Determinants of academic staff retention in Zimbabwean universities

ABSTRACT

The issue of academic staff retention has attracted major interest in many countries and Zimbabwe is not an exception. The purpose of this study was to establish the determinants of academic staff retention in Zimbabwean universities with particular reference to two universities in Matabeleland region. Specifically, the objectives of the study were to establish whether demographic factors (age, sex and marital status), remuneration, career advancement opportunities, training and development, educational qualifications and amount of workload have an influence on academic staff retention. The study was quantitative and it employed the survey design. The total population of the academic staff in the two universities covered by the study was 491. Stratified random sampling was used to select a sample of 119 from the two universities. Data was analysed using Eviews version 10 and SPSS Version 21 software. The study employed the logit model to estimate the results. The study revealed that marital status, educational level, training and development, workload, remuneration and career advancement opportunities significantly affect academic staff retention in Zimbabwean universities. Age and sex do not have a significant effect on academic staff retention. The study recommended the need for Zimbabwean universities to provide staff exchange programmes so that members of staff can share latest practices with staff members from other universities. It also recommended academic institutions to negotiate with banks to offer housing and vehicle loans to academic staff at reasonable interest rates. Another recommendation was that universities should conduct exit interviews with quitting members of staff so as to identify the major reasons for quitting. In addition, universities should provide continuous professional development programmes to their staff, so that they can obtain relevant training and appropriate skills.¹

1. INTRODUCTION

Skilled employees are the major distinguishing factor between successful and unsuccessful business organisations in today’s competitive business environment (Jureshma, 2012). A vital philosophy shared by successful organisations is that most of them value and invest in their staff. In order to gain competitive advantage in the international market, business organisations need to hire and retain proficient employees (Ahmad and Riaz, 2011).

In today’s dynamic business environment, organisations spend considerable amount of resources to train and develop employees, only for them to leave for greener pastures (Jeremy, 2014). Executives are finding it difficult to retain highly experienced employees as they are being attracted by competitors with various types of incentives (Lee, 2009). Unless organisations identify and apply suitable retention strategies, they will continue experiencing frequent turnover of key staff and hence losing their skilled manpower to competitors.

Academic institutions have not been spared the challenge of staff exodus. Academic staff retention has been a pertinent issue in higher education institutions for the past three decades (Ng’ethe, Iravo and Namusonge, 2012). High academic staff turnover has severe repercussions on the quality of academic graduates produced by these institutions. Staff turnover can have negative effects on students who receive poor service when teaching positions are vacated and then filled by inexperienced staff (Masaiti and Naluyeke, 2011).

While academic staff retention continues to be a challenge across the globe, the state of affairs in many African countries seems to be particularly urgent (Tettey 2010). There is inadequate teaching capacity in African universities because much of the expertise base of these institutions has been eroded. (Havenga, 2011). If something is not done urgently, African universities will lose their ability to protect the quality of intellectual life in the Africa region (Obayako, 2012).

In Zimbabwe, prevailing evidence suggests that there is a high rate of academic staff turnover. Zimbabwean institutions of higher learning are facing major challenges in retaining experienced and talented academic staff (Zhou, 2015). Mushonga (2015) notes that universities in Zimbabwe have developed into training grounds for other universities within the region and beyond. These institutions are failing to cope with a huge exodus of senior academic staff who have extensive research skills and teaching experience (Mushonga, 2015). High academic staff turnover has given rise to a large number of unfilled posts, understaffing, reliance on part time lecturers and high student to staff ratios (Zhou, 2015). This is counterproductive to the functionality of academic institutions.

Due to the excessive dependence on part- time staff, the ability of Zimbabwean state universities to deliver their mandate has been put to question (Mutambaziko, 2013). The performance of Zimbabwean universities in the world rankings is very pathetic with the University of Zimbabwe being the only university to appear among the top 3000 universities in the world at position 2351 as at the end of December 2016 (Webometrics, 2017).

This study therefore, seeks to establish the determinants of academic staff retention in Zimbabwean universities with particular reference to two universities from Matabeleland region (University A and University B). The real names of the two universities are not going to be disclosed for ethical reasons. The study involves a general investigation into the factors which influence academic staff retention in Zimbabwean State Universities. It specifically seeks to ascertain whether factors such as training, remuneration, workload, career advancement opportunities and demographic factors have an influence on academic staff retention in Zimbabwe.

2.1 The Conceptual Framework

The study adopted a conceptual framework developed by Shah and Burke (2013). The conceptual framework suggests that employee retention (dependent variable) is determined by workload, remuneration, career advancement opportunities, training and development, educational level and demographic factors such as age, sex and marital status (explanatory variables). The study analysed the extent to which the explanatory variables influence employee retention (dependent variable). Shah and Burke (2013)’s conceptual framework is illustrated in Figure 2.1.

Figure 2.1: The Conceptual Framework

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Source: Adapted from Shah and Burke (2013).

3.METHODOLOGY

3.1 Sources of Data

Data for the study was gathered from primary sources. The data used in this study originated from a quantitative survey of academic staff members from two universities in Matabeleland region (University A and University B). The target population for this study comprised of the entire full time academic staff members of the two universities. Table 3.1 illustrates the target population.

Table 3.1: Target Population

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Source : Survey Data (2017).

3.2 Sampling Design

A stratified random sampling technique was used to select the respondents out of a total population of 491 academic staff members. The study was divided into two strata which are University A and University B. Thereafter, simple random sampling was used to choose respondents within strata in order to ensure that all staff members stand equal chance of being selected.

3.3 Sample Size

Yamane (1967)’s formula was used to determine the sample size. The formula is given as:

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Where:

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According to Deming (2000), a margin of error of 8% will yield reliable results, hence this was adopted for this study. Thus, using Yamane (1967)’s formula and applying Deming (2000)’s acceptable margin of error rate, the sample size for academic staff population of 491 is 119 respondents. The sample size from each stratum is illustrated in table 3.2:

Table 3.2: Sample size from each stratum

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Source: Survey Data (2017).

3.4 Data Collection Instruments

Due to the fact that the study applied a quantitative research approach, questionnaires served as the means of gathering data. A pilot test with sample size of 18 respondents was carried out before the actual distribution of questionnaires. These were distributed to the respondents at the University B who were not included in the final study.

3.5 Theoretical Research Model: Binary Logit Model

This study analyses how much the hypothesised factors are related to academic staff retention in Zimbabwean academic institutions. The dependent variable is a dummy, thus it takes a value of zero or one depending on whether or not an academic staff member intends to leave his or her job. However, the independent variables are both continuous and discrete. The logistic regression (logit) model was adopted for this study.

Logistic regression is a form of regression used to explain and predicts a categorical dependent variable. It works best when the dependent variable is a binary categorical variable (Gujarati, 2010). The main advantage of logistic regression is that it is not restricted by the normality assumption which is a basic assumption in the ordinary least squares regression analysis (Maddala, 2009).

3.6 The Model

The logit model was estimated and it is given as:

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The dependent variable is called INTENT and is a binary variable, which is used to determine if a lecturer intends to leave (INTENT=1) or intends to stay (INTENT= 0) current employment. The explanatory variables include Age (the lecturer’s age measured in years), Sex (a dummy variable which measures whether the respondent is male or female), Mrst (a measure of one’s marital status), Training (the period of training received from the current employer), Remun (one’s remuneration), Workload (the lecturer’s number of teaching hours per week), Advance (perceived career advancement opportunities) and Education (which depicts the respondent’s level of education). The error term µ represents all other possible determinants of academic staff retention which are not explained by this model.

4. DISCUSSION OF RESULTS

4.1 Response Rate

From the survey, 21 questionnaires were distributed to respondents from University A and of these, 16 questionnaires were returned. This gives a response rate of 76.2%. In addition, out of the 98 questionnaires distributed to respondents from the University B, 81 were returned, hence a response rate of 82.7%. The overall response rate was 81.5%. Table 4.1 illustrates the response rate.

Table 4.1: Response rate

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Source: Survey data (2017).

4.2 Descriptive Statistics

Data gathered through questionnaires was first entered into Microsoft Office Excel. It was then exported to Eviews Version 10 software package for analysis.

4.3 Results of the Final Model Estimated

The model was estimated using Eviews Version 10. The variable coefficients, standard errors, probability values and some diagnostic statistics extracted from Eviews are shown in Table 4.2.

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¹ This document is a summary of my MBA dissertation. References provided at the end of this document were not edited. They are sources which were consulted for the entire dissertation.