Examining the Impact of Algorithmic Control on Uber Drivers’ Technostress

Challenge or Hindrance? The Dual Impact of Algorithmic Control on Gig Workers' Prosocial Service Behaviors

Algorithmic technological progress presents both opportunities and challenges for organizational management. The success of online labor platforms hinges on algorithmic control, making it imperative to explore how this control affects gig workers' prosocial service behaviors. Drawing from affective event theory, our study delves into the factors influencing gig workers' prosocial service behaviors in the online labor platform setting. We utilize the challenge-hindrance appraisal framework to highlight the pivotal role of algorithmic control. To rigorously test our hypotheses, we gathered empirical data from an online questionnaire survey of 660 gig workers. Our results indicate that challenge appraisals and hindrance appraisals in regard to platform algorithm control have a nuanced dual impact on gig workers' prosocial service behaviors. This relationship is clarified by the mediating function of work engagement. A challenge appraisal of platform algorithmic control can positively influence gig workers' prosocial service behaviors. However, hindrance appraisal of platform algorithmic control can negatively influence gig workers' prosocial service behaviors. Interestingly, workplace interpersonal capitalization boosts the effect of challenge appraisal on employees' prosocial service behaviors. However, it does not mitigate the adverse effects of hindrance appraisal on such behaviors. This study has multiple theoretical implications, and it also provides valuable practical insights into organizational management.

Navigating the decision-making landscape of AI in risk finance: Techno-accountability unveiled

The integration of artificial intelligence (AI) systems has ushered in a profound transformation. This conversion is marked by revolutionary extrapolative capabilities, a shift toward data-centric decision-making processes, and the enhancement of tools for managing risks. However, the adoption of these AI innovations has sparked controversy due to their unpredictable and opaque disposition. This study employs the transactional stress model to empirically investigate how six technological stressors (techno-stressors) impact both techno-eustress (positive stress) and techno-distress (negative stress) experienced by finance professionals and experts. To collect data for this research, an e-survey was distributed to a diverse group of 251 participants from various sources. The findings, particularly the identification and development of techno-accountability as a significant factor, contribute to the risk analysis domain by improving the failure mode and effect analysis framework to better fit the rapidly evolving landscape of AI-driven innovations.

Globalization, platform work, and wellbeing-a comparative study of Uber drivers in three cities: London, Helsinki, and St Petersburg

BACKGROUND

Globalization of platform work has become a challenge for wider social and employment relations and wellbeing of workers, yet on-location work remains governed also by local regulatory context. Understanding common challenges across countries and potential for regulatory measures is essential to enhance health and wellbeing of those who work in platform economy. Our comparative study on platform work analyzed concerns of Uber drivers in three cities with a different regulatory and policy context.

METHODS

Drawing from current understanding on employment and precarity as social determinants of health we gathered comparative documentary and contextual data on regulatory environment complemented with key informant views of regulators, trade unions, and platform corporations (N = 26) to provide insight on the wider regulatory and policy environment. We used thematic semi-structured interviews to examine concerns of Uber drivers in Helsinki, St Petersburg, and London (N = 60). We then analysed the driver interviews to identify common and divergent concerns across countries.

RESULTS

Our results indicate that worsening of working conditions is not inevitable and for drivers the terms of employment is a social determinant of health. Drivers compensated declining pay with longer working hours. Algorithmic surveillance as such was of less concern to drivers than power differences in relation to terms of work.

CONCLUSIONS

Our results show scope for regulation of platform work especially for on-location work concerning pay, working hours, social security obligations, and practices of dismissal.

Gig worker's perceived algorithmic management, stress appraisal, and destructive deviant behavior

With the advance of data technologies, gig platforms have applied data and algorithms to their management and put more stringent requirements on gig workers through algorithmic management. Gig workers might perform destructive deviant behavior when coping with algorithmic management. It is meaningful to examine how the algorithmic management applied to gig platforms could lead to gig workers' destructive deviant behavior. Based on the challenge-hindrance framework, we developed a research model and validated it with survey data collected from 423 food delivery riders. We employed multi-level linear regression analysis in data analysis and found that perceived algorithmic management was appraised as both a hindrance and a challenge. As a hindrance, it elicits working/family deviant behavior; as a challenge, it helps reduce working/family deviant behavior. Regulatory focus (a prevention focus vs. a promotion focus) moderates the effect of perceived algorithmic management on stress appraisals (hindrance appraisals vs. challenge appraisals). This study explains algorithmic management's impact on gig workers' destructive deviant behavior through the appraisal of algorithmic management as both a challenge and a hindrance. It also provides practical advice to gig platforms, gig workers and policymakers on how to balance the challenge and hindrance roles of algorithmic management in gig work.

Algorithmic management, preferences for autonomy/security and gig-workers' wellbeing: A matter of fit?

The objective of this study was to understand how gig-workers interpret the effects of their laboral activity on their wellbeing. We developed a grounded theory based on interviews with 57 Brazilian gig-workers. The results show that (1) workers and gig-work organizations have preferences for work relationships with more autonomy or security; (2) when there is a congruence of preferences, the worker experiences greater wellbeing, and when the preferences diverge, there are episodes of preference violations that, when repeated, reduce worker wellbeing; and (3) however, not everything is a matter of fit: when both individuals and organizations have the same preference (for example, for more autonomy and less security), worker wellbeing may be vulnerable to abuse, for example, in terms of an unsustainable workload. Our study draws attention to an integrated discussion of the benefits and harms of algorithmic management, which allows overcoming a polarized view in which it would be seen only as beneficial or harmful to workers.