An Examination of Counterfactual Thoughts and Fairness Theory

This article examines employee reactions to observing abusive supervision at work. Specifically, we integrate deonance theory with fairness theory and social cognitive theory to explore when and why employees who witness abuse toward a coworker engage in prosocial behavior intended to protect the victim. We first develop a moderated mediation model of prosocial responses to abuse, which identifies overall fairness as a first- and second-stage moderator, influencing the relationships between observed abusive supervision and observer deontic reactions and between deontic reactions and coworker protective behavior. Results from Study 1 support a model in which overall justice moderates the second link, indicating that fair norms and principles might prompt observers of abuse to direct their deontic anger toward coworker protective responses. Study 2 extends this work by further exploring the mediating mechanism by which overall fairness moderates the relationship between deontic anger and prosocial behavior for the coworker. Utilizing a critical incident technique, Study 2 reveals support for a mediated moderation model, indicating that the moderating effect of overall fairness occurs through its impact on ethical efficacy. These findings contribute to work on abusive supervision, third-party responses to injustice, and overall fairness by helping to better understand observer prosocial reactions to abusive supervision.

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Occurrence of rating distortions and ratees’ fairness perceptions per raters’ mood and affect

Career Development International ◽

10.1108/cdi-03-2016-0036 ◽

2016 ◽

Vol 21 (7) ◽

pp. 726-743 ◽

Cited By ~ 1

Author(s):

Sumrina Razzaq ◽

Muhammad Zahid Iqbal ◽

Malik Ikramullah ◽

Jan-Willem van Prooijen

Keyword(s):

Undergraduate Students ◽

Repeated Measures ◽

Content Type ◽

Fairness Perceptions ◽

Repeated Measures Analysis ◽

Negotiation Performance ◽

Fairness Theory ◽

Affect Infusion Model ◽

Analyse Data ◽

Different Levels

Purpose The purpose of this paper is to investigate the occurrence of rating distortions under raters’ different mood conditions and at different levels of interpersonal affect of raters towards ratees, and further its association with ratees’ perceptions of distributive and interpersonal fairness. Design/methodology/approach For the scenario-based experiment, the study recruited 110 undergraduate students as participants. Of them, 22 raters appraised the video-taped buyer-seller negotiation performance of 88 ratees. Repeated measures analysis was employed to analyse data. Findings Results revealed that under different mood conditions (pleasant and sad) and at different levels of interpersonal affect towards ratees (high and low), raters distorted ratings (inflated and deflated, respectively). These rating distortions shaped ratees fairness perceptions in such a way that ratees who received inflated ratings due to raters’ pleasant mood and high interpersonal affect perceived more distributive and interpersonal fairness than ratees who received deflated ratings due to raters’ sad mood and low interpersonal affect. Originality/value The paper is a step towards integrating the affect infusion model with distributive and interpersonal fairness theory. This integration can be of value for enhancing our understanding of how rater-centric rating errors take place, which subsequently shape ratees’ fairness perceptions.

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The Influence of UGC on Consumers’ Information Process on Service Failure

International Journal of E-Services and Mobile Applications ◽

10.4018/jesma.2012100103 ◽

2012 ◽

Vol 4 (4) ◽

pp. 32-47

Author(s):

Te-Lin Chung

Keyword(s):

Experimental Study ◽

Current Literature ◽

Service Failure ◽

Counterfactual Thinking ◽

User Generated Content ◽

Information Process ◽

Negative Experiences ◽

Fairness Theory ◽

Post Purchase

This study explores the influence of user-generated content (UGC) on consumers’ post-purchase information process when a service failure occurs. Fairness theory, which regards counterfactual thinking (CFT) and judgments of blame as two key constructs in processing negative experiences, is applied in this study. In a scenario-based experimental study, the presence of UGC (positive, negative, or not presented), and the transaction medium (online or offline) were manipulated. This study contributes to current literature on UGC with the aspect of service failure that is rarely discussed. It also adds to the literature on fairness theory, which has primarily focused on the offline contexts. Results are further explored in the article.

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Why Do Patients Move from Online Health Platforms to Hospitals? The Perspectives of Fairness Theory and Brand Extension Theory

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16193755 ◽

2019 ◽

Vol 16 (19) ◽

pp. 3755 ◽

Cited By ~ 3

Author(s):

Wei Le ◽

Po-Ya Chang ◽

Yu-Wei Chang ◽

Jiahe Chen

Keyword(s):

Behavioral Intentions ◽

Structural Equation ◽

Positive Impact ◽

Healthcare Services ◽

Extension Theory ◽

Equation Modeling ◽

Research Model ◽

Perceived Fairness ◽

Fairness Theory ◽

Doctor Communication

Online healthcare services are growing rapidly. Patients can receive health support through communication with online health professionals. However, previous research on eHealth has focused on patients’ online behavioral intentions. The purpose of this study is to investigate the effect of online patient–doctor communication on offline behavioral intentions and to propose a conceptual model for eHealth. We integrate fairness theory and band extension theory to develop a research model. This is further tested by structural equation modeling (SEM), with 279 valid sets of data from patients on the online health platform. Using partial least squares analysis (PLS), the results show that perceived fairness is an important factor affecting online satisfaction and the willingness to ask online doctors, which in turn has a positive impact on the willingness to go to offline doctors. These findings shed light on the research model for eHealth and offer suggestions for improving patients’ online satisfaction, as well as both online and offline behavioral intentions.

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