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The “Double-Edged Sword” Effect of Perceived Algorithmic Control on Platform Workers’ Work Engagement [PDF]

Behavioral Sciences
With the deep development and iterative upgrading of algorithmic technology, the management practice of platform enterprises using intelligent algorithmic technology has become a hot issue.
Jian Zhu, Yuhe Cao, Yanjun Wang
doaj   +4 more sources

Voice in the algorithmic era: how perceived algorithmic control influences gig workers’ voice behavior [PDF]

Frontiers in Psychology
Existing studies have focused on gig workers’ voice behavior and the mechanisms through which it is expressed via official and unofficial channels, the underlying logic of voice behavior generation, particularly its operational pathways under algorithmic
Ting Liang, Yufei Zhang, Dan Xiang, Lingtong Zhu   +3 more
doaj   +4 more sources

Perceived algorithmic control and gig workers’ work engagement: assessing the mediating role of psychological empowerment and the moderating effect of deep acting [PDF]

BMC Psychology
Background Online labor platforms rely on algorithmic control to manage gig work, but its impact on work engagement remains contested. Existing research predominantly adopts technological determinism perspectives, neglecting gig workers’ agency, and ...
Qingxiu Lin, Rui Sun, Qiuhua Zhu
doaj   +5 more sources

The double-edged sword effects of perceived algorithmic control on platform workers’ service performance

Humanities and Social Sciences Communications
Algorithmic control has been reflected in online labor platform management, but there is a lack of empirical research on how platform algorithmic control affects platform workers’ service performance.
Hui Wang
exaly   +3 more sources

Algorithmic platform management and risk-taking behavior among Chinese food delivery riders [PDF]

iScience
Summary: Food delivery riders in China face widespread risk-taking behavior. This behavior is tied to algorithmic platform management. It is become more pressing as the digital platform economy expands.
Wei Liu, Hong Yang, Shuqin Liu
doaj   +2 more sources

Resistance or compliance? The impact of algorithmic awareness on people's attitudes toward online information browsing [PDF]

Frontiers in Psychology
IntroductionWith the pervasive integration of big data algorithms into daily life, individuals increasingly rely on algorithmic filtering to access information of interest, yet simultaneously face the risk of confinement within “information cocoons ...
Ying Yuan, Yin Shi, Ti Su, HanXuan Zhang
doaj   +2 more sources

Algorithmic management and psychosocial risks at work: An emerging occupational safety and health challenge [PDF]

Scandinavian Journal of Work, Environment & Health
In recent years, algorithmic management (ALMA) systems have spread rapidly from platform work to more traditional sectors such as logistics, retail, and healthcare (1, 2).
Mairi Bowdler, Heidi Lahti, Marie Jelenko, Giuliana Buresti, Teppo Valtonen   +4 more
doaj   +2 more sources

Formation of fan psychological ownership and Its influence on fan enthusiasm: a hybrid approach using PLS-SEM and fsQCA [PDF]

Frontiers in Psychology
Driven by digital platforms and algorithmic ecosystems, contemporary fan culture has evolved into an unprecedented form of intensity and density. Fans willingly devote vast amounts of data labor, daily engagement in comment management, and financial ...
Qiuhao Huang, Yanxue Zou
doaj   +2 more sources

The Effect of Algorithmic Recommendations on Female University Students’ Willingness to Engage in Romantic Behaviour [PDF]

SHS Web of Conferences, 2023
With algorithmic recommendations becoming an important way and means of information distribution, social media platforms are developing rapidly, and students in colleges and universities today have become the main audience for their information access ...
Guo Conghui, Guan Xuexin
doaj   +1 more source

How do people react to AI failure? Automation bias, algorithmic aversion, and perceived controllability

Journal of Computer-Mediated Communication, 2022
Abstract AI can make mistakes and cause unfavorable consequences. It is important to know how people react to such AI-driven negative consequences and subsequently evaluate the fairness of AI’s decisions. This study theorizes and empirically tests two psychological mechanisms that explain the process: (a) heuristic expectations of AI’s ...
S. Mo Jones-Jang, Yong Jin Park
openaire   +1 more source