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The purpose of this paper is to examine the social capital factors of career advancement of female academic staff in Nigerian universities.

A measurement and structural analysis were conducted for the three independent variables and a dependent variable on 20 public universities. Data were collected using a structured self-administered questionnaire. The dependent variable was female academic staff career advancement and the independent variables were mentoring, networking and government machinery. Using stratified random sampling, 532 academic staff were selected as the study respondents. They represented sampling criteria such as federal and state universities.

Structural modeling analysis showed that social capital variables, specifically mentoring, networking and government machinery variables, were significant contributors to the career advancement of the female academic staff in Nigerian universities.

This study creates an insight into the knowledge of career advancement among female academic staff in public universities. These institutions dominate the university system in Nigeria and serve as the main avenue for university education in the country. At the level of higher institution, HRD is significant, particularly in creating awareness among academic staff about their career planning and aspirations, the role that the perceived environmental factors play in their advancement to higher positions in the university and how they should further utilize those factors.

The paper examines social capital factors (limited to mentoring, networking and government machinery) that are of concern to managing the career advancement of female academic staff in public universities.

This study aims to reach a perception of the advance of research on deviant organisational behaviours.

This research has been done through the text mining method. By reviewing, the papers were selected 360 papers between 1984 and 2020. Based on the Davis–Boldin index, 11 optimal clusters were gained. Then the roots were ranked in any group, using the Simple Additive Weighting technique. Data were analysed by RapidMiner and MATLAB software.

According to the results obtained, clusters are included leadership styles, job attitudes, spirituality in the workplace, work psychology, personality characteristics, classification and management of deviant workplace behaviours, service and customer orientation, deviation in sales, psychological contracts, group dynamics and inappropriate supervision.

This study provides a landscape and roadmap for future investigation on deviant organisational behaviours.