Navigating algorithmic bias and data ethics in the gig economy: Balancing transparency and privacy

PurposeAlgorithms 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.Design/methodology/approachDrawing 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.FindingsThis 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.Originality/valueThis 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.

The changing employment scenario has given rise to new employment models like the Gig Economy. Though autonomy and flexibility are key factors that attract women to this employment, their representation is low. This is explored by identifying the state of gender representation and the challenges faced by women in different gig platforms through a systematic literature review. The review is conducted using the PRISMA flowchart. Using Scopus and Web of Science, a total of 23 papers were used for the preparation of the final review. The result of the review shows that, in the gig economy too, women are more represented in already gender–biased jobs, such as childcare, and tutoring, and the gender earnings gap is prevalent in all types of gig platforms. Algorithmic biases and security and safety concerns are also identified in the platform economy. This study advances the literature on gender inequality in the workplace.

Purpose This study examines how gig economy participation drives startup innovation, especially in resource-constrained settings. Design/methodology/approach Drawing on job characteristics theory (JCT), resource-based view (RBV) and upper echelons theory (UET), this study integrates multi-level perspectives to explain how gig economy participation fosters startup innovation. Empirical insights are derived from data on 5,727 entrepreneurs from the Global Entrepreneurship Monitor (GEM). Findings Results show that gig economy participation significantly enhances innovation, particularly in emerging economies. These insights guide entrepreneurs and policymakers in leveraging the gig economy to drive innovation. Research limitations/implications Our findings confirm a significant positive relationship between entrepreneurial participation in the gig economy and startup innovation performance. This not only supports but extends prior work by conceptualizing the gig economy as a strategic resource within the resource-based view (RBV) of the firm. In doing so, the study contributes to a paradigm shift in understanding how startups mobilize and exploit external resources in digitally mediated environments. By redefining gig work as an innovation-enabling input rather than a labor cost alternative, this research advances theoretical conversations around entrepreneurial resource orchestration in the digital era. Practical implications For practitioners, this study underscores the strategic value of engaging with the gig economy to enhance innovation. Entrepreneurs and startup leaders are encouraged to integrate gig-based labor not only for operational flexibility but also as a source of specialized skills and rapid iteration. Importantly, psychological traits such as entrepreneurial self-efficacy must be managed to ensure optimal use of external resources. Furthermore, firms should consider aligning gig work structures with the principles of job characteristics theory (JCT), emphasizing autonomy, task identity and skill variety. Doing so can improve performance, foster creativity and enhance both participant development and organizational innovation outcomes. Social implications For policymakers and regulatory bodies, our findings highlight the gig economy’s potential to strengthen entrepreneurial ecosystems, particularly in underdeveloped and emerging economies where formal resource channels are limited. A supportive and inclusive gig economy infrastructure – guided by oversight from government and industry associations – can improve worker satisfaction, create meaningful employment opportunities, and promote inclusive national development. Effective policy must therefore strike a balance between enabling innovation and safeguarding fair labor standards, ensuring that gig platforms contribute not only to entrepreneurial performance but also to social equity and economic resilience. Originality/value The study clarifies how individual and institutional factors shape the gig–innovation link and provides strategic recommendations to maximize gig economy benefits, particularly across emerging markets, thus offering actionable insights for entrepreneurs, policymakers and platform designers.

Problem definition: We study the impact of bonus strategies on gig platforms and their welfare implications. We consider two types of bonus strategies used by gig platforms: 1) fixed bonus that is paid in addition to commissions as long as a service provider participates, and 2) contingent bonus that is paid only if a service provider participates consistently over time. Academic/practical relevance: The success of a gig platform is crucially driven by its ability to retain service providers. However, gig workers are independent contractors whose working schedules are not fully controlled by the platform. To overcome this challenge, gig platforms in reality have commonly relied on bonus strategies to drive participation of gig workers. Methodology: We develop a game theory model to study platform competition with bonus strategies. Results: Our analysis shows that the two types of bonuses will arise in equilibrium under different market conditions. First, when labor supply is thick relative to demand, fixed bonus will be offered. In this case, fixed bonus improves platform profit by eliminating a prisoner’s dilemma that arises when the platforms compete only on commissions. However, social welfare will be reduced because platform competition is softened. Second, when labor supply is thin relative to demand, contingent bonus will be offered. In this case, contingent bonus reduces platform profit because it intensifies platform competition and traps the platforms in a prisoner’s dilemma where they are forced to offer too much bonus. It further causes inefficiency in matching labor supply with demand and hence reduces social welfare. Managerial implications: First, bonus competition can lead to opposite impacts on gig platforms depending on the market condition. Second, social planners should be cautious about gig platforms’ use of bonus strategies in general.

The algorithm-based management exercised by digital gig platforms contributes to information and power asymmetries that are pervasive in the gig economy. Although the design of these platforms may foster unbalanced relationships, in this paper, we outline how freelancers and clients on the gig platform Upwork can leverage a network of alliances with external digital platforms to repossess their displaced agency within the gig economy. Building on 39 interviews with Upwork freelancers and clients, we found a dynamic ecosystem of digital platforms that facilitate gig work through and around the Upwork platform. We use actor-network theory to: 1) delineate Upwork's strategy to establish a comprehensive and isolated platform within the gig economy, 2) track human and nonhuman alliances that run counter to Upwork's system design and control mechanisms, and 3) capture the existence of a larger ecosystem of external digital platforms that undergird online freelancing. This work explicates the tensions that Upwork users face, and also illustrates the multiplicity of actors that create alliances to work with, through, around, and against the platform's algorithmic management.

The gig economy is an expanding sector that extensively utilizes mobile digital apps to connect freelancers with customers. This article is a review-based article that aims to examine the potential of the gig platform and identify possible regulatory strategies for Nepal's future development of the gig economy. The gig economy provides opportunities for earning revenue, enhances the flexibility of workers, and contributes to employment generation in the country's economy. However, gig workers lack benefits such as a minimum wage, paid maternity leave, social security payments, and protection against occupational and health hazards. There is a lack of suitable legislation to effectively regulate the gig economy. The rise of Nepal's gig economy is disrupted. It is imperative to formulate precise legislation that specifically addresses the rights and working conditions of gig workers. It is imperative for Nepal to establish comprehensive legal frameworks to effectively regulate the gig economy. The government should introduce measures to enhance working conditions and offer comprehensive insurance programs to ensure that gig workers do not experience discrimination compared to traditional workers.

PurposeThe alternative arrangements to traditional employment have become a promising area in the gig economy with the technological advancements dominating every work. The purpose of this paper is to explore the barriers to the entry of gig workers in gig platforms pertaining to the food delivery sector. It proposes a framework using interpretive structural modelling (ISM) for which systematic literature review is done to extract the variables. This analysis helps to examine the relationship between the entry barriers to gig platforms. The study further proposes strategies to reduce the entry barriers in gig sector which would help to enhance productivity and generate employment opportunities.Design/methodology/approachThe study uses interpretive structural model (ISM) to ascertain the relationship between various entry barriers of the gig workers to the gig platforms. It also validates the relationship and understand the reasons of their association along with MICMAC analysis. The model was designed by consulting the gig workers and the experts allied to food delivery gig platforms namely Zomato and Swiggy.FindingsIt was observed that high competition, longer login hours and late-night deliveries are the significant barriers with high driving power and low dependence power. Poor payment structures and strict terms and conditions for receiving the incentives are interdependent on each other and have moderate driving and dependence power. The expenses borne by the gig workers, such as Internet, fuel and vehicle maintenance expenses have high dependence power and low driving power. Hence, they are relatively less significant than other barriers.Research limitations/implicationsThe study is confined to food delivery sector of India, without considering other important sectors of gig economy for generalizing the framework. As the study is based on forming an ISM framework through literature review only, it does not consider other research methods for analysing the entry barriers to the gig platforms.Practical implicationsThe study attempts to dig out the low entry barriers for gig workers in food delivery platforms as there is a dearth of analysis of these factors. This study would weave them using ISM framework to help the gig platforms overcome these barriers at various levels, thus adding to the body of literature.Originality/valueThe study discusses the need for understanding relationship between the entry barriers in the form of ISM model to identify the dependent and driving factors of the same.

This study explores the impact of online gig platforms like TaskRabbit on the employment of incumbent service workers, focusing on the housekeeping sector. It highlights how TaskRabbit’s entry correlates with a decrease in middle-skilled roles such as supervisors due to automation, whereas low-skilled jobs like janitors remain stable because of their manual nature. Notably, many middle-skilled workers transition to self-employment within the sector instead of facing layoffs. Amidst competing narratives about the gig economy’s influence on labor markets, this research offers critical insights into how gig platforms redistribute the workforce and create new employment opportunities, suggesting significant implications for policymakers and practitioners. Policymakers should recognize the shift from traditional roles to self-employment facilitated by these platforms, which enhances local entrepreneurship. Meanwhile, it is crucial for them to ensure fair working conditions, wages, and benefits. Practitioners can see that gig platforms not only improve operational efficiency but also enable middle-skilled workers to launch their own businesses, indicating a potential strategy for new platforms looking to increase their service offerings and client base. Traditional companies, in response, need to adapt their job designs, incentive structures, and company culture to align with the changing needs for flexibility and autonomy in the gig economy.

Introduction: The growth of gig work has transformed the contemporary labor market, provided flexibility and independence but also subjected workers to high stressors like job insecurity, economic uncertainty, and irregular workloads. Gig workers work in various sectors, such as ridesharing, food delivery, freelancing, and on-demand services, and usually encounter distinctive occupational hazards. Gig Workers' Well-being (GWW) Model, based on the Job Demands-Resources (JD-R) model and Psychological Capital (PsyCap) theory, presents a systematic approach to understanding job demands and resources and their effect on stress resilience, mental well-being, and job performance among gig workers. This research explores how resilience interventions can reduce stress and improve work engagement and productivity among gig workers. Objectives: The main aim of this research is to create and test a multi-dimensional stress resilience framework that explains the dynamic interaction of multiple influences on wellbeing among gig workers. More specifically, the research intends to explore how job demands can intensify burnout while job resources promote work engagement, and to investigate the dual nature of gig-related factors that can benefit or detract from job performance. Furthermore, the study aims to investigate the contribution of technostress in exacerbating burnout, evaluate the effectiveness of boundary management in enhancing work engagement, and identify the degree to which resilience is responsible for enhanced job performance. Through this holistic examination, the study aims to offer detailed insights into the stress and coping processes in the gig economy, ultimately guiding strategies to enhance the wellbeing and performance of gig workers. Methods: A qualitative research design was used to obtain rich insights into the well-being of gig workers. A stratified random sampling strategy provided representative diversity across work categories, demographics, and geographic regions. Data were collected through structured online questionnaires distributed through LinkedIn, social media groups, and gig work platforms. The questionnaire contained validated scales assessing stress levels, financial security, work-life balance, resilience, and well-being. 400 questionnaires were distributed and 338 returned (84.5% response rate). After excluding incomplete or invalid responses, the final sample of 323 gig workers was analyzed. Statistical analysis using IBM SPSS 25.0, including correlation and regression analysis, was conducted to examine the relationships between job demands, burnout, resilience, and work engagement. Results: Correlation analysis indicated significant relationships between the most important variables. Job demands were positively correlated with job resources (r =.900, p <.01), indicating that as stressors, resource needs also increased. Burnout had significant correlations with job demands (r =.489, p <.01) and technostress (r =.804, p <.01), supporting the negative effect of workload unpredictability. Regression analysis showed that job demands strongly predicted burnout (β = 0.489, R² = 0.239, p <.000), whereas job resources positively affected work engagement (β = 0.415, R² = 0.172, p <.000). Gig-specific factors also significantly contributed to the formation of job performance (β = 0.368, R² = 0.135, p <.000). In addition, technostress strongly raised levels of burnout (β = 0.476, R² = 0.227, p <.000), whereas resilience helped job performance to the extent of β = 0.499, R² = 0.289, p <.000, highlighting its significance when managing stress. Conclusions: The results highlight the intricate interaction between job demands, resilience, and performance in the gig economy. Digital stressors and high job demands are sources of burnout, which is detrimental to well-being. Nevertheless, job resources such as autonomy, skill development, and social support improve work engagement and reduce stress. The GWW Model highlights the importance of resilience in mediating stress outcomes and proposes that interventions like boundary management, adaptive coping, and financial security programs can enhance mental health and performance. Policy measures should aim to augment gig workers' access to social security, training, and mental health care. By building resilience, gig platforms can establish a more sustainable and supportive work culture, guaranteeing long-term well-being and productivity.

With the global rise of the gig economy, platform-based jobs – such as food delivery riders, ride-hailing drivers (e.g. Didi), and social media content creators – have become key employment opportunities for people with disabilities. In China, rapid digital economic development has made gig work a crucial income source for this population. However, this seemingly flexible work also reveals persistent structural barriers, including entrenched discrimination, algorithmic bias, intersectional oppression, and inadequate barrier-free infrastructure. These challenges are not unique to China but are shared by gig workers with disabilities globally. This article critically examines the lived experiences and structural constraints of people with disabilities in China’s gig economy. It calls for inclusive reforms in policy and platform design by governments, corporations, and civil society to build a more equitable employment ecosystem for people with disabilities worldwide.

The gig economy's defining characteristics—real-time fulfillment, decentralized operations, and rapid payment cycles—create ideal conditions for sophisticated fraud schemes. This article examines the architectural frameworks and technical approaches required to implement effective AI-driven fraud prevention systems within gig platforms. Through analysis of the unique fraud landscape in gig environments, it explores multi-layered detection methodologies combining rule-based systems, statistical anomaly detection, machine learning classifiers, and graph analytics to identify fraudulent behaviors. The article details key architectural components including stream processing for live data ingestion, hybrid detection approaches, low-latency model serving infrastructure, decision orchestration, and comprehensive audit trails. Using a food delivery platform implementation as a case study, the article illustrates how these components function cohesively to detect and prevent fraud in real-time. Technical challenges including balancing speed with accuracy, ensuring algorithmic fairness, and scaling with platform growth are addressed alongside practical implementation considerations for data persistence, computational resource management, and API design. Finally, emerging technologies including federated identity solutions, behavioral biometrics, explainable AI, and privacy-preserving computation are evaluated for their potential to transform fraud prevention capabilities in gig economy environments

The success of a gig platform is crucially driven by its ability to compete for labor supply. However, gig workers are independent contractors whose working schedules are not fully controlled by the platform. To overcome this challenge, gig platforms have commonly relied on bonus strategies to drive the participation of gig workers. We study the impact of bonus strategies on gig platforms and their welfare implications. We consider two types of bonus strategies used by gig platforms: 1) fixed bonus that is paid in addition to commissions as long as a service provider participates, and 2) contingent bonus that is paid only if a service provider participates consistently over time. We develop a game theory model to study platform competition with bonus strategies. Our analysis shows that the two types of bonuses will arise in equilibrium under different market conditions. First, when labor supply is thick, fixed bonus will be offered. In this case, fixed bonus improves platform profit by eliminating a prisoner’s dilemma that arises when the platforms compete only on commissions. However, social welfare will be reduced because the utilization of the labor supply is reduced due to the softened platform competition. Second, when labor supply is thin, contingent bonus will be offered. In this case, contingent bonus reduces platform profit because it intensifies platform competition and traps the platforms in a prisoner’s dilemma where they are forced to offer too much bonus. It further causes inefficiency in matching labor supply with demand and hence reduces social welfare.

The gig economy reshapes the traditional employment frameworks, providing flexible job opportunities across different industries. Hence, the gig employees encounter various satisfaction levels and challenges depend on their engagement patterns. This study collects real-time data among the gig workers. The 201 respondents are responded to this study. Initially, Exploratory Data Analysis (EDA) is performed to understand the trends within the dataset and discovers the meaningful pattern among the gig workers. EDA assists knowing the insights such as various gig platforms, working hours per week, satisfaction level, and challenges encountered by multiple worker segments. In this study, the K-means Clustering algorithm classifies the gig workers as Active Seekers, Stable Performers, and Low Engaged. These three different cluster separation is based on various key metrics such as work per week, satisfaction level, challenges, and continue-to-work in gig platforms and recommending to another user. The finding shows both Active Seekers and Low Engaged users experiencing huge challenges, due to lack of stable opportunities and workloads. In contrast, Stable Performers tend to have balanced experiences. The study highlights the important inferences for gig platforms, underscoring the necessity health benefits, enhanced worker support and standard policy that encourage sustainable engagement. These findings can help the gig platform to increase the worker retention, improve satisfaction and develop a more resilient gig workspace.

Little is known about whether and how workers with disabilities participate in the many on-demand labor platforms that make up the growing gig economy. Understanding disabled gig workers' experiences is a vital step toward developing inclusive and equitable labor platforms. Through interviews with 24 disabled gig workers and observational fieldwork, we present a rich, in-depth picture of the opportunities and challenges presented by four main types of gig work (ridesharing, delivery, crowdwork, and freelancing) for workers with a wide range of disabilities. We identify a key tension: gig work can be a vital source of needed income for workers who have been excluded from traditional workplaces, but at the same time, the structure of gig platforms present workers with a host of new disability-related challenges, including around algorithmic control and performance evaluation. We discuss how this tension plays out in terms of how workers engage in gig work and protect themselves from risk. We also call attention to how many workers can face complicated, intersectional challenges based on multiple marginalized identities in addition to disability, such as race, gender, sexual orientation, and socioeconomic status. Our work contributes to research on the gig economy by centering the perspectives of workers who are marginalized based on disability and other identities. We show how workers face several penalties based on disability, including shouldering extensive invisible labor to mitigate the challenges they face. Based on our interviews, we suggest several ways that on-demand labor platforms can be designed to be more inclusive of disability, including how to improve the accessibility of various tasks while mitigating the discrimination and negative interactions faced by disabled workers.

Numerous studies have explored the situations of gig workers in the ‘new’ gig economy. While the existing literature focuses on intermediary platforms’ algorithmic control over workers, this paper emphasizes the interpersonal and negotiable aspects of gig worker-customer interactions through an accountability perspective. We posit that algorithmic control and accountability to customers constitute two coordination mechanisms for platform HRM. The latter has gained significance as intermediary platforms adopt algorithmic management and empower customers as visible supervisors. Algorithm-mediated customer control assigns customers direct influence, thereby positioning them as prominent accountability sources and compelling gig workers to meet their expectations. Nevertheless, how gig workers navigate their behaviors under these conditions remains unknown. This study employs a typological approach to explore gig workers’ coping strategies in response to accountabilities, providing micro-level insights into the functionality and dysfunctionality of HRM with gig platforms. Drawing from a qualitative study in the Chinese online delivery industry, we integrate metaphors from cognitive research (i.e. intuitive psychologists and intuitive economists) with the metaphor derived from accountability theory (i.e. intuitive politicians) to develop a fourfold typology grounded in two dimensions (i.e. cognitive modes and economic motives). Our findings reveal both the inevitability and possibility of accountability to customers, identifying four coping strategies (i.e. obedient, ingratiatory, defensive, and cooperative behaviors). This paper offers novel insights into the fields of accountability and HRM research, along with practical implications.