R&D innovation under uncertainty: a framework for empirical investigation of knowledge complementarity and goal congruence

Abstract

Purpose

This study aims to introduce a new prescriptive model to aid both managers and researchers in partner selection for innovation-orientated collaboration. This framework demonstrates how prospective partner firms’ complementing bodies of knowledge and goal alignment interact to affect the success of a collaboration.

Design/methodology/approach

The authors use geometric modeling to represent the interrelationships among knowledge similarity/dissimilarity, goal congruence, knowledge complementarity (KC) and innovation in alliance formation. Using this model as a framework, the authors derive relationships among predictors of innovation success and determine how they affect the nature of partnerships under varying conditions of KC.

Findings

This research shows how innovation success is strongly determined by partner selection. Specifically, the authors examine the influence of KC and partner goals on three aspects of a potential research and development (R&D) alliance – the potential level of innovation outcome for the alliance, the boundaries of knowledge sharing and limitations arising from knowledge and goal incongruence and the nature of cooperation.

Originality/value

Although there is broad empirical support that innovation success is influenced by the similarity of R&D partners’ knowledge, further research is still needed to model the relationship more precisely between partner KC and goal alignment. The authors address this gap by developing a model that is both prescriptive and predictive of how innovation success can be achieved in the context of disparate but complementing knowledge and goal sets. The authors conclude with practical implications for practice and future research directions.

Citation

Richard, A., Ahrens, F. and George, B. (2023), "R&D innovation under uncertainty: a framework for empirical investigation of knowledge complementarity and goal congruence", Journal of Modelling in Management, Vol. 18 No. 5, pp. 1635-1654. https://doi.org/10.1108/JM2-01-2022-0007