TECHNOLOGY ADOPTION IN INDIA: A FUTURE PERSPECTIVE WITH ANALYSIS OF IMPORTANT VARIABLES
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,003 | 0,009 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».