The Innovative Work Behavior of External Technology Experts in Collaborative R&D Projects: Uncovering the Role of Multiple Identifications and Extent of Involvement

Manufacturers increasingly involve external technology experts (ETEs) from suppliers in collaborative R&D projects. Through their innovative work behavior (IWB), these temporary employees help to creatively solve manufacturers' engineering problems. Previous literature suggests that organizational identification should be an important driver of ETEs' IWB, but the intricacy of ETEs is that they may identify with both the supplier and the manufacturer. Past studies on multiple identification do not account for the frictions or synergies that are fundamental to collaborative innovation settings, and which may alter the role of identification with either organization. In response, this study considers the interactive and non-linear associations of ETEs' dual identification with their IWB, and also investigates the moderating role of ETEs' extent of involvement in the manufacturer's R&D project. Using moderated multilevel polynomial regression on single-informant survey data from 186 ETEs, we find support for a linear association of identification congruence: as both supplier identification and manufacturer identification are high, ETEs' IWB is also high. Moreover, we find an inverted U-shape pattern of identification incongruence: as identification with one organization exceeds identification with the other, ETEs' IWB is lower than when both identifications have a comparable level. The congruence pattern is weaker for ETEs with high involvement (i.e., those individuals active in all innovation stages), while the incongruence pattern is stronger for these ETEs. Remarkably, our analyses indicate that the lowest IWB value for high involvement ETEs is found at a combination of low supplier identification and high manufacturer identification, a point where low involvement ETEs in fact report the highest levels of IWB. Together these findings extend literatures on multiple organizational identification and external contributions in open innovation processes, and also provide valuable insights to managers working in collaborative R&D settings.