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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.

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.

This work examines the intersection between traditional human resource management and the novel employment arrangements of the expanding gig economy. While there is a substantial multidisciplinary literature on the digital platform labor phenomenon, it has been largely centered on the experiences of gig workers. As digital labor platforms continue to grow and specialize, more managers, executives, and human resource practitioners will need to make decisions about whether and how to utilize gig workers. Here the authors explore and interrogate the unique features of human resource management (HRM) activities in the context of digital labor platforms. The authors discuss challenges and opportunities regarding (1) HRM in organizations that outsource labor needs to external labor platforms, (2) HRM functions within digital labor platform firms, and (3) HRM policies and practices for organizations that develop their own spin-off digital labor platform. To foster a more nuanced understanding of work in the gig economy, the authors identify common themes across these contexts, highlight knowledge gaps, offer recommendations for future research, and outline pathways for collecting empirical data on HRM in the gig economy.

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.

As businesses become more sophisticated and welcome new technologies, artificial intelligence (AI)-based methods are increasingly being used for firms' pricing decisions. In this review article, we provide a survey of research in the area of AI and pricing. On the upside, research has shown that algorithms allow companies to achieve unprecedented advantages, including real-time response to demand and supply shocks, personalized pricing, and demand learning. However, recent research has uncovered unforeseen downsides to algorithmic pricing that are important for managers and policy-makers to consider.

This chapter critically examines whether it may be possible to create an AI-based authentic leader, questioning the inherent contradiction between artificial and authentic. The authors pose central research questions: Does the application of AI – even just as a powerful resource – challenge the tenets of authentic leadership? What are the possibilities and limitations of the concept of authenticity in AI-based management systems? Moreover, with the help of three vignettes illustrating practical applications of AI-based systems in leadership and management tasks, the authors illustrate how technology may be used to either control or empower workers and leaders. The authors call for research to assess whether the search for authenticity in AI-based leadership could lead anywhere, warning that it could entrap us in unresolvable existential and conceptual ambiguity, ultimately diverting our focus from the essence of leadership altogether.