![]() Given DDM's higher costs, it is mainly used in operational settings for tasks such as prototyping, “soft tooling,” and the on-demand production of customized and spare parts (Campbell et al., 2018 Khajavi et al., 2015).Ĭonsidering these practical and theoretical motivations, we aim to study another emerging technology-unmanned aerial systems (UAS)-popularly known as drones. To date, however, in practice, the economic business rationale of DDM remains far from apparent (Holweg, 2015). As such, there could be major potential benefits of this if integrated with advanced design and modeling software, DDM could help optimize designs, inducing, for example, a reduction in weight and assembly steps by integrating the functionality of parts (Eyers & Dotchev, 2010). Some argue that DDM will lead to fundamental structural changes in established business models (Holmström & Partanen, 2014), replacing all traditional tool-based manufacturing (D'Aveni, 2015, 2018). Existing DDM research highlights many of the dilemmas associated with studying emerging technologies. Where there has been OM focus in this area, albeit limited, is in the field of direct digital manufacturing (DDM) or 3D printing (for example, see the special issue of Holmström et al., 2019). This represents a significant gap in OM research (Heim et al., 2021), which hinders our ability to guide managerial decision-making regarding emerging technologies. 1), as compared to commonly researched technology issues such as enterprise resource planning (ERP) and e-commerce. ![]() To date, operations management (OM) scholarship has “not focused as much effort on empirically examining impacts of emerging technology innovations within production operations, warehousing, transportation, logistics, distribution, or customer services” (Heim & Peng, 2020, p. With the proliferation of emerging “Industry 4.0” technologies, operations managers frequently explore, pilot, and scale these fast-changing and potentially disruptive technologies even before ascertaining their business case. 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. 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 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. 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. 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. 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.
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