Abstract
In Chap. 7 we found that firm-specific (structural) factors have explicit impact on the adoption pattern. There were also evidences at the micro-level on the role of external linkages on the adoption, having clear spatial connotations. This chapter extends our analysis by providing an econometric framework for explicating the impact of an array of the variables on firms’ adoption behaviour. The aim of this chapter is to test the empirical evidence on the possible set of factors determining adoption of AMTs in the Indian auto component industry. In line with our framework discussed in Chap. 3, the empirical exercise in this chapter is purported to take into account the influence of structural, (i.e., internal to the firm), and socio-economic (external to the firm) factors on the process of adoption. In congruence with the recent studies of technology adoption in the literature (as discussed in Chap. 2), the general framework of our analysis is specified where we distinguish a series of explanatory variables.
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Notes
- 1.
The various descriptive statistics relating to this aspect is presented and discussed in detail in the next chapter.
- 2.
A detailed exposition of the characteristics of AMTs with reference to their adoption process is presented in Chap. 2.
- 3.
Although we recognise that this proxy is not such a good representation of the capability needed for adoption and implementation of a new technology (where we would ideally require the skill level of workers instead of managerial staff), we use it in the absence of a better measure. Moreover by using the human capital base of the firm (through percentage of employees with technical/managerial qualifications) we try to capture the firm-level capabilities needed for evaluating and acquiring an advanced technology. In this sense this variable is expected to represent the absorptive capability of a firm.
- 4.
- 5.
For example, Mansfield (1989) explains the diffusion of industrial robots on the basis of differences in firms’ estimates of their own profitability of adoption.
- 6.
See Chap. 3 for a detailed discussion of the various economic benefits associated with AMTs use.
- 7.
See Maddala (1988) for an illustration of the problems associated with these models.
- 8.
We have used logit model as computational procedures are rather easier in this case. Moreover, the coefficients in the logit model have an immediate interpretation.
- 9.
Detailed descriptions of the properties of these models can be found in standard econometric textbooks (e.g., Greene 1997).
- 10.
See Chap. 7 for an elaboration of this issue of ‘adoption criterion’.
- 11.
The category large also includes the medium-sized firms.
- 12.
For some firms the first starting year of production is used due to non-availability of the year of establishment.
- 13.
OEM is used to refer to a company that acquires a product or component and reuses or incorporates it into a new product with its own brand name. It is interesting to note that OEM term originated in the automotive industry.
- 14.
Factor analysis is applied: (1) to reduce the number of variables and (2) to detect structure in the relationships between variables, that is to classify variables. A hands-on how-to approach on factor analysis can be found in Stevens (1986).
- 15.
Basically, the extraction of principal components amounts to a variance maximizing (varimax) rotation of the original variable space. The computational aspects of principal components analysis can be found in Stevens (1986).
- 16.
- 17.
In network terminology, the degree of a vertex or node is the number of edge connecting to it. Analogously, out degree is defined as the number of ties going out of the vertex to others in the network. In our case, out degrees are the number of firms to which a particular auto component firm has a supply relationship.
- 18.
This is calculated using UCINET. Chapter 4 presents in detail the network perspective and the various centrality measures in Indian auto component industry.
- 19.
A detailed discussion of this aspect of network analysis is presented in a previous chapter.
- 20.
A comparison of both the regressions for one model is provided in Appendix 8B.
- 21.
ll estimations have been carried out using STATA 13 package.
- 22.
Surprisingly RND is not found to be significant in the adoption intensity model. We try to give some intuitive reasons later in this section regarding the possible non-significance of this variable in the model.
- 23.
It may be pointed out that R&D and size variable seems to be highly correlated as large firms tend to have more regular R&D practice for which the effect of R&D is not coming out to be significant.
- 24.
The adoption pattern is observed to be largely similar across regions. Therefore, the significance/non-significance of regional dummies in adoption propensity (AMTTHREE) models are not very much interesting here. The regression results on AMTTHRE did not show any regional differences in adoption pattern. Hence the objective here is to test if there is regional impact on adoption intensity.
- 25.
As a rule of thumb, VIF of 10 or greater (equivalently a tolerance of 0.1 or less) is a cause for concern. For more detailed discussion of multicollinearity and its remedies refer to Berry and Feldman (1985).
- 26.
We have used STATA 8.2 package to estimate the predicted probabilities. It may be noted that the predicted values are generated for the most general model (conforming to model 3 in our analysis)
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Diebolt, C., Mishra, T., Parhi, M. (2016). AMT Diffusion in Indian Auto Components Industry: An Examination of the Determinants of Adoption. 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_8
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