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The purpose of the study is to identify barriers to collaboration among female administrators at a Ghanaian technical university (TU), based on the social identity theory (SIT).

A mixed-method approach was adopted, integrating qualitative interviews of 15 female administrators and completing structured questionnaires from 117 randomly sampled female administrators. The SIT, as the analytical framework, identified themes emerging from the data on barriers to collaboration among female administrators. While exploratory factor analysis identified measures of factors hindering collaborations, the use of structural equation modeling (SEM) enabled the confirmation of relationships among the barriers to collaboration with female administrators.

There existed statistically significant relationships between four of the barriers: intergroup relations conflict, trust with stakeholders and among females and structural barriers (SBs). For the quantitative analysis, it was found that SBs, intergroup relations, conflict and trust were statistically significant except for weak cultures. For the qualitative, results showed that SBs, lack of trust with stakeholders and among females and intergroup conflict hinder collaboration.

The study has a limited scope in using only one TU and focusing on a particular gender. The implications of this research will enrich the literature on barriers to female administrative collaboration in technical education based on the SIT.

Promoting administrative collaborations in the TU will ensure sustainability and efficient administrative systems.

Institutional policies should include gender inclusivity and equality on networking opportunities and provide mentorship programs for efficient administrative systems.

We used the SIT to determine barriers to collaboration among female administrators in a technical education institution, and the mixed methodology added a unique dimension to the study.

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.