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.

Unlocking effective coordination: A knowledge‐based multilevel perspective on supplier integration into product development

“…Knowledge complementarity has been investigated at an aggregate level in the innovation (Fang, 2011; Richard et al, 2023) and supply chain management (Cheng, 2020; Cheung et al, 2010; Li et al, 2017) literature. When knowledge complementarity is addressed, arguments concerning coordination provide a contextual background (Cheng, 2020; Makri et al, 2010), leading to a single‐view understanding of knowledge complementarity.…”

“…This article contends that the concept identified at the buyer–supplier level is instrumental in resolving the dilemma. Knowledge complementarity refers to the extent to which partners' technological problem solving relates to nonoverlapping knowledge subdomains of the same broadly defined knowledge domain (Makri et al, 2010; Richard et al, 2023). Acknowledging the multidimensionality of the concept (component, architectural, and predictive buyer–supplier knowledge complementarity are defined later) produces the following research question: What types of knowledge complementarities drive effective coordination in SIPD under different conditions of component and supplier knowledge modularity?…”

Crowdworking: Nurturing Expert-Centric Absorptive Capacity

“…Transfer knowledge from specific external knowledge sources (e.g., key suppliers and customers) (Zhang et al 2022). Develop knowledge complementarity (knowledge overlap and diversity) between R&D alliance partners (Richard et al 2023).…”