Akinlawon Olubukunmi AMOO Jamila Khatoon ADAM

The Impact of Supervisor Support, Performance Feedback and Workload on the Engagement of TVET Lecturers in Gauteng, South Africa

Employee engagement revolves around “job involvement, organisation commitment and job satisfaction”. Research has shown that many organisations fail in promoting employee engagement and as a result pay dearly for disengagement. The purpose of this paper is to explore what antecedents of engagement drive TVET college lecturers to be engaged with the outcomes of an organisation, in order to identify further strategies that organisations can implement to improve engagement. This study was part of a larger interdisciplinary research project in which a cross-sectional design with a survey data-collection technique was used. Five variables were considered with 6 hypotheses. Results indicate that supervisor support, performance feedback and adequate workload are important for job engagement among lecturers within the TVET sector, while performance feedback and adequate workload are both important for organisation engagement. Based on these findings, this study recommends the involvement of top management in the creation of strategic interventions that may enable more lecturers to be more productively engaged with their job and organisation.
JEL Classification O15, M10
Full Article

1. Introduction

The concept of employee engagement has received a great deal of attention since the past three decades, particularly because of the interest from academic scholars, who found through various empirical research that engagement offers a number of positive benefits (such as business profitability, workforce excellence, employee loyalty, etc.) to any organisation willing to pay its price (Saks and Gruman, 2014, Bakker, 2011; Shuck and Wollard, 2009). While the research on engagement continues, researchers have yet to find a common ground when it comes to the definition of the concept (Shuck, 2010). Kahn (1990) in his seminal work, defines engagement as “the harnessing of organisation members’ selves to their work roles; in engagement, people employ and express themselves physically, cognitively, and emotionally during role performances”. Since Kahn’s (1990) first definition, many scholars have made attempts to define the concept, with all agreeing at least, that employee engagement relates to job involvement, organisation commitment, job satisfaction and personal fulfilment (Saks and Gruman, 2014; Bakker, 2011; Shuck, 2010; Schaufeli and Bakker, 2002; Khan, 1990).

What is clear though, according to Kahn’s definition is that engagement requires people to consciously invest themselves into their job roles (Kahn, 1990; Schaufeli et al., 2002; Saks, 2006; Shuck, 2010). This conscious investment has been argued to yield enormous benefits for the individual, as well as the organisation, with more organisations now investing substantially into the promotion of their employee’s engagement (Shuck, 2010; Shuck and Wollard, 2009; Shuck et al., 2011) in order to leverage the benefits inherent in the concept (Shuck et al., 2011).

The more organisations invest in employee engagement, the more they realise that their employees are the greatest asset in a competitive business environment (Katzenbach, 2000; Habraken, 2013). It is thus important that more organisations embrace this great human resource asset if they must remain relevant (Katzenbach, 2000). Academic scholars are playing their part, conducting study upon study, unearthing key drivers (or antecedents) of engagement, and providing strategic recommendations to organisations who care to listen (Shuck and Wollard, 2009; Saks, 2006; Shuck et al.; 2011).

However, with the multitude of research conducted on employee engagement, few studies have considered antecedents of engagement in the Technical Vocational and Education and Training (TVET) colleges in Gauteng, South Africa (Mmako, 2016). There is no doubt that more research is required in this area, and this study seeks to add to the body of knowledge with respect to employee engagement within the TVET sector. This additional knowledge may help uncover key antecedents that drive engagement among TVET college lecturers and will serve as one of the few vital starting points for the management of such colleges in their adoption and implementation of employee engagement strategies.

This paper’s purpose is to investigate what antecedents (or variables) of engagement drive Technical and Vocational Education and Training (TVET) lecturers to be engaged with the outcomes of an organisation. The study focuses on lecturers in the engineering studies, business studies, utility studies and occupational programme departments at 8 public TVET colleges in South Africa (SA). The paper begins by providing some insight into the concept of employee engagement and the antecedents of engagement examined in the study, with relations to past studies. Next, the research methodology, using a survey for data collection is then described. Results of bivariate correlation and structural equation modelling, discussion and conclusion then follow.

2. Literature Review

2.1. Employee Engagement

Since employee engagement became popular among scholars, there has been no consensus on the definition of the concept (Saks and Gruman, 2014; Shuck, 2010). However, two popular definitions are commonly cited in related research – Kahn’s (1990) definition and Schaufeli et al’s (2002) definition (Saks and Gruman, 2014; Shuck, 2010). Kahn’s (1990) definition portrays the employee as playing a conscious part in their engagement at work. The employee knows they are at work and are fully involved on the job. Schaufeli et al. (2002) on the other hand, defines engagement as “a positive, fulfilling, work related state of mind that is characterized by vigor, dedication, and absorption”. In other words, Schaufeli et al. (2002) considers engagement to be a byproduct of the workplace environment. A healthy positive workplace environment may therefore promote engagement, while a negative unhealthy workplace environment may hamper engagement. Furthermore, Schaufeli et al’s definition is considered by many scholars as the opposite of burnout, and as such, concerns on whether it fully captures the meaning of the concept abounds (Saks and Gruman, 2014).

In recent publications, attempts have been made to capture the meaning of employee engagement, by expanding on the work of Kahn (1990) and Schaufeli et al., (2002). Shuck and Wollard (2009) define it as "an individual employee’s cognitive, emotional, and behavioral state directed toward desired organisational outcome’’, while Habraken (2013) defines employee engagement as “the state of emotional and intellectual involvement that motivates employees to do their best work”. It is evident from all the definitions, that employee engagement is a conscious effort to physically, mentally and emotionally behave positively on the job in order to accomplish job tasks and organisational goals and above all to experience personal fulfilment. It is an invocation of an employee’s complete presence on the job for result-driven performance. Kahn’s definition is considered a more complete definition because it depicts engagement as an intentionally triggered, positive on-the-job behaviour. This research is therefore based on Kahn’s (1990) definition of employee engagement. The antecedents (or variables) in the study are – (1) supportive supervisor relations, (2) performance feedback (3) workload questionnaire and (4) employee engagement (job engagement and organisation engagement).

2.2. Supervisor Support and Employee Engagement

Supervisor support has been described as employees’ interpretation of how much concern their supervisor show for their well-being and how much their contributions to the organisation is valued (Bhanthumnavian, 2003). Supervisors are integral to the growth and entrenchment of organisational culture - a culture that promotes employee excellence and success (Bakker and Demerouti, 2007) and all round work-life balance (Thompson et al., 1999; Hammer et al., 2007). Supervisor support can take many forms such as the provision of required job resources or the expressions of concern by the supervisor with the sole aim of enhancing the well-being of the employee (Kossek et al., 2005; Bhanthumnavian, 2003). Supervisors are vital to the development of organisational culture that motivates employees to excel (Bakker and Demerouti, 2007) and to the integration of employees' work and family roles (Thompson et al., 1999; Hammer et al., 2007).

Due to the fact that supervisors are widely regarded as organisation agents, who are responsible for coordinating employee performance, their feedback is often interpreted by employees as the organisation’s feelings towards them (Bakker et al., 2008). Furthermore, because employees’ perceptions mould attitude, behaviour and consequently engagement in the workplace (Shusha, 2013), it is vital for supervisors to actively play their part to ensure that employees feel valued and supported by the organisation, so they can respond with increased engagement (Eisenberger et al., 2001). Research has shown supervisor support to be a major driver of employee engagement, with many arguing that when employees receive adequate supervisor support, they respond with increased engagement (Shusha, 2013). In the academic context, past studies have shown that supervisor support is critical to better job performance (Uzun and Ozdem, 2017), the reduction of the academic staff’s intention to turnover (Afzal et al., 2019), and employee engagement (Rothmann and Jordaan, 2006; Barkhuizen et al., 2013; Silman, 2014). Based on the above, we propose the following hypotheses.

Hypothesis 1a: Supervisor support will be positively related to job engagement.

Hypothesis 1b: Supervisor support will be positively related to organisation engagement.

2.3. Performance Feedback and Employee Engagement

Many definitions have been provided for performance feedback, especially from the organisations’ viewpoint. Based on performance management process, Alston and Mujtaba (2009) defines feedback as “an exchange of information about the status and quality of work products”. Dobbeleaer et al. (2013) considers feedback to be the provision of information relating to the performance of an employee, with the aim of improving the performance of that employee.

In light of the above definitions, performance feedback is a means through which information about current and past performance is provided to employees. This information details what, and how well they have been performing in relation to their task requirements. Positive feedback can help improve employee performance. It has the ability to stimulate the recipient’s impetus for achieving the desired organisational and personal standards and is a source of valuable information to employees about their work, strengths and potential improvement (Krasman, 2012:19; Dobbeleaer et al., 2013:89).

Evidence from past studies (Demerouti et al., 2001; Van den Broeck et al., 2008; Jenaro et al., 2011; Bakker, 2011) attests to the pivotal role that performance feedback plays in promoting engagement among employees and how a lack of, or poor performance feedback is a good predictor of counterproductive behaviours, such as intention to quit. Within the academic context, Rothmann and Jordaan (2006), Rothmann and Barkhuizen (2008), Tamayo (2016) and Giles-Merrick (2018 all found in their studies that performance feedback is a significant positive predictor of employee engagement. In light of the above, the following hypotheses are proposed.

Hypothesis 2a: Performance feedback will be positively related to job engagement.

Hypothesis 2b: Performance feedback will be positively related to organisation engagement.

2.4. Workload and Employee Engagement

Workload can be described as “perceived pressure” linked to an enormous amount of work and work-task heaviness. It is the extent to which a job is demanding with regards to mental exertion, complexity and time taken to perform such work (Tomic and Tomic, 2010). An employee would be considered as having a heavy workload, when they have so much to do that, they are unable to meet employer-stipulated, task-related deadlines (Tomic and Tomic, 2010). Researchers have argued that employees possess a finite amount of energy to get through each work-day, and that engaged employees dispense this positive, physical and emotional energy in the execution of their work-related duties (Macey and Schneider, 2008), eventually giving way to fatigue and exhaustion, the mind’s natural response to declining resources and increasing demands (Bakker et al., 2004). Therefore, when faced with heavy workloads, employees have to deploy additional energy in order to match the job demands, speeding up the rate of exhaustion, leading to burnout and consequently disengagement (Bakker et al., 2004:87; Schaufeli and Salanova, 2007). Past studies (Ugwu and Onyishi, 2020; Upadyaya et al., 2016; Tomic and Tomic, 2010; Bakker et al., 2010) have all reported that workload is a statistically negative predictor of employee engagement. The common argument among these authors is that high workload could hamper employee engagement. Based on the evidence above, the following hypotheses are proposed.

Hypothesis 3a: Workload will be negatively related to job engagement.

Hypothesis 3b: Workload will be negatively related to organisation engagement.

The overall hypothesized model is shown in Figure 1.

Figure 1. Hypothesized model

Source: Own compilation

3. Research Methodology

This study was part of a larger interdisciplinary research project in which a cross-sectional design with a survey data-collection technique and simple random sampling was used. Ethical clearance was granted by the University’s ethics committee. Potential participants were told that participation is voluntary, and that their participation is anonymous and strictly confidential. Furthermore, the participants were informed that they could withdraw from participating in the project at any time during the data-collection stage without any consequence. The hypothesis previously mentioned were then tested in order to establish key relationships between the stated antecedents of engagement.

3.1. Participants

Employees (n = 190) from 8 public TVET colleges in Gauteng, South Africa, participated. 58.9% of the participants are female, while 41.15 are male. 62.6% are lecturers (PL1), 34.7% are senior lecturers (PL2), while 2.6% are head of departments (PL3). Over half (54.2%) of the participants are in the engineering studies department, 26.3% in the business studies department, 12.1% in the Utility studies department, while the rest (7.4%) are in the occupational programs department. In terms of education, 65.3% have a formal degree, 25.3% have a diploma, while 9.4% are in possession of a professional certificate. An overwhelming portion of participants (85.3%) are permanent employees, while 14.7% are temporary.

Participants’ age was grouped into five categories, 47.4% are in the 30-39 age group, 33.2% in the 40-49 age group, 11.1% in the 20-29 age group, 7.9% in the 50-59 age group, while the rest (0.5%) are 60 years or older. With regards to race, 74.2% of participants are black, 9.5% are white, another 9.5% are coloured, while the rest (6.8%) are Indians. The majority (67.9%) speak an indigenous African language, about 22.1% speak English, while the rest (10%) are Afrikaans-speaking. 61.1% of the participants are married, 34.7% are single, 2.6% indicate they are divorced, while 1.6% are separated.

3.2. Measurement and Research Instruments

The research instrument used for this study consisted of measures from several past studies, selected and adapted to help answer the research questions for this study. The instruments adapted are the supportive supervisor relations questionnaire (May et al., 2004), the performance feedback questionnaire (Linderbaum and Levy, 2010), the workload questionnaire (Moore, 2000) and the job engagement and organisation engagement questionnaire (Saks, 2006). A 5-point Likert scale is used in all the questionnaires (from 5 (strongly agree) to 1 (strongly disagree)).

3.3. Statistical Analysis

In the preliminary analysis, descriptive statistics and correlation analysis (via IBM’s SPSS) were generated using all the study variables. Afterwards, the internal consistency of the measurement instrument were computed based on Cronbach’s alpha coefficients, with values as low as 0.6 considered acceptable (Field, 2009; Byrne, 2012). In order to evaluate the discriminant validity as well as the convergent validity of the measures and latent variables used in the study, a confirmatory factor analysis (CFA) was performed. After screening, the data was exported to Mplus using a “.dat” file as input, and the hypothesized model estimated using the Satorra-Bentler's Maximum Likelihood Mean Adjusted (MLM) estimator.

Based on suggestions by (Byrne, 2012), the adequacy of the measurement model was evaluated using the “Chi-square goodness-of-fit test, Comparative Fit Index (CFI) [41], Tucker Lewis Index (TLI) (Tucker and Lewis, 1973), Root Mean Square Error of Approximation (RMSEA) and Standardized Root Mean square Residual (SRMR)” (Steiger and Lind, 1980). Regarding the thresholds used for model fit evaluation, this study used 0.06 as the cut-off value for the RMSEA, and a cut-off value of 0.90, for the TLI and CFI as proposed by (Hu and Bentler, 1999). To assess the effects of specific measurement items on the model fit, a standardized root-mean-square residual (SRMR) value with a threshold value of 0.08 or lower was adopted (Hu and Bentler, 1999). After confirming the full measurement model for the latent variables, the structural model was then evaluated. 

4. Results

To confirm discriminant validity among our study variables, a confirmatory factor analysis (CFA) was conducted. The goodness of fit of the hypothesised five factor model was compared with the goodness of fit of a one factor model in which all indicators were loaded on to one common factor. The results of these analyses show that the five factor model (CFI = 0.96; TLI = 0.95; RMSEA = 0.04; and SRMR = 0.08) provided a significantly better fit to the data than the single factor model factor (CFI = 0.83; TLI = 0.78; RMSEA = 0.08; and SRMR = 0.3). Factor loadings (Table 1) for the five factor model were all significant, ranging from 0.52 to 0.91. These findings provide evidence of discriminant validity among our five study variables.

The results also indicate that common method variance was not a concern in this study, because if it were, a single factor model would reveal acceptable goodness of fit similar to that of a more complex model” (Korsgaard and Roberson, 1995). Furthermore, means (M), standard deviations (SD), reliability coefficient (indicated by Cronbach’s alpha (CA)) and correlations among the study variables were computed. Table 1 shows the mean, standard deviation and reliability coefficients for all the variables used in the study, while Table 2 highlights the correlation coefficients among the studied variables of supervisor support (SS), performance feedback (PF), workload (WK), job engagement (JE) and organisation engagement (OE)

As shown in Table 1, job engagement has the highest reliability (0.92), with organisation engagement coming very close at 0.91. Performance feedback has the lowest reliability value (0.66) and workload has a reliability value of 0.67, both of which are still above the generally acceptable minimum reliability value of 0.6 (Field, 2009; Byrne, 2012). In addition, workload has the highest mean value, while performance feedback has the lowest mean value. In terms of standard deviation, supervisor support has the lowest value, an indication that the responses of participants to the questions making up the scale was very similar. Table 2 shows that the highest positive correlations was between PF – JE (r = 0.42, p < 0.01). The correlations serve as a preliminary method of testing the strength and direction of the relationships hypothesized previously and were not interpreted as causation. To establish causation, path analysis (a causal modelling approach) was performed, with the results of the measurement model highlighted in Table 3 and path analysis results discussed.

Table 1. Descriptive statistics

Latent variable Item code Factor loadings M SD CA
SS SS1 0.82 3.73 0.56 0.85
SS2 0.85
SS3 0.67
SS4 0.71
PF PF1 0.65 3.47 0.57 0.66
PF2 0.52
WK W1 0.60 3.90 0.60 0.67
W2 0.80
JE JE1 0.78 3.71 0.67 0.92
JE2 0.89
JE3 0.87
JE4 0.87
OE OE1 0.78 3.78 0.70 0.91
OE2 0.91
OE3 0.89
OE4 0.67
OE5 0.79

Table 2. Correlation coefficients

SS 1
RCW .31** 1
WK .37** .25** 1
JE .29** .42** .22** 1
OE .14 .12 .11 .36** 1

Note: **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).

4.1. Testing of the Research Model

As shown in Table 3, the hypothesised model depicted a good fit to the data: CFI = 0.93; TLI = 0.92; RMSEA = 0.05; and SRMR = 0.08. Next, the fit of this model was compared with that of a revised model, using modification indices suggested by the statistical software. The results of the model revision revealed that the goodness of fit improved slightly: CFI = 0.95; TLI = 0.95; RMSEA = 0.04; and SRMR = 0.08. Therefore, the final model used for path analysis was the revised model (Figure 2). Results of the path analysis and hypotheses tests are highlighted in Table 4.

Table 3. Comparison of structural models

Model 1 (Theorised) 194.205 126 0.93 0.92 0.05 0.08
Model 2 (Modified) 168.468 124 0.96 0.95 0.04 0.08

Table 4. Results of path analysis and hypotheses tests

Hypothesis Predicted Relationships Standard Path Loadings p-value Hypothesis Test Outcome
H1a JENG <--- SSUP 0.52 < 0.01 Supported
H1b OENG <--- SSUP 0.21 0.11 Not supported
H2a JENG <--- PF 0.71 < 0.01 Supported
H2b OENG <--- PF 0.91 < 0.01 Supported
H3a JENG <--- WKLOAD -0.40 < 0.05 Supported
H3b OENG <--- WKLOAD -0.58 < 0.05 Supported

Examination of the paths in the final model shown in Figure 2 and tabulated in Table 4, revealed the following:

- Hypothesis 1a: Supervisor support is a positive significant predictor of job engagement (β = 0.52, p < 0.01).

- Hypothesis 1b: The relationship between supervisor support and organisation engagement is not significant.

- Hypothesis 2a: Performance feedback is a positive significant predictor of job engagement (β = 0.71, p < 0.01).

- Hypothesis 2b: Performance feedback is a positive significant predictor of organisation engagement (β = 0.91, p < 0.01).

- Hypothesis 3a: Workload is a negative significant predictor of job engagement (β = -0.40, p < 0.05).

- Hypothesis 3b: Workload is a negative significant predictor of organisation engagement (β = -0.58, p < 0.05).

Overall, H1a, H2a, H2b, H3a, H3b were fully supported while H1b was not supported.

5. Discussion and Conclusion

5.1. Discussion

The current study investigated the antecedents of employee engagement by integrating elements of the job demand-resources (JD-R) model with Kahn’s (1990) theory of employee engagement. Though this study bears some resemblance to seminal and early studies on employee engagement (Saks and Gruman, 2014; Kahn, 1990; Schaufeli et al., 2002), it is unique in its own way because it is one of the first studies to investigate antecedents of employee engagement among lecturers at public TVET colleges in Gauteng, South Africa.

Figure 2. Modified measurement model

Source: Own compilation

The results of this study reveal that supervisor support is positively related to job engagement but not to organisation engagement. This implies that the more support the lecturers receive from their immediate supervisors, the more engaged they will be on the job. This finding is consistent with that of (Rothmann and Jordaan, 2006; Bakker et al., 2008; Shusha, 2013; Barkhuizen et al., 2013; Silman, 2014; Uzun and Ozdem, 2017; Afzal et al., 2019), all of whom found supervisor support to be a key driver of employee engagement. The lack of evidence between supervisor support and organisation support suggests that the lecturers receive no support from their supervisor when it comes to initiatives or activities that may help them engage more in the organisation. There is therefore a need for more research in this area, in other to better understand this relationship within the public TVET colleges in Gauteng, South Africa.

Furthermore, the path analysis reveal performance feedback is a statistically significant predictor of both job engagement and organisation engagement. This implies that in the context of this study, the lecturers consider performance feedback an important factor for employee engagement. They are of the opinion that performance feedback helps them engage more in their job roles and in the organisation. The better the quality of the performance feedback received, the more the lecturers are able to work towards closing any identified gap in their performance through better engagement with their job and the more they are able to participate in organisation-wide activities. This finding is consistent with those of (Schaufeli et al., 2009; Xanthopoulou et al., 2009; Bakker, 2011; Dobbeleaer et al., 2013).

This study also shows that workload is negatively related to job engagement and organisation engagement and is also a statistically significant predictor of both engagement. This is a clear indication that the lecturers believe that a heavy workload hampers their engagement levels, while adequate workload removes unnecessary stress which may lead to burn out and ultimately to disengagement. This result lends support to that of (Bakker et al., 2004; Schaufeli and Salanova, 2007; Tomic and Tomic, 2010; Bakker et al., 2010; Upadyaya et al., 2016; Ugwu and Onyishi, 2020).

5.2. Conclusion

The main purpose of this paper was to explore the antecedents of engagement that drive TVET college lecturers to be engaged with the outcomes of an organisation, by analysing questionnaire responses from a sample of TVET lecturers from 8 public TVET colleges in Gauteng, South Africa. Results show that supportive supervisor relations, performance feedback and the right amount of workload are important for job engagement among lecturers within the TVET sector, while performance feedback and adequate workload are both important for organisation engagement. 

Therefore, in order to maintain competitiveness and to ensure long-lasting success, organisations should endeavour to provide their employees with the resources needed to successfully perform their job duties. In the context of this study, supervisors should work tirelessly to develop a healthy relationship with their subordinates, in order to better provide the needed support for their daily tasks. The supervisors should show more concern for the well-being of their subordinates and should be willing at all times to provide necessary support and help employees embed into the organisation. Furthermore, constructive feedback relating to employee performance should be provided timeously, with the sole aim of helping the employee perform better on the job. Negative, morale killing feedback should be avoided at all cost. Management of TVET colleges should devise interventions based on the results of this study to help more lecturers achieve a higher level of engagement. In addition, strategic interventions that would lighten the workload of the lecturers should also be put in place by management. Lecturer workload should be reviewed periodically to ensure that no one is biting more than they can chew.

This study was not without its limitations. Firstly, the use of self-report questionnaires is known to introduce common source variance. This was mitigated by informing the participants that their participation is totally anonymous and confidential. Secondly, in terms of the generalisation of the findings, the results though applicable to other TVET colleges outside Gauteng, may not generalise well to other work contexts. In conclusion, the results of this study should be provided to top management of TVET colleges, so they can share with their employees, and together devise strategic measures that would improve lecturer engagement. These measures may also be beneficial to non-academic employees, which further research can investigate. Employee engagement policy, if available should be revised to reflect the findings of this study. If no such policy exists, management should work towards developing and implementing one. It is important to remember that the price we pay for engagement is cheaper than the cost of disengagement.


Author Contributions: Akinlawon Olubukunmi Amoo: Conceptualization, Methodology, Software, Data curation, Validation, Formal analysis, Writing – Original draft preparation. Jamila Khatoon Adam: Supervision, Writing - Reviewing and Editing.

Funding: This research received no external funding.

Conflicts of Interest: The authors state that they have no conflicts of interest.

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© 2022 The Authors. Published by Sprint Investify. ISSN 2359-7712. This article is licensed under a Creative Commons Attribution 4.0 International License. Creative Commons License
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Akinlawon Olubukunmi Amoo, Durban University of Technology, Department of Entrepreneurial Studies and Management, South Africa, ORCID ID: 0000-0002-5314-1638
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Akinlawon Olubukunmi AMOO
Durban University of Technology, South Africa

Jamila Khatoon ADAM
Durban University of Technology, South Africa