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Abstract

This study explores how the interplay between perceived autonomy and dependence in online crowdwork shapes workers’ perceptions of fairness and frustration. Promises of autonomy entice workers to crowdwork, yet these promises often mask deeper dependencies on platforms and requesters. This study finds that high autonomy, when coupled with high dependence, may have adverse effects on fairness and frustration. These findings contribute to a more nuanced understanding of the interplay between autonomy and dependence in online crowdwork. Workers with low autonomy are less affected by dependence, while those with high autonomy are more likely to perceive platforms as operating unfairly when coupled with high dependence. These findings support the fauxtonomy hypothesis, demonstrating how autonomy, under conditions of dependence, creates a sense of false self-employment and amplifies worker frustration. Addressing these disparities is critical to ensuring fair treatment and reducing frustration among platform workers, especially for those who feel trapped in high-dependence situations.

Keywords

autonomy crowdwork dependence fairness fauxtonomy work frustration

Introduction

Megatrends such as globalization and technological advances highlight an increasing pressure on decent and sustainable work (D’Cruz et al., 2022). Contemporary working arrangements are increasingly precarious (Duggan et al., 2020) as the “standard” employment contract makes place for weaker employment contracts, with organizations outsourcing work to external labor markets (Cropanzano et al., 2023). The gig economy and online crowdwork, in particular, represent clear markers of such new employment contracts. Notably, while the gig economy encompasses a variety of different forms of mediated work, including on-location app-work – e.g., Uber – high-skilled freelance work – e.g., Upwork – or capital platform work – e.g., Airbnb – this study focuses specifically on online crowdwork (also referred to as micro tasking; Duggan et al., 2020).

Crowdwork involves (micro-)task allocation and completion through digital labor platforms, such that requesters outsource small tasks to a large and undefined crowd of online workers who then complete tasks in batches. The online labor platform orchestrates the labor process by matching labor supply and demand and deploying algorithms to manage the workforce (Fieseler et al., 2019; Ihl et al., 2020; Kittur et al., 2013; Strunk and Strich, 2023). A hallmark of the gig economy is the cultivation of autonomy (Gerber, 2021), persuading workers to become “micro-entrepreneurs” and enjoy the unprecedented flexibility of working via a digital platform without a human supervisor (Howcroft and Bergvall-Kåreborn, 2019; Ihl et al., 2020). Indeed, one consistent finding in research on crowdworkers’ experiences is that workers may enjoy and value the autonomy afforded to them in this work arrangement (Cropanzano et al., 2023; Kuhn and Maleki, 2017; Schor et al., 2020). Autonomy refers to the perception of freedom and discretion in scheduling work, making decisions, and choosing the methods to perform tasks (Morgeson and Humphrey, 2006).

However, online crowdwork is rife with controversy over workers’ job conditions and dependence on labor platforms (Wood et al., 2023). In many cases, this has led research to focus on economic dependence (Cropanzano et al., 2023; Pichault and McKeown, 2019; Reynolds and Kincaid, 2025; Schor et al., 2020), highlighting how work conditions and worker classification create imbalances making the risks and consequences of employment more severe for workers than for employers (i.e., platforms) (Wood, 2019a, 2019b). However, economic dependence is a consequence of a deeper structural imbalance related to worker classification, working conditions, and worker empowerment (Wood et al., 2023), often accumulating in low pay (Hornuf and Vrankar, 2022). Notably, in many cases, online crowdworkers do not rely solely on this type of work for their economic livelihood, but rather supplement their incomes through online crowdwork (Morgan et al., 2023). For instance, a study across five countries by the European Trade Union Institute reported that only 12.6 % of online workers relied exclusively on these earnings (Piasna and Drahokoupil, 2019). Yet, regardless of income generated, all online crowdworkers face a structural imbalance (Wood et al., 2023), leaving them powerless to improve their work conditions (Gegenhuber et al., 2021; Ravenelle, 2019). Hence, we suggest following a broader conceptualization of dependence.

Dependence is defined here as a perceived power imbalance in which crowdworkers feel helpless in front of the platform and are afraid or unable to improve their working conditions (Wood et al., 2019a, 2019b). Extant research discusses dependence largely in terms of structural subordination in which workers are unable to exercise influence over pricing, performance criteria, or terms of engagement due to unilateral algorithmic governance, opaque rating systems, or lack of alternative employment options (e.g., Wood et al., 2025). This raises important questions about the value of autonomy under conditions of high dependence. Some studies highlight that (income) dependence may severely limit workers’ perceived autonomy (Fisher et al., 2025).

Although often treated as opposite ends of the same continuum, we argue that autonomy and dependence are orthogonal constructs. Autonomy refers to perceived discretion over one’s work execution, whereas dependence reflects a relational and structural inability to influence platform terms or working conditions. In other words, a worker can feel free to choose tasks or schedules (autonomy) yet be structurally reliant on the platform for shaping the conditions under which these tasks are performed (dependence) (Wood et al., 2023). Hence, it is possible, and perhaps increasingly common in the gig economy, for workers to feel autonomous and dependent at the same time. We suggest that this might create an illusion of empowerment, such that autonomy, in practice, transmutes into a mere semblance akin to a paper tiger.

This article contributes to the autonomy–control debate in gig work research by highlighting how high autonomy and high dependence create a false sense of autonomy – i.e., “fauxtonomy” – which misleadingly informs workers and contributes to a sense of unfair treatment (Fieseler et al., 2019) and work frustration. Hence, we examine how autonomy and dependence, independently, as well as their interplay, may lead to work frustration by eroding perceptions of fairness. Notably, while the constraints of autonomy have been theorized, this study moves beyond technological constraints embedded in the platform’s operating logic (Malhotra, 2021) and contributes to a better understanding of the antecedents and implications of perceived unfair treatment in online crowdwork (Fieseler et al., 2019). The contribution of this study lies in demonstrating that autonomy and dependency independently, and in interaction, shape perceived unfairness and ultimately work frustration. We use a psychological contract lens (Rousseau, 1989), as the absence of formal legal labor contracts gives rise to more implicit and fluid agreements between workers and platforms (Cropanzano et al., 2023). We suggest that the inducement of autonomy used to lure and retain workers can be perceived as unfair when there is a high degree of perceived dependency on the platform.

Our analysis highlights a pervasive interaction between autonomy and dependency, raising questions about fair employment for crowdworkers. Specifically, the new psychological contract in online gig work promises unprecedented autonomy (Cropanzano et al., 2023), while ironically representing a highly unilateral employment relationship characterized by the high dependency of workers on the online labor platform (Hickson, 2024; Shibata, 2019), ultimately frustrating workers in the gig economy through perceived unfairness. The findings of this study provide a comprehensive understanding of how the interaction between perceived autonomy and dependence fuels a profound sense of unfairness among crowdworkers.

Theoretical background and hypothesis development

Autonomy, dependence, and fairness

Online crowdworkers engage in a nonstandard form of employment that seemingly offers workers unprecedented autonomy – i.e., allowing them to choose when, where, and how much to work – believed to liberate them from the traditional constraints of conventional work environments and human oversight (Strunk et al., 2022). Platforms cultivate the promise of autonomy to attract workers to their platforms (Ahsan, 2020). Yet, gig workers often find themselves depending on the platform and the requesters for work conditions, continued income opportunities, and performance appraisals (Duggan et al., 2020; Malhotra, 2021; Möhlmann and Zalmanson, 2017). Extreme standardization and fragmentation of work tasks with clearly defined outputs create tight technological performance monitoring and direct dependence on the platform (Gerber and Krzywdzinski, 2019; Kittur et al., 2013).

Specifically, crowdworkers operate under a transactional contract that is narrowly focused, short-term, limited to economic concerns, easily monitored, and quid pro quo, morphing formal employment contracts into ultra-short pay-per-task exchanges (Cropanzano et al., 2023; Rousseau, 1995). Psychological contract theory is concerned with the individual-level cognitive interpretations of such exchange relationships (Rousseau, 1989). In online crowdwork, the psychological contract reflects how workers and platforms evaluate the quality and fairness of their labor exchange, which hinges on the extent to which implicit and explicit promises are fulfilled in the absence of long-term formal employment contracts (van Zoonen and Sivunen, 2024).

The psychological contract theory offers a robust framework for analyzing the implicit promises and expectations in crowdwork arrangements (Cropanzano et al., 2023) and conceptualizing fairness in individualized work arrangements (Shanahan and Smith, 2021). The concept of psychological contract is an important dimension in new types of employer–worker relationships, such as in crowdwork (van Zoonen and Sivunen, 2024), as it highlights the reciprocal but often implicit promises and obligations between worker and employer (Cullinane and Dundon, 2006). Hence, psychological contract theory is particularly relevant in the gig economy, where formal contracts often fail to capture the nuanced expectations of autonomy that workers are promised (Cropanzano et al., 2023; Duggan et al., 2020; van Zoonen and Sivunen, 2024). Broadly, the psychological contract can be understood as workers’ perception of a “fair” deal with their employer (Rousseau, 1989).

The psychological contract represents an implicit exchange based on the beliefs and perceptions about the obligations between workers and employers. In online crowdwork, platforms cultivate the notion of unprecedented autonomy to lure workers to the platform, presenting worker autonomy and market flexibility as fair exchanges (Shanahan and Smith, 2021). However, this may be misleading, as the purported autonomy offered often appears more limited than advertised (Alacovska et al., 2024b; Cropanzano et al., 2023). In light of high worker dependence on the platform (Wood et al., 2023), workers may perceive few opportunities to utilize their autonomy, which could potentially lead to a breach of the psychological contract (Shanahan and Smith, 2021). Indeed, Cropanzano et al. (2023) note that online crowdwork is particularly enriching when autonomy is high but dependence on the platform is low. However, in reality, the absence of labor protection and voice mechanisms hinders workers’ ability to exert meaningful control over their work conditions (Gegenhuber et al., 2021). As a result, in situations where autonomy is high and dependence is high, workers may experience unfairness and frustration as the promised autonomy does not represent a fair exchange at the expense of their independence. This resonates with Galanis et al. (2019), who highlight the importance of equal exchange in mitigating labor exploitation and safeguarding fair treatment. Similarly, van Zoonen and Sivunen (2024) used psychological contract theory to demonstrate that crowdworkers’ autonomy is particularly helpful in overcoming work challenges when this is coupled with a high locus of control. Hence, drawing on psychological contract theory, we propose that while autonomy in online crowdwork can enhance perceptions of fairness, dependence undermines these benefits by eroding workers’ control over the exchange.

Understanding autonomy dynamics in crowdwork requires focusing on dependency, too. Dependency generally refers to a power imbalance that may prevent workers from exerting power over the platforms or the conditions of their employment (Wood et al., 2023). Emerson (1962) noted that the power of one actor over another is equal to, and based upon, the dependence of the latter actor on the former. In platform work, the extent to which workers are dependent on the platform is derived from their investment in the relationship and their (lack of) alternatives (Kuhn and Maleki, 2017). While often described in economic terms (Schor et al., 2020; Vallas and Schor, 2020), dependence relates to workers’ bargaining power and ability to exert power over the outcomes of their work relationship (Maffie, 2020; Wood et al., 2023). Hence, (algorithmic) control should not be equated with dependence (Wood, 2019a, 2019b), as workers’ dependency exists regardless of the technological control exerted by online labor platforms. Dependence represents a perceived imbalance, such that workers are limited in exerting power over the conditions of the labor relationship in online crowdwork.

Building on psychological contract theorizing (Cropanzano et al., 2023), this study suggests that autonomy is positively associated with fairness perceptions, while dependence is negatively associated with fairness perceptions. The rationale behind this assumption is that crowdworkers who perceive greater autonomy will consider the platform to be credible in fulfilling the inducement of offering unprecedented autonomy. This may lead workers to conclude that the platform upholds its end of the psychological contract, implying that it allocates resources in a fair manner. In contrast, high dependence may signal a power imbalance that may lead workers to question whether the exchange between worker autonomy and their labor position is fair (Shanahan and Smith, 2023; Tan et al., 2021). Cropanzano et al. (2023) articulated various propositions based on psychological contract theorizing, including the idea that high autonomy in gig work is particularly advantageous when dependence is low (rather than high) (see also Kuhn and Maleki, 2017).

Hence, research has highlighted how autonomy may foster fairness (Fieseler et al., 2019), while (economic) dependence may undermine fairness perceptions (Schor et al., 2020). This is important because fairness perceptions “constitute a key appraisal element of emotional experience” (González-Gómez and Hudson, 2024: 429), such that fairness perceptions elicit positive emotions and unfairness perceptions elective negative emotions (Matta et al., 2014). Given the centrality of ethical challenges in the gig economy (Tan et al., 2021), it is no surprise that scholars have identified gig work as highly emotional, with feelings of anger and frustration among workers prevailing (Ashford et al., 2018). Drawing attention to workers’ emotional responses (Strunk et al., 2022) and (un)fair treatment, it is likely that perceived autonomy is negatively related to work frustration through increased perceptions of fairness (Deng and Joshi, 2016; Fieseler et al., 2019). Conversely, perceived dependence will be positively related to work frustration by reducing perceptions of fairness (Deng et al., 2016). Thus, we hypothesize:

H1: Perceived autonomy reduces perceptions of work frustration through increased fairness perceptions.

H2: Workers’ perceived dependence on the platform and requesters increases perceptions of work frustration through decreased fairness perceptions.

The fauxtonomy hypothesis

Research has highlighted ethical concerns regarding worker classification, for instance, labeling them as “independent contractors” (Tan et al., 2021). Labor platforms assume a role of mediator, thereby avoiding employer responsibilities and operating beyond the purview of labor regulation, exposing workers to harm (Schor et al., 2024). While some research highlights that there are domains within the broader gig economy (e.g., online freelancers) where the benefits (e.g., employment opportunities, additional income) outweigh the drawbacks, online crowdworkers seem particularly disadvantaged by low pay (Hornuf and Vrankar, 2022) and abysmal labor positions (Durward et al., 2020). Hence, as workers find themselves (increasingly) dependent upon the platform and requesters, their work autonomy is rendered an illusion devoid of substantive practical utility.

Indeed, research widely criticizes the proclaimed autonomy in gig work. For instance, Pichault and McKeown (2019) offer a nuanced framework that highlights how autonomy can be a multidimensional concept in the context of self-employment, critiquing the idea that independent professionals are inherently autonomous. Hickson (2024) offers more critical perspectives, noting that pro-gig economy discourse presents a less demanding vision of liberty, as the precarious conditions and vulnerable status of workers fail to ensure freedom and autonomy. Similarly, Shibata (2019) explicitly contrasts the “official discourse”, which predominantly highlights the benefits for gig workers, with a much bleaker reality: one in which the flexibilization of employment hollows out the job conditions of gig workers, severely undermining their autonomy and freedom. Hence, we propose that autonomy and dependence likely interact, as autonomy may only be experienced as meaningful when not overshadowed by dependence.

Importantly, the illusion of autonomy may mask the reality of their vulnerability (Vandaele, 2018) and dependence on the platform (Wood et al., 2023). Research on labor exploitation has emphasized how unequal or disproportionate labor exchanges, for instance, when labor contributions structurally exceed the benefits returned, may undermine distributive justice (Galanis et al., 2019). Arguably, false promises of autonomy under high dependence represent an exploitative mechanism in which proclaimed benefits cannot be fully realized by workers, undermining perceived fairness.

There is evidence to suggest that autonomy overall has positive implications for crowdworkers, for instance, by improving meaningfulness and satisfaction with crowdwork (Deng and Joshi, 2016), improving fairness perceptions (Fieseler et al., 2019), and reducing work frustration (van Zoonen et al., 2025). At the same time, these benefits have been shown to come at a significant cost to crowdworkers. Deng and colleagues (2016) theorized the duality of crowdwork, highlighting how workers felt both empowered by autonomy but simultaneously felt powerless and even exploited due to the structural marginalization of workers (Deng et al., 2016; Gegenhuber et al., 2020; Wood et al., 2019a, 2019b).

Indeed, research has widely problematized how crowdwork is a dependent form of self-employment where online platforms decisively shape the working conditions (Gerber and Krzywdzinki, 2019), leaving workers powerless at the whim of economic dependency (Schor et al., 2020), strict technological control (Gerber and Krzywdzinki, 2019), and exposed to structural challenges (Caza et al., 2020). However, we know little about whether perceived freedom and autonomy, one of the elements most valued by crowdworkers (Deng et al., 2016), is evaluated in isolation or in relation to the structural dependence many crowdworkers experience. A psychological contract perspective suggests that workers assess the quality of the exchange by weighing promised autonomy against their lived dependency, making it essential to explicitly test how autonomy and dependence interact to shape perceptions of fairness and frustration in online crowdwork.

The situation of crowdworkers bears a resemblance to that of workers who are believed to have some autonomy, such as self-employed professionals, but who face precarious conditions of dependence. Examples have been documented in construction work (Behling and Harvey, 2015; Vershinina et al., 2018), in the sex industry (Cruz et al., 2017), and more broadly in precarious forms of employment (Thörnquist, 2015). For instance, in the sex industry – specifically exotic dancing and prostitution – workers are often self-employed, yet their working conditions and dependence on the clubs they work at severely limit their autonomy and seem uncharacteristic of true self-employment (Cruz et al., 2017). Similarly, for crowdworkers, working conditions are often precarious, and workers have little power to demand better conditions or redress unfair treatment, due to high dependence on the platforms (Anwar and Graham, 2021; Wood et al., 2023). Hence, workers’ dependence on the platforms obstructs the fulfillment of the inducements to afford true autonomy to workers and empower them as “micro-entrepreneurs” (Howcroft and Bergvall-Kåreborn, 2019; Malhotra, 2021).

Thus, in online crowdwork, autonomy and dependence independently and collectively shape worker experiences. Research has often scrutinized the technological constraints of algorithmic organizing limiting workers’ autonomy (e.g., Malhotra, 2021). However, conditions of dependence are more structurally embedded in the psychological contracts of gig workers (Cropanzano et al., 2023; Wood et al., 2023). Hence, moving beyond technological constraints, the “fauxtonomy hypothesis” suggests that when promises of autonomy are false, they may have adverse effects under conditions of heightened worker dependence, such that bogus promises of empowerment decrease perceived fairness and fuel worker frustration.

Research on voice mechanisms in organizations has highlighted how the ability to voice concerns may fuel injustice and frustration when the organization fails to adequately address or alleviate the voiced concerns (Harlos, 2001). Additionally, research on autonomy and control in the context of technology-intense work environments documented that while the available technology may offer workers more autonomy over when, where, and how they work (e.g., Wang et al., 2020), several socio-technological issues may reduce workers’ control over their work, leading to perceptions of unfairness and worker frustration (González-Gómez and Hudson, 2024). Ciulla (2020) suggests that bogus or false conditions often elicit anger, disappointment, and disgust over hypocrisy, lies, or misrepresentation, stating, “This is how people feel when they are told that they are being empowered, but they know that they are not” (p. 178). The fauxtonomy hypothesis problematizes the promise of autonomy under conditions of high dependence.

Hence, psychological contract theory (Rousseau, 1989, 1995) emphasizes that workers form subjective evaluations of the employment relationship based on perceived mutual obligations. These perceptions are shaped by organizational inducements (e.g., promises of autonomy) and by the worker’s experiences of fulfillment or breach. In online crowdwork, we argue that autonomy is often a central inducement and is commonly interpreted by workers as a promise of flexibility, self-direction, and control (Cropanzano et al., 2023). However, when this autonomy is experienced in tandem with high levels of structural dependence, workers may come to view the autonomy as illusory. This mismatch between the promised and the lived experience constitutes a perceived breach of the psychological contract. As such, the framework helps us explain not only the independent effects of autonomy and dependence but also how they interact to shape worker outcomes such as perceived fairness and frustration. Thus, the following hypothesis is tested:

H3: Perceived autonomy moderates the impact of perceived worker dependency on fairness perceptions, such that the negative effect of dependency is stronger when autonomy is higher.

Methods

We focus on online crowdwork, rather than online freelance work (Sutherland et al., 2020), app-based work, or capital platform work (Duggan et al., 2020). We selected Clickworker as the digital platform to source the crowdworkers from because Clickworker is the largest and one of the most prominent microtasking platforms (Mayer et al., 2024). Clickworker claims to provide labor opportunities to millions of workers worldwide, and is even estimated to provide access to a labor force up to ten times larger than Amazon Mturk (Tahaei and Vaniea, 2022). Following recommendations by Silberman et al. (2018) about responsible research with crowdworkers, fair compensation and opportunities to contact the responsible researcher in the event of questions were ensured. Each individual crowdworker provided written consent for participation, and the research design went through ethical approval before data collection commenced.

To examine our indirect effect and moderation assumptions we collected survey data at two time points with a two-month interval. Two wave survey data can probe indirect effects (Cole and Maxwell, 2003) and the two month time interval was deemed appropriate as shorter intervals may not allow dependence or autonomy effects to manifest, while longer intervals may allow contaminating factors to intervene (Podsakoff et al., 2003). Furthermore, the two-month interval follows earlier research on worker experiences in the gig economy (Khan and Khan, 2024) and research on perceived fairness and emotional responses (e.g., Boswell and Boudreau, 2000).

Sample

The data underlying this study was obtained from 575 online crowdworkers. In the first wave, 1291 respondents completed the questionnaire who were then invited to participate in the second survey. A total of 575 crowdworkers completed both questionnaires and passed all attention checks, yielding a retention rate of 44.54%. Table 1 summarizes the sample statistics. Respondents reported an average age of 38.8 years (SD = 11.17) and had about three years of experience doing online crowdwork (SD = 5.53). The respondents further indicated that they worked 12.7 hours per week on the platform (SD = 11.44), generating about 21.3% of their personal income from crowdwork. Most workers reported other forms of employment next to their crowdwork, with 41.4% reporting full-time employment and 18% reporting no other forms of employment. Others indicated being students (8.5%), part-time employees (10.4%) or being self-employed (21.8%).

Table 1.

Sample description.

Characteristic	Mean (SD)	% of Respondents
Age (years)	38.8 (11.17)	–
Crowdwork experience (years)	3.0 (5.53)	–
Weekly hours on platform	12.7 (11.44)	–
% of income from crowdwork	21.3% (27.9)	–
Employment status outside platform work
Full-time employed	–	41.4%
No other employment	–	18.0%
Self-employed	–	21.8%
Part-time employed	–	10.4%
Student	–	8.5%

Measurement

All measurement items, taken at two time points, and factor loadings are reported in Table 2. Validity and reliability are discussed in the results section (see Table 3). Responses were recorded using seven-point Likert-type scales (1 = strongly disagree, 7 = strongly agree).

Table 2.

Measurement items and factor loadings.

	Time 1				Time 2
Item	R²	St. Factor loading	Unst. Factor loading	Se	R²	St. Factor loading	Unst. Factor loading	Se
Worker dependency
I am afraid to ask for better conditions	.58	.763	1.000	-	.45	.777	1.000	-
I am unable to ask for better conditions	.55	.743	1.035	.06	.56	.745	1.044	.06
I feel helpless	.51	.714	0.898	.06	.57	.756	1.000	.06
I fear the platform fees will increase	.42	.649	0.795	.06	.41	.643	0.842	.06
I fear the effects of unfair feedback on my income	.57	.757	0.954	.06	.52	.723	0.977	.06
Autonomy
Working on the platform allows me to make my own decisions about how to schedule my work.	.66	.814	1.000	-	.77	.875	1.000	-
Working on the platform allows me to decide on the order in which things are done in my work	.79	.888	1.178	.05	.79	.888	1.003	.04
Working on the platform allows me to plan how I do my work.	.67	.820	1.115	.05	.65	.806	0.921	.04
Fairness
The way this platform determines how to allocate work seems fair	.65	.805	1.000	-	.73	.855	1.000	-
This platform’s process for deciding which workers are promoted or restricted is fair	.71	.841	0.989	.04	.73	.853	0.981	.04
The decisions regarding how worker performance is evaluated is fair	.80	.895	1.071	.04	.75	.868	1.023	.04
The outcome of the decisions by the platform seems fair	.75	.866	1.059	.04	.78	.883	1.009	.04
Work frustration
Trying to get tasks done can be a very frustrating experience	.55	.738	1.000	-	.56	.746	1.000	-
Overall, I think crowd work is frustrating	.79	.888	0.841	.04	.80	.892	0.823	.03
I experience very much frustration doing this work	.78	.882	1.020	.04	.76	.873	0.933	.03

Table 3.

Validity and reliability statistics.

	Mean (SD)	MaxR (H)	CR	AVE	MSV	1	2	3	4	5	6	7	8	9	10	11	12
1. Dependency (T1)	3.76 (1.23)	.85	.85	.53	.41	.73
2. Autonomy (T1)	5.27 (1.19)	.89	.88	.71	.15	−.310*	.84
3. Fairness (T1)	4.48 (1. 13)	.92	.91	.73	.23	−.474*	.381*	.85
4. Frustration (T1)	3.42 (1.37)	.89	.88	.70	.41	.635*	−.392*	−.449*	.84
5. Dependency (T2)	3.89 (1.27)	.85	.85	.53	.39	.708*	−.391*	−.446*	.471*	.73
6. Autonomy (T2)	5.07 (1.30)	.90	.89	.74	.17	−.326*	.648*	.371*	−.394*	−.321*	.86
7. Fairness (T2)	4.39 (1.19)	.92	.92	.75	.30	−.524*	.295*	.677*	−.406*	−.518*	.397*	.87
8. Frustration (T2)	3.53 (1.42)	.89	.88	.71	.39	.599*	−.370*	−.463*	.734*	.634*	−.406*	−.540*	.84
9. Gender ^a	0.41 (0.49)	−	−	−	−	.086*	.041	−.036	−.046	.052	.004	−.054	−.030	−
10. Age	38.75 (11.17)	−	−	−	−	−.028	−.048	−.015	−.027	−.087*	−.068	−.017	−.069	−.028	−
11. Work hours	12.74 (11.44)	−	−	−	−	−.018	.105*	−.036	−.025	.056	−.025	−.080	.050	.056	.072	−
12. Tenure	4.77 (5.53)	−	−	−	−	.003	−.106	.055	−.065	−.150*	.051	.109	−.111*	.070	.125*	.160*	−
13. Income ^a	21.27 (27.90)	−	−	−	−	.111*	.167*	−.087*	.054	.152*	−.002	−.086*	.075	.074	−.131*	.493*	.018

CR: Composite Reliability; MaxR(H): Maximum Reliability H; AVE: Average Variance Extracted; MSV: Maximum Shared Variance (within wave). The diagonal elements display the Square Root of the AVE.

In the dataset, Gender was encoded as 1 for male and 0 for female, while Income was assessed as the percentage of personal income derived from crowdwork.

Statistically significant correlations are denoted by asterisks (*p < .05)

Autonomy was assessed using three reworded items from Morgeson and Humphrey (2006), adapted for crowdwork (Durward et al., 2020; Strunk et al., 2022). A sample item is: “Working on the platform allows me to make my own decisions about how to schedule my work.” Worker dependence was measured with five statements from Wood et al. (2023). Dependence refers to the extent to which workers are able to exert power over the platforms (Wood et al., 2023). Higher scores indicate greater dependency, e.g., “I fear the effects of unfair feedback on my income.” This approach aligns with prior research showing that dependence in platform work is often expressed through constrained voice and risk-averse behavior in relation to opaque platform governance (Meijerink and Keegan, 2019; Wood et al., 2021).

Perceived fairness was assessed with four items from Newman et al. (2020), measuring perceptions of equitable treatment. A sample item is: “The way this platform determines how to allocate work seems fair.” Work frustration was measured using three items adapted from Peters et al. (1980) for crowdwork (Strunk et al., 2022). Frustration refers to negative emotions arising from goal disruption. A sample item is: “Overall, I think crowdwork is frustrating.”

Analysis

The hypotheses were evaluated using Structural Equation Modeling (SEM) in IBM AMOS 25. Model fit was examined by employing various fit indices, including the chi-square/degrees of freedom ratio (χ²/df), Incremental Fit Index (IFI), Tucker-Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residual (SRMR). For satisfactory model fit, the following criteria were applied: χ²/df ratio below three, CFI and TLI exceeding .90 for good fit, and RMSEA and SRMR values below .08. Maximum likelihood estimation and bootstrapping with 5000 resamples were used to estimate model parameters and derive bias-corrected coefficients and confidence intervals.

To explore mediation effects with only two waves of data, the analyses adhered to the guidelines of Cole and Maxwell (2003). In the first step, the measurement model was estimated using a Confirmatory Factor Analysis (CFA). After establishing adequate validity and reliability of the model, equivalence tests across measurement occasions were conducted. Subsequently, the structural model was estimated. In this model, the error terms of indicators for the same latent constructs were permitted to covary with corresponding error terms in the other measurement occasion (Cole and Maxwell, 2003). Under the assumption of factorial invariance, a mediation effect can be assumed through the product of path a and path b. Path a was estimated by regressing M2 onto X1 while controlling for M1, and path b was estimated by regressing Y2 onto M1, while controlling for Y1. Assuming stationary data (factorial invariance), path b between M1 and Y2 was equivalent to a path b between M2 and Y3, as if three measurement occasions existed. Consequently, the product of ab represented the mediation effect of X1 on Y3 through M2 under this assumption. Moderation was probed using the Johnson-Neyman technique. This analysis enables a moderation analysis at a greater level of granularity by identifying the critical values of the moderator at which the interaction effect is most salient, and at which values the interaction fails to reach significance. Finally, we conducted several analyses to ensure the study was sufficiently powered, assess common method variance, establish measurement invariance across measurement occasions, and examine alternative models, which are available in the online appendix.

Results

Measurement model

A Confirmatory Factor Analysis (CFA) demonstrated an excellent fit between the hypothesized model and the empirical data. The Incremental Fit Index (IFI = .948) and Tucker-Lewis Index (TLI = .937) both exceed the established threshold of 0.90, underlining the model’s strong alignment with the observed data. Furthermore, the Root Mean Square Error of Approximation (RMSEA = .055) and Standardized Root Mean Square Residual (SRMR = .048) are well within the acceptable limits (< .08). Finally, the χ²/df =2.76, all supported the adequate model fit.

Upon further inspection of the model parameters, no reliability or validity concerns were detected (see Table 3). Composite reliability (CR) and maximum reliability (H) [MaxR(H)] all exceed the recommended threshold of .70, with values ranging between .85 and .92. Discriminant validity was assessed by examining the Fornell-Larcker criterion. Table 2 demonstrates that the square root of the average variance extracted for each construct is greater than the correlation with all other constructs (i.e., Fornell-Larcker criterion). It is further notable that the average variance extracted exceeds the maximum shared variance between constructs. Hence, discriminant validity can be assumed. In addition, convergent validity can be assumed as the average variance extracted for all constructs in the model exceeds .50. All factor loadings are significant on the intended constructs and sizeable, ranging between .65 and .89 (see Table 2).

Structural model

The structural model fitted the data well. The χ²/df was 2.83. Also, the incremental (IFI = .94 and TLI = .94) and absolute fit indices (RSMEA = .06 and SRMR = .06) suggested a good model fit. Note that the model specifies autoregressive paths for all constructs, correlations among latent constructs within each measurement occasion, and covariations of error terms between the same indicators of the latent constructs across both waves. Figure 1 represents a simplified hypothesized model with standardized regression results. Below, the unstandardized coefficients are reported. Table 4 provides a summary of the hypothesized relationships.

Figure 1.

Simplified hypothesized model.

Table 4.

Results for hypotheses testing.

		Bootstrapping			BC 95% CI
		B	SE	Beta	Lower	Upper	P	Result
Hypothesized effects
H1	Autonomy → Fairness → Frustration	−.001	.008	−.001	−.018	.016	.843	Not supported
H2	Dependency → Fairness → Frustration	.040	.015	.037	.015	.070	.001	Supported
H3	Autonomy * Dependency → Fairness	−.084	.037	−.057	−.107	−.008	.024	Supported

Hypothesis 1 reflects the assumption of a negative indirect relationship between autonomy and frustration through fairness. The results suggest autonomy at time 1 has a positive but non-significant relationship with fairness at time 2 (beta = .006, BC95% (−.088; .107) p = .887) while controlling for perceptions of fairness at time 1 (beta = .576, BC95% (.424; .728) p < .001). Furthermore, the results indicate that fairness at time 1 is negatively related to frustration at time 2 (beta = .594, BC95% (.524; .663) p < .001) while controlling for frustration at time 1 (beta = .726, BC95% (.627; .825) p = .001). These results suggest that the indirect effect (a*b) from autonomy on frustration through fairness is not significant (beta = −.001, BC95% (−.018; .016) p = .843). These results do not support Hypothesis 1.

Hypothesis 2 suggests that workers’ dependency is positively related to work frustration through fairness. The findings indicate that dependency at time 1 is negatively related to fairness at time 2 (beta = −.215, BC95% (−.323; −.121) p < .001), again controlling for fairness at time 1. Hence, the indirect effect of dependency on frustration through fairness is positive and significant (beta = .037, BC95% (.015; .070) p = .001). These results support Hypothesis 2.

Finally, Hypothesis 3 suggests that autonomy at time 1 moderates the relationship between dependency at time 1 and fairness at time 2. The moderation was probed using the J-N technique. The results suggest a negative and significant interaction of autonomy (beta = −.057, BC95% (−.107; −.008) p = .024). Figure 2 depicts the slope of dependency on fairness at levels of autonomy. The results suggest that at lower levels of autonomy, the relationship between dependency and fairness is not statistically significant. When the mean-centered value of autonomy surpasses −1.71 (in absolute values, 3.56 on a seven-point scale), an increasingly strong and significant negative effect of dependency on fairness emerges. This finding suggests that, at higher levels of autonomy, individuals perceive dependency as being more unfair, possibly due to the creation of a false sense of independence associated with higher levels of autonomy.

Figure 2.

J-N interaction plot.

Finally, it should be noted that the following controls were scrutinized: age, gender, work hours, tenure, percentage of personal income derived from crowdwork, and household income. Hence, our findings are robust against differences in economic dependence indicated by the percentage of personal income derived from platform work, and the total household income. We did not find significant relationships between the percentage of personal income derived from platform work (beta = .001, p = .565) and household income (beta = −.008, p = .556) and fairness. In addition, the percentage of personal income (beta = .001, p = .566) and household income (beta = −.016, p = .353) were not significantly related to frustration. Importantly, the inclusion of these variables did not affect the hypothesized relationships. Hence, following recommendations against the automatic inclusion of control variables (Spector and Brannick, 2011), the results of the model without the controls were presented to a) avoid potential bias from the inclusion of controls, b) because a thorough theoretical grounding for their inclusion is lacking, and c) because the model without the controls is more parsimonious.

Discussion

The findings show that perceived autonomy alone does not significantly reduce frustration in crowdwork through fairness. Perceived dependence, however, increases frustration through fairness perceptions. Importantly, the interaction effect between autonomy and dependence on fairness reveals an important interplay. Specifically, the findings demonstrate that at low levels of autonomy, the effect of dependence on fairness is not significant. This suggests that the perception of dependence does not impact fairness as strongly when individuals perceive limited autonomy. Conversely, at higher levels of perceived autonomy, the relationship between dependence and fairness is negative and increasingly strong. This implies that as workers perceive more autonomy, they become more concerned about their position of dependence vis-à-vis the platform and requesters, perceiving the platforms to operate more unfairly. This finding supports the fauxtonomy hypothesis, where workers perceive greater autonomy but do not perceive that their position relative to the platforms and requesters has improved. Thus, while autonomy is often celebrated in crowdwork, it also intensifies unfairness and frustration when coupled with high dependence. This can also be understood from the perspective of voice mechanisms. For instance, Harlos (2001) describes the deaf-ear syndrome, suggesting that the ability to voice concerns is not enough; employees also expect assurances through remedial actions. When voiced concerns or complaints appear to fall on deaf ears, injustice and frustration grow. Similarly, our study demonstrates that experiencing autonomy, under conditions of high dependence, fuels unfairness and frustration. These findings underscore the need to critically reevaluate autonomy in gig work and address the structural disparities that undermine worker empowerment.

Conclusion

These findings contribute to emerging theorization on the conditions and experiences of crowdworkers (Heeks et al., 2021; Kittur et al., 2013; Wood et al., 2023; van Zoonen et al., 2023a, 2023b). Specifically, this study grounds its inquiry into autonomy–dependence dynamics (Pichault and McKeown, 2019; Ravenelle, 2019; Wood et al., 2019a, 2019b) in psychological contract theory (Rousseau, 1989; Cropanzano et al., 2023), not merely framing autonomy and dependence as independently operating opposing forces, but as interacting factors that shape crowdworker perceptions of fairness and work frustration. In doing so, we contribute to research that applies psychological contract theorizing beyond traditional or direct employment situations (Shanahan and Smith, 2023). The findings provide direct evidence for the proposition that high autonomy is particularly advantageous when (economic) dependence is low (see proposition 12: Cropanzano et al., 2023). We extend this work by suggesting that it is not merely economic dependence but a broader structural dependency (Wood et al., 2023) that may render promised autonomy not only insufficient but potentially counterproductive. We suggest that this signals a breach of the psychological contract when workers experience fauxtonomy: the illusion of promised autonomy in the face of structurally constrained choices, limited influence, algorithmic control, and economic necessity.

Autonomy, often heralded as central to the gig economy and the new psychological contract (Cropanzano et al., 2023; Schor et al., 2020; Strunk and Strich, 2023), frequently masks deeper power imbalances (Krzywdzinski and Gerber, 2021; Schörpf et al., 2017). Our study reveals a prevalent ethical issue, fauxtonomy, where apparent autonomy is undermined by platform control, leaving workers dependent and disempowered. The findings align with Alacovska et al. (2024a), illustrating how dependence and autonomy in platform work are shaped by algorithmic paranoia, where opaque algorithms govern livelihoods. Defensive strategies, such as ingratiation and vigilance, while asserting control, reinforce worker dependence. Autonomy, therefore, remains fragile and constrained within structural power imbalances that perpetuate dependence.

Perceived autonomy paired with high dependence intensifies worker frustration by exposing the illusory nature of autonomy. Our findings echo Shapiro’s (2018) assertion that platforms’ promises of flexibility often lead to worker disillusionment, as dependence undermines the benefits of autonomy. In particular, the findings highlight that affording workers greater autonomy without addressing structural power imbalances in crowdwork may exacerbate rather than remedy the negative implications of worker dependence. Hence, beyond legal and jurisdictional efforts to improve workers’ positions, the findings of this study highlight the importance of reducing workers’ dependence on platforms.

This study also extends the concept of dependence beyond economic and technological factors to consider perceived dependence, reflecting workers’ limited ability to exert control over platform relationships (Wood et al., 2023). This perspective builds on research emphasizing how dependency stems from economic vulnerabilities (Vallas and Schor, 2020) and algorithmic control (Malhotra, 2021). Platform workers often juggle multiple jobs, a common practice due to the challenges of securing a stable income (Hornuf and Vankar, 2022; Piasna et al., 2022). Despite the variability in economic dependence, our findings demonstrate that the autonomy–dependence interaction we examined remains robust when controlling for indicators of economic dependence. This suggests that the relationships we observed between dependence, autonomy, fairness, and frustration are not significantly influenced by whether platform work constitutes a worker’s primary source of income or whether workers are from relatively higher-income households. Hence, we suggest that autonomy–dependence trade-offs represent a contested domain of organizational control, where workers’ perceived autonomy is paired with significant structural inequities (Bader and Kaiser, 2017; Green and Welsh, 1988), regardless of their economic dependence on platform work.

Finally, our findings contribute to debates on bogus self-employment (Cherry, 2016; de Stefano, 2015; Funke and Picot, 2021), demonstrating how power asymmetries in crowdwork exacerbate inequalities rather than fostering entrepreneurship (Auguste et al., 2023). Addressing these structural challenges requires reducing worker dependence and fostering fairer labor practices (Fieseler et al., 2019). Prior research offers solutions, including leveraging platforms’ technological capabilities for collective voice (Gegenhuber et al., 2021), creating support mechanisms (Strunk and Strich, 2023), and promoting bidirectional feedback (Gadiraju and Demartini, 2019). These measures could mitigate dependence and help ensure that autonomy is not merely an illusion but a reality in the gig economy.

Our findings have important policy implications. First, they highlight a critical challenge for legislators: while commendable efforts to improve worker protections have been made (European Council, 2023), the promise of autonomy in online crowdwork often masks significant power imbalances. Policymakers must address these structural disparities, which favor platforms and requesters, to reduce worker dependence and ensure autonomy becomes a reality rather than a false promise (Cantarella and Strozzi, 2021). Future initiatives should rebalance the power dynamics, improving workers’ positions and enabling a fairer digital labor market.

Second, platforms and requesters bear responsibility for fostering equitable labor conditions. Beyond ensuring transparent task requirements and fair remuneration, platforms can leverage technological capabilities to support collective representation and bidirectional feedback, as proposed by Gegenhuber et al. (2021). Reciprocal evaluation systems could help redistribute power between workers and requesters, ensuring fairer treatment. Similarly, in line with this suggestion, it would be interesting to explore in greater detail how workers may enact – e.g., through voice mechanisms – resistance to different forms of dependencies in online crowdwork, including economic, technological, and structural dependencies.

Finally, platforms must remain vigilant about the unintended consequences of datafication, gamification, and automation, which risk dehumanizing workers and perpetuating dependence. Maintaining human-centric work processes that recognize workers’ contributions is essential. Our findings call for platforms to embrace fair practices, fostering collaboration that upholds the promise of autonomy while addressing power imbalances in crowdwork.

Ultimately, our study emphasized that autonomy, when seemingly proffered within conditions of dependence, is not merely illusory; it is a catalyst for heightened perceptions of unfairness and frustration. This revelation points to a potential breach of the new psychological contract in gig work, laying bare the absence of genuine work independence. In doing so, this study implores future research to embark on a path that scrutinizes the nature of contemporary employment relationships and the dynamics of worker empowerment and support in the digital era.

Supplemental Material

sj-docx-1-wes-10.1177_09500170251380739 – Supplemental material for Autonomy’s Mirage: How Fauxtonomy Fuels Workers’ Frustration in the Gig Economy

Supplemental material, sj-docx-1-wes-10.1177_09500170251380739 for Autonomy’s Mirage: How Fauxtonomy Fuels Workers’ Frustration in the Gig Economy by Ward van Zoonen and Anu E Sivunen in Work, Employment and Society

Footnotes

Author Contributions

WvZ contributed to the conceptualization, methodology, formal analysis, investigation, and funding acquisition for this study. They also collected the data and were responsible for writing the original manuscript draft. AS provided feedback, comments, and contributed to writing and reviewing the manuscript.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by a grant from the Erasmus Trustfonds, grant number: 97010.2022.101.622/012/RB.

Ethical Approval

Ethical approval for the study was obtained from Erasmus University (ETH2122-0556). All participants in the study signed an informed consent prior to data collection.

ORCID iDs

Ward van Zoonen

Anu E Sivunen

Supplemental material

Supplemental material for this article is available online.

Ward van Zoonen (PhD, University of Amsterdam) is an Associate Professor at the Vrije Universiteit Amsterdam and at the University of Jyväskylä. His research focuses on issues related to how datafication and technologies shape workers’ collaboration and communication across time and space. Furthermore, he focuses on questions related to organizing and management in the gig economy.

Anu E Sivunen (PhD, University of Jyväskylä) is a Professor of Communication in the Department of Language and Communication Studies at the University of Jyväskylä, Finland. Her research focuses on communication processes in global teams and in other distributed work arrangements, workspaces, as well as the affordances of organizational communication technologies.

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