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      Linking trust and collaboration in project teams to project management success

      , ,
      International Journal of Managing Projects in Business
      Emerald

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          Abstract

          Purpose

          The purpose of this paper is to emphasise the importance of high levels of trust and collaboration for increasing the likelihood of project management (PM) success. However, the link between these three constructs remains unclear.

          Design/methodology/approach

          In this study, the authors use structural equation modelling (SEM) based on the findings from an international survey of 151 project practitioners to demonstrate the significance of project team trust and collaboration for increasing the likelihood of PM success.

          Findings

          The results indicate that PM success becomes more likely as the degree of collaboration improves which, in turn, is influenced by an increase in the level of trust between team members. The two factors of PM success are project performance and knowledge integration and innovation. The six factors of the degree of collaboration that were studied are physical proximity, commitment, conflict, coordination, relationships and incentives. The three factors of the level of trust investigated are expectations, knowledge exchange and imported trust.

          Practical implications

          The results of the study are expected to provide insight for project practitioners to increase the likelihood of PM success by taking cognisance of the factors that influence collaboration and trust. The results of the study may also provide insight into teaching and learning in tertiary education, in terms of professionalism and integrity issues.

          Originality/value

          This paper presents a new perspective for investigating PM success. SEM techniques are used to determine the likelihood of PM success by promoting trust and collaboration in the project team. This unique approach highlights the “human factors” that influence perceived PM success which should benefit both researchers and practitioners.

          Related collections

          Most cited references118

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          Comparative fit indexes in structural models.

          P. Bentler (1990)
          Normed and nonnormed fit indexes are frequently used as adjuncts to chi-square statistics for evaluating the fit of a structural model. A drawback of existing indexes is that they estimate no known population parameters. A new coefficient is proposed to summarize the relative reduction in the noncentrality parameters of two nested models. Two estimators of the coefficient yield new normed (CFI) and nonnormed (FI) fit indexes. CFI avoids the underestimation of fit often noted in small samples for Bentler and Bonett's (1980) normed fit index (NFI). FI is a linear function of Bentler and Bonett's non-normed fit index (NNFI) that avoids the extreme underestimation and overestimation often found in NNFI. Asymptotically, CFI, FI, NFI, and a new index developed by Bollen are equivalent measures of comparative fit, whereas NNFI measures relative fit by comparing noncentrality per degree of freedom. All of the indexes are generalized to permit use of Wald and Lagrange multiplier statistics. An example illustrates the behavior of these indexes under conditions of correct specification and misspecification. The new fit indexes perform very well at all sample sizes.
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            NOT SO DIFFERENT AFTER ALL: A CROSS-DISCIPLINE VIEW OF TRUST.

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              A reliability coefficient for maximum likelihood factor analysis

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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                International Journal of Managing Projects in Business
                IJMPB
                Emerald
                1753-8378
                April 16 2018
                May 02 2018
                April 16 2018
                May 02 2018
                : 11
                : 2
                : 432-457
                Article
                10.1108/IJMPB-06-2017-0068
                d21e00b9-9f8c-4471-96ed-46677518bbed
                © 2018

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