Academic Research Translation Capability: A Multilevel Predictive Framework

Abstract

Academic research-driven organizations (ARO) are increasingly building capabilities for advanced research, innovation, commercialization and entrepreneurship, with a vision to translate their scientific findings into ‘societal impact’. Such organizational capabilities can help them in consistently transforming their scientific knowledge into innovations which can be translated from academic labs to industrial markets. However, their definition, composition and the process to build such organizational capabilities appears to be nebulous at multiple levels within organizational settings. To overcome these shortcomings, we attempt to develop a theoretically-grounded multilevel framework to measure, compare or predict ‘academic research translation capability’ of academic research-driven organizations. Our framework consists of five independent constructs, two mediating constructs and one dependent construct along with relevant theoretical evidences. Empirical validation of this multilevel framework can help academicians as well as practitioners in identifying those antecedents which tend to significantly influence academic research translation capability (ARTC). Top Management Teams (TMTs) can use this framework for developing strategies to improve their innovation performance.