![]() The results from the current inquiry have theoretical implications regarding the dynamism of justice effects over short time frames and practical implications for whether (and how) organizations can sever or prolong the effects of given interactions.Ī third contribution we seek to make is adding to the growing literature on the implications of algorithmic performance management systems for gig worker reactions and performance (Kellogg et al., 2020). ![]() Despite these conclusions, we are unaware of research exploring how the justice-related effects of one encounter do or do not influence reactions to subsequent customer service encounters and performance in them. ![]() This question reflects the now established recognition that justice perceptions often stem from specific events and encounters (e.g., Matta et al., 2014) and the fact that most customer service positions entail one interaction after another. Specifically, we explore whether the effects of a justice event associated with one customer service encounter spill over and continue impacting impact reactions and behavior during a second encounter or, alternatively, cease to have an effect when justice events during the second subsequent customer encounter become salient. This is, however, a questionable assumption: as employees continually encounter new justice information in the environment, their perceptions of justice are likely to evolve.” In the current study, we examine the effects of successive justice-related events on subsequent reactions and performance. 1072) recently concluded, “in nearly all cases justice perceptions have been treated as static phenomena. Second, we extend the justice literature by examining the effects of justice over time. In the current research, we propose different mechanisms for various dimensions of justice and use experimental methods that allow both distilling each of their effects and their interplay, thus advancing the justice literature in several ways. Of particular relevance, scholars have questioned which dimension is most predictive of performance (Pattnaik & Tripathy, 2018) and have called for greater nuance in understanding the mechanisms through which the different dimensions operate, both separately and in tandem (e.g., Conlon et al., 2013). Several studies have found null or even negative findings (see Pattnaik & Tripathy, 2018), and Colquitt et al., ( 2013) found that most confidence intervals contained zero (suggesting moderation). Moreover, each of these relationships is highly variable. Although each dimension of organizational justice has a positive overall meta-analytic relationship with job performance, these relationships are moderate in size (with all corrected correlations being less than 0.30 Colquitt et al., 2013). First, these studies help explicate how different components of justice-both in isolation and jointly-impact task performance. More specifically, we see this set of studies as making four main potential contributions to the justice literature. The results replicated the findings of Study 1 and revealed two moderators of the unexpected distributive justice-performance relationship. ![]() Study 2 was an online simulation vignette scenario with 294 participants. Moreover, unexpectedly, perceived distributive injustice as caused by the customer rating had opposite (direct versus indirect) effects on service performance in the subsequent ride. ![]() Antecedent-focused emotion regulation strategies (ERS) reduced felt unhappiness. The results from 99 participants showed that perceptions of interpersonal injustice increased anger and unhappiness during the ride, in turn impairing driving and service performance. In Study 1, we modeled the passenger-driver interaction of the ridesharing context using a driving simulator in a laboratory setting to differentiate the real-time and carry-over effects of specific dimensions of injustice. Here, we report the results of two studies examining the unique effects of these respective dimensions of injustice on emotions and, ultimately, the driving performance and service quality in a ridesharing service context. An understanding of this topic can inform justice theory more broadly and help explain inconsistent findings in the literature. Aspects of gig work, including the transactional compensation arrangement, strict algorithmic rating system, and power asymmetry between drivers and customers, have implications for understanding how dimensions of distributive, informational, and interpersonal injustice manifest and impact job performance in the gig context. The nature of gig work and its growth have important implications for organizational justice theory. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |