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Digital Transformations and Structural Exclusion Risks: Towards Policy Coherence for Enabling Inclusive Trajectories

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Part of the India Studies in Business and Economics book series (ISBE)

Abstract

Weaving together the literature on technology evolution, information society and digital economy, this chapter argues that several intrinsic structural features of many digital markets make them inherently exclusionary. These, together with the strategies used by digital innovators and fast followers to sustain their competitive advantages and prevent new entry, cause the adverse incorporation of developing country economies into these markets and lead to asymmetric benefits for them. This presents new challenges to the Indian economy in the ability of its firms to survive. Despite the large canvas of new opportunities offered by digital technologies, digital technologies can therefore entrench some of the existing inequalities in technology access and use, and also generate new inequalities. With data as the prime driver of several new digital technology systems, policy choices in the digital space will decidedly influence India’s digital transformation trajectories in the services, manufacturing and agricultural production spaces, as well as their societal outcomes. In particular, the national data governance regime, in relation to data ownership, security standards and data access, has a critical role to play. In order to ensure that emerging technology and business models promote competition and broader developmental benefits, the government also needs to formulate stronger antitrust policies, which acknowledge the economy-wide implications of control by digital monopolies. Reformulating trade, investment and technology policies to reflect the above concerns also come in the ambit of the institutional/regulatory shake-up that is urgently needed to allow secure, sustainable and equitable digital transformation by India.

1 Introduction1

We are increasingly experiencing the transformative impact of information and communication technologies (ICT) in myriad ways across most areas of social and economic life. The greater ICT-led transformations that we are witnessing now across sectors (compared to the earlier internet phase) follow the emergence of the inter-related technology systems driven by cloud computing, automation, digital platforms, the internet of things(IOT), artificial intelligence (AI), and so on. The increased dynamism is reflected in the emergence of several developing country start-ups. In India, these are the most prominent in service sector activities such as retail, finance, transport, restaurants, hospitality, health and education. Seemingly leveraging India’s strength in the IT software domain and driven by new business models facilitated by the new technology systems, these sectors are undergoing significant digital transformation.

Despite the large spectrum of new opportunities offered by such digital technologies for levelling the playing field for developing country firms, digital technologies can entrench some of the existing inequalities in technology access and use, leading to differential benefits from the diffusion of technological advances. The focus in this chapter is on how digital technologies may entrench existing inequalities between developed and developing countries through the latter’s adverse incorporation into the global digital economy due to several inherent structural characteristics of the emerging dominant digital markets and the many strategies used by digital innovators and fast followers to sustain their competitive advantages and prevent new entry.

The chapter strives for a systematic understanding of technological change in the digital era and the dynamics of the accompanying competitive processes and transformations. There has been a significant evolution in the theoretical understanding of the processes underlying technological change from the time early neoclassical growth theories conceived technological progress as exogenous to firms. As technologies evolve in a complex process influenced by a variety of factors, the nature of competition in the market changes through the different phases, and the changed market structure, in turn, impacts the nature of innovation and diffusion of new technologies. Accordingly, as technologies and products evolve and mature, they present different opportunities and challenges to innovating firms and first movers, as well as for new entrants and fast followers. The rate and effectiveness of the changes in firm and industry structures, as well as the pace and effectiveness of efforts to adopt and master new technologies, depend on the institutional structures enveloping the socio-economic spheres—in particular, on the extent to which state policies facilitate productive transformation through each phase of a technological revolution. All these micro, meso and macro level processes of technological evolution and diffusion involving complex interactions have been captured within the framework of evolutionary (or neo-Schumpeterian) economics, which is found in the most synthesised form in the Venezuelan-British scholar Carlota Perez’s techno-economic paradigm.

After briefly explaining this framework in Sect. 2, the chapter analyses the features and phases of the ongoing ICT revolution, including the current digital era, in Sect. 3. It also discusses how transformations being brought about by digital technologies have to be understood at three levels: (i) those in the digital space itself; (ii) those associated with the digital transformation of services; and (iii) those associated with digital transformations in the production space whether in the manufacturing or agricultural sector. Subsequently, Sect. 4 discusses the opportunities and challenges presented at the firm level during each phase of the product/technological lifecycle, to innovators, fast followers and incumbents. Against this backdrop, Sect. 5 discusses different strategies used by innovators and fast followers in the digital era to increase their market share and to erect entry barriers for new entrants from developing countries. This leads to asymmetric benefits from digital transformations for the latter. Against the backdrop of these analyses, in the last section, the chapter discusses the various economic policy changes needed in India to enable inclusive digital transformation trajectories across sectors.

2 The Techno-Economic Paradigm (TEP)

2.1 A Brief Overview of the TEP

Perez synthesised the perspectives from the historical framework of Schumpeter and the Russian economist Kondratiev on cycles and long wave theories of economic development, as well as the neo-Schumpeterian research on innovation, technological trajectories, national innovation systems, and institutions. The latter stream is associated with the names of Richard Nelson, Bengt-Åke Lundvall, Giovanni Dosi, Luc Soete, and so on, and in particular, Chris Freeman and Francisco Louçã.2 For all these neo-Schumpeterian economists, the description of technological revolutions as processes of “creative destruction” applies not only to the economy but also to policies and institutions. However, as highlighted by Kattel et al. (2009), Perez has gone further than all of them by bringing in the fundamental role of financing in technological change. Perez’s framework incorporates the financial infrastructure interplay with innovations and economic activities. Thus, she is able to relate microeconomic innovations with macroeconomic policies and activities, by marrying the historical account with institutional change and macroeconomic and financial issues (Kattel et al. 2009).

Perez (2001) observed that even though there are specific differences between technologies, most technologies tend to follow a similar trajectory as regards the rate and direction of change, from initial innovation to maturity. The lifecycle of a technology commences with the introduction of a new product based on an emerging technology. According to her, after a radical innovation gives rise to the appearance of a new product capable of generating a new industry, there is an initial period of intensive innovation and optimisation, until the product gains acceptance in the corresponding market segment. Once market acceptance is achieved, they are subjected to a series of incremental innovations following the changing rhythm of a logistic curve. While new investment and economic growth are triggered by a radical innovation, investment expansion depends on numerous incremental innovations in product enhancement and process improvement that follow. These incremental innovations have an important impact on productivity increases and market growth. Changes occur slowly at first, while producers, designers/engineers, distributors and consumers engage in feedback learning processes, which become rapid and intensive once a dominant design is established in the market. This process culminates in the product reaching maturity, and changes begin to slow down once again as new investment begins to have diminishing returns (Perez 2001: 113–114; Perez 2009: 3).

The evolution of technology, therefore, is not random or isolated in Perez’s sequence model—it is a collective process involving different agents (producers, suppliers, distributors and many others, including consumers). Further, technologies interconnect and tend to appear in the neighbourhood of other innovations. Thus technological systems consist of successive new products, services and related industries that build upon the innovative space inaugurated by an initial radical innovation (leading to a new product/technology) and which is widened by followers (Perez 2001). Just as individual innovations are interconnected in technology systems, these are in turn interconnected in technological revolutions. The process of multiplication of innovations and technological systems, both up and downstream from the industries based on radical breakthroughs, forms the core of each technological revolution. In the whole process of diffusion and social assimilation of each successive technological revolution, from big-bang to maturity, there is the recurrence of a sequence—”irruption, frenzy, synergy and maturity” (see, Francis 2018 for a detailed discussion).

Successive technological revolutions and their TEPs are, as Perez showed, the fundamental feature of capitalism after the Industrial Revolution. Accordingly, there have been five distinct technological revolutions and five associated development surges during the last 250 years: (i) the eighteenth century Industrial Revolution; (ii) the age of steam and railways of the early nineteenth century; (iii) the age of steel, electricity and heavy engineering in the late nineteenth century; (iv) the age of oil, the automobile and mass production of the early twentieth century; and (v) the age of information and telecommunication from the second part of the twentieth century.3

As mentioned, one of the salient features of Perez’s framework is the incorporation of finance into the technology cycle. Under the TEP, the propagation of a techno-economic paradigm is divided into the installation period and the deployment period. In the installation period lasting 20–30 years or more, wherein a new technological revolution acts as the instigator of a new surge of development, financial capital plays a critical role in investments in new technologies. Finance is the handmaiden that allows the new TEP to be explored, exploited and installed before it is fully deployed (Kregel 2009). However, with yet limited scope in these new technologies, overinvestment in them and increased focus on financial profits eventually lead the way to the hyperinflation of asset values and creation of a major market bubble (Kregel 2009). The subsequent inevitable crash leads to the ‘Turning Point’ in the middle of the propagation of a TEP.

It is clear from Perez’s paradigm that it is only once the financial sector is reined in by regulation, and the incentives for investments have been tilted in favour of production simultaneously, the new technologies tend to spread their transformative power across the whole economy over the next two decades or so. The latter constitutes the deployment period (Perez 2017).4 Unleashing the growth potential of each technological revolution in the deployment period requires overcoming the basic tensions inherited from the installation period. According to Perez (2007: 24), this means:
  • favouring long-term over short-term investment;

  • stimulating productive investment and employment creation, rather than feeding the financial casino or housing bubbles;

  • aiming at innovations for true market expansion and not for quick financial gains, and inducing the search for profits from real production and not from manipulating money.

Each TEP gives rise to a great surge of growth initially in the core group of industrialised countries, where, in addition to the explosive expansion of new industries, new technologies also encompass and gradually rejuvenate most of the existing industries. Perez argues that it is towards the end of the process of deployment of a TEP, when the primary industries of a particular technological revolution face maturity and market saturation that the process spreads to the periphery, while in the core countries the next great surge is already irrupting.5

2.2 Catching-Up Opportunities for the Followers

Under Perez’s TEP, apart from the mature phase of technologies, the other moment when weaker players confront surmountable barriers is not in phases two or three, rather in the phase one of irruption. This is because catching-up supposes a dynamic development process that is fuelled by local innovation and growing markets, and this requires an entry as early as feasible. The irruption phase, thus, happens to be the most promising entry point.

In contrast to how the industries of the mass production paradigm were deployed nationally first before moving internationally, many industries in the ICT paradigm have operated globally from phase one. This has been, in large part, owing to the transformation of the business organisation from the rigid hierarchical pyramids of the mass production age into flexible organisation and adaptable networks in the fifth paradigm based on ICT. This opened up the possibility of participating in global value chains (GVCs) in many roles and with varied arrangements. Even though there are differences in network structures across industries, and crucially between manufacturing, services and natural resources, the ICT revolution has seen oligopolistic/monopolistic innovating firms from the developed countries (and subsequently, a few developing countries) externalising non-strategic activities through various network formations to reduce costs and to coordinate and rationalise various linkages in these network formations.6 At the same time, strategically, this allows them to increase barriers and alter market structures to their advantage.

However, the experiences of different countries concerning the degree of integration into value chains and net benefits drawn by them have varied. A significant body of empirical research has established that in the relatively strong catching-up region of East Asia (including China), the state has had a strong influence in each case, in particular by protecting the learning efforts (see the detailed discussion in Francis 2019b). It is because of such industrial policy-led learning efforts that these economies have managed to create sustainable links to global production and innovation networks as well as manage the rise of indigenous firms as market leaders.

Given that access to key ingredients or raw materials for technological progress is widely available, ultimately the capabilities to use them—technological knowledge, applications and production facilities—become the critical deciding factor in catching-up strategies. Thus, whether follower firms can “catch-up from behind” (phase four) or forge ahead from the frontlines (phase one), industrial policy has played differing roles in fostering and managing national technological development processes by protecting the learning efforts.

Further, the nature of specialisation or the kind of activities that a country is engaged in during each TEP also determines the factors influencing its ability to catch-up. This is something classical and development economists had pointed out long ago—the presence of increasing returns activities and synergies between different types of economic activities positively influence a country’s development trajectory (Reinert and Kattel 2010; Francis 2019b). Interestingly, Hausmann and Klinger (2006) has brought this idea back to conventional economics literature through the ‘product space’ concept. Note that Perez’s conceptualisation of technology systems, wherein new technologies, products and services tend to appear in the neighbourhood of earlier innovations, clearly contains the implication that catching-up would involve building up capabilities in the earlier and/or related technologies.

A country’s ability to catchup and use new technologies successfully also depends on the existence of certain important complementary factors such as dynamic advantages and different types of externalities, especially the physical, social and technological infrastructure, and often, the existence of competent and demanding local clients. These elements may have been built-up before with mature technologies, or they can be acquired through intensive learning processes and investments in the improvement of the social and economic environment.

It must be noted that since the early 1980s, evolutionary economists have emphasized that the characteristics of technology such as path dependencies, linkages, spill-overs, externalities, winner-takes-all markets and highly imperfect and dynamic competition engender long-term structural changes in economies in the form of technology trajectories, paradigms and geographical agglomerations (Karo and Kattel 2011: 182). While these trends and patterns from the earlier TEP will form the base, the uneven and varied response of governments, firms and industries to the threats and opportunities posed by a new wave of technologies will decide whether the new trajectories will tend to accentuate or even out the uneven processes of development globally and within specific economies and societies.

3 Phases and Features of the ICT Revolution

As observed by Perez (2001, 2007), the current ICT revolution had erupted in the early 1970s with its first technology system around microprocessors, their specialised suppliers and their initial uses. Many products of the microelectronics technology system reached phase two at the beginning of the 1980s. The 1990s were marked by the vigorous development of the new telecommunications infrastructure, the wider adoption of the internet leading to the structuring of the emerging industries, and the modernisation of the existing ones. Subsequently, there was an overlapping sequence of minicomputers and personal computers, software, telecoms and the internet, each of which opened new technology systems trajectories through the 2000s, while being strongly inter-related and inter-dependent. As they appeared, these systems inter-connected with intense feedback loops in both technologies and markets.

The inter-related upstream and downstream technology systems that have evolved under the current fifth technological revolution—the ICT revolution (variously referred to in the literature as information technology/IT revolution, information revolution, etc.)—are captured in Fig. 1.
Fig. 1

Technology systems under the ICT revolution

Source Perez (2001: 116)

The 2008 global financial crisis was initially considered the Perezian turning point for the ICT TEP (Drechsler et al. 2009). However, as Carlota Perez emphasizes, although there have been some post-crisis efforts to regulate speculative finance, policy efforts aimed at redirecting finance towards productive investments have not been intensive enough nor uniform across countries to make a difference.7 Similarly, despite the urgent need for a drastic change in development models towards environmentally sustainable ones, there is the persistence of the old fossil fuel-based technologies. Perez, thus, considers that we are in a “long turning point” of the ICT revolution. 

3.1 Digital Era and the Digitalisation of Services, Industry and Agriculture

The 2010s have seen the emergence of new technological systems within the ICT revolution, which we refer to as the digital era. These have been driven by simultaneous intertwining innovations in the areas of networking, interfacing and services/content/knowledge creation through Web 2.0. These advances have led to the emergence of the inter-related technological systems of cloud computing, automation, online platforms, the IOT, and AI (Fig. 2). In all these cases, the shift from the old simple internet technologies to Web 2.0 having interoperability (wherein the website or computing system can work smoothly with other products, systems and devices)8 has enabled information processes to be organised differently (Soete 2015).
Fig. 2

The digital technology systems of the ICT evolution—The 2010s

Source Author’s illustration

Cloud computing delivers computing services—data storage, computation and networking—to users at the time, to the location and in the quantity, they wish to consume, with costs based only on the resources used (Zysman and Kenney 2016: 7). This means that powerful computing resources can be assembled more easily and deployed as needed. In other words, cloud computing expands the availability of computing while lowering the cost of access to computing resources. Value in computing moves up the value chain from the provision of the basic data infrastructure to the creation and deployment of applications based on the same. And since computing can be moved from a capital expense to an operating expense, the ability to create, experiment with, and launch new products, platforms, and so on, is radically improved (Zysman and Kenney 2016: 7).

Thus digital technologies are themselves moving at an accelerating pace giving rise to new possibilities while having the capacity to continue transforming the old.

Simultaneously, we have been witnessing critical innovations in generic industrial technologies through the turning point and into the deployment period of the ICT revolution. Called “Advanced Manufacturing Technologies” in the US and “Key Enabling Technologies” in Europe, the latter allows for new ways of manufacturing existing products, as well as for manufacturing new products (Ernst 2016).

According to Montalvo (2014), Ernst (2016), Ross (2016) and Schwab (2016) the new enabling industrial technologies encompass, for instance:
  • Continuous manufacturing of pharmaceuticals and bio-manufacturing

  • Environmental and renewable energy technologies for sustainable manufacturing

  • Photonics9

  • Industrial biotechnology

  • Nanotechnology

  • Additive manufacturing (or 3D printing), etc.

With service activities converted into codifiable, computable processes, there has been progressive digitalisation of business processes and transactions (Ernst 2016). Manufacturing is, thus, getting further transformed through radical innovations in production organisation, product and business processes. Driven by advances in digital technologies, direct ICT application areas include control technologies, advanced visual and physical human–machine interfaces, navigation and perception technologies, monitoring and diagnostics devices, locomotion technologies and integrated product-process-production system design and simulation techniques (Alcorta 2014). Innovations in all these enabling technologies together with synthesised advanced materials and custom-designed and recycled materials are expected to act as enablers of new products and services that might create new niches and new industries. All these are also expected to lead to transformations in the supply chain dynamics.

The abundance of data storage, computing power and networking abilities—enabling the analysis of data on a scale never imagined before and cross-sectoral coordination—permits the reorganisation and transformation of not only services and manufacturing but also agriculture. As described by Ross (2016), huge masses of real-time data on weather, water, air quality, soil nutrient levels, diseases—specific not just to each farm or acre, but precise at the level of each inch of the farmland—can be collected through sensors located on and off the farm. Big data is evaluating these real-time data accumulated in the cloud combined with GPS and satellite-driven weather data, and is beginning to transform agriculture into “precision agriculture”. Algorithms based on such real-time analysis enable the customised delivery of fertiliser mix to each defined portion of the farmland.                                                                                                                                                                                                                                                                                                                                  Evidently, ICT technologies are refining and re-defining existing industries as well as introducing new technologies and industries, while transforming the material conditions of societies and driving new governance and institutional formats. Thus, from a purely technological view point—as pointed out in Francis (2018)—the current phase may appear to be the synergy phase of the ICT revolution as it moves into the deployment period of the ICT paradigm (Francis 2018). These trends have accelerated globally after the COVID-19 pandemic struck. Yet, this is how states are able to re-orient their financial sectors towards the real economy and resolve the many challenges related to ecologically sustainable development, which will critically influence how the synergy phase of the ICT revolution moves forward and brings wide-spread and inclusive benefits from the deployment of the ICT revolution.

Arguably, the digital technology systems of cloud computing automation and digital platforms are going through the phase of incremental innovations. Currently, the other technology systems like IOT and AI seem to be in their initial optimisation phases immediately following introduction. However, given their inter-related nature which enables them to build on each other, advances in any or some of these and other related technology systems can accelerate their movement through the intermediate phase.

3.2 The Platforms

Service sector digitalisation has been occurring at many levels; sometimes through distinct innovations (say, electronic payment), and at other times, transforming the old with advances originating in other technology systems.10 Zysman and Kenney (2016) observed that such digital/algorithmic transformation of services, which was initially observed in the early internet phase of ICT-enabled business processes in communications, finance, media, and so on, but which has since spread further across other services through digitisation, underpins the “platform phase” of the digital era.

According to the computational understanding of the term, the platform is an infrastructure that enables the development and deployment of applications. But from an economic point of view, platforms refer to intermediaries of multi-sided11 digital markets (credited to Evans 2003; Rochet and Tirole 2006), which create value by facilitating, shaping and intermediating the terms on which economic agents (often, but not always buyers and sellers of services or products) interact with one another in a manner that makes everyone better off (Ross 2016: 91; Evans and Schmalensee 2013). Companies operating a platform create products or services that facilitate value-creating exchanges between different types of market participants, and create new markets by doing so. Figure 3 presents several different types of platform markets that have been in operation.
Fig. 3

Platforms—The multi-sided markets

Source Author’s illustration

The products that facilitate these markets where distinct user groups interact are the internet platform services themselves: a search platform enables transactions between users, content providers and advertisers; and a social media platform helps users, advertisers and application developers to meet (Lehtiniemi 2016). The platform increases the value for these economic agents by solving a coordination problem between these groups and by reducing the transactions costs they must incur in order to interact (Evans and Schmalensee 2013: 7). In the process, platforms replace and rematerialize markets, restructuring both economic exchange and patterns of information flow (Cohen 2017, as cited in Gurumurthy and Bhathur 2018).

In terms of the TEP, within the platform technology system, while e-commerce, sharing service platforms, electronic payment platforms and streaming entertainment services appear to be in the incremental innovations phase of their evolution, blockchain is in the initial optimisation phase following introduction.

Three defining characteristics of platform companies can be considered to be the following:
  • Platforms are characterised by the existence of indirect network effects, whereby the presence of end-users on one side of the market creates a positive externality that makes participation for the other more attractive, and vice versa (Amelio et al. 2017).

  • The services provided to both end-users and customers and their terms (pricing/fee structures, etc.) are based on the collection and leveraging of data about users on all sides of the market; and

  • Based on the data-based manoeuvring of their algorithmic design, platforms use their fee/pricing structures to influence transactions between different users and maximise platform value (see Amelio et al. 2017; Evans and Schmalensee 2013; Rochet and Tirole 2006).

These characteristics arise from the fact that the multi-sided platform literature assumes the presence of multiple customer groups with demand that is interdependent in various ways (Evans and Schmalensee 2013; Rochet and Tirole 2006). Indirect network effects, thus, function something like economies of scale on the demand side and increase the value that economic agents can realise from the platform (Evans and Schmalensee 2013). The interdependence of demand and indirect network effects also mean that the prices charged on one side of the market need not reflect the costs incurred to serve that side of the market. If we define one side of the market as the buyer side and the other as the seller side, then the price charged to one side (say, the buyer side) will tend to be lower when either:
  • each additional buyer generates significant extra revenue on the seller side; or

  • it is difficult to persuade buyers to join the platform.

While the consumer end-users get the services of platforms free of charge, the profit-turning side of the market consists of paying businesses which often pay both a membership fee and a usage fee (Haucap and Heimeshoff 2013; Evans and Schmalensee 2013; Lehtiniemi 2016; Amelio et al. 2017). Thus, the businesses of online platforms are made possible by “datafication”, or the transformation of the social actions of their users into quantified data (Mayer-Schönberger and Cukier 2013, cited in Lehtiniemi 2016), which is used to capture value. The access-in-exchange-for-data regime of platforms has been variously referred to as a form of governmentality (Cova et al. 2011) involving disciplining, and as extractive and surveillance capitalism (Zuboff 2015).

4 Firm-Level Opportunities and Challenges

It must be emphasised that the opportunities available to different categories of firms in a particular market—innovators/first movers, fast followers and existing firms—differ in various stages of the evolution of a technology (see Francis 2018 for a detailed discussion). In the earlier technological revolutions, because mature technologies did not generally require much prior know-how and the processes could use unskilled labour, for example, in electronics final product assembly, the determining advantage was the comparative cost profile. Therefore, phase 4 had offered opportunities for follower firms from developing countries. However, in the current phase of the ICT revolution, technological change is occurring at a more rapid pace than before. Thus, the targets for catching up and development are constantly moving and market opportunities change quickly in today’s world. Accordingly, the requirements to access and apply new technologies and to capture market opportunities may be more difficult to meet than before. This is all the more so, given the distinctive features of digital markets.

4.1 Unique Characteristics of Digital Markets

Digital technologies are characterised by specific unique features. As pointed out by Soete (2000), electronic markets by their very nature are wrought with problems of non-excludability, non-rivalry, and often, non-transparency. Owners of digital commodities selling their products/services on the market will have difficulty in preventing buyers, or anyone else for that matter, from copying and reselling it. The creation and enforcement of excludability is, therefore, an absolute and first condition for such markets to exist. Consequently, a central response by innovators and fast followers has been to create artificial excludability by focusing on encryption, watermarks and various other forms of tracing and monitoring property rights. The creation and strengthening of property rules have of course immediate implications for market structure and the degree of competition in such markets. High levels of property protection create significant challenges for new entrants and lead to less than optimal competition in a market (Soete 2000). Apart from a focus on intellectual property rights, innovators in digital markets create excludability and erect entry barriers through various anti-competitive business strategies, as we will see in detail in Sect. 5. Here, the focus is on the innate characteristics of the digital markets.

Despite the tremendous opening up of trading possibilities and the seeming increase in market transparency, the actual exchange of a digital commodity also involves, almost by definition, a high degree of information asymmetry between sellers and buyers (Haucap and Heimeshoff 2013). Many of the new forms of internet markets—including the platform companies (generating value out of providing intermediation services)—are considered innovative responses to this problem of non-transparency. However, as discussed in the ensuing section, the anti-competitive business strategies of platforms often aim at leveraging and entrenching the information asymmetry between different sides of the markets they intermediate.

Here, it is useful to examine how platform companies deal with the issue of non-excludability. As pointed out in Caillaud and Jullienne (2003), intermediation services usually are not exclusive as users often rely on services of several intermediation providers. For instance, a web-surfer looking for some specific good or service will usually visit and register with several intermediation service providers, to increase his chances of finding a match. Similarly, firms offering various services register with different intermediaries—in their segment or on multi-sectoral platforms, to benefit from their different user bases. Literature refers to the parallel usage of different platforms as multi-homing.

Excludability is often imposed by intermediaries to ensure that their efforts in processing users’ demands end up with a transaction, or because registration involves the specific building of a profile that the intermediary may consider proprietary. Caillaud and Jullienne (2003) pointed out that the use of transaction fees is central in these pricing and business strategies. Platform matchmakers rely on two pricing instruments: registration fee, which is user-specific and paid ex-ante, and a transaction fee paid ex-post when a transaction takes place between two matched parties. The Ease of multi-homing depends, among other things, on (a) switching costs (if they exist) between platforms; and (b) whether usage-based tariffs or positive flat rates are charged on the platform (Haucap and Heimeshoff 2013). Even though switching costs between search engines are very modest for consumers, new firm entry into the search engine business is not easy due to the indirect network effects and the associated economies of scale.

While ruling out transaction fees raises intermediation profits most of the time, the fact that the user’s specific profile and usage patterns become controlled as the platform’s proprietary asset leads to the other central pillar of excludability for new entrants—the control over data and data-based intelligence (or digital intelligence) by the leaders. We will discuss this in detail soon.

4.2 Indirect Network effects and Control Over Digital Intelligence

It is evident that at one level, platform companies (search engine services such as Google, Bing or Baidu; e-commerce firms like Amazon, Alibaba, eBay, Flipkart, etc.) have increased competition. The entry of new firms is facilitated by the fact that their coded nature makes these markets available even to the smallest vendors/participants (Ross 2016). Many online markets have been characterised by a large degree of Schumpeterian competition, where one temporary monopoly is followed by another (Haucap and Heimeshoff 2013).12

At another level, however, in many of the digital markets, we see a highly concentrated structure with a monopoly or a duopoly (Caillaud and Jullienne 2003). The reasons for these high concentration levels are the excludability conditions imposed by platforms to capitalise on the indirect network effects that characterise platform companies as discussed in the previous section, as well as the economies of scale that the former gives rise to.

At one level, increasing returns to scale arises from the fact that typically, multi-sided markets are characterised by a falling average cost structure due to the relatively high proportion of fixed costs and relatively low variable costs. Most of the fixed costs arise from the technical infrastructure (servers, cloud, etc.) required for managing the respective databases, such that additional transactions within the capacity of the databases usually cause hardly any additional cost (Haucap and Heimeshoff 2013: 6–8).

At a more critical level, increasing returns to scale arise from the digital intelligence the proprietary platforms claim control over. This is again linked to the presence of network effects in platforms. For instance, when Uber increases the number of cars under its pool and under-prices its rentals, it incentivises more users to register on that platform. The latter in turn draws more car owners (and advertisers) to it. Benefits of this network effect get multiplied for the platform owning company through the greater amount of data that gets generated for analytics (i.e., for learning from the data) and predictive modelling, which enable the platform-owning company to improve its algorithm further. Greater the usage base (which is increased by imposing excludability conditions on new entrants), greater is the revenue potential that accrues to the company “owning” the data (Francis 2019d). Digital platform businesses, such as Swiggy/Zomato, Ola/Uber and Facebook/LinkedIn, generate their revenues by selling the data generated from the user interactions on their platforms to third parties. Equally important is the potential for future revenue generation from the innovations based on data analytics—both for the “original” data “owning” company as well as the third party who can buy data or data intelligence from it (Francis 2019d).

In the case of e-commerce, while the entry of new firms and new growth opportunities will depend on the substitution possibilities of physical commerce with electronic commerce, followers’ business strategies invariably focus on entering and expanding the market by differentiating the kind and variety of products and services offered. Whether in e-commerce or other platform companies, the higher the degree of heterogeneity among potential users and the easier it is for platforms to differentiate among users, the more diverse the platforms that emerge, and the lower the level of concentration and therefore barriers to entry for followers. Consequently, despite indirect network effects, every digital platform market is not automatically highly concentrated. For example, several competing platforms coexist in case of online real estate brokers, travel agents, online-dating sites, etc. (Haucap and Heimeshoff 2013).

That is, while on the one hand, indirect network effects, increasing returns to scale and the proprietary ownership of technology platforms, the extracted data, and the digital intelligence will drive increasing concentration in multi-sided markets, on the other, capacity limits (and the associated the risk of platform overload),13 product differentiation and the potential for multi-homing will decrease concentration levels (Evans and Schmalensee 2008, cited in Haucap and Heimeshoff 2013. See also Martens 2016).

Hackl et al. (2014) have shown that while entry and exit of e-commerce firms are very prevalent, newcomers are more likely than in well-established physical markets to be able to influence the market structure at the core. This is because of the cheap and easy establishment of online shops, and the fact that many such shops operate only online, without a brick-and-mortar store or physical storehouse. Investigating the impact of the number of firms on mark-ups and price dispersion in e-commerce using data from an Austrian online price-comparison site (price search engine) for digital cameras, they found that the number of firms had a highly significant and strong negative effect on mark-ups. Having one more firm in the market reduced the mark-up of the price leader by the same amount as the competition between existing firms in three additional weeks in the product lifecycle. This was found to be especially true for markets for consumer electronics, where product lifecycles are particularly short.

Even so, the success of new entrants in e-commerce is not guaranteed. This is partly because indirect network externalities give rise to a “chicken & egg” problem: to attract buyers, an intermediary should have a large base of registered sellers, but these will be willing to register only if they expect many buyers to show up (Caillaud and Jullien 2003: 310). This is related to the fact that as Haucap and Heimeshoff (2013) argue, it is not easy for sellers (or buyers) to use competing online trading platforms simultaneously—that is, to engage in multi-homing. This creates a fundamental bias against new e-commerce entrants. First of all, multi-homing is difficult for small sellers because they often sell unique items and heavily benefit from a large group of customers to find buyers for their products. Additionally, it is difficult to build up a reputation on several platforms, as reputation depends on the number of transactions a seller has already honestly completed on a given network.14 Investment into one’s reputation is typically platform-specific so that there are significant costs involved in switching to another e-commerce platform. Furthermore, selling on smaller platforms bears the risk of selling the product at prices below its market value, as the price mechanism works best with a sufficiently large number of market participants on both sides of the market, that is, with sufficient market liquidity or “thickness”. As long as sellers do not switch to other trading platforms, there is only a very limited benefit for consumers in starting to visit and to search through other trading platforms (Haucap and Heimeshoff (2013).

In this context, it must also be kept in mind that lower search costs and lower switching costs for internet users increase price elasticity (Smith et al. 1999a). That is, all strategies and techniques which increase the cost of switching to another platform lower price elasticity and simultaneously provide premium pricing opportunities for innovators and become entry barriers for followers. By reducing actual or potential competition, such high market entry costs make it possible for existing market players to sustain price premiums. This is helped along by the fact that customers’ search costs (which in turn reduces information asymmetry) and sellers’ menu costs are both lower online than in conventional outlets.15 Retailers may also be able to charge a price premium by leveraging customers’ switching costs.

Various types of loyalty programs are one such strategy used by platform companies to increase switching costs of the participants and to build in excludability. Towards this, goods might be offered for free, or paid for by advertising or by subsequent upgrades; or a limited preview of the goods might be offered for free; and so on (Soete 2000; Lehtiniemi 2016). Citing Rochet and Tirole (2006), Lehtiniemi (2016) also points out how platform companies’ pricing strategy often entails selling products on one market segment below cost. Losses on one market segment are incurred to stimulate the sales of products in other, profit-turning market segments, which subsidise the loss-incurring segment (see also Rieder and Sire 2014).16 This is also called the “divide and conquer” strategy (Evans and Schmalensee 2013: 10).

Such strategies whereby one group of buyers is locked in by the incumbent with very favourable offers, to prevent a potential entrant from reaching the critical scale, allow the incumbent to monopolise the rest of the market (Amelio et al. 2017). This exclusionary strategy deployed by the incumbent in a multi-sided market is identified in the literature as “naked exclusion” (Amelio et al. 2017). Caillaud and Jullien (2003: 310) had argued that in the case of exclusive services, an incumbent might forego all potential profits to protect a monopoly position. However, with non-exclusive services, intermediaries may avoid fierce price competition and make positive intermediation profits.

The integration of various forms of verticals may be seen as another strategy to generate customer loyalty. This, for instance, is visible in Amazon or Flipkart’s strategy to process a large part of their payments directly. They push their payment applications—Amazon Pay and PhonePe, respectively—with steep cashback and incentives for shoppers, which serves to increase switching costs.

Thus, at the firm level, growth in e-commerce will depend on how it can advantageously monetise the trade-offs involved in not only replacing the particular features and relative merits of physical commerce over electronic communication and exchange, including the payments of money, but also in the ability to build loyalty (and therefore, significant switching costs) through various strategies.

Perez had pointed out that once innovators’ and early adaptors’ experience accumulated with product, process and markets reach a high point, this speeds up their incorporation of subsequent innovations so that it is even more difficult for latecomers to catch up with the leaders. We can observe this in the case of digital technologies too. Amazon is a case in point. The value creation models of large-scale internet platforms can be observed in Amazon’s strategies to maintain lead market share, which has involved several of the above kind of innovative strategies. These have led to Amazon’s transformation from an online bookseller to a marketplace for third-party sellers with a premier membership program. As reported by LaVecchia and Mitchell (2016), already half of all US households were subscribed to the membership program Amazon Prime, half of all online shopping searches started directly on Amazon, and Amazon captured nearly one in every two dollars that Americans spend online. Amazon sells more books, toys, apparel and consumer electronics than any retailer online or offline, and is also investing heavily in its grocery business.17 Beyond acquiring Whole Foods, the US grocery store chain, Amazon has now shown that it is serious in expanding its physical presence by moving further into “offline” stores. Using computer vision, machine learning algorithms and sensors to figure out what people are grabbing off its store shelves, which are added to a virtual cart,18 it had also launched its Amazon Go concept in Seattle, which lets shoppers take goods off its shelves and just walk out. Amazon’s technology charges customers after they leave by charging the customer’s credit or debit card to a smartphone. There are no cashiers, no registers and no cash in this new business model of Amazon for store shopping.

Social networks such as Facebook also share many characteristics with other online platforms. To assess the potential for competition and potential barriers to entry for followers, here again, it is important to understand: (a) whether switching costs play a major role or not, and (b) how easy it is for consumers to engage in multi-homing (Haucap and Heimeshoff 2013). Overall, the market for social networks shows lower concentration levels than other internet markets because user preferences are more heterogeneous and, secondly, it is not very costly for users to be present on two social networks, that is, to engage in multi-homing. For example, one network (such as Facebook) may be used for social contacts, while a second network (e.g., LinkedIn) may be used for business-related contacts and exchanges. Given this market segmentation, the degree of competition between various business-related networks and various social networks may decline to some extent, as direct network effects are rather strong for social networks. The main value of the network lies in the number of members subscribed to the network. However, new networks (as Google+ did in 2011) can still emerge, as multi-homing is rather easy and switching costs are not too substantial (Haucap and Heimeshoff 2013).

In Perez’s framework, passing successfully through phases two and three (of frenzy and synergy) of a technological revolution requires growing support to the innovators or leads firms from the economic environment—especially, the physical, social and technological infrastructure/capabilities, constant innovation and the existence of competent and demanding local clients and consumers (see also Parthasarathy 2013). While state policy has a central role to play in enabling many of these—as will be discussed in Sect. 6, equally crucially, capital-intensive investments and great manoeuvrability in terms of markets and alliances also play major roles (Perez 2001). Meanwhile, innovators and fast followers are making use of various ingenious strategies to entrench their monopoly positions. Such anti-competitive strategies are being used to leverage various synergies enabled by digital technologies as well as to generate new synergies based on new business/organisational models and other innovations. We will discuss this in detail in Sect. 5.

4.3 Differing Value Propositions for Leaders and Followers

It is crucial to understand that through its deployment period, the ICT revolution indeed offers immense opportunities to fast followers and new disruptors/innovators. There are also opportunities for incumbent firms facilitating their reincarnations through digital transformations. The availability of platforms, cloud, data analytics, AI, blockchain, and so on as infrastructural utility services and an increasing array of other digitised services being offered through all of them, along with the availability of risk capital (from venture capital and other funds) enable fast followers and new disruptors to self-organise, scale up rapidly and generate rapid financial returns. As Teece (1986) has pointed out, sometimes greater profits from the original innovation may accrue to fast followers or imitators with certain complementary assets and successful business strategies for integration and collaborations, rather than to the original developers of intellectual property. This, together with their dynamic capabilities, can enable some of the fast followers to overtake the original innovators in some markets.

However, in the ongoing deployment phase of the ICT TEP, continuing interactions between the evolving technological systems and the subsequent ones that may emerge mean that lifecycles of the related new upstream and downstream products/services may involve much shorter phases of maturity than under the previous TEP. Some products may atrophy and die out before even reaching maturity. Further, the immense opportunities to small businesses do not usually lead to the same wealth generation capabilities for them as the owners of the platforms or cloud or AI or blockchain, since the value gets concentrated due to the latter’s proprietary ownership of platform design and monetisation of the data generated, as well as other anti-competitive strategies. This is dealt in detail in the following section.

5 Competitive Strategies by Innovators and Fast Followers

As discussed earlier, overlapping and inter-linked innovations, rapidly falling average total costs, zero marginal costs, strong network externalities, standards battles and path dependence (Ernst 2016) as well and intelligentsification are the hallmarks of digital technologies. Arguably, internet markets are no more as frictionless as suggested two decades ago by Smith et al. (1999b). At the time, they had attributed the former to low search costs, strong price competition, low margins, and low deadweight loss in internet markets for consumer goods. However, other characteristics peculiar to digital technologies (in particular, platform companies) discussed in the previous section and speeding waves of creative disruptions are creating increased concentration and greater friction in these markets. Therefore, challenges to competitors and followers have become more complex in the digital arena.

In the ensuing discussion, the main strategies adopted by innovators/fast followers to extend their market power for generating sustained competitive advantages and consolidating monopoly positions are grouped under four major categories. All of these put up significant entry barriers and challenges to other follower firms from developing countries.

5.1 Expansion of Private Property Rights to New Spheres and Standards-Setting

Various IP-related tactics followed by innovators aimed at extending, broadening, and leveraging their monopoly power involve strategic patenting as well as other strategies leading to patent thickets and patent trolling. Since innovation often requires the use of currently existing IP, high-tech companies are often unable to innovate without violating other companies’ intellectual property rights. This leads to blocks (sometimes called a patent thicket) that delay and reduce innovation because of the long and costly negotiations involved in obtaining the multiple permissions needed (Baker et al. 2017). Clearly, the greater the IP protection, the narrower the scope and opportunities for competitors and followers to “invent around or for innovating on the shoulders of the patent holder” (Burlamaqui 2006: 6). Thus overly high IP protection goes hand-in-hand with high entry barriers.

Some firms are also involved in “strategic patenting”, that is, acquiring patents that the firm has no intention of using/exploiting, but patenting it solely to prevent others from using and profiting from it (Burlamaqui 2006; Block and Keller 2011). Large corporations have been aggressive in acquiring substantial portfolios of strategic patents as a defensive manoeuvre (Block and Keller 2011). If they are sued by another firm for infringing an existing patent, they might use some of the patents in their portfolio to mount a countersuit against the other firm (remember the Apple vs Samsung battles not so long ago) to arrive at a negotiated settlement (Francis 2018). Sometimes, private equity (PE) firms, which make strategic investments in competing start-ups, also play a role. When the winner firm eventually takes over any of the competitors (often manoeuvred by the PE fund), this transfers the ownership of the acquired firm’s patents also to the acquirer, to be leveraged by them for entrenching their market position.                                                                                                                                                                                                                                                 Companies also invest large sums of money into emerging technologies that have not yet been deployed, not solely for the patents, but because they will also give them room to influence the setting of standards, which give them a long-term competitive advantage in several related markets (Francis 2018). This is currently the case with 5G, shorthand for fifth-generation wireless technology. Given the use cases being forecast for 5G, including in the spheres of autonomous cars and the internet of things IoT, and so on, setting standards also becomes a barrier to entry.

5.2 Increasing Embeddedness of Software in Hardware and Networked Products

In the ongoing deployment period of the ICT technological revolution with the widespread application of digital technologies across sectors and spheres, the maximum size of the market for digital products is defined by the possession of the hardware by users and the existence of communications links. Given that hardware possession would be determined by incomes and telecom network penetration which requires gigantic investments, the diffusion of digital technologies might not happen equitably, whether between developed and developing countries or within countries. As pointed out by Perez (2007: 22), this means that hardware and telecom networks penetration (that determines the extent and quality of internet access) are the true market frontiers for the ICT industries. This, we argue, is an important reason why global software companies are increasingly investing in hardware technologies, and vice versa, as a strategy to retain monopoly rents. Lead platform-owing firms are, thus, entering into several sectoral verticals in the production space. The fact that Alphabet—Google’s parent company, has an autonomous car technology on which it has invested hundreds of millions of dollars over nearly a decade, is just one such instance of this attempt to maintain leadership by entering hardware segments that will see growth due to advances in the emerging technological systems. There are also ride-sharing platform firms like Uber that sees its future as dependent on self-driving cars and is investing in the same.

Advances in data analytics combined with greater sets of data such companies gain access to through their various products and business strategies enable them to develop and offer new products. These can displace existing products in industrial segments different from the digital space that the firms originally inhabited, and even traditional sectors (e.g., through the emergence of precision agriculture). It will also create a demand for new networked products. In manufacturing, this has begun to happen in a variety of industries, starting with the electronics industry. In the electronics industry, rather than being stand-alone pieces of electronics with capabilities that were limited to the hardware and software inside the unit (Ross 2016), several new networked products are being launched.

Another example is where proprietary owners of personal assistant software are releasing their hardware as a strategy to increase integration of their software and create new product ecosystems and control the standards and markets. Reportedly, Google’s strategy is to get people used to talking to the Google Assistant, whether it is on their products or the ever-growing list of third-party products that leverage their Virtual Personal Assistant (VPA). Google wants the Assistant to be to devices what its search is to the web (Pahwa 2018). In October 2018, Google announced the integration of its Assistant with the Pixel handsets and Google Home (an audio-based device which can be used to search and play music, book services, and integrate with supporting devices such as Chromecast), and indicated how these would work with multiple services, including YouTube, Maps, Street View, among others. The home also allows using voice control for TV and audio via Chromecast, allowing one to pause content, or change the volume through “handsfree voice control”. Partners can integrate with the Google Assistant. To begin with, it supports external services like Nest, IFTTT, Philips and Samsung Smarthings. It is essentially becoming a single control device for a connected smart home. Google announced an open developer platform, which will allow anyone to build for the Google Assistant. Amazon has not lagged integrating its Alexa with its Echo. As a result, for instance, in the consumer electronics industry, smart speakers and other electronics are being powered by personal assistants and voice interfaces owned by Google (Google Assistant), and Amazon (Alexa), or both. Apple is also releasing its voice-controlled speaker, the HomePod—its first Siri-enabled smart speaker.

Fast followers are attempting to copy such strategies at their level. To fight the continuing risk of commoditisation in the consumer electronics space and to deal with competition from the leaders, the media streaming company Roku, for instance, is developing its virtual assistant and plans to license it to smart speaker manufacturers in an attempt to own the entertainment experience more fully.19

The transformation being brought about by digital technologies is not limited to the electronics industry. In the automobile industry, for instance, many cars (and other vehicles soon) are coming into the market with pre-installed IoT apps and devices, which capture huge amounts of data related to the vehicle, user, traffic, pollution, and so on (Francis 2019a).

The competition to make differentiated offers to consumers is also driving integration strategies being adopted at multiple levels such as product, software, services and marketing.

5.3 Acquisition of Competitors and Innovator Start-Ups

There have been several instances of mergers or acquisitions of competitors and start-ups by leading firms to protect their dominant market position. Many leading firms have cut back their R&D efforts or shifted funds towards product development because the financial orientation of top executives means that they see new technologies as simply another asset that can be acquired (Block and Keller 2011; Ernst 2016). Acquisitions enable the leading firms to achieve many advantages: (i) to transfer the ownership of patents on the latest technological advances; (ii) to absorb the capabilities, and (iii) to crush the competition and often significantly delay new competition until the leader can garner the premium profits in a new product/new market.

Buying other companies’ technology is widespread among innovator firms in the digital economy. For example, Google purchased Boston Dynamics, a leading robotics design company with Pentagon contracts, in 2013. It also bought DeepMind, a London-based artificial intelligence company founded by wonderkid Demis Hassabis, which had taught computers how to think in much the same way that humans do. Google has been applying its expertise in machine learning and systems neuroscience to power the algorithms as it expands beyond internet search into robotics (Ross 2016: 25).

Similarly, Apple bought up the technologies it needed to launch its iPhone X with face-tracking technology and Animoji, years before its eventual launch in 2017. Apple had bought up PrimeSense, maker of some of the best 3-D sensors on the market, as well as Perceptio, Metaio, and Faceshift, companies that developed image recognition, augmented reality, and motion capture technology, respectively. These technologies enabled Apple to eventually come out with the face tracking technology which allows users to unlock the phone with their face or to lend their expressions to a dozen or so emoji with Animoji (Stinson 2017).

A related strategy for lead innovator firms, especially when they enter emerging markets like China and India20 with specific domestic market requirements (such as stringent government regulations in China), is to acquire local start-ups. For instance, when it entered China, Amazon bought up an online book retailer Joyo.com. Sometimes acquisitions take place at the personal level of top-level executives of lead firms. An example is that of John Chambers, executive chairman of Cisco Inc. picking up a 10 per cent stake in the Chennai-based speech recognition solutions company Uniphore Software Systems Pvt. Ltd.21 In addition to their technologies, acquisitions in developing countries also provide global firms with valuable insights into the business models used by domestic firms.

More recently, companies have also been signalling interest in blockchain technology through strategic acquisitions. In 2016 and 2017, Airbnb, Daimler, Rakuten, and several others acquired blockchain-related start-ups, while the investment arms of Jaguar Land Rover, JetBlue, Verizon, and others made blockchain-related strategic investments.

It must be noted that such acquisitions are becoming strategically important in varied sectors, including agriculture, and transforming agri-business firms to ICT firms. Ross (2016) discusses how major investments are being undertaken by the largest agribusinesses such as Monsanto, DuPont and John Deere. Convinced about the opportunities in the use of big data to agriculture, Monsanto has gone on a buying spree of farm data analytics companies. Even if hardware costs on sensors, smartphones and tablets come down, the business model likely to be pushed by big agribusinesses will mean that costs, and, therefore challenges to followers will come from the cost of precision agriculture software as a service.

5.4 Proprietary Ownership of Technology Platforms and Networked Data

The central barriers to new entrants in the platform markets are the incumbent leader’s emphasis on proprietary technology platforms and its “ownership” of networked data. The platform companies own the proprietary platforms used for the peer-to-peer “intermediating” functions. Also, in the absence of data protection policies, they become de facto “owners” over the data assets that are extracted over a wide range of users and data sources.

The monetisation of the surplus-value produced by platforms is exclusionary, as the platform owners control the monetisation of the extracted data assets through its platform design and keep the users/producers out of that process. All decisions about what kind of data is extracted and what is learned from it are shaped by the underlying institutional market form of platform companies (Kostakis et al. 2016). They are embedded in ways platform companies organise their markets, and collect, store and use personal data about their users (Zuboff 2015). That is, digital platforms are regulatory structures, which set the rules and parameters of action for participants, whether it is Uber/Lyft, Google, Facebook, Airbnb or others (Zysman and Kenney 2016: 23). The governance rules of such sharing platforms are, as Larry Lessig argued years ago, an outcome of the code itself, and therefore, deeply exclusionary (Lessig 2015; Kostakis et al. 2016).

According to Zuboff (2015), the value creation process in the platform-based markets takes place in three phases: data extraction, behaviour prediction and monetisation of predictions. In the first phase, the company provides products or services for people to use, and targets the users with ubiquitous extraction processes to collect data about them. The users become the sources of what Zuboff calls surveillance assets, a raw material for later phases of production. In the next phase, the company uses the extracted data as input material to produce prediction products from surveillance assets. The conversion of surveillance assets to prediction products happens by employing highly specialised analysis capabilities. Predictions include qualities, preferences, characteristics, intentions, needs and wants of users. The third phase is about converting prediction products into revenue. Accordingly, revenues come from beneficiaries of prediction products, who are not limited to only advertisers. Thus the very revenue model of platform companies is to produce “objective and subjective data about individuals and their habits for the purpose of knowing, controlling, and modifying behaviour to produce new varieties of commodification, monetisation, and control” (Zuboff 2015: 85). As observed by Ross (2016: 93), how the intermediary company redirects each of the transactions between multiple user groups through their proprietary technology platform leads to greater concentration, because revenue flows to the owners of the platform rather than the participants of the transaction.

Further, the greater the volume of data (in terms of both breadth of data from a single user as well as the breadth of user base), the greater is its predictive power through analysis, and therefore, its revenue potential. Therefore, as Rieder and Sire (2013) have argued, these companies have incentives to collect as much information as possible from the users. Moreover, when extraction and analysis of data about user behaviour improves service quality, extracting more data leads to more users and advertisers choosing the particular service, which in turn leads to even better service (Lehtiniemi 2016, p. 4). Data-based intelligence on user behaviour is also leveraged by incumbent firms for various exclusionary practices in order to attract agents on each side of these multi-sided markets and reach a viable scale. These include strategies such as “divide and conquer” (Evans and Schmalensee 2013) or “limit pricing” (Dixit 1980). Such exclusionary strategies also allow the incumbent to monopolise the rest of the market. In the case of exclusive services, an incumbent might forego all potential profits in order to protect a monopoly position (Caillaud and Jullienne 2003). 

The leadership position of the innovator tends to get entrenched due to the advantageous access to the capabilities of data collection and analysis. The material and knowledge asymmetries—both in the data extraction phase and the analysis phase—institutionalise the leader’s position in multi-sided markets. This asymmetry has been rightly described as the “big data divide” by Andrejevic (see Zuboff 2015). This is reflected in the fact that Facebook and Google now control 73% of US revenue in the digital ad market.

LaVecchia and Mitchell (2016) shows how Amazon too increasingly controls the underlying infrastructure of the economy through its advantageous position as the innovator/leader. Its marketplace for third-party sellers has now become the dominant platform for digital commerce. On the other side, its Amazon Web Services division provides the cloud computing backbone for much of the country, powering everyone from Netflix to the CIA. Its distribution network includes warehouses and delivery stations in nearly every major US city, and it is rapidly moving into shipping and package delivery for both itself and others (LaVecchia and Mitchell 2016).

By controlling the critical infrastructure across various sectors (and not just verticals), Amazon both competes with other companies and sets the terms by which these same rivals can enter the market in each and all of these segments. Moreover, redirecting all these transactions through its proprietary technology platforms also enables this company to entrench its market leader position further through “ownership” of data collected from across sectors and benefit from the digital intelligence thus generated.22

6 Towards Policy Coherence for Inclusive Digital Transformations

In the rapidly evolving digital technology space, it is evident that there will be continuous inter-related innovations in technology systems, markets and organisational forms. They bring together synergies in ICT hardware and software capabilities together with access to data of all kinds (Francis 2019a). Digital technologies will be integrated into the production of most goods and services in myriad ways. India, therefore, faces critical policy choices in shaping her development trajectory in the context of such transformations. Policies have to take into account the following three kinds of ongoing transformations:
  1. (i)

    those in the digital space itself;

     
  2. (ii)

    those associated with the transformation of services; and

     
  3. (iii)

    those related to transformations in the manufacturing and agricultural production spaces.

     

Increasingly, policy choices in the digital space will influence the trajectories of digital transformations in the services, manufacturing and agricultural production spaces, as well as their societal outcomes (Francis 2018).

Data and digital intelligence have become the prime drivers of many of the new technology systems in the digital economy. With the capital and knowledge asymmetries between lead firms and follower firms from countries like India getting entrenched through the former’s control over extracted data, value also gets concentrated, especially given the latter’s proprietary ownership of platform design and monetisation of the data extracted. As observed earlier, greater the data for analytics and predictive modelling, the greater is the revenue potential for the owners of data as well as the innovation that follows it for future revenue generation. This also means that the immense opportunities to small businesses that utilise these platforms do not usually lead to the same wealth generation capabilities for them as the owners of the platforms or cloud (or Blockchain or AI).

The increased use of sensors in devices and application-driven machines, and equally importantly, the growth in networked devices are continuously increasing the scale and scope of real-time data extraction. All kinds of public data—whether of utility usage, traffic, domestic and international trade and financial sector transactions, health, farming practices or the weather, environment or ecosystems—are also the raw material for analytics-based innovation.23 Thus how access to data is managed by the public policy will have critical implications for the “digital development trajectory” and for ensuring that the new technologies can be used widely for solving the many development problems facing the country (Francis 2019d).

Additionally, across sectors, whether it is retail, health, finance and insurance, education, and so on, or in manufacturing, extensive foreign ownership, especially of platform businesses, may imply their de facto ownership of humungous volumes of data, which can be used to build digital intelligence. Advancements in the new technological systems such as AI, network technologies, robotic process automation and cloud robotics, blockchains, and so on are all also contingent on digital intelligence. This makes it crucial to ensure that various monopolistic tendencies and predatory pricing practices in the digital spheres, and particularly in the platform business segments, are reined by regulations. Otherwise, such practices will result in heavily concentrated sectors, with concentrated “data ownership”. This will have anti-competitive implications for not just the respective digital sectors, but also the “not-yet-digital” sectors and innovative potential in the economy as a whole. This is why protecting data is critical for the needs of an inclusive digital development trajectory.

Thus, digital transformations need to be guided by the government by quickly putting in place adequate regulatory systems and policies related to data ownership, privacy and security standards. We must not have a policy of first introducing the technologies and then imposing standards. Unfortunately, this is how things have been proceeding. Unlike the previous generation of internet, linked products, lack of security standards for the emerging generation of networked products is a call to disastrous consequences for national security given the vast range of channels through which security may be compromised (Francis 2019c). Further, to capture the broad synergies that will become available through ICT deployment, IP rules must strike a balance in favour of technology diffusion.

All these entail a challenging policy task to strike a balance between data needs for innovation on the one hand, and issues surrounding privacy, data protection and the ethics of data use (including for AI, for example) on the other. India’s draft Personal Data Protection Bill 2018, the draft national e-commerce policy and the one in the making on Non-Personal Data all have data localisation rules that address the flow of data outside the country’s borders. India must resist the pressures to dilute them. Further, India must not take up legal commitments in bilateral, regional or multilateral trade negotiations on e-commerce, cross-border data flow, and so on, before it develops its digital competitiveness.

Acknowledging and protecting data as the source of competitive advantage and inclusive digital development trajectories while securing strategic national interests, public digital infrastructure provision will also be critical in finance, energy, water supply, transportation, health, education, and so on. This will go a long way in ensuring equitable access to digital infrastructural layers and the broadest sharing of the benefits flowing from digital technologies by reducing overhead and transaction costs. This is also crucial for creating new competitive advantages for domestic firms in existing and emerging new technologies.

One of the arguments made against data localisation requirement has been that the cost of localisation measures will hit start-ups and small businesses disproportionately. To help enable smaller entrepreneurs over such entry barriers, fully secure, public cloud and local data storage rentals can be provisioned, similar to what the Chinese state of Guizhou has done by setting up the Guizhou Cloud Big Data Industry Co. Ltd. Europe is also offering useful examples from which India can learn (Singh 2018; Scholz 2016). Moreover, given that digital intelligence is becoming the focus of economic value generation even in agriculture, with global seed and agricultural machinery companies transforming themselves into digital companies, continuing along the current path could have adverse implications for India’s food sovereignty too.

Policy responses to evolving corporate strategies for value appropriation in the digital economy will determine the direction and impact of digital transformations and the associated income distribution effects on aggregate demand in the economy. To ensure that emerging digital economy players promote competition and entail broader economy-wide benefits, policies need to evolve quickly to support indigenous digital companies by reigning in monopolistic tendencies and practices in the digital space. Our competition policy should also discourage the takeover of Indian technology firms in different sectors by foreign technology companies and foreign private equity funds. This calls for a reformulation of the metrics used in merger reviews24 by adopting a more strategic approach to ensuring competition within and across sectors. Building up the national ecosystem for an inclusive digital economy also calls for a level-playing field for firms and start-ups founded in India by Indian entrepreneurs. Access to capital from the government through the setting up of ingenious financing methods is the dire need of the hour so that they do not have to depend on foreign venture capitalists and foreign private equity firms.

It must also be acknowledged that without improving indigenous capabilities in electronics hardware production rapidly to leverage as well as forge synergies with India’s software capabilities, India faces the risk of witnessing another wave of import surge with adverse macroeconomic implications. Lack of rapid indigenous technological improvements could lead to dwindling of its manufacturing base across several other industries, as the use cases of the new digital technologies expand in sectors as varied as healthcare and education to industrial automation, renewable energy, public safety, smart cities, finance and agriculture. Such an impact will vary depending on:
  • the existing levels of capabilities in various industries;

  • indigenous entrepreneurs’ abilities to foresee the synergies and leverage capabilities across different areas and activities; and

  • the government’s abilities to put in place the necessary ecosystem to improve the first two capabilities through a systemic approach (Francis 2019a).

Within the ICT sector, there is a critical need to support the domestic telecom equipment manufacturing segment as it provides the network connectivity and access for the digital economy. Depending on foreign equipment manufacturers for this critical digital infrastructure layer will leave India dangerously vulnerable as the country’s strategic sectors become more and more digitalised and integrated. Equally importantly, India must ensure that critical digital infrastructure network layer—telecommunications networks—are not kept open to foreign companies. This calls for tweaking of India’s FDI rules (Francis 2020).

Ensuring equitable access to data through data protection and security policies, building up secured ICT hardware/software synergies domestically and evolving adequate public digital infrastructure provision support are some of the most urgent inter-related interventions that India needs to take on a priority basis. Acknowledging all these challenges and seeking solutions not only call for a significant leap in the capacity of bureaucracy and policymakers, but also a huge step up in the resources allocated to for educating and skilling the population and public funds allocated to SMEs, start-ups, and R&D in strategic high-technology fields. Thus, India faces a critical governance challenge to envision an inclusive digital development trajectory within the deployment phase of the ICT techno, economic paradigm and to effect a high level of coordination between the development of technological, financial, industrial and institutional capacities and capabilities needed for equitable digital transformations.

Footnotes

  1. 1.

    This chapter draws from a working paper (Francis 2018) of the author published by Centre for WTO Studies, Indian Institute for Foreign Trade, New Delhi, and is re-used here with permission.

  2. 2.

    See the detailed discussion in Francis (2018).

  3. 3.

    It is now popular to use the Fourth Industrial Revolution or Industrie 4.0 to refer to the current transformations. According to Carlota Perez, this different numbering of the Industrial Revolutions sometimes arises from the conflating of the mechanisation era and the steam and railways era. Further, unlike Klaus Schwab’s conceptualization of Industrie 4.0, we consider that microprocessors, which led to the eruption of the ICT revolution, continue to be the kernel of most of the technology systems that we see today, while others like biotechnology are generic industrial technologies that have sprung up since the frenzy phase of the ICT revolution (detailed discussion follows in Sect. 3). Also, the Klaus Schwab argument about Industrie 4.0 does not take into account the socio-institutional processes of assimilation of new technologies, which is central in Perez’s TEP.

    As Perez (2002) highlighted, while the dominant TEP moves through its mature phases (late deployment and decline), a new paradigm is gestating and moving into the early phase of installation.

  4. 4.
  5. 5.

    Citing the case of countries which had little success in promoting their development during the mass production age, even though they applied “similar” procedures for making use of imported technology like the newly industrialised East Asian developing countries (such as South Korea and Taiwan), Perez (2001) argued that the reasons for the different outcomes are “connected with the nature of the windows of opportunity created by the technological evolution of the leading countries and the capacity for consciously or intuitively taking advantage of them.” During the late 1950s to the late 1970s, catch-up development strategies adopted by several developing countries were successful owing to the nature of the techno-economic paradigm in place at the time. While relocation of production from the mature industries in the advanced countries which were faced with “technological exhaustion and market saturation” in their countries provided the push factor, developing country governments adopted different models of import-substitution industrialisation strategies to attract relocation of production by multinational corporations (MNCs). The eruption of the ICT revolution along with the changes in the international trade rules that “penalise” import-substitution industrialisation strategies and promote export-led growth strategies that pushed several developing countries simultaneously into the export markets for similar products have together meant that these conditions have radically changed (Francis 2018).

  6. 6.

    See also Ernst (2016).

  7. 7.

    The author is grateful to Carlota Perez for highlighting this important point over a private email discussion.

  8. 8.

    Interestingly, the term Semantic Web (sometimes referred to as Web 3.0) has already been coined by Berners-Lee to refer to a web of content where the meaning can be processed by machines.

  9. 9.

    Photonics is a space where information signals carried by electrons are converted to photons and vice versa. It allows for optical transmission of information and applications cover a range of areas, including lasers, consumer electronics, telecommunications, data storage, biotechnology, medicine, illumination and defence. The main developments are being driven by the telecommunications industry for smartphones and increasing bandwidth for internet transmission (Alcorta 2014).

  10. 10.

    A good example for the latter is bot or chatbot, which is a computer program that provides a chat-based interface, where clients can interact with a company through text chats or voice commands. It can be embedded and used through any major messaging application. This is an AI-based automation of the business process service provided by a customer service assistant.

  11. 11.

    A market is typically called two-sided or multi-sided if indirect network effects (or cross-side network effects) are of major importance. Indirect network effects are distinguished from the so-called direct network effects. Direct network effects mean that the utility that a user receives from a particular service directly increases with an increase in the number of other users. For example, a service such as Skype is more attractive for users the larger the number of other Skype users, as the possibility to communicate increases with the number of users. In contrast, indirect network effects arise indirectly if the number of users on one side of the market attracts more users on the other market side, as the larger the number of users on one side of the market, the larger the expected gains on the other market side (Haucap and Heimeshoff 2013, p. 3). Thus the participation of at least one of the user groups in the market impacts the value of participation for the other group. Some of the key papers discussing theoretical and empirical issues relating to multi-sided markets are Caillaud and Jullien (2003), Rochet and Tirole (2006), Evans and Schmalensee (2013), Amelio et al. (2017). See also Lehtiniemi (2016).

  12. 12.

    A notable exception has been eBay which has managed to hold on to its dominant position for long (Haucap and Heimeshoff 2013).

  13. 13.

    Advertising space is often restricted since too much advertising is often perceived as a nuisance by users, and therefore, as decreasing the platform’s value in the recipients’ eyes (Haucap and Heimeshoff 2013: 6).

  14. 14.

    It has been documented by many studies that a good reputation on eBay translates into higher prices for sellers (Haucap and Heimeshoff 2013).

  15. 15.

    Menu costs are the costs incurred by retailers when they make price changes. In online markets, it is comprised primarily of the cost to make a single price change in a central database, rather than physically changing price labels on shelves. Offline retailers will only make a price change when the benefit of the price change exceeds the cost. Lower online menu costs allow Internet retailers to make significantly more, small price changes than conventional retailers (Smith et al. 1999a).

  16. 16.

    Varian had pointed to such complex and diversified set of exchange methods in which the value of the content offered by a seller is likely to differ strongly amongst individual consumers (Varian 1997 cited in Soete 2000). This so-called versioning can be seen to be influencing switching costs, and in turn, multi-homing.

  17. 17.

    For more on Amazon’s market power and anti-competitive practices, see also Budzinski and Köhler (2015).

  18. 18.
  19. 19.
  20. 20.

    Parthasarathy (2016) discussed these strategies as a form of frugal innovation.

  21. 21.

    “Uniphore’s products help government and companies reach rural customers by interacting with them in vernacular languages”, Hindustan Times, December 1, 2017. This monopoly power also enables Amazon to formulate new “business strategies” around setting the terms for government support, which go beyond the externalities they gain from publicly-funded research and development in the US. See the discussion in Section 5.1.6 in Francis (2018).

  22. 22.

    This monopoly power also enables Amazon to formulate new “business strategies” around setting the terms for government support, which go beyond the externalities they gain from publicly-funded research and development in the US. See the discussion in Section 5.1.6 in Francis (2018).

  23. 23.

    See Singh (2019) for an extremely useful framework for formulating non-personal data policies.

  24. 24.

    See Saraswathy (2019) for a critical analysis of the Competition Commission of India (CCI)’s assessment of the 2019 Flipkart-Walmart deal.

Notes

Acknowledgements

This chapter mostly draws upon “Evolution of Technology in the Digital Arena: Theories, Firm-level Strategies and State Policies”, Working Paper No. CWS/WP/200/47, Centre for WTO Studies, New Delhi; this was an extended pre-publication output to elicit comments from readers. This was the report of a study commissioned by the Centre for WTO Studies (CWS) and is available at https://wtocentre.iift.ac.in/workingpaper/WorkingPaper47.pdf. The author is very grateful to Abhijit Das, Head, Centre for WTO Studies for his valuable guidance and support, and to O. P. Wali, Professor, IIFT, Sanjay Bahl, Director General, CERT-In, and M. R. Anand, formerly, Principal Economic Advisor, Department of Telecom, for their very constructive suggestions on the original version of the paper, as well as to the participants at the seminar held at the Centre for WTO Studies, for sharing their views. She is indebted to Carlota Perez for her most kind comments on the Working Paper. The author is also grateful to Smita Purushottam, Founder Chairperson of SITARA (Science, Indigenous Technology and Advanced Research Accelerator) and SITARA co-members for the insights they have shared. However, the author remains solely responsible for any omissions and errors.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Consultant Institute for Studies in Industrial DevelopmentNew DelhiIndia

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