Artificial intelligence applications and manufacturing innovation outcomes: An exploratory qualitative multiple-case study using grounded-theory coding
Artificial intelligence (AI) is increasingly embedded in manufacturing activities, but adoption alone does not explain how AI is associated with innovation outcomes. This exploratory qualitative multiple-case study draws on semi-structured interviews with 15 managerial and technical participants from four manufacturing firms and one AI technology provider, four site visits, and supplementary organizational and public materials collected from July 2022 to October 2025. Grounded-theory coding techniques yielded 14 subcategories within 7 main categories. The analysis was made transparent through explicit coding steps, source-to-category tables, an illustrative end-to-end audit trail, comparison across available source types, examination of negative evidence, and a reserved-material category-sufficiency check. The available record does not support claims of iterative theoretical sampling, independent coding, inter-coder agreement, or theoretical saturation across contexts. The resulting qualitative framework links task characteristics and AI affordances to perceived task–technology fit and organizational embedding of AI; participants’ accounts further relate embedded AI use to dynamic-capability processes, innovation-network embeddedness, and exploitative and exploratory innovation. The framework offers context-bound interpretive propositions rather than causal or population-wide claims.
Ahuja, G. (2000). Collaboration networks, structural holes, and innovation: A longitudinal study. Administrative Science Quarterly, 45(3), 425–455. https://doi.org/10.2307/2667105
Benner, M. J., & Tushman, M. L. (2003). Exploitation, exploration, and process management: The productivity dilemma revisited. Academy of Management Review, 28(2), 238–256. https://doi.org/10.5465/amr.2003.9416096
Drechsler, K., Reibenspiess, V., Eckhardt, A., & Wagner, H.-T. (2025). Understanding the role of innovation actors’ social network in the digital age: A literature review and avenues for future research. IEEE Transactions on Engineering Management, 72, 409–425. https://doi.org/10.1109/TEM.2025.3526703
Duchek, S. (2020). Organizational resilience: A capability-based conceptualization. Business Research, 13(1), 215–246. https://doi.org/10.1007/s40685-019-0085-7
Eisenhardt, K. M. (1989). Building theories from case study research. Academy of Management Review, 14(4), 532–550. https://doi.org/10.2307/258557
Felicetti, A. M., Cimino, A., Mazzoleni, A., & Ammirato, S. (2024). Artificial intelligence and project management: An empirical investigation on the appropriation of generative chatbots by project managers. Journal of Innovation & Knowledge, 9(3), Article 100545. https://doi.org/10.1016/j.jik.2024.100545
Füller, J., Hutter, K., Wahl, J., Bilgram, V., & Tekic, Z. (2022). How AI revolutionizes innovation management—Perceptions and implementation preferences of AI-based innovators. Technological Forecasting and Social Change, 178, Article 121598. https://doi.org/10.1016/j.techfore.2022.121598
Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213–236. https://doi.org/10.2307/249689
Granovetter, M. (1985). Economic action and social structure: The problem of embeddedness. American Journal of Sociology, 91(3), 481–510. https://doi.org/10.1086/228311
Gulati, R. (1998). Alliances and networks. Strategic Management Journal, 19(4), 293–317. https://doi.org/10.1002/(SICI)1097-0266(199804)19:4<293::AID-SMJ982>3.0.CO;2-M
Hua, Y., Kang, F., Zhang, S., & Li, J. (2023). Impacts of task interdependence and equivocality on ICT adoption in the construction industry: A task-technology fit view. Architectural Engineering and Design Management, 19(1), 92–109. https://doi.org/10.1080/17452007.2021.2020084
Johnson, P. C., Laurell, C., Ots, M., & Sandström, C. (2022). Digital innovation and the effects of artificial intelligence on firms’ research and development—Automation or augmentation, exploration or exploitation? Technological Forecasting and Social Change, 179, Article 121636. https://doi.org/10.1016/j.techfore.2022.121636
March, J. G. (1991). Exploration and exploitation in organizational learning. Organization Science, 2(1), 71–87. https://doi.org/10.1287/orsc.2.1.71
Mittal, R., Rani, G., Pathak, V., Chhikara, S., Dhaka, V. S., Vocaturo, E., & Zumpano, E. (2023). Low-cost multisensory robot for optimized path planning in diverse environments. Computers, 12(12), Article 250. https://doi.org/10.3390/computers12120250
O’Reilly, C. A., III, & Tushman, M. L. (2013). Organizational ambidexterity: Past, present, and future. Academy of Management Perspectives, 27(4), 324–338. https://doi.org/10.5465/amp.2013.0025
Rammer, C., Fernández, G. P., & Czarnitzki, D. (2022). Artificial intelligence and industrial innovation: Evidence from German firm-level data. Research Policy, 51(7), Article 104555. https://doi.org/10.1016/j.respol.2022.104555
Rana, N. P., Pillai, R., Sivathanu, B., & Malik, N. (2024). Assessing the nexus of generative AI adoption, ethical considerations and organizational performance. Technovation, 135, Article 103064. https://doi.org/10.1016/j.technovation.2024.103064
Ruga, T., Zumpano, E., Vocaturo, E., Caroprese, L., & Arlia, C. (2025). Bias in dermatological datasets: A critical analysis of the underrepresentation of dark skin tones in melanoma classification images. In M. Paszynski, A. S. Barnard, & Y. J. Zhang (Eds.), Computational Science—ICCS 2025 Workshops: 25th International Conference, Singapore, Singapore, July 7–9, 2025, Proceedings, Part I (Lecture Notes in Computer Science, Vol. 15907) (pp. 434–448). Cham, Switzerland: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-97554-7_32
Strauss, A., & Corbin, J. (1998). Basics of qualitative research: Techniques and procedures for developing grounded theory (2nd ed.). New York, NY. Sage.
Teece, D. J. (2007). Explicating dynamic capabilities: The nature and microfoundations of (sustainable) enterprise performance. Strategic Management Journal, 28(13), 1319–1350. https://doi.org/10.1002/smj.640
Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533. https://doi.org/10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z
Uzzi, B. (1997). Social structure and competition in interfirm networks: The paradox of embeddedness. Administrative Science Quarterly, 42(1), 35–67. https://doi.org/10.2307/2393808
Volkoff, O., & Strong, D. M. (2013). Critical realism and affordances: Theorizing IT-associated organizational change processes. MIS Quarterly, 37(3), 819–834. https://doi.org/10.25300/MISQ/2013/37.3.07
Winter, S. G. (2003). Understanding dynamic capabilities. Strategic Management Journal, 24(10), 991–995. https://doi.org/10.1002/smj.318