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Top1. Introduction
Knowledge creation has been one of the essential drivers for enterprises to maintain growth in a globally competitive environment (Diaz-Diaz, Aguiar-Diaz, & Saa-Perez, 2006; Forés, & Camisón, 2016; Ibidunni, et al., 2018; Hussein, Rosita, & Ayuni, 2019). Previous research on the predictors of knowledge creation performance (KCP) has focused mainly on knowledge strategy, acquisition, storage, process, sharing, adoption, leverage and evaluation (Xu, Bernard, Perry, Xu, & Sugimoto, 2014; Åkerman 2015; Bhatti, Larimo, & Carrasco, 2016; Hempelmann, Sakoglu, Gurupur, & Jampana, 2016; Kao, & Wu, 2016; Baoli, Ling, & Xiaoxue, 2018). The SECI creation model (socialization, externalization, combination, internalization; Nonaka, Toyama, & Konno, 2000) and many reports available have made a significant contribution to the field. Each stage of the SECI affects the outcome of the following one. The socialization and externalization are critical since SECI performance depend on successful interactions and presentations that produce sufficient and relevant outcomes ready for the stages of combination and internalization. A more thorough understanding of the effect of the social capitals (structural, relational and cognitive) and knowledge properties (tacitness and complexity) on creation merits is required, and could bring noticeable benefits to knowledge management programs.
Knowledge can be divided into explicit and tacit, according to whether knowledge contents can be easy to codify or not (Nonaka, 1994; Spulber, 2012; Arnett, & Wittmann, 2014; Davies, 2015; Song, Jiang, & Liu, 2016; Iorio & Rossi, 2018). Explicit knowledge can be articulated, codified, and communicated using multiple media such as text, symbols, animations, or even video, whereas tacit knowledge generally covers intangible experiences, concepts, intuition, and feelings in a specific context, being comprised of both cognitive and technical components. The degree of explicitness or tacitness may result in diverse impacts on creation outcomes. For example, incremental innovation is usually achieved by reconfiguring existing explicit knowledge, whereas radical innovation is often achieved by utilizing tacit knowledge that provides a foundation for more novel ideas (Liu & Cui, 2012; Spulber 2012; Scaringella, & Burtschell, 2017; Roper, Love, & Bonner, 2017). Knowledge tacitness is therefore more likely to enhance creativity.
Knowledge properties including tacitness, complexity and specificity have been extensively studied (D’Eredita, & Barreto, 2006; Macher, and Boerner, 2012; Rodil, Nielsen, & Nielsen, 2018), with differences based on number of conceptual elements and their relationships among elements (Blome, Schoenherr, & Eckstein, 2014; Sanchiz, Chin, Chevalier, Fu, Amadieu, & He, 2017; Dong & Sarkar; 2015). Generally, complex knowledge is thought to be beneficial for creativity because more novel ideas are likely to be stimulated when people express complicated concepts and their relationships (Pérez-Luño, Medina, Lavado, & Rodríguez, 2011; Law, 2014; Silva, Howells, & Meyer, 2018). For example, external unrelated knowledge is often required, and its complexity necessitates multi-disciplinary knowledge to be combined (Silva et al., 2018). Accordingly, knowledge with different properties is more likely to enhance levels of creation.
Knowledge development via social exchange has been recognized as an aid to knowledge creation (Wu, Kao, & Shih, 2010; Mupepi, Modak, Motwani, & Mupepi, 2017). Capital embedded in social exchange generated within social networks are a critical asset for knowledge creation and sharing (Wu et al., 2010; Pérez-Luño et al., 2011). Social capital theory advocates that actual and potential embedded resources are derived from networks of relationships among individuals or social entities (Pérez-Luño et al., 2011; Yu, Hao, Dong, & Khalifa, 2013). Based on social psychology and organization theories, social capital theory provides an explanation for team work phenomena such as the behaviors of volunteering reliable information, honoring cause agreements, malfeasance reduction, tacit information sharing encouragement, and negotiating on the same wave-length (Gonzalez, 2017; Yu et al, 2013). The social capital approach suggests that the aspects of innovation or knowledge creation lie not only on partners and the structure of social networks, but also on the cohesiveness embedded in the social relationships of participants (Pérez-Luño et al., 2011; Yu, 2013).