A Qualitative Exploration of Decision-Making Processes in Multidisciplinary Engineering Innovation
Keywords:
multidisciplinary engineering, innovation, decision-making, qualitative research, semi-structured interviewsAbstract
This study aimed to explore how multidisciplinary engineering teams make innovation-related decisions under conditions of technical uncertainty, disciplinary diversity, organizational constraint, and market-oriented pressure. This qualitative study was conducted using semi-structured interviews with 24 participants from Tehran-based engineering and technology-oriented organizations. Participants included senior engineers, project managers, R&D specialists, product development experts, innovation managers, and technical consultants who had direct experience in multidisciplinary engineering innovation projects. Purposeful sampling was used to recruit information-rich participants, and data collection continued until theoretical saturation was achieved after the twenty-first interview, followed by three additional interviews to confirm thematic stability. Interviews were conducted face-to-face or online, lasted 45–75 minutes, and focused on participants’ experiences of innovation decision-making, cross-disciplinary coordination, technical evaluation, conflict resolution, risk assessment, and implementation choices. Audio-recorded interviews were transcribed verbatim and analyzed using thematic analysis with the support of NVivo software. Analysis produced five main categories: shared problem framing across disciplinary boundaries, evidence-based negotiation under uncertainty, distributed authority and decision ownership, iterative experimentation and risk balancing, and organizational alignment with innovation value. The findings showed that multidisciplinary engineering decisions were rarely linear or purely technical. Instead, participants described decision-making as an interactive process shaped by translation between disciplinary logics, negotiation over evidence, informal influence, prototype-based learning, and organizational expectations regarding cost, feasibility, timing, and market relevance. The study concludes that effective decision-making in multidisciplinary engineering innovation depends on the team’s ability to create shared interpretive spaces, combine analytical and experiential knowledge, clarify decision authority, tolerate controlled experimentation, and align technical novelty with organizational value. These findings contribute to engineering innovation literature by showing how decision-making operates as a socio-technical and negotiated practice rather than a purely rational selection process.
Downloads
References
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Brown, S. L., & Eisenhardt, K. M. (1995). Product development: Past research, present findings, and future directions. Academy of Management Review, 20(2), 343–378. https://doi.org/10.5465/amr.1995.9507312922
Bucciarelli, L. L. (1994). Designing engineers. MIT Press.
Carlile, P. R. (2002). A pragmatic view of knowledge and boundaries: Boundary objects in new product development. Organization Science, 13(4), 442–455. https://doi.org/10.1287/orsc.13.4.442.2953
Cross, N. (2004). Expertise in design: An overview. Design Studies, 25(5), 427–441. https://doi.org/10.1016/j.destud.2004.06.002
Dorst, K. (2011). The core of design thinking and its application. Design Studies, 32(6), 521–532. https://doi.org/10.1016/j.destud.2011.07.006
Dougherty, D. (1992). Interpretive barriers to successful product innovation in large firms. Organization Science, 3(2), 179–202. https://doi.org/10.1287/orsc.3.2.179
Dym, C. L., Agogino, A. M., Eris, O., Frey, D. D., & Leifer, L. J. (2005). Engineering design thinking, teaching, and learning. Journal of Engineering Education, 94(1), 103–120. https://doi.org/10.1002/j.2168-9830.2005.tb00832.x
Edmondson, A. C. (1999). Psychological safety and learning behavior in work teams. Administrative Science Quarterly, 44(2), 350–383. https://doi.org/10.2307/2666999
Edmondson, A. C., & Nembhard, I. M. (2009). Product development and learning in project teams: The challenges are the benefits. Journal of Product Innovation Management, 26(2), 123–138. https://doi.org/10.1111/j.1540-5885.2009.00341.x
Eisenhardt, K. M., & Zbaracki, M. J. (1992). Strategic decision making. Strategic Management Journal, 13(S2), 17–37. https://doi.org/10.1002/smj.4250130904
Guest, G., Bunce, A., & Johnson, L. (2006). How many interviews are enough? An experiment with data saturation and variability. Field Methods, 18(1), 59–82. https://doi.org/10.1177/1525822X05279903
Jansen, J. J. P., Van den Bosch, F. A. J., & Volberda, H. W. (2006). Exploratory innovation, exploitative innovation, and performance: Effects of organizational antecedents and environmental moderators. Management Science, 52(11), 1661–1674. https://doi.org/10.1287/mnsc.1060.0576
Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Sage.
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
Nembhard, I. M., & Edmondson, A. C. (2006). Making it safe: The effects of leader inclusiveness and professional status on psychological safety and improvement efforts in health care teams. Journal of Organizational Behavior, 27(7), 941–966. https://doi.org/10.1002/job.413
Pahl, G., Beitz, W., Feldhusen, J., & Grote, K.-H. (2007). Engineering design: A systematic approach (3rd ed.). Springer.
Simon, H. A. (1996). The sciences of the artificial (3rd ed.). MIT Press.
Star, S. L., & Griesemer, J. R. (1989). Institutional ecology, “translations,” and boundary objects: Amateurs and professionals in Berkeley’s Museum of Vertebrate Zoology, 1907–1939. Social Studies of Science, 19(3), 387–420. https://doi.org/10.1177/030631289019003001
Ulrich, K. T., & Eppinger, S. D. (2016). Product design and development (6th ed.). McGraw-Hill Education.
