Work engagement of online car-hailing drivers: the effects of platforms' algorithmic management

Abstract

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.

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

“…Initially proffered by Webster et al [ 16 ], Searle and Auton [ 17 ] subsequently validated new measures of challenge and hindrance appraisals, showcasing their utility in stress research and articulating a distinct CHAF. Many empirical studies have since adopted this framework to explore the effects of AI [ 18 , 19 ], algorithm management [ 12 , 20 ], employee demands [ 21 , 22 , 23 ] and other factors on workers’ behaviors and intentions.…”

“…To address the posited research questions and ameliorate the identified research gap, this study strategically amalgamates the affective events theory with the challenge–hindrance appraisal framework (CHAF) and the conceptualization of prosocial service behaviors (PSBs), thereby guiding the formulation of the theoretical model. In examining the impact of challenge–hindrance appraisals on PSBs, work engagement (WE) is selected as the mediator, given its previously established status as an antecedent of PSBs and an outcome of challenge–hindrance appraisals [ 11 , 12 ], thereby showcasing its appropriateness as a mediator between challenge–hindrance appraisals and PSBs. Moreover, delving into potential contingencies that shape the relationship between multifarious factors and gig workers’ behaviors is imperative.…”