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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.

This paper aims to analyse residents’ perceptions of tourism growth in Porto prior to the COVID-19 pandemic, aiming to determine the most appropriate strategies to mitigate negative tourism impacts. Studies on resident perceptions of tourism impacts are still scarce, particularly the ones addressing the topic in the context of Portuguese urban tourism areas.

Data was collected through an online survey, focusing on three categories of impacts: (i) economic, (ii) sociocultural (iii) and spatial-environmental, and the respective mitigation strategies, analysed from the perspective of Porto’s residents. Descriptive and bivariate statistics – T-test and Eta correlation – were used to analyse the collected data.

Respondents who live in the city centre experience specific tourism impacts more negatively, when compared to those living outside the inner-city area. Furthermore, no strong correlation is found between the said impacts and the respective mitigation strategies. However, creating awareness among tourists about acceptable behaviour in shared spaces is the strategy that stands out, as it has a medium correlation with all three impact categories. Most impact-strategy associations are weak, meaning that the defined strategies are not the most case-appropriate, which is something that policymakers should address.

To the best of the author’s/authors’ knowledge, this is the first study to adopt this approach in tackling the negative impacts of rapid tourism growth in Porto.

This paper aims to explore how young students experience the contribution of a pedagogical assemblage based on design thinking (DT) while contributing to the transition to a more just and sustainable university.

This qualitative research considers the case of two pedagogical experiences developed at Universitat Politècnica de Valencià, Spain. In both experiences, a methodological proposal that includes practices of care, just transitions and DT was implemented. The data obtained through in-depth interviews, surveys and digital whiteboard labels was analyzed under the lens of three relational categories in the context of sustainability.

Learnings are acquired through five categories: place-based learning, prior learning, embodied learning, collaborative teamwork and intersectionality. The research shows how the subjective knowledge of young students positions them as co-designers and leaders of a University that drives a more just and sustainable transition.

The originality of the paper lies in the shift of DT from a human-based approach to a justice-oriented relational approach.