Abstract
Diffusion of new technologies broadly refers to the mechanism or process that spreads the improved technologies across socio-economic structure such as individuals, firms, or societies. It has been rightfully pointed out that the process of ‘diffusion’ is a quintessential part in innovation since without being widely circulated the new technology would have little productive or socio-economic significance. In many ways, understanding the mechanisms by which innovations are being adopted among a group of firms or individuals also renders insights as to how firms and other agents pursue innovative activities (viz., launching new products or creating new processes) to generate higher economic and social welfare as the learning and feedback effects which arise during the process of adoption enhance the original innovation (Hall 2004). Thus, the process of adoption/diffusion is not only significant in itself due to its impacts but is also instrumental in triggering further improvements in technology that leads to higher innovation at large.
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Notes
- 1.
Baptista (1999) also surveys the main strands of diffusion literature in a similar tradition.
- 2.
We do not aim to provide a complete survey of diffusion models (given the empirical focus of this Book). Rather the thrust of our survey is to present a macroscopic view of the various schools of thought to thrash out their key focus so as to derive the factors determining the diffusion. Also see Baptista (1999), Hall (2004) for recent surveys on diffusion literature.
- 3.
The speed of diffusion reflects the time required between two levels of diffusion (Nabseth and Ray 1974).
- 4.
This approach is called as the disequilibrium approach as in this line of argument diffusion is understood to be a self-propagating adjustment process to a fixed end point; the process of adjustment being driven by uncertainty reduction due to information spreading as a result of usage.
- 5.
- 6.
In spite of several criticisms of the epidemic models these models remain as the basis or primary model as most of the latter models seem to adopt some of its assumptions in varying degrees. For example, Metcalfe (1981) described the epidemic models as the standard diffusion model, and Karshenas and Stoneman (1992) term their model (‘a flexible model of technology diffusion’) as ‘a new variant of the standard epidemic (or logistic) diffusion model’.
- 7.
- 8.
The adoption cost, here refers basically to the price of the technology.
- 9.
As illustrated by Karshenas and Stoneman (1993, 1995), and Baptista (2000) it is possible to subsume alternative theories of diffusion into one encompassing model. The specification of an encompassing model then enables one to test empirically which (if any) of the epidemic, rank, stock, and order effects play significant role in the diffusion process.
- 10.
Baptista (2000) argues that the results of stock and order effects on probability of adoption would be opposite. According to this, the stock effect focuses on the equilibrium number of adopters and the subsequent lower profitability of adoption, whilst order effect stresses on the anticipation of future adoptions. Hence, the stock effect has a negative impact on the probability of adoption, and the order effect has a positive impact.
- 11.
See Mahajan et al. (1990) for a description of the different functional forms used in empirical diffusion models.
- 12.
- 13.
- 14.
For instance, Griliches (1957) in his seminal article on hybrid corn reported spatial difference in the availability of innovation to be one of the factors affecting diffusion.
- 15.
As mentioned in some sources (Brown 1981 for example) the most immediate approach from which the present day work in geographical study of diffusion descends can be ascribed to Carl Sauer (1952) in the context of cultural geography. However, in general Hägerstrand’s work, though influenced by Sauer (Clark,) is considered to be more concerned with location and locational processes underlying the process of diffusion, where as Sauer’s work is more to do with culture and landscape (ibid, 1981). Therefore we discuss Hägerstrand’s work more elaborately here.
- 16.
A relevant issue in the context of geography and new technology diffusion is the often-cited phrase “Dealth of Distance” (see e.g., Cairncross 1997). It is often held that the ICT—the quintessential icon of globalisation, has altered the nuances of ‘distance’ by eliminating the role of physical distance. However, there is also increasing evidence and reasoning that the role of ‘space’ has become intensified rather than becoming absent in the present context. It is argued that though ICT has enabled the conquering of distance, it is more virtual than real. The subtle benefits of a close physical proximity is has become more important than ever with the increasing role of tacit elements in knowledge generation/diffusion. But, given the scope of the present study, a thorough analysis of the geographic implications of ICT and diffusion is reserved for future.
- 17.
For instance, Feldman (1994a, b), Audretsch and Feldman (1996) have shown pronounced evidence that innovative activity is substantially more concentrated than overall production and that industries that emphasize R&D tend to be more spatially concentrated. A related result is obtained by Jaffe et al. (1993), who show that patent citations are highly spatially concentrated.
- 18.
- 19.
We do not analytically differentiate between agglomeration and technology spillovers here as the conceptual link between the two is evident (see Koo 2005 for an explicit explanation on the differences and similarities).
- 20.
Industrial agglomeration, localisation, and spatial clustering are used more or less synonymously in the literature (Malmberg and Maskell 2002).
- 21.
A distinction is sometimes made between clusters in geographical space and those in economic space. Clusters in geographical space are the one we are interested in this Book.
- 22.
Empirical evidences have supported both the points of economies. A number of studies find evidence of localization economies. Henderson (1999) finds that localization scale externalities arise from the number of local own industry plants, or points of information spillovers. Also, Rosenthal and Strange (2001) find that localization externalities have much greater impact than urbanization externalities on the agglomeration of economic activities. While several empirical evidences also support the urbanisation economies argument. For instance, Rosenberg (1963) supports this view in a study of the spread of machine tools across industries and describes how an idea is transmitted from one industry to another. Also, Feldman and Audretsch (1999) find that diversity across complementary economic activities sharing a common science base is more conducive to innovation than is specialization. Some studies have highlighted that both types of externalities are important (e.g., see Harrison et al. 1996).
- 23.
Krugman’s approach bears a strong resemblance to Marshall (1920) and Weber (1929) in many ways. However, unlike the new industrial geography approach, which is also mostly based on the Marshallian model, Krugman placed less emphasis on technology spillovers as a source of externalities and stressed more on labor pools and specialized suppliers (Koo 2005).
- 24.
It is interesting to note that the New Growth Theory in its original and later versions (Romer 1986, 1987; Grossman and Helpman (1991a, b); and Aghion and Howitt 1992) did not have any spatial aspects. Geography was introduced into the model later by a group of urban economists, and the theory, came with a greater emphasis on geography, lending a new perspective to agglomeration and spill-over research. Notably, Lucas (1988) showed positive externalities of human capital accumulation arguing that new skills acquired by each worker can be shared or spill over to others in the same location, eventually making the entire labor pool more productive. Later, Black and Henderson (1999) related knowledge spillovers from human capital to spatial agglomeration by combining models several models (Koo 2005).
- 25.
However, the effects of the industrial system on technology adoption have never been tested empirically. Rosenthal and Strange (2001) find a related result, that the industrial structure affects the agglomeration of economic activities.
- 26.
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Diebolt, C., Mishra, T., Parhi, M. (2016). Theoretical and Empirical Literature on Diffusion: A Move Towards a Broader Perspective. In: Dynamics of Distribution and Diffusion of New Technology. India Studies in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-319-32744-0_2
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