The Effect of Algorithmic Management on Psychological Well-Being of Gig Workers: Moderating Role of Perceived Autonomy

1. INTRODUCTION

The digitisation of the labour market has substantially transformed the nature of labour and resulted in the formation of what is called the gig economy, where temporary and situational interactions are fully digitalised (Crisan et al., 2023). A key aspect of this change is the introduction of algorithmic management (AM), which can be described as deploying software algorithms to assign, track, organise, and assess work (Parent‐Rocheleau & Parker, 2022). AM systems have taken over the everyday lives of millions of workers, whether on a ride-hailing app or an online freelancing platform, determining how workers are assigned tasks, how their performance is evaluated, and how their accounts are terminated (Zhang et al., 2025). Although AM has brought efficiency and scalability to platform operators, it has sparked great academic discussion around the Psychological Well-being (PWB) of workers. Empirical studies conducted over the past few years indicate that the constant and intrusive surveillance inherent in AM can exacerbate stress, anxiety and emotional fatigue (Félix et al., 2023; Zhao & Liu, 2024). This phenomenon could be linked to a “chilling effect”, in which knowledge of the permanent algorithmic surveillance causes employees to police themselves and restrict behaviours even when there is no actual sanction. This phenomenon tends to increase psychological pressure. The widespread use of algorithmically mediated management creates a distinct psychosocial environment in which human managerial judgment is partly replaced by automated and often opaque decision-making. These practices can generate feelings of powerlessness, commodification, and dehumanisation (Zha et al., 2026; Spreitzer et al., 2017; Wan et al., 2024a; Parent‐Rocheleau & Parker, 2022), each of which may impose distinct cognitive and emotional demands on workers. In this study, algorithmic management (AM) refers to workers’ perceived exposure to algorithmically mediated monitoring, task assignment, and performance control.

While gig work has garnered significant scholarly interest, the particular mediating processes that link AM to PWB have not been well explored in an integrated moderation framework. Past studies have mostly highlighted performance, platform regulation, or overall job satisfaction, but paid insufficient attention to the psychological processes (Wan et al., 2024a). There are several important functions that are performed in algorithmic management, including monitoring, evaluation, and task assignment (Parent‐Rocheleau & Parker, 2022). These functions have the capacity to create or exacerbate certain cognitive and emotional stresses in people working in a job. However, as (Möhlmann et al., 2021) have found, algorithmic coordination of workers on the platforms changes the power dynamics between workers and platforms, making it difficult for labour to counterbalance. It is important to note here that the perceived autonomy, or how much freedom employees have in their work, work hours and work environment, deserves further study. According to Self-Determination Theory, autonomy is a key psychological need for motivation and well-being (Ryan & Deci, 2019). Platforms have been associated with a high degree of flexibility and freedom for gig workers, but this can often be different when it comes to the experiences of algorithmically mediated work (Veen et al., 2020). This paradox, which we can call the autonomy paradox, indicates that although workers may formally be able to decide when to be online, their perceived autonomy may be restricted through the use of algorithmic task allocation, the dynamics of the pricing system, and gamified reward systems (Gandini, 2019; Wood et al., 2019). Although the autonomy paradox is a conceptual framing used here to motivate the research, it was not directly operationalised as a tested construct in this study. The discrepancy between formal and perceived autonomy may have important implications for workers’ well-being.

This study focuses on the relationship between AM and gig workers’ PWB and examines whether perceived autonomy buffers this association. Relying on the Job Demands-Resources (JD-R) theory (Bakker et al., 2023), AM practices are conceptualised as job characteristics that can generate or intensify job demands through constant surveillance, limited transparency in assessment, and inconsistent enforcement of algorithmic rules (Bakker et al., 2023). In contrast, perceived autonomy may serve as a key employment resource that can help allay these demands by restoring the feeling of agency and predictability (De Stefano et al., 2021; Bakker & Demerouti, 2024; De Stefano & Aloisi, 2019). However, there has been limited empirical integration of these constructs into a validated moderation model. Past research has shown both positive and negative outcomes: some describe the stressfulness and unpredictability of algorithmic surveillance (Bucher et al., 2021; Duggan et al., 2023; Liu et al., 2024; Liu & Yin, 2024). While past studies have explored the link between AM and well-being (Zha et al., 2026; Zayid et al., 2024) as well as the moderating effect of autonomy in the JD-R approach (Wan et al., 2024b), this study contributes to the existing body of knowledge through its analysis of (a) a multi-platform sample of gig workers comprising location-based (ride-hailing) and online (freelance digital tasks) gig workers, as explained in Section 2.1, and (b) a focused measure of perceived AM covering real-time task monitoring, algorithmic task assignment, and algorithmic control over work performance, as explained in Section 2.2. Respondents were recruited from freelance platforms operating in the online task-based segment of the gig economy, in addition to the ride-hailing and delivery platforms mentioned above.

Thus, the main goal of this study is to examine the direct association between algorithmic management and the PWB of gig workers in an empirical study and to examine the moderating variable of perceived autonomy in this correlation. This study aims to accomplish three distinct goals: (1) examine the association between the perceived level of algorithmic management and the PWB of gig workers; (2) assess gig workers’ perceived level of autonomy as a construct within the moderation model; and (3) explore the role of perceived autonomy in moderating the strength of the AM effect on well-being. This study operationalises AM within the JD-R theoretical architecture and validates the moderation model using PLS-SEM. The findings are intended to provide practical guidance for platform

Based on the theoretical framework and literature reviewed above, the following hypotheses are proposed.

H1: Algorithmic management is negatively associated with the PWB of gig workers.

H2: Perceived autonomy is positively associated with the PWB of the gig workers.

H3: Perceived autonomy moderates the relationship between algorithmic management and PWB.

2. METHODOLOGY

2.1. Research Design, Sampling and Data Collection

A cross-sectional survey design was used to examine the hypothesised relationships between algorithmic management, perceived autonomy, and PWB. The target population comprised active gig workers who currently participate in gig work through digital platforms. Eligible participants reported current work on a digital labour platform during the data collection period, working on location-based (ridesharing) and online (freelance digital tasks) platforms. All participants were selected from one particular national setting of the gig economy, Pakistan, and the participants came only from urban and peri-urban areas of Lahore, Karachi, and Islamabad/Rawalpindi, where the platforms were more active. Participants from rural settings with fewer platforms were not included in the sample. This geographical concentration should be considered when generalising the findings to other regulatory, cultural, or platform market contexts. A total of 342 valid answers were obtained during a six-week period using a structured online questionnaire that was sent to special gig worker communities and social media groups. The sample size (N = 342) exceeded the minimum recommended threshold established by (Hair et al., 2022) for detecting both direct and interaction effects in PLS-SEM models of such complexity. Based on the inverse square root method and the number of arrows pointing to the endogenous construct (three predictors, including the interaction term), the minimum required sample size was 160, and the final sample of 342 exceeded this threshold. Consistent with the common minimum sample size guidelines for PLS-SEM, no formal a priori power analysis (for example, G*Power) was conducted to determine this threshold, which is acknowledged in the limitations and future direction section.

Purposive sampling was used to ensure that only workers who had firsthand experience in terms of the algorithmic distribution of tasks and performance appraisal were included. It is acknowledged that purposive sampling may introduce self-selection bias, as workers who were more engaged with the topic or more severely affected by AM may have been over-represented in the limitations and future direction section. Informed consent was obtained, and all data were anonymised before analysis. This study is best characterised as primarily confirmatory, testing three pre-specified hypotheses (H1–H3) derived from JD-R theory. The assessment of predictive relevance (Q²) also provides an explanatory element regarding the model’s out-of-sample explanatory power. A wide range of platform types was used to attract respondents, which increased the heterogeneity of the sample; however, because purposive sampling was employed, this heterogeneity does not, by itself, guarantee the generalisability of the results. The instruments were extensively redesigned from valid scales used in previous research studies. Before data collection, all scale items were considered conceptually relevant to the gig work context. Items were adapted and not simply copied so they would fit within the context of the items, but original wording was used where it was relevant, and some minor language changes were made to reflect the platform labour context. A limitation of this is the lack of formal expert panel and content validity process. Future work should do this. The adaptation process involved three steps: First, items were pilot tested in a platform work setting with a small sample of gig workers (n = 15) and some changes were made in items wording as needed to ensure items were clear and understandable; Second, the adapted items were tested against the original measures to ensure that the meaning of the constructs was kept intact. Such a procedure serves as preliminary evidence of content and face validity, but it does not include a structured expert panel content validity test, such as content validity index ratings by experts in the subject matter, which represents a methodological limitation for further scale validation research in this area.

2.2. Measurement Instruments and Common Method Bias Assessment

The Algorithmic Management scale initially comprised 10 items modified by (Parent‐Rocheleau et al. 2024), representing the core dimensions of perceived AM exposure: real-time task monitoring, automated performance ratings, and algorithmic task assignment. Although AM is a multidimensional construct, this study focuses on three key aspects represented by the retained indicators. These aspects include real-time monitoring, algorithmic task assignment, and algorithmic control over work performance. This operationalisation captures workers’ perceptions of algorithmic control intensity rather than objective system-level metrics. Perceived Autonomy was a 6-item scale based on (Zhu et al., 2024), which included the latitude of decision-making and scheduling, task acceptance, and work pace. The Psychological Well-Being scale is an 8-item scale which relies on existing flourishing and PWB scales applied to the work setting (Zayid et al., 2024), including the dimensions of positive affect, lack of distress, engagement, and sense of purpose. All items were graded on a 5-point Likert scale ranging from 1 to 5 (strongly disagree/strongly agree). Age, gender, months of platform tenure, and average working hours per week were collected as demographic and work-related variables. These variables were not included as covariates in the final structural model. To assess common method bias (CMB), both Harman’s single-factor test and full collinearity VIF assessment were employed. The single-factor test indicated that no single factor accounted for most of the covariance. Additionally, all full collinearity VIF values were below the recommended threshold of 3.3 (Ringle et al., 2012).

2.3. Indicator Selection and Item Purification

All scale items were administered in the survey as part of the full measurement. Item reduction was conducted only during the measurement model evaluation stage in SMART-PLS. Indicators were removed sequentially if they exhibited low outer loadings (< 0.70), cross-loadings, or redundancy, which weakened convergent and discriminant validity. This approach follows the established PLS-SEM guidelines, which recommend retaining indicators that demonstrate adequate reliability while maintaining the conceptual coverage of the construct domain (Hair et al., 2022). Importantly, item removal was not performed solely on statistical grounds; retained indicators were selected to ensure the representation of the theoretical measures of each construct (monitoring, evaluation, and task assignment for algorithmic management; scheduling and decision latitude for autonomy; and emotional well-being and purpose for PWB). A summary of the removed indicators and their loadings is provided in the Results section. The final retained measurement items used in the PLS-SEM analysis are provided in the Appendix A.

2.4. PLS-SEM Analysis Strategy and Rationale

Data analysis was conducted in two consecutive stages using SmartPLS 4.0. PLS-SEM was selected because the study tests pre-specified hypotheses and includes a latent interaction term for moderation. The analysis also evaluated the predictive relevance of the model. This method is suitable for the model structure and distributional characteristics of the survey data (Hair et al., 2022). There were specific reasons why PLS-SEM was chosen as the method of analysis.

(a) PLS-SEM supports the estimation of the latent interaction term AM × AUT in the proposed moderation model.

(b) This study tests three pre-specified hypotheses based on the JD-R framework and assesses the predictive relevance of the structural model.

(c) The model includes a moderate number of indicators, and the survey data show distributional characteristics that support the use of PLS-SEM.

2.5. Measurement and Structural Model Assessment

The first step in conducting the analysis included the evaluation of the measurement model: indicator reliability (outer loadings), internal consistency reliability (Cronbach’s alpha and Composite Reliability), as well as convergent and discriminant validity (AVE and HTMT). The second stage involved estimating the structural model to test the direct and moderating hypotheses. Perceived autonomy was moderated by establishing a latent interaction measure (AM × AUT) using SmartPLS 4.0. This is a fitting method when the predictor and moderator are both characterised as reflective constructs (Hair et al., 2022). The moderation effect was analysed using bootstrapping with 5,000 resamples. To decode the interaction, the conditional effects of algorithmic management on PWB were tested at four levels of perceived autonomy: low (−1 SD), mean, high (+1 SD), and very high (+2 SD).

Fig. (1) presents the conceptual model of this study. Perceived autonomy was positioned as the moderating variable (Mod), with an interaction term (AM × AUT) included in the structural model. The model corresponds to the three hypotheses (H1–H3) formally stated in the Introduction.