Literature DB >> 15189630

A four-component model of procedural justice: defining the meaning of a "fair" process.

Steven L Blader1, Tom R Tyler.   

Abstract

Two studies test the prediction of the four-component model of procedural justice that people evaluate the fairness of group procedures using four distinct types of judgment. The model hypothesizes that people are influenced by two aspects of the formal procedures of the group: those aspects that relate to decision making and those that relate to the quality of treatment that group members are entitled to receive under the rules. In addition, people are hypothesized to be separately influenced by two aspects of the authorities with whom they personally deal: the quality of decision making by those authorities and the quality of the treatment that they receive from them. The results of two studies support the hypothesis of the four-component model by finding that all four of the procedural judgments identified by the model contribute to overall evaluations of the fairness of group procedures.

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Year:  2003        PMID: 15189630     DOI: 10.1177/0146167203029006007

Source DB:  PubMed          Journal:  Pers Soc Psychol Bull        ISSN: 0146-1672


  6 in total

1.  Concrete Construction Employees: When does procedural fairness shape self-evaluations?

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Journal:  J Appl Soc Psychol       Date:  2006-03

2.  A Qualitative Study of Unfairness and Distrust in Smoke-free Housing.

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3.  Being Ostracized Versus Out of the Loop: Redundant or Unique Predictors of Variance in Workplace Outcomes?

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Journal:  J Appl Soc Psychol       Date:  2020-09-21

4.  A longitudinal and experimental study of the impact of knowledge on the bases of institutional trust.

Authors:  Lisa M PytlikZillig; Christopher D Kimbrough; Ellie Shockley; Tess M S Neal; Mitchel N Herian; Joseph A Hamm; Brian H Bornstein; Alan J Tomkins
Journal:  PLoS One       Date:  2017-04-17       Impact factor: 3.240

5.  Employee Age Alters the Effects of Justice on Emotional Exhaustion and Organizational Deviance.

Authors:  Justin P Brienza; D Ramona Bobocel
Journal:  Front Psychol       Date:  2017-04-06

6.  Integrative urban AI to expand coverage, access, and equity of urban data.

Authors:  Bill Howe; Jackson Maxfield Brown; Bin Han; Bernease Herman; Nic Weber; An Yan; Sean Yang; Yiwei Yang
Journal:  Eur Phys J Spec Top       Date:  2022-04-09       Impact factor: 2.891

  6 in total

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