TY - JOUR
AU - Taalbi, Josef
AB - Abstract This study examines the factors that shaped the long-term evolution of the information and communication technology (ICT) industry in Sweden, 1950–2013. Using a new historical microdatabase on actual innovation output, the driving forces and technological interdependencies in the third industrial revolution are chronicled. The results of this study support some stylized facts about technological Interdependencies in general-purpose technologies: a closely knitted set of industries has provided positive and negative driving forces for the development of ICT innovations. The historical evolution of GPTs can in this perspective be described as a sequence of development blocks. 1. Introduction What forces shape the long-term evolution of technological systems? The received view unanimously tells us how technological development is an inert process that takes time, simply because technology diffusion is characterized by the coordination and coming into place of several complementary technologies and institutional arrangements. There is however no consensus about what forces actually shape the evolution of technological systems; what types of interdependencies that matter, and how industries upstream and downstream interact to produce technological advances. Some accounts have tended to stress the role of innovational complementarities, the increased incentives to innovate in an industry owing to technological advances elsewhere, while others stress innovation as being induced by the appearance of obstacles and imbalances elsewhere in the technological system (Dahmén, 1942/1991, 1988/1991; Rosenberg, 1969; Hughes, 1987; David, 1990; Bresnahan and Trajtenberg, 1995; Lipsey et al., 2005; see also Markard and Hoffmann, 2016). Though such interdependencies have attracted interest from scholars of innovation and technology, remarkably few studies have investigated empirically from a long-term perspective what driving forces actually matter in the evolution of general-purpose technologies (GPTs) and how the formation of interdependencies in technological systems takes place. This study explores the history (1950–2013) of information and communication technology (ICT) in Sweden through the lens of a new historical micro-database encompassing in its entirety more than 6000 innovation output objects (Sjöö et al. 2014; Sjöö, 2014; Taalbi, 2014). In doing so, it is possible to address several issues raised in the theoretical literature on GPTs and major technology shifts that previously have received relatively little attention in empirical studies. The issues at hand concern the character and structure of interdependencies in technological systems. How did opportunities and problems that emerged in the technological system drive innovation in the ICT revolution? What industries supplied and used innovations? In this regard, the aim of this study is to juxtapose predictions made in theoretical literature with empirical patterns observed in Swedish ICTs. By examining innovation biographies, this study chronicles the driving forces of ICT innovation in terms of the obstacles, problems, and opportunities that have induced innovation activity in the hardware and software ICT industries from the mid-20th century. Second, an innovation flow matrix is constructed (Taalbi, 2014, 2017a) to study the supply and use of innovations and the structure of interdependencies in the ICT technological system. The article is organized as follows. Section 2 critically examines historical perspectives on major technology shifts and the third industrial revolution and derives conflicting predictions as regards driving forces of innovation and the structure of technological interdependencies. Section 3 discusses the methods and data used in the article. Section 4 speaks to driving forces of innovation in ICT. This section presents basic results on the origin of ICT innovations and narrates the driving forces in the history of the Swedish ICT innovations, making use of the collected innovation biographies for the period 1950–2013. Section 5 investigates the structure of the ICT innovation network. The concluding section discusses the corollaries of the empirical results for the theories of GPTs, technology shifts, and long-run economic growth. 2. Perspectives on the third industrial revolution The history of modern capitalism is certainly fundamentally shaped by the diffusion of pervasive and radical technologies, among which steam power, electricity, the combustion engine, biotechnology, and microelectronics are especially notable. Economic historians and economists have come up with several theories and concepts to explain these major technology shifts and their interplay with the process of economic development. According to a perspective popular among economic historians, we have in modern times seen three major technology shifts, starting with the industrial revolution of the 18th century, based on the development of steam powered technologies. A second industrial revolution was based on the internal combustion engine and electric motor, enabling the electrification of factories and homes and the postwar expansion of automotive vehicle infrastructure. We are now in the midst of a third industrial revolution, centered on the diffusion of two of the most canonical examples of GPTs: microelectronics and the Internet. How do such major technology shifts take place? What do we know about the evolution of technological systems centered on GPTs such as microelectronics and the Internet? It is well known that innovation activity and industry evolution are shaped through interdependencies between firms, technologies, industries, and institutions (Nelson, 1994; Malerba, 2002, 2005), such as collaboration networks of innovating firms, knowledge spillovers, and knowledge flows (see Verspagen, 1997; Ahuja, 2000; Jaffe et al., 2000; Neffke et al., 2011; Acemoglu et al., 2016). This study specifically examines technological interdependencies between industries, viz., interdependencies at the boundaries of sectoral innovation systems (Malerba, 2002), and how such interdependencies have shaped innovation in ICT during the course of its history. Technological interdependencies may be understood as the architecture of technological systems (Simon, 1965; Frenken, 2006), or dynamically in the sense that technological advances in a certain industry will give rise to incentives for innovation elsewhere (the sense used in this study). While there is a sizeable number of studies of firm-level networks, spillovers, and knowledge complementarities in ICTs (Corrocher et al., 2007; Fransman, 2010; Antonelli et al., 2010; Strohmaier and Rainer, 2016; Marsh et al., 2017), the role of dynamical technological interdependencies in ICTs has not been appraised in long-run studies (exceptions are discussed below). These types of interdependencies have been most significantly theorized in the context of large technological systems, spanning several theoretical approaches. Though diverse, the literature seems to agree on a couple of things. Much of the theoretical literature would agree that a GPT is “a single generic technology” that “initially has much scope for improvement,” “eventually comes to be widely used, to have many uses,” and has innovational complementarities, or “many spillover effects” (Lipsey et al., 2005: 98) Using this definition, Lipsey et al. (2005) could single out 24 GPTs, including in modern times the steam engine, the factory system, electricity, the computer, Internet, and potentially nano-technology. It appears however that much of the research on GPTs has become caught up in discussions of how to appropriately measure the GPT character of particular technologies (Hall and Trajtenberg, 2004; Feldman and Yoon, 2012), which technologies that are GPTs,2 the extent of their productivity effects and, in the most critical vein, if the notion of GPT is a useful concept to economists and economic historians in the first place (see Moser and Nicholas, 2004; Field, 2008; Bekar et al., 2016). This study argues that while the concept of GPTs has a clear place in economic–historical research, there is scope for a critical reappraisal of the GPT framework by comparison with historical frameworks proposed to explain and detail the evolution and workings of technological systems. Table 1 relates the broad historical contours of three different frameworks that have been put to use to describe major historical technology shifts: GPTs (Lipsey et al., 2005), techno-economic paradigms (TEPs; Perez, 1983, 2002; Tylecote, 1992; Freeman and Louça, 2001) and development blocks (DBs; Dahmén, 1950; Schön, 2006, 2010; Kander et al., 2014) (for comparisons of these frameworks, see Lipsey et al., 2005; Taalbi, 2016). Superficially, the frameworks appear to be similar in that they stress dynamical technological interdependencies, understood in the sense that technological advances in one place will produce incentives for innovation elsewhere. These frameworks also usually accept each other under a condition of inclusion, since they in part refer to different aspects of major technology shifts. For instance, GPTs describe the key inputs of TEPs, and some DBs are similarly formed around GPTs (see Lipsey et al., 2005; Schön, 2010; Taalbi, 2016). However, important differences appear when scrutinizing how technology shifts are described. This concerns two important aspects of the evolution of technological systems: the interdependencies that drive innovation and the structure (or “topology”) of interdependencies in the technology shift. Thus, quite importantly, though compatible with GPT theory, the TEP and DB frameworks challenge assumptions made in the literature on GPTs. This study will eventually address the mechanisms at play in the unfolding of ICT in Sweden, but let us first see in what way the frameworks differ. Table 1. Industrial revolutions, major innovations, and DBs Industrial revolution . Technological revolutionsa . GPTs . Major DBsb . First ca 1780 1. Water-powered mechanization of industry Steam engine and factory system Cotton spinning and coal 2. Steam-powered mechanization of industry and transport Railways and iron steamship Steam engines, railway infrastructure, and machine tools Second ca 1870 (1890)c 3. Electrification of industry, transport, and the home Internal combustion engine, and electricity Electrification 4. Motorization of transport, civil economy, and war Automobile, airplane, and mass production Automotive vehicles and transportation Third ca 1970 5. Computerization of entire economy Computer, lean production, Internet, and biotechnology Factory automation, telecommunications, and biotechnology Industrial revolution . Technological revolutionsa . GPTs . Major DBsb . First ca 1780 1. Water-powered mechanization of industry Steam engine and factory system Cotton spinning and coal 2. Steam-powered mechanization of industry and transport Railways and iron steamship Steam engines, railway infrastructure, and machine tools Second ca 1870 (1890)c 3. Electrification of industry, transport, and the home Internal combustion engine, and electricity Electrification 4. Motorization of transport, civil economy, and war Automobile, airplane, and mass production Automotive vehicles and transportation Third ca 1970 5. Computerization of entire economy Computer, lean production, Internet, and biotechnology Factory automation, telecommunications, and biotechnology Note: This table summarizes a broad literature that is only superficially consentient. Periodizations of long waves differ considerably between the Swedish structural cycle perspective (Schön, 2010), the Marxist long wave theory (Mandel, 1995) and the TEP framework(s) (Perez, 1983, 2002; Tylecote, 1992; Freeman and Louça, 2001). Economic historians (such as Mokyr, 1990; David, 1990; Schön, 2010) tend to employ the notions of three industrial revolutions, whereas the TEP framework discusses technological revolutions. a Based on Tylecote (1992), Perez (2002), and Freeman and Louça (2001). b Based on Schön (2006; 2010). c The dating of the second industrial revolution differs between authors. Open in new tab Table 1. Industrial revolutions, major innovations, and DBs Industrial revolution . Technological revolutionsa . GPTs . Major DBsb . First ca 1780 1. Water-powered mechanization of industry Steam engine and factory system Cotton spinning and coal 2. Steam-powered mechanization of industry and transport Railways and iron steamship Steam engines, railway infrastructure, and machine tools Second ca 1870 (1890)c 3. Electrification of industry, transport, and the home Internal combustion engine, and electricity Electrification 4. Motorization of transport, civil economy, and war Automobile, airplane, and mass production Automotive vehicles and transportation Third ca 1970 5. Computerization of entire economy Computer, lean production, Internet, and biotechnology Factory automation, telecommunications, and biotechnology Industrial revolution . Technological revolutionsa . GPTs . Major DBsb . First ca 1780 1. Water-powered mechanization of industry Steam engine and factory system Cotton spinning and coal 2. Steam-powered mechanization of industry and transport Railways and iron steamship Steam engines, railway infrastructure, and machine tools Second ca 1870 (1890)c 3. Electrification of industry, transport, and the home Internal combustion engine, and electricity Electrification 4. Motorization of transport, civil economy, and war Automobile, airplane, and mass production Automotive vehicles and transportation Third ca 1970 5. Computerization of entire economy Computer, lean production, Internet, and biotechnology Factory automation, telecommunications, and biotechnology Note: This table summarizes a broad literature that is only superficially consentient. Periodizations of long waves differ considerably between the Swedish structural cycle perspective (Schön, 2010), the Marxist long wave theory (Mandel, 1995) and the TEP framework(s) (Perez, 1983, 2002; Tylecote, 1992; Freeman and Louça, 2001). Economic historians (such as Mokyr, 1990; David, 1990; Schön, 2010) tend to employ the notions of three industrial revolutions, whereas the TEP framework discusses technological revolutions. a Based on Tylecote (1992), Perez (2002), and Freeman and Louça (2001). b Based on Schön (2006; 2010). c The dating of the second industrial revolution differs between authors. Open in new tab The first facet of interest is the mechanisms that drive the evolution of ICTs. In common to all these frameworks is the basic notion that major technology shifts are driven by interdependencies. However, the types of interdependencies that are put in center differs greatly. In its canonical form, the theory of GPTs (Bresnahan and Trajtenberg, 1995; Helpman, 1998; Lipsey et al., 2005) describes technology shifts in terms of innovational complementarities that emerge between supplier and user sectors. The diffusion of general-purpose innovations is thus induced through the increasing returns between innovation in GPTs and application sectors, forming a coordination game for which there is a Nash equilibrium (Bresnahan and Trajtenberg, 1995). In other words, the theory of GPTs is essentially a story of positive inducement mechanisms: opportunities and complementarities. Likewise, in the framework of TEPs, the pulse of technological revolutions is mediated by positive feedback mechanisms: “major innovations tend to be inductors of further innovations; they demand complementary ones upstream and downstream and facilitate similar ones, including competing alternatives” (Perez, 2010: 188). However, a long-standing claim, stressed especially in the framework of DBs (Dahmén, 1942/1991, 1988/1991; Carlsson and Stankiewicz, 1991; Enflo et al. 2008; Schön, 2010; Taalbi, 2016) and technological systems (Gille, 1978; Hughes, 1983, 1987), is that systems of technologies to an equal extent evolve in response to technological imbalances and critical problems that unsolved may hamper the development of the technological system. Innovation is here understood as problem-solving in no small part (compare Simon, 1965; Nickerson and Zenger, 2004; Frenken, 2006), or as Rosenberg (1969) famously noted: “the history of technology is replete with examples of the beneficent effects of this sort of imbalance as an inducement for further innovation” (Rosenberg, 1969: 10). This applies also to more well known and fundamental innovations. For example, the main imbalance of early steam engines was the loss of steam. The commercial feasibility of steam engines came only through inventions that directly focused on resolving these critical problems. After years of trying, John Wilkinson’s invention of the boring mill (1774) solved the problem of producing accurately bored cylinders. This in turn allowed James Watt to solve the problem of steam loss with his separate condenser in 1776. Another example is electricity. The phenomenon of electricity was known long before its economic breakthrough, but it was not until the 1890s that innovations of alternating current in a three-phase system solved the critical problem of transforming higher and lower voltage, making possible the expansion of the electricity grid (Hughes, 1983). Previous empirical studies have also stressed to the importance of critical problems in the technological development in parts of the ICT sector (see Fransman, 2001; Dedehayir and Mäkinen, 2008). One is thus not hard pressed to come up with relevant examples of the role played by critical problems as focusing innovation activity. Before carrying on, I wish to make an important clarification to the statement made here. The presence of growth bottlenecks is not foreign to the concept of GPTs. In fact, it is central (see David, 1990; Bekar et al., 2016). As the lack of measurable productivity effects of ICT was initially puzzling, famously expressed by Solow,3David (1990) pointed out that the conjunction of the initial diffusion of ICT with a productivity slowdown, is hardly a conundrum given the collected historical knowledge of the inert and time-consuming diffusion of general-purpose engines in the past; the first and second industrial revolutions were, despite being called revolutions, protracted processes facing obstacles that had to be resolved. For instance, the diffusion of electric power technology “was a long-delayed and far from automatic business,” in part due to the switching costs faced in factory electrification (David, 1990: 356). Further, Goldfarb (2005) has shown that the adoption rate of electricity was dependent on (the complexity of) technical obstacles. In brief, both productivity slowdowns and periods of stalled technological development may well characterize GPTs in early stages. However, this literature has not fully recognized imbalances, technological obstacles, and problems as being part of the driving forces and mechanisms that may focus (Rosenberg, 1969) innovation activity in the diffusion of GPTs. While recent contributions have taken some steps toward a closer examination of growth bottlenecks (Bresnahan and Yin, 2010; see also Cantner and Vannuccini, 2012), there is a large literature that would suggest that the theory underrates an important mechanism. Accordingly, there is work to be done to assess and theorize the explicit role of imbalances and technological obstacles in the diffusion of GPTs, alongside innovational complementarities. The second issue concerns the topology of interdependencies that form in the diffusion of a GPT or a major technology shift. Also in this matter there are different assumptions that have not been examined empirically in a long-term perspective. Stylized pictures of the structure of interdependencies between technologies are contrasted in Figure 1. Nodes are taken to be industries producing a technology, and linkages (edges) are taken to imply the supply and use of innovations. Typically, the interdependencies are understood in terms of the relationship between a GPT sector and several application sectors, i.e., sectors that apply the general-purpose engine (Bresnahan and Trajtenberg, 1995). Incidentally, Bresnahan and Trajtenberg (1995) envisioned the supply and use of semiconductors. The technological system is posited to have a star-like structure (Figure 1a). Though not pictured, it is often assumed that there are feedbacks and reciprocal inducement mechanisms from application sectors to the development of the GPT sector. Figure 1. Open in new tabDownload slide Stylized topologies of technological systems. General-purpose industries in red. Figure 1. Open in new tabDownload slide Stylized topologies of technological systems. General-purpose industries in red. However, as indicated by the notions of TEPs and DBs, more complicated structures could well be at play. A structure in which several basic technologies interact is illustrated in Figure 1b and c. In Figure 1b, the interdependencies are still hierarchical, with little feedback from application sectors, while in Figure 1c there is greater reciprocity (Garlaschelli and Loffredo, 2004; see also section 5). There have been some attempts to understand major technology shifts along similar lines. A first contribution to situate the position of industries in the broader technological system was given by Perez (1983), who developed a typology of the relation between producers and users of new technologies in a “techno-economic paradigm.”4 In these technology shifts, “motive branches” produce the “key inputs,” such as microelectronic components, and have “the role of maintaining and deepening their relative cost advantage” (Perez, 1983). Carrier industries implement the “key input” and induce new investment opportunities, e.g., computers, software, and mobile phones (Perez, 2010). The “induced industries” follow and innovation is a consequence of the introduction of key innovations in the motive branches. Moreover, the infrastructures, e.g., railroads, electricity, roads, and the Internet, are pivotal in a mature TEP. This typology is suitable for understanding the position of industries in technology shifts. However, on a yet finer scale, the DB approach, originating from Dahmén’s (1942/1991, 1950) work, sets focus on a core mechanism that allows us to study the diffusion of ICT in greater detail, namely, that technology shifts take place by way of sequences of complementarities that are advanced as innovation solves imbalances and tensions. Accordingly, a DB was defined as “a sequence of complementarities which by way of a series of structural tensions, i.e., disequilibria, may result in a balanced situation” (Dahmén, 1988/1991: 138; see also Carlsson and Stankiewicz, 1991; Carlsson, 1995). DBs are conceptually akin to the presence of subgroups or communities in a network that provide an impetus for innovation to their close neighbors (Taalbi, 2017a). In this view, the diffusion of GPTs is thus contingent on internal and history-specific driving forces that may develop in discrete steps among smaller sets of interdependent technologies. Thus, the diffusion of a GPT can be conceived as a series of DBs. Accordingly, this study hypothesizes that the evolution of GPTs, such as ICTs, can be understood as temporally localized sets of complementarities and imbalances between technologies or industries that for some period of time provide the core impetus for further development. Clearly, this makes the historical analysis of imbalances and complementarities the center of attention, while also suggesting that important interdependencies between industries should take place in a closely knitted set of core industries. 2.1 Summary The foremost concern of this study is to understand the types of interdependencies that have induced ICT innovation and the structure of such interdependencies across industries. From the previous literature review, it is apparent that theories of GPTs, TEPs, and DBs emphasize different types of interdependencies as playing a role in inducing innovation. Second, theory suggests that the structure of interdependencies is important to understand different outcomes in terms of innovation dynamics. The above discussion makes it interesting to ask the following empirical questions about the nature of technological interdependencies: Q1 To what extent have technological opportunities upstream or technological imbalances and problems served as inducement mechanisms for ICT innovation? Q2 How has the locus of innovation activity as responding to opportunities and imbalances changed in the course of time? Q3 Is the structure of innovation networks hierarchical or non-hierarchical? Was the interaction between upstream and downstream industries reciprocal or non-reciprocal? 3. Methods and data This study approaches the aforementioned issues by using both a quantitative and narrative historical approach, aiming mainly to describe interdependencies at the industry level of analysis. Innovation biographies are used to narrate how complementarities and imbalances emerging in the ICT technological system have influenced individual innovation processes and industry evolution. This allows a qualitative understanding of the role of interdependencies in ICT evolution and the extent to which innovations have been focused on certain imbalances and opportunities in sets of DBs. At this juncture, while more formal operationalizations are possible (see Enflo et al., 2008; Taalbi, 2017a), this study understands DBs as sets of imbalances or opportunities that drive innovation during a limited period of time and in a limited set of technologies. Second, we study the supply and use of innovations across industries, to assess the structure of flows of ICT innovation across industries. To this end, this study employs a recently constructed longitudinal micro-database, which contains extensive information about innovations commercialized by Swedish firms between 1970 and 2013 (Sjöö et al. 2014).5 This database collects biographical and statistical information about actual innovation objects from trade journals following the Literature Based Innovation Output method (Kleinknecht and Bain, 1993). The data the manufacturing sector and ICT services and is employed throughout the article. Moreover, an extension of the database for the engineering industry has been constructed for 1950–1969, here employed as sources for the historical description of early ICT innovations.6 The database was constructed by scanning 15 Swedish trade journals covering the manufacturing industry for independently edited articles on product innovations (see Appendix Table A1). Innovations are, in keeping with the Oslo manual (OECD, 2005), defined as an entirely new or significantly improved good, process, or service that is transacted on a market, and innovations were only included upon explicit description of such novelty. Apart from ensuring a coverage of all major manufacturing industries, these trade journals were selected with the criteria that journals cannot be affiliated with a company or otherwise biased and that the journal has an editorial mission to report on technological development of the industry. Moreover, only innovations developed by Swedish companies were covered, in part since the editorial mission of the trade journals is more or less confined to the Swedish market. Immediate limitations of the methodology stem from the focus on commercialized innovations. First, in-house process innovations appear in trade journals but are likely to be under-represented. Moreover, there is a success bias, i.e., due to the criterion of commercialization, failed innovation projects do not appear (Aldrich and Rueff, 2006). Hence, the historical narrative of origins of innovation (Section 4) does not account for business failures or failed development projects.7 Applying this methodology, over 6000 innovation objects have been registered through the reading of trade journals for 1950–2013. The trade journal articles provide detailed information on the innovating firm, as well as descriptions of the development and commercialization of individual innovations. This information has been used to produce time series of the commercialization of innovations and to classify innovations according to economic, social, and other factors that led to or contributed to their development. Thus, it is possible to simultaneously assess when innovations were launched, and the types of problems and opportunities that drove their development. Table 2 describes the basic data used in this study. The information available in the trade journals has enabled the construction of data on the product group and user industries of the innovations, both classified in ISIC Rev. 3 (henceforth ISIC). The product types help define our main object of analysis, i.e., ICT innovations, as consisting of five industries: computers and office equipment (ISIC 30), electrical apparatus (ISIC 31), telecommunication equipment (ISIC 32), electronic and optical equipment (ISIC 33), and software (ISIC 72). In total, 1524 of the innovations were classified as ICT innovations (see Appendix Table A2). Table 2. Description of key variables Variable . Description . Commercialization year Year of commercialization of the innovation Product type The product code (ISIC Rev. 3) of the innovation User sector The sector in which the innovation is used according to the journal article. User sector specified as industries (ISIC Rev. 3), final consumers, or general purpose Problem-solving The articles cite a problem as an impulse to or motivating factor for the development of the innovation Opportunities The articles cite a new technology or scientific advance as enabling the innovation Other The innovation was developed to improve performance or satisfy a consumer demand Variable . Description . Commercialization year Year of commercialization of the innovation Product type The product code (ISIC Rev. 3) of the innovation User sector The sector in which the innovation is used according to the journal article. User sector specified as industries (ISIC Rev. 3), final consumers, or general purpose Problem-solving The articles cite a problem as an impulse to or motivating factor for the development of the innovation Opportunities The articles cite a new technology or scientific advance as enabling the innovation Other The innovation was developed to improve performance or satisfy a consumer demand Open in new tab Table 2. Description of key variables Variable . Description . Commercialization year Year of commercialization of the innovation Product type The product code (ISIC Rev. 3) of the innovation User sector The sector in which the innovation is used according to the journal article. User sector specified as industries (ISIC Rev. 3), final consumers, or general purpose Problem-solving The articles cite a problem as an impulse to or motivating factor for the development of the innovation Opportunities The articles cite a new technology or scientific advance as enabling the innovation Other The innovation was developed to improve performance or satisfy a consumer demand Variable . Description . Commercialization year Year of commercialization of the innovation Product type The product code (ISIC Rev. 3) of the innovation User sector The sector in which the innovation is used according to the journal article. User sector specified as industries (ISIC Rev. 3), final consumers, or general purpose Problem-solving The articles cite a problem as an impulse to or motivating factor for the development of the innovation Opportunities The articles cite a new technology or scientific advance as enabling the innovation Other The innovation was developed to improve performance or satisfy a consumer demand Open in new tab All these variables are possible to study over the period 1970–2013, since the year of commercialization is recorded for all marketed innovations. The information from innovation biographies also allows detailed description of the origins of innovation, which has been classified according to two main categories: technological opportunities (Klevorick et al., 1995) and problem-driven search (Cyert and March, 1963; Rosenberg, 1969; Antonelli, 1989). The distinction of innovations that exploit technological opportunities is based on explicit mentioning in the journal articles of a technology, which contributed to or enabled the development of the innovation. Innovations were classified as problem-solving under the condition that the development of the innovation was explicitly described as aiming to overcome an obstacle or problem as defined previously. For the problem-solving innovations, a note was taken of this textual evidence, which has served as the basis of qualitative descriptions of innovation activity (see Taalbi, 2014 for further details). Those innovations that could not be categorized as opportunity-driven or problem-driven were developed to improve a product in some dimension of performance or to accommodate customer requirements and market niches. As these did not account for a large share of the innovations, they are presented jointly in this work as “other.” 3.1 Studying interdependencies It is possible to study some of the interdependencies that have shaped the evolution of ICT by examining the supply and use of innovations across industries. This information is contained in the variables “product type” and “user sector” (Table 2). Taalbi (2017a) constructs a technology flow matrix for Sweden 1970–2007 by mapping the innovations supplied by industry i to industry j. This study focuses on flows to and from ICT industries during the period 1970–2013. The underlying innovation flow matrix is in principle constructed by counting the number of innovations that flow from sector i to sector j. However, as any innovation may have several user industries, we let each linkage between sectors obtain a weight, such that the sum of all linkages of an innovation to industries is equal to one.8 The network analysis of this study is only concerned with the flow of innovations that are supplied by, or used by ICT industries, the so-called ego network of ICT innovations, illustrated in Figure 2 and detailed further in Section 5. This enables a description of the linkages between ICT industries and other industries and the potential role played by feedback mechanisms in the technology shift. It is also possible to investigate the structure of technological interdependencies in terms of the two structural aspects of networks referenced above: hierarchy and reciprocity. The hierarchical character is accessed through an investigation of structural characteristics, centrality, and weight distributions (see McNerney et al., 2013). The reciprocity is tested following Garlaschelli and Loffredo (2004). Figure 2. Open in new tabDownload slide Simplified map of the flows studied. Figure 2. Open in new tabDownload slide Simplified map of the flows studied. 4. A history of Swedish ICT innovation This section portrays the history of Swedish ICT through the lens of innovation biographies, with the aim of describing in particular the imbalances and opportunities that have led to ICT innovation. The basic results on the long-term structural change of the ICT industry are summarized in Figures 3–5 (see also Appendix Tables A2 and A3). Figure 3 shows the total number of ICT innovations and the contribution of three broadly defined subsectors: electrical apparatus (ISIC 31), computers and electronic and optical equipment (ISIC 30 and 33), and telecommunication equipment and software (ISIC 32 and 72). It is fairly apparent that ICT innovations have arrived in a pattern of two upswings. One upswing in innovation activity occurred during and following the structural crisis of the 1970s, culminating in the mid-1980s. Another upswing began in the early 1990s, culminating in the mid-2000s. These upswings have together signified a major technology shift carried by the exploitation of microelectronics. During the early stages of the microelectronic revolution, ICT innovation was embodied in new control systems, computer-controlled machinery, automation equipment, and automatic guided vehicles, mainly developed for industry automation. Figure 4 also shows that most of the innovations during the period 1970–1989 were driven by the exploitation of new opportunities. Moreover, ICT innovations were developed predominantly by large corporate groups (see Figure 5), such as ASEA (ABB from 1988), Saab-Scania (split in 1995), Electrolux, and Volvo. The role played by these large firms in the development of ICT technology during its early stages has been highlighted by others (Carlsson, 1995). Figure 3. Open in new tabDownload slide ICT innovations, total and by subsector (5-year centered moving averages). Figure 3. Open in new tabDownload slide ICT innovations, total and by subsector (5-year centered moving averages). Figure 4. Open in new tabDownload slide ICT innovations in total and by origins (5-year centered moving averages), 1970–2013. Figure 4. Open in new tabDownload slide ICT innovations in total and by origins (5-year centered moving averages), 1970–2013. Figure 5. Open in new tabDownload slide Size distribution of ICT innovators by number of employees, 1970–2013. Figure 5. Open in new tabDownload slide Size distribution of ICT innovators by number of employees, 1970–2013. The second upswing was, as shown in Figure 3, carried entirely by telecommunication and software innovations. The driving forces in the second upswing were the wider exploitation of microelectronics and the resolution of imbalances that appeared in the development of Internet and telecommunication infrastructures. Toward the end of the period, the telecommunications and software innovations were increasingly targeting performance improvement and market niches. From the 1980s, ICT innovation was increasingly carried out by smaller and younger firms observing market niches or responding to technological imbalances. Though large actors, e.g., Ericsson, were still important innovators in telecommunications in the 1990s, a strikingly small share of innovations was launched by large firms (with more than 200 employees) by the end of the period. 4.1 Origins of ICT innovation, 1950–2013 As indicated in Figure 4, the history of Swedish ICT innovations is to a considerable extent a history of creative response to both opportunities and imbalances emerging between parts of the technological system. The below sections chronicle the history of innovations as mirrored by innovation biographies. The main results are summarized in Table 3 at the end of this section. Table 3. A characterization of imbalances and opportunities in Swedish ICT Subperiod (ca) . Industries . Imbalances . Supplied opportunities . 1950–1970 Circuits, magnetic tape memory, and data machines Increasing complexity of transistor-based systems and insufficient memory space Integrated circuits, magnetic tape memory 1960–1989 Factory automation and AGVs Insufficient capacities of control systems Computerized NC equipment, electronics-based industrial robots 1970–1980 Laminate for electronic components and microchip production technologies Under-etching, bottlenecks in production of integrated circuits Laser lithography technologies 1980–2013 Secure payment and secure identification technologies Security issues in Internet networks Biometric identification technology (inter alia) 1990–2013 Telecommunication networks and components Capacity requirements of network standards Development of network standards (e.g., ATM, VDSL, and VoIP) Subperiod (ca) . Industries . Imbalances . Supplied opportunities . 1950–1970 Circuits, magnetic tape memory, and data machines Increasing complexity of transistor-based systems and insufficient memory space Integrated circuits, magnetic tape memory 1960–1989 Factory automation and AGVs Insufficient capacities of control systems Computerized NC equipment, electronics-based industrial robots 1970–1980 Laminate for electronic components and microchip production technologies Under-etching, bottlenecks in production of integrated circuits Laser lithography technologies 1980–2013 Secure payment and secure identification technologies Security issues in Internet networks Biometric identification technology (inter alia) 1990–2013 Telecommunication networks and components Capacity requirements of network standards Development of network standards (e.g., ATM, VDSL, and VoIP) Open in new tab Table 3. A characterization of imbalances and opportunities in Swedish ICT Subperiod (ca) . Industries . Imbalances . Supplied opportunities . 1950–1970 Circuits, magnetic tape memory, and data machines Increasing complexity of transistor-based systems and insufficient memory space Integrated circuits, magnetic tape memory 1960–1989 Factory automation and AGVs Insufficient capacities of control systems Computerized NC equipment, electronics-based industrial robots 1970–1980 Laminate for electronic components and microchip production technologies Under-etching, bottlenecks in production of integrated circuits Laser lithography technologies 1980–2013 Secure payment and secure identification technologies Security issues in Internet networks Biometric identification technology (inter alia) 1990–2013 Telecommunication networks and components Capacity requirements of network standards Development of network standards (e.g., ATM, VDSL, and VoIP) Subperiod (ca) . Industries . Imbalances . Supplied opportunities . 1950–1970 Circuits, magnetic tape memory, and data machines Increasing complexity of transistor-based systems and insufficient memory space Integrated circuits, magnetic tape memory 1960–1989 Factory automation and AGVs Insufficient capacities of control systems Computerized NC equipment, electronics-based industrial robots 1970–1980 Laminate for electronic components and microchip production technologies Under-etching, bottlenecks in production of integrated circuits Laser lithography technologies 1980–2013 Secure payment and secure identification technologies Security issues in Internet networks Biometric identification technology (inter alia) 1990–2013 Telecommunication networks and components Capacity requirements of network standards Development of network standards (e.g., ATM, VDSL, and VoIP) Open in new tab 4.1.1 Beginnings, 1950–1969 The early history of ICT was marked by imbalances as strong incentives for innovation. The breakthrough innovations were made with the digital computer, called ENIAC (1945), and the transistor (1947).9 The first Swedish computers were developed by the Swedish Board for Computing Machinery (SBCM) in the early 1950s, called BARK (Binär Automatisk Relä-Kalkylator), and BESK (Binär Elektronisk Sekvens-Kalkylator). While the research activities of SBCM were later cancelled, the experience from the construction of BARK and BESK lays the basis of the continued development of computers.10 Meanwhile however, the increasing complexity of transistor-based systems, known as the “tyranny of numbers,” made assembly costs high and became a strong incentive for further innovation. Moreover, the size of complex circuits impeded efficiency in computers (Langlois, 2002). These were precisely the problems that motivated two Americans, Robert Noyce at Fairchild and Jack Kilby of Texas Instruments, to (independently) develop the first prototypes of integrated circuits in 1961. Swedish innovators responded to these problems as well. To overcome the bottlenecks of increased data processing power and the increasing requirements for memory space, Swedish firms (e.g., SAAB and AB Åtvidaberg Industrier) developed magnetic tape memory to enable information storage without large physical space requirements. From this experience, SAAB was able to continue development of process control equipment in a Machine Tool Control (MTC) series, the innovations MTC-6 (launched 1965) and MTC-7 (launched 1967) being especially notable. By the late 1960s, firms in the machine tool industry were integrating numeric control (NC) equipment into new innovations, the beginning of an ensuing wave of factory automation. 4.1.2 Irruption, 1970–1989 The key event in the modern history of ICTs was of course the development of the microprocessor, launched by Intel in 1971. Now, the information processing capacity of a digital computer was contained on a single chip and could be mass produced at low cost. This enabled the explosion in numeric capacity and the wider diffusion of computers and microelectronics (Bresnahan and Trajtenberg, 1995; Langlois, 2002). Innovation biographies suggest that strong incentives for product innovation emerged from the opportunities brought about by the new microelectronics-based technologies. The diffusion of microprocessor-based technology enabled new generations of machinery and instruments for control and measurement with vastly improved performance. Control systems and computer equipment were central to the wider application of microprocessors in factory automation. NC systems had already been introduced into machinery during the course of the 1960s, but predominantly among large firms. Swedish ASEA was one of the pioneers of the development of commercially available Computer Numeric Control systems with its introduction of Nucon in 1972 and Nucon 400 in 1977. ASEA’s robotics division was also at the forefront, launching several notable robot innovations during the period studied. For example, ASEA’s IRB 6, launched in 1973, was the first wholly electrical microprocessor-based robot on the market. In 1977, ASEA began research and development of a new robot system with computer-based image processing technology. The result, “ASEA Robot Vision,” was commercialized in 1983. The development of microelectronics also enabled the solution of some technological imbalances in the 1970s. A case in point is the introduction and further development of automated guided vehicles (AGVs), an integral part of the Swedish factory automation industry (Carlsson, 1995). Before the breakthrough of microelectronics, control systems were bulky and had limited capacity. Solutions to these problems were made possible as integrated circuits and microelectronics were improved. In Sweden, this led to several AGV development projects during the 1970s, notably involving the firms Netzler & Dahlgren Co (NDC), Volvo, and Tetra Pak. Our innovation biographies also convey that critical problems were in themselves important incentives for innovation during the 1970s. As it were, industrial transformation during the 1970s had both “positive” and “negative” sources.11 There were also imbalances present in the improvement of the key input, microelectronic circuits, becoming the target of new technologies. In the 1970s, demand emerged for printed circuit boards with higher packaging density. Underetching however emerged as a limiting factor. Perstorp AB was one of several international manufacturers to initiate search for a laminate material with thinner copper plates to solve the problem. Similarly, the manufacturing of masks for integrated circuits with the technology available at the time (photographic lithography) tended to become a production bottleneck due to the complexity of mask patterns. In response to these bottlenecks, one firm, Micronic, developed a new laser-based method for the production of masks for integrated circuits. In the 1980s, a wave of entrant firms emerged, aiming to exploit new opportunities (see Taalbi, 2014, ch. 7). Many of these small firms specialized in developing computer-aided design and computer-aided manufacturing innovations, based on previous advances in robot or control systems technology. During the course of the 1980s, the factory automation industry however came under increased competitive pressure, and many Swedish suppliers of machine tools and flexible manufacturing systems were forced out of business. A similar fate was suffered by the Swedish computer industry during the economic crisis, 1990–1993. By that time, new forces of growth had emerged. In the 1980s, ICT innovation was mostly focused on factory automation, but the 1980s also saw the entry of a handful of ICT firms in the segment of consumer electronics, notably the successful firms Axis Communications and Array Printers. As seen in Figure 3, ICT innovation subsequently shifted from factory automation toward the growing telecommunication and data communication industries. 4.1.3 Infrastructure, 1990–2013 From the 1990s, innovation activity was driven by the opportunities stemming from computerization and imbalances and opportunities that emerged through the expansion of telecommunications and Internet infrastructures. The main breakthroughs were made in the 1980s, but it was not until the abolishment of state-owned Televerket’s monopoly with the Telecommunications Act of 1993 that a veritable expansion took off. For instance, mobile telephone networks were introduced in Sweden with NMT (Nordic Mobile Telephone system), invented by engineer Östen Mäkitalo and launched in 1981 by Ericsson. Similarly, the first Swedish network was connected to the Internet in 1984. Internet did not however become publicly available in Sweden until 1994, when a small start-up firm, Algonet, connected Internet with the Swedish telephone network. In the ensuing boom in telecommunications, Ericsson naturally accounted for a large share of innovations. Ericsson for instance developed the first WAP phone (2000), the first Bluetooth product and the first mobile telephone supporting both Bluetooth and MMS (Multimedia Messaging Service). However, the deregulation of telecommunications and the launching of Internet signaled the burgeoning opportunities ahead and a wave of new firms emerged. These new firms often developed innovations that were aiming to solve critical problems in the deployment of Internet and telecommunication networks. Innovations in transmission systems, network switches, and electronic components for data and telecommunications were responding to obstacles to the introduction of broadband access technologies such as Digital Subscriber Line (DSL), transmission standards such as Asynchronous Transfer Mode (ATM), or Voice-over-IP (VoIP). One of many instances of this dynamics between network standards and network components was ATM, developed to fulfil the requirements of broadband and enabling digital transmission of data, speech, and video and the unification of telecommunication and computer networks. For this technology, fast circuits were needed. Ericsson developed an ATM circuit for broadband networks, aimed to increase performance and fulfil security requirements. Switchcore launched a circuit aimed to resolve the problem that data switches were becoming a capacity bottleneck as traffic volumes increased.12 Similarly, the lack of network processors compatible with the requirements of fast routers prompted Xelerated to develop and launch a network processor capable of 40 gigabytes per second. Another example of a technology shaped by obstacles is the introduction of mobile VoIP technology. The perhaps most well-known Swedish innovation for VoIP is Skype, launched in 2003 (Telekom idag, 2005: 4, 47; 2005: 8, 38; 2006: 7, 38–9), but several other development projects were aiming to overcome specific obstacles in the introduction of VoIP technology. For instance, Nanoradio was started in 2004 to solve the problem of synchronizing mobile phones with VoIP. The then-available WLAN circuits were power consuming, so Nanoradio developed a small WLAN circuit that enabled a fast synchronization of mobile telephones.13 A last noteworthy imbalance that spurred innovation activity was the problem of Internet and data communication security. In the early 1980s, there were a few Swedish innovations aimed to prevent database hacking or computer thefts. With the expansion of Internet technology in the 1990s, and as more transactions were carried out over the Internet, several firms also emerged that were attempting to eliminate obstacles to secure transaction online and e-commerce: in particular the problem of secure transactions. Some innovative start-up firms were targeting this bottleneck, e.g., Surfbuy and Buyonnet. Other firms developed systems for secure identification online or in mobile phones, exploiting fingerprint recognition technology.14 4.2. Summary Our analysis shows that innovation activity in ICTs has undergone vigorous transformation. A summary of the imbalance and opportunities observed is given in Table 3. One major result is that ICT innovation took place in two upswings: one focused on computers and factory automation and a second focused on communications and Internet infrastructure. It is also clear that opportunities and imbalances have shifted between technology “components” and “networks.” The early history of ICT was characterized by the high assembly costs, and insufficient memory space, resolved, e.g., through the integrated circuit. This enabled the development of drastically improved computerized NC systems, resolving an imbalance in the burgeoning factory automation. In the technological development of Internet and telecommunication networks, the imbalances were frequently related to insufficient capacity of network components or standards (see also Fransman, 2001). In other words, the opportunities and imbalances that drove ICT innovation have widened from pertaining to the key input and industrial applications to being focused on infrastructural investment. Thus, innovation biographies suggest that innovation activity has evolved in temporally localized sets of opportunities and imbalances. 5. Pathways of innovation in the third industrial revolution We now turn to an analysis of the interdependencies created in the evolution of ICT. Following the notion of microelectronics as a “key input” (Perez, 1983; Freeman and Louça, 2001), we first briefly examine the broader role played by microelectronics in overall innovation activity since the basic innovations of the transistor, the integrated circuit, and the microprocessor. We then examine the structure of supply and use of innovations between ICT industries (“carrier industries”) and other sectors. Figures 6 and 7 show the count and share of Swedish innovations exploiting microelectronics in their core functions, as inferred from trade journal articles. What is striking is the apparently S-shaped curve of the percentage of microelectronics-based innovations, converging toward an average share of 58% (culminating at 70.6% in 2003). This share may seem low given the pervasiveness of microprocessors, computers, and electronics but can in part be explained by a sizeable number of, e.g., pharmaceuticals, plastic and metal innovations, as well as electrical, nonelectronic machinery, and transport equipment. Figure 6. Open in new tabDownload slide Count of microelectronics-based innovations, 1970–2013. Figure 6. Open in new tabDownload slide Count of microelectronics-based innovations, 1970–2013. Figure 7. Open in new tabDownload slide Share of innovations based on microelectronics, 1950–2013 and logistic curve. Note: The generalized logistic curve (Richards, 1959) was fitted according to y(t)=K(1+τe−α(t−β))1/τ where K, τ, α, and β are estimated parameters. Figure 7. Open in new tabDownload slide Share of innovations based on microelectronics, 1950–2013 and logistic curve. Note: The generalized logistic curve (Richards, 1959) was fitted according to y(t)=K(1+τe−α(t−β))1/τ where K, τ, α, and β are estimated parameters. Visualized in Figure 8, the most salient industries exploiting microelectronics, “carrier industries” in Perez’ terminology, are machinery and various electronic equipment and software, i.e., the ICT industries. While this is unsurprising, it is more surprising that no other industries, except machinery equipment, have exploited microelectronics in their core functions to a significant extent. Figure 8. Open in new tabDownload slide Count of microelectronics-based innovations in “carrier industries,” 1970–2013. Figure 8. Open in new tabDownload slide Count of microelectronics-based innovations in “carrier industries,” 1970–2013. 5.1 The structure of technological interdependencies The pathways of innovations in these “carrier industries” can be analyzed directly through a mapping of the flow of innovations between industries. Some of the innovations were classified as being of a general-purpose character (Figure 9), i.e., as being possible to use across the board. This measure allows an immediate corroboration of the general-purpose character of ICT industries: some 40% of all ICT innovations were aimed for use throughout the industry up until 2000, as compared with some 15% on average in other industries. ICT innovations have thus, unsurprisingly, a clear general-purpose character. Figure 9. Open in new tabDownload slide Share of general-purpose innovations among ICT and non-ICT industries, 1970–2013. Figure 9. Open in new tabDownload slide Share of general-purpose innovations among ICT and non-ICT industries, 1970–2013. Figure 10. Open in new tabDownload slide Centrality in the ICT ego network, 1970–2013. (a) Out- and in-strength. (b) Edge weights. (c) Eigenvector centrality. (d) Strength reciprocity. ρ = −0.038 [P > 0.1]. (e) Edge reciprocity. ρ = −0.011 [P > 0.1]. Figure 10. Open in new tabDownload slide Centrality in the ICT ego network, 1970–2013. (a) Out- and in-strength. (b) Edge weights. (c) Eigenvector centrality. (d) Strength reciprocity. ρ = −0.038 [P > 0.1]. (e) Edge reciprocity. ρ = −0.011 [P > 0.1]. The remainder of the ICT innovations were developed for specific industrial use. These industry-specific ties formed by ICT innovations inform of local interdependencies that played a role in the evolution of ICTs. These interdependencies are analyzed in an innovation flow matrix, which maps the number of innovations that are supplied by industry i to industry j. The innovation flow matrix is a weighted directed network, which means that both the count of innovations and the direction of the connections between industries matter. For a directed weighted network, each edge from node i∈V to another node j∈V ⁠, has a weight. Using matrix notation, the intersectoral supply and use of innovations can be expressed as a N × N matrix A, having as elements aij the amount of innovations supplied by industry i to industry j (see Section 3.1 for underlying considerations). Here we restrict our study to the flows to and from ICT sectors (see also Figure 2). Formally, with the set of ICT sectors I ⁠, we define the entries of the ICT ego network W as: wij=aij(δi+δj−δiδj),(1) where δi and δj equal 1 for i,j∈I ⁠, otherwise 0. From this adjacency matrix, we can define core statistics that inform about the structure of flows between ICT industries and other industries. The theory section outlined two main characteristics of networks of interest: hierarchy and reciprocity. We are first of all interested in describing to whether innovation activity is characterized by a star-like structure or rather a nonhierarchical structure where several industries have played a large part. This can be done by describing the centrality of nodes (industries) and the weight distribution of the network (see McNerney et al., 2013). The simplest notion of centrality is node strength. The out-strength of an industry is defined as the column sums of the innovation flow matrix: kiout=∑jwij(2a) and the in-strength as the row sums kjin=∑iwij.(2b) The notion of eigenvector centrality expands on this basic understanding of centrality, by noting that nodes are more central in the network if they have strong linkages to other central nodes. Since this measure is recursive, we look for a positive vector vi≥0 that solves the eigenvalue problem: ∑jviwij=λvi.(3) λ is taken as the maximum eigenvalue of vi as, per the Perron–Frobenius theorem, this guarantees a positive eigenvector. The reciprocity of a network can be defined in terms of the degree to which outward flows between industry i and j is also reflected by feedback innovation flows from industry j to industry i. We measure this following Garlaschelli and Loffredo (2004) as the correlation coefficient: ρ=∑i≠j(wij−w¯)(wji−w¯)∑i≠j(wij−w¯)2,(4) where wij as before denotes the flow of innovations to or from ICT industries, wji the reciprocal flows, and w¯ the average flow in the ICT ego network. In the present context, our interest lies in accounting for the main flows to and from ICT industries, but also the dynamics of the linkages between ICT industries and other industries. The distribution of out- and in-strength of industries in the ICT ego network shows that most industries have low out- and in-strength, while only a few industries have high out- and in-strength, an indication of a hierarchical network structure. Total 4.7% of the industries supplied more than 100 innovations. Similarly, 1.9% of the industries used more than 100 innovations. The cumulative distribution of weights is proportional to a power law wij−1.53 ⁠. A similar result is found for eigenvector centrality (Figure 11c): only a small fraction of the industries has high forward/backward centrality. These results thus inform us of a hierarchical structure, where some nodes play the role as principal suppliers or users of innovation, and where most nodes receive or supply only a small number of innovations. In Figure 11, the strength reciprocity and the edge reciprocity of the network clearly indicate a highly asymmetric network, where industry ties are not reciprocated. Figure 11. Open in new tabDownload slide Network reciprocity, 1970–2013. (a) 1970–1989. (b) 1990–2013. Figure 11. Open in new tabDownload slide Network reciprocity, 1970–2013. (a) 1970–1989. (b) 1990–2013. To provide further intuition for these results, Figures 11a and 12b visualize the “ego network” of ICT industries, i.e., the number of innovations used in ICT industries, or supplied by ICT industries over the periods 1970–1989 and 1990–2013. The layout of the network applies the Fruchterman–Reingold algorithm to a fast greedy community detection algorithm (Clauset et al., 2004), which groups closely related industries. The figures indicate a star-shaped supply structure for some of the ICT industries, notably measuring instruments, computers, and software, implying that these industries are mainly suppliers of innovations while using relatively few innovations from other industries. Figure 12. Open in new tabDownload slide Ego network of the ICT industries based on absolute flows of innovations. Layout by communities, applying the fast greedy community detection algorithm (Clauset et al., 2004). Figure 12. Open in new tabDownload slide Ego network of the ICT industries based on absolute flows of innovations. Layout by communities, applying the fast greedy community detection algorithm (Clauset et al., 2004). For a detailed breakdown of the interdependencies among ICT innovations, Table 4a and b shows the industries with highest shares of innovations flowing to and from the ICT sectors. In the first half of the period, main user sectors of ICT innovations were health care (11%), final consumption (9%), other business activities (6%), and publishing and printing (6%). Most of the innovations supplied to ICT industries came from other ICT industries: measuring equipment accounted for 30%, computers 13%, medical equipment 9%, and software 7%. In total, 87% of the innovations used by ICT industries were supplied by other ICT industries. The second half of the period saw a more pronounced shift in the user sectors toward final consumption (17%), health care (9%), and the software, telecommunication equipment, and service industries (together these accounted for 13%). Meanwhile, software (22%), measuring instruments (21%), telephones (11%), computers (6%), and medical equipment (9%) together accounted for 69% of all innovations used in ICT. Table 4. 20 industries with strongest linkages to ICT, 1970–1989 and 1990–2013 (shares in total number of innovations supplied or used by ICT industries) Industry . User of ICT (%) . Supply to ICT (%) . Total linkages (%) . (a) 1970–1989 1. Measuring instruments 1 30 15 2. Computers 1 13 7 3. Health care 11 0 5 4. Medical equipment 0 9 5 5. Final consumption 9 0 5 6. Electronic components 5 4 5 7. Telephone and radio transmitters 1 7 4 8. Software 1 7 4 9. Other business activities 6 0 3 10. Optical instruments and photographic equipment 0 6 3 11. Publishing and printing 6 0 3 12. Basic metals 4 1 3 13. Electrical equipment n.e.c. 0 5 2 14. Industrial process control equipment 0 4 2 15. Telephone and radio receivers 1 2 2 16. Electricity, gas, and water supply 4 0 2 17. Land transportation 3 0 2 18. Construction 3 0 2 19. Wood 3 0 1 20. Office machinery 1 2 1 (b) 1990–2013 1. Software 2 22 12 2. Measuring instruments 0 21 11 3. Final consumption 17 0 9 4. Telephone and radio transmitters 4 11 7 5. Electronic components 3 7 5 6. Medical equipment 1 9 5 7. Health care 9 0 5 8. Computers 1 6 4 9. Optical instruments and photographic equipment 1 5 3 10. Telephone and radio receivers 1 4 3 11. Post and telecommunications 3 1 2 12. Research and development 4 0 2 13. Electricity, gas, and water supply 4 0 2 14. Motor vehicles 3 0 2 15. Electric motors 0 3 2 16. Pulp and paper 3 0 2 17. Other business activities 3 0 1 18. Electrical equipment n.e.c. 0 3 1 19. Basic metals 2 1 1 20. Wood 2 0 1 Industry . User of ICT (%) . Supply to ICT (%) . Total linkages (%) . (a) 1970–1989 1. Measuring instruments 1 30 15 2. Computers 1 13 7 3. Health care 11 0 5 4. Medical equipment 0 9 5 5. Final consumption 9 0 5 6. Electronic components 5 4 5 7. Telephone and radio transmitters 1 7 4 8. Software 1 7 4 9. Other business activities 6 0 3 10. Optical instruments and photographic equipment 0 6 3 11. Publishing and printing 6 0 3 12. Basic metals 4 1 3 13. Electrical equipment n.e.c. 0 5 2 14. Industrial process control equipment 0 4 2 15. Telephone and radio receivers 1 2 2 16. Electricity, gas, and water supply 4 0 2 17. Land transportation 3 0 2 18. Construction 3 0 2 19. Wood 3 0 1 20. Office machinery 1 2 1 (b) 1990–2013 1. Software 2 22 12 2. Measuring instruments 0 21 11 3. Final consumption 17 0 9 4. Telephone and radio transmitters 4 11 7 5. Electronic components 3 7 5 6. Medical equipment 1 9 5 7. Health care 9 0 5 8. Computers 1 6 4 9. Optical instruments and photographic equipment 1 5 3 10. Telephone and radio receivers 1 4 3 11. Post and telecommunications 3 1 2 12. Research and development 4 0 2 13. Electricity, gas, and water supply 4 0 2 14. Motor vehicles 3 0 2 15. Electric motors 0 3 2 16. Pulp and paper 3 0 2 17. Other business activities 3 0 1 18. Electrical equipment n.e.c. 0 3 1 19. Basic metals 2 1 1 20. Wood 2 0 1 Open in new tab Table 4. 20 industries with strongest linkages to ICT, 1970–1989 and 1990–2013 (shares in total number of innovations supplied or used by ICT industries) Industry . User of ICT (%) . Supply to ICT (%) . Total linkages (%) . (a) 1970–1989 1. Measuring instruments 1 30 15 2. Computers 1 13 7 3. Health care 11 0 5 4. Medical equipment 0 9 5 5. Final consumption 9 0 5 6. Electronic components 5 4 5 7. Telephone and radio transmitters 1 7 4 8. Software 1 7 4 9. Other business activities 6 0 3 10. Optical instruments and photographic equipment 0 6 3 11. Publishing and printing 6 0 3 12. Basic metals 4 1 3 13. Electrical equipment n.e.c. 0 5 2 14. Industrial process control equipment 0 4 2 15. Telephone and radio receivers 1 2 2 16. Electricity, gas, and water supply 4 0 2 17. Land transportation 3 0 2 18. Construction 3 0 2 19. Wood 3 0 1 20. Office machinery 1 2 1 (b) 1990–2013 1. Software 2 22 12 2. Measuring instruments 0 21 11 3. Final consumption 17 0 9 4. Telephone and radio transmitters 4 11 7 5. Electronic components 3 7 5 6. Medical equipment 1 9 5 7. Health care 9 0 5 8. Computers 1 6 4 9. Optical instruments and photographic equipment 1 5 3 10. Telephone and radio receivers 1 4 3 11. Post and telecommunications 3 1 2 12. Research and development 4 0 2 13. Electricity, gas, and water supply 4 0 2 14. Motor vehicles 3 0 2 15. Electric motors 0 3 2 16. Pulp and paper 3 0 2 17. Other business activities 3 0 1 18. Electrical equipment n.e.c. 0 3 1 19. Basic metals 2 1 1 20. Wood 2 0 1 Industry . User of ICT (%) . Supply to ICT (%) . Total linkages (%) . (a) 1970–1989 1. Measuring instruments 1 30 15 2. Computers 1 13 7 3. Health care 11 0 5 4. Medical equipment 0 9 5 5. Final consumption 9 0 5 6. Electronic components 5 4 5 7. Telephone and radio transmitters 1 7 4 8. Software 1 7 4 9. Other business activities 6 0 3 10. Optical instruments and photographic equipment 0 6 3 11. Publishing and printing 6 0 3 12. Basic metals 4 1 3 13. Electrical equipment n.e.c. 0 5 2 14. Industrial process control equipment 0 4 2 15. Telephone and radio receivers 1 2 2 16. Electricity, gas, and water supply 4 0 2 17. Land transportation 3 0 2 18. Construction 3 0 2 19. Wood 3 0 1 20. Office machinery 1 2 1 (b) 1990–2013 1. Software 2 22 12 2. Measuring instruments 0 21 11 3. Final consumption 17 0 9 4. Telephone and radio transmitters 4 11 7 5. Electronic components 3 7 5 6. Medical equipment 1 9 5 7. Health care 9 0 5 8. Computers 1 6 4 9. Optical instruments and photographic equipment 1 5 3 10. Telephone and radio receivers 1 4 3 11. Post and telecommunications 3 1 2 12. Research and development 4 0 2 13. Electricity, gas, and water supply 4 0 2 14. Motor vehicles 3 0 2 15. Electric motors 0 3 2 16. Pulp and paper 3 0 2 17. Other business activities 3 0 1 18. Electrical equipment n.e.c. 0 3 1 19. Basic metals 2 1 1 20. Wood 2 0 1 Open in new tab In brief, the data convey that ICT industries supplied broadly to other sectors, mostly health care, final consumption, or developed for general-purpose use, while ICT industries have used innovations almost exclusively from other ICT industries. Thus, the main dynamics took place within the ICT sectors with little feedback from what Perez (2010) has called “induced industries.” These results speak in favor of a hierarchical network topology with little feedback from application sectors. 6. Conclusions The results of this study convey several facts about the long-term evolution of ICT innovation. In particular, four main results emerge, some of which have been predicted in previous theoretical literature: Both innovational complementarities and imbalances have greatly mattered as driving forces of ICT innovation. The diffusion of GPTs is a localized process, confined to certain industries in certain points of time. This may suggest an interpretation of the evolution of GPTs as a series of DBs, defined as sequences of innovations that respond to history-specific imbalances and opportunities. The network of ICT innovation is hierarchical and locally “star-like.” That is to say, the supply and use of innovations took place mainly between what Perez (1983) has called the “key input” and “carrier industries,” with little feedback from “induced industries.” The actors behind ICT innovation have changed from large incumbents with experience in the traditional electrical sector to small entrant firms observing market niches and new opportunities, which stresses the disruptive impact of GPTs on industrial structure. First of all, the evolution of ICT has been shaped by both innovational complementarities and the sequential appearance of imbalances that has focused innovation activity in discrete phases of industrial development. This relates to the second main result. The shifting patterns of driving forces motivate a view of the diffusion of the GPT of microelectronics in terms of sequences in which significant innovations were often driven by attempts to overcome major obstacles and imbalances, while other innovations drew on technological opportunities created elsewhere. Such sequences of imbalances are possible to view as discrete DBs (Dahmén, 1988/1991; compare Taalbi, 2017a). The early history of ICT was characterized by high assembly costs and insufficient memory space, ultimately resolved through the invention of the integrated circuit. This enabled the development of drastically improved computerized NC systems, resolving an imbalance in the burgeoning factory automation. With the “big bang” of the innovation of the microprocessor, salient opportunities emerged for innovation in factory automation. Toward the 1990s, the sources of innovation activity had shifted—in part through a painful process of dismantling the domestic computer industry—toward the expansion of Internet and telecommunication infrastructure. A large set of innovations was now centered on solving imbalances created in the mismatch between technological capacities and requirements. Moreover, the focal point of innovation activity shifted from industry-oriented innovation activity, toward consumer electronics in the 1990s. Third, both the qualitative analysis and a quantitative analysis of the network of innovations provide support for an interpretation that this dynamics in the diffusion of ICT as a GPT was largely contained within the ICT industry, rather than stemming from feedback mechanisms between ICT industries and other industries. This is reflected in a hierarchical structure and locally star-like structure of the innovation networks. Hence, while the impact of ICT has been generic, the central impetus of the technological system to evolve appeared in the interaction between core components in DBs. A last result is that the ICT industry transformed from being driven by a few large actors developing automation technologies, such as Saab and ASEA (ABB from 1988), evolving into a large DB being increasingly dominated by smaller actors. In the beginning of the 1950s, automation technology and computers were mainly developed in research laboratories of institutes and a few large Swedish firms. Already toward the mid-1980s, new actors had emerged that produced innovations in a number of interrelated areas such as industrial automation machinery, AGVs, telecommunication equipment, computers, and electronic components. As telecommunication markets were deregulated in the 1990s, a further wave of entrants followed and toward the end of the period studied only a minor part of ICT innovations was developed by firms with more than 200 employees. These results amount to a clear message about the long-term evolution of GPTs. In particular, this study has found support that the ICT industry in Sweden can be understood as the diffusion of a GPT in terms of several DBs, localized in time and centered both on a set of opportunities and imbalances, while the structure of technological interdependencies is hierarchical, confined to core industries and locally star-like. In keeping with core insights on industrial evolution (Nelson, 1994; Malerba, 2002), the results of this study allude to the importance of understanding sectoral innovation outcomes with respect to the structure and character of dynamic interdependencies. Our results align with a broad literature suggesting that systemic complexity has impact on search strategies, in this case to direct search strategies to focus on opportunities, bottlenecks, or imbalances (Simon, 1965; Nickerson and Zenger, 2004; Taalbi, 2017b). If such interdependencies play a role in fostering innovation, the identification of opportunities and imbalances should be of the utmost importance to innovation policy, including policies devoted to achieving sustainable transitions. In particular, our analysis would stress that the allocation of knowledge and resources toward the identification and resolution of structural and technological imbalances is key. However, understanding how the structure of technological interdependencies and network topologies affects innovation outcomes and industrial dynamics empirically requires further efforts. Limitations of this study lie in part in being restricted to studying innovation output data for a single European country. Though these data are a unique source of qualitative information on the sources of innovation and the character of inter-industrial interdependencies, not all types of interdependencies and imbalances can be studied through innovation networks. For instance, our study does not inform of feedback mechanisms through demand or knowledge complementarities. The suggestions of this study could hence be further investigated by using other types of longitudinal data, e.g., industrial economic output data and patent citation networks to understand the relationship between network topology, complexity, and industrial dynamics (compare Fleming and Sorenson, 2001; Nickerson and Zenger, 2004; Acemoglu et al., 2016). However, our results also speak in favor of developing approaches for the empirical analysis of how critical problems, technological imbalances, and growth bottlenecks focus innovation activity in what here has been called “development blocks” and how this affects long-run industry outcomes (see Dedehayir and Mäkinen, 2008, 2011; Bresnahan and Yin, 2010). Acknowledgments The author wishes to thank two anonymous referees for valuable comments on a previous draft. Funding The author gratefully acknowledges funding support from the Jan Wallander and Tom Hedelius foundation (grant number W2015–0445: 1) and VINNOVA (grant number 2014–06045). Footnotes 1 Previously prepared for the 16th International Schumpeter Society Conference, July 6–8, 2016, Montréal. 2 For instance, Moser and Nicholas (2004) argued against a general-purpose character of electricity, with aid from historical patent citation data from the early 20th century. Though the general-purpose character of microelectronics and Internet technologies is uncontroversial, doubts have also been raised by some (notably Gordon, 2000, 2016) with regard to productivity effects and pervasiveness. 3 “We can see the computers everywhere but in the productivity statistics” (cited in David, 1990). 4 The notion of “techno-economic paradigms” describes the successive technological revolutions brought about by sets of radical innovations (Freeman and Louça, 2001; Perez, 2002). The use of “paradigms” alludes of course to Kuhn (1963) and stresses that there is a strong direction and sense of progress in technological change. 5 A first version covered 1970–2007. An extension of the database to 2014 was finished in May 2016. 6 Due to this selection, statistical information is not comparable between the data set collected for 1950–1969 and the full database 1970–2013, why the early sample is only exploited for a qualitative description of the history of ICT innovation in Sweden. 7 For further details on methods and selection procedures, see Sjöö et al (2014). 8 Formally, consider a set of N innovations indexed by k∈{1,2,…,N} where each innovation has a number of observed user industries, denoted U. The weight a for a linkage of innovation k is then ak=(1/Uk) ⁠. Assigning each weight to its respective supply and user industry, i and j, respectively, we obtain the innovation flow matrix A with elements aij=∑k(aijk) ⁠. 9 ENIAC built on an earlier invention developed in the 1930s and 1940s by John Atanasoff and Clifford E. Berry. Alan Turing’s electronic computer, developed to break the Enigma code, and IBM’s Automatic Sequence Controlled Calculator are other, perhaps more famous, precursors. 10 Main players in the early development of computers during the 1950s and 1960s were L. M. Ericsson, Facit (Åtvidaberg Industrier AB), AB Addo, SAAB, Standard Radio, and Telefon AB. These firms were all well established, having started in the 1930s or earlier (respectively, 1876, 1906, 1918, 1937, and 1938). Facit and SAAB developed their own versions of BESK under the names “Facit EDB” and “SARA,” respectively. 11 One should note that the crisis of the 1970s also meant a negative pressure to transform. Facing negative performance, some firms were pushed to diversify their production toward growing markets in electronics. For example, Sweden’s first personal computer, called ABC 80, was launched on the Swedish market in 1978. It was developed by three Swedish companies, Luxor Industri AB, Scandic Metric AB, and Dataindustrier AB, to meet difficulties arising from the saturated market in home electronics (TV and audio systems). 12 The circuit was developed in a research project in which Ericsson Components, Saab Dynamics, the Royal Institute of Technology, and the Universities of Linköping and Lund participated. The circuit was customized for IP switches and routers for the Gigabit Ethernet standard. 13 Another example is Ericsson Research that developed a technology “to solve the basic problems with the mobile Internet”. A major problem was that IP phones would be more expensive to use than GSM mobiles, if the then best-practice Internet technology was used. 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General coverage Ny Teknik Foodstuff (ISIC 15) Livsmedelsteknik/Livsmedel i Fokus Textiles and clothing (ISIC 17–19) Textil och konfektion/Struktur Wood and wood products (ISIC 20) Sågverken/Trävaruindustrien/NTT (Nordisk Trä Teknik) Wood, paper and pulp (ISIC 20–21) Svensk trävaru-och pappermassetidning/Svensk Papperstidning Printing and publishing (ISIC 22) Aktuell Grafisk Information Chemical and pharmaceutical industry (ISIC 23–24) Kemisk Tidskrift/Kemivärlden Plastics and rubber (ISIC 25) Plastforum Basic metal, mining (ISIC 13, 27) Bergsmannen med Jernkontorets annaler Engineering industries (ISIC 28–34) Verkstäderna Automation- and production process technology (ISIC 30, 33) Automation Electronic components and equipment, telecommunication equipment (ISIC 30–33, 72) Modern Elektronik/Elektroniktidningen/Elteknik Information- and communication technology, software (ISIC 32, 64, 72) Telekom Idag Land, air and shipping transportation, packaging (e.g., ISIC 34–35, 60–63) Transport teknik/Teknik i Transport/Transport Idag Energy, ventilation systems (e.g., ISIC 29, 37, 40) VVS/VVS & Energi/Energi & Miljö Corresponding industries . Journal name . General coverage Ny Teknik Foodstuff (ISIC 15) Livsmedelsteknik/Livsmedel i Fokus Textiles and clothing (ISIC 17–19) Textil och konfektion/Struktur Wood and wood products (ISIC 20) Sågverken/Trävaruindustrien/NTT (Nordisk Trä Teknik) Wood, paper and pulp (ISIC 20–21) Svensk trävaru-och pappermassetidning/Svensk Papperstidning Printing and publishing (ISIC 22) Aktuell Grafisk Information Chemical and pharmaceutical industry (ISIC 23–24) Kemisk Tidskrift/Kemivärlden Plastics and rubber (ISIC 25) Plastforum Basic metal, mining (ISIC 13, 27) Bergsmannen med Jernkontorets annaler Engineering industries (ISIC 28–34) Verkstäderna Automation- and production process technology (ISIC 30, 33) Automation Electronic components and equipment, telecommunication equipment (ISIC 30–33, 72) Modern Elektronik/Elektroniktidningen/Elteknik Information- and communication technology, software (ISIC 32, 64, 72) Telekom Idag Land, air and shipping transportation, packaging (e.g., ISIC 34–35, 60–63) Transport teknik/Teknik i Transport/Transport Idag Energy, ventilation systems (e.g., ISIC 29, 37, 40) VVS/VVS & Energi/Energi & Miljö Open in new tab Table A1. Trade journals Corresponding industries . Journal name . General coverage Ny Teknik Foodstuff (ISIC 15) Livsmedelsteknik/Livsmedel i Fokus Textiles and clothing (ISIC 17–19) Textil och konfektion/Struktur Wood and wood products (ISIC 20) Sågverken/Trävaruindustrien/NTT (Nordisk Trä Teknik) Wood, paper and pulp (ISIC 20–21) Svensk trävaru-och pappermassetidning/Svensk Papperstidning Printing and publishing (ISIC 22) Aktuell Grafisk Information Chemical and pharmaceutical industry (ISIC 23–24) Kemisk Tidskrift/Kemivärlden Plastics and rubber (ISIC 25) Plastforum Basic metal, mining (ISIC 13, 27) Bergsmannen med Jernkontorets annaler Engineering industries (ISIC 28–34) Verkstäderna Automation- and production process technology (ISIC 30, 33) Automation Electronic components and equipment, telecommunication equipment (ISIC 30–33, 72) Modern Elektronik/Elektroniktidningen/Elteknik Information- and communication technology, software (ISIC 32, 64, 72) Telekom Idag Land, air and shipping transportation, packaging (e.g., ISIC 34–35, 60–63) Transport teknik/Teknik i Transport/Transport Idag Energy, ventilation systems (e.g., ISIC 29, 37, 40) VVS/VVS & Energi/Energi & Miljö Corresponding industries . Journal name . General coverage Ny Teknik Foodstuff (ISIC 15) Livsmedelsteknik/Livsmedel i Fokus Textiles and clothing (ISIC 17–19) Textil och konfektion/Struktur Wood and wood products (ISIC 20) Sågverken/Trävaruindustrien/NTT (Nordisk Trä Teknik) Wood, paper and pulp (ISIC 20–21) Svensk trävaru-och pappermassetidning/Svensk Papperstidning Printing and publishing (ISIC 22) Aktuell Grafisk Information Chemical and pharmaceutical industry (ISIC 23–24) Kemisk Tidskrift/Kemivärlden Plastics and rubber (ISIC 25) Plastforum Basic metal, mining (ISIC 13, 27) Bergsmannen med Jernkontorets annaler Engineering industries (ISIC 28–34) Verkstäderna Automation- and production process technology (ISIC 30, 33) Automation Electronic components and equipment, telecommunication equipment (ISIC 30–33, 72) Modern Elektronik/Elektroniktidningen/Elteknik Information- and communication technology, software (ISIC 32, 64, 72) Telekom Idag Land, air and shipping transportation, packaging (e.g., ISIC 34–35, 60–63) Transport teknik/Teknik i Transport/Transport Idag Energy, ventilation systems (e.g., ISIC 29, 37, 40) VVS/VVS & Energi/Energi & Miljö Open in new tab Table A2. Count of innovations in ICT . Electrical apparatus . Computers and electronic equipment . Telecommunications and software . ICT, total . 1970–1989 74 383 121 578 1990–2013 83 409 454 946 Total 157 792 575 1524 . Electrical apparatus . Computers and electronic equipment . Telecommunications and software . ICT, total . 1970–1989 74 383 121 578 1990–2013 83 409 454 946 Total 157 792 575 1524 Open in new tab Table A2. Count of innovations in ICT . Electrical apparatus . Computers and electronic equipment . Telecommunications and software . ICT, total . 1970–1989 74 383 121 578 1990–2013 83 409 454 946 Total 157 792 575 1524 . Electrical apparatus . Computers and electronic equipment . Telecommunications and software . ICT, total . 1970–1989 74 383 121 578 1990–2013 83 409 454 946 Total 157 792 575 1524 Open in new tab Table A3. Driving forces of innovation in ICT . Technological opportunities . Problem-driven . Other . 1970–1989 379 209 108 1990–2013 453 371 261 . Technological opportunities . Problem-driven . Other . 1970–1989 379 209 108 1990–2013 453 371 261 Open in new tab Table A3. Driving forces of innovation in ICT . Technological opportunities . Problem-driven . Other . 1970–1989 379 209 108 1990–2013 453 371 261 . Technological opportunities . Problem-driven . Other . 1970–1989 379 209 108 1990–2013 453 371 261 Open in new tab © The Author(s) 2018. Published by Oxford University Press on behalf of Associazione ICC. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com © The Author(s) 2018. Published by Oxford University Press on behalf of Associazione ICC.
TI - Origins and pathways of innovation in the third industrial revolution
JF - Industrial and Corporate Change
DO - 10.1093/icc/dty053
DA - 2019-10-01
UR - https://www.deepdyve.com/lp/oxford-university-press/origins-and-pathways-of-innovation-in-the-third-industrial-revolution-GQp2nhij4H
SP - 1125
EP - 1148
VL - 28
IS - 5
DP - DeepDyve
ER -