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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 perusal of extant literature suggests asymmetric devotions in empirical research that overlooks unique social and industrial dynamics of married working women in highly competitive male-dominated industries. Drawing on the social role theory, the study addressed this asymmetry by proposing a multivariate regression model that examined performance drivers of married female real estate brokers, as mediated by organizational, cognitive and social influences.

The study employed a descriptive design. A structured electronic questionnaire was used to collect data from a sample of 256 married female brokers who are active on online messaging platforms. Snowball sampling was used to reach the research participants. Collected data were subjected to multivariate regression analysis.

The results infer that social influences were the most significant drivers of married female brokers in the real estate industry (r = 0.932, p < 0.05, F = 556.581). Significant interventions of diversity management, work-life balance, family support and access to economic opportunities were found.

In acknowledging the societal stereotype and culturally imposed burdens married women face in emerging economies, the study advanced theoretical, practical and policy initiatives for a more inclusive, supportive and learning-oriented work environment.

The novelty of the paper lies in its exposition of the unregulated real estate brokerage market in an emerging economy and the dynamic organizational, cognitive and societal influences of married female brokers.

This study aims to examine the association of employee resilience and agility with innovative performance and subjective well-being. Moreover, it tests job crafting as the underlying mechanism through which resilient and agile employees perform innovatively and experience higher subjective well-being.

The study used a survey-based research design and structural equation modeling technique to examine the proposed hypotheses. Data was collected from a sample of 380 employees working in the Indian information technology sector using survey questionnaires.

The results show that highly resilient and agile employees participate in job crafting that positively influences their innovative performance and subjective well-being. Job crafting fully mediates the association of resilience with work and well-being outcomes and partially mediates agility and outcomes.

There is a value in promoting the development of employee resilience and agility to foster ways in which employees can craft their jobs and, thus, maximize their innovative performance and subjective well-being.

This study makes an important contribution by underscoring the importance of personal resources (resilience and agility) as drivers of job crafting for higher innovative performance and subjective well-being.

The authors' work aims to identify the employer-specific drivers of the college (or university) wage gap, which has been identified as one of the major determinants of the dynamics of overall wage and income inequality in the past decades. The authors focus on three employer-level features that can be associated with asymmetries in the employment relation orientation adopted for college and non-college-educated employees: (1) size, (2) the share of standard employment and (3) the pervasiveness of incentive pay schemes.

The authors' establishment-level analysis (data from the Basic Survey on Wage Structure (BSWS), 2005–2018) focusses on Japan, an economy characterised by many unique economic and institutional features relevant to the aims of the authors' analysis. The authors use an adjusted measure of firm-specific college wage premium, which is not biased by confounding individual and establishment-level factors and reflects unobservable characteristics of employees that determine the payment of a premium. The authors' empirical methods account for the complexity of the relationships they investigate, and the authors test their baseline outcomes with econometric approaches (propensity score methods) able to address crucial identification issues related to endogeneity and reverse causality.

The authors' findings indicate that larger establishment size, a larger share of regular workers and more pervasive implementation of IPSs for college workers tend to increase the college wage gap once all observable workers, job and establishment characteristics are controlled for. This evidence corroborates the authors' hypotheses that a larger establishment size, a higher share of regular workers and a more developed set-up of performance pay schemes for college workers are associated with a better capacity of employers to attract and keep highly educated employees with unobservable characteristics that justify a wage premium above average market levels. The authors provide empirical evidence on how three relevant establishment-level characteristics shape the heterogeneity of the (adjusted) college wage observed across organisations.

The authors' contribution to the existing knowledge is threefold. First, the authors combine the economics and management/organisation literature to develop new insights that underpin the authors' testable empirical hypotheses. This enables the authors to shed light on employer-level drivers of wage differentials (size, workforce composition, implementation of performance-pay schemes) related to many structural, institutional and strategic dimensions. The second contribution lies in the authors' measure of the “adjusted” college wage gap, which is calculated on the component of individual wages that differs between observationally identical workers in the same establishment. As such, the metric captures unobservable workers' characteristics that can generate a wage premium/penalty. Third, the authors provide empirical evidence on how three relevant establishment-level characteristics shape the heterogeneity of the (adjusted) college wage observed across organisations.

This study aims to examine the relationship between internal and external factors and job satisfaction, and between job satisfaction and auditors’ performance.

This research used deductive approach. Data was gathered from 83 auditors in the Saudi Organisation for Certified Public Accountants (SOCPA) database. By implementing the partial least squares-structural equation modelling (PLS-SEM) technique, the suggested hypotheses were examined.

The results show that internal factors, i.e., achievement, advancement, recognition and growth, significantly impact job satisfaction. Subsequently, the external factors, i.e., company policies, relationship with a peer and relationship with supervisor, significantly impact job satisfaction. In contrast, work security has no relationship with job satisfaction. Furthermore, job satisfaction is a significant driver for auditors' performance.

This research sheds light on the relationships between internal and external factors, job satisfaction and auditors' performance in the Saudi context. It would be interesting to investigate these relationships in a different setting, such as a different country, time or industry. Future studies should broaden the sample frame to include different types of employees to obtain more generalisable results.

This study may help managers of auditing departments formulate appropriate strategies and design effective programs to increase the level of job satisfaction between auditors by enhancing such factors, which will lead to improving the auditors' performance.

This research provide an empirical evidence to support the theoretical assumptions of Herzberg's which is much needed.

Construction frameworks are recognised by the UK Government as an integrated procurement path to deliver efficiency, collaboration and sustainability. This research paper aims to develop a performance-driving model for framework procurement to align with the government construction policy objectives.

A quantitative hierarchical regression analysis was conducted to examine the causal relationships between project outcomes and performance drivers. A successive qualitative multiple-case study involving eight typical construction framework projects was examined to further explain casual relationships.

Conclusive results from the quantitative and qualitative analyses validated the performance-driving model, generalising a framework procurement principle that project outcomes of time, cost, quality, sustainability and closer relationships are significantly driven by supplier's task and contextual performance drivers, and client's organisational drivers.

The investigation followed two major public-sector organisations in the UK: infrastructure offices of local authorities and estate offices of universities. Further research should be extended to other public-sector situations to enhance the model further.

Using this model, framework managers can drive project outcomes by applying task and contextual performance drivers in order to select the best suppliers at the procurement phase, and by adopting the client's organisational factors at both procurement and construction monitoring phases.

The empirical evidence supports the use of frameworks by public sector policymakers as a key strategy to develop the performance drivers further and improve efficiency and quality outcomes.

The results will be of value to support the policy of public-sector works organisations contemplating using framework procurement for executing continuing construction activities.

This chapter aims to contribute to the literature on sustainability in the public sector by discussing how human resource and human resource management can help to integrate environmental management into organizations and improve environmental performance. Public sector scholars have neglected the study of Green Human Resource Management (GHRM) until now. Nevertheless, implementing such practices could lead to positive outcomes regarding awareness of environmental issues, organizational reputation and attractiveness, job satisfaction and organizational performance. The authors discuss the relevance and the necessity of developing a field of research on GHRM in public organizations. Starting from a conceptual review of the main literature on GHRM, this chapter provided some directions for future research.

Drawing on the social identity theory, this paper explores the impact of rider–driver ethnicity match on the driver’s expected ride satisfaction and willingness to perform, and rider’s trust on the driver.

The study relies on scenario-based online experiments with 291 ride-hailing drivers and 282 riders in the USA.

The findings indicate that ethnicity match between ride-hailing drivers and riders positively impact driver’s ride satisfaction and willingness to perform, and rider’s trust in the driver. The study also revealed a significant positive moderation effect of ethnic identity on the relationship of ethnicity match and those constructs.

While it may be challenging to influence an individual’s level of ethnic identity, managers can take steps to educate and train their employees regarding the impact of ethnic identity and discrimination, with a particular focus on those individuals who possess a strong sense of ethnic identity.

The findings of this research provide theoretical contributions to the existing literature on ride-hailing services and adds to the limited stream of logistics research that examines the impact of ethnicity on ride-hailing operations.

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.

This research examines what motivates professional truck drivers to engage in eco-driving by linking their self-reports with objective driving scores.

Theory of Planned Behavior (TPB) is illustrated in an embedded, single-case study of a Finnish carrier with 17 of its truck drivers. Data are obtained through in-depth interviews with drivers, their fuel-efficiency scores generated by fleet telematics and a focus group session with the management.

Discrepancies between drivers’ intentions and eco-driving behaviors are illustrated in a two-by-two matrix that classifies drivers into four categories: ideal eco-drivers, wildcards, wannabes and non-eco-drivers. Attitudes, subjective norms and perceived behavioral control are examined for drivers within each category, revealing that drivers’ perceptions did not always align with the reality of their driving.

This study strengthens the utility of TPB through data triangulation while also revealing the theory’s inherent limitations in elucidating the underlying causes of its three antecedents and their impact on the variance in driving behaviors.

Managerial insights are offered to fleet managers and eco-driving solution providers to stipulate the right conditions for drivers to enhance fuel-efficiency outcomes of transport fleets.

This is one of the first studies to give a voice to professional truck drivers about their daily eco-driving practice.