Evidence map›Paper›PMID 36684460›Full record

ArticleCurrent psychology (New Brunswick, N.J.)2023

The real-time and carry-over effects of injustice on performance and service quality in a ridesharing driver scenario.

Xue Lei, Seth A Kaplan

Abstract read
In one paragraph

Article in Current psychology (New Brunswick, N.J.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Xue LeiSchool of Business, East China University of Science and Technology, No. 130 Meilong Road, Shanghai, 200237 China.ORCID 0000-0002-0205-9386
Seth A KaplanDepartment of Psychology, George Mason University, Fairfax, VA USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The nature of gig work and its growth have important implications for organizational justice theory. 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. An understanding of this topic can inform justice theory more broadly and help explain inconsistent findings in the literature. 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. 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. 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. Antecedent-focused emotion regulation strategies (ERS) reduced felt unhappiness. Moreover, unexpectedly, perceived distributive injustice as caused by the customer rating had opposite (direct versus indirect) effects on service performance in the subsequent ride. Study 2 was an online simulation vignette scenario with 294 participants. The results replicated the findings of Study 1 and revealed two moderators of the unexpected distributive justice-performance relationship. Supplementary Information: The online version contains supplementary material available at 10.1007/s12144-022-04215-3.

Indexed as

Algorithmic managementCarry-over effectsJusticePerformanceRideshare

Identifiers

PMID36684460
PMCPMC9838455

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.