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Introduction
Although knowledge management and dissemination have become increasingly important (Lam and Chua, 2005), not all employees share knowledge voluntarily (Cabrera and Cabrera, 2002). Employees’ willingness to share knowledge depends on the effectiveness of the knowledge management system, how the knowledge is used, and the nature of the knowledge itself. Moreover, some employees even conceal knowledge that is requested of them.
Knowledge hiding has gained attention, as the seminal work of Connelly et al. (2012). This study established a range of strategies used by employees to conceal knowledge from their fellow workers and managers. Subsequently, knowledge hiding has been linked to organizational factors including job characteristics, perceived motivational climates and counterproductive work behaviours (Abubakar et al., 2019; Arshad and Ismail, 2018; Černe et al., 2017; Khalid et al., 2018; Serenko and Bontis, 2016).
These studies were notable in highlighting that knowledge hiding is driven by organizational factors; it is not merely individual deviant behaviour. Some researchers (Serenko and Bontis, 2016; Vasconcelos, 2018) have further investigated the antecedents of knowledge hiding. Issac and Baral (2018) describe knowledge hiding as a distinct behaviour, emphasizing that it is neither a lack of knowledge sharing nor knowledge hoarding. Consequently, it is likely that the factors driving knowledge sharing and knowledge hiding are different.
In a recent special edition, Connelly et al. (2019) observed that knowledge hiding had been investigated through a range of theoretical perspectives, including self-determination theory, social identity and learning theory, leadership and socially embedded thriving. Connelly et al., observed that although this work had expanded understanding of knowledge hiding, there was still a considerable need for further research on individual, team and organizational antecedents. They called for research using a broader range of methods, including social network and qualitative studies. Moreover, there is not yet a unified model of knowledge hiding that incorporates insights from each perspective.
Responding to this need, we apply a relatively novel and underused approach to develop a comprehensive, integrative model of knowledge hiding and identify its most important antecedents. This method is total interpretive structural modelling (TISM) and Matrice d’Impacts Croises Multiplication Appliquée a un Classement (MICMAC) analysis. As we later elaborate, TISM/MICMAC analyses involve an inductive, systematic approach to synthesizing insights from the existing research...