Too much light blinds: The transparency-resistance paradox in algorithmic management

Hu, Peng; Zeng, Yu; Wang, Dong; Teng, Han

doi:10.1016/j.chb.2024.108403

Computers in Human Behavior

2024

DOI: 10.1016/j.chb.2024.108403

|View full text |Cite

Too much light blinds: The transparency-resistance paradox in algorithmic management

Peng Hu,

Yu Zeng,

Dong Wang

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article3

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 76 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

The AI interface: Designing for the ideal machine-human experience

Sundar,

Russell-Rose,

Kruschwitz

et al. 2024

Computers in Human Behavior

View full text Add to dashboard Cite

The AI interface: Designing for the ideal machine-human experience

Sundar,

Russell-Rose,

Kruschwitz

et al. 2024

Computers in Human Behavior

View full text Add to dashboard Cite

Decoding algorithm fatigue: The role of algorithmic literacy, information cocoons, and algorithmic opacity

Yang,

Li,

2024

Technology in Society

View full text Add to dashboard Cite

How Algorithmic Management Influences Gig Workers’ Job Crafting

Liu,

Yin

2024

Behavioral Sciences

View full text Add to dashboard Cite

Algorithmic management, as a digital management tool in the gig economy, has become a hotspot of concern at the intersection of theory and practice. However, there is a lack of research on the mechanisms and boundary conditions through which algorithmic management affects gig workers’ job crafting. Based on the social information processing theory, this study constructed a dual-mediation model of how algorithmic management influences gig workers’ job crafting through gameful experience and perceived job autonomy. Data from 687 valid samples were collected through a two-stage survey and statistically analyzed using structural equation modeling (SEM). The results demonstrate that algorithmic management increases gig workers’ promotion-focused job crafting behaviors (increasing job resources and challenging job demands) by stimulating their gameful experiences and increases gig workers’ prevention-focused job crafting behaviors (decreasing hindering job demands) by inhibiting their perceived job autonomy. In addition, the higher-order personality trait core self-evaluation moderates the relationship between algorithmic management and gameful experience and perceived job autonomy, as well as the indirect effects of algorithmic management on job crafting through gameful experiences and perceived job autonomy. This study advances empirical research on algorithmic management in the field of the gig economy and human resource management practices. Crucially, it provides practical insights for optimizing algorithmic systems in platform companies, potentially enhancing their efficiency and economic benefits.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Too much light blinds: The transparency-resistance paradox in algorithmic management

Cited by 3 publications

References 76 publications

The AI interface: Designing for the ideal machine-human experience

The AI interface: Designing for the ideal machine-human experience

Decoding algorithm fatigue: The role of algorithmic literacy, information cocoons, and algorithmic opacity

How Algorithmic Management Influences Gig Workers’ Job Crafting

Contact Info

Product

Resources

About