Managing disruptive technologies and innovation
van Bracht, Ruth; Piller, Frank Thomas (Thesis advisor); Kleer, Robin (Thesis advisor)
Aachen (2019, 2020) [Dissertation / PhD Thesis]
Page(s): 1 Online-Ressource (xvi, 215 Seiten) : Illustrationen, Diagramme
Throughout the last decades, we have seen a continuous rise of importance and relevance of digital technologies, emerging as the phenomena of ‘digitalization’, ‘Industry 4.0’, or ‘the fourth industrial revolution’. These technologies are on the verge of changing many industries. One of the industries that could be severely altered is the manufacturing industry, where the question of how to bridge the physical and digital worlds increasingly stimulates organizations to act and change. Digital technologies include computers, information technology in general, automation technologies, and others that embody the move from analog to digital. Additive manufacturing (AM) is one of the most mature of these technologies, promising great opportunities while bearing large challenges at the same time. Many organizations currently attempt to adopt AM to keep up. At the same time, we are at a crossroads in academic research: Previous research predominantly concentrates on post-hoc analyses of disruptive technologies but digitalization increasingly mandates support for adopting them ex-ante. Furthermore, there is a missing distinction between different kinds of disruptive innovations and a lack of research on the employee level, even though we see that organizational struggles are inherently dependent on individual behavior. Therefore, the objective of this dissertation is to enhance extant research to gain a holistic understanding of the ecosystem, organizational, and individual-level processes transpiring while technology adoption is ongoing. I strive to complement literature with my research on these different levels. Furthermore, I deliver a contribution by investigating a more differentiated view of disruptive technological innovations and potentially disruptive technologies from an ex-ante perspective. Methodologically, I employ a mix of research methods and analyze qualitative as well as quantitative data to address the outlined research questions in a series of three studies. The first paper explores the future of AM through a Real-Time Delphi survey method to derive future scenarios. I ask experts to evaluate future projections regarding political, economic, socio-cultural, and technological aspects to gain an understanding of the future ecosystem. I derive a most probable future scenario spanning the entire value chain and find that there are still massive amounts of uncertainty lingering in the minds of organizations and the individuals comprising them. Results of the study suggest that organizations should, despite that lingering uncertainty, act and start the adoption process of such technologies early. As this task is not easy, I find the need for further analysis of adoption endeavors in organizations. The second paper addresses this need by shedding light on organizations and how they make sense of new technologies during adoption endeavors. Looking at the organizational perspective, I investigate how organizations approach AM within several case studies. As a result, I find that perceptions of threat and opportunity inherently influence adoption. I derive three sensemaking patterns that lead to ongoing adoption. I observe that employee sensemaking is a deciding factor during technology adoption, and I conclude that managers need to continuously monitor and support their employees when planning to adopt a potentially disruptive technology. The third paper continues the analysis of Paper 2, investigating individual employees within organizations that are affected by the emergence and development of AM. I specifically survey employed engineers who are confronted with technological change. I inquire about threat and opportunity perception and engagement intentions in favor of AM to gain answers to several hypotheses regarding their interrelations. I find that threat and opportunity do predict engagement intentions, which are, in turn, directly related to behavior and individual adoption decisions. Furthermore, the results show that the cognitive style of individuals affects these main effects by moderating the relationship, suggesting that team compositions should be adjusted according to the adoption endeavor that is pursued. Overall, the results of the research papers help improve our understanding of disruptive technology adoption in established organizations, providing a holistic view on the underlying ecosystem, organizational, and individual-level processes.