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The evolution of the global digital platform economy: 1971–2021

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The emergence of digital technologies has significantly reduced the economic costs of data—search, storage, computation, transmission—and enabled new economic activities. Over the years, firms able to create a platform-based ecosystem have become a force of “creative construction.” Economic activities (C2C, B2C, B2B) have been reorganized around platform-based ecosystems for value creation and value appropriation, which are orchestrated by multisided platforms via the “digital hand.” To further understanding of the Digital Platform Economy, this paper provides a conceptual framework consisting of three interrelated concepts: digital technology infrastructure, multisided digital platforms, and platform-based ecosystems (users and entrepreneurs). Using a unique database over five decades, we revisit the hypothesis that new firms were needed to introduce digital technologies.

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Some years, like some poets and politicians, are singled out for fame far beyond the common lot and 1971 was clearly such a year. One of the events of 1971 was the inventions of the microprocessor, a computer on a chip. This invention led to the creation of the personal computer, the internet, the smart phone, and cloud computing. Over the past 50 years, economic activities have been reorganized from large bureaucratic firms to a more networked form of organization for creating value for consumers and making money for companies. To further our understanding of this digital revolution, we provide a framework consisting of three interrelated concepts: digital technology infrastructure, multi-sided digital platforms, and platform-based ecosystems. Using a unique database over five decades, we test the hypothesis that new firms were needed to introduce digital technologies. Countries that did not promote new firms fell behind in adopting the new technologies.

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  1. Wall Street Journal, Daniel Casse, ‘The Future Turns 50 This Year’, January 1, 2021.

  2. Source: count.

  3. Sachs identifies seven ages of globalization including the Industrial Age (1800–2000) and the Digital Age (2000- present).

  4. Also see Greenwood and Jovanovic (1999).

  5. Also see, Guenther, C., Lehmann, E.E. David B. Audretsch: Clarivate Citation Laureate 2021. Small Bus Econ (2021).

  6. Jensen’s concern was that incumbent firms may not exit because of free cash flow and the failure of internal control mechanisms (Jensen, 1986). Also see Fama & Jensen (1983); Jensen and Meckling (1976).

  7. For a review of the literature, see Jia, Cusumano, and Chen (2019).

  8. Also see Peitz and Waldfogel (2012).

  9. See Nambisan (2017); Srinivasan and Venkatraman (2018); Nambisan et al. (2018); and Sahut et al. (2019).

  10. For a comparison across countries, see, Acs et al., 2020, “The Digital Platform Economy Index: 2020, The GEDI Institute,

  11. Transaction cost economics explain the rise of the managerial economy and the hierarchical organization as a choice between markets or hierarchies. The shift from the managed economy to the platform economy requires a different approach—digital technology. Digital technology changes economic activity by reducing costs, and agents solve an optimization problem (Goldfarb & Tucker, 2019). There is a key assumption in transaction cost models—that the transaction is the unit of analysis. Agents check factors that impact transaction costs, including transaction properties (asset specificity, information asymmetries, and uncertainty) and agents’ properties (bounded rationality and opportunism). Once these costs are estimated, the agent chooses the cost-minimizing governance structure—that is, the market or the hierarchy. These properties primarily create three types of costs: information costs (searching supplies, distribution channels, etc.); bargaining costs (contract regulating relationship between the firm and the suppliers or customers); and monitoring costs (quality control). How do transaction costs differ from information costs? Information costs are part of transaction costs, but they are different in at least two ways. First, information cost strategies mostly use the agent (and its effort) as the unit of analysis that solves an optimization problem in a model. Second, information costs usually occur when an agent acts or makes an effort, and these costs often are not minimized but optimized; in fact, optimization is the desired goal. Good managers do not try to save money by scouting a few potential suppliers; they strive to find the best or close-to-best suppliers.

  12. These approaches were both underpinned by endogenous growth theory (Romer, 1990).

  13. See Acs and Audretsch (1987, 1988, 1990); Audretsch (1991); Acs et al. (1992, 1994); Audretsch and Feldman (1996); Anselin et al. (1997); Acs et al. (2002).

  14. For a review of the literature, see Rysman (2009); Gawer (2009); McIntyre and Srinivasan (2017); de Reuver, Sorensen, and Basole (2018); Jacobides, Cennamo, and Gawer (2018); Jia, Cusumano, and Chen (2019).

  15. Malecki (2018) emphasized the regional aspect of entrepreneurial ecosystems, and Cavallo et al. (2018) focused on the present debates and future directions.

  16. Nambisan, Wright, and Feldman (2019a, 2019b) approached the subject from the digital transformation side and how it has transformed entrepreneurship and innovation.

  17. See also Nambisan (2017); Srinivasan and Venkatraman (2018); Nambisan et al. (2018); Sahut et al. (2019).

  18. Our competitors aren’t taking our market share with devices; they are taking our market share with an entire ecosystem.

  19. See Rochet and Tirole (2003, 2006); Gawer (2009); Evans and Schmalensee (2008, 2016).

  20. See

  21. See

  22. See

  23. See

  24. See

  25. See

  26. See

  27. See

  28. See

  29. See

  30. See

  31. See

  32. See

  33. See,of%20Beijing%20seven%20years%20ago.

  34. See

  35. See

  36. See

  37. Gilder (1981, p. 43) explained that “the source of the gift of capitalism is the supply side of the economy.”.


  39. The knowledge spillover theory of entrepreneurship posits the people become entrepreneurs to seize opportunities accruing from knowledge created but not commercialized in one organizational context through innovative activity in the context of a new firm or organization (Acs et al., 2009).

  40. See

  41. “Why did Britain vote for Brexit? Looking at an individual-level analysis, Clarke et al. (2017) found both the economic influence and immigration-terrorism cost–benefit factors played a very significant role in explaining the vote to leave. However, what has not been carefully research is what aspect of the economic influence was important in leaving? Was it innovation, technology, entrepreneurship, type of industry or human capital? What the above analysis shows is that the UK has a rather strong twenty-first century digital entrepreneurial ecosystem but is stuck in a dysfunctional twentieth century European Union bureaucracy. It rejoined the LME.

  42. That the public sector was responsible for the evolution of the DPE in the late twentieth century is greatly exagerated by Mazzucato (2013). The bureaucratic structure of the managed eaconomy was unable to initiate the implementation of the new technologies and the state had even less success. See Wennberg (2019) for an alternative view.


  44. Both Taiwan and South Korea are interesting cases and deserve further integration. Taiwan, a global leader in semiconductors and computers, created new firms based on an American model. South Korea, a leader in smart phones and semiconductors, used existing firms by tweaking the Japanese model. The Koreans modified the Chaebols model by giving the private sector more leverage (Campos and Root, 1996).

  45. See

  46. See

  47. See

  48. See,of%20Beijing%20seven%20years%20ago.

  49. See

  50. See

  51. See

  52. See

  53. See

  54. See

  55. See


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This paper grew out of a larger research project on systems of entrepreneurship and entrepreneurial ecosystems, which was conducted over the past decade at Imperial College Business School and the London School of Economics. The project builds on earlier work on national systems of entrepreneurship and the Global Entrepreneurship Index at the University of Pecs, George Mason University, The Max Planck Institute in Jena, Germany, and the GEDI Institute. We would like to thank Esteban Lafuente, Robert Wuebker, Connie L. McNeely, Hilton L. Root, Silvio Vasmara, Daniel Gerlowski, Avi Goldfarb, and Saul Estrin for valuable comments, and seminar participants at the 2018 and 2020 Conference on Digital Entrepreneurial Ecosystems hosted by the Center for Entrepreneurship and Public Policy (George Mason University), the 2019 Frontiers in International Business Conference, “The Digital Economy in a Multi-Polar World” at Darla Moore School of Business (University of South Carolina), the 2020 “5th Annual Global Strategy and Emerging Markets (GSEM) Conference: Competing in the Digital World,” SC Johnson College of Business (Cornell University), the 2020 “Measuring the Digital Entrepreneurial Ecosystem: Business Policy Implications” Research Policy Special Issue Conference for helpful comments, and two anonymous referees for valuable comments. The usual caveat applies.

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Appendix The firms of the digital platform economy

Appendix The firms of the digital platform economy

1.1 Appendix A: Digital users

Access to the internet and smartphone ownership is the pre-requisites to participating in the DPE. Internet World Stats keeps track of internet users globally (see Appendix Table 5.Footnote 45 The penetration rate varies widely by continent, from a high of 90% in North America to a low of 42% in Africa. Because over 70% of the world’s population is in Africa and Asia, the worldwide internet penetration rate is 62%. These numbers translate into 4.8 billion out of 7.8 billion people in the world having access to the internet.

Table 5 Digital users: demand side. World internet usage and population statistics, 2020 (Q2 estimates)

The number of smartphone users has increased steadily and is forecast to grow by several hundred million in the next few years (see Appendix Table 6. According to Newzoo, worldwide smartphone ownership in 2016 was 2.5 billion; the number had increased to 3.5 billion by 2020.Footnote 46 China, India, and the USA have the highest number of smartphone users, each with over 100 million. Smartphone unit sales are leveling off, but the market still has high potential to grow, particularly in the emerging markets, such as China and India. The leading smartphone vendors are Samsung, Apple, and Huawei, which make up half of global sales. Putting these numbers together, we estimate that between 3.5 billion and 4.8 billion people make up the demand-side users.

Table 6 Digital users: demand side. Number of smartphone users worldwide from 2016 to 2021 (in billions)

Supply-side users are digital users that supply a good or service on a platform. Broadly speaking, digital users on the supply side include Uber drivers and Airbnb hosts, sellers on eBay and Amazon Marketplace, and all the small and medium-size enterprises that have an online presence, whether through their own website, a Facebook page, a LinkedIn profile, a YouTube channel, or a Twitter account. Finding good indicators to measure the supply-side users (especially economy-wide) was more challenging. Consider a few examples. Amazon has 6 million suppliers on its platform (supply-side users).Footnote 47 Uber has 3.9 million drivers globally (supply-side users). Didi Chuxing has 31 million drivers.Footnote 48 Airbnb has more than 650,000 hosts worldwide.Footnote 49 All of these are users on the supply-side.

Another group of supply-side users are advertisers. The highest number of active advertisers is on the Google platform, but the company does not disclose the number of international advertisers it has. According to research conducted by Macquarie, in 2015, Google had 4 million advertisers and Facebook had 2 million. As of 2019, Google App campaign catalogs 3 million sites and apps and reaches more than 800 million active users.Footnote 50

Unlike Google, Facebook publishes information on its advertisers, which is the only reliable way to estimate the number of advertisers worldwide. Furthermore, according to the Social Media Examiner, Facebook is the leading social media platform used by marketers worldwide: 94% of marketers using social media platforms rely on Facebook to advertise their business (see Appendix Table 7). As of 2020, Facebook has 9 million active advertisers (see Appendix Table 8), which include major corporations, as well as local mom-and-pop shops.Footnote 51 Most of these advertisers are small and medium-size enterprises that depend on Facebook to reach their customers.

Table 7 Digital users: supply side. Social media platforms used by marketers worldwide 2020
Table 8 Digital users: supply side. Number of active advertisers on Facebook from 1st quarter 2016 to 1st quarter 2020 (in millions)

Digital advertising spending is one way to measure the number of the supply-side users. According to the 2018 IAB Internet Advertising Revenue Report released by IAB, almost 70% of digital advertising spending (about $73 billion) goes to Google, Facebook, and Amazon. This means that total digital advertising spending worldwide tops $100 billion.Footnote 52

1.2 Appendix B: Digital entrepreneurs

In the DPE, digital entrepreneurs are the complementors that build on the platform-based ecosystem. These are software developers building mobile apps and web-based services that increase the value proposition of multisided digital platforms. There is limited information on who constitutes these digital entrepreneurs. According to Evans Data Corporation’s Global Development Population and Demographics Study published in 2016, the number of developers involved in mobile app development was 12 million in 2016Footnote 53; when the company began tracking the number of app developers for the first time in 2006, the number was less than 2 million. The number of developers who target Android platforms is 5.9 million, the number who target iOS platforms is 2.8 million. Developers targeting iOS outnumber those targeting Androids by more than 200,000 in North America, but developers in the rest of the world more often target Android platforms. According to Janel Garvin, CEO of Evans Data, “Mobile devices are everywhere, but while most modern applications support mobile devices, not all developers are working on the client target side. Some are server or backend oriented or are concentrating more on the application logic or more and more on newer machine learning implementations.”Footnote 54

There are now various listings online that track mobile application development companies. The most comprehensive listing is Clutch, which claims to have vetted 4,000 app development companies to find the best. As of September 2020, the list includes just under 20,000 firms. Software World has identified more than 7,000 mobile app development companies in the USA and curated a list of the best global companies.Footnote 55 The list in A2 ranks the top 88 firms globe-wide. The average age of these firms (measured in 2020) is 11.2 years. The youngest are year-old startups (InnovationM UK, MyAppGurus, and SegWitz Tech), and the oldest is 31 years old (Zco). The USA accounts for about half of these companies (43), followed by India (20), Ukraine (7), UK (4), Canada (3), and Russia and Romania (each 2). Belarus, Egypt, Israel, Malaysia, Poland, Australia, and Vietnam have one each. About 30 firms employ between 1 and 50 workers; 44 employ between 50 and 250 workers; only about 14 employ between 250 and 1,000 workers. Project sizes range between $1,000 and $75,000. The most common range was between $10,000 and $50,000. The hourly rate ranged from $25 to $199.

Table 9

Table 9 Top global mobile application development companies

Table 10

Table 10 Additional digital multisided platform companies

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Acs, Z.J., Song, A.K., Szerb, L. et al. The evolution of the global digital platform economy: 1971–2021. Small Bus Econ 57, 1629–1659 (2021).

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