Search results

Drawing upon the needs–supplies fit theory, this study aims to examine how the expected and perceived algorithmic autonomy support (AAS) influences platform workers’ work stress and incivility, with a focus on the moderating role of self-direction.

Using data collected from 422 platform workers in China through multiple waves, the model is tested through polynomial regression and response surface analysis.

The results indicate that a mismatch between expected and perceived AAS is associated with higher levels of incivility among platform workers, and this relationship is mediated by work stress. These findings are particularly salient when self-direction is emphasized.

This study suggests that a universally high level of AAS may not necessarily reduce workers’ stress and incivility. Instead, it is important for platforms to ensure that their workers’ expectations of autonomy support are met and for workers to be given the space to exercise self-direction.

Previous studies have highlighted the need to pay attention to workers in mobile and ambiguous environments, and this study adds to this literature by focusing specifically on platform organizations and workplaces. This study provides valuable insights into the relationship between AAS, work stress and platform workers’ incivility.

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.

Social media fatigue (SMF) has been widely recognized; however, previous studies have included various concepts into a single fatigue construct. Fatigue has typically been explored from the stressor-strain-outcome (SSO) or stimulus-organism-response (SOR) perspectives. To further investigate SMF, the authors split it into the two constructs of exhaustion and disinterest. Furthermore, the authors introduced the concept of emotional labor and identified rules that may affect surface and deep acting strategies.

The authors designed and conducted a survey to collect data from social networking platform users.

Results from 364 users of social networking platforms supported most of the authors' hypotheses. First, most of the display rules affect the choice of deep or surface acting. Second, both types of acting lead to exhaustion, but only surface acting leads to disinterest. Third, discontinuance intention is affected by both types of fatigue.

This study contributes to SMF research by adding more antecedents (deep and surface acting) based on the emotional labor perspective and showing the impacts of communication rules on emotional labor. In addition, this study also distinguishes disinterest-style fatigue from exhaustion.

The purpose of this study is to critically reflect on visually impaired customers’ technology assistance needs and the perceptions of existing technologies’ performance in the contexts of hospitality and tourism.

Following a qualitative approach, this study used in-depth semistructured interviews with 19 participants with visual impairments.

Positive and negative sides of technology-assisted experiences in the hotel, restaurant, and travel domains were summarized, and room for improvement was discussed to enhance the quality of life and travel experience of visually impaired customers.

Findings from this study offer actionable implications and future directions to technicians and managers to make hospitality and travel experiences more inclusive.

This timely reflection addresses a critical situation by offering original ideas and calling for more discussion of under-represented groups with visual impairments.

Food delivery apps (FDAs) have rapidly developed due to mobile technologies, changes in modern consumption and increased online use during the COVID-19 pandemic. Despite this, the platforms are struggling to build a stable customer base and make a profit. This study combined tech-driven motivators in an information system success model (ISSM) and emotional attachment operated by affect transfer theory (ATT) to explain why people might continue to use an FDA.

An online quantitative cross-sectional survey was carried out via the Prolific platform. Data were collected from 416 FDA users and analyzed to test the hypotheses using the partial least squares based structural equation modeling.

The study found that system quality, information quality and emotional attachment directly influence the continued use of FDA. Moreover, the results suggest that perceived social interactivity and perceived playfulness are significant antecedents of emotional attachment.

The findings of this study enrich the current literature on continuance intention in online food delivery services by integrating the technology- and affective-based factors. The findings also provide various practical implications for app designers and marketers to foster an emotional bond with users, resulting in high retention rate.

The study proposes the integrated framework of ISSM and ATT for enhancing understanding of consumer behaviors in the post-adoption stage for FDA.