Abstract
For many years, it has remained unquestioned that developing innovation mainly happens within the boundaries of organizations' own research and development (R&D) or marketing departments, that is, an activity based on using or reusing local expertise. The negative effect of this local search behavior on the novelty of the outcome, however, is one of the reasons researchers and innovation managers are increasingly discussing the idea of opening up innovation processes by drawing on external problem solvers. In particular, problem solvers located in contextually distant but analogous domains (i.e., domains linked by similar problems) are capable of contributing to overcoming local search bias: as they do not suffer from functional fixedness but experience a similar (i.e., analogous) problem, they are capable of coming up with highly novel solutions. In theory, a recently introduced search approach known as pyramiding holds great potential for crossing domain‐specific boundaries and identifying problem solvers from contextually distant domains. Although initial practical applications of this search method, for example, in the course of applying the lead user method, provide anecdotal evidence, systematic research on the potential of pyramiding for crossing domain‐specific boundaries is still lacking to date. This study addresses this gap by analyzing 1,147 interviews conducted in the course of pyramiding search processes in eight lead user studies. The study found that pyramiding is an apt means of systematically crossing domain‐specific boundaries: more than one third of those interviewees who were able to provide a valid referral in their interview performed the creative task of referring into one or more analogous domains previously unknown to the searching organization. The interviewees' levels of expertise as well as their domain origins influence the likelihood of a domain‐crossing referral. Moreover, the type of industry in which the search field is located is found to moderate the effect of expertise on the likelihood of a referral into an analogous domain.
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