AI Impact on Manufacturing Industry in Chennai

Authors

  • Dr.A. MYTHILY Assistant Professor, Department of Management Studeis, Adaikalamatha College, Vallam, Thanjavur - 613402. Author
  • Dr. Prabu A Author

DOI:

https://doi.org/10.66210/jy8bw187

Abstract

This research investigates the factors that determine perceived benefits in organizational AI adoption specifically technological readiness, management support, workforce expertise, and cost perceptions. Data was gathered through a structured survey of organizations who are in the consideration or implementation phase of AI initiatives. Quantitative analysis was utilized in SPSS for descriptive statistics and reliability testing, and AMOS for structural equation modeling to assess the proposed relationships. The findings indicate that technological readiness and management support positively influence perceived benefits of AI, with cost perceptions having a partial mediation on relationship paths. The study highlights leadership commitment to AI adoption as an implicit organization resource, and the commitment of resources to AI demonstrates its organizational value. Practical implications suggest that managers should commit to employee skill development or upskilling employees to fully capture value from AI. Limitations include the use of cross-sectional design and data gathered from specific sectors. Future research could adopt a longitudinal approach to understanding AI's adoption, as well as extend the model to discuss additional contextual variables and link measurable financial or operational performance metrics to a better understanding of AI's strategic impact.

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References

1. Baker, J. (2012). The technology–organization–environment framework. Information Systems Theory, 231–245.

2. Baker, J. (2012). The technology–organization–environment framework. In Y. K. Dwivedi et al. (Eds.), Information systems theory (pp. 231–245). Springer.

3. Balasubramanian, S., & Prasad, A. (2020). Industry 4.0 and its impact on manufacturing sector in India. Journal of Manufacturing Technology Management, 31(7), 1345–1365.

4. Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120.

5. Brown, A., & Taylor, M. (2022). Employee attitudes toward AI adoption: The role of leadership and change resistance. Journal of Organizational Change Management, 35(4), 512–528. https://doi.org/10.1108/JOCM-2022-0456

6. Brynjolfsson, E., & McAfee, A. (2017). Machine, platform, crowd: Harnessing our digital future. W. W. Norton & Company.

7. Bughin, J., Seong, J., Manyika, J., Chui, M., & Joshi, R. (2018). Notes from the AI frontier: Modeling the impact of AI on the world economy. McKinsey Global Institute Report.

8. Chatterjee, S., Rana, N. P., & Dwivedi, Y. K. (2021). Assessing organizational readiness for AI adoption. Information Systems Frontiers, 23(2), 1–18.

9. Chatterjee, S., Rana, N. P., & Dwivedi, Y. K. (2021). Social media marketing: A practitioner’s perspective. Journal of Business Research, 135, 1–9.

10. Chatterjee, S., Rana, N. P., Dwivedi, Y. K., & Baabdullah, A. (2021). Understanding AI adoption in manufacturing: Insights from the TOE framework. Technological Forecasting and Social Change, 170, 120880.

11. Chaudhuri, A., & Bose, I. (2022). Exploring the barriers to AI adoption in supply chains: Evidence from an emerging economy. Information Systems Frontiers.

12. Chen, J., Ishfaq, R., Ashraf, S., Sarfaraz, A., & Wang, H. (2023). Managerial competencies for digital transformation: The role of AI in organizational performance. Technological Forecasting and Social Change, 190, 122350. https://doi.org/10.1016/j.techfore.2023.122350

13. Creswell, J. W., & Plano Clark, V. L. (2018). Designing and conducting mixed methods research. Sage publications.

14. Daire, A. P., Henson, R. K., & Fisher, T. A. (2008). Statistical considerations in structural equation modeling. Journal of Counseling & Development, 86(1), 92–99.

15. Dalenogare, L. S., Benitez, G. B., Ayala, N. F., & Frank, A. G. (2018). The expected contribution of Industry 4.0 technologies for industrial performance. International Journal of Production Economics.

16. Erol, S., Schumacher, A., & Sihn, W. (2016). Strategic guidance towards Industry 4.0–A three-stage process model. Procedia CIRP.

17. European Commission Joint Research Centre. (2024). Strategic alignment of artificial intelligence adoption in European organizations. Publications Office of the European Union. https://doi.org/10.2760/ai-strategy-2024

18. Frank, A. G., Dalenogare, L. S., & Ayala, N. F. (2019). Industry 4.0 technologies: Implementation patterns in manufacturing companies. International Journal of Production Research.

19. Gangwar, H., Date, H., & Ramaswamy, R. (2015). Understanding determinants of cloud computing adoption using an integrated TAM-TOE model. Journal of Enterprise Information Management, 28(1), 107–130.

20. Gerbing, D. W., & Anderson, J. C. (1992). Monte Carlo evaluations of goodness-of-fit indices for structural equation models. Sociological Methods & Research, 21(2), 132–160.

21. Gupta, H., & Shukla, N. (2022). Barriers to implementing Industry 4.0 in emerging economies: A case of Indian manufacturing industries. Journal of Manufacturing Technology Management.

22. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2006). Multivariate data analysis (6th ed.). Pearson Prentice Hall.

23. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (3rd ed.). Sage.

24. Hair, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2019). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications.

25. Hecklau, F., Galeitzke, M., Flachs, S., & Kohl, H. (2016). Holistic approach for human resource management in Industry 4.0. Procedia CIRP.

26. Hernandez, R., & Liu, Y. (2023). Measuring perceived benefits of AI adoption: Toward standardized approaches. Information Systems Frontiers, 25(6), 1421–1438. https://doi.org/10.1007/s10796-023-10345-7

27. Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55.

28. Hu, L. T., & Bentler, P. M. (2006). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Multivariate Behavioral Research, 41(4), 537–555.

29. Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis. Structural Equation Modeling, 6(1), 1–55.

30. Ifinedo, P. (2011). An empirical analysis of factors influencing Internet/e-business technologies adoption by SMEs in Canada. International Journal of Information Technology & Decision Making, 10(4), 731–766.

31. Ifinedo, P. (2012). Technology acceptance by health professionals in Canada: An analysis with a modified UTAUT model. International Journal of Medical Informatics, 81(2), 76–87.

32. Ifinedo, P. (2012). Technology acceptance of ERP systems. Computers in Human Behavior, 28(3), 977–986.

33. Journal of Cleaner Production. (2024). Enabling Industry 4.0: Assessing technologies and prioritization framework for agile manufacturing in India, 447, Article 141488. (ScienceDirect)

34. Kallio, H., Pietilä, A. M., Johnson, M., & Kangasniemi, M. (2016). Systematic methodological review: developing a framework for a qualitative semi‐structured interview guide. Journal of Advanced Nursing, 72(12), 2954–2965.

35. Kannan, P. (2018). Qualitative Analysis and Impact Assessment of Chennai Automotive Industry Cluster. South Asian Journal of Management. (Cluster study of Chennai automotive ecosystem). (SAGE Journals)

36. Khan, M., Alam, S., & Usman, M. (2022). Artificial intelligence adoption in SMEs: The mediating role of resource and ecosystem constraints. Journal of Small Business Management, 60(7), 1550–1572. https://doi.org/10.1080/00472778.2022.2034579

37. Kline, R. B. (1998). Principles and practice of structural equation modeling. Guilford Press.

38. Kumar, A., Singh, R. K., & Dwivedi, Y. K. (2021). Application of industry 4.0 technologies in SMEs for sustainable organizational performance. Production Planning & Control.

39. Lee, J., Bagheri, B., & Kao, H. A. (2023). A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Letters.

40. Lee, J., Bagheri, B., & Kao, H. A. (2023). Recent advances and trends of cyber-physical systems and big data analytics in industrial applications. International Journal of Advanced Manufacturing Technology. (Industry 4.0/AI integration review). (ScienceDirect)

41. Machado, C. G., Winroth, M. P., & Ribeiro da Silva, E. H. D. (2019). Sustainable manufacturing in Industry 4.0: An emerging research agenda. International Journal of Production Research.

42. Maroufkhani, P., Tseng, M. L., Iranmanesh, M., Ismail, W. K. W., & Khalid, H. (2022). Big data analytics adoption: Determinants and performance outcomes in manufacturing firms. Journal of Business Research, 137, 1–13.

43. Moeuf, A., Pellerin, R., Lamouri, S., Tamayo-Giraldo, S., & Barbaray, R. (2018). The industrial management of SMEs in the era of Industry 4.0. International Journal of Production Research.

44. Müller, J. M., Hopf, H., & Kraus, S. (2020). Management support and governance in Industry 4.0 adoption. International Journal of Production Economics, 227, 107668. https://doi.org/10.1016/j.ijpe.2020.107668

45. Müller, J. M., Kiel, D., & Voigt, K. I. (2018). What drives the implementation of Industry 4.0? The role of opportunities and challenges in the context of sustainability. Sustainability.

46. Natarajan, R., & Balasubramanian, S. (2011). The Technical Efficiency of Chennai Auto Industry Cluster. SAE Technical Paper 2011-28-0100. (Efficiency and structure of Chennai auto cluster). (SAE International)

47. Oliveira, T., & Martins, M. F. (2011). Literature review of information technology adoption models. Electronic Journal of Information Systems Evaluation, 14(1), 110–121.

48. Parhi, S., Joshi, K., Wuest, T., & Akarte, M. M. (2022). Factors affecting Industry 4.0 adoption – A hybrid SEM-ANN approach. Computers & Industrial Engineering, 168, Article 108062. (ResearchGate)

49. Parhi, S., Joshi, K., Wuest, T., & Akarte, M. M. (2022). Factors affecting Industry 4.0 adoption – A hybrid SEM-ANN approach. Computers & Industrial Engineering, 168, Article 108062. (ResearchGate)

50. Premkumar, G., & Roberts, M. (1999). Adoption of new information technologies in rural small businesses. Omega, 27(4), 467–484.

51. Prifti, L., Knigge, M., Kienegger, H., & Krcmar, H. (2017). A competency model for “Industrie 4.0” employees. WI 2017 Proceedings.

52. Rao, S., & Mehta, K. (2022). Managerial optimism bias in AI adoption: Evidence from a longitudinal study. Journal of Business Research, 149, 245–257. https://doi.org/10.1016/j.jbusres.2022.04.012

53. Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.

54. Roy Ghatak, R., & Garza-Reyes, J. A. (2024). Investigating the barriers to Quality 4.0 adoption in the Indian manufacturing sector: insights and implications for industry and policy-making. International Journal of Quality & Reliability Management, 41(6), 1623–1656. (Emerald)

55. Shamim, S., Cang, S., Yu, H., & Li, Y. (2017). Management approaches for Industry 4.0: A human resource management perspective. Proceedings of International Conference on Technology Management.

56. Singh, A., & Patel, R. (2024). Trust, explainability, and managerial endorsement in generative-AI adoption. AI & Society, 39(2), 587–603. https://doi.org/10.1007/s00146-024-01678-4

57. Singh, R., Yadav, V., Gaur, T. S., et al. (2025). Barriers to adopting industry 4.0 in Indian textile and clothing industries. Discover Applied Sciences, 7, Article 560. (SpringerLink)

58. Sony, M., & Naik, S. (2020). Industry 4.0 integration with socio-technical systems theory: A systematic review and proposed theoretical model. Technology in Society.

59. Teece, D. J. (2018). Business models and dynamic capabilities. Long Range Planning, 51(1), 40–49.

60. Teece, D. J. (2018). Dynamic capabilities as systems of sensing, seizing, and transforming. Journal of Management Studies, 55(7), 1155–1190.

61. Tornatzky, L. G., & Fleischer, M. (1990). The Processes of Technological Innovation. Lexington Books.

62. Tornatzky, L. G., & Fleischer, M. (1990). The processes of technological innovation. Lexington Books.

63. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.

64. Venkatesh, V., Thong, J. Y. L., & Xu, X. (2013). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178.

65. Wang, X., Chen, Y., & Zhao, L. (2022). Cross-functional leadership and governance in AI-enabled organizations. Journal of Business Strategy, 43(6), 390–402. https://doi.org/10.1108/JBS-2022-0065

66. Wikipedia. (2025). Economy of Chennai. Retrieved from Wikipedia. (Wikipedia)

67. Windmann, A., Wittenberg, P., Schieseck, M., & Niggemann, O. (2024). Artificial Intelligence in Industry 4.0: A review of integration challenges for industrial systems. AI in Production/Industrial Systems (review). (SpringerLink)

68. World Scientific. (2022). Factors affecting Industry 4.0 adoption in Indian SMEs. Journal of Industrial Integration and Management. (World Scientific)

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2026-06-01

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How to Cite

AI Impact on Manufacturing Industry in Chennai. (2026). International Journal of Management Research & Innovation, 1(2), 80-102. https://doi.org/10.66210/jy8bw187

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