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The purpose of this study is to pinpoint the factors that affect teacher motivation in this particular setting. It specifically looks at how job satisfaction, the reward system, training and development and work situational factors affect teachers’ motivation.

Through nonrandom sampling, data from 229 teachers from private schools in Lebanon were gathered.

The findings showed that professional training and development opportunities and a supportive work environment have a strong impact on teachers’ motivation.

The findings highlight the need for more research to guide comprehensive improvement strategies in Lebanese private schools for motivating teachers. The Ministry of Education and private schools in Lebanon can benefit from the insights provided by the findings in terms of policy and practice. Among the suggestions include bringing up-to-date rules to boost teacher satisfaction, raising educational standards, creating focused training sessions, changing compensation systems and improving both the physical and mental aspects of the workplace.

Competition among higher education institutions has pushed universities to expand their competitive advantages. Based on the assumption that the core functions of universities are academic, understanding the teaching–learning process with the help of student evaluation of teaching (SET) would seem to be a logical solution in increasing competitiveness. The paper aims to discuss these issues.

The current paper presents a narrative literature review examining how SETs work within the concept of service marketing, focusing specifically on the search, experience, and credence qualities of the provider. A review of the various factors that affect the collection of SETs is also included.

Relevant findings show the influence of students’ prior expectations on SET ratings. Therefore, teachers are advised to establish a psychological contract with the students at the start of the semester. Such an agreement should be negotiated, setting out the potential benefits of undertaking the course and a clear definition of acceptable performance within the class. Moreover, connections should be made between courses and subjects in order to provide an overall view of the entire program together with future career pathways.

Given the complex factors affecting SETs and the antecedents involved, there appears to be no single perfect tool to adequately reflect what is happening in the classroom. As different SETs may be needed for different courses and subjects, options such as faculty self-evaluation and peer-evaluation might be considered to augment current SETs.

Existing literature on algorithmic management practices – defined as autonomous data-driven decision making in people's management by adoption of self-learning algorithms and artificial intelligence – suggests complex relationships with employees' well-being in the workplace. While the use of algorithms can have positive impacts on people-related decisions, they may also adversely influence job autonomy, perceived justice and – as a result – workplace well-being. Literature review revealed a significant gap in empirical research on the nature and direction of these relationships. Therefore the purpose of this paper is to analyse how algorithmic management practices directly influence workplace well-being, as well as investigating its relationships with job autonomy and total rewards practices.

Conceptual model of relationships between algorithmic management practices, job autonomy, total rewards and workplace well-being has been formulated on the basis of literature review. Proposed model has been empirically verified through confirmatory analysis by means of structural equation modelling (SEM CFA) on a sample of 21,869 European organisations, using data collected by Eurofound and Cedefop in 2019, with the focus of investigating the direct and indirect influence of algorithmic management practices on workplace well-being.

This research confirmed a moderate, direct impact of application of algorithmic management practices on workplace well-being. More importantly the authors found out that this approach has an indirect influence, through negative impact on job autonomy and total rewards practices. The authors observed significant variation in the level of influence depending on the size of the organisation, with the decreasing impacts of algorithmic management on well-being and job autonomy for larger entities.

While the influence of algorithmic management on various workplace practices and effects is now widely discussed, the empirical evidence – especially for traditional work contexts, not only gig economy – is highly limited. The study fills this gap and suggests that algorithmic management – understood as an automated decision-making vehicle – might not always lead to better, well-being focused, people management in organisations. Academic studies and practical applications need to account for possible negative consequences of algorithmic management for the workplace well-being, by better reflecting complex nature of relationships between these variables.