Evidence map›Paper›PMID 42792487›Full record

ArticleBehavioral sciences (Basel, Switzerland)2026

Algorithmic Gamification and Gig Workers' Proactive Service Behaviour: An Attribution Theory Perspective.

Yepeng Wu, Xin Yan, Yuanyuan Jiao

Abstract read
In one paragraph

Article in Behavioral sciences (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

3 authors.

Yepeng WuSchool of Economics and Management, Hubei University of Technology, Wuhan 430068, China.ORCID 0000-0002-2033-1136
Xin YanSchool of Business Administration, Anhui University of Finance and Economics, Bengbu 233030, China.ORCID 0009-0004-9086-0203
Yuanyuan JiaoBusiness School, Nankai University, Tianjin 300071, China.

Funding

Anhui Provincial Department of Education 2025AHGXSK40052Hubei Provincial Department of Education 25Y059Hubei Provincial Department of Education Philosophy and Social Science Research 23Q112Hubei Provincial Social Science Foundation Project HBSKJJ20233239Hubei University of Technology XJ2022004701National Natural Science Foundation of China 72402063, 72172065
6 · The paper itself

Abstract

Algorithmic gamification has become an important tool for gig platforms to shape labour input; however, its relationship with the proactive service behaviour of gig workers remains unclear. Drawing on attribution theory, this study developed a dual-path model linking algorithmic gamification to gig workers' proactive service behaviour through commitment attribution and control attribution, with algorithmic procedural fairness as a boundary condition. The results indicate that algorithmic gamification is positively associated with proactive service behaviour through commitment attribution and negatively associated with it through control attribution. Further analyses revealed that the indirect effect through commitment attribution was larger in absolute magnitude than that through control attribution, yielding a statistically significant positive total indirect effect. Moreover, algorithmic procedural fairness strengthens the positive association between algorithmic gamification and commitment attribution and attenuates its positive association with control attribution. At high levels of algorithmic procedural fairness, the conditional indirect effect of control attribution was not statistically significant. By examining workers' attributions of platform intent, this study identifies two mechanisms linking algorithmic gamification to extra-role service behaviour and offers managerial implications for the design of algorithmic gamification on gig platforms.

Indexed as

algorithmic gamificationalgorithmic procedural fairnessattribution theorygig workersproactive service behaviour

Identifiers

PMID42792487
PMCPMC13603112

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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.