“Oops, I Did It” or “It Wasn’t Me:” An Examination of Psychological Contract Breach Repair Tactics View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-03-12

AUTHORS

Kevin E. Henderson, Elizabeth T. Welsh, Anne M. O’Leary-Kelly

ABSTRACT

This study investigated ways an organization might mitigate the negative effects of psychological contract breach. Drawing on the trust repair literature and organizational justice theory, we examined six general repair tactics (i.e., full penance, partial penance, denials, apologies, excuses, and combined apology/excuse) in terms of whether they improve trust and diminish the negative emotions following a breach. Data were obtained via two experimental studies employing 918 participants, including both college students and working adults. All of the repair tactics were effective at enhancing trust and easing negative emotions, except for denying that the breach occurred. Full penance (i.e., offering full reparation) was the most effective, with the next best option depending upon what outcome was being addressed and the population studied. The type of contract and magnitude of breach did not play a significant role in the effectiveness of repair tactics. The results of this study show that companies can do something to “fix” breaches. We extend the trust repair research to the context of breach and show that the effectiveness of repair tactics differs across outcomes. Practically, based upon our results, we advise companies to use these repair tactics (except denial) when breaches occur. This study is the first to empirically examine how companies might address breaches to avoid their negative consequences. It is also among only a few studies on trust repair to include emotions, initial trust, more than three repair tactics, and a sample of working adults. More... »

PAGES

1-16

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10869-019-09624-z

DOI

http://dx.doi.org/10.1007/s10869-019-09624-z

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112703238


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