Evidence map›Paper›PMID 36156905›Full record

ArticleJournal of business research2023

Remote work and the COVID-19 pandemic: An artificial intelligence-based topic modeling and a future agenda.

Majid Aleem, Muhammad Sufyan, Irfan Ameer, Mekhail Mustak

Abstract read
In one paragraph

Article in Journal of business research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

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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

4 authors.

Majid AleemDepartment of Marketing and International Business, Turku School of Economics, University of Turku, Rehtorinpellonkatu 3, 20500 Turku, Finland.
Muhammad SufyanDepartment of Marketing and International Business, Turku School of Economics, University of Turku, Rehtorinpellonkatu 3, 20500 Turku, Finland.
Irfan AmeerChrist Church Business School, Canterbury Christ Church University, N Holmes Rd, Canterbury CT1 1QU, UK.
Mekhail MustakDepartment of Marketing and Sales Management, IESEG School of Management, Univ. Lille, CNRS, UMR 9221 - LEM - Lille Economie Management, Lille, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As remote work has become more common than ever throughout the COVID-19 pandemic, it has drawn special attention from scholars. However, the outcome has been significantly sporadic and fragmented. In our systematic review, we use artificial intelligence-based machine learning tools to examine the relevant extant literature in terms of its dominant topics, diversity, and dynamics. Our results identify-eight research themes: (1) Effect on employees at a personal level, (2) Effect on employees' careers, (3) Family life and gender roles, (4) Health, well-being, and safety, (5) Labor market dynamics, (6) Economic implications, (7) Remote work management, (8) Organizational remote work strategies. With further content analysis, we structure the sporadic research into three overarching categories. Finally, for each category, we offer a detailed agenda for further research.

Indexed as

EmployeeHuman resource managementLiterature reviewOrganizationRemote workWork from home

Identifiers

PMID36156905
PMCPMC9489997

What OpenQuestion holds

Textmetadata
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.