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      “I deserve better grades.” Compliance-gaining perspective of dark triad traits, power distance and academic entitlement in Chinese higher education

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      Kybernetes
      Emerald

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          Abstract

          Purpose

          Dark triad (DT) personality traits, that is, Machiavellianism, narcissism and psychopathy, are socially unenthusiastic and predict a range of antisocial behaviors. Academic entitlement, similarly, demonstrates the students’ rising temptation of higher grades without putting corresponding efforts. Based on coercion theory, this research investigated power distance as mediator between DT and academic entitlement (externalized responsibility and entitled expectations). Further, this study examined DT model of personality as a predictor of academic entitlement in Chinese higher education institutions (HEIs).

          Design/methodology/approach

          Chinese HEIs were ascertained for data collection. Students (with bachelor’s, master’s and Ph.D. degrees) responded to 719 questionnaires for data analysis. Hypothesized relationships were examined through partial least squares structural equation model (PLS-SEM).

          Findings

          This study established positive and significant direct link between DT and academic entitlement. The indirect effect through power distance was found insignificant between the relationship of DT and academic entitlement.

          Originality/value

          Based on coercion theory, this study extends the prevailing literature through unexplored effects of DT on academic entitlement and role of power distance in Chinese HEIs. This study validated the effect of DT on academic entitlement; however, direct-only no-mediation of power distance between the relationships is novel in Chinese HEIs. Moreover, power distance as mediator is novel between the relationships. Hence, this study provides an understanding of mentioned associations and contributes to the literature.

          Related collections

          Most cited references74

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          Common method biases in behavioral research: A critical review of the literature and recommended remedies.

          Interest in the problem of method biases has a long history in the behavioral sciences. Despite this, a comprehensive summary of the potential sources of method biases and how to control for them does not exist. Therefore, the purpose of this article is to examine the extent to which method biases influence behavioral research results, identify potential sources of method biases, discuss the cognitive processes through which method biases influence responses to measures, evaluate the many different procedural and statistical techniques that can be used to control method biases, and provide recommendations for how to select appropriate procedural and statistical remedies for different types of research settings.
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            Reconsidering Baron and Kenny: Myths and Truths about Mediation Analysis

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              Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models.

              Hypotheses involving mediation are common in the behavioral sciences. Mediation exists when a predictor affects a dependent variable indirectly through at least one intervening variable, or mediator. Methods to assess mediation involving multiple simultaneous mediators have received little attention in the methodological literature despite a clear need. We provide an overview of simple and multiple mediation and explore three approaches that can be used to investigate indirect processes, as well as methods for contrasting two or more mediators within a single model. We present an illustrative example, assessing and contrasting potential mediators of the relationship between the helpfulness of socialization agents and job satisfaction. We also provide SAS and SPSS macros, as well as Mplus and LISREL syntax, to facilitate the use of these methods in applications.
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                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                Kybernetes
                K
                Emerald
                0368-492X
                January 03 2024
                January 03 2024
                Article
                10.1108/K-08-2023-1454
                43d667ae-e514-46a3-bf8d-49b2be34c33c
                © 2024

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