TECHNOLOGY ADOPTION IN INDIA: A FUTURE PERSPECTIVE WITH ANALYSIS OF IMPORTANT VARIABLES
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Bibliographic record
Abstract
As one of the top two fastest growing economies in the world, a study of technology adoption in India is a relevant and important topic today. This is especially true for two reasons; first, the economic ties between USA and India are growing increasingly stronger, and second, there is relatively a larger importance of industrial sector in the growth of developing economies. However, this appears to be the first survey of advance manufacturing technology (AMT) adoption in India that leads to the first measurements of various important variables related to AMT adoption process and presents the emergent implications for its future. In addition to current status, future implementation plans of AMT adoption and planned future investment for AMT adoption, our survey includes most of the important aspects of AMT adoption process: methods of AMT adoption processes used, critical success factors of AMT implementation, personnel shortages in AMT adoption process, benefits/results of AMT adoption, major obstacles to AMT adoption, and the relation of AMT adoption to research and development in the organization. For a relative perspective on important success factors, comparison is made between Indian firms and firms in Singapore for which data is available. Similarly, regarding the benefits of AMT adoption, obstacles to AMT adoption, and relationship of R&D with AMT adoption, we present a brief comparison with Canadian firms. Our analysis shows that four technologies that we can expect to move from the low current adoption level to high future adoption level are computer aided manufacturing, automated systems used for inspection/testing, benchmarking, and just-in-time inventory control. In terms of investment, five technologies that are likely to be heavily invested are computer aided design, computer aided manufacturing, MRP/ERP, Plant certification, and local area networks. Our statistical test results also reinforce the expectation that larger companies under various conditions are more likely to adopt AMT in future than smaller companies. We find that the most frequently used methods of AMT adoption in India are by Purchasing Equipment and by Customizing Existing Technology in house rather than by licensing new technology from outside. Out of the 19 success factors for AMT adoption surveyed, on a seven point scale from 1 to 7, the most important top three in India are Management Commitment and Support (score: 6.32), Top-down Planning and Bottom-up Implementation (score: 6.04), and Active Participation by In-house Engineers (score: 6.04). Reflecting the considerable differences in the two economies, there is significant divergence in these factors from the Singapore study. On the same scale, Improved Worker Safety is the most important AMT adoption benefit with a mean score 6.04, followed by Product Quality (5.92), Product Flexibility (5.92), and Set-up Time (5.92, note same scores) among the seventeen benefits surveyed. Most of the benefits are weighted roughly the same in the Canadian survey except differences in the Profitability, Equipment Utilization and Set-Up Time Reduction benefits. The survey also shows that variables related to the lack of financial justification is the largest obstacle to AMT adoption, followed by lack of technical support; the result being quite parallel to the survey of AMT obstacles in Canadian firms. Further, classifying 25 AMT into three levels (simple — Level I, moderate — Level II, sophisticated — Level III), by statistical analysis, we can conclude that Indian firms we surveyed have high adoption degree of Level I technologies, are going to adopt more Level II technologies in the future, and do not yet seem poised to invest in Level III technologies. This classification should be useful for a briefer, more easily communicable explanation for managerial personnel regarding the status of various adoption levels of the 25 major advanced manufacturing technologies presented here.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it