Sharma and Naude (2021) state that the South African automotive industry, which is the most significant sector in the South African economy, needs to function efficiently to compete globally. The gross domestic product (GDP) of a country is one of the main indicators used to measure the performance of a country’s economy (StatsSA, 2021). The automotive industry is currently a significant contributor to South African GDP (6.8%), with 110 000 people employed across vehicle and component manufacturers. It also has an estimated indirect impact on 1.5 million people (BusinessTech, 2021). In South Africa, the artisan occupation is facing two important issues: the need for expansion and the need for transformation (Teti, 2016). There are widespread claims of a shortage of artisans in South Africa as well as a general agreement that artisan development is crucial (Wildschut & Ralphs, 2016).

Hufkie (2021) states that reskilling of artisans within the automotive industry in South Africa is needed towards Fourth Industrial Revolution (4IR) readiness. Many South African artisans are being deskilled and the remaining artisans are being reshuffled to assembly lines likely to be automated with time (Chigbu & Nekhwevha, 2020). The study of Mey, Poisat and Stindt (2021, p. 1) revealed that:

Leadership support is vital in endorsing an enabling environment that cultivates talent. Without leadership’s utilisation of opportunities to provide an enabling environment, talent is not secured and will not thrive (Shikweni, Schurink, & Van Wyk, 2019). A transformation leadership style within the South African automotive industry will, therefore, be needed to develop interventions to manage the talent of artisans and to increase their work engagement.

It is essential that a culture is created where talent and engagement can emerge, and where leaders and managers are willing to put practices in place to ensure the development of talented and engaged employees (Savarimuthu & Jothi, 2020). Real leadership is needed in the automotive industry’s competitive environment to guide subordinates so that they share goals, attitudes, values, and work towards the achievement of organisational strategies (Gautam & Enslin, 2019). Leaders also need to stimulate employees’ work engagement through progressive policies and work practices (Decuypere & Schaufeli, 2020). By combining research about talent management, leadership and work engagement, it is possible for managers to develop strategies and approaches to work engagement and talent management that become self-reinforcing. However, such studies conducted in the South African automotive industry from an artisan perspective are not known. This study is, therefore, conducted to close this research gap by investigating the relationship between talent management, transformational leadership and work engagement within the automotive industry from an artisan perspective. Furthermore, it is hoped that this study will contribute to an in-depth understanding of the intervening effect that talent management has on transformational leadership and work engagement.

The theory of transformational leadership was first coined by Downton (1973). Downton (1973) categorised three leadership tendencies, which are transactional leadership, charismatic leadership and inspirational leadership. Avolio, Waldman and Yammarino (1991) mentioned the four I’s of transformational leadership namely, idealised influence (modelling ethical and socially desirable behaviour, exhibiting enthusiasm about company strategy), inspirational motivation (to impart a vision that is appealing and worthy), intellectual stimulation (empowering employees to constantly be learning) and individualised consideration (giving employees a feeling of ownership in company goals and independence in the workplace).

Idealised influence is described by Hamidianpour, Esmaeilpour and Mashayekh (2016), wherein followers demonstrate respect and loyalty towards a leader who conveys an ideal sense and makes followers consider the leader as a perfect sense model of behaviour. Inspirational motivation is defined by Steinmann, Klug and Maier (2018) as the method in which a transformational leader inspirationally motivates followers to achieve more than expected by sharing his or her vision in order to inspire them towards a more effective and efficient performance of their duties. Intellectual stimulation refers to the leader’s ability to instil innovative and creative capabilities into followers as a way of building their critical thinking and problem-solving skills (Atiku & Anane-Simon, 2020). Individualised consideration is a dimension of transformational leadership whereby a leader pays special attention to individual differences, followers’ personal growth and needs for achievement by acting as a mentor or coach for them (Gonfa, 2019).

Swanwick (2017) opined that developing transformational leadership with training in the four I’s can enhance a leader’s effectiveness and satisfaction. Khorshid and Pashazadeh (2014) maintained that transformational leaders create an atmosphere of trust and motivate employees to work for the organisation beyond their self-interests. Transformational leadership has proven to be a pervasive model, inspiring shared purpose, engaging others and sharing the vision of an organisation (Swanwick, 2017). Mittal and Dhar (2015) also put forth the fact that a transformational leader is one who inspires followers to do more than originally expected.

Lewis and Heckman (2006) mentioned that there are three distinct perspectives regarding talent management. The first perspective defines talent management as a collection of typical human resource department practices, functions, activities or specialist areas such as recruiting, selection, development and career and succession management (Mercer, 2005). The second perspective on talent management focuses primarily on the concept of talent pools by focusing on the internal workforce (Schweyer, 2004). Employees who experience a talent management system that is based on an inclusive or stable philosophy are likely to feel supported and valued by their organisation because they work in surroundings that are generally appreciative of their talents (Meyers & Van Woerkom, 2014). A third perspective on talent management focuses on high performing talent (Axelrod, Handfield-Jones, & Michaels, 2002) on the one hand and generic talent (Walker & Larocco, 2002) on the other hand.

New perspectives on talent management by Rhodes and Fletcher (2013) bring approaches that are focused on both the employment and the work relationship and that consider the influence and well-being of role players at multiple levels. Talent management is an integrated, strategic and deliberate activity that is performed to direct how organisations source, attract, select, train, develop, retain, promote and deploy employees (Van Rooyen & Whittle, 2011). Johnson (2015) posits that talent management is about the early identification of key role players and articulation of duties, leading to ownership and clear accountability to ensure organisational success. Ogbari, Onasanya, Ogunnaike and Kehinde (2018) are of the opinion that one of the main components of talent management is continuously training personnel to maintain the required level of performance. Talent management contributes to creating a high-performance work culture within an organisation (Poisat et al., 2018).

Work engagement refers to a positive, affective-motivational state of high energy combined with high levels of dedication and a strong focus on work (Schaufeli & Bakker, 2010). Engaged employees demonstrate vigour, dedication and absorption when executing tasks in the workplace (Schaufeli, Salanova, Gonzáles-Romá, & Bakker, 2002). Dedication and vigour are regarded as the core components of work engagement (Schaufeli, 2013; Taris, Ybema, & Van Beek, 2017). Vigour means enhanced levels of mental resilience and energy and includes employees’ preparedness to put effort into their work (Schaufeli, Martinez, Pinto, Salanova, & Bakker, 2002; Sonnentag, 2017). Employees with dedication feel challenged, proud, inspired and enthusiastic, and experience a sense of significance about their work (Schaufeli et al., 2002a; Sonnentag, 2017). Absorption entails the immersion in one’s work (Schaufeli et al., 2002b).

Individuals who are engaged in their work have high levels of energy, are enthusiastic about their work and are completely immersed in their work activities (Bakker & Albrecht, 2018). The majority of studies have adopted a between-person approach, showing that there are mean level differences in work engagement between individuals as a function of working conditions, personal characteristics and behavioural strategies (Bakker, Demerouti, & Sanz-Vergel, 2014).

In this study, nine research hypotheses were developed and investigated. The development of each of these hypotheses is discussed in the following sections.

According to the research conducted by Ghafoor, Qureshi, Khan and Hijazi (2011), transformational leadership was positively related to talent management. Transformational leadership has an effect on talent management (Onyango, 2015). Widodo and Mawarto (2020) found that there is a significant relationship between talent management and transformational leadership.

The relationship between transformational leadership and talent management highlighted the following hypotheses:

According to research conducted by Church (2013), talent management positively relates to work engagement. The findings of Sopiah, Kurniawan, Nora and Narmaditya (2020) confirmed that talent management positively influences work involvement and employee performance. There is a significant positive relationship between talent management and work engagement (Toka & Hamida, 2020). Ishayama (2021) found that talent management has an impact on work engagement.

The relationship between work engagement and talent management highlighted the following hypotheses:

A positive leadership style, such as transformational leadership, has a relatively large correlation with work engagement (Hoch, Bommer, Dulebohn, & Wu, 2018). The findings of Gautam and Enslin (2019) indicated that transformational leadership moderately influenced work engagement more than transactional leadership and there was a positive relationship between these two constructs. An increasing number of studies suggests that transformational leadership influences work engagement (Breevaart et al., 2014; Tims, Bakker, & Xanthopoulou, 2011; Tuckey, Bakker, & Dollard, 2012). Leadership styles can potentially complement the known benefits associated with transformational leadership, particularly in explaining the emergence and maintenance of engagement in dynamic team-based working contexts (Bakker & Albrecht, 2018). Hayati, Charkhabi and Naami’s (2014) investigation of transformational leadership suggested that transformational leadership would likely be the most predictive characteristic of an optimal leader. In support of this statement, Avolio, Bass and Jung (1999) stated that transformational leaders embody characteristics of being charismatic and influential in their ability to make employees do more than expected at work.

The relationship between work engagement and transformational leadership highlighted the following hypotheses:

According to Lai et al. (2020), work engagement mediates the relationship between transformational leadership and employees’ task performance and helping behaviour. In addition, transformational leaders can enhance followers’ performance and foster their helping behaviours because they induce members’ work engagement and enable them to exceed expectations. Furthermore, Lai et al.’s (2020) research also depicted that role-based self-efficacy, transactional leadership and work engagement fully mediate the positive relationship between transformational leadership and members’ task performance and helping behaviours (Lai et al., 2020). Sopiah et al. (2020) found that work engagement had a moderating role between talent management and employee performance.

Talent management as a mediator between transformational leadership and work engagement highlighted the following hypothesis:

A graphic presentation of the hypotheses is presented in Figure 1.

Empirical research, which was conducted in this study, should primarily be embedded in theory. For the purpose of this study, current, proven and tested questionnaires on the variables of transformational leadership, talent management and work engagement were used. In other words, data were directly collected from the sample group. From the analysis of the statistical data gathered, conclusions were drawn.

This study was conducted at a large organisation that provides training to all artisans in the South African automotive industry. Automotive organisations were represented by automotive brands selling vehicles in all nine provinces in South Africa. From a total of 1045 artisans targeted, 609 respondents completed the questionnaire on transformational leadership, talent management and work engagement.

For the purpose of this research, artisans from 152 automotive South African organisations were targeted to complete the questionnaire. Purposive sampling was used to target the artisans who attended training at three different training venues at a specific point in time.

The study takes the ontological position of objectivism, with a positivistic epistemology. According to Botma, Greeff, Mulaudzi and Wright (2010), epistemology determines the methodologies, theories and procedure application throughout the research process. Therefore, a quantitative and non-experimental research approach was adopted. Leedy and Ormrod (2016) highlighted that descriptive quantitative research entails the identification and exploration of characteristics of the phenomenon and possible correlations amongst the multiple phenomena. A self-administered questionnaire survey design was utilised for data collection purposes. Cross-sectional and survey research designs were used in this study.

The following instruments discussed namely, transformational leadership, talent management and work engagement are those that were used in the study.

According to Bass and Riggio (2006), the most widely accepted instrument to measure transformational leadership is the Multifactor Leadership Questionnaire (MLQ), which assesses the Full Range of Leadership (FRL) model, including laissez-faire leadership, the components of transactional leadership, namely management by exception (both active and passive forms) and contingent reward, as well as components of transformational leadership. In the study of Louw, Muriithi and Radloff (2017), the Cronbach’s alpha assessment was done to establish the reliability of the scales of the measuring instrument and the scores were considered very reliable compared to the minimum acceptable score of 0.7 (Nunnally, 1978). For all factors, coefficients of greater than 0.7 (Nunnally, 1978, p. 226) were returned. Sufficient evidence of validity and reliability for the scales measuring the independent and dependent variables is thus provided. The specific scores for transformational leadership sub-constructs were: individualised consideration (0.892), idealised influence behaviour (0.844), inspirational motivation (0.868), intellectual simulation (0.869) and idealised influence attributes (0.849). The alpha scores were in agreement with similar studies, thereby making the measuring instrument and data reliable (Hayward, 2005).

A questionnaire on talent management was adapted from Van Hoek (2014), it consisted of 29 questions. The instrument was previously used in the study conducted by (Van Hoek 2014). In this study of Van Hoek (2014), performance management obtained the Cronbach’s alpha value of 0.845, however remuneration obtained Cronbach’s alpha of 0.619, finally career development obtained the Cronbach’s alpha value of 0.629. The Cronbach’s alpha should be more than 0.70 to make the scale more reliable, in this case two factors, that is, remuneration and career development obtained the Cronbach’s alpha value just below the acceptable level and performance management obtained an acceptable level of 0.845 of the Cronbach’s alpha value, that made the scale more reliable (Van Hoek, 2014).

Schaufeli et al. (2006) set out to develop a short questionnaire to measure work engagement and validate its scores. This questionnaire was used in this research to measure engagement. In the study of Hoole and Bonnema (2015), Cronbach’s alpha scores of 0.93 and 0.72 were obtained for the Utrecht Work Engagement Scale (UWES) and Performance management system (PMS), respectively. In the case of the PMS, one should take into consideration that reliability also tends to be lower when a scale consists of fewer than 10 items (Pallant, 2005).

The questionnaires which are in English were manually distributed to the artisans. Sealed boxes were used for collection in order to ensure anonymity and confidentially. The returned responses with missing values were excluded from the analysis.

The Stata version 15 statistical analysis software was used for the analysis of data. Descriptive statistics were reported in a form of frequencies and percentages. In terms of inferential statistics, the structural equation modelling (SEM) using the partial least squares path modelling approach (PLS-PM) was performed (Hair, Sarstedt, Hopkins, & Kuppelwieser, 2014). Bootstrapping was used to estimate the parameters and associated standard errors in the direct and indirect effect in the statistical analysis of the structural equation model through the non-parametric method.

Measurement model evaluation is aimed to evaluate the consistency and validity of the manifest variables. Consistency evaluations are through individual manifest and construct reliability tests. Whilst validity of the variables is tested based on convergent and discriminant validity, individual manifest reliability explains the variance of individual manifest relative to latent variable by calculating standardised outer loadings of the manifest variable. Structural equation modelling consists of two parts, namely the measurement model evaluation and the testing of the structural model. Measurement model evaluation is intended to evaluate the validity and consistency of the manifest variables. Validity of the variables is tested based on convergent and discriminant validity, and individual manifest reliability explains the variance of individual manifest relative to latent variable by calculating standardised outer loadings of the manifest variable, whilst consistency evaluations are done through individual manifest and construct reliability tests.

Approval was granted by the automotive industry to conduct research on this topic with their artisans. The study was conducted within the approved ethical framework. The participation by respondents was voluntary, and their completed questionnaires were treated as confidential. Permission to conduct this study was obtained from the company under study. Ethical permission was obtained from the High ethical standards that were followed in all aspects of the research process. All possible respondents were provided with an overview of the study, before inviting them to voluntarily participate. Participants were assured of the anonymity and confidentiality of their responses and were informed that they could withdraw from the study at any stage, should they feel it necessary to do so. All participants indicated their consent.

A total of 609 respondents participated in the study, and the response rate was 100%. The data revealed that there were more male respondents (96.72%) than female respondents (3.28%) who participated in the study. The data show that most respondents were Africans with 43.64%, followed by their white counterparts with 31.74%, mixed races represented 15.87% and the remaining 876% represented Indian participants. Thirty-four (34) (5.6%) were in the age group of 31–40 years, 13 (2.1%) were in the age group of 41–50 years and only 2 (0.3%) were over the age of 51 years. With regard to the education of the respondents, the majority of the respondents (56.83%) had grade 12% and 17.7% possessed an N4–N6 qualification. In terms of tenure, the data showed that 77.80% of the respondents who completed the questionnaire had a tenure of service between 1 and 5 years, 14.50% of the respondents had a tenure of service less than 1 year, 5.9% of the respondents had a tenure of service between 6 and 10 years of service, 1% of the respondents had a tenure of service between 11 and 20 years of service, 0.3% of the respondents had a tenure of service between 21 and 30 years of service and 0.50% of the respondents had a tenure of service more than 31 years. The percentage distribution of vehicle petrol mechanics was 78.9% compared to vehicle diesel mechanics representing 18.5%, vehicle spray painter 1% and the other individual fields of work less than 1% each.

The results presented in Table 1 show that the Cronbach’s alpha values for all the variables are between the cut-off points of 0.5 and 0.7. Dillon–Goldstein’s rho for all the variables was above 0.7 that indicates homogeneity of the factor. The results suggest that all the variables are reliable with regard to internal consistency. The quality of a measurement model was examined by testing the convergent validity, which shows the degree to which the indicators under the constructs are related. Convergent validity shows the amount of variance the indicators have in common: high factor loading and average variance extracted (AVE). According to Hair et al. (2014), AVE loadings above 0.5 are indicators of convergent validity. The results displayed in Table 1 indicate that the AVE loadings were above 0.5, except for continuance commitment with 0.3. The final model factor loadings are also reported in Table 1.

The direct effect in the statistical analysis of the structural equation model was determined through the non-parametric method. In the analysis of the non-parametric method, the identification of a loading close to 1 indicates a strong relationship, with the Perc 0.975 equal to a 95% confidence interval. Further loadings with a zero involved in the reading of the interval (–0.068:0.154) become insignificant. Furthermore, bootstrapping was used to estimate the parameters and associated standard errors. Table 2 indicates the different paths resulting from the statistical results (see Figure 2). TM1 is significantly related to TL. The relationship is strong and positive. This means that, when TM1 increases, TL also increases. Similarly, TM2 is significantly related to TL; however, the relationship is weak. TM1 has a moderate and positive relationship with WE1 and WE2; however, it is not as strong as with TL. Should TM1 increase, WE1 and WE2 increase. Furthermore, the results show that TM1 is a better predictor of TL than TM2. The relationship between TL and both WE1 and WE2 is not significant. The effect of TL on WE as a whole is not significant. TM2 is significantly related to WE1 and WE2. The relationship is strong and positive. Talent management is significantly related to TL, but not as strongly as to WE1 and WE2. Transformational leadership is not significantly related to WE1 and WE2. The relationship is very weak and not positive.

Bootstrapping was used to estimate the parameters and associated standard errors in the direct and indirect effect in the statistical analysis of the structural equation model using the non-parametric method. Table 3 indicates the total effect of the different paths from the direct and indirect statistical results. TM1 is still significantly related to TL. This relationship is positive. This means that, when TM1 increases, TL also increases. Similarly, TM2 is significantly related to TL, but this relationship is not as positive as the direct estimation analysis only. However, the relationship between TM2 and TL is still weak. The results show that TM1 is still a better predictor of TL compared to TM2. The relationship between TL and both WE1 and WE2 is low and weak and thus not significant. The low and weak effect of TL on WE as a whole is not significant. Goodness of fit (GoF) can be used as a global criterion that helps to evaluate the performance of the model in both the inner and the outer models. Basically, GoF assesses the overall prediction performance of the model. The main drawback with the GoF index is that there is no threshold that allows one to determine its statistical significance. Unfortunately, there is also no guidance about what number could be considered a good GoF value. GoF can be considered as an index of average prediction for the entire model. Although this is not entirely true, it helps in understanding GoF values. The calculated global GoF is 0.479, which exceeds the threshold of 0.36 suggested by Wetzels, Odekerken-Schröder and Van Oppen (2009). This led the researchers to conclude that the research model has a good overall fit.

The purpose was to determine the relationship between talent management, transformational leadership and work engagement from an automotive artisan perspective within South Africa.

The results showed that transformational leadership is negatively related to work engagement, talent management is positively related to transformational leadership and work engagement is negatively related to transformational leadership. Lastly, talent management is a mediator between transformational leadership and work engagement. These results could assist managers to become transformational leaders in order to properly manage talent amongst artisans in the South African automotive industry so that these artisans’ work engagement can improve. A talent management strategy that includes transformational leadership and work engagement should, therefore, be developed to guide the automotive industry on the way forward.