Factors affecting lecturers’ decision to stay or leave academia, Namibia

Orientation: Attracting and retaining academic staff needs to become a priority for tertiary institutions.Research purpose: Instead of spending funds on replacing staff members, tertiary institutions need to invest in interventions to keep staff committed to and embedded into the organisation.Motivation for the study: Lecturing staff are valuable resources that need to be nurtured, taken care of, and retained to keep tertiary institutions functioning effectively. This study focused on factors that impact lecturing staff’s decision to stay at or leave the University of Namibia.Research approach/design and method: Making use of quantitative research (a questionnaire), data was collected from lecturing staff (n = 242) to investigate the relationship between organisational commitment, job embeddedness and turnover intention.Main findings: Affective commitment, normative commitment, organisational fit and organisational sacrifices reported significant negative relationships with turnover intention. Affective commitment, organisational fit and organisational sacrifice were found to be significant predictors of turnover intention.Practical/managerial implications: Including lecturing staff in the decision-making process, ensuring that there is an equitable exchange for their labour and being fair during interactions enhances psychological meaningfulness and affective commitment. Providing career development opportunities enhances organisational fit, effectiveness of staff and reduces turnover intention. Assessing the job demands-resources relationship remains of cardinal importance.Contributions/value-add: Provision of benefits, employees identifying with the organisation and having the needed resources mitigate the demands of the job, reduce exhaustion, enhance levels of commitment and decrease turnover intention.

Abstract licence: CC BY

33 more references, whose wording the paper’s licence keeps private.

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.