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|Title:||A Knowledge Economy Model for Government- Academia-Industry Collaboration in Automotive Industry in India|
|Supervisor:||Nangia, Vinay K.|
|Keywords:||Government-Academia-Industry Collaboration;Motivational Drivers;Mediation;Channels of Interaction;Production Benefits;Innovation Benefits;Improved Operations' Performance;Knowledge Economy|
|Abstract:||Knowledge economy is not only about pushing back the frontiers of knowledge; it is also about the more effective exploitation of all types of knowledge in all manner of economic activity. The capacity to generate new knowledge, to integrate and transfer it depends increasingly on the “triangle of knowledge”, that includes education, research and innovation. In this era of high innovation intensity, bringing government, academia and the industry together, to serve as instruments of change, channelizing their unique capabilities and capacities into a seamless intra and inter-play that models synergy in innovation and economic regeneration, has become quintessential. Such collaborative associations have often bridged the gaps between research, technology development and market application. But despite the optimism, the social and technological discontinuities still exist, which underline the need to pay greater attention to the factors that aid in the mapping and harmonizing of different resources when the actors come together. The driving force behind any collaboration amongst the actors arises from an ‘expectation of profit’, and this term has different connotations for the actors involved. For firms in the industry, the expectation of profit has found expression in two broad dimensions, innovation and production benefits. For academics, the expectation of profit has found expression in the intellectual and economic benefits. The objective of the present research has been to develop a government–academia–industry collaboration model for the automotive industry in India. The study builds on government – academia–industry collaboration architecture that establishes relationships amongst the actors through a set of enablers for a defined set of deliverables. Using the collaboration architecture, a set of critical success factors have been identified and a generic model of government– academia–industry collaboration has been proposed. Two questionnaires, one for academia and the other for firms in the automotive industry, have been used as survey instruments to collect data from academics, working in select institutes of national importance, and from the firms in the automotive industry in the North-western region of India. Data from 129 academics and 54 firms in the automotive industry has been collected and analysed separately, using Statistical Package for the Social Sciences, SPSS, v. 21 and SmartPLS, v. 3.2 for data coding and statistical analysis. xv In the case of academics, the results of the analysis of data collected revealed that two primal drivers, past collaborative experience of the academic and importance of criterion for career advancement build the case for the academic to consider the possibility of engaging with the industry. Both the primal drivers selectively, but significantly influence the decisional drivers of the academic. The decisional drivers express the anticipated intellectual and economic benefits resulting from the collaboration process for the academic. The intellectual motivational drivers serve as a significant positive predictor of the outcomes of collaboration related to the enhanced networks of knowledge creation and utilization, while the economic motivational drivers significantly improve not only the networks of the academic, but also enhance the joint research activity. While the resulting outcomes of enhanced networks of knowledge creation and utilization of the academic significantly influence the teaching and research activity of the academic, the outcomes of enhanced joint research activity significantly improve only the research activity of the academic. The selection of the channels of interaction and the frequency of use are based on the anticipated benefits. In the case of academics collaborating with firms in the automotive industry, the increased use of traditional and service channels of interaction serve as a significant contributor in aggrandizing the outcomes of enhanced networks of knowledge creation and utilization. But the increased use of these channels has no significant effect in enhancing the joint research activity of the academic. The increased use of bi-directional channels of interaction also has no significant effect in either enhancing the networks and insight of the academics or in improving the joint research activity. In the case of firms in the automotive industry, the results of the analysis of data collected revealed that the government initiatives in encouraging collaboration between academia and the industry significantly influenced the firms’ motivation to engage. This motivation was underpinned by the perceived long-term benefits that would stitch a long-term relationship between academia and the industry. Both the short and long-term motivational drivers served as decisional drivers for the firms to participate in the collaboration process. The short-term motivational drivers not only served as strong predictors of the production benefits realized by the firms, but also had a significant influence on the realization of innovation benefits. The phenomenon of the short-term motivational drivers influencing the innovation benefits has been explained by the type of problems faced by the firm that necessitated collaboration and required path breaking innovative solutions, rather than the conventional routinized solutions. The long-term motivational drivers also significantly influence in the realization of the production and innovation benefits. The influence of long-term motivational drivers on the xvi production benefits is indicative of the incremental approach to innovation resulting from collaboration between academia and the industry. Interestingly, the production benefits resulting from collaboration between academia and the industry do not contribute significantly in improving the overall operations’ performance of the firm. On the other hand, the innovation benefits realized through collaboration have a strong and significant influence on the overall operations’ performance of the firm. With respect to the channels of interaction and the frequency of their use, only the frequency of use of the service channel is a significant predictor of the outcomes of production and innovation benefits. In addition, the increased use of the service channel of interaction completely accounts for the influence of short-term motivational driver on the production benefits, while the innovation benefits resulting from short-term motivational drivers are only partially accounted for by the frequency of use of the service channel. In the case of the industry collaboration with academia, the increased use of either the traditional or the bi-directional channels failed to explain the influence of the short-term or long-term motivational drivers on the production or innovation benefits. Thus, for firms in the automotive industry, the increased use service channels in their interaction with academia, serve in adequately addressing the production related issues. On mapping the enablers for each actor for a defined deliverable, the study revealed that service channels of interaction served as the most preferred channel of interaction, when the firms envisage realizing production and innovation benefits from collaboration and the academics pursue benefits that result in enhancing the networks of knowledge creation and utilization. In order to validate the results of the quantitative model, a case study has been conducted at Thapar University (TU). Five academics from three engineering departments and two schools of applied sciences were identified as subjects of the study and case study evidence was collected through semi-structured interviews. Word tables displaying the individual case study data provided the start of the analysis. The analysis of a collection of word tables facilitated in drawing a cross-case comparison and the descriptive statistics from the quantitative model was used to compare the choices of the respondents with those of the subjects in the case study. The findings of the case study showed a significant concurrence with the results of the quantitative study. Majority of the variables considered in the quantitative study showed similar importance, when the case study evidence was analysed.|
|Appears in Collections:||Doctoral Theses@MED|
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