Evidence map›Paper›PMID 41750047›Full record

ArticleBehavioral sciences (Basel, Switzerland)2026

Making Artificial Intelligence Work at Work: The Role of Human Resource Practices and Personal Attitudes in Fostering Meaningful Work with Artificial Intelligence.

Cataldo Giuliano Gemmano, Danila Molinaro, Diego Bellini, Silvia De Simone, Maria Luisa Giancaspro, Marina Mondo, Carmela Buono, Barbara Barbieri, Paola Spagnoli, Amelia Manuti

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. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

10 authors.

Cataldo Giuliano GemmanoDepartment of Educational Sciences, Psychology, Communication, University of Bari "Aldo Moro", 70121 Bari, Italy.ORCID 0000-0003-4519-8594
Danila MolinaroDepartment of Human Sciences, LUMSA University, 00193 Rome, Italy.ORCID 0000-0003-4099-4241
Diego BelliniFaculty of Social and Communication Sciences, Universitas Mercatorum, 00186 Rome, Italy.
Silvia De SimoneDepartment of Pedagogy, Psychology, and Philosophy, University of Cagliari, 09123 Cagliari, Italy.ORCID 0000-0001-7539-1665
Maria Luisa GiancasproFaculty of Human Sciences, Education and Sport, Pegasus Telematic University, 80143 Napoli, Italy.
Marina MondoDepartment of Pedagogy, Psychology, and Philosophy, University of Cagliari, 09123 Cagliari, Italy.ORCID 0000-0003-1625-2248
Carmela BuonoFaculty of Social and Communication Sciences, Universitas Mercatorum, 00186 Rome, Italy.ORCID 0000-0002-9525-5289
Barbara BarbieriDepartment of Political and Social Sciences, University of Cagliari, 09121 Cagliari, Italy.ORCID 0000-0001-7706-2363
Paola SpagnoliDepartment of Psychology, University of Campania "Luigi Vanvitelli", 81100 Caserta, Italy.ORCID 0000-0002-0637-9205
Amelia ManutiDepartment of Educational Sciences, Psychology, Communication, University of Bari "Aldo Moro", 70121 Bari, Italy.ORCID 0000-0001-5308-7494

Funding

Italian Ministry of University and Research (MUR) PNRR - PRIN 2022 program (D.D.NO.1409/222).
6 · The paper itself

Abstract

The rapid diffusion of Artificial Intelligence (AI) is transforming job characteristics, raising important questions about how to implement these technologies in organizations in ways that support employee well-being and performance. Drawing on the High-Involvement Management framework, this study examined employee-centered Artificial Intelligence implementation (ECAII) practices (defined as transparent communication, consultation, and training initiatives) as strategic levers to foster positive employee outcomes during Artificial Intelligence-driven transformations. Survey data were collected from 168 Italian white-collar employees who actively used Artificial Intelligence in their work. Structural equation modeling was employed to test direct and indirect relationships among employee-centered Artificial Intelligence implementation practices, work meaningfulness, job satisfaction, and job performance, as well as the moderating role of personal attitudes toward AI. Results showed that employee-centered Artificial Intelligence implementation practices had significant direct effects on both job satisfaction and performance, as well as indirect effects through work meaningfulness. Latent moderated mediation analyses further revealed that these indirect effects were stronger among employees with more positive attitudes toward Artificial Intelligence. Overall, the findings highlighted the importance of employee-centered strategies for enhancing meaningfulness and fostering positive outcomes during technological change. This study contributed to Human Resource Management (HRM) and meaningful work research by extending classic theoretical frameworks to Artificial Intelligence-enabled workplaces. Furthermore, from a practical perspective, our findings provided valuable guidance for organizations by highlighting the importance of transparent communication, employee involvement, and targeted training in reducing uncertainty and helping employees perceive their roles as relevant during the implementation of Artificial Intelligence.

Indexed as

Artificial Intelligenceattitudes toward AIHR practicesjob performancework meaningfulness

Identifiers

PMID41750047
PMCPMC12938308

What OpenQuestion holds

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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.