Online Labor Platforms Research Articles

Living one’s calling independently: the role of online labor platforms for different dynamics of calling enactment

PurposeThis study investigates the diverse career experiences of gig workers by looking at a sample of gig workers that are living a calling through online labor platforms (OLPs). Drawing from the systems informed positive psychology (SIPP) framework we examine the experiences and dynamics through which individuals can enact their calling through OLPs.Design/methodology/approachWe conducted 34 semi-structured interviews with gig workers who are able to live their calling through OLPs. The interviews were analyzed by using a grounded theory approach.FindingsOur findings reveal that gig workers sense of living a calling is rooted in the experience of being and feeling independent. Furthermore, we identify three dynamics of calling enactment through OLPs which are: (1) engaging in personal development, (2) constructing social fit and (3) stabilizing finances.Originality/valueWhile many studies document the precarious nature of the gig economy, our paper offers rich insight into positive career experiences of gig workers. Through taking a systemic view on individuals’ ability to live a calling, we illustrate how individuals interact with different features of OLPs to enact their calling. Finally, we suggest that besides direct dynamics of calling enactment there are also indirect dynamics of calling enactment that play an important role in individuals’ ability to live a calling in the gig economy.

Promotion of Self-employment: Manager Justifications for Online Labor Platform Operations

The emergence of online labor platforms (OLPs) has raised concerns about the treatment of platform workers and the legality of OLPs’ contractual relations and control. This study examines how managers (CEOs/founders) justify OLPs’ contractual relations and market control within the frames of critical platform work discussions and the Finnish labor market model. By using justification theory and market control classification as analytical tools, this qualitative study reveals that managers justify OLPs’ control and contractual relations by referring to critical discussions and features of Finnish labor market model. In justifications, managers either adopt the normative features of the model or criticize the societal neglect of self-employed workers. The model’s emphasis on full-time employment and legal questions around the platform economy seems to challenge the legitimacy of all OLP companies on some level. Despite OLPs’ operational differences, all managers justify OLPs with the idea of promoting self-employment within the Finnishlabor market.

Challenge or Hindrance? The Dual Impact of Algorithmic Control on Gig Workers' Prosocial Service Behaviors.

Algorithmic technological progress presents both opportunities and challenges for organizational management. The success of online labor platforms hinges on algorithmic control, making it imperative to explore how this control affects gig workers' prosocial service behaviors. Drawing from affective event theory, our study delves into the factors influencing gig workers' prosocial service behaviors in the online labor platform setting. We utilize the challenge-hindrance appraisal framework to highlight the pivotal role of algorithmic control. To rigorously test our hypotheses, we gathered empirical data from an online questionnaire survey of 660 gig workers. Our results indicate that challenge appraisals and hindrance appraisals in regard to platform algorithm control have a nuanced dual impact on gig workers' prosocial service behaviors. This relationship is clarified by the mediating function of work engagement. A challenge appraisal of platform algorithmic control can positively influence gig workers' prosocial service behaviors. However, hindrance appraisal of platform algorithmic control can negatively influence gig workers' prosocial service behaviors. Interestingly, workplace interpersonal capitalization boosts the effect of challenge appraisal on employees' prosocial service behaviors. However, it does not mitigate the adverse effects of hindrance appraisal on such behaviors. This study has multiple theoretical implications, and it also provides valuable practical insights into organizational management.

Monitoring and Home Bias in Global Hiring: Evidence from an Online Labor Platform

Online labor platforms increasingly use monitoring systems to manage remote workers. This study assesses whether and how these systems mitigate employer bias in hiring foreign versus domestic workers. Leveraging the exogenous introduction of a monitoring system for time-based projects on a leading online labor platform, we employ a difference-in-differences model to estimate the impact of monitoring systems on mitigating employers’ tendency to bias against hiring foreign workers (home bias). Results indicate a significant reduction in home bias, along with a 15% increase in the hiring of foreign workers following the introduction of the monitoring system. The mitigation effect is notably stronger in high-routine projects or when employers lack prior positive experiences with foreign workers, two scenarios characterized by low external uncertainty and high internal uncertainty, respectively. Moreover, employers no longer exhibit a stronger home bias in scenarios of lower moral hazard risk or coordination costs. These findings lend support to the effectiveness of monitoring systems in mitigating employers’ home bias through facilitating contractual control and coordination. Our study offers important implications for the design of online labor platforms and policymaking.

Does information disclosure affect the gender gap in bidding behavior? Empirical evidence from a natural experiment on a large online labor platform

Exploiting an exogenous change in information disclosure on one of the largest online labor platforms worldwide, we assess how the provision of more information on the employers’ willingness to pay (WTP) and the required experience level of workers affects the gender gap in bidding behavior. We find that female workers make lower wage proposals than male workers if the employers’ WTP for a project, as given by its budget, is disclosed in addition to the (exogenously enforced) experience level of workers that employers deem necessary for the job, i.e., low, intermediate, or high. In addition, we do not find robust empirical support for the hypothesis that female workers’ under-confidence in their skills increases the gender gap in bidding behavior. Finally, we find a statistically significant gender wage gap of 16.8 %, which is reduced to 1.5 % when we control for employer, worker, and project characteristics. Once we include workers’ wage proposals in our agreed-upon wage regressions, the gender wage gap virtually disappears (0.2 %) and is statistically insignificant. This suggests that gender differences in wage expectations pertain to gender differences in wages.

Two's company, platforms make a crowd: Talent identification in tripartite work arrangements in the gig economy

The gig economy provides a novel setting that challenges many established ways of working. This paper unpacks the nature of talent identification in the gig economy through the role of three central actors; the online labor platform firm, the requester/customer and the gig worker. Talent identification in this context is especially novel as it emerges from tripartite relationships among independent economic actors, in contrast to traditional settings where talent identification is studied from a dyadic perspective (i.e., talented workers and the organization). We decipher the heterogeneity across online labor platforms and their gig workforces through the practice of talent identification. We provide an agenda to guide future research on the inclusive versus exclusive nature of talent identification in the gig economy as well as on online labor platforms as independent, yet powerful players who identify talents themselves alongside shaping talent identification processes between workers and hiring organizations. Accordingly, this paper extends the parameters of talent identification scholarship along with providing a different lens by which we examine work in the gig context.

Informing or persuading? Unveiling the complex dynamics of live chat in online labor markets

This paper delves into the impact of live chat signals, manifested by persuasive and informative signals, on contract decision and the moderating role of business familiarity. Utilizing a dataset from a Chinese online labor market, our results confirm that politeness, response frequency, and reply speed positively influence contract decision. Regarding the moderating effects, business familiarity undermines the impact of politeness and enhances that of reply speed on contract decisions. The implications are as follows: first, this paper is among the early studies to empirically explore the live chat mechanism in online labor platforms. Second, this paper integrates the differential roles of live chat signals and business familiarity into a comprehensive model to specify the intricacies involved in the signal-behavior linkages. Third, our study illustrates how live chat works and prescribes how to effectively use this tool to bring benefits to vendors and platform managers, thus further extending the literature regarding live chat.

The duality of global online labour platforms as restrictive‐expansive sites of workplace learning and skill development

AbstractThe paper analyses global online labour platforms (OLPs) through the lens of the Expansive‐Restrictive Learning Environments framework. The framework articulates a set of structural factors that enable or constrain workplace learning and development. The paper draws on multistakeholder, mixed‐method empirical data to illustrate how OLPs are emerging as learning environments, where new and reconfigured skills, learning practices, and new forms of learning support emerge in response to the radically distributed and fragmented nature of this work. Against portrayals of OLPs as places of deskilling work devoid of learning opportunities, the paper contributes a more nuanced understanding of the duality of OLPs as simultaneously restrictive and expansive. Three dualities of OLPs emerge from the study: (i) their espoused vision restricts organisational support for workforce development, yet stimulates self‐directed learning; (ii) their enacted workplace curriculum is patchy and opaque, yet offers novel structural features supporting learning and development; (iii) workplace learning practices in OLPs are autonomous, yet not atomised. The paper illustrates how structure and individual agency interact in OLPs to create and configure learning opportunities for workers and informs practitioners about the current learning and development features and practices in OLPs.

Does algorithmic control facilitate platform workers’ deviant behavior toward customers? The ego depletion perspective

Online labor platforms widely implement algorithmic control to ensure that workers consistently deliver quality services. However, extensive evidence suggests that platform workers under the tight monitoring of algorithms still engage in customer-directed deviant behavior, which raises questions about the effectiveness of algorithmic control. Thus, we draw on ego depletion theory to examine the critical issue of why and when algorithmic control fails to reduce workers' undesirable behavior toward customers. This study conducted a three-phase online questionnaire survey with 377 ride-hailing drivers in China. Data were analyzed using the PROCESS macro model. The results show that algorithmic control excessively drains workers' limited self-control resources and drives them into an ego-depleted state with low control ability, which further creates conditions for more deviance. Algorithmic transparency alleviates the influence of algorithmic control on ego depletion, whereas financial dependence on platform work mitigates the impact of ego depletion on customer-directed deviance. The indirect effect of ego depletion is most pronounced when both algorithmic transparency and financial dependence are lower. We shed light on the mediating mechanism and boundary conditions of the unexpected facilitating influence of algorithmic control on workers’ customer-directed deviant behavior, providing feasible directions for optimizing algorithmic control system design and reducing customer-directed deviant behavior.

The duality of reputation portability: Investigating the demand effect of imported ratings across online labor markets

Online labor platforms have been criticized for fueling precarious working conditions. Due to their platform-bound reputation systems, switching costs are prohibitively high and workers are locked-in to the platforms. One widely discussed approach to addressing this issue and improving workers’ position is the portability of reputational data. In this study, we conduct an online experiment with 239 participants to test the effect of introducing reputation portability and to study the demand effect of imported ratings. We find that the volume of imported ratings stimulates demand, although to a lower degree than onsite ratings. Specifically, the effect of imported ratings corresponds to about 35% of the effect of onsite ratings. The results imply the possibility of unintended cross-market demand concentration effects that especially favor workers with high rating volumes (“superstars”).

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

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. To address this gap, drawing upon the transactional theory of stress and regulatory focus theory, this study sheds light on how perceived algorithmic control affects the platform workers’ service performance. Data collected from 286 platform workers was used for empirical study. Findings indicate: (1) perceived algorithmic control indirectly positively affects service performance through job crafting; (2) perceived algorithmic control indirectly negatively affects service performance through withdrawal behavior; (3) the indirect effect of perceived algorithmic control on service performance via job crafting is stronger when there is a high promotion focus and weaker in the case of high prevention focus; and (4) the indirect effect of perceived algorithmic control on service performance via withdrawal behavior is weaker in situations of high promotion focus and stronger in those of high prevention focus. The theoretical and practical implications are also discussed in this work.

Beyond Categories: A Flow-Oriented Approach to Social Justice on Online Labour Platforms

Online labour platforms (OLP) are transforming long-established employment relations, raising questions for researchers and policymakers alike as to the social justice implications of this increasingly pervasive, algorithmic, and platform-mediated form of work. In investigating this issue, this paper makes a case for complementing current category-based approaches to social justice, prevalent in literature and policy on OLPs, with a flow-oriented approach that recognises the diversity of gig-work trajectories when it comes to the situated enactment of social (in)justice. Inspired by the recent work of Tim Ingold and building upon seminal work on social justice by Rawls and Sen, we develop a synthetic framework for revealing the social justice implications of OLPs, in terms of the enactment of opportunities and/or barriers, from three perspectives: access to resources, capabilities to function, and correspondences with flows. The latter perspective temporally reinterprets the former two and offers a processual flow-oriented approach to social justice. We further substantiate and showcase the value-added of our approach through an empirical investigation of different gig-working stories on the Amazon MTurk platform and discuss how our flow-oriented approach leads to revealing social justice implications not foregrounded through other approaches. Finally, we derive remedial design and policy principles that can serve to reshape dialogue about social justice on OLPs, both theoretically and practically, in a manner that is more relevant and responsive to the fluid and evolving realities faced by gig-workers.

A bibliometric analysis and future research agenda for online labour platforms

Online labour platforms (OLPs) are profit-oriented companies utilising technology to connect independent contractor workers with short-term service labour needs, offering a digital marketplace for posting tasks, receiving bids, and finalising agreements across various domains such as writing, design, programming, and digital marketing. This paper presents a bibliometric analysis of the concurrent body of knowledge on OLPs, explaining how the specificities of this emerging form of labour are researched from various academic standpoints. The study is based on a dataset of 358 papers on OLPs published from 2013 to August 2023. The main finding of the study is that scholarly interest in OLPs is steadily growing in the observed period. However, geographical dispersion of the scientific output is not in line with the actual level of utilisation of OLPs, and scholarly interest in OLPs spans across diverse disciplines, including industrial relations and labour, management, economics, sociology, law, and computer science. The results of this paper can help better understand the dynamics of scholarly publishing on OLPs and further leverage underexploited subtopics in this field.

Algorithmic management of crowdworkers: Implications for workers’ identity, belonging, and meaningfulness of work

Online labor platforms function as meta-organizations, blending elements of an open market and hierarchical structure by algorithmically governing goals and objectives. This study investigates how this algorithmic management approach influences the perceived meaningfulness of work among crowdworkers, with a particular focus on its effects through identity and belonging. Drawing on survey data collected from 1291 crowdworkers, our findings highlight that algorithmic control and algorithmic matching are differently associated with the meaningfulness of work. Algorithmic control, characterized by directive oversight, exhibits a negative association with perceived meaningfulness. In contrast, algorithmic matching, which pairs workers with online tasks, fosters a positive perception of meaningfulness. We demonstrate that these associations are (partially) mediated by identity and belonging mechanisms. Specifically, we demonstrate that crowdworkers tend to self-organize support and social confirmation using online communities, which provides a sense of meaningfulness. This research advances our understanding of the experiences of crowdworkers within online labor platforms, shedding light on the multifaceted implications of algorithmic management. It emphasizes the importance of fostering supportive and communicative environments in work settings characterized by algorithmic governance mechanisms.

Should Gig Platforms Decentralize Dispute Resolution?

Problem definition: Disputes on online labor platforms have traditionally been mediated by the platform itself, which is often viewed as unhelpful or biased. However, there are emerging platforms that promise to resolve disputes with a novel tribunal system and relegate dispute resolution to individual platform users through a voting mechanism. We aim to examine the dispute resolution systems used by traditional platforms (i.e., the centralized dispute system) and emerging platforms (i.e., the decentralized dispute system) in order to assess whether the latter has an advantage over the former. Methodology/results: We use game theory to analyze both the centralized and decentralized dispute systems, and we model the tribunal’s voting game using the global games framework. Our findings indicate that in order to achieve a fair voting outcome, it is crucial to have sufficient heterogeneity in the assessments of tribunal members. Moreover, the decentralized dispute system outperforms the centralized dispute system only when the freelancer’s skill level is sufficiently high. Lastly, the decentralized dispute system has the potential to induce a more socially optimal quality level from the freelancer. Managerial implications: Our findings provide insights on the optimal adoption and implementation of the decentralized dispute system. The decentralized dispute system is more effective for tasks that involve subjective evaluations, and platforms should avoid strategies that homogenize the assessments of tribunal members. Moreover, platforms should consider switching to the decentralized dispute system only if they are able to verify the skill level of freelancers through certification or other means. Lastly, the decentralized dispute system may be more appealing to policy makers because of its potential to induce a more socially optimal outcome. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0398 .

Kicking off a Gig Work Career: Unfolding a Career Learning Cycle of Gig Workers.

We observe gig workers' retrospective sense-making of their career development, from creating an account on online labor platforms to managing gigs successfully. Our data reveals that gig workers advance through three career stages in their initial career learning cycle. We identify each stage as characterized by stage-specific emotions and that they react with specific behaviors to gig work challenges. Gig work challenges that occur in the platform environment are namely the newbie challenge, the positioning and relational challenge, and the balancing challenge, which workers need to overcome in order to transition to the next stage. In line with contemporary career and protean career theory on career learning cycles, gig workers need to build a set of specialized skills and meta-competencies to successfully navigate their careers. As an outcome of the here-described career learning cycle, gig workers develop an entrepreneurial identity aspiration, as they are empowered and can use the platform as a playground or stepping stone for entrepreneurial activities. Our paper, thus, develops an understanding of gig workers' initial career learning cycle by examining the factors that enable gig workers to kick off a career and allow them to thrive and advance on the platforms professionally.

‘If we lower our responsiveness, the algorithm likes us less’. A biographical perspective on (losing) control in the platform economy

ABSTRACT While there have been numerous – mostly qualitative – studies on the experience and the conditions of platform labour, the exploratory potential of biographical research has so far been overlooked. This article responds to this gap in research by presenting a case study of a French crowdworker, who was interviewed in the context of a larger European research project on precariousness and unpaid labour. On the basis of her autobiographical narrative, the authors reconstruct how her relationship with two online labour platforms evolved throughout the course of two years. The aim of this article is to illustrate the benefits of adopting a longitudinal, process-oriented perspective on the experience of online gig work in a biographical context. By giving informants the time and space to openly narrate and reflect about how their lives evolved, it also becomes possible for them to work through difficult past experiences and gain new perspectives. Furthermore, this research approach can be a tool to amplify ‘voices from below’ as a corrective against powerful corporate strategies. The authors aim to make the process of interpreting autobiographical narrative interviews transparent by highlighting the analytical relevance of selected formal textual features.

Putting Online Labor Platform Workers Front and Center: Successful Career Development Across Time

Putting Online Labor Platform Workers Front and Center: Successful Career Development Across Time

Does Mood affect Sexual and Gender Discrimination in Hiring Choices? Evidence from Online Experiments

We explore whether there is a link between mood and hiring decisions. This research examines how positive mood affects the discrimination faced by homosexual and female job candidates compared to heterosexual and male ones. We randomly assign respondents to one of two mood-inducing videos (positive and neutral), and we allow subjects to make a series of hiring choices prior and immediately after watching the mood-inducing video. Our experiment being conducted in the online labor platform Amazon Mechanical Turk, allows us to track the complete hiring process and monitor employers’ behavior within and without our treatment context. Constructing pairs of curriculum vitae, distinguished only by the sexual orientation or the gender of the applicants in each case, leads to the observation that women and gay men faced a significantly lower chance of getting hired. We also find that female employers proposed higher levels of discrimination only in the case of female applicants. Our positive mood manipulation leads to a decrease of discrimination levels. Thus, there is substantial experimental evidence to suggest that discrimination based on sexual orientation and gender also exists in online labor markets. An additional experiment with negative mood manipulation, also, gives evidence for the opposite direction of the effects, contributing to a broader picture of the relationship between mood and discrimination behavior. Contributions to the literature on hiring discrimination, mood research and the online economy are discussed.

Precarious Participants, Online Labour Platforms and the Academic Mode of Production: Examining Gigified Research Participation

In an economic environment defined by precarious and gig-based labour contracts, academic research has been reimagined as a source of income for research participants. In addition, with the rise of online labour platforms, researchers have turned to online labour platforms as a solution to the increasing difficulty in recruitment of participants in research. This present context makes explicit the hidden labour that research participants have always done in the production of research outputs within academia. This paper develops a Marxist lens through which we can understand the material conditions of the circulation of capital through academia and the role of research participants in this mode of production. By developing this broad analytical framework for the academic mode of production, this paper further argues that our present economic epoch of the gig economy and specifically the use of digital labour platforms for academic research, has accelerated the subsumption of research participation as a source of income through the fragmentation of work and the gigification of everyday life.