CoChat
Sign Up
Cited by 117Technology Adoption and User BehaviourDigital Transformation in IndustryInnovation Diffusion and Forecasting

Emerging technologies and the use case: A multi‐year study of drone adoption

Open access
Maghazei, Omid, Lewis, Michael A., Netland, Torbjørn H.·2022·Journal of Operations Management
DOI 10.1002/joom.1196
Open this paper in CoChat — free accountOpen PDF

Abstract

Although disruptive “Industry 4.0” technologies often lack a clear business case, vendors are advocating and companies are actively exploring their use in operations settings. The technology management literature suggests that successful adoption derives from an appropriate fit between the specific technology and (1) economic and strategic factors, (2) operational and supply chain factors, and (3) organizational and behavioral factors. Through a five‐year research project, we explore how drones—an archetypal emerging technology supported by a thriving vendor ecosystem—transitioned from early ideas to experimental applications to full adoption in daily operations. We analyze a range of data, including exploratory interviews with drone ecosystem actors, a secondary dataset, and case studies of drone applications in Geberit and IKEA. Key findings relate to our observation that technology adoption patterns for emerging technologies do not always follow the traditional linear logic of technology fit. We find that emerging technologies are characterized by a dynamic interaction between technology push from a thriving ecosystem and market pull from companies exploring meaningful operational and business value using the concept of “use case.” Based on these findings, we contribute to the technology management literature with an alternative technology adoption framework for emerging “Industry 4.0” technologies.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1002/joom.1196

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 85

  1. Agatz, N., Bouman, P., & Schmidt, M. (2018). Optimization approaches for the traveling salesman problem with drone. Transportation Science, 52(4), 965-981.
  2. Avital, M. (2000). Dealing with time in social inquiry: A tension between method and lived experience. Organization Science, 11(6), 665-673.
  3. Babich, V., & Hilary, G. (2020). OM forum-Distributed ledgers and operations: What operations management researchers should know about blockchain technology. Manufacturing & Service Operations Management, 22(2), 223-240.
  4. Bala, H., & Venkatesh, V. (2016). Adaptation to information technology: A holistic nomological network from imple- mentation to job outcomes. Management Science, 62(1), 156-179.
  5. Baldwin, C. Y. (1991). How capital budgeting deters innovation- And what to do about it. Research-Technology Management, 34(6), 39-45.
  6. Bastani, H., Zhang, D., & Zhang, H. (2021). Applied machine learn- ing in operations management. In V. Babich, J. R. Birge, & G. Hilary (Eds.), Innovation technology at the interface of finance and operations (pp. 189-222). Springer Nature.
  7. Beatty, C. A., & Gordon, J. R. (1988). Barriers to the implementa- tion of CAD/CAM systems. MIT Sloan Management Review, 29(4), 25.
  8. Beaudry, A., & Pinsonneault, A. (2010). The other side of accep- tance: Studying the direct and indirect effects of emotions on information technology use. MIS Quarterly, 34, 689-710.
  9. Bendoly, E., & Cotteleer, M. J. (2008). Understanding behavioral sources of process variation following enterprise system deployment. Journal of Operations Management, 26(1), 23-44.
  10. Bessant, J. (1994). Towards total integrated manufacturing. Interna- tional Journal of Production Economics, 34(3), 237-251.
  11. Bittner, K., & Spence, I. (2003). Use case modeling. Addison-Wesley.
  12. Boyer, K. K., Leong, G. K., Ward, P. T., & Krajewski, L. J. (1997). Unlocking the potential of advanced manufacturing technolo- gies. Journal of Operations Management, 15(4), 331-347.
  13. Bravo, R. Z. B., Leiras, A., & Cyrino Oliveira, F. L. (2019). The use of UAV s in humanitarian relief: An application of POMDP- based methodology for finding victims. Production and Opera- tions Management, 28(2), 421-440.
  14. Brynjolfsson, E., Rock, D., & Syverson, C. (2021). The productivity J-curve: How intangibles complement general purpose technol- ogies. American Economic Journal: Macroeconomics, 13(1), 333-372.
  15. Burcher, P., Lee, G., & Sohal, A. (1999). Lessons for implementing AMT: Some case experiences with CNC in Australia, Britain and Canada. International Journal of Operations & Production Management, 19(5/6), 515-526.
  16. Carlsson, J. G., & Song, S. (2018). Coordinated logistics with a truck and a drone. Management Science, 64(9), 4052-4069.
  17. Chamayou, G. (2015). Drone theory. Penguin UK.
  18. Cheng, Z., & Nault, B. R. (2007). Industry level supplier-driven IT spillovers. Management Science, 53(8), 1199-1216.
  19. Chew, W. B., Leonard-Barton, D., & Bohn, R. E. (1991). Beating Murphy's law. MIT Sloan Management Review, 32(3), 5.
  20. Chowdhury, S., Emelogu, A., Marufuzzaman, M., Nurre, S. G., & Bian, L. (2017). Drones for disaster response and relief opera- tions: A continuous approximation model. International Jour- nal of Production Economics, 188(2017), 167-184.
  21. D'Andrea, R. (2014). Guest editorial can drones deliver? IEEE Transactions on Automation Science and Engineering, 11(3), 647-648.
  22. Das, A., & Narasimhan, R. (2001). Process-technology fit and its implications for manufacturing performance. Journal of Opera- tions Management, 19(5), 521-540.
  23. D'Aveni, R. (2015). The 3-D printing revolution. Harvard Business Review, 93(5), 40-48.
  24. Duimering, P. R., Safayeni, F., & Purdy, L. (1993). Integrated manufacturing: Redesign the organization before implementing flexible technology. MIT Sloan Management Review, 34(4), 47.
  25. Efstathiades, A., Tassou, S. A., Oxinos, G., & Antoniou, A. (2000). Advanced manufacturing technology transfer and implementa- tion in developing countries: The case of the Cypriot manufactur- ing industry. Technovation, 20(2), 93-102.
  26. Eisenhardt, K. M. (1989). Building theories from case study research. Academy of Management Review, 14(4), 532-550.
  27. Eyers, D., & Dotchev, K. (2010). Technology review for mass cus- tomisation using rapid manufacturing. Assembly Automation, 30(1), 39-46.
  28. Fenn, J., & Raskino, M. (2008). Mastering the hype cycle: How to choose the right innovation at the right time. Harvard Business Press.
  29. Field, J. M., Heim, G. R., & Sinha, K. K. (2004). Managing quality in the e-service system: Development and application of a pro- cess model. Production and Operations Management, 13(4), 291-306.
  30. Frohlich, M. T., & Dixon, J. R. (1999). Information systems adapta- tion and the successful implementation of advanced manufacturing technologies. Decision Sciences, 30(4), 921-957.
  31. Gehman, J., Glaser, V. L., Eisenhardt, K. M., Gioia, D., Langley, A., & Corley, K. G. (2018). Finding theory-method fit: A comparison of three qualitative approaches to theory build- ing. Journal of Management Inquiry, 27(3), 284-300.
  32. Goldenberg, J., Lehmann, D. R., & Mazursky, D. (2001). The idea itself and the circumstances of its emergence as predictors of new product success. Management Science, 47(1), 69-84.
  33. Hayes, R. H., & Jaikumar, R. (1991). Requirements for successful implementation of new manufacturing technologies. Journal of Engineering and Technology Management, 7(3-4), 169-175.
  34. Hayes, R. H., & Wheelwright, S. C. (1979). Linking manufacturing process and product life cycles. Harvard Business Review, 57(1), 133-140.
  35. Heim, G. R., Nair, A., & Bendoly, E. (2021). Aims and criteria for advancing technology management research. Journal of Opera- tions Management, 67(8), 920-925.
  36. Heim, G. R., & Peng, D. X. (2010). The impact of information tech- nology use on plant structure, practices, and performance: An exploratory study. Journal of Operations Management, 28(2), 144-162.
  37. Heim, G. R., & Peng, X. (2020). Technology management in a global context: From enterprise systems to technology disrupting oper- ations and supply chains. Retrieved June 16 from https:// onlinelibrary.wiley.com/pb-assets/assets/18731317/JOM% 20CFP%20-%20Technology%20Management%20in%20a% 20Global%20Context-1570138240457.pdf.
  38. Heim, G. R., Wentworth, W. R., Jr., & Peng, X. (2009). The value to the customer of RFID in service applications. Decision Sciences, 40(3), 477-512.
  39. Holmström, J., Holweg, M., Khajavi, S. H., & Partanen, J. (2016). The direct digital manufacturing (r) evolution: Definition of a research agenda. Operations Management Research, 9(1), 1-10.
  40. Holmström, J., Holweg, M., Lawson, B., Pil, F. K., & Wagner, S. M. (2019). The digitalization of operations and supply chain man- agement: Theoretical and methodological implications. Journal of Operations Management, 65(8), 728-734.
  41. Holmström, J., Ketokivi, M., & Hameri, A. P. (2009). Bridging prac- tice and theory: A design science approach. Decision Sciences, 40(1), 65-87.
  42. Holmström, J., & Partanen, J. (2014). Digital manufacturing-driven transformations of service supply chains for complex products. Supply Chain Management: An International Journal, 19(4), 421-430.
  43. Jacobs, F. R., & Bendoly, E. (2003). Enterprise resource planning: Developments and directions for operations management research. European Journal of Operational Research, 146(2), 233-240.
  44. Jacobson, I. (1987). Object-oriented development in an industrial environment, conference proceedings on object-oriented program- ming systems, languages and applications (pp. 183-191). Associ- ation for Computing Machinery, New York, NY.
  45. Jacobson, I. (2004). Use cases-yesterday, today, and tomorrow. Soft- ware & Systems Modeling, 3(3), 210-220.
  46. Jaikumar, R. (1986). Postindustrial manufacturing. Harvard Busi- ness Review, 64(6), 69-76.
  47. Khajavi, S. H., Partanen, J., Holmström, J., & Tuomi, J. (2015). Risk reduction in new product launch: A hybrid approach combin- ing direct digital and tool-based manufacturing. Computers in Industry, 74(2015), 29-42.
  48. Kurpjuweit, S., Wagner, S. M., & Choi, T. Y. (2021). Selecting startups as suppliers: A typology of supplier selection arche- types. Journal of Supply Chain Management, 57(3), 25-49.
  49. Langley, A., Smallman, C., Tsoukas, H., & Van de Ven, A. H. (2013). Process studies of change in organization and manage- ment: Unveiling temporality, activity, and flow. Academy of Management Journal, 56(1), 1-13.
  50. Langley, A., & Truax, J. (1994). A process study of new technology adoption in smaller manufacturing firms. Journal of Manage- ment Studies, 31(5), 619-652.
  51. Latour, B. (1987). Science in action: How to follow scientists and engi- neers through society. Harvard University Press.
  52. Leonard-Barton, D. (1990). A dual methodology for case studies: Synergistic use of a longitudinal single site with replicated mul- tiple sites. Organization Science, 1(3), 248-266.
  53. Leonard-Barton, D., & Deschamps, I. (1988). Managerial influence in the implementation of new technology. Management Science, 34(10), 1252-1265.
  54. Loch, C. H. (2017). Creativity and risk taking aren't rational: Behav- ioral operations in MOT. Production and Operations Manage- ment, 26(4), 591-604.
  55. Maghazei, O., & Netland, T. (2019). Drones in manufacturing: Exploring opportunities for research and practice. Journal of Manufacturing Technology Management, 31(6), 1237-1259.
  56. Malhotra, M. K., Heine, M. L., & Grover, V. (2001). An evaluation of the relationship between management practices and com- puter aided design technology. Journal of Operations Manage- ment, 19(3), 307-333.
  57. Meredith, J. R. (1987). Implementing new manufacturing technolo- gies: Managerial lessons over the FMS life cycle. Interfaces, 17(6), 51-62.
  58. Meredith, J. R., & Suresh, N. C. (1986). Justification techniques for advanced manufacturing technologies. International Journal of Production Research, 24(5), 1043-1057.
  59. Morris, M. G., & Venkatesh, V. (2010). Job characteristics and job satisfaction: Understanding the role of enterprise resource planning system implementation. MIS Quarterly, 34(1), 143-161.
  60. Mowery, D., & Rosenberg, N. (1979). The influence of market demand upon innovation: A critical review of some recent empirical studies. Research Policy, 8(2), 102-153.
  61. Murray, C. C., & Chu, A. G. (2015). The flying sidekick traveling salesman problem: Optimization of drone-assisted parcel deliv- ery. Transportation Research Part C: Emerging Technologies, 54(2015), 86-109.
  62. Nemet, G. F. (2009). Demand-pull, technology-push, and government- led incentives for non-incremental technical change. Research Policy, 38(5), 700-709.
  63. Olsen, T. L., & Tomlin, B. (2020). Industry 4.0: Opportunities and challenges for operations management. Manufacturing & Ser- vice Operations Management, 22(1), 113-122.
  64. Pandza, K., Horsburgh, S., Gorton, K., & Polajnar, A. (2003). A real options approach to managing resources and capabilities. Inter- national Journal of Operations & Production Management, 23(9), 1010-1032.
  65. Park, Y., & Moon, I. (2019). UAV set covering problem for emer- gency network. In F. Ameri, K. E. Stecke, G. V. Cieminski, & D. Kiritsis (Eds.), IFIP international conference on advances in production management systems (pp. 84-90). Springer Nature.
  66. Pentland, B. T., & Feldman, M. S. (2007). Narrative networks: Patterns of technology and organization. Organization Science, 18(5), 781-795.
  67. Pettigrew, A. M. (1985). Contextualist research and the study of organizational change processes. Research Methods in Informa- tion Systems, 1(1985), 53-78.
  68. Pettigrew, A. M. (1990). Longitudinal field research on change: The- ory and practice. Organization Science, 1(3), 267-292.
  69. Pettigrew, A. M., Woodman, R. W., & Cameron, K. S. (2001). Studying organizational change and development: Challenges for future research. Academy of Management Journal, 44(4), 697-713.
  70. Puhakainen, P., & Siponen, M. (2010). Improving employees' compliance through information systems security training: An action research study. MIS Quarterly, 34(4), 757-778.
  71. Ravishankar, M., Pan, S. L., & Leidner, D. E. (2011). Examining the strategic alignment and implementation success of a KMS: A subculture-based multilevel analysis. Information Systems Research, 22(1), 39-59.
  72. Rogers, E. M. (2003). Diffusion of innovations (5th ed.). The Free Press.
  73. Roscoe, S., Cousins, P. D., & Handfield, R. (2019). The micro- foundations of an operational capability in digital manufactur- ing. Journal of Operations Management, 65(8), 774-793.
  74. Schwab, K. (2016). The fourth industrial revolution. Penguin UK.
  75. Small, M. H., & Yasin, M. M. (1997). Advanced manufacturing tech- nology: Implementation policy and performance. Journal of Operations Management, 15(4), 349-370.
  76. Suresh, N. C., & Meredith, J. R. (1985). Justifying multimachine sys- tems: An integrated strategic approach. Journal of Manufactur- ing Systems, 4(2), 117-134.
  77. Swamidass, P. M. (1987). Planning for manufacturing technology. Long Range Planning, 20(5), 125-133.
  78. Swamidass, P. M., & Waller, M. A. (1990). A classification of approaches to planning and justifying new manufacturing tech- nologies. Journal of Manufacturing Systems, 9(3), 181-193.
  79. Tyre, M. J., & Orlikowski, W. J. (1994). Windows of opportunity: Temporal patterns of technological adaptation in organizations. Organization Science, 5(1), 98-118.
  80. Upton, D. M. (1995). Flexibility as process mobility: The manage- ment of plant capabilities for quick response manufacturing. Journal of Operations Management, 12(3-4), 205-224.
  81. van Aken, J., Chandrasekaran, A., & Halman, J. (2016). Con- ducting and publishing design science research: Inaugural essay of the design science department of the Journal of Operations Management. Journal of Operations Management, 47-48(2016), 1-8.
  82. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204.
  83. Wagner, S. M. (2021). Startups in the supply chain ecosystem: An organizing framework and research opportunities. Interna- tional Journal of Physical Distribution & Logistics Management, 51(10), 1130-1157.
  84. Waldeck, N. E., & Leffakis, Z. M. (2007). HR perceptions and the provision of workforce training in an AMT environment: An empirical study. Omega, 35(2), 161-172.
  85. Yin, R. K. (2013). Case study research: Design and methods. Sage Publications.

Cited by

Sorted by citations

Authors
Maghazei, O., Lewis, M. A., & Netland, T. H.
Affiliations
  • ETH Zurich (CH)
  • University of Bath (GB)
Corresponding author
Maghazei, Omid
ORCID
Venue
Journal of Operations Management
Publisher
Wiley
Published
2022-06-20
Volume
68
Issue
6-7
Pages
560-591
ISSN
  • 0272-6963 (ISSN-L)
  • 1873-1317
DOI
10.1002/joom.1196
OpenAlex
W4283212403
Cited by
117
References
2
Citations by year
2026: 16 · 2025: 34 · 2024: 39 · 2023: 25 · 2022: 3
FWCI
21.13
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Operations Management (publishedVersion, cc-by)
  • Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by)
  • ETH Zürich Research Collection
Funders
  • Eidgenössische Technische Hochschule Zürich
  • National Metal and Materials Technology Center
Topics
  • Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Innovation Diffusion and Forecasting
Keywords
emerging technologies, Industry 4.0, use cases, technology adoption framework, technology push, market pull, technology fit, supply chain operations, Geberit, IKEA, drone delivery

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review