A contingency model of knowledge and learning in organizations

Today's competitive environment is more knowledge intensive than ever. Effective knowledge and learning strategies within a firm can represent a source of competitive advantage, and great variance in knowledge transfer strategies and outcomes exists. This study sought explanations for variance in effectiveness of knowledge transfers through the lens of a contingency model that linked the context of a particular problem-solving situation and knowledge transfer strategy (as evidenced by a firm's resources and incentives). The research question that guided this study was: Does the fit between situational context and knowledge transfer strategy have an impact on knowledge transfer effectiveness? To answer this question three models were developed and tested. Model 1 tested the situational context, Model 2 tested the knowledge transfer strategy, and Model 3 tested the fit and impact on effectiveness. Theoretical support for the ideas presented herein was garnered from strategy, organization and psychology fields—with a focus on the contingency theory, resource-based theory, knowledge-based view, and organizational learning. The empirical evidence for this study came from a random, stratified sample of over 5,000 management consultants and auditors from 5 different firms and resulted in 1,189 usable responses. Factor analysis, multivariate correlations and polynomial regression methodologies were used to test the models' hypotheses. The primary unit of analysis was the individual, although the data was examined for variance patterns at the industry level as well.;The results show that the fit between the situational context and the knowledge strategy does matter and has a significant impact on knowledge transfer effectiveness. Additional testing on the situational context revealed four important predictors of the Desired Degree of Personalization in knowledge transfer: Articulability, Embeddedness, Extraversion and the Industry. Testing related to the Actual Degree of Personalization in knowledge transfers indicated the following important predictor variables: Personalization Mechanisms, Computer Based Archives, Dedicated People, and Reciprocity. Overall, the contingency model and supporting models are supported by the data.;This research advances the aforementioned theories by offering a new model of contingency that integrates previously unassociated constructs. Another contribution is the unique operationalization and testing of existing and new variables. This research also informs practice by offering guidance to managers seeking the right mix of knowledge and learning-related resources and incentives, given certain organizational structures and particular situations, to achieve competitive advantage. This research offers a better understanding of ways that firms can achieve more effective internal knowledge transfer.