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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.

Existing literature on algorithmic management practices – defined as autonomous data-driven decision making in people's management by adoption of self-learning algorithms and artificial intelligence – suggests complex relationships with employees' well-being in the workplace. While the use of algorithms can have positive impacts on people-related decisions, they may also adversely influence job autonomy, perceived justice and – as a result – workplace well-being. Literature review revealed a significant gap in empirical research on the nature and direction of these relationships. Therefore the purpose of this paper is to analyse how algorithmic management practices directly influence workplace well-being, as well as investigating its relationships with job autonomy and total rewards practices.

Conceptual model of relationships between algorithmic management practices, job autonomy, total rewards and workplace well-being has been formulated on the basis of literature review. Proposed model has been empirically verified through confirmatory analysis by means of structural equation modelling (SEM CFA) on a sample of 21,869 European organisations, using data collected by Eurofound and Cedefop in 2019, with the focus of investigating the direct and indirect influence of algorithmic management practices on workplace well-being.

This research confirmed a moderate, direct impact of application of algorithmic management practices on workplace well-being. More importantly the authors found out that this approach has an indirect influence, through negative impact on job autonomy and total rewards practices. The authors observed significant variation in the level of influence depending on the size of the organisation, with the decreasing impacts of algorithmic management on well-being and job autonomy for larger entities.

While the influence of algorithmic management on various workplace practices and effects is now widely discussed, the empirical evidence – especially for traditional work contexts, not only gig economy – is highly limited. The study fills this gap and suggests that algorithmic management – understood as an automated decision-making vehicle – might not always lead to better, well-being focused, people management in organisations. Academic studies and practical applications need to account for possible negative consequences of algorithmic management for the workplace well-being, by better reflecting complex nature of relationships between these variables.

AI is an emerging tool in HRM practices that has drawn increasing attention from HRM researchers and HRM practitioners. While there is little doubt that AI-enabled HRM exerts positive effects, it also triggers negative influences. Gaining a better understanding of the dark side of AI-enabled HRM holds great significance for managerial implementation and for enriching related theoretical research.

In this study, the authors conducted a systematic review of the published literature in the field of AI-enabled HRM. The systematic literature review enabled the authors to critically analyze, synthesize and profile existing research on the covered topics using transparent and easily reproducible procedures.

In this study, the authors used AI algorithmic features (comprehensiveness, instantaneity and opacity) as the main focus to elaborate on the negative effects of AI-enabled HRM. Drawing from inconsistent literature, the authors distinguished between two concepts of AI algorithmic comprehensiveness: comprehensive analysis and comprehensive data collection. The authors also differentiated instantaneity into instantaneous intervention and instantaneous interaction. Opacity was also delineated: hard-to-understand and hard-to-observe. For each algorithmic feature, this study connected organizational behavior theory to AI-enabled HRM research and elaborated on the potential theoretical mechanism of AI-enabled HRM's negative effects on employees.

Building upon the identified secondary dimensions of AI algorithmic features, the authors elaborate on the potential theoretical mechanism behind the negative effects of AI-enabled HRM on employees. This elaboration establishes a robust theoretical foundation for advancing research in AI-enable HRM. Furthermore, the authors discuss future research directions.

The purpose of this paper is to explore parallels between scientific management and the new scientific management to gain insight into applications of machine learning and artificial intelligence (AI) to human resource management and employee assessment.

Analysis of Taylor’s work and its interpretation by scholars is contrasted with modern analysis of human resource analytics to demonstrate conceptual and methodological commonalities between the old and the new forms of scientific management.

The analysis demonstrates how the epistemology, ethos and cultural trajectory of scientific management has resulted in a mindset that has influenced the implementation and objectives of the new scientific management with respect to human resources analytics.

This paper offers an alternative to the view that machine learning and AI as applied to work and employees are beneficial and points out why important challenges have been overlooked and how they can be addressed.

Commonalties between Taylorism and the new scientific management have been overlooked so that attempts to gain an understanding of how machine learning is likely to influence work, employees and work organizations are incomplete. This paper provides a new perspective that can be used to address challenges associated with applications of machine learning to work design and employee rights.

This article aims to advance a multifaceted framework for preserving algorithms and algorithmic systems in an archival context.

The article is based on a review and synthesis of existing literature, during which the authors observe emergent themes. After introducing these themes, the authors follow each theme as manifest in existing digital preservation projects, starting with algorithms' earliest conceptual starting points and moving up through themes' eventual implementation within a complex social environment.

The authors find current literature is largely divided between that which addresses algorithms primarily as computational artifacts and that which views them instead as primarily social in nature. To bridge this gap the authors propose that “the algorithm,” as the algorithm is frequently deployed in popular discourse, is best understood as not as either the algorithm's technical or social components, but rather the sum total of both.

The study is limited by its methodology as a literature review. However, the findings point toward a new framing for future research that is less divided in terms of social or material orientation.

Creating multifaceted records of algorithms, the authors argue, enables more effective regulation and management of algorithmic systems, which in turn help to improve their levels of fairness, accountability, and trustworthiness.

The paper offers a wide variety of case studies with the potential to inform future studies, while contextualizing the studies together within a new framework that avoids prior limitations.

The 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.

The 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.

It 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.

The 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.

The 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.

The 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.

The purpose of this study is to understand the scope and nature of information sharing as a form of workplace learning among gig workers.

Data were collected from public social media communities of gig workers in Thailand. In total, 338 posts and 3,022 comments on the posts were analyzed (data corpus N = 3,360). Thailand was selected for the context of this study given its high level of social media penetration, a high percentage of digital service consumption of internet users and the prevalence of app-based gig workers. This study used thematic analysis using inductive and semantic coding to generate themes.

Findings showed two overarching themes of information sharing, which included on-the-job experience and inquiries. One surprising finding was the extent to which gig workers used social media to help others even when it potentially undermined their success.

This study adds evidence to the role of information sharing in workplace learning and illustrates how gig workers who do not have access to traditional training and learning opportunities use social media communities to fill this need.

Given the surge of digitalization and internet infrastructure leading to the rise of gig work worldwide, this study provides a closer look at how gig workers are using social media communities to facilitate workplace learning and support one another amid otherwise difficult and insecure working conditions. It also discusses the role that culture plays in facilitating a cooperative rather than a competitive environment among drivers.

This paper aims to investigate algorithmic governmentality – as proposed by Antoinette Rouvroy – specifically in relation to law. It seeks to show how algorithmic profiling can be particularly attractive for those in legal practice, given restraints on time and resources. It deviates from Rouvroy in two ways. First, it argues that algorithmic governmentality does not contrast with neoliberal modes of government in that it allows indirect rule through economic calculations. Second, it argues that critique of such systems is possible, especially if the creative nature of law can be harnessed effectively.

This is a conceptual paper, with a theory-based approach, that is intended to explore relevant issues related to algorithmic governmentality as a basis for future empirical research. It builds on governmentality and socio-legal studies, as well as research on algorithmic practices and some documentary analysis of reports and public-facing marketing of relevant technologies.

This paper provides insights on how algorithmic knowledge is collected, constructed and applied in different situations. It provides examples of how algorithms are currently used and how trends are developing. It demonstrates how such uses can be informed by socio-political and economic rationalities.

Further empirical research is required to test the theoretical findings.

This paper takes up Rouvroy’s question of whether we are at the end(s) of critique and seeks to identify where such critique can be made possible. It also highlights the importance of acknowledging the role of political rationalities in informing the activity of algorithmic assemblages.

How to improve continuance commitment for platform workers is still unclear to platforms' managers and academic scholars. This study develops a configurational framework based on the push-pull theory and proposes that continuance commitment for platform workers does not depend on a single condition but on interactions between push and pull factors.

The data from the sample of 431 full-time and 184 part-time platform workers in China were analyzed using fuzzy-set qualitative comparative analysis (FsQCA).

The results found that combining family motivation with the two kinds of pull factors (worker's reputation and algorithmic transparency) can achieve high continuance commitment for full-time platform workers; combining job alternatives with the two kinds of pull factors (worker's reputation and job autonomy) can promote high continuance commitment for part-time platform workers. Particularly, workers' reputations were found to be a core condition reinforcing continuance commitment for both part-time and full-time platform workers.

The findings suggest that platforms should avoid the “one size fits all” strategy. Emphasizing the importance of family and improving worker's reputation and algorithmic transparency are smart retention strategies for full-time platform workers, whereas for part-time platform workers it is equally important to reinforce continuance commitment by enhancing workers' reputations and doing their best to maintain and enhance their job autonomy.

This study expands the analytical context of commitment research and provides new insights for understanding the complex causality between antecedent conditions and continuance commitment for platform workers.

This paper investigates the strategic behavior of algorithmic trading firms from an innovation economics perspective. The authors seek to uncover the sources of competitive advantage these firms develop to make markets inefficient for them and enable their survival.

First, the authors review expected capability, a quantitative behavioral model of the sustainable, or reliable, profits that lead to survival. Second, they present qualitative data gathered from semi-structured interviews with industry professionals as well as from the academic and industry literatures. They categorize this data into first-order concepts and themes of opportunity-, advantage- and meta-seeking behaviors. Associating the observed sources of competitive advantages with the components of the expected capability model allows us to describe the economic rationale these firms have for developing those sources and explain how they survive.

The data reveals ten sources of competitive advantages, which the authors label according to known ones in the strategic management literature. We find that, due to the dynamically complex environments and their bounded resources, these firms seek heuristic compromise among these ten, which leads to satisficing. Their application of innovation methodology that prescribes iterative ex post hypothesis testing appears to quell internal conflict among groups and promote organizational survival. The authors believe their results shed light on the behavior and motivations of algorithmic market actors, but also of innovative firms more generally.

Based upon their review of the literature, this is the first paper to provide such a complete explanation of the strategic behavior of algorithmic trading firms.