Evidence map›Paper›PMID 42063670›Full record

SynthesisFrontiers in sociology2026

Algorithmic management in the global gig economy: an interdisciplinary systematic literature review and critical discourse analysis.

Bianca Ifeoma Chigbu

Abstract readSystematic Review
In one paragraph

Synthesis in Frontiers in sociology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

1 author.

Bianca Ifeoma ChigbuFaculty of Law, Social Sciences & Humanities (Department of Social Sciences), Walter Sisulu University, Mthatha, South Africa.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Algorithmic management-the delegation of managerial functions to algorithmic systems has become a defining feature of work in the global gig and platform economy. This article presents a systematic literature review (SLR) of 103 scholarly sources, complemented by a targeted critical discourse analysis (CDA) to examine how algorithmic management operates across diverse regions and is discursively framed. While gig work engages millions of workers worldwide, scholarly attention has centered disproportionately on Western contexts. I develop an interdisciplinary research framework that integrates sociology, labor studies, digital economy, and law to analyze the implications of algorithmic management for labor practices, worker autonomy, and regulation. The study asks: (1) what dominant themes and findings emerge in the literature across regions and contexts; (2) how these themes are discursively framed in scholarly and policy texts; and (3) what research gaps and policy implications emerge from this synthesis. The SLR reveals several prevalent themes: the tension between promised flexibility and algorithmic control, worker resistance and agency, biases and fairness in algorithmic systems, and the influence of local institutional contexts, while the CDA uncovers how academic narratives often oscillate between portraying algorithmic management as an extension of digital Taylorism and a catalyst for novel forms of worker autonomy. This review offers contributions by mapping the state of knowledge, highlighting a critical gap in global and comparative analysis, and proposing an agenda for future research and policymaking.

Indexed as

algorithmic controlalgorithmic managementdigital laborgig economyglobal laborplatform economyplatform workwork regulation

Identifiers

PMID42063670
PMCPMC13124556

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.