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With the rapid growth of the gig economy worldwide, gig workers’ perceived algorithmic control has been proven to have a crucial impact on the service performance, well-being and mental health of gig workers. However, the literature suggests that gig workers’ perceived algorithmic control may be a double-edged sword. The purpose of this research is to explore how the perceived algorithmic control of gig workers can accelerate thriving at work.

Based on the model of proactive motivation and work design literature, a three-wave survey was employed, yielding 281 completed responses. The structural equation modeling method was used to test the theoretical hypothesis.

The results indicate that gig workers’ perceived algorithmic control has positive and indirect effects on thriving at work through the mediating role of job crafting. In addition, job autonomy can moderate the mediated relationship; specifically, when job autonomy is high, this mediated relationship will be stronger.

The health and well-being of gig workers is a concern around the world. The findings provide insights for service platform enterprises and gig workers.

Perceived algorithmic control is critical to mental health and positive work experiences during a gig worker’s service process. However, the current literature focuses more on the negative aspects of algorithmic control. This paper provides a comprehensive research agenda for how to accelerate thriving at work for gig workers.

This paper aims to explore the implications of algorithmic management on careers and employment relationships in the Nigerian gig economy. Specifically, drawing on labour process theory (LPT), this study provides an understanding of the production relations beyond the “traditional standard” to “nonstandard” forms of employment in a gig economy mediated by digital platforms or digital forms of work, especially on ride-hailing platforms (Uber and Bolt).

This study adopted the interpretive qualitative approach and a semi-structured interview of 49 participants, including 46 platform drivers and 3 platform managers from Uber and Bolt.

This study addresses the theoretical underpinnings of the LPT as it relates to algorithmic management and control in the digital platform economy. The study revealed that, despite the ultra-precarious working conditions and persistent uncertainty in employment relations under algorithmic management, the underlying key factors that motivate workers to engage in digital platform work include higher job flexibility and autonomy, as well as having a source of income. This study captured the human-digital interface and labour processes related to digital platform work in Nigeria. Findings of this study also revealed that algorithmic management enables a transactional exchange between platform providers and drivers, while relational exchanges occur between drivers and customers/passengers. Finally, this study highlighted the perceived impact of algorithmic management on the attitude and performance of workers.

The research presents an interesting case study to investigate the influence of algorithmic management and labour processes on employment relationships in the largest emerging economy in Africa.

A growing body of research finds that gig economy platforms use gamification to enhance managerial control. Focusing on technologically mediated forms of gamification, this literature reveals how platforms mobilize gig workers’ work effort by making the labour process resemble a game. This chapter contends that this tech-centric scholarship fails to fully capture the historical continuities between contemporary and much older occurrences of game-playing at work. Informed by interviews and participatory observations at two food delivery platforms in Amsterdam, I document how these platforms’ piece wage system gives rise to a workplace dynamic in which severely underpaid delivery couriers continuously employ game strategies to maximize their gig income. Reminiscent of observations from the early shop floor ethnographies of the manufacturing industry, I show that the game of gig income maximization operates as an indirect modality of control by (re)aligning the interests of couriers with the interests of capital and by individualizing and depoliticizing couriers’ overall low wage level. I argue that the new, algorithmic technologies expand and intensify the much older forms of gamified control by infusing the organizational activities of shift and task allocation with the logic of the piece wage game and by increasing the possibilities for interaction, direct feedback and immersion. My study contributes to the literature on gamification in the gig economy by interweaving it with the classic observations derived from the manufacturing industry and by developing a conceptualization of gamification in which both capital and labour exercise agency.

This research focuses on how algorithmic management, as a primary method of platform governance, affects job burnout among gig workers. Drawing on self-determination theory, our study examines the various effects of algorithmic management’s aspects on gig workers' job burnout.

This study targeted gig workers (car drivers and food-delivery workers) and was conducted in two waves. Data analysis was facilitated using SPSS 22.0 and MPlus 8.4, a tool for CB-SEM (covariance-based structural equation modeling).

Algorithmic evaluation and discipline increase job burnout by negatively impacting gig workers' basic psychological needs. Algorithmic direction, in contrast, alleviates job burnout by enhancing basic psychological needs among gig workers.

Platform companies should address gig workers’ burnout by implementing advanced algorithmic management and providing autonomy-supportive environments. Adopting human-centric algorithmic practices can strengthen the platform–worker relationship, boost competence and reduce resistance to oversight.

Our study contributes to the literature by examining the various effects of algorithmic management on gig workers. By applying self-determination theory, we provide a novel perspective on understanding the mechanisms of job burnout in the gig economy.

Algorithms are widely used to manage various activities in the gig economy. Online car-hailing platforms, such as Uber and Lyft, are exemplary embodiments of such algorithmic management, where drivers are managed by algorithms for task allocation, work monitoring and performance evaluation. Despite employing substantially, the platforms face the challenge of maintaining and fostering drivers' work engagement. Thus, this study aims to examine how the algorithmic management of online car-hailing platforms affects drivers' work engagement.

Drawing on the transactional theory of stress, the authors examined the effects of algorithmic monitoring and fairness on online car-hailing drivers' work engagement and revealed the mediation effects of challenge-hindrance appraisals. Based on survey data collected from 364 drivers, the authors' hypotheses were examined using partial least squares structural equation modeling (PLS-SEM). The authors also applied path comparison analyses to further compare the effects of algorithmic monitoring and fairness on the two types of appraisals.

This study finds that online car-hailing drivers' challenge-hindrance appraisals mediate the relationship between algorithmic management characteristics and work engagement. Algorithmic monitoring positively affects both challenge and hindrance appraisals in online car-hailing drivers. However, algorithmic fairness promotes challenge appraisal and reduces hindrance appraisal. Consequently, challenge and hindrance appraisals lead to higher and lower work engagement, respectively. Further, the additional path comparison analysis showed that the hindering effect of algorithmic monitoring exceeds its challenging effect, and the challenge-promoting effect of algorithmic fairness is greater than the algorithm's hindrance-reducing effect.

This paper reveals the underlying mechanisms concerning how algorithmic monitoring and fairness affect online car-hailing drivers' work engagement and fills the gap in the research on algorithmic management in the context of online car-hailing platforms. The authors' findings also provide practical guidance for online car-hailing platforms on how to improve the platforms' algorithmic management systems.

As algorithms permeate nearly every aspect of digital life, artificial intelligence (AI) systems exert a growing influence on human behavior in the digital milieu. Despite its popularity, little is known about the roles and effects of algorithmic literacy (AL) on user acceptance. The purpose of this study is to contextualize AL in the AI environment by empirically examining the role of AL in developing users' information processing in algorithms. The authors analyze how users engage with over-the-top (OTT) platforms, what awareness the user has of the algorithmic platform and how awareness of AL may impact their interaction with these systems.

This study employed multiple-group equivalence methods to compare two group invariance and the hypotheses concerning differences in the effects of AL. The method examined how AL helps users to envisage, understand and work with algorithms, depending on their understanding of the control of the information flow embedded within them.

Our findings clarify what functions AL plays in the adoption of OTT platforms and how users experience algorithms, particularly in contexts where AI is used in OTT algorithms to provide personalized recommendations. The results point to the heuristic functions of AL in connection with its ties in trust and ensuing attitude and behavior. Heuristic processes using AL strongly affect the credibility of recommendations and the way users understand the accuracy and personalization of results. The authors argue that critical assessment of AL must be understood not just about how it is used to evaluate the trust of service, but also regarding how it is performatively related in the modeling of algorithmic personalization.

The relation of AL and trust in an algorithm lends strategic direction in developing user-centered algorithms in OTT contexts. As the AI industry has faced decreasing credibility, the role of user trust will surely give insights on credibility and trust in algorithms. To better understand how to cultivate a sense of literacy regarding algorithm consumption, the AI industry could provide examples of what positive engagement with algorithm platforms looks like.

User cognitive processes of AL provide conceptual frameworks for algorithm services and a practical guideline for the design of OTT services. Framing the cognitive process of AL in reference to trust has made relevant contributions to the ongoing debate surrounding algorithms and literacy. While the topic of AL is widely recognized, empirical evidence on the effects of AL is relatively rare, particularly from the user's behavioral perspective. No formal theoretical model of algorithmic decision-making based on the dual processing model has been researched.

Organizations are increasingly deploying algorithmic human resource management (HRM) for decision-making. Despite algorithms beginning to permeate HRM practices, our understanding of how to interpret and leverage the functions of algorithmic HRM remains limited. This study aims to review the stock of knowledge in this field of algorithmic HRM and introduce a theoretical perspective of functional affordance to enhance the understanding of the value of algorithmic HRM.

A systematic literature review was conducted in this study based on 283 articles. The articles are extracted from the Web of Science and Scopus. The content of the articles was then integrated to formulate the framework for this study.

Functional affordance highlights algorithmic HRM can be systematically embedded within the organizational environment, with its characteristics naturally suggesting the functionalities or actions available for HR managers to choose from. The findings of this study demonstrate five features of algorithmic HRM from the perspective of functional affordance: awareness of algorithmic HRM, alignment with business model design, action readiness, adaptation to business context and attribution to individuality.

This study provides a novel perspective for understanding the insufficiently theorized application of algorithmic HRM within organizations. It presents an integrated framework that elucidates the key features of algorithmic HRM and elaborates on how organizations can better develop algorithm-driven capabilities based on functional affordance.

The rise of app-based work in the gig economy, particularly within the food delivery sector, challenges traditional employment paradigms and raises questions about the potential for achieving meaningful work experiences. This study explores whether such work can be considered meaningful for food delivery couriers in Poland.

This research adopts a qualitative, case-study approach, conducting 30 in-depth interviews with food delivery couriers in Poland. The study investigates how these workers perceive the meaningfulness of their work, focusing on the interplay between subjective and organisational aspects of their work.

The findings reveal that despite the precarious nature of app-based work, couriers often find meaningful experiences through perceived autonomy, gamified control and the physical demands of their job. The study highlights the dual nature of app work, where the same elements that contribute to worker engagement and a sense of independence also perpetuate exploitation and job insecurity.

The study’s reliance on a convenience sample of 30 interviews conducted via social media may not represent the broader population of food delivery couriers. Future research should expand the sample size and include a more diverse range of participants to improve generalisability.

The insights from this study can inform platform designers and policymakers to create more supportive environments for gig workers. Enhancing algorithmic transparency, providing better social protections and implementing fair gamification strategies can help mitigate the negative aspects of gig work and improve job satisfaction.

The study underscores the need for regulatory changes to ensure minimum guaranteed earnings and health and safety provisions for gig workers. By fostering a supportive and transparent work environment, the gig economy can better contribute to worker well-being and social equity.

This research contributes to the limited body of literature on meaningful work within the gig economy, particularly focusing on food delivery couriers in Poland. It provides new insights into how workers create and perceive meaningful work in a highly digitised and algorithmically managed environment.

This study investigates perceptions of the use of online tracking, a passive data collection method relying on the automated recording of participant actions on desktop and mobile devices, for studying information behavior. It scrutinizes folk theories of tracking, the concerns tracking raises among the potential participants and design mechanisms that can be used to alleviate these concerns.

This study uses focus groups composed of university students (n = 13) to conduct an in-depth investigation of tracking perceptions in the context of information behavior research. Each focus group addresses three thematic blocks: (1) views on online tracking as a research technique, (2) concerns that influence participants' willingness to be tracked and (3) design mechanisms via which tracking-related concerns can be alleviated. To facilitate the discussion, each focus group combines open questions with card-sorting tasks. The results are analyzed using a combination of deductive content analysis and constant comparison analysis, with the main coding categories corresponding to the thematic blocks listed above.

The study finds that perceptions of tracking are influenced by recent data-related scandals (e.g. Cambridge Analytica), which have amplified negative attitudes toward tracking, which is viewed as a surveillance tool used by corporations and governments. This study also confirms the contextual nature of tracking-related concerns, which vary depending on the activities and content that are tracked. In terms of mechanisms used to address these concerns, this study highlights the importance of transparency-based mechanisms, particularly explanations dealing with the aims and methods of data collection, followed by privacy- and control-based mechanisms.

The study conducts a detailed examination of tracking perceptions and discusses how this research method can be used to increase engagement and empower participants involved in information behavior research.

Purpose – This chapter considers the economic and political relationship between artificial intelligence tools such as facial recognition software and Lesbian, Gay, Bisexual, Transgender and Queer (LGBTQ) identity construction and identification. In doing so, the chapter considers the threats and opportunities to diverse LGBTQ identities from algorithmic governance.

Methodology/approach – The author analyzes public discourse on these issues and its relationship to agency for LGBTQ communities. The conceptual approach integrates research into surveillance capitalism and neuroliberalism with “digiqueer” criminology to map the relationship between digital media technologies, institutional legitimacy and negotiations for LGBTQ rights, recognition and resources.

Findings – The discussion shows that the surveillance capitalist principles of blurred consent and redistributed privacy are underpinned by geopolitical and technological forces that have undermined the legitimacy of governments and big tech companies. LGBTQ community resistance to harms perpetrated through digital media platforms is one positive consequence of the ambiguities of surveillance capitalism, but which also reflects the investment required by such communities to secure basic protections that the general population might take for granted.

Originality/value – Research into the relationship between recognition and redistribution through access to rights granted to different social groups on the basis of sexuality, sexual expression and identity is under-interrogated. This chapter responds to that gap with a focus on the role that digital media technologies can play in securing recognition and redistribution of resources for LGBTQ communities, or the significance of their absence and/or diminution in current contexts.