In the rapidly evolving digital economy, the gig workforce has emerged as a pivotal force reshaping the nature of work and employment. Gig workers, who often navigate flexible yet precarious jobs mediated through digital platforms, face unique challenges that differ significantly from traditional employment settings. A groundbreaking study published in BMC Psychology in 2025 delves deeply into the psychological and behavioral dynamics underpinning gig workers’ engagement with their work, focusing on the intricate interplay of algorithmic control, psychological empowerment, and emotional labor—specifically deep acting.
The research conducted by Lin, Sun, and Zhu investigates how perceived algorithmic control—the degree to which gig workers feel governed by the automated systems and algorithms dictating their tasks—influences their work engagement. Unlike conventional management that relies on human judgment and interpersonal interaction, algorithmic control is characterized by its opaque, data-driven directives, and real-time performance monitoring. This shift introduces new dimensions of worker experience, particularly concerning autonomy, motivation, and job satisfaction.
Central to the study is the concept of psychological empowerment, a crucial mediating factor linking algorithmic control and work engagement. Psychological empowerment encapsulates an individual’s intrinsic motivation, sense of meaning, competence, self-determination, and impact concerning their job role. By dissecting how algorithmic systems affect these psychological states, the authors explore a nuanced pathway by which technology shapes human behavior in the workplace. The hypothesis holds that if gig workers perceive algorithmic supervision as excessively controlling and restrictive, it may erode their psychological empowerment, thereby diminishing their enthusiasm, commitment, and overall engagement.
What makes this research particularly compelling is the moderating role of deep acting, an emotional labor strategy whereby individuals attempt to genuinely feel the emotions they are expected to display at work rather than simply faking them. Deep acting involves conscious regulation of emotions to align inner feelings with outward expressions, a challenging psychological process. The study posits that gig workers who engage in deep acting are better equipped to buffer the potentially detrimental effects of algorithmic control on their empowerment, thereby sustaining higher levels of engagement despite the pressures of platform management.
Gig economy platforms like Uber, Deliveroo, and TaskRabbit represent ecosystems where algorithmic control mechanisms are not just managerial tools but defining elements of the work environment. These platforms assign tasks, set performance standards, and administer feedback predominantly through invisible algorithms, which leaves workers in a state of continuous adaptation and uncertainty. By quantifying how such algorithmic dynamics intersect with the emotional and motivational determinants of work engagement, the study advances a critical understanding of contemporary labor relations shaped by technology.
Moreover, the research underscores the psychological complexities inherent in deep acting within gig work contexts. Unlike traditional service roles that involve interpersonal exchanges with supervisors or colleagues, gig workers often encounter clients briefly and primarily through app-based interactions. Despite this, emotional labor remains vital in maintaining client satisfaction and securing repeat engagements. This emotional investment, when managed via deep acting, becomes an essential psychological resource that mitigates stress and enhances resilience against the austerity of algorithmic directives.
The investigators employed rigorous methodological frameworks, integrating quantitative survey data from a diverse cohort of gig workers alongside psychological scales measuring perceived autonomy, competence, emotional labor strategies, and engagement indices. Their findings reveal a statistically significant mediation model where psychological empowerment fully mediates the relationship between algorithmic control and work engagement. Furthermore, deep acting consistently moderated this effect, substantiating its role as a psychological buffer.
This nuanced elucidation moves beyond simplistic dichotomies of technology as either purely enabling or controlling. Instead, it places gig workers’ lived experiences—resilience, emotional labour, and identity negotiation—at the center of the discourse on algorithmic management. The study eloquently articulates how workers’ internal psychological mechanisms critically determine their capacity to thrive or falter within platform economies. It challenges platform operators and policymakers to reconsider how algorithmic designs impact worker well-being and calls for integrating psychological empowerment frameworks into technological governance.
The implications of this research reverberate through multiple dimensions of the gig economy. For one, it suggests that enhancing transparency and worker involvement in algorithmic decision-making could bolster empowerment and engagement. It also highlights the need to support workers’ emotional labor by fostering environments that recognize and validate deep acting efforts, potentially through training, peer support, or platform design that humanizes interactions. These insights collectively advocate for a more humane and psychologically informed approach to algorithmic management.
Notably, the authors emphasize that while algorithmic control introduces efficiencies and standardization, it must be balanced against the psychosocial needs of gig workers to avoid burnout, alienation, and disengagement. The study warns against the uncritical adoption of surveillance-intensive management systems that risk transforming workers into mere cogs within inscrutable digital machinery, eroding motivation and morale. By integrating psychological empowerment and emotional labor constructs, the research sets a new benchmark for understanding technology-mediated work.
In a broader context, this work contributes to the interdisciplinary dialogue spanning organizational psychology, human-computer interaction, labor economics, and digital sociology. It offers empirical evidence that the future of work hinges not only on technological innovation but equally on the intricate psychological processes that govern human adaptation to these innovations. The balance between control and autonomy, emotional authenticity and performative compliance, emerges as a crucial dynamic shaping the gig economy’s sustainable growth.
Furthermore, Lin, Sun, and Zhu’s research opens avenues for subsequent investigations into how different types of gig work—ranging from ridesharing and delivery to freelance creative tasks—might differentially experience algorithmic control and emotional labor. It also prompts exploration of cultural, demographic, and personality factors that influence workers’ psychological empowerment and engagement under algorithmic governance.
The viral potential of this study lies in its timely revelation of the hidden struggles and adaptive strategies of gig workers globally. As millions increasingly rely on gig platforms for livelihood, understanding the psychological ramifications of algorithmic management becomes not merely academic but urgent social knowledge. Media outlets and industry observers have already begun amplifying these findings, fueling discussions on the ethics of platform design and workers’ rights in the digital age.
In conclusion, Lin, Sun, and Zhu’s 2025 study is a seminal contribution that intricately maps the psychological terrain of gig work under algorithmic control. By clarifying how psychological empowerment mediates and deep acting moderates work engagement, it illuminates pathways to enhance worker motivation and well-being amidst the rising tide of digital labor platforms. This research beckons scholars, practitioners, and policymakers alike to foreground human psychology within the architecture of the gig economy, ensuring that technology empowers rather than diminishes the workforce at its core.
Subject of Research: The impact of perceived algorithmic control on gig workers’ work engagement, with a focus on psychological empowerment as a mediating factor and deep acting as a moderating factor.
Article Title: Perceived algorithmic control and gig workers’ work engagement: assessing the mediating role of psychological empowerment and the moderating effect of deep acting.
Article References: Lin, Q., Sun, R. & Zhu, Q. Perceived algorithmic control and gig workers’ work engagement: assessing the mediating role of psychological empowerment and the moderating effect of deep acting. BMC Psychol 13, 1237 (2025). https://doi.org/10.1186/s40359-025-03570-7
Image Credits: AI Generated
