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      Impact of knowledge sharing and absorptive capacity on project performance: the moderating role of social processes

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      Journal of Knowledge Management
      Emerald

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          Abstract

          Purpose

          This study aims to propose an integrated model to examine the impact of knowledge governance, knowledge sharing and absorptive capacity (ACAP) on project performance in the context of project-based organizations (PBOs). This study also examines the moderating role of social processes on the relationships among these variables.

          Design/methodology/approach

          To test the proposed model, cross-sectional data were collected regarding projects from 133 PBOs in Pakistan’s information technology/software industry. The data were analyzed using the partial least squares – structural equation modeling (PLS-SEM) method and PRCOESS tool. Finally, this study also uses causal asymmetry analysis to check asymmetric relationship in the key constructs.

          Findings

          The results generally support the proposed model. Knowledge governance and knowledge sharing are important antecedents for improving the ACAP of the project, which in turn significantly improves project performance. Additionally, social processes positively moderate the relationship between knowledge sharing and ACAP, as well as between ACAP and project performance.

          Research limitations/implications

          The findings suggest that PBOs should invest in developing a knowledge governance system that guides and stimulates knowledge sharing within and between projects. This would boost the ACAP of projects and lead to superior project performance.

          Originality/value

          This study addresses the important issue of knowledge management in IT/software projects. It proposes a unique model that integrates the key constructs of knowledge management and describes their effect on project performance.

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          Most cited references100

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          Evaluating Structural Equation Models with Unobservable Variables and Measurement Error

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            A new criterion for assessing discriminant validity in variance-based structural equation modeling

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              A power primer.

              One possible reason for the continued neglect of statistical power analysis in research in the behavioral sciences is the inaccessibility of or difficulty with the standard material. A convenient, although not comprehensive, presentation of required sample sizes is provided here. Effect-size indexes and conventional values for these are given for operationally defined small, medium, and large effects. The sample sizes necessary for .80 power to detect effects at these levels are tabled for eight standard statistical tests: (a) the difference between independent means, (b) the significance of a product-moment correlation, (c) the difference between independent rs, (d) the sign test, (e) the difference between independent proportions, (f) chi-square tests for goodness of fit and contingency tables, (g) one-way analysis of variance, and (h) the significance of a multiple or multiple partial correlation.
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                Author and article information

                Journal
                Journal of Knowledge Management
                JKM
                Emerald
                1367-3270
                February 23 2018
                March 27 2018
                February 23 2018
                March 27 2018
                : 22
                : 2
                : 453-477
                Article
                10.1108/JKM-10-2016-0449
                f40359e4-11b5-4726-b96b-3d664d4ebed8
                © 2018

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