Chapter 2. Literature review: Asia’s digital transformation
2.2 The digital economy
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displacement. To transform the workforce and sustainably release labor into the tertiary sector considering ongoing trends of servitization (Kuo et al., 2019), public and private stakeholders are to establish cohesive institutional complementarities.
Industrial relations and vocational training/education should be targeted to ensure reskilling and upskilling of the workforce based on domestic industrial profiles, for instance, through flexible labor markets in tandem with constant monitoring of industries at risk, professional conversion programs, and social safety nets guaranteeing stable interim periods, or selective immigration policies to attract foreign talents like AI researchers (Araral, 2019; Hawksworth & Fertig, 2018).
Moreover, intensified multilateral collaboration may become necessary because other leaders in semiconductor manufacturing markets, such as Japan, South Korea, or Singapore are strong performers (Rasser et al., 2019). Therefore, they have the potential to set global standards in hardware production or convergence of hardware with big data-driven applications (e.g. South Korea’s smart semiconductor ambitions), as well as regulatory standards these data-derived applications are based on.
2.2 The digital economy
In establishing and preserving the foundations of an economy’s wealth, the capitalist mode of production has innate that it not only produces but also needs growth to function by exploiting labor and resources using available and allowing new technologies to boost productivity and add value. Even in the digital age, this principle remains at the core of capitalist logic, but the new resource to leverage is data – big data (Buyya, Yeo, Venugopal, Broberg, & Brandic, 2009; Srnicek, 2016).
As a hot topic of the 21st century, artificial intelligence, which is feeding on data as a resource, is namely the potential new driver of reshuffling and reconfiguring supply chains across the globe as well as for emerging new business models based around IoT. Some may associate AI with progressive improvement and enhancement of human development, others may react leery to the exponentially growing field of AI application with regard to job displacement and dystopian surveillance state scenarios à la George Orwell. However, artificial intelligence has reached new levels of
maturity over the past years and is gradually becoming a driver of digitalization and autonomous systems in all life areas, not only in the private sector for
commercialization purposes but also for public use. Therefore, the state, society,
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economy, administration, and scientific stakeholders are required to cope with AI emergence, development, and applications to adequately address opportunities and risks.
Countries worldwide have set up AI and ICT strategies to enable digital policy-finding processes that incorporate data as a resource into domestic industrial and societal development frameworks. These strategies are propelled by tremendous progress in research and application of AI systems dealing with unprecedented amounts of data, which gave scope to the recognition of digital data infrastructure being a matter of global relevance. For instance, the joint AI statement of all G7 countries, the Charlevoix Common Vision for the Future of Artificial Intelligence (2018), to promote the development and application of human-centric AI may pave the way towards global guidelines and (non-)binding codes of conducts regarding future data-handling to effectively apply and monetize on human-centric applications and solutions to humanity’s problems and needs on a global scale. For example, the statement touches upon privacy and personal data protection in tandem with the free flow of information to achieve inclusive and equality-enhancing participation rates of all societal and socio-economic stakeholders. If we go back to the connotation of data as a resource considering the internet as the infrastructure to provide and access data on a global scale, it is, therefore, important to develop common-sense towards how to cope with, extract, allocate and share this resource for the benefit of all. Scholarship advocates that “algorithm technologies are a part of broader social realities … and thus their design and development should be grounded in users’ interests and rights within a social, political, and cultural milieu” (Shin, 2019, p. 276). However, it can be argued that due to the variety of socio-political and cultural milieus in a globalized world, the perceived potential and benefits of data-driven technologies differ from country to country, depending on needs, vested interest, and application scope and scale. For instance, European countries mainly see the economic potential in AI whereas Japan goes beyond 4.0 connotations jumping straight to something they call society 5.0, a vision of AI as the next step in human evolution and an unavoidable part in everybody’s life. China emphasizes a variety of potentials from the military to civil society, whereas India stresses the social aspects of AI such as the potential to
alleviate poverty.
A thriving digital economy depends on a country’s capacity to leverage ICT and innovate. If data provides the resource for cyber-physical spaces in which
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economic and societal stakeholders interact, ICT technologies with the Internet and IoT as a platform may represent a vital public good referred to as digital utility (Chen
& Qiu, 2019; Sawada, Park, & Dembowski, 2018). The concept is also in line with computer scientists considering computing, especially cloud computing, to be the fifth utility after water, electricity, gas, and telephony (Buyya et al., 2009).
It has been long established in economics that utility markets are prone to monopolization (Newbery, 2002), so they require state regulation to balance the interests of investors and consumers for the sake of system stability and the common good (Demsetz, 1968). Usually, the state is seen as the main facilitator of utility infrastructure. Now, multi-purpose platforms such as Google in the West, Tencent’s WeChat in China, or Indonesia-based multi-service mobile application Gojek
operating in many countries Southeast Asia as well as their regional competitor Grab from Singapore, have achieved a tremendous scale beyond their core businesses from social media apps to mobile payment systems – a phenomenon referred to as
“infrastructuralization” of platforms (Chen & Qiu, 2019, p. 276). This thesis,
therefore, scrutinizes the forms in which governmental policies address and shape the distribution of digital utilities to public and private stakeholders. Cooperation between stakeholders is needed at any level to guide the technical change that Schumpeter coined with the term ‘creative destruction’ and Soete (2013) highlighted as
‘destructive creation’, benefiting the few at the expense of the many. Castells (2009) also poses the question of who contributes and creates value, and whether tech-savvy elites will be the only ones benefitting. This touches upon the discourse around data ownership and the extent to which private and public data are monetized upon.
Lundvall (2017) adds that there is a link between neoliberal deregulation that led to the 2008 crisis and ICTs, which might actually slow down the formation of a new techno-economic paradigm based around AI, the IoT, etc. However, the 2008 crisis also showed that pronounced state intervention helped Asian economies to stand out and bring back into question the extent to which governments should act on laissez-faire principles or strengthen their role in the transition to the digital economy.
The private sector is a major facilitator for data-related innovation and AI development, and viable interconnected ecosystems are strategic assets driving the private sector. In the leading countries, the US and China, globally operating
corporations and young tech companies are the main drivers of the vivid dynamics of AI development. For instance, in Japan and South Korea, globally operating and
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hardware-oriented conglomerates drive AI development. While in the US, these dynamics are reinforced by deregulation, China gradually tends towards increased state control of large technology companies. Strong market-oriented development in both Asia and the US put them in an advantageous position vis-à-vis AI development and application due to more liberalized regulatory frameworks regarding data
handling as opposed to continental Europe where firms are falling behind. Connecting AI-related research to the needs of industries has been a major challenge whereas in the US, these connections between science and the economy established over the course of decades already. In order to develop better solutions, there is a need for researchers, talented developers, tremendous amounts of data, computing capacities, strategic entrepreneurs and experienced investors, and versatile legislature. While these factors are most conducive to the successful commercialization of AI in the US and China, in Europe only the United Kingdom is beginning to do so. In Japan and South Korea, these factors are concentrated within large corporations and
conglomerates such as the chaebols in South Korea, however, local start-up
ecosystems remain small. But it is the latter that should be actively supported to the extent necessary in order to achieve inclusive and broad data-fueled ecosystems to establish thriving digital economies.
Nonetheless, innovation systems must be steered by adequate policies.
National innovation systems, as described by Nelson, Freeman, Lundvall, and Pelikan (1988) and Lundvall (2017), are strong and sustainable with cohesive institutional complementarities, which in the case of Asia’s rapid industrialization pose more of an obstacle than to Europe and the U.S. with a fairly longer period of institutionalization and constant refining of the latter (Lee & Shin, 2018). A large portion of VoC
literature predicts global convergence towards total liberalization which often is roughly referred to as the ‘more market and less government’ principle. Carney et al.
(2009) summarized VoC literature in an Asian context and offer a resourceful repertoire of VoC theory on the case of Asia-Pacific 4.0. The government steering through liberalization may enhance digital connectivity but it should also intervene and constrain neoliberal forces for the sake of all stakeholders in the society if deregulation widens the technology and the wealth gap and, thus, increases or generates new channels for inequality.
Path-dependent forces still condition policy formulation and institutional change. Liberalizing elements have been introduced in Asian economies, however,
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old patterns of policymaking that used to work back in the days of technology imitation are often still relied on to cope with new challenges of industrial upgrading in East and Southeast Asia (Felker, 2009; Kalinowski, 2009; Mahbubani, 2009;
Ozawa et al., 2001; Park, 2000; Schot & Steinmueller, 2018; Wong, 2011). The shift towards innovation can be referred to as a path-dependent function of cultural and institutional drivers and inhibitors. In their VoC compilation, Carney et al. (2009, Table 1) analyze Dodgson (2008) on national innovation systems and institutional adaptability and find that whereas “Taiwan’s network-based innovation strategy resembles liberal market economy[,] Korean firms retain commitment to large business group capital allocation methods that may retard leading-edge
entrepreneurship”. Kalinowski (2009) finds similar path-dependent forces impacting national innovation policies in Korea. Nonetheless, Mahbubani (2009) adds that path-dependence does not intrinsically impede economic change because oligopolistic chaebols factually embraced it, however, they were less interested in sharing or giving up their power and position, thus, impeding a structural change of the VoC sphere of industrial state-enterprise relations. Schot and Steinmueller (2018) elucidate this general pattern of path-dependence by saying that there is “a balance … between major disruptive innovations that alter the trajectories of search and improvement (path-disrupting), and cumulative innovations that reinforce and strengthen existing strengths and centers (path-reinforcing)” (p. 1558).
However, to explore potential fields of the nascent digital economy, different countries choose different approaches not only according to their comparative advantages and natural endowments but also with progressive policy attempts to diversify their national economic landscape. For instance, the governments of Korea and Japan invested in and nurtured the creative industry and promoted Korean and Japanese pop-music to an extent that gave rise to an entirely new sector dedicated to digital content and new marketing strategies based on entertainment: In 2018, Japan and South Korea had the third and fifth largest sales of digital media in the world and their sectors continue to grow (Holroyd, 2019, p. 13). Seoul’s newly erected Digital Media City, Korea's first creative cluster, houses broadcasting channels and was set up to connect small businesses with big players through subsidizes office rooms, etc.
(Cohen, 2013). Such creative hubs have become a target point in creative policy formulation with regard to open up new channels of enhancement for the digital economy. However, if poorly steered by the government, this can bring up new
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issues. Bunnell (2002) notes how the Malaysian multimedia corridor led to new forms of social and spatial exclusion through financial exclusion from the privatization of high‐tech spaces.