The purpose of this paper is to create a technology foresight method in which the visual analogue scale is used to harness the wisdom of expert crowds, namely, industry experts, in anticipating potential disruptions in an industry. In an empirical demonstration, we investigate experts’ views and perceptions of possible future disruption caused by digitalization in an established machine-building industry. We demonstrate the usability of the proposed method in detecting future worldviews of experts grouped by their position in the value chain. The results show polarized responses, with considerable clustering among groups. For example, respondents who were inclined to view digital technologies as disruptive (i.e., as changing the paradigm of value creation in machine-building) also viewed them as related more to service and business models than to products and operation. We discuss the theoretical and practical contributions of the proposed method and suggest fruitful avenues for future research.

Introduction

Disruptive innovation brings to an industry new performance parameters that existing products do not provide (Christensen, 1997), and disruptive innovations often promise lower prices. The offering of disrupters then contrasts with incumbent firms that provide performances that overshoot mass markets with expensive price tags. Disruption in an industry is also a process that comes about with new business models utilized by disrupters, thus shaking the positions of incumbents (Christensen et al., 2015). Disruptive innovation theory has been under close scrutiny in academic research [see further e.g. (King and Baatartogtokh, 2015; Markides, 2006; Yu and Hang, 2010)] while spreading widely to the practicing community (Nagy et al., 2016; Sampere et al., 2016). The need to detect and anticipate disruptive innovations is the cornerstone of disruptive innovation (Christensen, 2006; Mäkinen and Dedehayir, 2014; Paap and Katz, 2004), and the normative purpose of disruptive innovation theory is to seek an understanding of why incumbents, in many cases with ample resources, fail to compete with smaller disrupters. The question of how to anticipate disruptive innovations has attracted much attention [see e.g. (Adner, 2002; Hüsig, 2009; Keller et al., 2008)] and various approaches have been proposed [see e.g. (Cheng et al., 2017; Dotsika and Watkins, 2017; Klenner et al., 2013; Momeni and Rost, 2016)] urging industry agents to exercise forward-looking searches and foresight activities. Moreover, there have recently been calls for more empirical research on these forwardlooking search processes (Rohrbeck et al., 2015).