Adoption of digital technologies, business model innovation, and financial and sustainability performance in start-up firms

Autio, Erkko et al. Working Paper Adoption of digital technologies, business model innovation, and financial and sustainability performance in start-up firms ADB Economics Working Paper Series, No. 734 Provided in Cooperation with: Asian Development Bank (ADB), Manila Suggested Citation: Autio, Erkko et al. (2024) : Adoption of digital technologies, business model innovation, and financial and sustainability performance in start-up firms, ADB Economics Working Paper Series, No. 734, Asian Development Bank (ADB), Manila, https://doi.org/10.22617/WPS240356-2 This Version is available at: https://hdl.handle.net/10419/301973 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/3.0/igo/ ASIAN DEVELOPMENT BANK ASIAN DEVELOPMENT BANK 6 ADB Avenue, Mandaluyong City 1550 Metro Manila, Philippines www.adb.org ADOPTION OF DIGITAL TECHNOLOGIES, BUSINESS MODEL INNOVATION, AND FINANCIAL AND SUSTAINABILITY PERFORMANCE IN START-UP FIRMS Erkko Autio, Chiraphol Chiyachantana, Cynthia Castillejos-Petalcorin, Kun Fu, Raymund Habaradas, Yothin Jinjarak, Anang Muftiadi, Donghyun Park, Pattarawan Prasarnphanich, Pham Minh Quyên, and Willem Smit ADB ECONOMICS WORKING PAPER SERIES NO. 734 July 2024 Adoption of Digital Technologies, Business Model Innovation, and Financial and Sustainability Performance in Start-Up Firms This report examines how digitalization affects firm performance using survey data from 681 digital entrepreneurs in six Association of Southeast Asian Nations (ASEAN) countries. It finds that select digital applications and firm’s business model digitalization drive business model experimentation. These findings underscore the significant value for the design of entrepreneurial and digitalization policies in Asian developing economies and in emerging economies more widely. The analysis points to important performance implications of digital technology adoption by entrepreneurial businesses. About the Asian Development Bank ADB is committed to achieving a prosperous, inclusive, resilient, and sustainable Asia and the Pacific, while sustaining its efforts to eradicate extreme poverty. Established in 1966, it is owned by 68 members —49 from the region. Its main instruments for helping its developing member countries are policy dialogue, loans, equity investments, guarantees, grants, and technical assistance. ASIAN DEVELOPMENT BANK The ADB Economics Working Paper Series presents research in progress to elicit comments and encourage debate on development issues in Asia and the Pacific. The views expressed are those of the authors and do not necessarily reflect the views and policies of ADB or its Board of Governors or the governments they represent. ADB Economics Working Paper Series Adoption of Digital Technologies, Business Model Innovation, and Financial and Sustainability Performance in Start-Up Firms Erkko Autio, Chiraphol Chiyachantana, Cynthia Castillejos-Petalcorin, Kun Fu, Raymund Habaradas, Yothin Jinjarak, Anang Muftiadi, Donghyun Park, Pattarawan Prasarnphanich, Pham Minh Quyên, and Willem Smit No. 734 | July 2024 Erkko Autio (erkko[email protected]) is a professor and chair in the Technology Venturing at Imperial College Business School. Chiraphol Chiyachantana ([email protected]) is an assistant professor at Singapore Management University. Kun Fu (kun. [email protected]) is a senior lecturer at Loughborough University London. Raymund Habaradas (raymund. [email protected]) is a professor at De La Salle University. Anang Muftiadi (anang.muftiadi@unpad. ac.id) is head magister at the Universitas Padjadjaran. Pattarawan Prasarnphanich (pattaraw[email protected]) is a lead researcher at Chulalongkorn University. Pham Minh Quyên ([email protected].vn) is a lecturer at Thu Dau Mot University. Willem Smit (willem.smit@ fulbright.edu.vn) is lead faculty for entrepreneurship at Fulbright University Vietnam. Cynthia CastillejosPetalcorin ([email protected]g) is a senior financial sector officer in the Sectors Group; Yothin Jinjarak ([email protected]) is a senior economist at the East Asia Department; and Donghyun Park ([email protected]) is an economic advisor at the Economic Research and Development Impact Department, Asian Development Bank. Creative Commons Attribution 3.0 IGO license (CC BY 3.0 IGO) © 2024 Asian Development Bank 6 ADB Avenue, Mandaluyong City, 1550 Metro Manila, Philippines Tel +63 2 8632 4444; Fax +63 2 8636 2444 www.adb.org Some rights reserved. Published in 2024. ISSN 2313-6537 (print), 2313-6545 (PDF) Publication Stock No. WPS240356-2 DOI: http://dx.doi.org/10.22617/WPS240356-2 The views expressed in this publication are those of the authors and do not necessarily reflect the views and policies ofthe Asian Development Bank (ADB) or its Board of Governors or the governments they represent. ADB does not guarantee the accuracy of the data included in this publication and accepts no responsibility for any consequence of their use. The mention of specific companies or products of manufacturers does not imply that they are endorsed or recommended by ADB in preference to others of a similar nature that are not mentioned. By making any designation of or reference to a particular territory or geographic area inthis document, ADB does not intend to make any judgments as to the legal or other status of any territory or area. This publication is available under the Creative Commons Attribution 3.0 IGO license (CC BY 3.0 IGO) https://creativecommons.org/licenses/by/3.0/igo/. By using the content of this publication, you agree to be bound bytheterms of this license. For attribution, translations, adaptations, and permissions, please read the provisions andterms of use at https://www.adb.org/terms-use#openaccess. This CC license does not apply to non-ADB copyright materials in this publication. If the material is attributed toanother source, please contact the copyright owner or publisher of that source for permission to reproduce it. ADB cannot be held liable for any claims that arise as a result of your use of the material. Please contact [email protected] if you have questions or comments with respect to content, or if you wish toobtain copyright permission for your intended use that does not fall within these terms, or for permission to use theADB logo. Corrigenda to ADB publications may be found at http://www.adb.org/publications/corrigenda. Note: In this publication, ADB recognizes “Vietnam” as Viet Nam. ABSTRACT This report investigates the impact of digitalization on firm-level performance using survey data from 681 digital entrepreneurs across six Association of Southeast Asian Nations (ASEAN) countries. Results show that the reliance of the business on select digital applications and the digitalization of different aspects of the firm’s business models were found to be potent drivers of business model experimentation in entrepreneurial businesses. We also observed consistent mediation effects of digitalization variables on performance through their effect on business model experimentation, although the digitalization variables also exhibited strong direct effects on performance. This last observation signals that the adoption of digital technologies by entrepreneurial businesses has more wide-ranging beneficial impacts than their facilitating effect on business model experimentation. We consider the findings reported here to be of significant value for the design of entrepreneurial and digitalization policies in Asian developing economies and in emerging economies more widely. Our analysis points to important performance implications of digital technology adoption by entrepreneurial businesses. Keywords: digitalization, business model innovation, entrepreneurial performance, ASEAN, sustainability performance JEL codes: L26, O32, O33 1 Introduction Over recent decades, advances in digital technologies have precipitated a major structural transformation in the organization of society and the economy. Ubiquitous digital connectivity has enabled economic and societal processes to be increasingly re-organized to take advantage of digital technologies. This process has also transformed the context within which entrepreneurs discover and pursue entrepreneurial opportunities and compete against established firms (Nambisan, 2017). Arguably the most important characteristic of digital technologies is their ability to enable business model innovation—i.e., a radical re-think of how entrepreneurial businesses organize for the creation and delivery of customer value and capture this value as business profit (Bouwman, Nikou, and De Reuver, 2019; Massa and Tucci, 2013; Rachinger et al., 2019). This is a particularly important opportunity driver for entrepreneurs, as established businesses tend to focus on optimizing their existing business models, which may hamper their ability to take advantage of the latest digital opportunities (Autio et al., 2018). Yet, surprisingly little is known about the performance effects of digital technology adoption by entrepreneurial businesses. In this report, we explore such performance effects by means of a six-country survey of digital entrepreneurial businesses. Although the importance of digitalization and its impact on entrepreneurship through business model innovation are widely recognized (Autio et al., 2018), surprisingly little is still known about the firm-level performance effects of the adoption of digital technologies in the business model (Bouwman, Nikou, and De Reuver, 2019). There is widespread acceptance that digitalization has a transformative effect on entrepreneurial opportunity landscapes in countries and on the optimal modes of entrepreneurial opportunity pursuit. Due to digitalization, entrepreneurial activities have become less constrained by spatial, temporal, and sectoral boundaries (Nambisan, 2017). The digitally-induced lifting of conventional constraints limiting entrepreneurial agency means that entrepreneurial opportunity pursuit has become a viable occupational option to larger audiences than ever before. At the same time and largely because of the same reasons, the effective means of pursuing entrepreneurial opportunities have been transformed, with entrepreneurs increasingly adopting innovation techniques and practices originally pioneered elsewhere, such as Design Thinking, Design Sprints, Growth Hacking, and Agile Development (Brown, 2009; Kimbell, 2009; Contigiani and Levinthal, 2019; Bocken and Snihur, 2020). Such ideas have prompted a novel, iterative approach to entrepreneurial opportunity discovery and validation, often referred to as ‘Lean Entrepreneurship’ (Blank, 2013; Ries, 2011). The lean entrepreneurship approach builds on the insight that entrepreneurial opportunities seldom appear readily formed, in the ’market’, ready to be exploited by entrepreneurs. Instead, opportunities need to be gradually created and shaped through entrepreneurial experiments by which the entrepreneur tests ideas and hunches, discarding those that do not appear to work, and retaining those that receive supportive feedback (Camuffo et al., 2019; Dimov, 2016; Romme and Reymen, 2018). In the boundaryless and interconnected digital world, steady-state, independently existing and objectively discoverable ‘market opportunities’ have become a rarity, and enterpreneurs are better off by harnessing digital technologies for an iterative process of opportunity development. The above narrative rests on two important assumptions: first, that the adoption of digital technologies enables entrepreneurs to experiment more effectively, and second, that the validated ideas are operationalized through their incorporation in the firm’s business model, or its operational architecture for the discovery, creation, delivery, and capture of customer value. 2 These assumptions imply that both the adoption of digital technologies in themselves, and the iterative experimentation with these in the firm’s business model should constitute important drivers of entrepreneurial firm performance in the digital age. If entrepreneurs shape and pursue opportunities more effectively through iterative experimentation, and if that experimentation is enhanced by the adoption of digital technologies, both should support more effective opportunity development, and therefore, enhance the performance of entrepreneurial new businesses. However, these assumptions have seldom been subjected to a direct empirical test, and the few tests that have been conducted have mostly taken place in the context of high-income Western economies, with only rare exceptions (Bouwman, Nikou, and De Reuver, 2019; Camuffo et al., 2019; Ferreira, Fernandes, and Ferreira, 2019; Liu, Liu, and Gu, 2021). The evidence regarding the impact of digitalization on entrepreneurial performance remains scarce in general and particularly so for emerging economies. This is an important gap, since emerging economies arguably stand to benefit the most from digitalization, as digital technologies offer the opportunity of catching up through leapfrogging steps conventionally required to advance economic development (Michelle, 2009; Xiong et al., 2021). We address this gap by means of an interview survey of ‘digital entrepreneurs’ in six ASEAN countries: Indonesia, Malaysia, the Philippines, Singapore, Thailand, and Viet Nam. In a project sponsored and coordinated by the Asian Development Bank and conducted in collaboration with research teams from six leading academic institutions from the six ASEAN countries, we identified and interviewed a population of 685 digital entrepreneurs in these countries, focusing particularly on their adoption of digital technologies in their business models, their business model experimentation activities, and explored the implications of these processes for the business performance, and also, for their performance in terms of conforming to and advancing United Nations’ Sustainable Development Goals. We designed novel operationalizations of businesslevel digitalization and business model experimentation in order to test mediating relationships between digitalization, business model experimentation, and business and sustainability performance. Our structural equation modelling analysis reveals that digital technology adoption by entrepreneurial businesses is a potent enabler of business model experimentation, which is a potent driver of business and sustainability performance. Our analysis also shows that the adoption of digital technologies also exercises a strong direct effect of business and sustainability performance in addition to its mediating effect through business model experimentation, revealing that digital technologies have broad performance implications for entrepreneurial businesses. Our analysis makes several important contributions. First, this is one of the relatively few studies contributing insight on the relationships between business model digitalization, business model experimentation, and business and sustainability performance. The evidence contributed in this study should help inform the design of entrepreneurship and digitalization policies. Second, we contribute first-hand evidence on the effect of digitalization on the performance of entrepreneurial businesses in developing Asian economies, thereby addressing an important gap. Third, we provide a theory-grounded account of how and why digitalization should impact performance in entrepreneurial new businesses, thereby illuminating the mechanics of this important dynamic. Fourth, we contribute new and enhanced operationalizations of business model experimentation, digital technology adoption in business models, and sustainability performance, thereby facilitating further data collection in this domain. Finally, we contribute reflections and insights for entrepreneurship policy design. 3 This report is structured as follows. We next review theorizing on digitalization, business models, and entrepreneurship. This review introduces key features of digitalization, how it enables business model innovation, and how it shapes and transforms entrepreneurship and entrepreneurial opportunity pursuit. We then construct our theoretical model, which explicates relationships between business model digitalization, business model experimentation, and business and sustainability performance. We then describe our empirical design and present our methods, analysis, and findings. We conclude by discussing implications for policy and practice. 2 Digitalization, Business Model Innovation, and Entrepreneurship 2.1 Transformative Properties of Digital Technologies and Infrastructures Digital technologies possess several features that distinguish them from other advanced technologies and explain why they are exercizing such a transformative impact on society. The key distinguishing feature of digital technologies is the very fact that they are digital and not physical, in the sense that digital technologies are defined by their digital and logical features and less by their physical characteristics (Yoo et al., 2012). Digital technologies are Turing machines: they accept bits as input and produce bits as outputs. In other advanced technologies, the key properties of the technology—and therefore, the technological effect produced—are coded in physical arrangements of atoms in matter. A machine tool shapes physical objects with sharp blades that have themselves been machine tooled into desired form. An engine creates rotational movement by harnessing the power of burinng fuel that is channeled to pistons that operate a rotating axis. A laser cutting device creates the desired cutting effect by concentrating large amounts of wave-synchronized electromagenetic radiation into a small space. In contrast to desired technical effects produced through manipulating physical arrangements of atoms in matter, digital devices manipulate information, as expressed in bits. Although those bits, too, are ultimately coded in physical media (e.g., electrons, photons), what matters for the operation of the digital device is the arrangement of those bits in the abstract, and the logical algorithms they can be designed to accomplish. As digital devices accept bits as inputs, and as the instruction sets that inform how to process inputs are themselves expressed as bits, digital devices can be flexibly reprogrammed to perform different functions with minimal cost and energy expenditure. In contrast, physical technologies are asset specific: they cannot be easily repurposed to perform different functions without significant loss of utility or significant expenditure of energy (Tilson, Lyytinen, and Sørensen, 2010). The reprogrammability and consequent flexibility of digital technologies means that digital technologies are generic technologies: they can be flexibily combined with other technologies and programmed to perform or enhance virtually any desired function in any sector. As generic technologies are adapted through the economy, they will inevitably precipitate changes in how the economy organizes its functions and open opportunities through their innovative application and through the enablement of new functionalities (Yoo et al., 2012). The impact of digital technologies is particularly pervasive, since digital programmability enables the coding of complex and knowledge-intensive functions that might not have been possible before. This feature, then, is the key driver of digitalization, or the application of digital technologies in the economy and society such that those technologies become infrastructural (Tilson, Lyytinen, and Sørensen, 2010). Through the process of digitalization, digial technologies and infrastructures become a 4 core element of the context in which business firms organize for the creation, delivery, and capture of economic and customer value, allowing these to perform desired functions in radically new ways. A core aspect of the process of economic digitalization is that through their pervasive application, digital technologies open up new opportunities for innovative combination of existing functions and across product and sector boundaries. This makes digitalization a potent driver of combinatorial innovation—i.e., the creation of new functions and functionalities by combining existing functions (Henfridsson et al., 2018). This dynamic drives digitally-induced structural transformation by breaking down barriers that used to separate conventional industry sectors. As all industry sectors increasingly rely on the pervasive digital infrastructure for their operation, opportunities to fundamentally re-think how the economy and society might work are created and the prospect of novel combinatorial innovations enhanced, opening unprecedented opportunities for entrepreneurs to discover, invent, and advance new digital reality. When considering the effects of digitalization on the organization of economic, innovative, and entrepreneurial activity, it is useful to distinguish between three manifestations of digital technologies: digital artifcats, digital platforms, and digital infrastructures (Nambisan, 2017). Digital artifacts are digital components, applications, or digital content and media (including data and machine learning algorithms) that offer specific functionalities or value to the end user (Kallinikos, Aaltonen, and Marton, 2013). Many digital artifcats result from digitally-induced servitization by which existing, often physical services are encoded into digital form (e.g., fintech applications), on the one hand, or by which physical artifacts are servitized by wrapping them into a digital envelope (e.g., redefining conventional car ownership as a service offered as part of a digitally coordinated mobility service). Such artifacts exploit the ability of digital technologies to decouple physical form from related information (Cecez-Kecmanovic et al., 2014). This enhances combinatorial flexibility, as digital artifacts can be easily combined with one another and with digital platforms to enable new functionalities and applications, thereby boosting innovative experimentation with novel combinations. Digital platforms are shared sets of services, architectures, interfaces, and technical standards that enable many hierarchically independent stakeholders to make their offerings available to wide audiences and combine their digital artifacts with those of others (Gawer, 2020; Gawer and Cusumano, 2008; Thomas, Autio, and Gann, 2014; Van Alstyne, Parker, and Choudary, 2016). Although digital platform operate as an important medium for distributing and accessing digital artifcats, they also operate their own dynamic, as platform owners seek to harness generativity and network effects for the creation and capture of economic and user value through, e.g., data network effects (Gregory et al., 2020). By operating as venues that enable large, nonhierarchically related audiences to coordinate and combine their activities, digital platform ecosystems have emerged as an important novel form of economic organization in their own right, operating as hubs of wide-ranging activity systems (Thomas and Autio, 2020). So doing, they have been transforming entrepreneurial opportunity landscapes and greatly expanding the reach of entrepreneurial opportunities to new audiences previously disconnected from them. Digital infrastructures are defined as digital technology tools and systems that offer connectivity, communication, collaboration, and computing capabilities to support innovation, entrepreneurship, and other forms of economic activity (Nambisan, 2017). Digital infrastructures provide the fabric that underpins and enables modern societies. Because of mobile connectivity, edge computing, Internet of Things, cloud computing, and other connectivity technologies and digital resources, virtually anything anywhere can be connected to digital infrastructures at any 11 - Reliance on Mobile and Web applications (our business relies on... (1) our own mobile applications; (2) our own applications in the Internet) - Reliance on Industrial Internet technologies (our business relies on... (1) Internet of Things (IoT), Industrial Internet of Things (IioT); (2) Robotics, intelligent machinery; (3) Blockchain, distributed ledgers) The scale values were then computed as weighted averages of individual statements, using factor loadings as weights. Application of digital technologies in the firm’s business model. The application of digital technologies by the business queried how the businesses used digital technologies in different aspects of their business model. For these scales we sought inspiration from previous literature on digitalization and business models (e.g., Bouwman, Nikou, and De Reuver, 2019; Parida, Sjödin, and Reim, 2019; Proksch et al., 2021). Consistent with received conceptualizations, we defined a business model as the firm’s architecture of activities for the creation, delivery, and capture of customer value (Zott and Amit, 2007, 2010). Drawing on and inspired by received theory, previous empirical operations, and our own reasoning, we designed the questionnaire to incorporate a total of 23 statements querying the application of digital technologies in four aspects of the firm’s business operations: (1) internal activities (8 items); (2) marketing, sales, and customer interactions (7 items); (3) products and services (3 items); (4) partnerships (4 items). Principal component analyses yielded four factors with Eigenvalues greater than 1. After removing items with no strong loadings on any factor and items with strong loadings on more than one factor, a total of 17 individual items were retained: six for internal activities; six for marketing, sales, and customer interactions; three for products and services; and two for partnerships. The scale values were computed as weighted averages of individual statements, using factor loadings as weights. The scale compositions are shown in Table 1. Table 1 Application of Digital Technologies in the Firm’s Business Model: Scale Composition We are interested in how you use digital technologies in your business. How well do the following statements describe your operations? (1=not at all … 5=perfectly) Scale Items Internal Activities Our human resource processes are fully digitalized (e.g., salary payments, recruitment, training…) Our customer management system and customer databases are fully digitalized Our accounting system is fully digitalized We use digital technologies and data to optimize our manufacturing, service, and logistics We use digital technologies for resource and inventory planning We are a fully data-driven company Marketing, Sales, Customer Interactions We advertise our products and services primarily through digital channels We constantly use social media to interact with customers (e.g., Facebook, Instagram, TikTo k , LinkedIn, Twitter, Line) We constantly monitor how our customers interact with our website and social media (e.g., clicks, views, etc) Continued on the next page 12 We are interested in how you use digital technologies in your business. How well do the following statements describe your operations? (1=not at all … 5=perfectly) Our customers can order or pay online (or both) We actively monitor our online ratings and customer reviews online We operate our own online user community Product and Service Our products and services are fully digital Our products and services are connected to a mobile app We use digital platforms to test new products and services and get user feedback Partnerships We actively work with partners to increase sales We collaborate with partners to create new services for our customers Source: Adapted from Djukic (2024). Business Model Experimentation. In measuring business model experimentation, we wanted to capture the degree to which the firm had recently adjusted aspects of its business model. Any change in the business model was interpreted as an experiment to improve the business operation. Seeking inspiration from received empirical and theoretical literature (e.g., Parida, Sjödin, and Reim, 2019; Spieth and Schneider, 2016; Zott and Amit, 2007, 2010), we created 11 items that queried the degree to which the firm had changed any aspects of its business model over the past year (1=no change ... 5=complete re-think). A principal component analysis showed that all statements loaded cleanly on a single factor. The scale value was then computed as the weighted average of individual statements, using factor loadings as weights. The scale composition for the business model experimentation variable is shown below. Table 2 Business Model Experimentation: Scale Composition Over the past 12 months, have you changed any of the following elements of your business model? (1=no change … 5=complete re - think) Scale Items Business Model Experimentation Our target customers and customer segment Our sales and marketing operations How we interact with our customers How we make and deliver our products and services Our partnerships (i.e., who we work with—other than suppliers) Our suppliers Our products and services What activities we do ourselves and what activities our partners do How we generate revenue (e.g., how we charge for our products) What business opportunities we address Our entire business model—i.e., how our company does business and organizes its operations Source: Authors. 13 4.2.3 Outcome Variables We assessed two sets of firm-level performance variables in the study: business performance and sustainability performance. The first set of outcome variables measured the firm’s business performance and sought to capture any effects of firm-level digitalization on business performance, as mediated by the firm’s digitally-enhanced ability to experiment with and adjust its business model to take the best possible advantage of the business opportunity. The second set of outcome variables focused on the sustainability performance of the business and sought to capture any effect of firm-level digitalization and business model experimentation for three dimensions of business sustainability: environmental sustainability, social sustainability, and stakeholder welfare. In tracking the business performance of the firms, we faced a dilemma of choosing between coverage and data quality. Our target population was new, entrepreneurial businesses that used digital technologies. No readily available records existed tracking their financial performance. The country teams also thought that if the survey were to inquire about financial details, this would likely push up non-response rate and make it difficult to sample a large enough number of companies. Therefore we opted for more qualitative proxies of business performance that did not require querying potentially sensitive information. Instead of measuring performance based on accounting data, we queried business performance in two different ways. First, we asked the company to assess how well their business had performed, as compared against the goals and expectations that they had had for their companies 12 months earlier. Six statements were developed, some of which focused more on financial performance (sales growth, profitability, and number of paying customers), and three focusing more on operational performance (new products and services, operational efficiency, and ability of the business to cope with the COVID-19 crisis). Second, we asked the respondents to compare the performance of their business against a typical competitor over the past 12 months. The same six scales were used. As expected, the performance-against-own-expectations statements loaded on two factors, both of which had an Eigenvalue over 1. One set of statements captured financial performance and the other operational performance, as shown in Table 3. As before, the scales were computed as weighted averages of the statements, using factor loadings as weights. Table 3 Business Performance Against Entrepreneur’s Expectations: Scale Composition Comparing against your goals and expectations you had for the company one year ago, how well has your company performed during the past 12 months? (1=much worse … 5=much better) Scale Items Financial Performance Against Expectations Sales growth Profitability Number of paying customers Operational Performance Against Expectations Development of new products and services Efficiency of our operations Our ability to cope with the COVID-19 crisis Source: Authors. 14 In contrast to performance against own expectations, the statements inquiring the companies’ self-assessed performance against typical competitors all loaded on a single factor with an Eigenvalue over 1. This probably reflects the fact that the entrepreneurs might not have had a detailed understanding of the different aspects of the performance of their competitors. In addition, the statement concerning ability to cope with the COVID-19 pandemic did not load strongly on the factor and was excluded from the final composite variable. The statements measuring selfassessed performance against peers are shown in Table 4. Table 4 Business Performance Against Peers: Scale Composition How does your company’s performance compare against your typical competitor over the past 12 months? (1=much worse … 5=much better) Scale Items Performance Against Peers Sales growth Profitability Number of paying customers Development of new products and services Efficiency of our operations Source: Authors. Our business performance measures being qualitative self-assessments, our analysis does not provide ‘hard’ data on financial performance. However, qualitative performance metrics also have advantages, especially when measuring the performance of new, entrepreneurial businesses that are still evolving rapidly. Generally speaking, financial performance metrics apply best to going concerns, who are fully developed and established as a steady-state business operation. It usually takes roughly a decade for an entrepreneurial business to reach that stage. Because different entrepreneurial businesses might be going through different stages in their development, measures of performance against the owner’s reasonable expectations may be less susceptible to bias resulting from that fact. In addition, our measure of operational performance also captures some aspect of the resilience of the business in the face of the COVID-19 pandemic, which would have impacted the surveyed businesses during the period of study. Finally, performance expectations are calibrated by general performance expectations in a given sector, which is helpful given the cross-sector nature of our sample. Finally, we measured the self-assessed sustainability performance of the businesses. Consistent with UN Sustainable Development Goals and related literature, we sought self-assessments of three aspects of business sustainability: environmental sustainability, social sustainability, and stakeholder sustainability (Fiksel, 2012; Lüdeke-Freund et al., 2018; Muhmad and Muhamad, 2020; Nikolaou, Tsalis, and Evangelinos, 2019; Parida, Sjödin, and Reim, 2019; Roberts and Tribe, 2008). Environmental sustainability approximates the impact the business operation has on its natural environment, or the size of its ‘environmental footprint’. A business with a large environmental footprint would generate a large negative externality on its natural environment. Social sustainability measures the impact the business operation has on its local community at large. A socially sustainable business would create a positive externality on its local social community. Stakeholder sustainability measures how well the business treats its key 15 stakeholders, such as employees, suppliers, and business partners. A stakeholder sustainable business would treat its stakeholders fairly and equitably. Drawing on received literature, we designed a total of 21 statements to measure different aspects of business sustainability performance. Nine of these measured environmental sustainability, six statements measured social sustainability, and six statements measured stakeholder sustainability. The principal-components factor analysis (orthogonal varimax rotation) revealed that all statements measuring social sustainability loaded on a single factor with an Eigenvalue greater than 1. Four of the five statements measuring stakeholder sustainability also loaded on a single factor. However, the statements measuring environmental sustainability loaded on two separate factors, each with an Eigenvalue over 1. A closer inspection revealed that three of the statements measured the environmental sustainability, as practiced in the internal operations of the business. Six of the statements measured externally-oriented environmental sustainability, as reflected in the sustainability mission of the business. The statement composition of the four measures of sustainability performance are shown in Table 5. Table 5 Business Sustainability Measures: Scale Composition We are interested in any actions you may have taken to enhance the environmental and social sustainability of your business. How well do the following describe your company? (1=not at all … 5=perfectly) Scale Items Environmental Sustainability (internal) We go well beyond the minimum required by legal authorities to minimize any negative impact of our business on the environment (e.g., waste, recycling, etc) We take great effort to use renewable and environmentally friendly materials in our products and operations We recycle all our waste Environmental Sustainability (external) We have applied for or been awarded a green label or certification We monitor our suppliers closely to ensure they are environmentally sustainable We often donate to environmental causes We have a clearly defined mission to help save the environment and planet We are widely recognized as an environmentally friendly company We have a system in place to ensure we keep focused on environmental friendliness Social Sustainability We go well beyond the minimum required by legal authorities to minimize any negative impact of our business on our local community We take great effort to make a positive contribution to the social community where we operate We have a clearly defined social mission in addition to our business mission We often donate to those in need It is very important for us to be a good corporate citizen in our community Continued on the next page 16 We are interested in any actions you may have taken to enhance the environmental and social sustainability of your business. How well do the following describe your company? (1=not at all … 5=perfectly) We have a system in place to ensure we keep focused on our social mission Stakeholder Sustainability We take extra effort to treat our employees well, like family It is very important for us to treat our suppliers and partners fairly and not take unfair advantage over them We pay close attention to workplace safety It is important for us to treat all our employees equally regardless of gender, age, ethnicity, or religion Source: Authors. 5 Analysis and Findings We tested two mediation models to verify our hypotheses. These were: H1 Digital Technology Adoption enables Business Model Experimentation; H2 Business Model Experimentation Drives Business Performance; H3 Business Model Experimentation Drives Sustainability Performance; and H4 Business Model Experimentation mediates the impact of Digital Technology Adoption on Business and Sustainability Performance. All hypothesis tests were carried out with structural equation modelling, using the ‘sem’ command of Stata 12. Structural equation modelling offers the benefit of allowing to estimate the share of the mediated influence of independent variables on the outcome variable relative to the direct influence of these on the outcome variable. In other words, it permits the estimation of the relative strength of mediation in the model. Table 6 shows sample descriptives by country. The mean age in the overall sample was 4.4 years and the mean employment size (full-time equivalents) was 38.6 employees. Table 6 Sample Descriptives by Country Country n Mean (age) Min (age) Max (age) Mean (size) Min (size) Max (size) Indonesia 114 5.5 0 38 57.1 0 588 Malaysia 139 3.5 0 8 22.0 1 500 Philippines 109 3.0 1 10 13.1 0 350 Singapore 124 3.0 0 20 12.5 0 150 Thailand 100 4.1 0 12 39.8 0 588 Viet Nam 100 7.6 1 57 102.2 3 588 Employment size was winsorized at 1%, hence max(size) of 588. n=686. Source: Survey data. Table 7 shows the correlation matrix. We can see significant correlations among digitalization variables, as expected. Firm age exhibits a negative bivariate correlation with business model experimentation, indicating that the frequency of business model experimentation tends to 17 attenuate over time. Interestingly, the firm’s reliance on mobile and web applications is not correlated with its reliance on industrial internet applications (IoT, IIoT, Robotics, Blockchain). Table 7 Correlation Matrix Variables 1 2 3 4 5 6 7 8 1 Mobile and Web application 1 2 IoT, IIoT, Robotics, Blockchain 0.00 1 3 Internal Activities 0.17* 0.23* 1 4 Marketing, Sales, Customer Interactions -0.01 0.10* 0.42* 1 5 Products and Services 0.11* 0.48* 0.46* 0.27* 1 6 Partnerships 0.15* 0.21* 0.39* 0.23* 0.40* 1 7 Business Model Experimentation 0.03 0.14* 0.13* 0.10* 0.20* 0.27* 1 8 Environmental Sustainability (internal) 0.19* 0.14* 0.08* 0.10* 0.09* 0.12* 0.09* 1 9 Environmental Sustainability (external) 0.09* 0.05 0.13* 0.09* 0.07 0.13* -0.02 0.00 10 Social Sustainability 0.08* 0.15* 0.29* 0.31* 0.24* 0.28* 0.18* 0.40* 11 Stakeholder Sustainability 0.07 -0.07 0.17* 0.17* 0.00 0.15* 0.04 -0.05 12 Financial Performance 0.07 -0.03 0.11* 0.16* 0.04 0.06 -0.02 0.02 13 Operational Performance 0.05 0.13* 0.29* 0.24* 0.19* 0.11* 0.17* -0.05 14 Performance (peer comparison) 0.11* 0.09* 0.33* 0.30* 0.14* 0.16* 0.13* 0.11* 15 Firm Age 0.18* 0.18* 0.10* 0.05 0.07 0.12* -0.08* 0.20* 16 Firm Size (FTE) 0.24* 0.14* 0.06 -0.01 0.01 0.05 -0.02 0.18* * = p< 0.05, n=681. Variables 9 10 11 12 13 14 15 16 9 Environmental Sustainability (external) 1 10 Social Sustainability 0.38* 1 11 Stakeholder Sustainability 0.23* 0.26* 1 12 Financial Performance 0.00 0.03 0.05 1 13 Operational Performance 0.10* 0.16* 0.12* 0.00 1 14 Performance (peer comparison) 0.07 0.21* 0.14* 0.39* 0.43* 1 15 Firm Age 0.03 0.10* 0.01 0.09* 0.02 0.12* 1 16 Firm Size (FTE) -0.02 0.07 -0.01 0.01 0.04 0.09* 0.42* 1 * = p< 0.05, n=681. Source: Authors’ calculations. 18 Before conducting the mediation analysis, we first consider the influence of the reliance on mobile and fixed-line Internet applications on the propensity of the firm to experiment with its business model. The results of this structural equation modelling analysis shown in Table 8. The table shows effects for direct pathways. Table 8 Influence of Reliance on Digital Tech Applications on Business Model Experimentation (Direct Pathways) Business Model Experimentation Coef. Std. Err. Reliance on Mobile and Web Applications 0.1148** 0.040 Reliance on Industrial Internet Applications 0.1715*** 0.040 Controls Firm Age Included Employees (FTE) Included* Malaysia Included Philippines Included Singapore Included Thailand Included Viet Nam Included *** = p<0.001, ** = p<0.01, * = p< 0.05, n = 681, One-tailed significances. Source: Authors’ calculations. Table 8 confirms the basic effect of digital technologies on business model experimentation: greater reliance on mobile and web applications was strongly associated with the likelihood of the business introducing non-trivial changes in its business model over the past 12 months (p<0.01**). Similarly, the reliance of the business on industrial internet applications was also strongly associated with introductions of non-trivial changes in the firm’s business model over the past 12 months (p<0.001***). Both these associations were consistent with hypothesis H1. Regarding control variables, firm size was negatively associated with business model experimentation: businesses with a greater number of full-time equivalent employees were less likely to have introduced non-trivial changes in their business models over the past 12 months. However, although statistically significant, the effect size was minor. As such, this association is not surprising, as larger businesses tend to be more mature and more likely to be in the scale-up phase, where the business model is more likely to be set and the need for business model experimentation will gradually grow smaller. The effects of digitalization of different aspects of the firm’s business model are shown in Table 9. We show the direct effects of each of the digitalization variables separately—i.e., for internal activities, marketing and sales, products and services, and for partnerships. As can be seen in the table, all digitalization variables exhibited strong and statistically significant effets on business model experimentation: greater degrees of digitalization in the firm’s activities were associated with greater likelihood of non-trivial business model changes during the past 12 months. These findings further reinforce support for our first hypothesis (H1): that the application of digital 19 technologies in the firm’s business model enhances the firm’s ability to make changes to its business model, and therefore, experiment with alternative business model configurations. Note that when entered together, the digitalization of internal activities is shown as a non-significant influence on business model experimentation. This is likely due to strong correlations between the digitalization variables, which may be confounding the structural equation modelling results. Regarding control variables, firm size in full-time employees exhibits a mild negative effect on the likelihood of business model experimentation. Of the country dummies, those for the Philippines and Viet Nam show significant negative effects, indicating that the interviewed firms in these countries were less likely to report business model changes over the past 12 months. Table 9 Effect of Digital Technology Application in the Firm’s Business Model on Business Model Experimentation (Direct Pathways) Digitalization Variables Model 1 Model 2 Model 3 Model 4 Digitalization of Internal Activities 0.1395 *** Dig’n of Marketing and Sales 0.1707 *** Dig’n of Products and Services 0.1930 *** Dig’n of Partnerships 0.2823 *** Control Variables Firm Age n.s. n.s. n.s. n.s. Employees (FTE) + + + * Malaysia n.s. n.s. n.s. + Philippines ** *** * *** Singapore n.s. n.s. n.s. n.s. Thailand n.s. n.s. n.s. n.s. Viet Nam *** *** *** *** *** = p<0.001, ** = p<0.01, * = p< 0.05, + = p < 0.1, n = 681. Source: Authors’ calculations. We next consider the effects of digitalization variables on performance. Table 10 shows the effects of the reliance of the business on Mobile and Web Applications and on Industrial Internet Applications, respectively, on sustainability performance and business performance. The ‘Direct Effect’ column shows the direct effects of the predictor variables on performance only. The ‘Indirect Effect’ column shows only the effects of the reliance of digital applications on performance, as mediated through their effect on business model experimentation. The ‘Total Effect’ column shows the combined direct and mediated effects. The ‘% Med.’ column shows the proportion of the effect of the independent variables that were mediated through their effect on business model experimentation. For simplicity, we do not show the effects of control variables, although these were included in all equations. 20 Table 10 Effects of Reliance on Digital Technologies on Sustainability and Business Performance Direct Effect Indirect Effect Total Effect %Med. Environmental Sustainability (internal) Business Model Experimentation Reliance on Mobile and Web Applications Coef. Std. Err. 0.1125*** 0.0325 0.1538*** 0.0341 Coef. 0.0129* Std. Err. (no path) 0.0058 Coef. Std. Err. 0.1125*** 0.0325 0.1667*** 0.0342 7.7% Reliance on Industrial Internet Applications 0.0442+ 0.0345 0.0193** 0.0072 0.0635* 0.0343 30.4% Environmental Sustainability (internal) Business Model Experimentation Reliance on Mobile and Web Applications Coef. Std. Err. 0.0127 0.0382 0.1026** 0.0401 Coef. 0.0015 Std. Err. (no path) 0.0044 Coef. Std. Err. 0.0127*** 0.0382 0.1041*** 0.0398 n.s. Reliance on Industrial Internet Applications 0.0758* 0.0405 0.0022 0.0066 0.078*** 0.04 n.s. Social Sustainability Business Model Experimentation Reliance on Mobile and Web Applications Coef. Std. Err. 0.2163*** 0.0372 0.0489 0.039 Coef. 0.0248** Std. Err. (no path) 0.0096 Coef. Std. Err. 0.2163*** 0.0372 0.0737*** 0.0397 33.7% Reliance on Industrial Internet Applications 0.0946** 0.0394 0.0371*** 0.0108 0.1317*** 0.0399 28.2% Stakeholder Sustainability Business Model Experimentation Reliance on Mobile and Web Applications Coef. Std. Err. 0.092** 0.0375 0.0269 0.0394 Coef. 0.0106* Std. Err. (no path) 0.0057 Coef. Std. Err. 0.092*** 0.0375 0.0374*** 0.0393 28.2% Reliance on Industrial Internet Applications -0.0239 0.0398 0.0158* 0.0074 -0.0082*** 0.0394 n.s. Financial Performance (vs expectations) Business Model Experimentation Reliance on Mobile and Web Applications Coef. Std. Err. -0.0089 0.0394 0.0705* 0.0414 Coef. -0.001 Std. Err. (no path) 0.0045 Coef. Std. Err. -0.0089*** 0.0394 0.0695*** 0.0411 n.s. Reliance on Industrial Internet Applications 0.0039 0.0418 -0.0015 0.0068 0.0023*** 0.0413 n.s. Operational Performance (vs expectations) Coef. Std. Err. Coef. Std. Err. Coef. Std. Err. Business Model Experimentation 0.1442 *** 0.0374 (no path) 0.1442 *** 0.0374 Reliance on Mobile and Web Applications 0.0627+ 0.0393 0.0166* 0.0072 0.0792*** 0.0394 20.9% Reliance on Industrial Internet Applications 0.1585*** 0.0397 0.0247** 0.0086 0.1832*** 0.0396 13.5% Performance vs Peers Business Model Experimentation Reliance on Mobile and Web Applications Coef. Std. Err. 0.1295*** 0.038 0.102** 0.0399 Coef. 0.0149* Std. Err. (no path) 0.0068 Coef. Std. Err. 0.1295*** 0.038 0.1168*** 0.04 12.7% Reliance on Industrial Internet Applications 0.0843* 0.0403 0.0222** 0.0083 0.1066*** 0.0401 20.8% Controls Firm Age Employees (FTE) Malaysia Philippines Singapore included included included included included included included included included included included included included included included Thailand included included included Viet Nam included included included *** = p<0.001, ** = p<0.01, * = p< 0.05, + = p < 0.1. Two-tailed significances shown. Source: Authors’ calculations. We first consider the effect of business model experimentation on performance. Looking at the Total Effect column, we can see that all associations between the business model experimentation variable and the different outcome variables are statistically highly significant, confirming the basic thesis that business model experimentation is an important driver of both sustainability and business performance. However, for one performance variable—the firm’s 27 survey data from 681 digital entrepreneurial businesses from Indonesia, Malaysia, the Philippines, Singapore, Thailand, and Viet Nam. Our analysis provided broad and consistent support to our theoretical model: the reliance of the business on select digital applications and the digitalization of different aspects of the firm’s business models were found to be potent drivers of business model experimentation in entrepreneurial businesses. Business model experimentation was found to be a potent predictor of both business performance and sustainability performance. We also observed consistent mediation effects of digitalization variables on performance through their effect on business model experimentation, although the digitalization variables also exhibited strong direct effects on performance. This last observation signals that the adoption of digital technologies by entrepreneurial businesses has more wide-ranging beneficial impacts than their facilitating effect on business model experimentation. We consider the findings reported here to be of significant value for the design of entrepreneurial and digitalization policies in Asian developing economies and in emerging economies more widely. Our analysis points to important performance implications of digital technology adoption by entrepreneurial businesses. Because a non-trivial part of this dynamic operates through business model experimentation, this makes digital entrepreneurial businesses potent drivers of digital transformation in the economy. Unconstrained by legacy investment in legacy business models, entrepreneurial businesses are free to explore ways to take advantage in their business models of advances in digital technologies and infrastructures. So doing, they challenge established industry incumbents who compete with legacy business models, forcing these to restructure their operations in response. This dynamic should help drive Total Factor Productivity in the digital economy. As digitalization offers promise for developing economies to leapfrog stages in development, this dynamic means that facilitating the digitalization of entrepreneurial businesses should be a high priority for governments in such economies. In practice this means investing in digital infrastructures, extending the geogrpahical coverage of these infrastructures, and making sure that those infrastructures can be accessed at an affordable cost. It is important to develop the digital literacy of entrepreneurs such that these will be better positioned to benefit from advances in digital technologies and infrastructures. Governments should also invest in facilitating regional entrepreneurial ecosystems, as these tend to operate as important hubs of business model experimentation and innovation. Finally, because digitalization tends to make entrepreneurial opportunity pursuit a viable and accessible career option for increasingly large audiences, governments should make sure that educational systems develop entrepreneurial skills such as opportunity recognition, action orientation, experimentation, teamwork, and collaboration. Although reporting important evidence, this study is not without limitations. In order to secure a large enough respondent sample, we did not ask for financial accounting data from the businesses. Instead, we used qualitative performance measures, as self-reported by the interviewed entrepreneurs. 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ASIAN DEVELOPMENT BANK ASIAN DEVELOPMENT BANK 6 ADB Avenue, Mandaluyong City 1550 Metro Manila, Philippines www.adb.org ADOPTION OF DIGITAL TECHNOLOGIES, BUSINESS MODEL INNOVATION, AND FINANCIAL AND SUSTAINABILITY PERFORMANCE IN START-UP FIRMS Erkko Autio, Chiraphol Chiyachantana, Cynthia Castillejos-Petalcorin, Kun Fu, Raymund Habaradas, Yothin Jinjarak, Anang Muftiadi, Donghyun Park, Pattarawan Prasarnphanich, Pham Minh Quyên, and Willem Smit ADB ECONOMICS WORKING PAPER SERIES NO. 734 July 2024 Adoption of Digital Technologies, Business Model Innovation, and Financial and Sustainability Performance in Start-Up Firms This report examines how digitalization affects firm performance using survey data from 681 digital entrepreneurs in six Association of Southeast Asian Nations (ASEAN) countries. It finds that select digital applications and firm’s business model digitalization drive business model experimentation. These findings underscore the significant value for the design of entrepreneurial and digitalization policies in Asian developing economies and in emerging economies more widely. The analysis points to important performance implications of digital technology adoption by entrepreneurial businesses. About the Asian Development Bank ADB is committed to achieving a prosperous, inclusive, resilient, and sustainable Asia and the Pacific, while sustaining its efforts to eradicate extreme poverty. Established in 1966, it is owned by 68 members —49 from the region. Its main instruments for helping its developing member countries are policy dialogue, loans, equity investments, guarantees, grants, and technical assistance.