thereby, open innovation aligns businesses with the challenges that are shaping societies. Through such mode capacities can be distributed for exchanging knowledge across geographical and organizational boundaries. Open innovation reduces trade-offs and tensions between knowledge sharing with partners and to ensure confidentiality, value creation, and value capture. Though there may be tensions at times, when open innovation may involve competitors, not-invented-here and not-­ shared-­here attitudes, it can combine and integrate opposing goals by providing intellectual property rights and contracts to keep proprietary technologies safely protected within the boundaries of an integrated firm and govern the collaboration and knowledge exchange across large entrepreneurial ecosystems across the globe (McGahan et al., 2021). The entrepreneurial ecosystems along with integration of open innovation can be powerful and can act as a tool to mitigate the grand challenges, and the related trade-offs. Thereby, integrating all the parts of the chapter together, Fig. 2.4 looks at the integrative approach of open innovation with international entrepreneurial ecosystems. This figure shows that the international entrepreneurial ecosystems both with nation and universities at the center when connected with open innovation model lead to economic growth and productivity. The

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Open innovation model Knowledge sharing Beyond boundaries Ideas generation

Protection through intellectual property rights

Nations at the centre Triple Helix at the centre Entrepreneurial ecosystem R&D Finance Human resources Regulations Infrastructure

Escalate to global levels by strategic alliances, joint ventures, partnerships and others

Leads to innovation value creation and entrepreneurship development globally

Facilitating technology transfer leading to high absorptive capacity

Fig. 2.4  Integrating open innovation with international entrepreneurial ecosystems

knowledge sharing across these actors brings value creation and value capture. It also bridges the literature on innovation, entrepreneurship, strategy, and operations within the legal framework of any nation. It is here, that the governments play a vital role as policymakers as they need to look into their intellectual property rights laws to fit into the changing scenario of innovation that has moved beyond the geographical boundaries of every nation. It is imperative for any economy for rethink on how to ensure legal flexibility in addition to ensuring that the nation’s own industries continue to contribute to economic growth and development and further advocating new knowledge creation on global frontiers. Figure 2.4 highlights that information is central to the entrepreneurial ecosystem which in turn provides stimulus for knowledge, know-how, skills, and expertise for driving the innovation process. As open innovation involves two-way flow of knowledge sharing, it leads to economic growth in terms of knowledge transfer also known as technology transfer. This brings into high levels of complexity as it subsumes into building knowledge base through organizational learning. Since open innovation speaks about beyond boundaries knowledge sharing, it leads to various opportunities that technology transfer can bring to the ecosystem of the nations and the world at large. At this juncture, the legislation by governments becomes vital for clearing ways for technology transfer across borders and thus contribute to greater social good. This is possible through various collaborative efforts, even with competitors, in the form of strategic alliances, partnerships, joint ventures, etc. This fits in with the basic premise of open innovation that manifests openness into two inbound processes  – sourcing and acquiring technology and two outbound processes – revealing and selling technology (Dahlander & Gann, 2010).

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Thereby, technology transfer makes technology move from one boundary to another whether local, regional, national, or global, and can take variety of forms such as product, process, piece of equipment, technical knowledge, expertise, or any other way of doing things. It also involves movement of ideas, knowledge, and information for effective application to industry and commerce (Seaton & Cordey-­ Hayes, 1993). Since knowledge is tangible in nature in addition to being tacit and explicit in nature, the transfer of knowledge should lead to action in the form of various projects and actions of any firm. Such an external process pushes the internal process of innovation wherein the firm, whether new or existing, can capture, assimilate, and apply technology to commercial ends (Trott & Cordey-Hayes, 1996). Thus, the innovation expenditure is viewed as an investment for any firm to achieve “absorptive capacity” (Cohen & Levinthal, 1990) wherein an organization’s ability to evaluate and utilize external knowledge is related to its prior knowledge and expertise and that this prior knowledge is, in turn, driven by prior R&D investment. Absorptive capacity is therefore important for the success of open innovation (Enkel & Heil, 2014; Fabrizio, 2009) which in turn requires strong entrepreneurial ecosystem. So, these domains get intertwined with each other leading to organizational learning as evident in Fig. 2.5 which is expanded from Fig. 2.4. Supporting the above integrative model, one of the latest initiatives had been experienced during the COVID-19 pandemic wherein the urgency fueled open innovation. Through various initiatives by various economies, firms started to offer free licenses to their intellectual property for the purpose of fighting the pandemic. There were new and unforeseen collaboration across both organizations, industries, and national boundaries emerged. Thus, when challenges were grand, complex, and urgent the integrative model led to entrepreneurial ecosystem supporting the technology transfer gained through open innovation in turn impacting the absorptive capacity of the firms and thereby building on organizational routines that added up to the knowledge transfer and database building with organizational learnings at all

Entrepreneurial Technology ecosystem transfer support and open innovaon

Pushes for absorpve capacity of the firm

Connuous flow of tacit and explicit knowledge

Knowledge base building and organizaonal learning

Firms at -Local -Regional -Naon -Global

Levels of learning -Individual and group (skills) -Organizaon (rounes)

Fig. 2.5  Integrative model – open innovation and entrepreneurial ecosystem leading to knowledge transfer and organizational learning across firms at different levels

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firm levels. We envisage a similar trend with the rising complexity and urgency of the climate change crisis slowly engulfing the global communities. We now try to reconceptualize the entire purpose of the latest model of innovation along with the importance of the international entrepreneurial ecosystems. The first outcome is to have exciting and beneficial social outcomes. However, there have concerns about growing levels of job security and distributional inequality leading to persistent poverty and migration crises across the world. Though energy, transportation, and communication technologies have proved to be boon to society they have caused extensive harm to environment. The world today faces issues like pandemic such as COVID-19, climate crisis, authoritarianism, loss of privacy and security, and others. We now need to innovate innovation and find new ways to overcome these global challenges. The other outcome is to create new ways of thinking about innovation due to the advent of artificial intelligence, machine learning, big data, and advanced analytics. With the advent of ChatGPT, the global challenges continue to be exacerbated. It is time that the integrative model looks at ways to redesign and recycle productively and redirect consumer attention towards healthier options. This requires the intervention of governments with effective governance and system change to improve social outcomes at the core. Here lie the challenges before open innovation to discern and integrate a vision for business and social outcomes to radically improve the response to pandemic, climate emergency, and other terrific challenges. One way that the United Nations community look at these challenges is through its 17 Sustainable Development Goals (SDGs) (with detailed subgoals and specific, measurable targets for their achievements) that aim for value creation by firms in alignment with economic achievements by various economies. These SDGs aim for making boundaries non-existent as the world looks for resource allocation to sustain the economies, their engagement with various stakeholders with the specified timeframes. For example, SDG 1 is “No poverty” and SDG 8 is “Good jobs and economic growth” that aim for economic goals, SDG 5 is “Gender equality” and SDG 10 is “Reduced inequalities” that aim for social-justice goals, SDG 6 is “Clean water and sanitation,” SDG 7 is “Clean energy” and SDG 13 is “Protect the planet” that aim at environmental goals and responsibilities, SDG 4 is “Quality education” and SDG 11 is “Sustainable cities and communities” that aim at institutional goals and SDG 16 is “Peace and justice” meant for governance goals and improvements. Such SDGs unlock hidden opportunities for various firms to move from closed value chains to open innovation ideas and cyclic knowledge-sharing ecosystems thereby, creating new avenues for entrepreneurial ecosystems, investments, and profits. The governments should be cautious with the trade-offs that can be vast and may not always be aligned with economic growth. These steps become an integral part of the innovation agenda that the firms may pursue to be relevant, justified, legitimate, and value-creating (McGahan et al., 2021). To conclude, it should be noted that the process of innovation was largely treated as an organizational issue. However, we find that the innovation process is a management process wherein it becomes demanding, long-term, high-risk, complex,

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and interactive as determined by the local, national, and global context within which the firms operate. Thus, innovation requires economic and social conditions to establish longer-term vision of competitiveness, survival, and sustained growth. Such interactions strive to mix competition and cooperation through networks, market, and hierarchical relations through external environment including entrepreneurial ecosystems. The economic transformation can be achieved through technology diffusion, new technology development, and efforts to develop own capabilities through supportive entrepreneurial ecosystems at global levels. This builds knowledge base for products and services and create high values through strategic intervention into legal infrastructure development, institutional building, human capital formations, and other innovative capacity formations. It is essential to remember that innovation is socially and institutionally embedded, but it cannot be separated from local, national, or global contexts and from political and social processes. Its connection with entrepreneurial ecosystems at global levels should not be seen in short-term but with long-term implications because risk-taking for long-term is inherent for economies to enjoy sustained growth and development and become powerful players in the global economy.

2.5 Outcome and Observations The chapter emphasizes that collaborations are an increasingly important part of open innovation that provides framework for addressing society challenges for issues like sustainability, health, climate crisis, and even COVID-19 pandemic. Open innovation also aligns well with the growing stakeholder theory of the firm and knowledge-based economy, and it mobilizes knowledge from these different entities toward useful and sometimes non-pecuniary objectives. It is crucial that we refocus away from sustaining the profitability and survival of organizations that contribute to the climate crises, inequalities, poverty, and disease. Knowledge sharing and learning through resources and capabilities in such ecosystems can be deployed to break trade-offs to create social and economic values at national and global levels. We need to relook at open innovation benefits and entrepreneurial ecosystems integrating within themselves and become a new system to resolve world’s most pressing problems. There has been limited recognition of the full integration of entrepreneurial ecosystem and open innovation. Indeed, there has been a dearth of connection between entrepreneurship and innovation policies by various economies. There needs to be a convergence between the two to ensure optimization of complementarities and meet global challenges. Unfortunately, all too often, innovation policies do not incorporate entrepreneurship as a focus. Yet we know that entrepreneurship involves the act of innovation and that entrepreneurs are essential to convert knowledge into economic and social benefits. The chapter bridges this gap and looks at various perspectives of integrating the two by firms to meet the challenges at local, regional, national, and global levels.

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Not only firms but also nations through its bilateral and multilateral relations should accentuate various collaborative efforts, even if done with competitors in different nations, in the form of strategic alliances, partnerships, joint ventures, etc. Such an external process in the form of open innovation can drive knowledge and technology transfer and enhance absorptive capacity within the firms, thus contributing to economic growth and development. The governments should look at open innovation model and work cautiously with the trade-offs and tensions that may indirectly get imbibed in the different entrepreneurial ecosystems and policies to meet business, industry and society well-­ being, economic growth, and development across various levels of the globe. The government should ensure that the international entrepreneurial ecosystem should interweave itself with every nation’s legal framework to foster open innovation move beyond geographical boundaries.

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Chapter 3

Tech Innovation and New Age Business Models Divya Sharma

3.1 Technology Innovation and Transformation The dictionary defines innovation in two ways. First, as “the introduction of something new,” and second as “a new idea, method, or device” (“Innovation,”, 2022). Innovation can, therefore, be understood as both a process and as an outcome (Kahn, 2018). The Oslo Manual (OECD & Eurostat, 2018) tries to resolve this duality by differentiating innovation as a process from innovation as a product. Innovation as a process includes all the innovation activities (developmental, financial, and commercial) that are intended to produce innovation as an outcome. Conversely, innovation as an outcome focuses on a new product or new service which is the output of innovation as a process. The innovation process involves a discovery phase, a design phase, and a deliver phase (Kahn, 2018). In the discovery phase potential opportunities are identified. Out of these potential opportunities, offerings are designed for the promising opportunities in the design phase. Finally, the deliver phase aims at the purposeful use of the offering by the consumer through the innovation’s market acceptance. Technological innovation relates to innovation as an outcome (Edwards-­ Schachter, 2018), where innovation includes technologically new or improved products or processes that differ from the previous products and processes of the firm (OECD & Eurostat, 2018). An essential aspect of technological innovation is that the innovation must have been “implemented,” that is, the product (good or service) must have been introduced in the market for use by intended consumers, or the process must have been brought into use in a firm’s operations.

D. Sharma (*) Management Development Institute (MDI) Gurgaon, Sukhrali, Gurugram, Haryana, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Patnaik et al. (eds.), Global Trends in Technology Startup Project Development and Management, Innovation, Technology, and Knowledge Management, https://doi.org/10.1007/978-3-031-40324-8_3

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Technological innovations are the result of marketing-push and/or technological-­ push (Garcia & Calantone, 2002). While in the former case, the emergence of new marketplaces or marketing skills lead to innovation, in the latter case, paradigm shift in technology, new R&D resources, and/or new production processes lead to innovation. Recent advances in technology, such as cloud computing, mobility, Internet of Things (IoT), artificial intelligence (AI), virtual/augmented reality (VR/ AR), blockchain, etc., are resulting in new waves of technological innovation (Edwards-Schachter, 2018). In this chapter, we focus on innovation and new business models resulting from this wave of technological development. But first we discuss some important concepts that are closely related to technological innovation.

3.1.1 Digital Innovation The ubiquity of digital technology and infrastructures, such as mobile computing, cloud computing, data analytics, AI, AR/VR, IoT, etc., has enabled the production of novel products through the new combinations of digital and physical components, enabled by digitization, that is, the digital representation of analog information (Yoo et  al., 2010). Furthermore, these new product ideas can be quickly formulated, implemented, and modified through repeated cycles of experimentation that are mediated by digital technologies (Nambisan et al., 2017). This experimentation may result in a new idea – that is, a product, process, or business model – that constitutes digital innovation. Digital innovation refers to a new idea that requires significant changes on the part of the adopters, and is embodied in or enabled by Information Technology (IT) (Fichman et al., 2014). Stated differently, digital technology and digital processes form an essential part of the new idea, its development, diffusion, and assimilation (Nambisan et al., 2017). For example, digital infrastructures such as social media and online communities enable digital innovations to get distributed among multiple actors, often possessing different objectives and motives (Boudreau, 2010; Tiwana et al., 2010). The involvement of multiple actors – beyond the primary innovator – using open standards and digital platforms has led to collaborative innovation in the form of crowdsourcing (e.g., Innocentive), crowdfunding (e.g., Kickstarter), and sharing platforms (e.g., GitHub). The process of digital innovation consists of four stages (Fichman et al., 2014) – discovery, development, diffusion, and impact. • Discovery: This stage involves the identification of new ideas that may be developed into a product, process, or business model innovation. This stage involves the activities of invention, which is the creation of something new through a creative process, or selection, which is identifying and evaluating an existing innovative technology in the external environment to further develop or adopt. • Development: This stage results in the development of a usable innovation based on the idea of a core technology identified in the discovery phase. The key

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a­ ctivities in this stage are developing and refining the core technology, and packaging complementary products and services with it to produce a solution that can be effectively used by an adopter. In the case of process innovation, rather than packaging, configuring the core technology in terms of adaptation of the technology, integration of the technology with other organizational technologies, and associated organizational changes for adoption of the technology become important. • Diffusion: In this stage, the digital innovation resulting from the development stage spreads across a population of potential users. The key activity in this stage is that of deployment, which implies assembling all the resources essential to convince potential users to adopt and use the innovation, so that it gets assimilated into their daily lives. • Impact: The emphasis in this stage is on intended and unintended consequences of the diffused digital innovation on individuals, organizations, markets, and societies. The key activities in this stage are value appropriation and transformation. Value appropriation implies securing the profits from the adoption of digital innovation by effectively managing intellectual property and the complementary ecosystem of products and services. Transformation refers to the continuous changes in technology and organizations to take advantage of the opportunities made available by the digital innovation developed.

3.1.2 Digital Disruption Digital disruption is the result of digital innovation that causes turbulence with respect to the traditional approaches used for creating and capturing value (Skog et  al., 2018). Digital disruption is often confused with the concept of disruptive innovation which is concerned with business model innovations that help new entrants compete with incumbents by offering low-performing products at a cheaper price (Christensen et al., 2015). Hence, disruptive innovation as a concept explains competitive dyads comprising new entrants and incumbents, rather than system impacts of the innovation on industries. Digital disruption interrupts the planned trajectory of firms entrenched in old business logics. While these firms are pressured to respond to the changes induced by digital technologies, it is difficult to change the historically successful business structures and business logics that have previously served the firms well (Lucas & Goh, 2009). For example, digital photography had a disruptive impact on Kodak, which had thrived in the era of film-based photography. Digital photography completely changed the way images were captured, displayed, and transmitted. Despite investing heavily in digital photography and reorganizing multiple times for a digital future, Kodak failed to leverage the emergent digital technology. This was because Kodak was unable to capitalize on its technology assets and capabilities owing to its culture, hierarchical structure, and internal resistance from its managers.

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Digital disruption possesses three fundamental characteristics (Skog et  al., 2018). First, digital disruption arises from digital innovations that transform the competitive landscape. Second, digital disruption involves the reconfiguration of linkages between resources, thereby impacting the value creation system. Third, while the digital innovation process may be led by one or a few firms, the eventual disruption is a systemic effect.

3.1.3 Digital Transformation Digital transformation involves a change in the way a firm uses digital technology to develop new business models that help in creating and appropriating more value for the firm (Verhoef et al., 2021). Digital transformation differs from digital disruption in two respects (Skog et al., 2018). First, digital disruption is the manifestation of specific innovation processes, while digital transformation is associated with the aggregated effects of several innovations. Second, digital disruption is more rapid than digital transformation. Therefore, digital transformation may be understood as “the combined effects of several digital innovations bringing about novel actors (and actor constellations), structures, practices, values, and beliefs that change, threaten, replace or complement existing rules of the game within organizations, ecosystems, industries or fields” (Hinings et al., 2018). Digital transformation is usually the result of a three-phase process that starts with digitization, followed by digitalization, and eventually culminates with digital transformation (Verhoef et al., 2021). • Digitization is the representation of analog information in a digital format that can then be easily stored, processed, and manipulated using computers. Examples include the use of digital forms and digital applications. • Digitalization refers to the use of digital technologies for changing business processes, such as communication, distribution, customer relations, etc. For example, social media platforms have altered the way customers reach out to firms. Digitalization enables organizations to optimize existing processes, improve coordination between processes, and enhance customer value. • Digital transformation involves a pervasive change that leads to the development of new business models using new business logic and/or value creation and capture processes. Digital transformation helps firms achieve competitive advantage by leveraging existing competencies or developing new ones. Digital transformation is of import to incumbent firms as they compete with new age entrants which are often “digital natives.” It can be very challenging for firms to transform their business models and value propositions owing to their legacy mindset and obsolete ways of working. As a result, many firms start with efforts toward digitization and digitalization, before progressing on a company-wide digital transformation.

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Let us consider the example of Audi, the auto-manufacturer, to understand the phases of digital transformation (Mocker & Fonstad, 2017). Between 2011 and 2015, Audi digitized the paper-based “vehicle tracking card” that provided assembly line workers with a list of all the parts to be assembled for a specific car with an electronic vehicle tracking card (EVTC). The EVTC was nothing but a monitor visualizing only the parts relevant to the current stage of the car assembly and specifying where to place them. The EVTC would issue an alert if a wrong part was picked and stall the assembly line until the error was corrected. In this way the number of errors and mistakes in the assembly process drastically reduced. In the year 2013, Audi introduced “Audi Enterprise 2.0” – a suite of five different social media applications for document sharing, networking, collaborating, knowledge sharing, and communicating news and information. This initiative digitalized the way employees interacted and collaborated, resulting in increased efficiency, innovation, and workplace attractiveness. By 2014, Audi started experimenting with different ride sharing business models to complement their traditional business model. Audi’s ride sharing model digitally transformed its value offering from car-as-a-­ product to mobility-as-a-service, and enabled Audi to extract value on a pay-per-­ use basis.

3.2 New Age Business Models Business model implies “how the enterprise creates and delivers value to customers, and then converts payment received to profits” (Teece, 2010). With the development of new information and communication technologies that power e-commerce and social media, customers have access to greater choice, they can easily express their needs, and can easily reveal their concerns. At the same time, businesses have more transparency with respect to supply chains, can utilize various offline and online channels, and offer novel value propositions to their customers. It has, therefore, become imperative for businesses to adapt their existing business models or develop novel business models that enable them to create and appropriate value for their novel tech-enabled offerings (Tongur & Engwall, 2014). It is also well understood that the potential value of a technology can be extracted only if it is commercialized using an appropriate business model (Chesbrough, 2010). If a firm that develops a technology embraces an inferior business model, there is every likelihood of competitor firms developing a better business model and extracting more value than even the originator firm itself. Hence, developing and executing a business model innovation in the wake of technological change is fraught with the challenge of overcoming the conflicts with existing assets and business models of the firm. The execution of a business model involves identification of features and technologies to be embedded in the products/services and determination of how the consumption of their offering can benefit the customers. This is followed by the

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identification of market segments to be targeted and delineating the revenue streams. Finally, the business must design mechanisms to capture value and convert it into profits. Figure 3.1 illustrates these elements of a business model. In what follows, we describe and discuss three new age business models that have emerged because of technology advancements and innovations.

Fig. 3.1  Elements of business model. (Adapted from Teece (2010))

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3.3 Cloud-Based Business Model 3.3.1 Cloud Computing The National Institute of Standards and Technology (NIST)1 defines cloud computing as “a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction.” Hence, the cloud can be used to provision hardware and IT infrastructure (i.e., Infrastructure-as-a-Service or IaaS), platform for software development (i.e., Platform-as-a-Service or PaaS), as well as software applications (i.e., Software-as-a-Service or SaaS). Amazon’s EC2 is an example of IaaS that provides virtualized computing infrastructure, such as server hardware, network, and data storage, that can be accessed over the Internet without the customer having to purchase or administer the physical infrastructure. An example of PaaS is Microsoft Azure that can be used by customers to design, develop, test, and deploy their own software applications by accessing a cloud-­ based platform over the Internet without bothering about configuring the software development environment. Finally, Salesforce CRM is an example of SaaS where the customers can access a CRM application hosted on the cloud without having to install it on their on-premises infrastructure. There are five characteristics of cloud computing: • On-demand self-service: The resources made available on the cloud can be accessed, managed, and monitored by users themselves without the intervention of another human administrator. • Multi-tenancy and resource pooling: Physical computing resources are pooled together and provisioned on-demand to multiple customers or tenants in an uncommitted manner. • Broad network access: The computing resources can be accessed through a standard network such as the Internet. • Rapid elasticity: The computing resources being utilized by a customer can be scaled up or down on a need basis. When the computing resource is no longer being used by the customer, it is returned to the resource pool for other customers to provision. • Measured service: Resource utilization is tracked on a per user basis. This characteristic forms the basis of the pay-per-use model.  The NIST Definition of Cloud Computing by P.M.  Mell in 2011, accessed from https://www. govinfo.gov/app/details/GOVPUB-C13-74cdc274b1109a7e1ead7185dfec2ada 1

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3.3.2 Cloud Business Model Innovations Cloud computing has emerged as a technology innovation that not only simplifies IT outsourcing but also enables the development of business model innovations for gaining competitive advantage (Muhic & Bengtsson, 2021). 3.3.2.1 Cloud-Sourcing Model Cloud sourcing refers to a shift from on-premises IT infrastructure and software application to cloud-based alternatives. Cloud-based solutions, whether in the form of IaaS, PaaS, or SaaS, offer businesses the ability to manage wide variations in scale with ease. This is specifically important from the perspective of entrepreneurial and start-up firms which might have a non-deterministic lifespan at inception. Using cloud-based solutions, such as Amazon Web Service (AWS) for hosting applications, Salesforce’s online CRM, Adobe’s suite of cloud-based creative applications, Google’s suite of cloud-based digital marketing tools, and Microsoft 360 as a cloud-based office productivity suite, have become the de facto approach for enterprises, small or large, as well as, young or old. Since, the management of computing resources on the cloud, including upgrades and resolving bugs, is the prerogative of the cloud-vendor, businesses that shift to the cloud-sourcing model also benefit from higher reliability and availability of the computing resources, enforced through strictly defined service-level agreements (SLAs). This also helps businesses transfer the tasks and costs associated with in-­ house maintaining and managing their IT infrastructure to the cloud-vendor. Hence, cloud-sourcing has specific benefits for firms whose core competence is not the design, development, deployment, and maintenance of technology infrastructure. Cloud sourcing emancipates such organizations from the burden of managing their in-house IT departments and infrastructure and enables them to focus on their core business. The ubiquitous access to computing resources over the cloud also enables organizations to expand their operations without geographical constraints. Organizations can reach out to customers across the globe through cloud services that may be accessed through a web-browser over the Internet, and at the same time can maintain consistent customer relationship through cloud-hosted Customer Relationship Management (CRM) systems. Similarly, managing supplier relationships and transparency in the supply chain has become much easier using online Supply Chain Management (SCM) systems. Furthermore, organizations can also expand their physical presence in other geographies by ensuring that their offices and staff are all enabled by and integrated through the same IT infrastructure in terms of cloud-­based Enterprise Resource Planning (ERP), networking, and collaboration systems.

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Finally, cloud-based solutions use pay-per-use pricing, preempting the need for upfront capital expenditure on IT infrastructure and software licenses. Cloud-based solutions are also sometimes beneficial from an accounting perspective as they enable a shift from capital expenditure in the traditional model of owned IT infrastructure to operating expenditure on services used over the cloud. 3.3.2.2 Service-Based Model Cloud computing has enabled servitization, that is the transformation process from product-centric to service-oriented business models (Kowalkowski et  al., 2017). This change has caused a transition in the expectations of customers, who might either expect additional services along with a product, or might be willing to only pay for the product use by consuming it as a service (Frank et al., 2019). While the combination of digitalization with servitization is expected to enhance a firm’s competitive advantage through cost reduction and revenue growth, it has also been seen that many times firms invest in developing services but are unable to garner returns or they invest in developing digital assets which do not pay off. Hence, it is argued that firms should instead develop hybrid product-service offerings in the form of integrated digital solutions (Gebauer et al., 2021). In the recent past many manufacturing and heavy industry organizations, like General Electric (GE) and Roll-Royce, have adopted service-oriented business models. For example, Rolls-Royce has shifted from its traditional model of selling aeroengines to an alternate model of providing power by the hour to its customers, and charging the customers only for the power bought while also providing all the required support. Similarly, GE is now producing smart equipment which is enabled by IoT sensors to capture performance data of heavy equipment, like oil rigs and turbines, that is in turn analyzed for predicting downtime, improving operational efficiency, and enhancing value creation for the customer. A suite of applications offered as services enable customers to monitor equipment, measure equipment performance, and predict downtime. Using a service-based model enables such heavy industry firms to collate customer data and use it for improving their hardware. However, a challenge for such firms is that the improved hardware comes with an enhanced life cycle that jeopardizes the future revenue of the firms from sale of new equipment. Nonetheless, service-based models offer the opportunity for continuous monetization of offerings in the form of services through a pay-per-use model. Moreover, in transitioning from a product-based model to a service-based model these companies also grapple with the challenges of: • Acquiring new capabilities in software development to complement their core engineering expertise. • Hiring new talent adept at sale of services that are charged on a recurrent basis, rather than products that have a longer life. • Shifting from product-centricity to customer-centricity.

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The service-based model is particularly popular in the software industry, where most businesses have moved away from selling software licenses to selling software as a pay-per-use service. For example, Adobe, which is among the leading providers of creative applications like Photoshop, Illustrator, Lightroom, and Dreamweaver, no longer offers its software as licensed products. Rather, the software is now available as a service to customers who access it over a web browser. There are many reasons for the predominant transition from a license-based model to a service-­ based model, including to: • Explore new markets and grow market share by offering a service on a per use basis (a low monthly price might entice new customers). • Avoid loss due to piracy of software licenses. • Prevent loss of revenue due to customers not upgrading to newer versions. • Enhance the predictability of revenue by reducing the length of the purchase cycle – from upgrades every few years to monthly payments for services used. • Enable continuous enhancement and upgrades to the offering without worrying about clubbing a large set of improvements in a new version release. Even from the perspective of consumers, service-based provision of software enables easier budgeting, enhances affordability, and provides ubiquitous access even to the latest versions of the software.

3.3.3 Challenges The transition from a product-centric model to a cloud-based service-centric model comes with its own set of challenges. Firstly, the shift to a service-based model requires an organizational reorientation, where the business must decide whether to make the transition in a phased or a big-bang way. The phased approach allows time for customers to adapt to the change and enables the business to extract the waning stream of revenue from the legacy licensed version. However, it is a challenge to manage two different business streams in the interim. Inability to manage the balance between the two streams can disgruntle customers and provide competitors the opportunity to woo loyal customers. There are also many imperatives for the successful implementation of a cloud-­ based business model transformation. A business attempting the transformation must understand that it is potentially cannibalizing a secure stream of revenue from the licensed version by offering a cheaper SaaS alternative. The business is also required to invest in its own IT infrastructure (which may in turn be sourced as a service) to ensure availability and reliability of its offering and meeting SLAs. Also, the business must understand the nuanced usage of its different customer segments, as the importance of usability, reliability, and feature richness might differ across segments, from amateur to professional users, new to experienced users, and individual to corporate users.

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3.4 Platform-Based Business Model 3.4.1 Technological Platforms A platform is an interface that enables interactions between different entities, often referred to as the different sides of the platform. Advances in technology have enabled the development of technology-enabled platforms that mediate interactions and transactions between multiple sides – usually comprising the demand side and the supply side. Most such platforms consist of a modular technology architecture composed of a core and a periphery. For example, Facebook is a platform with social media users, advertisers, and app developers as its multiple sides, where the social graph of personal relations constitutes the core that can be accessed, productized, and monetized by peripheral applications and websites. Such platforms act as meta-organizations where the agents constituting the platform are legally autonomous and not bound by employment relations (Gulati et al., 2012). Platforms serve the purpose of federating and coordinating these agents as they compete and innovate, and in doing so the platforms create value through economies of scope in supply and/or demand (Gawer, 2014).

3.4.2 Platform-Based Business Model Innovations A platform-based business model creates value by facilitating interactions and transactions and orchestrating resources, rather than utilizing the linear pipeline logic of value creation, where inputs are converted into outputs along a value chain (van Alstyne et al., 2016). Some of the leading businesses such as Meta, Amazon, Apple, Alphabet, and Microsoft derive much of their valuation from their platform-­ based offerings. Even Uber and Airbnb – both platforms – are valued much higher than many of the large traditional taxi and hotel businesses respectively. Firms that employ a platform-based business model generate value by leveraging network effects – that is the phenomenon of change in the utility of an offering with a change in the size of its user base. Network effects may be direct or indirect, and positive or negative. Direct network effects exist when a change in size of user base on one side of the platform changes the utility of the platform for that side itself. For example, more users joining a social networking platform, say Instagram, enhances the value of that platform for other users. Conversely, indirect network effects imply that a change in the size of user base on one side of the platform changes the utility of the platform for users on another side. For example, an increase in the number of drivers on Uber’s platform enhances the utility of the platform for riders. Both the above examples of Instagram and Uber are those of positive network effects, as the increase/decrease in the user base on one side increases/decreases the utility of the platform for other users on the same/other side. However, network effects may also be negative when an increase/decrease in the user base on one side of the platform decreases/increases the utility of the platform for users on the same or other side.

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For example, an increase in the number of advertisers on Facebook decreases the utility of the platform for other social media users (interested in socializing, rather than advertising), implying the existence of negative network effects. The success of a platform-based business model is based on the focal firm’s ability to kick-off interactions between the different sides of the platform. This is operationalized by choosing appropriate strategies to enhance the positive network effects. Some of the means by which direct network effects may be enhanced are: • Low pricing: A firm may entice more users to join by pricing its platform offerings at a low point. For example, when the ride-sharing app, Ola, launched in India it offered rides at Rs. 6 per kilometer which was much below the standard kilometer fare charged by taxis. • Lucrative offers: Offering cashbacks, bonuses, and  rewards to customers can also incentivize them to join a firm’s platform. For example, at the time of its inception, PayTM, the Indian payments platform, offered cashbacks as high as the value of the payment itself to entice users to join. • Product features: Enhanced product features, over and above those possessed by competing offerings, can also encourage users to join a platform. For example, most users shifted from MySpace to Facebook when the latter was launched owing to its better features. • First-mover advantage: A firm launching a platform can also take advantage of being a first mover by capturing most of the available market share before the launch of other competing platforms. Since platforms are prone to customer lock-in owing to the utility derived from the size of the user base, it is usually difficult for late entrants to poach users from the first mover. Netflix used its first-­ mover advantage in the on-demand video space to succeed as a platform. The success of a platform-based business model also depends on the strength of indirect network effects. Some of the ways in which a firm may enhance indirect network effects are: • Incentives: Firms can offer incentives to users on the supply side to enhance the utility of the platform for users on the demand side in the case of positive indirect network effects. For example, when Uber launched in India, it offered drivers a fixed salary of Rs. 30,000 over and above their commission. This served as a potent incentive for drivers to transition from driving taxis to plying cars on Uber. • Open access: Opening access to the platform to anyone without restriction can also improve the success of the platform. For example, the success of the Android platform can be attributed to its openness, where anyone could contribute apps, in contrast to Apple’s closely guarded platform. • Multi-homing and migration: Multi-homing implies the  possibility of users adopting and using multiple platforms at the same time. Platform offerings where multi-homing is allowed enable the supply-side to extract value from being present on multiple platforms at the same time, while  enhancing indirect network effects for the platform. Similarly, the possibility of migrating complementary products developed for one platform to other competing platforms can also enhance the value capture for complement developers.

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Since the success of platform-based business models depends on kicking off network effects, garnering a large enough user base on the supply side, as well as on the demand side, is crucial. This is often referred to as the “chicken-and-egg problem,” since users on the supply side would join only if there were sufficient users on the demand side, and vice versa. It is difficult to answer which side – demand-side or supply-side – should be given precedence. However, there are some strategies that firms may use to resolve the chicken-and-egg problem of platformization (Moazed, 2015). • Entering with a pre-investment: Making a significant pre-investment in the platform signals to the users that it is safe for them to join, since the firm has a large stake in the platform. Microsoft used this strategy when it launched the original Xbox, by announcing a promotional budget of $500 million for the Xbox platform. • Cooperative strategy: Cooperating with other stakeholders can also help in resolving the chicken-and-egg problem. For example, to compete with Apple in the mobile search space, Google created an Open Handset Alliance (OHA) of leading handset manufacturers who also considered Apple as a formidable competitor. Thereafter, Google used the OHA as a sales channel to diffuse its Android platform and retain leadership in mobile search. • Act as a producer: Instead of depending on complementors to provide for the supply-side, a firm may choose to act as the producer itself on the platform. For example, when Apple launched the iPhone, it did not allow third parties to produce apps. Instead, it produced all the apps in-house, eliminating its reliance on app suppliers. • Evolutionary strategy: Instead of setting network effects into motion from scratch, a firm can also utilize existing network effects from another network. For example, Facebook’s acquisition of Instagram to tap into the young-age population that found favor with Instagram, was a strategy of building upon the network effects that existed for Instagram. • Marquee strategy: A firm can also solve the chicken-and-egg problem by enticing high-value customers, which may in turn, encourage others to join. For example, Facebook launched as a social media platform only for Ivy league schools, that helped in marketing it to other schools later. • Single-user group to fill both sides: Firms can also create platform offerings where the same user can participate on the demand-side and the supply-side. For example, the creative goods platform, Etsy, finds consumers and producers in the same target segment. • Provide single-user utility: A platform can also succeed if one side of the platform can find utility in the platform irrespective of the other side. For example, the table-booking-app, OpenTable, that allowed users to book a table at a restaurant, allowed restaurants to use its app as a back-end table booking system even if none of the reservations came through the app. In this way, the restaurants which were primarily using a pen-paper system to manage reservations, saw inherent value in adopting OpenTable.

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3.4.3 Challenges It is often challenging for product-based companies to transition to a platform-based model (Gupta, 2018). This is because rather than focusing on developing the best product, a platform-orientation requires building a network of complementors. Hence, the role of the firm in the case of product development is to control operations, while in the case of a platform is to manage partners on the supply and demand sides. Also, the success of the platform, unlike that of a product is measured not as a function of its profit or sales, but as a function of transactions and overall adoption of the platform. Additionally, the transition also requires a reorientation of the way the firm thinks about its offering. For example, the development of products within a firm is often a closely guarded affair with the involvement of a few trusted parties, but platforms benefit from being open to anyone without restriction.

3.5 Decentralized Business Model 3.5.1 Blockchain Technology The blockchain is often considered a technology innovation that is expected to have an impact as profound as that of the Internet. Blockchain technology, first manifested as the Bitcoin, is a distributed ledger of transactions involving any kind of asset – money, goods, property, or votes – shared across a distributed network of computers called nodes (Beck & Müller-Bloch, 2017). There exists no central authority that acts as guarantor for a transaction. Instead, the nodes are responsible for verifying each transaction through a combination of mathematical problem solving and cryptography, an approach referred to as proof of work. Once a transaction is verified it gets added as a block to the blockchain in a chronological manner. All past transactions recorded on the blockchain are transparent to all the nodes participating on the blockchain network. This peer-to-peer (P2P) nature of the blockchains offers novel business model innovations that may pose a challenge for business models that rely on third parties for verification and trust, such as banks and insurance companies. The cryptographic design of the blockchain makes it tamper proof, as it is almost impossible to alter or reverse transactions. Any such attempt to change transactions in the blockchain requires reverification of all the blocks of the blockchain, which is combinatorically challenging owing to the cryptographic nature of the blockchain. This characteristic confers immutability to the blockchain. Fig.  3.2 represents the six steps involved in asset exchange on the blockchain (Morkunas et al., 2019). The blockchain technology can be used in permissionless or permissioned manner (Morkunas et al., 2019). The permissionless or public blockchain allows anyone to participate as a node in the blockchain network and see all the transactions on the blockchain. The nodes participating on a permissionless blockchain may be

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Fig. 3.2  Asset exchange on the blockchain (Morkunas et al., 2019)

anonymous or pseudonymous. For all nodes on a permissisionless blockchain to remain in sync, substantial computational power needs to be expended by all the nodes for proclaiming proof of work. The permissioned or closed blockchain allows only select few parties to access the blockchain. In comparison to the permissionless blockchain, the permissioned blockchain comes with additional privacy, trust and security as only pre-validated entities can participate on the blockchain. Hence, for many use cases involving private and sensitive transaction data, permissioned blockchains are preferred over their permissionless counterparts. Blockchain technology can also be used to implement smart contracts which, much like traditional legal contracts, comprise rules, rights, and consequences. But smart contracts are coded into the blockchain and can be automatically enforced depending on pre-defined rules and scenarios.

3.5.2 Blockchain-Based Business Model Innovations Blockchain technology is being utilized to foster many kinds of decentralized business model innovations. We discuss a few below. 3.5.2.1 Decentralized Finance (DeFi) Traditionally, financial institutions have mediated and structured financial transactions that would have been difficult to execute owing to the transaction costs involved. The role of these financial institutions is to connect the transacting parties and build trust. However, the development of blockchain technology is enabling a shift toward decentralization and disintermediation in financial transactions. Some promising characteristics of decentralized finance are (Chen & Bellavitis, 2020): • Decentralization: Since transactions on the blockchain are immutable and verifiable through distributed consensus, blockchain technology enables distributed trust, which in turn reduces the cost of searching, contracting, and enforcing. In the case of decentralized finance there is no possibility for a single entity – such as the Bank of America or PayPal – to monopolize the network and decide on the rules of participation in the network.

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• Innovativeness: Since decentralized finance does not include a controlling central agency, third-party developers have open access to the permissionless network and can experiment and develop new offerings. For example, DeFi platforms, such as Bitcoin, Ethereum, and Libra, share underlying core technology, unlike centralized financial services based on patents, copyrights, and trademarks. • Interoperability: Unlike the silos that exist in centralized finance, where each financial institution maintains its own ledgers, blockchain technology enables interoperability, where all DeFi applications build on the same blockchain standard and are interoperable, enabling capital and value to flow seamlessly. • Borderlessness: Centralized finance is restricted by the bounds of geography and legality of fiat currency. Contrarily, DeFi offers the opportunity for capital and value to move across geographic boundaries without friction or delay. • Transparency: The public ledger technology of blockchain ensures full transparency in terms of the transactions that take place on the blockchain. Further, since most blockchains are based on open-source technology, it is possible for anyone to check the business logic coded in the blockchain and expose biases and risks. Some of the business models based on DeFi are described below: • DeFi is enabling business models for decentralized currencies, like Bitcoin. The currencies can be transacted in a borderless manner and without involving any central guarantor for the transaction. Since, the Bitcoin’s supply cannot be altered at will by a central authority, as in the case of fiat currency, it is largely anti-­ inflationary. As a result, Bitcoin has become the primary store of value for the blockchain industry. • DeFi is also powering decentralized payment networks, such as Libra, that allow instant, low-cost, borderless transfer of capital. Since decentralized networks provide low-cost transactions, they can be utilized by merchants to lower their costs and be used where no similar alternatives exist. • DeFi is also allowing decentralized fundraising. Raising venture capital has always been fraught with the challenge of establishing trust. But blockchain technology is enabling initial coin offerings (ICOs) for specific projects that require funds. In an ICO, tokens are created on a public blockchain and are then sold to potential investors for raising funds. In this way, access to capital is becoming easier. • DeFi allows decentralized contracting through smart contracts that execute automatically when certain pre-specified conditions are met. Smart contracts not only reduce the reliance on a central authority to facilitate the contract, but also reduce the transaction costs involved in drafting and enforcing the contract. For example, platforms like Compound facilitate lending and borrowing using smart contracts.

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3.5.2.2 Non-fungible Tokens Non-fungible tokens (NFTs) are unique, tokenized representations of digital files that can be transmitted and exchanged over blockchain technology. Technically, an NFT is the combination of digital content with a smart contract. As the name suggests, NFTs are non-fungible, an attribute derived from the immutability of blockchain technology. NFTs attribute a unique identity and ownership to digital content, preempting rampant piracy and illegal distribution of digital content. While it is possible to replicate digital content at no cost, NFTs allow digital content to also be characterized as rare, owing to provenance of the digital content that is stored on the blockchain. Though NFT technology is still in its infancy, it is already showcasing the possibility of various business model innovations. • NFTs can be used for engaging fans of sporting teams. The uniqueness associated with NFTs can confer voting rights on the fans to influence team decisions. The fans can also be provided access to exclusive offers that cannot be re-utilized by someone else. • NFTs can be used to enhance customer relationships by enabling the tracking of ownership of a product/service. Based on the ownership, novel segmentation and engagement opportunities can emerge for businesses. • NFTs can enable new revenue models by creating digital scarcity. With NFTs it becomes possible for digital content to become exclusive, enabling businesses to sell digital content as limited-edition goods and collectibles. • NFTs provide conditions for a new creator economy. Instead of a one-time sale of a digital asset, creators can receive recurring royalties. In the creator economy, all client-creator interactions will be logged on to the blockchain, enabling clear ascription of ownership. Smart contracts will be used to release payments, which will enable the payment of royalty upon each transaction. This model will help reduce loss of value for the creators due to piracy.

3.5.3 Challenges While blockchain-based business models offer many opportunities, they also pose certain challenges. Firstly, such models enable money laundering, so businesses must consider risk management strategies using appropriate customer authentication methods. Secondly, there is an immense environmental impact of such models owing to the essentiality of proof of work, an energy-intensive mechanism. As a respite, recently some blockchains, including Ethereum, have started using alternative consensus mechanisms based on proof of stake, where a validator stakes

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collateral that may be destroyed if the validator behaves dishonestly. Thirdly, there are legal and regulatory challenges associated with cross-border jurisdiction when borderless transactions are executed. Also, there are potential implications from multiple legal domains including contract laws, IP laws, property laws, privacy laws, and security laws.

3.6 Conclusion In this chapter, we have explored how advances in technology, and associated innovations in products and services, are transforming the way businesses create and capture value. First, we discussed digital innovation and how it triggers digital disruption and transformation. Thereafter, we focused on business model innovations prompted by digital disruption and transformation. In this chapter, we emphasized three dominant technology-enabled business model innovations, namely, the cloud-­ based business model, the platform-based business model, and the blockchain-­ based business model. We also discussed the enablers, advantages, and challenges of technology-led business model innovations. The cloud-based business model can be leveraged by firm by either sourcing cloud-based IT infrastructure, or by offering their products as services or as product-­service bundles hosted and served from the cloud. The primary benefit of this model is cost reduction and ubiquitous access to resources and products. The platform-­based business model is often powered through cloud infrastructure and creates value by enabling different entities to interact and transact with each other. The success of this business model, therefore, depends on garnering a critical mass of adopters and initiating an appropriate mix of direct and indirect network effects. Finally, many business model innovations are being powered by blockchain technology that provides immutable digital ledger infrastructure, decentralized control, and automatically executable contracts. While the true potential of these blockchain-­based business models is yet to be realized, the blueprints of such models are already being tested in the domains of decentralized financial and non-fungible tokens. Technology-led business models are enabling firms to not only commercialize digital technology, but also benefit from using digital technology as an essential aspect of their business models. Innovative business models are helping firms to use emerging technologies to enhance their value propositions, customer engagement, supplier relations, distribution channels, and cost/revenue structures. With the continued developments in this domain, firms will not only be able to provide greater value to their customers but will also be able to extract this value for their own growth.

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Chapter 4

Enhancing the Commercialization of University Research Andrew Maxwell

4.1 Introduction Universities, especially major research universities, play a key role in national and regional economic development, with a critical component being the conversion of scientific inventions to innovation through the commercialization of university research. Unfortunately, commercialization success rates from university research are low, despite the introduction of many initiatives to increase the regional economic importance of knowledge mobilization and growing recognition of the regional benefits of using new technologies to catalyze venture creation. In this chapter, we examine the multiple ways in which universities and their communities’ benefit from the successful commercialization of university research, especially when this is through the creation of new ventures. We provide insights into major causes of low commercialization success, which can be explained by the context in which it occurs, a lack of process understanding, and fundamental changes in the technologies being commercialized as well as the role of venture creation. We structure this chapter by offering a historical perspective on how university technology commercialization has evolved, and the importance of technology commercialization recognized. We examine the context in which research commercialization occurs, to highlight systemic challenges facing those trying to increase commercialization activity, before exploring the research commercialization process that occurs in most universities. This allows us to identify contextual issues and specific process aspects that contribute to the high failure rates and suggest, based on our research and experience, specific ways in which universities can modify their approach and process so that more faculty and graduate students can participate in A. Maxwell (*) Lassonde School of Engineering, York University, Toronto, ON, Canada e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Patnaik et al. (eds.), Global Trends in Technology Startup Project Development and Management, Innovation, Technology, and Knowledge Management, https://doi.org/10.1007/978-3-031-40324-8_4

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commercialization activities. We conclude the chapter by sharing two frameworks we have deployed and now share as Open Educational Resources.

4.2 Importance of Commercializing University Research Most research institutions still view the number of research publications as their key performance metric, reinforced by being used, alongside teaching, as a key metric in government evaluations, when attracting faculty and graduate students and as part of the research funding process. Over the past fifty years, research universities have increasingly seen technology commercialization as a third additional mission (Etzkowitz, 2003; Compagnucci & Spigarelli, 2020). However, there is still some way to go before universities’ societal contribution, such as creating new products, services, and industries, are viewed as important motivators for faculty and graduate student activity. This coupled with the high failure rate and long time before the success of commercialization activities can be observed, make it challenging for universities to either respond to commercialization opportunities despite best intentions or to measure their outcomes (Link & Siegel, 2005).

4.3 Historical Perspective The widespread misalignment between the current process and policies in use by many institutions and those necessary to enhance commercialization activity and success rates requires an understanding of the historical context of the evolution of the commercialization of university research over the past fifty years. In the 1970s, the US government (along with other governments globally) became increasingly aware that most of the inventions arising from public research funding never reached the marketplace. Initially, there was some debate about the cause of this issue, with some arguing that the fundamental problem was caused by the gap between academia and the marketplace, while others felt that universities were not designed to exploit the outcomes of the faculty research. This issue was exacerbated by a philosophical issue within the “Ivory Tower” of the university, where there was a feeling that too close connections between industry and research activity has the potential for conflict of interest that might negatively impact research outcomes (Behrens & Gray, 2001). In the United States, politicians, industrialists, and academics came together1 to identify the primary causes of the lack of translation of academic research into market application, concluding there was:

 The University of New Hampshire archive provides working documents developed during the creation of the Bayh-Dole Act. 1

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• Lack of investment in the type of activities and resources required to turn research into marketable products. • Lack of knowledge and expertise about the commercialization and licensing process. • Confusion about who should be responsible for commercialization activity and who should benefit. To address this, the Bayh-Dole Act was enacted in 1980 by US Congress, reversing the presumption that the government held title to the results of federally funded research, instead permitting a university, small business, or non-profit institution to elect to pursue ownership of an invention. This changed how universities looked at technology commercialization, putting the onus on universities to establish Technology Transfer Offices (TTOs) to file for patents and undertake commercialization activities through exclusive licenses (Bercovitz & Feldman, 2006; Mowery et al., 2015). The apparent success of this approach was replicated in many countries faced with the same concerns about the mobilization of research commercialization success rates (Baldini, 2009). However, this approach had several unintended consequences which have contributed to ongoing poor success rates. • The role of commercialization was embedded in a single entity, the University TTO, creating a one size fits all approach (Colyvas et al., 2002). • The focus of technology commercialization is on licensing codified knowledge (through patents) ignoring the importance of transferring tacit knowledge. • Universities, able to attract license revenues, established TTOs with unrealistic income targets, which straight jacketed the types of technologies commercialized and licenses negotiated. • Universities’ IP ownership created a disconnect between innovator and commercialization organization with the potential for lack of process alignment and conflicts between institution and innovators, around how the technology should be mobilized, and about how any benefits should be shared. However, while universities were being asked to transform from institutions of teaching and research to institutions with a knowledge mobilization objective, most have not changed their policies and procedures to make this happen. For example, many institutions still recruit and incent faculty  based on  publication  potential, rather than potential for knowledge transfer.

4.4 Evidence of Poor Success Rates Since the introduction of Bayh-Dole, Universities have spent significant resources on filing patents and attempting to commercialize IP, however, the overall results have been disappointing: • There has been a substantial increase in university research activities, as well as an increasing array of technology fields in which to conduct research – so while

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the absolute number of patents filed has increased, the percentage commercialized has reduced (Link et al., 2008). • University performance is increasingly measured by provisional patents filed, increasing attention on the filing process, but in practice 70% of provisional patent submissions are never filed as actual patents, less than 10% become patents (Blake, 1993), and less than 5% of those become the basis of a license agreement (AUTM). • The poor record of universities in developing commercializable technologies can be seen in the lack of research funding from industry, accounting for only 5% of university research funding in 2015, indicating that industry does not view academia as a source of commercializable IP (NSF). Aggregating data from US research universities provides insights into the magnitude of the problem. For example, in 2018, in the United States, more than $71 billion was spent on federally funded research at universities, yet in the same year, only $2.94 billion was generated in university licensing revenue. Indeed, in only 10% of universities, did the revenue from the University Technology Transfer Office (TTO) exceed its operational and patenting costs (Nag et al., 2020). Even when we consider the role of venture creation in contributing to the commercialization of university research (i.e., SUN, Google, OpenText) it is the exception, rather than the rule. In reality venture creation success stories are outliers, with only about 6% of universities creating spun-off companies between 2012 and 2017, and only 0.3% of these companies achieving annual revenues over $100 million (NSF). In an extensive review of the impact of Bayh-Dole, Grimaldi et al. (2011) conclude that while there have been improvements in certain aspects of the commercialization of university research, the benefits have not been as hoped.

4.5 The Context in Which Technology Commercialization Occurs Most universities, faced with Bayh-Dole or similar legislation, developed structures and policies to facilitate the commercialization model assumed by Bayh-Dole, the transfer of knowledge to third parties through licensing. However, the separation of research and commercialization activities disconnected the technology expertise from the user input creating sub-optimal commercialization outcomes. Further Universities equated responsibility for ensuring technologies are commercialized with the rights of ownership of that technology, creating a misalignment between the innovator and the TTO. The TTO wants to see short-term revenue, the innovator usually wants to see long-term impact (Phan & Siegel, 2006; Nag et  al., 2020). Other issues that resulted from this approach include the focus on patenting to license codified knowledge, limits on commercialization options, and the adoption of a one-size fits all process and business model for technology commercialization which might not be appropriate for many technologies and market conditions (Siegel et al., 2004). Other issues include:

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• Over-valuation of IP by the TTO leading to sub-optimal license negotiation outcomes, and even a no-deal, despite the very real potential of the technology (Clarysse et al., 2007). • Focusing resources and time on patenting, despite the costs of applying for and enforcing them. • Measuring impact of university commercialization based on license revenues and patents filed (because this is easier) rather than measuring more meaningful impact (such as regional job creation). One of the biggest challenges in making a TTO office, responsible for commercialization activities, is that TTO staff are not equipped to fully understand the applications and nuances of innovative technologies, while faculty have limited motivation and incentives to commercialize their research (Di Gregorio & Shane, 2003). Indeed, most faculty are actively discouraged from losing their focus on research activities and are not incented to participate both from a compensation perspective, or because commercialization activity is not usually considered as a tenure requirement.2 Failure to consider commercialization activity as a way of assessing faculty contribution has a consequence when in measuring the impact of university research. Universities are keen to provide evidence that demonstrates a Return on Investment (ROI) for government research investments; however, measuring this can be complex, as it involves many direct and indirect factors, occurs over the longer term and is difficult to capture. Most universities choose to measure direct input (research dollars earned) rather than research outcomes (even then they only consider patents issued and license revenue despite limited evidence of their direct impact) (Grimpe & Fier, 2010). While universities are increasingly under pressure to report on more meaningful impact metrics, such as number of ventures created, or regional economic impact, the lack of standard metrics and agreed methods for measuring this data, means that collected and quoted data is often seen as subjective. As Weingart (2017) notes, this can lead to the conclusion that outcome measurements are hype. The need for universities to highlight successful research outcomes (rather than the inevitable failures) leads to a desire not to report on failures or analyze their causes. Unfortunately, from a quality improvement perspective, this has the consequence of reducing our ability to identify specific causes of failure that could help identify specific actions that might be undertaken to eliminate them. This is the approach taken in this chapter, identifying common causes of failure in the technology commercialization process, in order to suggest specific actions that institutions and stakeholders might take to both improve the level of commercialization activities and increase their resulting success rates. We summarize some of these causes of failure  and make suggestions as to ways to address them in Table 4.1 (Zarringhalam, 2020)  We generalize the problem, noting that many institutions/funding agencies already encourage research commercialization activities to be considered in faculty achievements. For example, in Canada, NSERC has significantly expand the definition of research contribution (NSERC, n.d.). 2

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Table 4.1  Top ten factors that contribute to low commercialization success rates  (Based on Zarringham, 2020) Factor Context factors 1 Constrained TTO skills, capabilities, and resources

Explanation

TTO staff have limited skills and experience and access to resources (especially for venture creation) Tenure process discourages Faculty incentives discourage involvement faculty from spending time undertaking commercial in commercialization activity activity

Potential solution Increase access to appropriate support for the TTO

Find new ways to incent faculty, graduate students, and others within the university and its community to engage in commercialization activities and projects Commercialization process is Develop a range of paths to 3 Standard approach to market by enhancing the commercialization used different for different technologies, and can require a function, role, and capabilities of for each opportunity the TTO, especially in the range of paths to market development of networks. Limited entrepreneurial effort Develop metrics to measure the TTOs viewed as a potential 4 Misalignment of TTO total impact of technology and researcher financial revenue source, so focus on commercialization and use them projects more likely to be goals to drive TTO engagement with financially rewarding (often faculty, graduate students, and less risky) the community Adopt technology 5 Lack of wholistic view Knowledge mobilization not commercialization and of commercialization viewed as important as entrepreneurship as a third teaching and research mission, that engages individuals across the university Process factors Find ways to align incentives and Rights to commercialize 6 Disconnect created by technology pass from inventor engage faculty (and especially the transfer of IP to TTO creating at the point of graduate students) directly in the ownership at the point commercialization activity disclosure creating of disclosure misalignment of process Limited comprehension of the Actively engage with faculty and 7 Lack of commercialization process and students interested in both commercialization process understanding users’ adoption decision or of understanding the technology commercialization process and the potential use of different pursuing it business models Develop alternate performance Placing too high a value on 8 IP valuation too high metrics to align outcomes, and patents or innovations by the and negotiation with address common issues that limit TTOs constrains potential TTO challenging commercial success, especially buyers and investors when this is through new venture creation 2

(continued)

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4  Enhancing the Commercialization of University Research Table 4.1 (continued) 9

Factor Multiple paths to market and potential business models

Explanation Link to local industries, receptors, and partners who can complement university commercialization activity Establish flexible resources to support commercialization across university

10 Lack of resources to support long-term venture development

a. Research

Percentage failures at each stage

b. Disclosure

70%

c. Evaluation

Potential solution Foster new industry partnership that can help identify use cases for technology and establish the relationships that can turn interest into adoption. Build a community of expertise with access to resources to support commercialization of technology innovation at any stage in the process

d. Business Plan

60%

40%

e. License/ Implementation

80%

Fig. 4.1  Simplified technology commercialization process (indicating failure percentage by stage)

4.6 Improving the Technology Commercialization Process In the previous section, we have identified systemic challenges to improving commercialization success rates; in this section, we discuss the commercialization process itself to identify specific opportunities for improvement  at each stage. We initially focuss (Fig. 4.1 on the traditional license-based commercialization model) before examining the Advanced Commercialization Process (Fig. 4.2 includes the option for venture creation) which creates alternative paths to market and encourages venture creation.

4.6.1 Stages of the Traditional Technology Commercialization Process The traditional technology commercialization process, shown in Fig. 4.1, includes: (a) Research, (b) Disclosure, (c) Evaluation, (d) Business Plan, and (e) Licensing/ Implementation which we briefly describe below: The starting point for the traditional commercialization process is the federally funded3 research activity undertaken by the innovator based on curiosity, the  We exclude industry funded research from our model, because it usually includes a commercialization plan that involves the transfer of IP ownership to the industry partner (although usually not an alternate plan if the technology cannot be used by the funder).

3

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Fig. 4.2  Commercialization process with options for venture creation

funding award, and the innovator’s desire to publish. Normally, the outcomes from this research are shared at conferences and published in a paper. If the innovator feels there is a potential commercial use, they will sign a disclosure agreement with the TTO. In most cases, the commercialization activity only starts once research has been completed and through completing a disclosure document the innovator passes responsibility for commercialization activity to the TTO, and only of the TTO determines it has commercialization potential (Friedman & Silberman, 2003). The separation of research and commercial activity is a core factor in the low commercialization success rates, as: • Research is completed without understanding user needs, and therefore optimized for publication not for utility as a researcher working on advanced technology is always going to strive for a better performing solution (as this is both intellectually challenging and more likely to be published) than a solution that is worse performing but can be delivered at 20% of the cost of the existing solution. Disconnects between research activity and commercial application contribute to the high rate of failure (Jensen et al., 2003). • Research completed without input from the user can also be suboptimally designed, not from the perspective of core features, but around important user-­ centric issues, such as compatibility or ease of use. Lack of direct user engagement can lead to the development of solutions that embed (rather than address) substantive barriers to adoption, which limit the potential for commercial success). • The technology commercialization process involves several stages (with as noted a high rate of failures at each stage). A failure to understand the criteria for each stage (and ultimately user adoption) creates process inefficiency and a lack of process alignment that contributes to high failure rates. • Technology Transfer offices are usually presented with more disclosures than they can support. While the decisions to support a specific technology are significantly influenced by the likelihood and magnitude of commercial success (Litan et al., 2007). They are also influenced by the knowledge and experience of the TTO officer, as well as the policies, agenda, and resources of the TTO (Lockett & Wright, 2005). If an innovator wants their technology to be commercialized they need to adapt their research strategy to increase the likelihood that it will meet the TTO criteria for acceptance.

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4.6.2 Disclosure Once the research is completed, innovators interested in pursuing the commercial application of the technology are required to complete a disclosure document that describes the nature of the research/discovery, who has been involved, any IP issues, such as freedom to operate, prior art or patenting potential for patenting. The disclosure document can sometimes include use case and potential customers, but only if the innovator has come across these opportunities during the research phase. Importantly, completing the disclosure document, if the innovation is accepted by the TTO, represents a fundamental change in ownership and commercialization motivation, as it is at this point that commercialization rights and resulting benefits pass from the innovator to the TTO.  This change of ownership and motivation means that the innovator has limited subsequent  responsibility for commercial activity, creating an obvious disconnect between the exploration and exploitation activities. The transfer of ownership and responsibility further contributes to low commercialization success rates, as: • It further limits innovator motivation to spend time on commercialization activity. • It limits the potential to truly exploit the technology as we know that successful technology commercialization requires the transfer of both tacit and codified knowledge. • It limits the additional benefits that can accrue from technology commercialization, related to additional research activity, knowledge transfer, and the creation of employment opportunities for university graduates. • It constrains innovators’ understanding of technology development requirements linked to the use of their technology, limiting the success of technology transfer, and the opportunity to address related problems. It is important to note that on occasion, innovators choose not to disclose a potential commercializable opportunity  to the TTO, either because of general distrust, ignorance of the process, or the belief that there is a better opportunity available outside the standard process (Markman et al., 2005).

4.6.3 Evaluation TTO staff have limited resources to choose whether to pursue each disclosed opportunity to pursue and must consider several external and internal factors when so doing. While the TTO’s initial assessment of a disclosed technology involves an assessment of the market potential, a determination of the likelihood of success and its magnitude, the final decision is mediated by the fact that deciding to move forward with a specific technology requires that so doing is aligned with the available resources and mandate of the TTOs (Siegel & Phan, 2005; Siegel & Wright, 2015).

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While the most common reason for rejection is the lack of market potential, other reasons include: • Lack of user engagement during the research stage means that the innovators cannot provide the TTO with evidence of technology utility or market application (meaning that the TTO has to assess the opportunity in a vacuum. • High-impact potential opportunities are often rejected because the TTO is constrained by their own expertise, and/or resources as well as their reliance on a single business model (Siegel et al. 2003). • Financial returns to the TTO are often measured in the short term when greater impact (e.g., through venture creation might be the optimum path to success). • The basic approach makes assumptions around the linear nature of the commercialization process, that does not consider potential pivots or changes in business model at each stage. It is difficult for the individuals working in the TTO to take a more entrepreneurial approach to technology commercialization, based on market or user feedback, that is associated with increased likelihood of success (Etzkowitz et al., 2000).

4.6.4 Business Plan In line with the culture of the host institution, a TTO proposing to commercialize a technology needs to develop a business plan to both allocate resources and establish financial performance metrics. However, these plans often fail to recognize the reality of the early-stage phases of technology commercialization, which rarely proceeds in a linear fashion, and requires an entrepreneurial (and often risky) approach, and challenges to the initial assumptions  (Clark, 1998). Indeed, TTOs tend to assume a standard business model for commercialization that is based on a single license and an obvious licensee. Given the path to market common in the deployment of innovative technologies, the real opportunity often arises once the commercialization effort is underway. TTO policies and constrained resources and timelines can mean that either commercialization activities are unsuccessful or fail to achieve their potential impact. These issues are exacerbated by: • The need to make rapid decisions, respond to new opportunities and embrace and mitigate risk, as the project evolves, in ways that deviate from the original business plan, require a different cultural perspective than normally found within an academic institution. • TTOs have to manage a portfolio of products and technologies and need to make tradeoffs between them in moving forward. This requirement means that for an individual technology, suboptimal decisions may be made (often due to time, financial, and human resource limitations).

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4.6.5 Implementation/License In the traditional commercialization model, the basic business plan involves the TTO identifying one or more appropriate licensees who can find a path to take the technology to market. Interestingly, recent evidence from Canada suggests that most licenses are granted outside the economic jurisdiction of the university, meaning that the economic value created frequently accrues to a different regime than contributes to the research funding in the first place (CIGI). In general, creating and managing a license (or several overlapping licenses) is a much better fit with TTO resources, capabilities, culture, and expectations than developing a range of business models and managing multiple commercialization options (including venture creation). Under the license agreement, there is usually a royalty payment that is paid on a regular basis (often within a few years of the license being issued), that is based on a fraction of future revenue earned by the commercialization partner (Siegel & Phan, 2005). The challenge for venture creation in the university environment is that the real financial return usually does not accrue until at least seven years after the investment is made (and beyond the tenure of TTO staff, or even VC funding arrangements). Further, much of the benefit accrues to the region and not the institution. That said, simple licensing of technologies is not without challenges that can contribute to unsuccessful or suboptimal outcomes, based on: (i) Difficulty in forecasting the commercial success of the licensed product (despite the ease with which revenue predictions are bandied about during license negotiations). (ii) Challenges in finalizing license agreements because the TTO frequently overvalues the patent (Clarysse et al., 2007), and underestimates the commercialization effort required as well as the inherent risk (Hertzfeld et al., 2006). (iii) The fact that license agreements relate to the use of a specific manifestation of the technology solution, which the licensee is then incented to replace in order to avoid paying ongoing royalties (Maxwell & Levesque, 2011).

4.7 Changing Nature of Research Commercialization: New Technologies and Business Models We have discussed both the contextual constraints of technology commercialization within a university, and the inherent limitations of the traditional technology commercialization process. However, the problems of high failure rates have been exacerbated by fundamental changes in the nature of the commercialization of university research. and the need to change processes, policies, and business models, to take advantage of these new opportunities (Link et al., 2008). The nature of technological innovation has changed since 1980, with a broader range of technologies with commercial potential being developed in universities

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(Geuna & Muscio 2009). Most significantly with the advent of information technology, universities have increasingly focused on research areas such as computer science, data science, and artificial intelligence, which have led to the development of new technologies such as Artifical Intelligence, e-commerce, and social media, transforming industries and creating new markets. In addition, advances in biotechnology have revolutionized the fields of medicine and agriculture, leading to the development of new drugs, vaccines, and genetically modified crops, which has also enabled new discoveries in areas such as genetics, genomics, and proteomics. Other emerging fields, such as nanotechnology, leading to the development of new materials with unique properties, such as superconductors and nanotubes, have created new applications in electronics, energy, and medicine. While interest in clean energy (and sustainability) has driven research in areas such as solar, wind, and biofuels, leading to the development of new materials, devices, and processes for generating and storing energy, and for improving energy efficiency. Advances in robotics have led to the development of autonomous vehicles, drones, and surgical robots that can be used in a variety of healthcare, transportation, and manufacturing applications, while advances in materials such as composites, ceramics, and alloys have found applications in areas such as aerospace, construction, and electronics. These new technologies and disciplines use patenting in a variety of different ways, for example, advances in important fields such as gene-based solutions and software innovations are usually not suitable for patenting (Pradhan, 2016). The move away from a focus on patenting and licensing is seen as evidence that successful commercialization often requires the combination of tacit and codified knowledge, with the ability to access additional ongoing support key to commercial success. While in the field of computer science, as well as others, most important method of transferring academic knowledge to industry is through scientific output (publications and conferences), students and informal contracts, as well as collaborative contract research (Grimpe & Hussinger, 2013). Importantly, many of these technological innovations are inherrently disruptive, changing the nature of the marketplace, and removing the inherent advantage of incumbants, highlighting the need to consider venture creation as the optimum path to exploit a disruptive technology. In parallel with the changing nature of technologies being commercialized, the commercialization of university  research is increasingly seen as the catalyst for venture creation (Weingart, 2017). As Maxwell and Levesque (2011) observe, venture creations offer several additional significant benefits to the University and the regional ecosystem, especially created in close proximity to the university from which they originate, creating several interlinked economic and employment benefits (Bercovitz & Feldman, 2006; Boh et al., 2016), including: • The development of local receptor capacity, and dedicated incubation support for early-stage technology ventures, that enhance regional innovation. • The growth of local  ventures who  can become significant regional  employers recruiting undergraduate and graduate students, as well as co-op and eWIL students. • The source of additional research contracts with the university and active partners with university researchers seeking government funding.

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• Local ventures offer indirect benefits to the university through the development of regional economies, which ultimately provides a tax base for the funding of university research, and encourage the community to participate in the educational programs offered. • Local ventures can stimulate interest in investing in early-stage ventures, by employees, university alumni, and the broader community. • Local ventures are often early adopters of new technologies, increasing their productivity and growth, and stimulating the local economy. This can foster the creation of additional start-up companies that either supply the university start-­ ups or take advantage of the new technology launched. While there are multiple benefits to the university and regional economy from the creation of new ventures to exploit a commercialization opportunity, it is important to highlight why new ventures can often be the preferred business model for commercialization of a new technology, including: • New ventures can be optimized to exploit an emerging market opportunity and are not constrained by the requirement to operate within a legacy operation or the limited resources and limited approaches available to a TTO (Bradley et al., 2013). • New ventures are, by definition, entrepreneurial in nature, making decisions quickly, under conditions of uncertainty, without full information. These are essential characteristics for commercial success but hard to find within the academic confines of a TTO. • New ventures are ideally suited to prototype new business models that enhance the potential for disrupting markets enabled by novel technologies. • New ventures can recruit a specialist leadership team and critical resources to take advantage of an emerging market and react in a timely fashion as the business develops. • New ventures can raise funds and require talent to take on the associated risks of early-stage technology commercialization that can be too high for existing organizations. For universities, an additional major benefit in fostering venture creation is that rather than relying on revenue from a single license, universities can (over the longer term) enjoy revenue from selling equity (e.g., Sun and Google at Stanford) instead of relying on the revenue from a single license agreement, as revenue is less dependent on a single codified patent, but offers a variety of value creation options.  The challenges of successful commercialization of a novel technology, are often exacerbated by the limitations under which the TTO itself operates, including the process (which  was designed  for licensing), and the team expertise who were recruited to work in a different environment (Phan & Siegel, 2006). Allowing the innovator (or the TTO) to choose from a variety of paths to market, allows innovators and their various commercialization partners to choose the commercialization path most likely to succeed beyond the simple “one-size fits all” licensing model, which we attempt to illustrate in Fig. 4.2, (Bradley et al., 2013). The ability to consider new processes, and business models witll sginficantly increase the likelihood of commercialization success.

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4.8 Opportunities to Enhance the Success Rate of University Research Commercialization In the chapter, we have identified several context-specific factors and process issues that limit the successful commercialization of university research, often based on faulty, or outdated, assumptions about the traditional commercialization process. While many of the causes of failure are due to a reliance on incorrect assumptions, and a lack of process understanding, changes in the technology, marketplace, and business models have created additional challenges to commercialization success. In this section (Table  4.1), we summarize previously identified key context and process causes of failure, to allow us to provide, in the next section, some suggestions as to how the current low success rates might be improved. Where possible we provide examples from our own work in this space and in Sect. 4.10 we share the TechConnect process which helps innovators better understand the technology commercialization process, and VentureStart, where we offer a framework that helps inform critical decisions around the choice of venture creation or licensing as the optimum approach for commercial success.

4.9 Opportunities for Improvement (Removing Causes of Failure) Based on the challenges facing universities in increasing commercialization activities and supporting venture creation identified above, we use a quality management approach to identify specific a opportunities to reduce the current causes of failure, in order to increase the level of commercialization activity, the number of ventures formed, and the success rates of technologies with commercial potential: 1. Increase access to appropriate support for the TTO: Develop the capabilities to support a wide range of technology commercialization activities (e.g., by hiring people with a broad range of technology and commercialization experience and knowledge). This can be facilitated by building a network of talent and local and global partners with a wide range of resources and expertise that can be deployed on-demand. The TTO can offer workshops and related activities to foster interest in, and awareness of, the commercialization process (perhaps integrated into the academic curriculum) and provide resources and facilities (e.g., incubators) for those interested in testing ideas around technology commercialization, or venture creation (experimental entrepreneurship). TTOs can expand their mission by building partnerships with community members and directly supporting venture creation. 2. Provide financial and other incentives to faculty: Universities can offer financial incentives to faculty members who successfully commercialize their research as well as to their graduate students who might join the start-ups created (Lockett et al., 2003). These incentives may include royalties or licensing

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fees, equity in start-up companies, or bonuses. Financial incentives can help to motivate researchers to pursue commercialization and reward them for their efforts. This can also be directly to tenure and promotion where appropriate, for example, by giving credit for commercialization activities in the tenure and promotion process, such as patents, licensing agreements, or start-up companies.  Faculty can also be motivated by non-financial incentives (Baycan & Stough, 2013). 3. Develop a range of paths to market; Enhance the function, role, and capabilities of the TTO, especially in the development of local and global  networks. Universities can also promote a culture of entrepreneurship and innovation, where commercialization is seen as a natural extension of academic research. TTOs can work across campus (including university administration, staff members, faculty members, and grad students) to develop entrepreneurial approaches to teaching, research, and work experience, to address challenges (facilitated by engaging directly with entrepreneurs where they can learn from both success and failure), for example, through the development of entrepreneurial universities and the development of the campus as a Living Lab. 4. Develop commercialization  metrics: Define and measure the total impact of technology commercialization and use the  results  to drive TTO engagement with faculty, graduate students, and the community. This should include providing data on regional wealth and job creation, ongoing research activities, and the multiplier impact of university commercialization (incubators already have similar metrics to justify their continued funding). 5. Adopt technology commercialization and entrepreneurship as a third mission: Universities should recognize, foster, and develop mechanisms and policies to support a wide range of informal commercialization processes, organizations, and activities in addition to the formal commercialization paths and organizations currently in place to support technology commercialization. This includes more direct support of entrepreneurial ventures, especially those linked to university research and/or employing university graduates. This will ­ likely  require adjustments, over time, to recruitment and promotion  policies. This should be complemented by enhanced activities to foster collaboration and reduce barriers within the university. 6. Engage faculty and graduate students in the commercialization activity: Provide funding to support graduate RA activity, including prototyping and customer discovery alongside traditional  research activity. Running workshops to help understand the actual commercialization process that includes faculty and graduate (and perhaps undergraduate students). In addition, there are other members of the community (both university staff and entrepreneurs in the local eco-system) who would love to better understand the opportunities created by the research on campus. 7. Create a culture that values commercialization and supports researchers: Develop mentorship and networking opportunities within the community and with the broader academic community. A special case, which we identify in Sect. 4.10 is, by offering graduate courses and workshops that expose faculty

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and graduate students to the process and outcomes of technology commercialization by introducing user needs and value propositions in parallel with the research activity. 8. Develop alternate performance metrics: Agree and measure outcomes definitions that include regional economic impact, and venture and wealth creation to align outcomes, and create paths to the market when TTO licensing is not the option most likely to succeed. This will include expanding the role of the TTO and moving towards a number of commercialization options. This includes developing relationship with local innovation system partners (and possible funders) 9. Foster industry partnership that can help identify technology  use cases: Establish collaborative  relationships that can turn initial  interest into widespread adoption through industry and community partnerships, providing faculty members with opportunities to collaborate with industry partners on research projects. This can help to create a stronger connection between academic research and industry needs, making it more likely that research findings will have commercial applications. 10. Provide access to funding and other market-oriented resources: Commercializing research requires funding, and access to market facing resources that universities can provide to faculty members to support commercialization efforts. This may involve providing seed funding for start-up companies, offering grants for commercialization activities, or connecting faculty members with investors or funding agencies.

4.10 Our Contributions (TechConnect and Venture Start) (a) TechConnect: Faced with poor success rates in commercializing university technology the authors developed and deployed a Design Thinking approach (TechConnect) that leverages the development of an innovative technology to create a competitive advantage. This methodology explores and tests: users’ functional and emotional needs, sustainable competitive advantage, job/application fit, compelling value proposition and alternate business models, in order to develop a go-to-market strategy,  described as “Technology Driven and Customer Centric.” Importantly, the Tech Connect process is designed to be implemented with faculty and graduate students while the research activity is still underway. Key aspects of the multi-stage process include: • Encouraging the innovator to consider factors that might enhance the likelihood of success while undertaking the research activity. • Clarifying the nature of the sustainable technology competitive advantage. • Embeding user functional and emotional needs into the technology development plan. • Adapting the technology to make it easier for users to adopt and deploy.

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• Reinforcing the motivation to adopt by creating a compelling value proposition through innovative technology design. • Developing a sustainable and viable business model to encourage adoption that might, in turn, influence the technology design. The process was developed to embed user needs into technology development, in order to significantly increase the likelihood that the resulting technology would be adopted by the target user. This includes considering different benefits, costs, and risks of adopting the solution in order to improve the likelihood of commercial success. While initial data shows that outcomes from using the process quadrupled the likelihood of commercial success (often due to modifying the technology, target application/user, or business model) the nature of the process also identifies rapidly and cost-effectively, technologies that are not likely to achieve commercial success. Importantly, the process creates links across each  stage of the commercialization  process, which reduces the gap between innovator and the commercializing organization. Further, it allows both graduate students and members of the local community to be involved in commercialization, creating options and resources that can subsequently engage in the commercialization process itself. The TechConnect approach (available as a free and downloadable open educational resource from www.innovationcartogrpahy.com) embeds the following ten steps: I. Understand what the technology can do. II. Identify jobs that the technology can do better than alternates. III. Find users who need job doing. IV. Validate most promising user needs/job fit. V. Gather evidence of compelling value propositions. VI. Assess barriers to adoption. VII. Explore technology and business model strategies to adoption barriers. VIII. Design and test prototype. IX. Create go-to-market strategy. X. Develop implementation business model and plan.

overcome

The TechConnect process can be freely deployed in any academic environment (likely supported by mentors). It is also available in a series of in-person or online workshops, or as a graduate course. We have found that learning about the commercialization process not only significantly improves the likelihood of commercial success, but also helps participants understand the challenges of bringing new technologies to market – an essential skill for those hoping to work in the technology industry. The availability of the content in a global classroom format reinforces the true value of bringing multi-disciplinary teams and diverse perspectives to creative problem solving. In Fig.  4.3, we highlight how the approach enhances the traditional technology commercialization process, by: (i) Offering a toolset and process to augment the traditional technology commercialization process.

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Fig. 4.3  Modified technology commercialization process with embedded TechConnect

(ii) Focuses on gathering meaningful evidence at each stage in order to guide the technology development process and challenge (or confirm) assumptions. (iii) Can be used for technologies that are most suited to being licensed or used as the basis for venture creation (iv) Blurring the lines between stages of the process, moving backward and forward between the various stages of the process, in order to validate assumptions, test hypotheses, and possibly pivot, at each stage. (v) Highlight fundamental challenges that reduce the likelihood of commercial success, suggesting either a completely new approach (or use case) or the need to stop working on the commercial potential of the innovation. (b) VentureStart: is a framework we developed, based on Porter’s Five Forces, to help those involved in the commercialization process make optimum decisions about whether the optimum path for commercial success is through venture creation or licensing. It considers five different factors that influence the choice of venture creation (over licensing) identifying specific factors that lead to the conclusion that venture creation is the preferred approach (Fig. 4.4). Below we provide more details and specifics on the factors and trends that influence venture creation as the preferred technology commercialization option (rather than licensing). (i). Market dynamics • Market trends (market is growing, market is segmenting, niches opening up). • Product life-cycle (short, so decisions are frequent and repeat buying common). • Market concentration (low, so that extant players don’t dominate the market, allowing new entrants). • Cost relative to total cost (easier to address discrete opportunities, rather than complex systems). (ii). Disruptive potential • Underserved customers (ability to address currently underserved users is high with limited offerings). • Changes in price/performance (potential created by significant reductions in cost, or improvement in performance). • Alternate business models (ability to use new business models to facilitate adoption, that are difficult for existing players to replicate).

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iii. Input factors

ii. Disruptive potential

i. Market dynamics

iv. Demand conditions

v. Barriers to entry

Fig. 4.4  Five forces approach to Venture creation decision

• Defendable patent, or strong barrier to potential additional entrants (allows company to exploit opportunity, defend their market and raise capital). (iii). Input factors • Supplier concentration (should be low, otherwise existing players can use their purchasing power to stifle innovation). • Distributor concentration (should be low creating a number of distributor or partner paths to market – where required). • Capital requirements (should be low, allowing the new entrants to raise the required capital to exploit the opportunity). • Economies of scale (should be low, to give the new entrant the time to build a market presence without a fundamental disadvantage.) (iv). Demand conditions. • Customer motivated to change current practice (benefits of adoption high and valuable). • Perceived need (high – fills a strong user functional or emotional need). • Switching costs (low costs of switching, training, and education enhance user willingness to adopt a solution from a new supplier). • Government legislation (minimal changes in legislation or approvals required to launch technology). (v). Barriers to entry. • Dominant technology (does not dominate market, allowing for new entrants). • Level of vertical integration (low allowing a new player to enter market without supply chain issues).

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• Brand loyalty and established relationships (low, allowing new entrants to be accepted by users). • Freedom to operate (high, allowing for new entrants to enter market – and possibly create a subsequent barrier for follow-on players).

4.11 Conclusions Recognizing the importance of the commercialization of university research and current low success rates, we review both the context in which technology commercialization occurs and the details of the technology commercialization process. We also provide a brief explanation of the fundamental changes in the nature of technology commercialization activity, based on changes in technology, and the interest in venture creation, that have challenged existing university approaches to this activity. This analysis helps us to identify systemic challenges facing universities looking to increase commercialization success, and then to suggest specific ways in which universities can address these issues. Our analysis of the technology commercialization process itself allows us to identify common causes of failure in the process, in order for us to suggest specific opportunities for process improvement. We then complement our  general findings, by providing tools and frameworks that help guide researchers through the stages of the technology commercialization process, and provide an objective framework for making the choice between venture creation and licensing as the preferred route to market. Wider deployment of these tools, and the ability to create a shared learning experience, linking technology roadmaps to user adoption, will massively increase commercialization success rates. We hope that this chapter motivates those involved in the commercialization process to explore assumptions about the process and policies within their own institutions, and use the approaches identified to find different ways to address them. Further, we hope that our quality management approach to the technology commercialization process will encourage those involved in the process to adopt some of the key suggestions made, to reduce the causes of failure at each stage, and between stages. These insights highlight the need to gather data on the process itself (in order to improve it) and to ensure that the technology commercialization process starts much earlier in the research activity.  This  will have the impact of improving the likelihood of developing a technology that achieves commercial success.

References Baldini, N. (2009). Implementing Bayh–dole-like laws: Faculty problems and their impact on university patenting activity. Research Policy, 38(8), 1217–1224. Baycan, T., & Stough, R. R. (2013). Bridging knowledge to commercialization: The good, the bad, and the challenging. The Annals of Regional Science, 50(2), 367–405.

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Behrens, T. R., & Gray, D. O. (2001). Unintended consequences of cooperative research: Impact of industry sponsorship on climate for academic freedom and other graduate student outcome. Research Policy, 30(2), 179–199. Bercovitz, J., & Feldman, M. (2006). Entrepreneurial universities and technology transfer: A conceptual framework for understanding knowledge-based economic development. The Journal of Technology Transfer, 31(1), 175–188. Blake, D.  A. (1993). The university’s role in marketing research discoveries. The Chronicle of Higher Education, 39(36), A52–A52. Boh, W. F., De-Haan, U., & Strom, R. (2016). University technology transfer through entrepreneurship: Faculty and students in spinoffs. The Journal of Technology Transfer, 41(4), 661–669. Bradley, S. R., Hayter, C. S., & Link, A. N. (2013). Models and methods of university technology transfer. Foundations and Trends® in Entrepreneurship, 9(6), 571–650. Clark, B. R. (1998). Creating entrepreneurial universities: Organizational pathways of transformation. Issues in higher education. Elsevier science regional sales, 665 avenue of the Americas, New York, NY 10010 (paperback: ISBN-0-08-0433545; hardcover: ISBN-0-08-0433421, $27). Clarysse, B., Wright, M., Lockett, A., Mustar, P., & Knockaert, M. (2007). Academic spin-offs, formal technology transfer and capital raising. Industrial and Corporate Change, 16(4), 609–640. Colyvas, J., Crow, M., Gelijns, A., Mazzoleni, R., Nelson, R.  R., Rosenberg, N., & Sampat, B.  N. (2002). How do university inventions get into practice? Management Science, 48(1), 61–72. Compagnucci, L., & Spigarelli, F. (2020). The Third Mission of the university: A systematic literature review on potentials and constraints. Technological Forecasting and Social Change, 161, 120284. Di Gregorio, D., & Shane, S. (2003). Why do some universities generate more start-ups than others? Research Policy, 32(2), 209–227. Etzkowitz, H. (2003). Innovation in innovation: The triple helix of university-industry-government relations. Social Science Information, 42(3), 293–337. Etzkowitz, H., Webster, A., Gebhardt, C., & Terra, B. R. C. (2000). The future of the university and the university of the future: Evolution of ivory tower to entrepreneurial paradigm. Research Policy, 29(2), 313–330. Friedman, J., & Silberman, J. (2003). University technology transfer: Do incentives, management, and location matter? The Journal of Technology Transfer, 28(1), 17–30. Geuna, A., & Muscio, A. (2009). The governance of university knowledge transfer: A critical review of the literature. Minerva, 47, 93–114. Grimaldi, R., Kenney, M., Siegel, D., & Wright, M. (2011). 30 years after Bayh–dole: Reassessing academic entrepreneurship. Research Policy, 40(8), 1045–1057. Grimpe, C., & Fier, H. (2010). Informal university technology transfer: A comparison between the United States and Germany. The Journal of Technology Transfer, 35(6), 637–650. Grimpe, C., & Hussinger, K. (2013). Formal and informal knowledge and technology transfer from academia to industry: Complementarity effects and innovation performance. Industry and Innovation, 20(8), 683–700. Hertzfeld, H. R., Link, A. N., & Vonortas, N. S. (2006). Intellectual property protection mechanisms in research partnerships. Research Policy, 35(6), 825–838. Jensen, R. A., Thursby, J. G., & Thursby, M. C. (2003). Disclosure and licensing of university inventions: ‘The best we can do with the s** t we get to work with’. International Journal of Industrial Organization, 21(9), 1271–1300. Link, A. N., & Siegel, D. S. (2005). Generating science-based growth: An econometric analysis of the impact of organizational incentives on university–industry technology transfer. European Journal of Finance, 11(3), 169–181. Link, A. N., Rothaermel, F. T., & Siegel, D. S. (2008). University technology transfer: An introduction to the special issue. IEEE Transactions on Engineering Management, 55(1), 5–8. Litan, R. E., Mitchell, L., & Reedy, E. J. (2007). The university as innovator: Bumps in the road. Issues in Science and Technology, 23(4), 57–66.

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Lockett, A., & Wright, M. (2005). Resources, capabilities, risk capital and the creation of university spin-out companies. Research Policy, 34(7), 1043–1057. Lockett, A., Wright, M., & Franklin, S. (2003). Technology transfer and universities' spin-out strategies. Small Business Economics, 20(2), 185–200. Markman, G.  D., Phan, P.  H., Balkin, D.  B., & Gianiodis, P.  T. (2005). Entrepreneurship and university-based technology transfer. Journal of Business Venturing, 20(2), 241–263. Maxwell, & Levesque, M. (2011). Technology incubators: Facilitating technology transfer or creating regional wealth? International Journal of Entrepreneurship and Innovation Management, 13(2), 122–143. Mowery, D. C., Nelson, R. R., Sampat, B. N., & Ziedonis, A. A. (2015). Ivory tower and industrial innovation: University-industry technology transfer before and after the Bayh-dole act. Stanford University Press. Nag, D., Gupta, A., & Turo, A. (2020). The evolution of university technology transfer: By the numbers, 22, 20–21. https://www.researchgate.net/publication/340766806 NSERC, (n.d.). “Guidelines on the assessment of contributions to research, training and mentoring”. Downloaded from https://www.nserc-crsng.gc.ca/NSERC-CRSNG/PoliciesPolitiques/assessment_of_contributions-evaluation_des_contributions_eng.asp Phan, P. H., & Siegel, D. S. (2006). The effectiveness of university technology transfer. Foundations and Trends® in Entrepreneurship, 2(2), 77–144. Pradhan, A. (2016). The evolution of technology transfer. Research Policy, 29(2000), 313–330. Siegel, D.  S., & Phan, P. (2005). Analyzing the effectiveness of university technology transfer: Implications for entrepreneurship education. Advances in the study of entrepreneurship, innovation, and economic growth, 16(1), 1–38. Siegel, D.  S., & Wright, M. (2015). Academic entrepreneurship: Time for a rethink? British Journal of Management, 26(4), 582–595. Siegel, D. S., Waldman, D., & Link, A. (2003). Assessing the impact of organizational practices on the relative productivity of university technology transfer offices: An exploratory study. Research Policy, 32(1), 27–48. Siegel, D. S., Waldman, D. A., Atwater, L. E., & Link, A. N. (2004). Toward a model of the effective transfer of scientific knowledge from academicians to practitioners: Qualitative evidence from the commercialization of university technologies. Journal of Engineering and Technology Management, 21(1-2), 115–142. Weingart, P. (2017). Is there a hype problem in science? If so, how is it addressed. The Oxford handbook of the science of science communication, 111–118. Zarringhalam, R. M. (2020). Enhancing University Technology Commercialization Success Rate downloaded from https://yorkspace.library.yorku.ca/items/d4e58d89-e0bf-4b5a-a3868287d92313b3

Chapter 5

Mapping Information Systems Flexibility with Organization’s Manufacturing Strategy Somen Dey and R. R. K. Sharma

5.1 Introduction The manufacturing and industrial sectors have undergone drastic changes during the last three decades. It has shifted in many aspects from conventional manufacturing practices to a more integrated approach. Use of automation and computers (i.e., CNC machines, Robots, MRP systems) (Lee, 1993; Lu & Yang, 2015) in manufacturing and deployment of IT tools (ERP systems, MIS systems) (Caggiano, 2018) to connect the various players in the supply chains have significantly increased the sensitivity and responsiveness of the manufacturing firms. To accommodate the changing needs of customers, manufacturing organizations need to implement a flexible manufacturing environment, where the various production-related activities can be aligned and modified to produce products as per customer orientation and market trends (Devece et al., 2017). Incorporating flexibility implies that changes or shifts are incurred, included, and absorbed smoothly with less or no penalties with respect to time, cost, and quality (Yu et al., 2021; Ku, 2022). Initial applications of IS in manufacturing and supply chain management were only restricted to automation of supporting functions. IS can have a direct impact on firm’s value creation through integration of different supply chain functions and lead to superior product quality, improved productivity, efficient space and machine utilization, and increased S. Dey (*) School of Management Studies, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, UP, India e-mail: [email protected] R. R. K. Sharma Department of Industrial and Management Engineering, Indian Institute of Technology Kanpur, Kanpur, UP, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Patnaik et al. (eds.), Global Trends in Technology Startup Project Development and Management, Innovation, Technology, and Knowledge Management, https://doi.org/10.1007/978-3-031-40324-8_5

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logistics efficiency (Kaltwasser, 1990a, b; Kim & Narasimhan, 2002; Abdolvand & Sepehri, 2016). With the gradual increase in competition and environmental uncertainty, firms started to realize the need for IS flexibility and its utilization for enhancing the capabilities to cope up with the sophisticated and fluctuating customers’ needs and meeting the quality standards of products (Bardi et al., 1994; Shukla & Sushil, 2020; Carter & Narasimhan, 1996). IS flexibility is an essential aspect for organizations (Bhatt et al., 2010a, b; Abrantes & Figueiredo, 2021; Qin et al., 2021; Seran et al., 2022). All tactical, strategic, and operational decisions are taken based on the information support from IS.  The present research identifies the different dimensions of IS flexibility (Dey & Sharma, 2018) and then relates these dimensions to two specific strategies, i.e., defenders or cost leaders and prospectors or differentiators in the form of related hypotheses (i.e., H1–H13).

5.2 Literature Review In this section, an exhaustive literature review is carried out on the essential constructs, i.e., organizational strategies and IS flexibility.

5.2.1 Review of Literature on Organizational and Manufacturing Strategy The strategy is concerned with the long-term direction of the organization, the scope of an organization’s activities, the matching of organization’s activities to its environment and resource capabilities, the allocation of significant resources within the organization, and the consideration of expectations and values of stakeholders (Langfield-Smith, 1997). Different researchers interpreted strategy in various ways as highlighted in Table 5.1, which gives a clearer understanding of the concept. From Table 5.1, a common idea about the concept of strategy and how organizations perceive and attribute it can be drawn. Most of the definitions of strategy conceptualized by different researchers share some common characteristics and help in understanding the different attributes of strategy. Figure 5.1 depicts some significant contributions to strategic typologies proposed by various authors from time to time. Our entire research framework is based on two widely cited strategic typologies, i.e., [1] Defenders (Miles et al., 1978) or Cost Leadership (Porter, 1980) which are a stable form of organizations with limited product range catering to a narrow segment of the market. They compete on cost and quality and hence also termed as cost leaders. They are efficient in the production and distribution of goods. These types of organizations highly emphasize technological efficiency. Within these organizations finance, production, and engineering functions dominate marketing and R&D functions.

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Table 5.1  Definitions of the strategy proposed by various authors Authors Barnard (1938)

Von Neumann and Morgenstern (1947) Drucker (1954)

Definition proposed Strategy is concerned with an organization’s effectiveness in defining objectives in accordance with the external environment and to effectively balance the communications between different members within the organization for the successful accomplishment of common goals. Strategy is a series of steps/actions undertaken by an organization in response to a specific situation or condition.

Strategy is defined as scanning the external environment and optimum utilization of available resources accordingly to change the situation in one’s favor. Chandler (1962) Strategy is the determinant for setting the long-term objectives of an organization, and it determines the course of action for optimum utilization of resources for the achievement of the objectives. Ansoff (1965) Strategy defines a set of rules for making decisions in synergy with market scope, product characteristics, growth, and competition. Mintzberg (1967) Strategy is reflected in all the decisions taken by an organization in all aspects through a continuous process of learning by the management. Cannon (1968) These are directional action decisions for the achievement of organizational goals. Learned et al. Strategy constitutes the entire framework (i.e., the pattern of objectives, (1969) policies, goals, and plans) for achieving the long-term objectives of an organization. Newman and Strategies are forward-looking plans that anticipate change and initiate Logan (1971) remedial action to take advantage of opportunities integrated into the concepts or mission of the firm. Schendel and Strategy is the primary goals and objectives of the organization, the Hatten (1972) significant action programs adopted to achieve these goals and objectives, and the dominant pattern of resource allocated to match the external environment. Uyterhoeven et al. Strategy provides both direction and cohesion to an enterprise. (1973) Ackoff (1974) Strategy is concerned with long-range objectives and methods to integrate these into the system as a whole. Paine and Macro-actions or patterns of actions for achieving the objectives of the Naumes (1975) company McCarthy and Analysis of the external environment based on the organization’s location Minichiello Selection of optimal alternatives that will direct the resources in synergy (1975) with the objectives of the organization Glueck (1976) Unified, comprehensive, and integrated plans designed for the fulfillment of objectives of the organization Michel (1976) Strategy is reflected in decisions for the acquisition of suitable resources to exploit opportunities and minimize risks. McNichols (1977) Strategy is reflected in policy-making, a series of decisions that reflect the primary objectives of an organization’s business, and optimal utilization of capabilities and internal resources to achieve these objectives. (continued)

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Table 5.1 (continued) Authors Definition proposed Steiner and Miner Strategy is the formulation of missions, purposes, primary organizational (1977) goals, policies, and programs, and the methods needed for proper implementation of all these. Ansoff (1979) Strategy is a set of rules for decision making under conditions of partial ignorance and concerns a firm’s relationship with its ecosystem. Mintzberg (1979) Strategy acts as a mediating force between the organization and its environment. Schendel and Strategy guides the firm to achieve its objectives and to respond to Hofer (1979) opportunities and threats in the external environment effectively. Bracker (1980) Strategy has two characteristics: Situational or environmental analysis to determine the company’s current position in the market and optimal utilization of company resources to achieve its objectives. Hambrick (1980) Strategy is reflected in the organization’s all internal and external structure and processes, determines its relationship with the external environment and affects performance. Porter (1980) Strategy reflects an organization’s choice concerning critical decision variables (i.e., price, promotion, quantity, and quality). Mintzberg and Strategy is a pattern in a chain of actions or decisions. McHugh (1985) Porter (1985) Strategy is a set of offensive or defensive actions to create a defensible position in an industry, to tackle the external competitive forces; and to achieve a higher rate of return on investment. Fahey (1989) Strategy explains the optimum utilization of resources Henderson (1989) Strategy is the focused use of imagination and logic to respond to the environment. Strategy pertains to a set of rules for decision making to guide the behavior Ansoff and of an organization concerning meeting objectives, targets, relationships with McDonnell the external environment, product strategy, business strategy, and (1990) operational policies. Andrews (1991) Strategy is the pattern of settlement for a company in the form of defining its objectives, purposes, and targets. It guides the business framework, the economic and human organization and the nature of the economic and non-economic benefits generated for shareholders, employees, and communities. Henderson (1991) Strategy is the deliberate search for an action plan to develop and adjust the competitive positioning of a company. Mintzberg and Strategy helps in securing the competitive position for a firm in a highly Quinn (1991) volatile and competitive market. Rumelt et al. Strategy defines the direction of organizations. It helps managers to draw a (1994) clear distinction about the factors governing the success or failure of organizations. Miller and Dess Strategy is a set of plans or decisions made to help organizations achieve (1996) their objectives. Porter (1996) Strategy means performing activities differently than competitors. Wright et al. Strategy is the set of guidelines to achieve results consistent with the (1997) organizational mission and objectives. (continued)

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Table 5.1 (continued) Authors Barney (2001)

Definition proposed Strategy is the theory of the firm on how to compete successfully. It also considers performance as a factor influenced by strategy, as to compete successfully means having a satisfactory performance.

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Source: Reproduced from Mainardes et al. (2014)

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Fig. 5.1  The strategic tree. (Chatterjee, 2014)

[2] Prospectors (Miles et al., 1978) or Differentiators (Porter, 1980) are the organizations, which continuously look for new products and market opportunities. They continually strive for enhancing their products, by introducing design changes with enhanced features. Accommodating changing customer requirements from time to time is the way by which they differentiate themselves from defenders. Customer satisfaction rather than cost comes first in their priority. They produce multiple variants of a product in lots of small batches, rather than going for a mass production strategy. Within these organizations, marketing and R&D functions dominate finance, production, and engineering functions.

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5.2.2 Review of Literature on Information Systems Flexibility Information systems (IS) form an essential component of organizations whether manufacturing or service. These systems function to cater to the information requirements of the organization. Proper, accurate, and timely information support from IS department equips managers to make informed decisions from the operational perspective. Organizing, assimilating, and retrieval of valuable knowledge from data are the chief priorities of an organizational IS. Based on this retrieved knowledge, the managers estimate about the current status of various functional divisions (i.e., production, inventory, procurement, workforce, sales, marketing, demand forecasting, and market conditions to name a few). Under these priorities, the role of an IS becomes quite critical for organizations. Additionally, the fast-­ changing state of affairs within the competitive environment made it necessary to associate flexibility features with IS. Many researchers associated IS flexibility as one of the antecedents to the firm’s agility and supply chain capability. Flexible IS can deliver rapid, sustainable growth and performance of organizations in an increasingly dynamic market environment (Han et al., 2017). IS flexibility can be attained by focusing on IS/IT infrastructure (Armstrong & Sambamurthy, 1999; Bhatt et al., 2010a, b; Byrd & Turner, 2000; Duncan, 1995; Fink & Neumann, 2009; Liu et al., 2013; Nelson & Ghods, 1998; Ngai et al., 2011; Ray et al., 2005; Tafti et al., 2012) and value creation through IS (Gosain et al., 2004; Bush et al., 2010; Cheng et al., 2014; Saraf et al., 2007). Successful implementation of IS flexibility also requires its proper and effective alignment with the organization’s intended strategic orientation (Chan & Reich, 2007; Peppard & Ward, 2004; Melville et al., 2004; Piccoli & Ives, 2005; Wade & Hulland, 2004). Arvidsson et al. (2014) proposed three critical dimensions for successful IS strategy implementation, i.e., the realization of strategic intent, alignment between the strategic plan and IT capabilities, and successful IT implementation (Fig. 5.2).

Fig. 5.2  Three critical dimensions for IS strategy implementation. (Arvidsson et al., 2014)

Realization of Strategic Intent

Alignment between Strategic intent and IT capabilities

Successful IT implementation

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IS is the backbone of any organizational structure and continually reflects the volatile and strategic business requirements of an organization. The strategic orientation of any organization is reflected through the way their organization IS works and its implementation and support. IS departments in organizations face great difficulty to cope up with the rapid changes in a highly complex competitive environment (Hopper, 1996; Scott, 1998; Murray, 2000). Organizational IS should be designed and organized in such a way they should support the information needs at the correct time and in the right direction without any delay or ambiguity. Organizational IS needs to be effective, efficient, and flexible, i.e., it must be capable of accommodating business and technological changes (Ramraj, 2010; Alter, 2000; Chakravarthy, 1997; Evans, 1999). IS flexibility is defined as the ability to align IS architectures and systems with the changing information needs of the organization as it responds to changing customer demand (Duclos et al., 2003). Flexible IS infrastructure and adaptable systems have been critical issue for managers (Duncan, 1995; Prahalad & Krishnan, 2002; Sambamurthy et al., 2003). Some significant contributions made by researchers in the past to identify and study the different dimensions of IS flexibility are presented in Table 5.2.

5.3 Theoretical Framework and Hypothesis Development In our research framework, thirteen dimensions of IS flexibility extensively researched in literature, are taken into consideration and related to two specific strategies, i.e., defenders or cost leaders and prospectors or differentiators in the form of related hypotheses.

5.3.1 Modularity or Distributed Systems (ISF1) This dimension of IS flexibility is defined as the degree to which a system component (i.e., hardware, software or data component of infrastructure) can be separated, recombined, and modified with ease without much penalty or efforts (in terms of time and cost) (Chanopas et al., 2006). Modularity function is specific and context based. In industrial design, it refers to techniques that build larger systems by combining smaller systems. In the manufacturing domain, modularity relates to the use of interchangeable parts or components in the fabrication of products. Even, within information systems, modularity is conceptualized in many ways, i.e., modular programming, network modularity, and modular software design, which refers to a logical partitioning of the software design that allows complex software to be manageable for implementation and maintenance. Organizations pursuing defender or cost leadership strategy will have a more centralized architecture of IS. All information about their products, processes, customers, and operations are procured and processed centrally based on which tactical decisions are chalked out. On the

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Table 5.2  Literature review of the various dimensions of IS flexibility References Duncan (1995) Turner (1999)

Identified IS flexibility dimensions Connectivity, compatibility, and modularity Connectivity, compatibility, modularity. Sub-dimensions of modularity are scalability, adaptability, portability, openness of systems, the autonomy of systems, accessibility, and interoperability. Byrd and Turner Connectivity, compatibility, modularity, human dimension of IS flexibility, (2000) i.e., IT personnel competency. Schwager et al. Connectivity, compatibility, modularity, and IT personnel (2000) Chung et al. Connectivity, compatibility, modularity, and IT personnel (2005) Chanopas et al. Connectivity, compatibility, modularity, IT personnel competency, scalability, (2006) continuity, rapidity, facility, and modernity. Ramaraj (2010) IT infrastructure (portability, connectivity, and maintainability), compatibility, connectivity, and modularity, interfacing change requests from users and IS response, technical changes, and modifications to IS/IT systems. Palanisamy Effectiveness and quality of system administrator-user interface et al. (2009) Kumar and IT infrastructure, i.e., connectivity, compatibility, modularity, software Stylianou (2014) systems flexibility, e-business architecture, robustness, scaling, responsiveness, reusability, and human IT infrastructure. Agarwal (2004) IS flexibility is a function of strategy, design, architecture (client-server/ centralized-decentralized), development, implementation open source initiative (OSI) usage, deployment, and functionality measured through interoperability, modularity, scalability, and upgradability Zhang (2005) IS support for product flexibility and IS support for cross-functional coordination. IS-based product flexibility Scherrer-Rathje System connectivity, process integration, hierarchical integration, user and Boyle customizability, and consistency (2012)

contrary, organizations with prospector or differentiator strategy will likely to have a distributed or modular IS architecture supporting their strategic orientation and needs. Therefore, we hypothesize: H1.a  Organizations with defender or cost leader strategy have IS which are low in modularity. H1.b  Organizations with prospector or differentiator strategy have highly modular or distributed IS.

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5.3.2 IS Integration (ISF2) System integration is a process of bringing together the component subsystems into one single system or an aggregation of subsystems and ensuring that the components or subsystems function together as a single entity delivering the overarching functionality. Within the IS domain, it is defined as a process of linking together different computing systems and software applications physically or functionally, to act as a coordinated whole (Gallego et al., 2015; Rauniar et al., 2013). IS integration enables organizations to identify optimal inventory levels at various stocking locations, effective warehouse management with reduced space, and increased inventory turnover (Kaeli, 1990; Kaplan, 1986; Shull, 1987; Narasimhan & Kim, 2011). Integrated supply chain through IS can lead to higher product quality, enhanced productivity, efficient machine utilization, reduced space, and increased logistics flexibility (Gross, 1984; Kaltwasser, 1990a, b). An integrated IS can help firms achieve economies of scale through long-term strategic alliance and procurement (Coleman et al., 1995; Goldhar & Lei, 1991). Organizations with either a defender or prospector strategy will require a highly integrated IS to enable access resources and valuable information across the various supply chain partners to make informed decisions. Therefore, we hypothesize: H2.a  Organizations with defender or cost leader strategy have IS which are highly integrated. H2.b  Organizations with prospector or differentiator strategy have IS which are highly integrated.

5.3.3 IS Interoperability (ISF3) Interoperability is the property that allows for the unrestricted sharing of information and resources between different systems connected through local area networks/wide area networks such that all end users interpret in similar ways. Interoperability is feasible through hardware and software components that conform to open standards. Technological interoperability means that the organizations must share the same goals and have compatible management strategies so that the different IS components can share, exchange, and interpret data in similar ways. Zayati et al. (2012) in their work showed the application of interoperable IS platforms for the realization of agile and lean production strategies. Interoperability federates the different IS components (Fig. 5.3), i.e., enterprise resource planning (ERP) systems, manufacturing execution system (MES), and machines on a workshop to support agile processes, workflows, business, and production descriptions (Ferrarini et al., 2006). Organizations with defender or cost leadership strategy work with distinct functional modules matching to their narrow and stable product requirements. These

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Fig. 5.3  Production control system organization. (Zayati et al., 2012)

functional modules conform to common open standards. Hence, they seamlessly share, exchange, and modify information as per strategic needs. On the contrary, prospectors or differentiators type of organizations work with different kinds of IT/ IS systems to match their constantly changing product requirements. These systems may not share a common executable operating platform. Hence, it may be difficult to share and interpret data seamlessly between them. Therefore, we hypothesize: H3.a  Organizations with defender or cost leader strategy have IS which are high in interoperability. H3.b  Organizations with prospector or differentiator strategy have IS which are low in interoperability.

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5.3.4 Loose Coupling (ISF4) This dimension of flexibility is defined as an architectural principle to interconnecting different functional modules or components in a network or a system in such a way that there is a least possible dependency among these components. Hence, these components can be easily disconnected from the existing system and reconsidered from a different set-up depending upon operational requirements. Coupling refers to the degree of direct knowledge that one component has of another. It is opposite to tight coupling. The different functional modules comprising a loosely coupled system are replaced with alternative arrangements that provide precisely the same functionality, less constrained to platform, language, operating system, and build environment. It mitigates risks and promotes system reusability. Beekun and Glick (2001) suggested a multi-dimensional framework of loose coupling consisting of coupling elements, coupling domains, dimensions of coupling (i.e., strength, directness, consistency, dependence), and coupling mechanisms (i.e., differentiation and integration). Organizations with a defender or cost leadership strategic orientation are characterized by IS which are tightly coupled owing to their narrow and fixed product requirements. On the other hand, prospector or differentiator type of organizations are marked by IS/IT systems which supports loose coupling to match their changing product requirements. Therefore, we hypothesize: H4.a  Organizations with defender or cost leader strategy have IS which are low in loose coupling characteristics. H4.b  Organizations with prospector or differentiator strategy have IS which are high in loose coupling characteristics.

5.3.5 Connectivity (ISF5) This dimension is defined as the ability of any component within IS to attach to any other elements inside and outside the organization environment. It gives a measure of the extent to which various elements or nodes connected and the ease with which they can converse. Connectivity is also one of the most talked dimensions of flexibility in the literature (Duncan, 1995; Turner, 1999; Byrd & Turner, 2000; Schwager et al., 2000; Chung et al., 2005; Chanopas et al., 2006). Mondragon et al. (2009) emphasized the role of IS connectivity in ICT applications for facilitating multimodal logistics and supply chain management. Connectivity ensures high levels of visibility and control across supply chain partners, i.e., suppliers, distributors, and third-party logistics providers. It enables them to share information relating to order status, product schedules, sales records, inventory positions to coordinate and plan production, logistics, and marketing promotions (Gunasekaran & Ngai, 2004; Kumar, 2001). Organizations with either a defender or prospector strategy will require a highly connected IS to access resources from different functional

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departments and valuable information across the various supply chain partners to make informed decisions. Therefore, we hypothesize: H5.a  Organizations with defender or cost leader strategy will acquire IS having high connectivity. H.5b  Organizations with prospector or differentiator strategy will acquire IS having high connectivity.

5.3.6 Compatibility (ISF6) Many researchers extensively studied this particular dimension of flexibility as evident from literature (Duncan, 1995; Turner, 1999; Byrd & Turner, 2000; Schwager et al., 2000; Chung et al., 2005; Chanopas et al., 2006). It is the ability to share any information across any technology component. Alternatively, it represents the capacity of two systems to work together without having to be altered or modified to do so. Compatible software applications use the same data formats. Within the IS domain, compatibility refers to a situation when different systems, system components, or system activities operate in harmony, i.e., can communicate effectively or exchange records with a minimum of effort. Systems are compatible when the results of processing in one system are immediately and directly usable by other organizations having similar but not necessarily identical systems. The principal reason for seeking compatibility is to facilitate cooperation or resource sharing among organizations. Compatibility is closely related to standardization and consistency. Organizations with either a defender or cost leader strategy will have IS that are highly compatible as most of the IS/IT components are fixed, acquired altogether, and operate within the same environment. On the other hand, prospectors will have more issues related to incompatibility of IS/IT systems as new components/modules are added incrementally as per strategic requirements working in different environments. Therefore, we hypothesize: H6.a  Organizations with defender or cost leader strategy have IS which are high in compatibility. H6.b  Organizations with prospector or differentiator strategy have IS which are low in compatibility.

5.3.7 Scalability (ISF7) This flexibility dimension is the capability of the system to manage the increasing workload or its potential to get enlarged to accommodate the growth within the system (Turner, 1999). A scalable system exhibits increased efficiency and output

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performance under renewed load complexity on the addition of software and hardware resources proportional to the load capacity. Chanopas et al. (2006) identified scalability as one of the dimensions of IS flexibility and defined it as a degree to which hardware/software can be scaled and upgraded on existing infrastructure without impacting the contribution margin. Organizations with a defender or cost leader strategy will be characterized by IS which are highly scalable to support increasing mass production targets of their products. On the other hand, IS scalability will be low for prospectors or differentiators as their IT systems lend support to different modules incrementally or in batches as per modifications or requirements. Therefore, we hypothesize: H7.a  Organizations with defender or cost leader strategy will acquire IS which are highly scalable to satisfy their strategic requirements by making huge investments. H7.b  Organizations with prospector or differentiator strategy have IS which are low in scalability.

5.3.8 Continuity (ISF8) IS continuity is defined as the degree to which the existing hardware and software resources, data, and IT personnel can seamlessly serve the users in an organization without any disruption. It is a holistic approach to managing technology systems in the event of a significant outage (Chanopas et al., 2006). Organizations should identify the critical IT functions, essential for business continuity. A set of policies and failover mechanisms is formulated and implemented ahead of time. Improving upon IS continuity is an organization-wide endeavor to which organization has to be committed. IS continuity harness technologies to enhance business continuity. The three major phases of IS continuity process are preparing for IS incidents, coping and mitigating the impact of IS incidents and recovering from IS incidents. Organizations with a defender or cost leader strategy will operate with a highly continuous IS under stable market conditions with no significant disruptions for a considerable duration of time. Its IS access resources and valuable information across the various supply chain partners to make informed decisions. On the other hand, prospectors or differentiators will have low IS continuity compared to defenders owing to the implementation of discontinuous innovative changes in their products. Therefore, we hypothesize: H8.a  Organizations with defender or cost leader strategy have IS which are highly continuous. H8.b  Organizations with prospector or differentiator strategy have IS which are low in continuity.

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5.3.9 Rapidity (ISF9) Rapidity is defined as the degree to which hardware/software can deliver information whenever it is needed (Chanopas et al. 2006). Rapidity demands standardization of all IT/IS components/elements throughout the organization. Some of the major IS rapidity implications include use of a digital subscriber line technology or leased line channel to create external linkages that support a high-speed link and a high data volume, implement network topology (i.e., bus, ring, star) that fits the internal usage, periodically monitor bottlenecks within the networks to find possible remedies. In manufacturing organizations, the critical decisions to be taken include strategic positioning of the product, correct timing of entry based on proper market orientation. These decisions require support from the organizational IS which should respond rapidly to capture the necessary data and make it available whenever needed. Rapidity as a distinct IS characteristic can be related to the business/market environmental conditions which can be stable or dynamic. Hence, IS rapidity is low for defenders due to environmental stability, but it should be high for prospector owing to unstable or dynamic environmental conditions. Therefore, we hypothesize: H9.a  Organizations with defender or cost leadership strategy have IS which are low in rapidity due to the presence of stable environmental conditions. H9.b  Organizations with prospector or differentiator strategy have IS which are high in rapidity owing to the presence of dynamic or competitive environmental conditions.

5.3.10 Facility (ISF10) Facility is defined as the degree to which hardware/software can be used with ease and without any complications. Its implications may be building user-friendly applications, i.e., web-based or menu driven and creation of manuals/documentation for each hardware/software components. Additional features include the use of single terminals to operate on different platforms/operating systems and ease of use of applications by non-IT professionals without intensive training (Chanopas et al., 2006). Organizations with cost leader or defender strategy have IS/IT systems of fixed or limited characteristics. The IS resources are procured keeping their specific product requirements, which function over a considerable period without any modifications to implement recent trends and enhance IS facility. On the other hand, prospectors or differentiators in IS/IT systems are more complicated owing to the handling of multiple traits matching operational and strategic requirements of products. Therefore, we hypothesize: H10.a  Organizations with defender or cost leader strategy implement single process ERP/IS which are low in facility.

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H10.b  Organizations with prospector or differentiator strategy implement multi-­ process ERP/IS which are high in facility.

5.3.11 Modernity (ISF11) Modernity is defined as the degree to which hardware/software are based on well-­ known products and technological trends (Chanopas et al., 2006). It refers to implementing hardware/software that is reputable or based on current technological trends. Organizations with either a defender or cost leader strategy will be characterized by IS/IT systems which are limited in technical capabilities suiting to the respective strategic product requirements. On the contrary, prospector-type organization’s IS/IT resources adapt to the continuously changing technological trends matching the individual strategic perspective. They continuously implement enhanced features in existing products and are supported by the latest IT tools. Therefore, we hypothesize: H11.a  Organizations with defender or cost leader strategy have IS which are low in modernity. H11.b  Organizations with prospector or differentiator strategy have IS which are high in modernity.

5.3.12 IT Personnel Competency (ISF12) Human resources form an essential component of organizational IS together with the technical IT infrastructure components as pointed out from studies in the literature. The human element includes the expertise, knowledge, and skills possessed by an organization’s IT professional. The impact of human IT capabilities on organizational flexibility and agility is studied subsequently by many past researchers (Fink & Neumann, 2009; Lee et al., 1995; Chanopas et al., 2006; Karimikia et al., 2022). The effect of human IT capabilities comprises of large functions such as strategic business-IT alignment, technology management, interpersonal management, effective communication, collaborative and cross-functional activities, and proactiveness (Panda & Rath, 2017). In an unstable environment, the human IT competency delineates the necessary knowledge, skills to face any unanticipated changes. According to Lee et al. (1995), three IS-related capabilities essential for IS personnel are learning new technologies, focusing and understanding updated technological trends, understanding policies and goals of the organization, planning for future technical challenges, and able to lead IT/IS projects by quickly teaming up. He further suggested that it is imperative that IS personnel acquire sufficient functional expertise to effectively re-engineer the old internal business processes by the adoption of

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advanced IT tools. We refer IT personnel competency as a flexibility dimension of IS/IT systems and relate it to the organization’s operational and manufacturing strategy. Organizations with a differentiator strategy will acquire a highly motivated workforce characterized by high IT competency. Therefore, we hypothesize: H12.a  Organizations with defender or cost leader strategy have low IT/IS personnel competency. H12.b  Organizations with prospector or differentiator strategy have high IT/IS personnel competency.

5.3.13 Reconfigurability (ISF13) Reconfigurability as a dimension of flexibility is defined as the ability to reconfigure the computing capability of a system so that its behavior can be changed to match the current business requirements. Reconfigurability is the ability to add, remove, and rearrange in a timely and cost-effective manner the components and functions of the system which can result in the desired set of alternate configurations (Farid & McFarlane, 2007; Farid, 2017). Conventional manufacturing information and planning systems were rigid and centralized. The design and development of reconfigurable IS for manufacturing planning and control is becoming increasingly essential to support the required degree of manufacturing flexibility demanded by changing business environments. This change in manufacturing planning systems over the years is shown in Fig. 5.4. Reconfigurable systems can repeatedly achieve distinct configurations and different functional capabilities. The configuration changes allow the system to carry out different roles, evolve in capacity, and survive environmental disturbances or internal failures. We refer Reconfigurability as a flexibility dimension of IS/IT systems and relate it to the organization’s operational and manufacturing strategy. Organizations with either a defender strategy will have IS which have certain fixed or limited specifications matching the strategic requirements of production. On the other hand, prospector-type organizations will have IS which is reconfigured according to the changing needs and strategic manufacturing requirements. Therefore, we hypothesize: H13.a  Organizations with defender or cost leader strategy have IS which are low in Reconfigurability. H13.b  Organizations with prospector or differentiator strategy have IS which are high in Reconfigurability.

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Fig. 5.4  Evolutionary developments of different planning systems. (Beach et al., 1998)

5.4 Empirical Investigation of the Developed Theoretical Framework Structured questionnaires are used as an instrument for surveying, to capture data for empirical investigation of the developed theoretical research framework in form of the hypotheses (H1-H13). Fowler (1993) argued that the survey method is appropriate for testing hypotheses that are derived from existing theory. Most of the items representing the various constructs in the questionnaire are directly adopted from literature (Table 5.3) and accordingly modified in consultation with the experienced professional executives after the pre-testing exercise.

5.4.1 Process of Data Collection Convenience sampling which is a non-probability sampling technique is utilized for the study (Bhattacherjee, 2012). Data collection is through the questionnaire designed for the study, administered through two different mediums, i.e., (i) Offline and (ii) Online through a mail survey. Most of the respondents agreed to respond to the questionnaires on conditions of anonymity for themselves and their respective organizations. After removing all the incomplete responses, a total of 116 responses are collected.

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Table 5.3  Research constructs and their measures Questionnaire research constructs Organizational strategy IS flexibility dimensions

References Miles et al. (1978), Chatterjee (2009), Saha (2017), Vishvakarma (2016) Chanopas et al. (2006)

5.4.2 Psychometric Measurement of Scale Questionnaire items used for the measurement of various constructs are tested for reliability through the Cronbach’s alpha value which should be higher than 0.7 (Nunnally, 1978; Hair et  al., 2006). Validity of the items in the questionnaire is assessed through exploratory factor analysis. The various output measures of factor analysis, i.e., Kaiser–Meyer–Olkin (KMO > 0.5), measure of sampling adequacy, Bartlett’s test of sphericity (significant value p 0.40 and cross-loading 1.0, and total variance explained, were found to be significant as shown in Table 5.4.

5.4.3 Data Analysis and Results The data collected through the questionnaire after being examined and prepared is divided into two clusters or groups based on their identified strategy using the k-means clustering algorithm. For the empirical investigation of the developed research framework, independent-samples t-test is conducted on the two clusters to assess the level of the 13 different dimensions of IS flexibility (i.e., dependent variables) across the two independent strategic groups.

5.4.4 Cluster Analysis Using k-Means Clustering Algorithm K-means clustering is a type of supervised learning technique, used to arrange and group unlabeled data. The aim is this method is to identify groups in the data, with the number of groups represented by variable K. The detailed algorithm is described in Chap. 5. The results of k-means clustering are presented below. Table 5.5 shows the final cluster centers obtained for clusters 1 and 2. The final means of all or most of the items related to organizational strategy are higher for cluster 2 than cluster 1. Therefore, cluster 1 corresponds to defenders or cost leaders whereas cluster 2 corresponds to prospectors or differentiators. Table 5.6 depicts the final distance between the two clusters formed. The ANOVA results for cluster formation are given in Table 5.7. From the results, it can be concluded that the strategy parameter OS12 with F value of 172.513

5  Mapping Information Systems Flexibility with Organization’s Manufacturing Strategy Table 5.4  Reliability and validity results of the research constructs Construct Strategy parameters IS flexibility dimensions Modularity or distributed systems IS Integration IS Interoperability Loose coupling IS Connectivity IS Compatibility IS Scalability IS Continuity IS Rapidity IS Facility Modernity IT personnel competency Reconfigurability

Cronbach’s alpha 0.811

KMO statistics 0.533

Eigenvalues –

Percentage of variance –

0.762

0.657

2.527, 1.010

50.541, 20.210

0.983 0.756 0.818 0.903 0.881 0.864 0.853 0.863 0.806 0.776 0.907 0.998

0.733 0.538 0.638 0.684 0.756 0.683 0.609 0.650 0.749 0.500 0.776 0.500

3.803 2.503 2.201 3.629 4.000, 1.389 4.097, 1.119 2.826 3.349 2.562 1.405 3.649 1.996

95.076 62.567 73.365 72.590 66.664, 23.147 58.529, 15.990 70.659 66.977 64.044 70.240 72.976 94.822

Table 5.5  Final cluster centers Strategy parameters OS1 OS2 OS3 OS4 OS5 OS6 OS7 OS8 OS9 OS10 OS11 OS12 OS13 OS14 OS15 OS16

Cluster 1 3.80 1.68 3.46 3.73 3.76 3.66 3.76 3.12 2.73 3.63 2.37 1.98 3.27 2.80 2.61 3.56

Cluster 2 3.71 3.49 3.57 4.08 4.32 4.05 3.96 3.92 3.75 3.95 3.93 3.91 3.95 3.65 3.75 4.13

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Table 5.6  Distance between final cluster centers Cluster 1 2

1

2 3.832

3.832

Table 5.7  ANOVA results for cluster formation Strategy parameters OS1 OS2 OS3 OS4 OS5 OS6 OS7 OS8 OS9 OS10 OS11 OS12 OS13 OS14 OS15 OS16

Cluster Mean square 0.256 86.884 0.320 3.216 8.429 4.132 1.102 16.883 27.308 2.589 65.131 98.850 12.199 19.083 34.264 8.684

df 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Error Mean square 1.053 0.698 0.601 0.523 0.367 0.658 0.828 1.069 1.002 0.959 0.931 0.573 0.683 0.556 0.456 0.726

df 114 114 114 114 114 114 114 114 114 114 114 114 114 114 114 114

F value 0.243 124.393 0.533 6.154 22.945 6.280 1.330 15.787 27.251 2.700 69.929 172.513 17.867 34.299 75.200 11.961

Significance 0.623 0.000 0.467 0.015 0.000 0.014 0.251 0.000 0.000 0.103 0.000 0.000 0.000 0.000 0.000 0.001

contributed the most in the cluster formation followed by OS2 with F value of 124.393. In a total of 116 firms, 41 firms belong to cluster 1 and are classified as defenders whereas 75 firms belong to cluster 2 and are classified as prospectors.

5.4.5 Independent Samples T-Tests The independent samples t-test compares the means between two unrelated groups on the same continuous, dependent variable. The test is used to test the null hypothesis that the means of two populations are the same (H0: μ1 = μ2) or different, i.e., alternate hypothesis (HA: μ1 ≠ μ2) when a sample of observations representative of each population is available (Landau & Everitt, 2004). The two independent samples represent the two strategic groups or clusters, i.e., defenders or cost leaders and prospectors or differentiators. The dependent variable in our study constitutes the 13 dimensions of IS flexibility, considered separately for each of the two samples.

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5.4.6 Assumptions of T-Tests The first assumption of t-test, i.e., homogeneity of variances is established through Levene’s test; and the second assumption of Normality is assessed through Shapiro-­ Wilk test and Kolmogorov-Smirnov test. The results of Levene’s test, Shapiro-Wilk test, and Kolmogorov-Smirnov test are shown in Tables 5.8 and 5.9. It is evident from the results of Shapiro-Wilk and Kolmogorov-Smirnov Tests that most of the data collected for the measurement of the IS flexibility dimensions (dependent variables) do not strictly follow a normal distribution. In argument for non-agreement with the normality assumption situation, suggestion by Lucake (1996) and Chatterjee (2009) is that a large amount of research has investigated to what extent violations of normality affect the conclusions of t-tests, and the consensus is that violation of normality has little effect on type-I error rate. Landau and Everitt (2004) also establish their view that independent samples t-tests can be conveniently applied to ignore that normality and homogeneity must be strictly valid. The third assumption is regarding the independence of clusters or groups. The obtained clusters for the study are independent of each other as any member of the defender is not related to the prospector group.

5.4.7 Results of the T-Tests After satisfying and validating all the assumptions of independent samples t-test, we proceed with the t-tests. The null hypothesis for this test assumes that there is no significant difference between the mean values of the IS flexibility dimensions (i.e., Table 5.8  Results of Levene’s test for equality of variances Variable ISF1 ISF2 ISF3 ISF4 ISF5 ISF6 ISF7 ISF8 ISF9 ISF10 ISF11 ISF12 ISF13

Construct Modularity or distributed systems IS integration Interoperability Loose coupling Connectivity Compatibility Scalability Continuity Rapidity Facility Modernity IT personnel competency Reconfigurability

F value 0.456 0.081 4.446 5.029 5.611 1.727 0.698 0.056 1.440 10.086 1.046 0.245 3.819

EV Equal variance assumed, UEV Unequal variance assumed * Significant at 0.05 confidence level

Sig (p-value) 0.501* 0.777* 0.037 0.027 0.020 0.191* 0.405* 0.814* 0.233* 0.002 0.309* 0.622* 0.053*

Variance EV EV UEV UEV UEV EV EV EV EV UEV EV EV EV

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Table 5.9  Results of Kolmogorov-Smirnov and Shapiro-Wilk tests

Variable IS flexibility dimension ISF1 Modularity or distributed systems 0ISF2

IS integration

ISF3

Interoperability

ISF4

Loose coupling

ISF5

IS connectivity

ISF6

Compatibility

ISF7

Scalability

ISF8

Continuity

ISF9

Rapidity

ISF10

Facility

ISF11

Modernity

ISF12

IT personnel competency

ISF13

Reconfigurability

Clusters Prospectors Defenders Prospectors Defenders Prospectors Defenders Prospectors Defenders Prospectors Defenders Prospectors Defenders Prospectors Defenders Prospectors Defenders Prospectors Defenders Prospectors Defenders Prospectors Defenders Prospectors Defenders Prospectors Defenders

Kolmogorov-­ Smirnov Statistic df 0.146 75 0.200 41 0.302 75 0.257 41 0.173 75 0.237 41 0.231 75 0.201 41 0.164 75 0.148 41 0.125 75 0.166 41 0.139 75 0.138 41 0.187 75 0.256 41 0.132 75 0.154 41 0.091 75 0.135 41 0.181 75 0.243 41 0.142 75 0.153 41 0.225 75 0.226 41

Sig 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.024 0.005 0.006 0.001 0.046 0.000 0.000 0.002 0.015 0.200 0.059 0.000 0.000 0.001 0.017 0.000 0.000

Shapiro-Wilk Statistic df Sig 0.947 75 0.003 0.886 41 0.001 0.830 75 0.000 0.787 41 0.000 0.880 75 0.000 0.894 41 0.001 0.894 75 0.000 0.867 41 0.000 0.164 75 0.000 0.942 41 0.036 0.931 75 0.001 0.915 41 0.005 0.937 75 0.001 0.913 41 0.004 0.931 75 0.001 0.832 41 0.000 0.947 75 0.004 0.929 41 0.013 0.969 75 0.064 0.917 41 0.006 0.899 75 0.000 0.906 41 0.003 0.930 75 0.000 0.877 41 0.000 0.878 75 0.000 0.878 41 0.000

dependent variables) considered separately for each of the two unrelated strategic groups, i.e., defenders and prospectors. Alternatively, we can reject the null hypothesis or accept the alternate hypothesis, if there exist significant differences between the mean values of the IS flexibility dimensions, considered separately for two independent strategic groups. Table 5.10 shows the results of the group statistics, indicating mean, standard deviation, and standard error mean of all the 13 IS flexibility dimensions examined among two independent strategic groups. Table 5.11 shows the results of t-tests for equality of means, showing the t-static, degrees of freedom (df), significance, mean difference, and standard error difference.

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Table 5.10  Group statistics Variable IS flexibility dimension ISF1 Modularity or distributed systems ISF2

IS integration

ISF3

Interoperability

ISF4

Loose coupling

ISF5

Connectivity

ISF6

Compatibility

ISF7

Scalability

ISF8

Continuity

ISF9

Rapidity

ISF10

Facility

ISF11

Modernity

ISF12

IT personnel competency

ISF13

Reconfigurability

Cluster Defender Prospector Defender Prospector Defender Prospector Defender Prospector Defender Prospector Defender Prospector Defender Prospector Defender Prospector Defender Prospector Defender Prospector Defender Prospector Defender Prospector Defender Prospector

N 41 75 41 75 41 75 41 75 41 75 41 75 41 75 41 75 41 75 41 75 41 75 41 75 41 75

Mean 3.4732 3.5920 2.0488 3.8667 3.7133 3.1463 2.3180 3.5597 2.8556 3.5395 3.5853 2.8859 3.6793 2.6054 2.6341 3.5533 3.3902 3.4880 3.1707 3.7033 2.8902 3.8200 2.8683 3.8213 3.2195 3.7000

Std. deviation 0.66597 0.63963 0.81819 0.76927 0.91597 0.47426 0.49921 0.95040 0.52642 0.94369 0.82317 0.59626 0.56063 0.53504 0.63022 0.63445 0.91537 0.73023 0.91931 0.64291 0.72035 0.59639 0.82778 0.77080 1.29445 0.94082

Std. error mean 0.10401 0.07386 0.12778 0.08883 0.10577 0.07407 0.07796 0.10974 0.08221 0.10897 0.09505 0.09505 0.06474 0.08356 0.09842 0.07326 0.14296 0.08432 0.14357 0.07424 0.11250 0.06886 0.12928 0.08900 0.20216 0.10864

5.4.8 Discussion of Results of T-Tests The results of the t-tests reveal that all the hypotheses are supported. Hypotheses sets H1, H3, H4, H6, H7, H9, H10, H11, H12, and H13 find support through rejection of the respective null hypothesis. The mean values corresponding to these IS flexibility dimensions are higher for prospectors than for defenders, i.e., (μprospectors > μdefenders). The second set of hypotheses, i.e., H2, H5, and H8, also finds support through acceptance of the null hypothesis. It indicates that there is no significant difference in mean values of the corresponding IS flexibility dimensions between the two strategic groups. The observed difference between the group mean values is by chance. From the above discussion of the results of t-tests, it can be inferred that our research framework stands true as all the hypotheses are supported. This again strongly puts forward our proposition that IS flexibility is an essential component of an organization’s strategic orientation.

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Table 5.11  Results of independent samples t-tests t-test for Equality of means

Variable ISF1 ISF2 ISF3 ISF4 ISF5 ISF6 ISF7 ISF8 ISF9 ISF10 ISF11 ISF12 ISF13

t −0.943 −11.896 −4.391 −9.224 −5.010 −4.793 −10.021 −7.477 −0.629 −3.295 −7.449 −6.201 −2.294

df 114 114 113.762 113.880 113.930 114 114 114 114 61.858 114 114 114

Sig 0.000 0.059 0.000 0.000 0.060 0.000 0.000 0.057 0.000 0.000 0.000 0.000 0.024

95% Confidence interval of the Mean Std. error difference difference difference Lower Upper −0.11883 0.12605 −0.36853 0.13088 −1.81789 0.15281 −2.12061 −1.51516 −0.56699 0.12912 −0.82279 −0.31119 −1.24168 0.13462 −1.50836 −0.97501 −0.68386 0.13650 −0.95427 −0.41345 −0.69948 0.14594 −0.98859 −0.41037 −1.07397 0.10717 −1.28627 −0.86166 −0.91919 0.12294 −1.16273 −0.67565 −0.09776 0.15540 −0.40560 0.21008 −0.53260 0.16163 −0.85571 −0.20950 −0.92976 0.12481 −1.17701 −0.68251 −0.95304 0.15368 −1.25749 −0.64860 −0.48049 0.20941 −0.89533 −0.06564

Null hypothesis Rejected Accepted Rejected Rejected Accepted Rejected Rejected Accepted Rejected Rejected Rejected Rejected Rejected

5.5 Managerial Implications and Conclusion The study concentrates on the IS flexibility requirements, specific to two organizational strategies, i.e., defenders and prospectors. From the organization’s competitive perspective, IS flexibility is a critical feature to fulfill the information-related requirements of organizations. It will aid organizations in making informed decisions aligned with their strategic orientation. Huge capital investment is associated with the procurement of IS facilities. Improper selection of IS will not serve the purpose. Study III can guide the managers about the specific IS flexibility characteristics to be looked for and procured matching their strategic orientation. From the results of the study, it can be well concluded that our research proposition finds adequate support. The comparison of group means, i.e., independent samples t-tests results for the different IS flexibility dimensions revealed that group representing defender’s strategy is high in IS integration, interoperability, connectivity, compatibility, scalability, and continuity. On the other side, this group representing defender strategy is low in IS modularity, loose coupling, rapidity, facility, modernity, IT personnel competency, and Reconfigurability. Exactly, the opposite results are obtained for the group representing prospector strategy. It gives strong support to our research framework of study. Proper, accurate, timely, and transparent information is an essential requirement for manufacturing organizations which will assist them in making informed decisions as per their operational and strategic needs. Information Systems or IS collect, organize, store, and retrieve the necessary information for their parent organizations. A considerable amount of capital is invested by firms to acquire and maintain

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the IS.  Flexibility in IS becomes a more crucial issue owing to the dynamic and consistently fluctuating environmental conditions. It becomes essential for organizations to match or align their IS with their operating strategy. The study considers 13 dimensions of IS flexibility drawn from the existing source of IS literature. These dimensions are related to the idea of organizational strategy, realized through two popular and significant philosophies, i.e., mass production or cost leadership strategy and product differentiation strategy. All the hypotheses related to the study stand supported. The theoretical implication of this study is that it bridges the gap between operating organizational strategy and its IS flexibility requirements.

5.6 Future Scope of the Study The study takes a macroscopic view on the IS flexibility construct and establishes its relationship with organizational strategy. In our research framework, IS flexibility is composed of 13 dimensions namely IS integration, interoperability, connectivity, compatibility, scalability, continuity, modularity, loose coupling, rapidity, facility, modernity, IT personnel competency, and Reconfigurability. Future scope of the study can be undertaken to study each of the above dimensions individually in greater depth to understand its effective implementation within the organizational IS to support the strategic requirements. Simultaneously, other dimensions apart from these 13, can also be investigated to understand and comprehend IS flexibility in a more comprehensive manner.

References Abdolvand, N., & Sepehri, M. M. (2016). Antecedents of strategic information systems alignment in Iran. Journal of Global Information Technology Management, 19(2), 80–103. Abrantes, R., & Figueiredo, J. (2021). Information systems and change in project based organizations. Procedia Computer Science, 181, 367–376. Ackoff, R. (1974). Redesigning the future. Wiley. Agarwal, A. (2004). Flexibility in information technology systems and organizational competitiveness. Global Journal of Flexible Systems Management, 5(1), 35–38. Alter, A. E. (2000, May). IT leaders require strategy, flexibility. Computerworld, 8, 36. Andrews, K. (1991). The concept of corporate strategy. Ansoff, H. (1965). Corporate strategy: An analytic approach to business policy for growth and expansion. McGraw-Hill. Ansoff, H. (1979). Strategic management. Macmillan. Ansoff, H., & McDonnell, E. (1990). Implanting strategic management. Prentice Hall. Armstrong, C. P., & Sambamurthy, V. (1999). Information technology assimilation in firms: The influence of senior leadership and IT infrastructures. Information Systems Research, 10(4), 304–327. Arvidsson, V., Holmstrom, J., & Lyytinen, K. (2014). Information systems use as strategy practice: A multi-dimensional view of strategic information system implementation and use. Journal of Strategic Information Systems, 23, 45–61.

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Chapter 6

Role of Human Resource Management Practices and HR Analytics in Start-Ups Malabika Sahoo

6.1 Introduction In the world of technological era, start-ups play a significant role in enhancing economic growth and employment opportunities. Start-ups are small ventures created for the purpose of creating new products and services. These ventures usually create high-tech innovative products without prior experience (Kulkarni et  al., 2020). Although start-ups are trivial organizations, they are a great source of creating jobs, innovation, creativity, and competition. They substantially contribute to economic growth, economic dynamism, and value creation for humanity. Along with immense contributions to economic growth, start-ups generate money and mutually benefit multiple stakeholders. New technology-based firms (NTBFs) are rising and are an imperative source of development and creation of  new jobs (Colombo & Grilli, 2010). Several factors contribute to the rise in start-ups, such as technological advancement, easier access to funding, and growth in the global market. Additionally, outbreak of Covid-19 pandemic has accelerated the digital transformation of varied industries, leading to an increasing demand for tech start-ups. Exemplary start-ups even turned to be giant multinational corporations such as Facebook which started in 2004 with small coding projects and within a span of 12 years’ worth more than $350 billion (Carlson, 2010; La Monica, 2016). Some more examples could be Apple, Google, Amazon, etc. Every start-up has its inimitable model of development, which changes over time. Nevertheless, in general, the growth of such organizations is reinforced by factors such as opportunities in the market, organizational culture, capacity to raise funds, innovative and creative ideas, appropriate business models, effective human resources, etc. The M. Sahoo (*) KIIT School of Management, KIIT Deemed to be University, Bhubaneswar, Odisha, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Patnaik et al. (eds.), Global Trends in Technology Startup Project Development and Management, Innovation, Technology, and Knowledge Management, https://doi.org/10.1007/978-3-031-40324-8_6

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entrepreneurship literature highlights the promotion of start-ups (Hanandeh et al., 2021). The contemporary tech-era characterized by multiple technological and cognitive variables, has implications for different aspects of human life in organizations (Hanandeh et al., 2017). Human resource management (HRM) is critically relevant for organizations irrespective of its type and nature. The competitive business era demands organizations to focus on enhancing effectiveness and strategic decision-making. An effective HRM system acts as backbone of the organizations and is applicable in start-ups too. Mostly, organizations aim to retain and develop talented people (Ahsan et al., 2013). Human resources (HR) are critical assets for a organizational development and success. Human resources and human capital are treasured in well-established organizations and are equally important for start-ups (Hornsby & Kuratko, 1990). Organization’s human capital determines the quality of service and product it offers (Nahapiet & Ghoshal, 1998). Human resource management system primarily focuses on manpower planning, recruitment, training and development, performance management, job analysis, compensation of the employees, succession planning, and industrial relations. Though start-ups contribute significantly to economic  growth and are a great source of job creations (Baumol & Strom, 2007) yet they experience huge problems in terms of finance and human resources. Start-ups can utilize effective human resource management strategies for survival and growth. Alignment of HRM policies and practices significantly contributes to the growth stage of start-ups for adjusting internal complexity. An early implementation of suitable HRM practices in start-ups can perform as a catalyst for creativity and innovation. Human resource management includes all activities associated with the employee-employer relationship and not deals with only traditional HRM practices such as recruitment, induction, development, and compensation but also includes motivation, ability, and opportunity in job design (Boxall & Purcell, 2003).

6.2 HRM Practices in Start-Ups Start-ups must implement varied HRM practices such as staffing, manpower planning, training and development, compensation management, job design, flexible work assignments, etc. Some researchers even suggest using a high-performance work system (HPWS) (bundle of HR practices, namely selection, training, merit-­ based performance appraisal and promotion, shared ownership rewards, and flexible approach of work) in the early phases of start-ups. The early stage is crucial as there is an increasing possibility of expansion (Bendickson et al., 2017; Messersmith & Wales, 2013). Once any start-up reaches high sales growth, it is essential to formalize HRM policies and practices (Rutherford et al., 2003). Commitment-oriented and collaboration-oriented HRM systems enhance creative and innovative capacity of start-ups and SMEs (Curado, 2018). Below are a few significant HR practices essential for sustainable growth of start-ups.

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6.2.1 Recruitment and Selection Recruitment and selection are one of the important HR practices for start-ups. Recruitment helps search for prospective employees and attract them to apply for jobs. The right man for the right job at the right time can produce effective results and help in organizational development. Start-ups need to have an effective recruitment policy. Appropriate staffing can assist start-ups to achieve competitive advantages. Without an appropriate recruitment policy, it is difficult for start-ups to attract prospective employees. Start-ups need to define each job, characteristics of the idle candidates, devise proper ways to attract candidates, and must follow proper selection procedures to get the right fit for the right job.

6.2.2 Job Analysis Though start-ups ignore this practice at the beginning, it is necessary to have a proper job analysis. Start-ups must devise job specifications and job descriptions for each post so that work can be done effectively by the employees without any role ambiguity. Job specification indicates the essential requirements for the job, and job description specifies the duties and responsibilities to be performed by the employees. Job descriptions usually include job identification, job summary, duties performed, supervision required, machines, tools and materials involved, working conditions. Hence, employees will get a fair idea about the jobs and what is expected from them.

6.2.3 Training and Development Training and development plays a significant role for the employees to acquire and enhance knowledge, skills, and abilities/attitudes (KSAs) (Sahoo et  al., 2018). Every organization must have a proper training and development policy for their employees. As start-ups are new organizations, technical and soft skills training are essential for growth of the employees and organizations. The significance of soft-­ skill training is on the rise (Sahoo & Mishra, 2022). Well-designed training properly conducted can enhance both individual and organizational performance. Varied training outcomes may include technical skill enhancement, life skills development, work-life balance, boosting creativity and innovation, better employee performance, organizational productivity, and many more. Cross-training helps in empowering employees, as well as internal dependency reduction. Pushing for more formal and planned training is found to be a top priority for the HR managers (Jebali & Meschitti, 2020). The training cycle includes training need assessment, design, delivery, and evaluation. Training progrrammes should be designed after proper

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need assessment as every training involves cost. Organizations must be careful about each phase of training cycle for ensuring effectiveness.

6.2.4 Compensation Management Compensation management is vital for any start-up. Following the job evaluation process, the relative worth of the job is decided. Fixing the compensation package is dependent on the relative worth of the job. A well-defined compensation structure provides opportunities for organizations to attract and retain employees. Specifically, compensation initiatives may occur through profit sharing, employee ownership, a comparatively high pay level, performance pay, contingent pay, and team-based pay (Evans & Davis, 2005). Start-ups may be limited in cash but can take part in better compensating individuals through equity options. Apart from salary, the start-ups can provide benefits, allowances, and incentives to motivate and retain their employees.

6.2.5 Performance Appraisal In the era of competition, for the survival and escalation of business, it is crucial to measure the performance of the employees. Performance appraisal is the formal evaluation of job performance in the organizations. Performance appraisal can be conducted in many ways. Start-ups must devise a performance appraisal system for their employees, which will help in job confirmation, promotion, training, deciding pay and rewards, and motivating the employees. Out of different performance appraisal methods such as management by objectives, 360-degree performance appraisal system, balance score card method, forced distribution the start-ups can select and implement any suitable method for their organizations. However, they need to be careful in performance appraisal to avoid different types of appraisal errors.

6.2.6 Employee Engagement Programs and Motivational Schemes Human resources play a significant role in success of  any business. Employee engagement is essential for achieving excellence. The rate of retaining employees depends on the organization’s ability to engage and manage employees (Kennedy & Daim, 2010). Start-ups should manage human resources with employee engagement programmes as it consider the physical, emotional, and cognitive involvement of the employees (Kulkarni et  al., 2020). Employee engagement comprises two

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dimensions: job engagement and organizational engagement (Saks, 2006). Exemplary employee engagement includes learning and development, compensation management, and shaping organizational brand value (Bicho et  al., 2018). Training initiatives for start-ups should concentrate on enhancing skills related to flexibility, communication, creativity, innovation. Employee engagement initiatives must aim to create a high-performance work system and effective management of employees in start-ups (Bendickson et al., 2017). It is vital for start-ups to implement varied motivational schemes to boost  employees’ morale, confidence, and motivation.

6.3 HR Analytics in Start-Ups In today’s increasingly interconnected and interdependent world, organizations face  a business climate that is vulnerable, uncertain, complex, and ambiguous (George & Kamalanabhan, 2016). Starting a business and seeing it through to success in these days and age is no easy feat. Therefore, the objectives of any business are to make sound decisions and maintain a competitive edge. Start-ups, being new and dynamic set-ups, face ups and downs frequently. Usually, the organizational structure has less hierarchy and less formal work culture. In start-ups, employees have multiple roles, and multi-tasking is inevitable. Usually, at the beginning employees are involved in core activities having no clear-cut roles and responsibilities of the specific domain areas. For instance, it could happen that the founder might be looking at financial and day-to-day activities and a marketing department head manages human resource functions and likewise. Hence in such challenging situations, technology and human resource analytics (HRA) can act as catalysts for start-ups (Verma & Dutta, 2021). Organizations are interested in data-driven decision making, and start-ups are no exception. HR analytics, artificial intelligence, and machine learning are buzzwords nowadays. Moreover, adoption of HR technology inclusive of human resource information systems, cloud platforms, and uses of different software enabled the HR departments to collect, analyze, and manage large volumes of employee data (Bondarouk & Brewster, 2016; Kim et al., 2021). HRA is the segment in the domain of business analytics which involves the  application of analytics processes, data-­ driven decision-making, and use of artificial intelligence in different functions of HR department. In the era of increased competition, globalization and uncertainty, use of HRA and artificial intelligence (AI) in start-ups is crucial to achieve cost-effectiveness, speed in fulfilling the proper resource demand, and preparation for future HR difficulties (Dearborn & Swanson, 2017). HRA is a method that combines quantitative and qualitative data on employees and analyses it using statistical tools and techniques to draw out significant insights that can be used to improve future decision-making. AI describes the programming of robots to act and think in ways analogous  to humans. HRA and AI have a lot to offer young companies. In order to guarantee the

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company’s indefinite survival, a start-up in its formative years must implement careful planning, diligent staffing, and a fantastic operations system. Start-ups that want to grow must prioritize the scalability of their processes and the retention of their best employees. Implementing HRA mitigate risks and proactively improve work  morale and cooperation among teams appears to be the best course of action. AI can fuel effective recruitment. Applications of analytics and AI have detected redundancy, speeded talent  search, promoted employee engagement, and reduced employee churn in a world where digital communication, digitization of work, and the rapid use of computer power have all increased. With the help of AI, a start-up can become one of the firms of great renown with healthy employee policies by providing tailored employee experience across the whole employee lifecycle, from recruitment and onboarding to HR service delivery and career pathing. Implementing HRA in a start-up’s setting can aid in making more informed choices about employee retention, performance management, turnover, talent, risk, and forecasting. With the advent of HRA, start-ups will be able to boost their efficiency. There are different HR metrics to measure the effectiveness of different HR functions, such as cost per hire, time to fill vacancies, yield ratio, absence metrics, attrition rate, training cost factor, training headcount investment, return on training investment. Organizations across the globe have realized the significance of HRA, internet-of-things, big data, artificial intelligence and how all these enable the automation of different HR processes for better performance (Sivathanu & Pillai, 2018). Measuring effectiveness and efficiency is essential for start-ups to have a competitive advantage. Quoting famous management thinker Peter Ducker said, “What gets measured gets managed and what gets managed gets done.” Though it is difficult to measure all intangible benefits of all the HR functions, start-ups can use different metrics to measure some functions bearing relevance. The following section highlights the uses of HRA in different HR functions.

6.3.1 Analytics in Recruitment and Selection This function ideally deals with identifying the number of positions to be filled and closing the positions as per a timeline. The subfunctions under this vertical are job analysis, human resource planning, job posting, application screening, process of selection, and induction process. Analytics can be used in job analysis for employee profiling which is essential for start-ups. Employee profiling is the collection of information through online sources such as social networking platforms or generic search engines to assess prospective employees and monitor existing employees with reference to their job fitment. Start-ups should  employ  analytics for recruitment and selection processes  as well. Usually, recruitment involves application generation, maintaining a data bank, and influencing job choice. The selection procedure  follows an effective  recruitment process. It is important to ensure the reliability and validity of the selection

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process. Metrics like sourcing channel effectiveness, sourcing channel cost, offer acceptance ratio, average retention time, and cost per hire satisfaction can be used for measuring recruitment and selection process effectiveness.

6.3.2 Use of Artificial Intelligence in Recruitment Getting the right man for the right job is always a challenge for the HR department, and the challenge is complex for start-ups. Looking at the feasibility, start-ups can use software tools for shortlisting candidates and in all other stages till the final selection of the candidates. Chat-bots can also be used to have better engagement of the candidates. Chat-bots help answer general queries of the candidates and keep them engaged. Application tracking softwares are also used for the recruitment process.

6.3.3 Analytics in Training and Development As discussed earlier in this chapter, training and development is crucial for start-­ ups. However, measuring return on investment (ROI) is equally important for training effectiveness. Following the classic Kirkpatrick training evaluation model, start-ups can evaluate training effectiveness. There are four levels of evaluation in the Kirkpatrick model (Kirkpatrick & Kirkpatrick, 2016), namely reaction, learning, impact, and result. Analytics can be used at each stage to measure training success. Some of the significant metrics are training ROI, training man days, position-wise employees trained, percentage of employees with no training need, participation satisfaction, money spent in training.

6.3.4 Analytics in Performance Appraisal Referring to the significance of performance appraisal earlier in this chapter, start-­ ups can utilize different metrics in performance appraisal. Metrics like monitoring percentage promotion department-wise, lateral job rotation rate, average promotion rate, the  participation rate in career coaching programs, revenue per full-time employee, net income for full-time employee. HRA can help start-ups to evaluate performance of employees, identify areas of improvement, and curate more effective performance management process.

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6.3.5 Analytics in Employee Engagement Employee engagement is one of the critical domains for start-ups to use analytics. Start-ups can practice different engagement metrics to measure engagement scores, such as satisfaction index, commitment index, absenteeism rate for employees, turnover intention, and number of suggestions per employee.

6.3.6 Analytics in Compensation Management The metrics helpful in using analytics in compensation are average salary position-­ wise, salary benchmarking, with-in-time settlement, negotiating counter offers, and investigating pay equity. Start-ups can use these metrics for performing analytics.

6.3.7 Analytics in Compliance HRA can help start-ups to ensure compliance with different labor codes such as equal pay law, maternity law, paternity law, provident fund act,  and  anti-­ discrimination laws.

6.4 Conclusion The chapter discussed the significance of different HR functions in start-ups and how they can use HRA for growth and sustenance. A well-defined HR policy at the beginning can help organizations to attract manage and retain talent. The success of start-ups depends on effective management of people. Analytics can be used for data-driven decision-making. HRA helps organizations manage essential data and make data-driven decisions using varied statistical models and methodologies. However, human resource personnel may lack expertise to use HRA tools  effectively, or there may be problems with data quality or data governance that make it difficult to implement HRA at workplace. As a result, the leadership may not support the group’s effort to resolve problems. In spite of these obstacles, HRA and AI are useful in helping firms acquire a competitive edge, resolve HR-related issues, boost organizational performance, and enhance HR’s core role. It is essential for the start-ups to adopt appropriate HRM practices and HR policies that act as a catalyst for growth and innovation.

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Chapter 7

Corporate Entrepreneurship and Its Effect on Business Performance: Evidence from Digikala Omid Tajeddini and Javad Tajdini

7.1 Introduction The World Health Organization (WHO) has announced COVID-19 (Gohil et  al., 2021) in late 2019 stunned the world due to the gravity of the implications and circumstances it presented. By March 2020, the novel virus had developed into a worldwide pandemic (WHO, 2020). As such, the spread of COVID-19 has resulted in completely new market circumstances that have been marked by a significant decline in demand for many services and products (Kuckertz et al., 2020). The pandemic, particularly the post-COVID-19 period, has been one of significant economic disruptions, some of which have been the most profound in recent memory as it has changed the danger of replacement in many businesses. Countless non-­ essential physical stores have been compelled to close until further notice, despite the economic benefits created by the scale of their skills and powers into the broader service economy. Additionally, reduced/rationed product availability as well as social distance rules have negatively impacted stores that have remained open. At the same time, consumers have changed their purchasing habits as a result of these measures, which has led to a greater reliance on e-commerce platforms and supply chains associated with these systems. Consequently, COVID-19 has compelled organizations to become more flexible, innovate new ideas, modify their business

O. Tajeddini (*) Tokyo International University, Ikebukuro, Japan J. Tajdini Eastern Mediterranean University, Famagusta, North Cyprus © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Patnaik et al. (eds.), Global Trends in Technology Startup Project Development and Management, Innovation, Technology, and Knowledge Management, https://doi.org/10.1007/978-3-031-40324-8_7

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models, and develop entrepreneurial strategies as integral components of their overall strategies in order to deal with the epidemic and its ramifications (Ivanov & Dolgui, 2020; Spurk & Straub, 2020). Unlike the traditional concept of entrepreneurship, intrapreneurship (i.e., corporate entrepreneurship) is described as the practice of “internal innovation” or “entrepreneurial behavior” within an existing organization. Intrapreneurship can be conceptualized as a company's emergent behavioral intents and actions that are linked to deviations from the norm (Antoncic & Hisrich, 2003; Tajeddini & Mueller, 2009, 2018). Studies previously conducted that intrapreneurship has a favorable effect on financial and non-financial performance (e.g., Qalati et al., 2023; Zahra & Covin, 1995). Intrapreneurship incorporates broad patterns of firms' resource commitments for the purpose of creating, developing, and implementing new ideas and activities in the form of tangible and intangible products (Wakkee et al., 2010). In a dynamic environmental atmosphere, where complex market environments change quickly and competitive benefits are not sustainable, entrepreneurial resources are recognized as useful mechanisms to discern the capabilities of enterprises in order to acquire excellent performance paths (Covin & Lumpkin, 2011). Meanwhile, the emergence of COVID-19 triggered changes in online shopping behaviors toward servitization among firms and customers. For this purpose, utilizing corporate social media platforms and websites has played a crucial component in many businesses, as they allow them to keep in touch with, learn from, and listen to consumers in a more effective manner (Jones et al., 2015). Likewise, researchers have discovered that utilization of social media continues to rise at an exponential rate (Watanabe et al., 2021), as it has quickly developed into a major business management trend in the world economy (Tajvidi & Karami, 2021). In this scenario, entrepreneurs and intrapreneurs have become increasingly interested in the ramifications of social media/websites to generate greater value for customers (Yang et  al., 2016). Similarly, in Iran, a variety of small and medium-sized firms have modified their business plans from traditional advertising (e.g., print advertising, direct mail advertising, TV and radio ads), to digital advertising (i.e., online and digital channels). This has been accomplished in order to conduct business in the newly emerging, post-pandemic digital landscape. Despite considerable attention focused on corporate entrepreneurship in academic literature since the mid-1980s (Tajeddini et al., 2023; Tajeddini & Mueller, 2012), little knowledge exists as to how service firms can enhance their level of intrapreneurship (intra-firm entrepreneurship) and take full advantage of its benefits (Do & Luu, 2020). Service firms have evolved into an extremely effective part of the modern economy (Tajeddini, 2011, 2013, 2015) and currently account for more than 70% of global gross domestic product (GDP) (Chen et  al., 2016). This paper strives to donate to our knowledge of how marketing managers perceive and act as intrapreneurs and how their activities can enhance firm performance.

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7.2 Background 7.2.1 Intrapreneurship The term “intrapreneurship”’ is unquestionably linked to the concept of business entrepreneurship. Intrapreneurship is widely determined as a significant benefactor to a business's success. Kuratko (2016) states that an intrapreneurial organization is eager to take movement, explore possibilities, and promote new, creative goods and services. In effect, intrapreneurship is noticed as a procedure through which an individual (or a cluster of individuals) creates new ventures, modifies the behavioral patterns of firms, and introduces new firm innovation at the heart of the business. In other words, intrapreneurs take dreams or ideas and turn them into profitable ventures within their respective companies. Nevertheless, intrapreneurship faces multiple barriers such as management opposition, the administration bureaucracy, insufficient resources, and poor compensation for successful intrapreneurial ventures (Kuratko, 2016; Merkle et al., 2020). Research shows that various intrapreneurs (e.g., Sony PlayStation, Texas Instruments, Google, Facebook, etc.) have increased their growth and undergone transformation through the creation of efficient, intrapreneurial ideas (Vocoli, 2014).

7.2.2 Social Media In contrast to traditional media, web-based social networking platforms or “social media” allow individuals to generate, distribute, and share data in digital environments (Kaplan & Haenlein, 2010). Social media’s interactive nature permits users to post comments or other types of content (photo, video, etc.). In recent years, particularly during the coronavirus pandemic, different entrepreneurs and intrapreneurs have created different enterprises through the implementation of social media as the primary distribution channel. Corporate social media enables businesses to keep in touch with a large number of customers and lets them share predetermined content with stakeholders in an efficient manner so that they may circumvent traditional media for promotional purposes (Tajvidi & Karami, 2021).

7.3 Method The e-commerce website was chosen as the context for this study due to the fact that it has grown exponentially through its online presence. Based on the research literature, the emergence of COVID-19 triggered changes in shopping behaviors with regard to certain products. Limited knowledge as to the effectiveness of social media by intrapreneurs requires exploratory research design. Thus, qualitative

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research with an adopted grounded theory approach was employed to explore the perception of a marketing manager (digital products department) as an intrapreneur. A combination of purposeful (judgmental) and convenience sampling was used to collect data from an established e-commerce website called “Digikala” (Graham et al., 2020; Mehta & Tajeddini, 2016). Both the firm’s website (Digikala.com) and Instagram account (@digikala including posts, likes, comments) were carefully reviewed to determine added value strategies. Digikala is one of Iran's most well-­ known internet-based businesses, which owns 85% of the Iranian market, has an equity value (or net asset value) of 30 million dollars, and boasts more than 30 million monthly visitors (Corporate digital website 2021). In order to develop available data points, the firm’s marketing manager was invited to a phone call interview. A range of various comprehensive questions were sent to him via email two days before the interview (Fan et al., 2022) and the possible impacts of the website and social media (Jones et  al., 2015) on corporate performance. The phone interview was held on December 27th, 2021, and lasted for approximately 25 min. The process of the interview began with general questions regarding the structure of the organization, which then evolved into questions that pertained to the variety of digital products, quality, price, and challenges.

7.4 Findings Digikala was established in 2006 to provide a variety of products online which include clothes, toys, car accessories, books, stationeries, appliances, cosmetics as well as health, edible, and digital products. As such, the company sells over 4 million types of products through one of the largest e-commerce websites in Iran (Digikala, 2021). As a private company, there are around 8,200 full-time and part-time staff, as well as 150,000 personal sellers. The interviewee explained that the main strategy of the company is "to expand the business." He stated that the firm’s culture is based on fostering a shared approach to creativity and innovation. The interviewee also stated that employees are encouraged to share their insights with the company and make new recommendations in terms of solutions in order to foster and further enhance the company's objective. All new marketing campaigns which pertain to new products, new offers, etc., are listed on the firm's website and Instagram. In keeping with earlier research (e.g., Begkos & Antonopoulou, 2020; Kallmuenzer et  al., 2022), the firm evaluates social media influencers through the amount of its followers. Prior research (De Veirman et al., 2017) has indicated that Instagram as a social media tool seems to be more agreeable/attractive than other online commerce platforms (for the purpose of online selling). Nevertheless, the interviewee explained that the firm’s Instagram account, with more than 1.4 million followers and 2,894 posts, does not affect firm performance. In accordance with their internal research, the firm’s website has a significantly greater impact on customer satisfaction than the firm’s Instagram account. The firm provides various unique and novel

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promotional tools within the country such as seven days money-back guarantee, possibility to buy in installments, payment at the front door, free shipping to all parts of certain cities, competitive pricing, as well as a guarantee on all products. The interviewee further stated that these actions were key to the success of the firm’s business plan. In a similar vein, Kuratko (2016) believes that a successful intrapreneurial firm is enthusiastic to take movement, explore possibilities, and promote unique, creative goods and services. Through the information provided, it appears as though creativity and certain entrepreneurial activities have in fact created economic growth and prosperity for Digikala. The informant also explained that he had encouraged and motivated employees to find any innovative ways to present the products and provide an “excellent” service for the customers. This is the reminiscent of product champion who contributes to the innovation process (Kuratko, 2016). The informant further explains that the firm has attempted to provide various intrinsic (challenging and flexible job, recognition) and extrinsic (e.g., bonuses, family support for holidays) motivations for the employees (Darvishmotevali et al., 2023; Darvishmotevali & Tajeddini, 2019).

7.5 Conclusion The coronavirus has had a profound impact on worldwide trends. As a result, e-commerce has emerged as a viable and significant alternative for entrepreneurial firms to exploit new opportunities. Despite plentiful evidence which illustrates the effect of intrapreneurial activities, little has been studied within the context of Iran. The goal of this essay is to investigate the perspective of an entrepreneurial firm’s marketing manager in order to provide insights into its e-commerce website within an Iranian context. Utilizing an inductive approach, a combination of judgmental (purposive) and convenient sampling methods was adopted to investigate the effect of an intrapreneur’s presence on the success of a business in a sensitive, pandemic situation. The phone interview revealed some insightful and interesting observations. Overall, it appears as though intrapreneurship has a favorable influence on firm success. The employment of corporate websites and Instagram (a social platform) can be influential toward the firm’s overall success. However, it was noted that the Instagram platform was not critical to its success but complementary to it. This is consistent with Jones et al observations (Jones et al., 2015) who defined a possible link between social media and a firm’s website. In contrast, Abu Bakar and Ahmad (2019) found that corporate performance is in fact largely unaffected by social media. In light of the findings of this research, it is clear that business owners should actively encourage intrapreneurs toward the establishment and development of new ideas within their businesses. In doing so, they may be able to transform their organizations into ones that are more capable of capitalizing in the constantly evolving social media landscape.

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7.6 Implications According to the case study, it appears that COVID-19’s spread has led to entirely dissimilar market circumstances, with a considerable drop in demand for numerous services and products (Kuckertz et  al., 2020). This study offers some practical insights for intrapreneurs who intend to run an e-commerce website. Limitations, regulations, and restrictions related to the pandemic situation as well as the external and internal constraints that have been put in place have affected organizations in innumerable ways. The process has forced firms and organizations to look for out-­ of-­the-box ideas in order to conduct their business and become more innovative. In order to achieve these aims, marketing campaigns should be more effective and efficient in regard to their online presence. As such, the organization should build a better social media presence to enhance brand image and in turn improve firm performance. The result of this brief study indicates that a firm's website may pave the way to improve results for the intrapreneur. Intrapreneurs can take a step toward value creation by being innovative, creative, and accepting calculated risk.

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Tajeddini, K., Gamage, T. C., Tajeddini, O., & Kallmuenzer, A. (2023). How entrepreneurial bricolage drives sustained competitive advantage of tourism and hospitality smes: The mediating role of differentiation and risk management. International Journal of Hospitality Management, 111, 103480. Tajvidi, R., & Karami, A. (2021). The effect of social media on firm performance. Computers in Human Behavior, 115, 105174. https://doi.org/10.1016/j.chb.2017.09.026 Vocoli. (2014). 10 Inspiring Examples of Successful Intrapreneurship., Viewed, 27 Dec 2021, Available: https://www.vocoli.com/blog/may-­2014/10-­inspiring-­examples-­of-­ successful-­intrapreneurship/ Wakkee, I., Elfring, T., & Monaghan, S. (2010). Creating entrepreneurial employees in traditional service sectors: The role of coaching and self-efficacy. International Entrepreneurship and Management Journal, 6, 1–21. https://doi.org/10.1007/s11365-­008-­0078-­z Watanabe, N. M., Kim, J., & Park, J. (2021). Social network analysis and domestic and international retailers: An investigation of social media networks of cosmetic brands. Journal of Retailing and Consumer Services, 58, 102301. https://doi.org/10.1016/j.jretconser.2020.102301 World Health Organization. (2020). WHO Timeline - COVID-19, Available at: https://www.who. int/news/item/27-­04-­2020-­who-­timeline%2D%2D-­covid-­19. Accessed December 27, 2021. Yang, S., Lin, S., Carlson, J. R., & Ross, W. T., Jr. (2016). Brand engagement on social media: Will firms’ social media efforts influence search engine advertising effectiveness? Journal of Marketing Management, 32(5–6, 526), –557. https://doi.org/10.1080/0267257X.2016.1143863 Zahra, S.  A., & Covin, J.  G. (1995). Contextual influences on the corporate entrepreneurship-­ performance relationship: A longitudinal analysis. Journal of Business Venturing, 10(1), 43–58. https://doi.org/10.1016/0883-­9026(94)00004-­E

Chapter 8

Corporate Governance and Firm Performance: Evidence from Microfinance Institutions in Ghana David Boohene and Rosemond Dentaah Agyepong

8.1 Introduction The search for solutions to problems that enhance firm performance in Ghana is still ongoing, and there is a wealth of literature on the subject (Fiador, 2016). Fiador’s study found that a variety of factors, encompassing economic, industry, and company-­level variables, determine how well a firm performs. The corporate governance system or structure is one of the key elements that determine a firm’s success or failure. Corporate governance has become a crucial topic in many businesses due to the beneficial and negative externalities of the parting of ownership and management in the contemporary firm as well as current commercial trends. Ghana’s financial industry has changed from being heavily regulated to being mostly driven by the market. The country dealt with one of the worst financial crises in its history as a result of the regulators’ clean-up exercise, despite the fact that the institutional and regulatory structure had significantly improved (Bank of Ghana Report, 2018). Mulili and Wong (2011) contend that developing nations differ from industrialized nations in many ways, and as a result, developing nations must create their own peculiar corporate governance forms that consider the partisan, social, and technological changes occurring in each African nation. Over the years, the Bank of Ghana has shut down several MFIs around the nation because of operational inefficiencies and their failure to meet capital requirements (Bank of Ghana Report, 2020; Belnye, 2011). According to a report by the Ghana Microfinance Institutions Network (GHAMFIN, 2014), certain MFIs are unable to fulfill their financial responsibilities to their clients in the first and last quarters of the year. As a result, MFIs that are D. Boohene (*) University of Energy and Natural Resources, Sunyani, Ghana R. D. Agyepong Kwame Nkrumah University of Science and Technology, Kumasi, Ghana © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Patnaik et al. (eds.), Global Trends in Technology Startup Project Development and Management, Innovation, Technology, and Knowledge Management, https://doi.org/10.1007/978-3-031-40324-8_8

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unable to manage this liquidity crisis are eventually forced out of business and collapse (Boohene & Agyepong, 2022; Boohene et  al., 2018). Unscrupulous and unlawful practices, poor management, and indifference for due diligence are the problems MFIs in Ghana face, according to (BoG, 2015; Boateng et  al., 2016). When these internal problems are compounded by outward problems like fright drawings and other economic instabilities, the risk levels of MFIs become unmanageable. In light of this, there has been increased attention by government agencies, academics, and others to investigate the phenomenon of corporate governance and its impact on performance. For instance, Black et al. (2012) discovered that in large Korean companies, the audit committee – a feature of corporate governance is positively connected with firm performance, but they found no association in smaller organizations. Nigerian businesses, however, did not experience this outcome (Black et al., 2012). Board size is another factor characterized by corporate governance and used in other surveys (Adams & Mehran, 2012). Both Jackling and Johl (2009) and Kajola (2008) discovered a strong and favorably skewed connection. As a result, despite extensive studies on corporate governance and its connection to performance, many financial institutions in Ghana still fail (BoG, 2018). For instance, by 2019, 347 of Ghana’s 484 licensed MFIs were shut down due to insolvency (BoG, 2019). This demonstrates that there is not only a tumultuous and unclear association between firm performance and corporate governance but also a concentration on large enterprises, with evidence pointing to firm-specific characteristics as the primary determinants of this association. As a result, this study investigates the degree to which corporate governance and MFI performance are related, as well as the challenges with corporate governance’s efficiency with regard to MFIs.

8.1.1 Theoretical Point of View on Corporate Governance Many theories and concepts have been proposed to describe corporate governance and how the concept may influence the outcomes of managerial actions. Stakeholder theory, agency theory, and stewardship theory are a few of the concepts under corporate governance. 8.1.1.1 Stakeholder Theory Freeman (2015) acknowledges that stakeholders are a collection of people who may be able to influence or be impacted by the corporation’s operations in realizing its goals. The presence of stakeholder sets within the business may lead to the emergence of new aims aside from increasing the number of stakeholders. The opinions of these stakeholder sets, which include clients, personnel, investors, and the local municipal, will differ on what the organization’s aims would be (Watson & Head, 2007). According to the investor proposition, every stakeholder in an organization has various demands, and the business should try to meet all of these different requirements.

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8.1.1.2 Agency Theory Agency theory explains how directors and specialists within the corporate setting interact with one another (Jensen & Meckling, 1976). Additionally, this theory seeks to provide solutions for issues that may arise in the interactions between authorities, particularly between organization owners and their representatives (leaders who have been selected to run the organization). 8.1.1.3 Stewardship Theory The agency theory is distinct from the stewardship theory. Donaldson and Davis (1991) argue by saying that newly hired executives are regarded as excellent stewards who will act in their shareholders’ best interests. The stewards’ acts are collectivist and pro-organizational, and as a result, they will have a higher utility than selfish activities. Since they are operating in the organization’s best interests, the activities of authorized directors acting in the capacity of stewards will be adjusted toward the company’s goals (Davis et al., 1997).

8.1.2 Mechanisms of Corporate Governance Corporate governance is the arrangement of market and institutional elements that encourage self-serving managers to surge the value of the organization’s remaining revenues to optimal levels in the interest of the business’s owners. Any conflicts between management’s and shareholders’ interests must be resolved for a corporate governance structure to significantly and favorably affect a company’s value and performance (Denis, 2001). Different components are thusly arranged with the understanding that they will at last improve the firm’s output and increase stakeholder capital. A portion of the corporate governance instruments incorporate board size, financial expertise of directors, structure and process, independence of directors, board diligence, ownership structure, board composition, and the audit committee’s impartiality. 8.1.2.1 Board Size Researchers agree that a huge board size causes a harmonization issue among individuals. According to (Malik et al., 2014; Jensen, 1993) board dimensions or size in the United States are frequently excessively large and should not exceed 8 directors. This creates a strong positive connection between bank execution and board size. They deduce from the discovery that an enormous board size can improve performance.

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8.1.2.2 Board Composition The term “board composition” refers to the presence of independent non-executive directors in addition to executive directors and directors who are non-executive on the board. According to SCGC, there ought to be areas of strength for the board that can exercise free and impartial judgment on business undertakings. No one or a select minority should be permitted to control the board’s decision-making processes. Directors’ participation on the board is absolutely vital. They contribute a vast amount of knowledge and their expertise in remarkable fields to the organization. In any case, board interaction mostly refers to the board’s dynamic activities. It has to do with having a strong and, at times, in-depth discussion about company concerns and issues so that decisions may be made and upheld. Once more, the design is susceptible to the composition of the board, outside advisors, board leadership, and committee structure (McNulty et al., 2012). 8.1.2.3 Independence of Directors Boards of directors’ capacity to go about as active components depend on their freedom from the board. The extent of leaders’ autonomy to autonomous directors has been identified by researchers as a sign of board freedom (Koh, 2007; Peasnell et al., 2005). A few past examinations have recommended that free chiefs are powerful screens since they don’t have financial interests in the organization or mental connections to the executives. They are in a superior place to impartially contest the board (Abbott et al., 2004). 8.1.2.4 Ownership Structure The efficiency of the ownership focus is disputed between the functions of monitoring and expropriation. In the 1980s, fixation proprietorship was accepted to limit organizational issues because a higher grouping of ownership gives enormous investors more grounded motivations and more prominent power at a lower cost to screen executives (Hu & Izumida, 2008). Large investors will take a vigorous role in corporate decisions as they will benefit from their observation efforts (Grossman & Oliver, 1986). The benefits of enormous investors can be veered from marginal investors’ advantages (Hu & Izumida, 2008). Regulating investors can take advantage of the benefits of marginal investors through associated party exchanges as well as misrepresentations of fiscal reports (Hu & Izumida, 2008). Following Demsetz and Lehn’s (1985) research, procedures to gauge possession convergence nearly concentrate on gathering the ownership of the “5,” “10,” or “20” largest investors. Nonetheless, Earle et al. (2005) argued that group amassing can hide collaborations between large investors and the example of fixation. These ideas may affect how fixation affects performance. Earle et al. (2005), on the other hand, contended that

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group amassing can conceal collaborations between large investors and the example of fixation. These viewpoints could have an impact on the impact of fixation on execution. 8.1.2.5 Independence of the Audit Committee An independent review advisory group is significant on the grounds that it assists with keeping up with straightforwardness, helps the board of directors, forestalls and controls insufficient strategic approaches, and supports the oversight interaction of financial revealing. Moreover, audit committees can work on improving the value of monetary reporting and decreasing audit risk by working on the nature of detailed income (Contessotto & Moroney, 2014). In this manner, the individuality of the audit committee has a significant role in supervising and checking an organization’s administration determined to shield the benefits of the owners (Kallamu & Saat, 2015). It is perceived that a compelling audit committee concentrates on improving company effectiveness and attractiveness, especially in an altering business climate that is beyond the control of the organization (Herdjiono & Sari, 2017). 8.1.2.6 Board Diligence The sum of board meetings held yearly affects the board’s capacity for oversight. Effective board monitoring may be threatened by having too many or too few meetings. Too few meetings could mean that the directors aren’t paying enough attention to the business, while too many meetings could mean that there are issues with the company (Kang et al., 2007; Vafeas, 2005). Regular board meetings increase the likelihood that members will carry out their responsibilities with diligence and effectiveness, which improves the degree of monitoring (Yatim et al., 2006). The boards of directors must take proactive steps to guarantee accurate and timely annual reports with open disclosures (Kent & Stewart, 2008). 8.1.2.7 Financial Expertise of Directors A well-rounded and effective board should comprise associates with a variety of skills, experience, and knowledge that have an impact on many parts of the company’s procedures, according to the Blue-Ribbon Committee Report (1999); such diversified aids are frequently made by various directors. Financial literacy is also described as “a vital component of the overall standards of care, expertise, and diligence needed by company directors.” The directors’ understanding of the repercussions of simple financial decisions is aided by their financial literacy. The acquisition of financial literacy is possible through official and independent learning (ASX CGC, 2007; Livingston, 2002).

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8.1.3 An Empirical Assessment of Corporate Governance and Firm Performance Peong Kwee Kim and Devinaga Rasiah (2010) assert that corporate governance procedures and bank performance are related. Further, after doing more analyses to explore the connection between performance and corporate governance, Fooladi and Nikzad (2011) discovered a negative link between performance and CEO duality. They also included return on value and return on resources fractions. Despite the finding that board independence, board size, and ownership structure were all positively correlated with performance, there was no impact on firm success in any case. In order to establish the effect of board composition, specifically, the representation of outside free directors, on organizations’ financial performance in Bangladesh, Rashid et al. (2010) conducted a review. There was no indication of a substantial relationship between the number of external independent directors on the board and business performance. This result implies that the independent external directors are unable to raise the value of the Bangladeshi company. In light of the presentation measures, it was determined that the board’s size had an influence on the firm’s bookkeeping execution. The connection between productivity, or financial performance, and the level of corporate performance disclosure (CGD) by registered non-­ financial enterprises in Bangladesh was later examined by Rouf (2012). Rouf’s study concludes that there was a clear association between the size of the organization and the exposure level of corporate governance. Furthermore, strongly and fundamentally linked to profitability, or financial performance, is corporate governance disclosure (CGD). 8.1.3.1 Performance Management Prior to being able to properly assess performance, it is essential to identify the elements that support strong performance indicators. Oakland (1993) stated that quantifiable, meaningful, and vital to the operation of the company are the characteristics of effective performance indicators. It should likewise be significant, and the expense of getting it ought to be about the same as the advantage that would be gotten from it. Different strategies might be utilized to quantify a company’s performance. As per Kiel and Nicholson (2003), pecuniary measures utilized in observational research on corporate governance fit into two general classes, in particular bookkeeping-based procedures and market-based procedures. Some performance measurement techniques are Return on Equity, Earnings per Share, and Return on Assets.

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8.1.3.2 Challenges Associated with the Effectiveness of Corporate Governance on MFIs Assuring long-term firm survival and enhancing firm performance are both possible with good corporate governance (Agbokah et  al., 2022). Since corporate governance is currently seen as one of the weakest areas in the financial industry, it has become increasingly important for microfinance (Labie & Mersland, 2010). For instance, a board of directors may occasionally be corrupted by conflicts of interest when making decisions. Most frequently, this has led to certain elected officials abusing the members’ trust by mismanaging and misappropriating funds (Mudibo, 2005). Furthermore, Labie and Périlleux (2008) claim that corporate governance is typically more complicated in management structures of MFIs because of their ownership as well as their democratic decision-making concept. The lack of clearly defined operational regions presents another difficulty. This is due to the fact that, per Bank of Ghana (2004), in some instances, the roles, responsibilities, and operations of different stakeholders – which are meant to be complementary – overlap. Another difficulty facing the microfinance sector is capacity development and funding (Boateng et al., 2015). According to Navissi and Naiker (2006), evidence about the link between share ownership distribution and business value is a crucial component of the corporate governance process. Their results also demonstrate that institutions with board participation have stronger incentives to oversee management, hence their presence should enhance firm value. However, at large ownership levels, institutional investors with board participation could persuade boards of directors to take unfavorable actions (Lakmal, 2014).

8.2 Materials and Methods An exploratory and descriptive methodology was employed to thoroughly investigate the phenomenon. There were 118 employees sampled from eight (8) MFI Institutions across Ghana. The researcher collected data by administering a questionnaire. The questionnaire used structured questions, divided into two sections “A” and “B.” Section “A” consisted of the demographical background of respondents. Section “B” involves questions related to the focus of the case study, that is highlights on the extent to which corporate governance affects the performance of MFIs in the New Juaben Municipality, Koforidua, and further takes an exploratory approach in soliciting views on the challenges associated with the effectiveness of corporate governance mechanisms on MFIs in New Juaben Municipality, Koforidua at a significance level which shows strong evidence against the null hypothesis. The questionnaires were administered by the researcher to the target group personally. The sampled demographic characteristics of survey respondents are given in Table 8.1.

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8.2.1 Analysis and Interpretation Table 8.2 uses the regression model that allows all independent variables to be considered by way of selection as one single step. In this survey corporate governance is looked at in the elements of Ownership structure, Independence of Audit committee, Board diligence, Independence of directors, Financial expertise of directors, Current board size and composition. As observed the predictor ability as explained by the R Square is (0.221) for the selected corporate governance activities, namely Ownership structure, Independence of Audit committee, Board diligence, Independence of directors, financial expertise

Table 8.1  Demographic profile of respondents Demographic profile case processing summary Gender Age category

Highest qualification

Respondent’s years with MFI

MFI number of years in operation

Male Female Below 25 years 25–34 years 35–44 years 45–54 years Certificate Degree Masters Others 1–5 years 6–10 years 11–15 years Above 15 years 6–10 years 11–15 years Above 15 years

Total

N 60 58 21 68 14 15 46 44 23 5 22 55 36 5 20 44 54 118

Marginal % 50.8% 49.2% 17.8% 57.6% 11.9% 12.7% 39.0% 37.3% 19.5% 4.2% 18.6% 46.6% 30.5% 4.2% 16.9% 37.3% 45.8% 100%

Table 8.2  Model summary Model 1

R .470a

R square .221

Adjusted R square .179

Std. error of the estimate .915

Predictors: (constant), ownership structure, independence of Audit committee, board diligence, independence of directors, financial expertise of directors, current board size and composition a

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Table 8.3  Model summary (backward regression model) Model 1 2 3 4

R .470a .469b .462c .444d

R square .221 .220 .213 .197

Adjusted R square .179 .185 .186 .176

Std. error of the estimate .915 .912 .912 .917

Predictors: (constant), ownership structure, independence of audit unit, board diligence, independence of directors, financial expertise of directors, current board size and composition b Predictors: (constant), ownership structure, board diligence, independence of directors, financial expertise of directors, current board size and composition c Predictors: (constant), ownership structure, board diligence, independence of directors, financial expertise of directors d Predictors: (constant), ownership structure, independence of audit unit, financial expertise of directors a

of directors, and Current board size and composition. Thus, the R Square explains 22.1% of the variations in the dependent variable. The backward regression selection method is used in Table 8.3. Here, each variable is introduced into the equation before being gradually eliminated. Therefore, if a variable satisfies the criteria for removal, it is first examined for removal if it has the weakest partial correlation with the dependent variable. Thus, the first set of data, which included all six corporate governance study variables, had the highest predictor ability as indicated by the R Square (22.1). The subsequent second, third, and fourth sets had some corporate governance elements being removed, notably among them are independence of directors, current board size and composition, board diligence, and independence of audit unit. The remaining corporate governance elements, namely Ownership structure and financial expertise recorded relatively high predictor ability for the dependent variable and therefore were not omitted in the backward regression model summary as per this survey. The percentage change in the dependent variable’s coefficient of determination (R Square) is shown in Table 8.4. For the different facets of corporate governance at each stage as per the study, the F value estimated the implication of R Square using F-distribution at 5% level of significance. The model was determined to fit at a significance level of (0.000), which is less significant than (0.05). The ownership structure, closely followed by the directors’ extensive financial knowledge, is listed in Table 8.5 as the key component of corporate governance that affects MFIs’ success. Their high mean values of (2.64) and (2.52), respectively, demonstrate this. Due to the high numeric values, both the mean values for the financial knowledge of the directors and the ownership structure are high; on a three-point scale, (3) is understood as the highest and (1) as the lowest. Table 8.6 uses mean comparisons to investigate the issues relating to the efficacy of corporate governance on MFIs. The chart shows that the primary issues influencing the effectiveness of corporate governance in MFIs are lack of transparency, closely followed by lack of independence of the audit unit. Their high mean values of (4.84) and (4.11), respectively, demonstrate this.

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Table 8.4 ANOVAa Model 1 Regression Residual Total 2 Regression Residual Total 3 Regression Residual Total 4 Regression Residual Total

Sum of squares 26.352 93.013 119.364 26.201 93.163 119.364 25.473 93.892 119.364 23.523 95.841 119.364

df 6 111 117 5 112 117 4 113 117 3 114 117

Mean square 4.392 .838

F 5.241

Sig. .000b

5.240 .832

6.300

.000c

6.368 .831

7.664

.000d

7.841 .841

9.327

.000e

Predictors: (constant), ownership structure, board diligence, independence of directors, financial expertise of directors, current board size and composition b Predictors: (constant), ownership structure, board diligence, independence of directors, financial expertise of directors c Predictors: (constant), ownership structure, independence of directors, financial expertise of directors d Dependent variable: performance of MFIs e Predictors: (constant), ownership structure, independence of audit committee, board diligence, independence of directors, financial expertise of directors, current board size and composition a

Table 8.5  Mean ranks: corporate governance activities in MFIs Corporate governance activities mean rank Current board size and composition Independence of directors Financial expertise of directors Board diligence Independence of audit unit Ownership structure

2.10 1.98 2.52 1.96 1.83 2.64

Table 8.6  Mean rank: challenges associated with the effectiveness of corporate governance on MFI’s Challenges Conflict of interest on the part of the board of directors Lack of transparency Inability to liquidate firms’ assets Communication gap Lack of Independence of audit unit Ineffective ownership structure Friedman Test (N: 118 Chi-Square 49.413; Df 5; Asymp. Sig. .000)

Mean rank 3.11 4.84 1.81 3.72 4.11 3.50

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Table 8.7  Variables entered/removed (stepwise regression) Variables removed

Model Variables entered 1 Ownership structure 2

Method Stepwise (criteria: Possibility-of-F to enter is < or = to .050, possibility -of-F to delete is > or = to .100) Stepwise (criteria: Possibility -of-F to enter is < or = l to .050, possibility -of-F to delete is > or = to .100)

Financial expertise of directors

Dependent variable: performance of MFIs Table 8.8  Model summary Model 1 2 a

R .368a .415b

R square .135 .172

Adjusted R square .128 .158

Std. error of the estimate .943 .927

Predictors: (constant), ownership structure Predictors: (constant), ownership structure, financial expertise of directors

b

Table 8.7 employs the stepwise regression model which saw the deletion of four elements  (current board size and composition, independence of directors, board diligence and independence of audit unit) of the corporate governance under study with the exception of ownership structure and financial expertise of directors as their probability to remove is sufficiently large (Possibility-of-F-to-delete >= 0.100). Table 8.8 shows the model summary of the information given in Table 8.7.

8.3 Discussion According to the results of the survey, the R Square’s explanation of the predictive ability is (0.221) for the selected corporate governance activities, namely ownership structure, independence of audit committee, board diligence, independence of directors, financial expertise of directors, and current board size and composition. Thus, the R Square explains 22.1% of the variations in the performance of MFIs. Additionally, the variable with the smallest partial correlation to the dependent variable is taken into consideration for removal first if it satisfies the criteria for removal using the backward regression selection procedure, where all the variables are added to the equation, then they are successively eliminated. Here, the R Square in the first set, which included all six factors of corporate governance employed for this study, explains the predictive ability as being (22.1). Some corporate governance components were eliminated in the next second, third, and fourth sets, as shown in Table 8.7. Notably among them are independence of directors, current board size and composition, board diligence, and independence of audit unit. The remaining corporate governance elements, namely ownership structure and financial expertise of directors, recorded relatively high predictor ability for the dependent variable and

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therefore were not omitted in the backward regression model summary as per this survey. This proves that among the six factors, ownership structure and directors’ financial acumen have a big impact on how well firms’ function. Furthermore, the Friedman rank test result shows very significant differences in employers’ replies to the issues related to corporate governance’s efficacy on MFIs (P