ORIGINAL RESEARCH

 

Impact of contextual factors on organisational performance mediated by talent management

 

 

Gladys B. Zake^I; Petronella Jonck^{II, III}; Anna-Marie Pelser^II

^IBusiness School, North-West University, Potchefstroom, South Africa
^IIDepartment of Global Innovative Forefront Talent, Faculty of Economic Management Sciences, North-West University, Mafikeng, South Africa
^IIIDepartment of Industrial Psychology and People Management, University of Johannesburg, Johannesburg, South Africa

Correspondence

 

 

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ABSTRACT

ORIENTATION: Talent management has become a significant concept in human resource management because of the potential influence thereof on organisational performance in a competitive business environment.
RESEARCH PURPOSE: This study investigated the impact of contextual factors on talent management and organisational performance of small, medium and micro enterprises (SMMEs) in the Gauteng manufacturing sector.
MOTIVATION FOR THE STUDY: There is a paucity of research on how organisational context influences the implementation of talent management and related organisational performance.
RESEARCH APPROACH/DESIGN AND METHOD: A quantitative cross-sectional research design was adopted. The target population included proprietors, general managers and human resource practitioners employed at manufacturing SMMEs in the Gauteng province. A self-designed survey was utilised to gather data from 395 participants. Statistical analysis included structural equation (direct effect) and mediation modelling (indirect effect).
MAIN FINDINGS: Exogenous and endogenous contextual factors and specifically perceived importance of talent management and SMME size, statistically significantly predicted talent management and subsequently organisational performance. Coronavirus disease 2019 (COVID-19) impact, perceived talent management importance, SMME size and enterprise age statistically significantly influenced talent management directly. Similarly, competition perception, COVID-19 and talent management importance impacted organisational performance.
PRACTICAL/MANAGERIAL IMPLICATIONS: To promote strategic human resource management and significantly contribute to organisational performance, SMMEs in the manufacturing sector should comprehend the strategic role of talent management, as well as the influence of exogenous and endogenous contextual factors.
CONTRIBUTION/VALUE-ADD: This study extends the corpus of knowledge regarding talent management and organisational performance in SMMEs, underscoring exogenous and endogenous contextual factors.

Keywords: exogenous factors; endogenous factors; organisational performance; talent management; SMME size, establishment.

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Introduction

Talent management is a strategic imperative for organisations nationally and internationally, with a paucity of studies investigating talent management in developing markets (Jayaraman et al., 2018). Al Aina and Atan (2020) argued that talent management should be considered as a strategic tool that has to be implemented to obtain the desired organisational results. Talent is emphasised as a principal factor in organisational success (Macpherson et al., 2023). Zake et al. (2024, p. 2) citing Sohel-Uz-Zaman et al. (2022) defined talent management as a strategic and universal approach to human capital to ensure organisational performance. The previous authors expound that talent management in the context of organisational performance centres on customised and dedicated human management functions and practices to achieve organisational objectives resulting in organisational performance (Sohel-Uz-Zaman et al., 2022; Zake et al., 2024). Dedicated human management functions and practices underscore 'activities and processes that involve the systematic attraction, identification, development, engagement, retention and deployment of those talents which are deemed valuable to an organisation toward creating strategic sustainable success' (Gallardo-Gallardo et al., 2020, p. 458). Rohida and Akbar (2019) defined talent management as integrated organisational initiatives that include recruiting, developing and maintaining talent as well as sustainable human resources to meet organisational requirements towards developing a competitive advantage as future leaders. Macpherson et al. (2023), reflecting on research by Phillips (2018), expounded that effective talent management has been a periodic stumbling block for South African organisations. As a result, the country has only 16% of the required talent to successfully compete on a global scale (Macpherson et al., 2023 citing Phillips, 2018). Moreover, talent management to achieve sustainable performance in developing economies is yet in the infancy stage (Mujtaba & Mubarik, 2020). Hence, the context appears to be pivotal in the study of talent management.

Gallardo-Gallardo et al. (2020) opined that talent management cannot be comprehended as a separate phenomenon ascribed to the implementation thereof within an organisational context, which, in turn, is part of a broader societal and even global context. Thunnissen and Gallardo-Gallardo (2019) expounded that the impact of contextual factors on talent management conceptualisation and implementation has largely been neglected. In the same vein, Aleksy and Urban (2022) found that research underscoring the impact of contextual factors on talent management practices is scarce. Irfan et al. (2023) evaluated the impact of human resource management on organisational performance in Pakistan, underscoring exogenous (e.g., competition perception and nature of services) and endogenous (e.g., [SMME] size and enterprise age) factors. A paucity of studies focuses on the impact of contextual factors on talent management and organisational performance in the South African context. Therefore, the research reported on aims, firstly, to ascertain the impact of contextual factors associated with talent management and organisational performance in the context of the manufacturing sector. Secondly, to ascertain whether talent management mediates the relationship of contextual factors with organisational performance. The significance of the reported research lies in the empirical evidence that demonstrates the value of talent management as a strategic tool fostering organisational performance specifically in SMMEs.

 

Research purpose and problem statement

Contextual factors influence talent management and organisational performance even though the stated has been underappreciated in the corpus of knowledge (Gallardo-Gallardo et al., 2020) with limited empirical research in the South African context. Pocztowski and Pauli (2021) confirmed a dearth of empirical studies relating to the impact of contextual factors on talent management specifically. Moreover, Harney and Alhkalaf (2021) noticed a dearth of human resource management, including talent management research across various types of SMMEs, with a seemingly limited consideration for SMME characteristics and contextual factors. Roumpi and Delery (2019, p. 431) opined that SMMEs provide 'a unique but under-researched context' validating the assertion by Katz et al. (2000) as cited in Harney and Alhkalaf (2021, p. 5) that SMME contexts provide vital insights for human resource management research of which talent management is part and parcel of. Similarly, Shafeek (2016) alluded to a dearth of research underscoring the study of human resource practices in SMMEs. To date, a similar study to that of the research reported on could not be identified in the South African context. Considering the forestated, the study aimed to investigate and test by means of a hypothesised statistical model the direct effect of exogenous and endogenous contextual factors on talent management and organisational performance, specifically with reference to SMMEs in the Gauteng manufacturing sector. Additionally, burgeoning research underscores the strategic imperative of talent management based on a growing demand to link human resources with organisational performance (Sohel-Uz-Zaman et al., 2022; Zake et al., 2024). Therefore, the research under discussion explores the strategic role of talent management, which subsumes mediating the impact of contextual factors on organisational performance (viz. indirect effect). The rationale for the study is based on the belief that investigating the impact of contextual factors on talent management would elucidate the effectiveness thereof (Gallardo-Gallardo et al., 2020).

 

Literature review

Antecedent - Contextual factors

Gallardo-Gallardo et al. (2020) explained that talent management is a context-dependent phenomenon, necessitating its conceptualisation as it must operate within an organisational context. The contextualisation of talent management is viewed through a multilevel approach according to Muratbekova-Touran et al. (2018), further emphasising the importance of adopting a contextualisation approach in explaining and identifying the co-existence of the various factors surrounding the environment within which effective talent management operates (Muratbekova-Touron et al., 2018). Similarly, King and Vaiman (2019) emphasised the contextual milieu mainly at macro and micro levels, with the macro level characterised by exogenous contextual factors such as economic, political, regulatory, technological and cultural conditions. The micro level takes into consideration the endogenous contextual factors at organisational level wherein monitoring and effective strategic systems are implemented towards the achievement of effective talent management (King & Vaiman, 2019). At a micro level, management considers the implementation of talent management practices and how both exogenous and endogenous contextual factors interrelate to achieve effective talent management (King & Vaiman, 2019). Gallardo-Gallardo et al. (2020) reflecting on previous research (see, e.g., Paauwe, 2004; Paauwe & Farndale, 2017) explained that exogenous factors that influence talent management subsume competition market mechanisms, technology, innovation, product-market combination and institutional mechanisms, viz. social, political, cultural, legal and regulatory facets inherent to the organisational environment. Endogenous factors include organisational history, organisational size, strategy, culture and human capital (Gallardo-Gallardo et al., 2020). Furthermore, Jonck (2023, p. 315), reflecting on previous research, explained that organisational performance is contingent on the business environment consisting of exogenous and endogenous factors. Newman and Sheikh (2014) conducted a study in the Chinese SME context and found statistically significant correlations between external organisations, firm size, enterprise age, growth orientation and adoption of human resource practice. Furthermore, the Small Enterprise Development Agency (SEDA, 2023) identified enterprise age as an SMME contextual characteristic.

This study focused on both exogenous and endogenous contextual factors indicative of talent management and organisational performance as depicted in the hypothesised model. The exogenous contextual factors were operationalised as competition perception (viz. competitive market mechanism), nature of services rendered (viz. product-market combination) and coronavirus 2019 (COVID-19) impact (viz. social and legal facets indicative of a specific period). The endogenous contextual factors subsume the perceived importance of talent management practices (viz. implementation of talent management and human capital related), SMME size and enterprise age underscoring organisational history.

Mediator - Talent management

Effective talent management is pivotal with reference to 'responding to market demands, fast-tracking competitiveness and enhancing organisational capabilities' (Irudayaraj, 2018, p. 1). Researchers have attempted to explain the phenomenon of talent management in various ways. Nevertheless, the corpus of knowledge emphasises that talent management should be implemented within the context of the specific environment, taking into consideration the interrelated factors (Kravariti & Johnston, 2020; Mitchell & Alfuraih, 2018). As such, Kravariti and Johnston (2020) defined talent management in the public sector in relation to employees who possess the required competencies, knowledge and values related to the public sector's principles underscoring a shared strategic goal. Macpherson et al. (2023) citing Bussin (2014) defined talent management as an umbrella term referring to integrated activities to ensure that a suitable candidate is employed in the most appropriate position at the appropriate time to significantly contribute to organisational strategic objectives towards organisational performance. Hongal and Kinange (2020) provided a simplified explanation of talent management, focusing on how candidates enter the organisation and how they progress within or out of the organisation. Considering the aforesaid, there is an emphasis on the influence of talent management on organisational strategic intent, emphasising the role of effective talent management as a strategic tool towards the improvement of organisational performance. Almaaitaha et al. (2020) noticed that talent management statistically significantly influences organisational performance. Moreover, talent management is confronted with exogenous and endogenous contextual challenges, such as increased job mobility, changing workforce demographics, economic challenges, expanded use of information and communication technologies and globalisation (Cizmić & Ahmić, 2021).

Considering the mediating role of talent management, Chadee and Raman (2012) reported that talent management has a strong mediating effect on the relationship between an antecedent (viz. external knowledge) and organisational performance in India, specifically among offshore information technology (IT) providers. Almohtaseb et al. (2020) found that a facet of talent management, namely, effective performance management, mediated the relationship between talent management and organisational performance in the Jordanian healthcare sector.

Model outcome - Organisational performance

Organisational performance is defined as the measure of outcomes compared to inputs towards achieving the organisational strategic goals (Nene & Pillay, 2019). Elaborating on this definition, Almaaitaha et al. (2020) defined organisational performance as the outcome of all operations and a reflection of resource utilisation towards the achievement of strategic goals. Organisational performance is subject to the business environment consisting of exogenous and endogenous contextual factors (Jankowić et al., 2016). Exogenous contextual factors subsume competition, globalisation, markets, crime, labour and regulations, which influence organisational performance according to Olawale and Garwe (2016). Other exogenous factors, inter alia, economic, social, political, legal and technological factors impact organisational performance (Jankowić et al., 2016). In the current research competition, product-market combination, social and legal facets were emphasised. Endogenous factors that influence organisational performance include human capital (Irfan et al., 2023), organisational culture (Shahzad et al., 2012) and organisational structure (Nene & Pillay, 2019). In the human capital research report, talent management and organisational structure, viz. SMME size and enterprise age, were specifically investigated. Moreover, Cizmić and Ahmić (2021) reported that talent management had a positive statistically significant influence on organisational performance in Bosnia and Herzegovina, which is also in a developing country context corresponding to the South African developing country context.

Hypothesised model

A hypothesised model based on the literature review was developed and graphically illustrated in Figure 1, where contextual factors consisting of exogenous and endogenous factors are the antecedents that predicted organisational performance, viz. model outcome mediated by talent management practices.

 

 

Based on the above-hypothesised model, the following hypotheses were tested for this study:

H1: Competition perception statistically significantly influences talent management in the SMME manufacturing sector.

H2: Nature of service delivery statistically significantly influences talent management in the SMME manufacturing sector.

H3: COVID-19 impact statistically significantly influences talent management in the SMME manufacturing sector.

H4: Perceived talent management importance statistically significantly influences talent management in the SMME manufacturing sector.

H5: The SMMEs size statistically significantly influences talent management in the SMME manufacturing sector.

H6: Enterprise age statistically significantly influences talent management in the SMME manufacturing sector.

H7: The variance in organisational performance of SMMEs in the manufacturing sector can statistically significantly be attributed to competition perception.

H8: The variance in organisational performance can statistically significantly be attributed to the nature of services.

H9: The variance in organisational performance can statistically significantly be attributed to COVID-19.

H10: The variance in organisational performance can statistically significantly be attributed to perceived talent management importance.

H11: The variance in organisational performance can statistically significantly be attributed to the size of the SMME.

H12: The variance in organisational performance can statistically significantly be attributed to enterprise age.

H13: Talent management statistically significantly influences the organisational performance of SMMEs in the manufacturing sector

H14: The statistically significant impact of competition perception on organisational performance is mediated by talent management practices.

H15: The statistically significant impact of the nature of services on organisational performance is mediated by talent management practices.

H16: The statistically significant impact of COVID-19 on organisational performance is mediated by talent management practices.

H17: The statistically significant impact of perceived talent management importance on organisational performance is mediated by talent management practices.

H18: The statistically significant impact of SMME size on organisational performance is mediated by talent management practices.

H19: Talent management practices statistically significantly mediates the impact of enterprise age on organisational performance.

 

Research method

Participants and procedure

A quantitative cross-sectional analytical research design was implemented between June 2022 and July 2023. The cross-sectional characteristic of the research study refers to a structured observation based on data gathered from a sample at a singular point in time without repeat measures to make inferences (Botha et al., 2023). Kesmodel (2018, p. 389) explained that cross-sectional research designs could either be descriptive or analytical in nature where the last-mentioned aims to ascertain the nexus between different variables. The target population of the study comprised proprietors, general managers and human resources practitioners employed at manufacturing SMMEs in the Gauteng province. Statistics South Africa (2023, p. 4) defined manufacturing SMMEs as enterprises registered for value-added tax engaged in the manufacturing of food, textiles, clothing, glass, basic metals, furniture, chemicals, professional equipment and transport equipment to mention a few. The only exclusion criterion implemented was registration. Thus, unregistered informal sector SMMEs were excluded, while SMMEs operating from fixed building structures on business premises demarcated as such by municipal town planning regulations were included. An extrapolated 468 000 employees were employed in manufacturing SMMEs in the Gauteng province in 2021 (Statistics South Africa, 2023), which serves as the population of the research reported on. A total of 395 (n = 395) participants were included in the sample generated by a convenience sampling technique.

Measuring instrument

Data were collected by means of a self-constructed coded measuring instrument. The questionnaire comprised four sections. Section A underscored obtaining demographic information, such as gender, age, highest academic qualification, work experience and rank. Section B emphasised contextual factors relating to the SMME, inter alia, SMME size, date of establishment (viz. enterprise age), type of services rendered and level of competition. Section C addressed talent management practices, including recruitment and retention (12 items), succession planning and promotion (10 items), performance appraisal (9 items) and talent development (14 items). Additionally, Section C contained a 19-item sub-section that focussed on organisational performance. Section D comprised 13 items relating to COVID-19 impact. A four-point Likert scale was utilised for Sections C and D with 1 denoting 'strongly disagree' and 4 representing 'strongly agree'. Typical questions included, for example, 'most of the employees in the SMME are recruited through competitive processes' (sub-scale recruitment and retention), 'good governance such as organisational transparency and accountability are adhered to and of great importance in the SMME' (sub-scale organisational performance) and 'the SMME had to put in place measures to reduce the impact of COVID-19 on company performance such as work from home' (sub-scale COVID-19 impact). The pilot study reported a Cronbach's alpha coefficient of 0.951 for the total scale. A breakdown of the reliability per sub-category revealed that recruitment and retention had a Cronbach's alpha of 0.891, succession planning and promotion α = 0.766, performance appraisal α = 0.860, talent development α = 0.846, organisational performance α = 0.913 and COVID-19 impact α = 0.700. In this study, the total scale had an alpha coefficient of 0.948, and the internal consistency reliability coefficients for the sub-scales ranged between 0.64 (COVID-19 impact) and 0.90 (recruitment and retention).

Statistical analysis

The Statistical Package for the Social Sciences (SPSS) version 28, SPSS Amos and the SPSS PROCESS macro programme were utilised to analyse collected data. Statistical significance was set at the 95th (p ≤ 0.05) or 99th (p ≤ 0.01) percentile. Descriptive statistics were computed to provide a demographic profile of the sample, which revealed that the majority of the participants were identified as male (n = 279; 72.1%) and were in the early adulthood life stage, viz. 25-45 years (n = 307; 77.7%). Most of the participants held a Grade 12 qualification (n = 269; 68.6%) and occupied middle managerial level positions (n = 302; 83.9%) in the manufacturing industry. Most of the participants were employed at micro-enterprises with an annual turnover of less than R200 000.00 (n = 271; 70%), followed by small enterprises with an annual turnover between R200 000.00 and R399 000.00 (n = 79; 20.4%) and medium enterprises with a turnover of between R400 000.00 and R599 000.00 (n = 37; 9.6%). Lastly, 44 participants representing 12.2% of the sample were SMME proprietors, 14 participants (n = 3.5%) were general or senior managers and 76.5% (n = 302) of the sample comprised human resource practitioners. Additionally, descriptive statistics were used to summarise the data set specifically with reference to the contextual factors relating to SMMEs in the manufacturing sector and to determine the measures of central tendency and normality, viz. skewness and kurtosis (Botha et al., 2023). Skewness values below ± 3 and kurtosis values below ± 10 could be deemed indicative of a normal distribution (Kline, 2015). Furthermore, the psychometric suitability of the measuring instrument was assessed. Cronbach's alpha coefficient was computed to ascertain the internal consistency and reliability with guidelines elucidated by McCallaghan et al. (2019), inter alia, α ˃ 0.80 (exemplary), α ˃ 0.70 (extensive) and α ˃ 0.60 (moderate). Composite reliability was determined and ought to exceed the recommended threshold of 0.7 with an average variance extracted (AVE) value exceeding 0.5 indicative of convergent validity. Moreover, the AVE should be greater than the maximum shared variance (MSV) to indicate discriminant validity (Cheung et al., 2024). Ascribed to the potential for common method bias and the cross-sectional nature of the research design, confirmatory factor analysis (CFA) was performed (Kock et al., 2021). Common method bias occurs when both independent and dependent variables are measured using a similar response scale, resulting in correlation parameter estimate bias between variables (Du Plessis, 2023). If an unrotated solution produces one factor that accounts for more than 50% of the variance, common method bias is present (Kock et al., 2021). Structural equation modelling (SEM) in SPSS Amos was used to estimate the theoretical model and determine the model fitness indices (Ramlall, 2017).

Pearson R correlation was performed to determine the strength and direction of the bivariate relationship between variables using the following criteria to interpret results: r = 0.1 to r = 0.29 (small effect), r = 0.30 to r = 0.49 (medium effect) and r = 0.50 to r = 1.0 (large effect) (see Botha et al., 2023 and seminal work by Cohen, 1992). The bias-corrected percentile bootstrap method was computed to ascertain the mediating effect of talent management, with 95% lower level (LLCI) and upper level (ULCI) ranges excluding zero (McCallaghan et al., 2019). PROCESS version 4.3 SPSS macro (model 4) software was utilised to determine the indirect mediation effect of talent management (Du Plessis, 2023).

Ethical considerations

Ethical approval for this study was obtained from the Economics and Management Sciences Research and Ethics Committee (EMS-REC) reference number (NWU-00610-22-A4). Furthermore, standard ethics protocol was adhered to, which included permission to conduct the study, informed consent, voluntary participation, right to anonymity, confidentiality, privacy and honesty in presenting the results. Specifically, data were de-identified prior to data analysis to ensure data confidentiality, and informed consent was mandatory for participation.

 

Results

Firstly, the primary aim of the study was twofold in that the direct impact of contextual factors on talent management and organisational performance was investigated. Secondly, it investigated whether talent management mediates the hypothesised impact between contextual factors and organisational performance. Results of the study are presented in terms of the reliability and validity assessment, measurement model fit, common method bias and construct descriptive analysis, followed sequentially by the bivariate correlation analysis, structural equation modelling and, lastly, the mediation analysis.

Psychometric properties of the questionnaire

The psychometric properties, inter alia, reliability and construct validity of the questionnaire were evaluated with results depicted in Table 1.

 

 

As can be seen in Table 1, recruitment and retention had a Cronbach's alpha coefficient of 0.90 (exemplary), succession planning and promotion α = 0.86 (exemplary), performance appraisal α = 0.77 (extensive), talent development α = 0.86 (exemplary) and organisational performance α = 0.89 (exemplary). The COVID-19 impact had an alpha coefficient of 0.64, which can be deemed moderate. The composite reliability scores exceeded the recommended threshold of 0.7, while the convergent reliability AVE scores were 0.5 and above (Cheung et al., 2024). Specifically, the scores were as follows: recruitment and retention (CR = 0.92; AVE = 0.51), succession planning and promotion (CR = 0.89; AVE = 0.53), performance appraisal (CR = 0.93; AVE = 0.64), talent development (CR = 0.83; AVE = 0.50), organisational performance (CR = 0.85; AVE = 0.50) and COVID-19 impact (CR = 0.88; AVE = 0.66). Thus, results support the convergent validity of constructs in the measurement model. Discriminant validity is also supported, signifying that the constructs vary significantly from each other because the AVE scores exceed MSV scores (Van der Walt, 2018). Based on the results presented, the measuring instrument is deemed reliable and valid.

Common method bias

The common method bias test entails performing an exploratory factor analysis (EFA) with an unrotated factor solution to ascertain the number of factors that count for 50% of the variance (Du Plessis, 2023; Kock et al., 2021). To this end, the Kaiser-Meyer-Olkin (KMO) test for sampling adequacy returned a value of 0.936, and Bartlett's test of sphericity reverted a statistically significant p-value on the 99th percentile or p ≤ 0.01 (χ² = 12898.535; df = 2850; p = 0.000**). McCallaghan et al. (2019) explained that a value close to 1 would be indicative of patterns of correlations, which are relatively compact. Thus, factor analysis would revert individual and reliable factors. Exploratory factor analysis with oblique rotation was calculated, which indicated that 15 components had an eigenvalue exceeding 1, accounting for 61.484% of the total variance. Nonetheless, an inspection of the scree plot suggested a clear break after the fifth factor. To statistically verify the number of factors, a Monte Carlo simulation analysis was performed (Masukela et al., 2023). Lim and Jahng (2019) found that parallel analysis is deemed an accurate method to determine which factors to retain. Results obtained from the parallel analysis indicated that five components had eigenvalues exceeding the corresponding criterion value for a randomly generated data matrix consisting of 1000 cases. Hence, CFA was performed with a forced five-factor rotation. The CFA results revealed that the first factor accounted for 26.139% of the variance and underscored talent management practices, specifically recruitment, retention, succession planning and promotion, with factor loadings ranging between 0.750 and 0.313. Factor two accounted for 7.142% of the variance that emphasised organisational performance, with factor loadings ranging from 0.650 to 0.355. Factors three and five, respectively, accounted for 5.437% and 2.841% of the variance and focused for the most part on the perceived COVID-19 impact. Lastly, factor four accounted for 3.299% of the variance and included items relating to performance appraisal and talent development, with factor loadings ranging between 0.713 and 0.352. Considering these results, common method bias was absent.

Measurement model fit

Structural equation modelling, specifically, the maximum likelihood estimation in SPSS Amos 28, was calculated to assess the structural model or model fitness. Results reverted a satisfactory fit (χ²= -61447; p = 1.000; df = 15; Chi-square/degree of freedom [CMIN/df] = -4096; normed fit index [NFI] = 6.710; Tucker-Lewis's index [TLI] = -1.692; comparative fit index [CFI] = 1.000; root mean square error of approximation [RMSEA] = 0.000). An interpretation of the results revealed that the CFI and TLI exceed 0.90, representing a good fit. The CFI exceeds 0.90, and the RMSEA value is below 0.05, therefore indicating an acceptable model fit (Van Zyl & Ten Klooster, 2022). It is, therefore, concluded that the structural model fits the data and is appropriate for further analysis.

Construct descriptive analysis

Table 2 sets out the descriptive information computed for the measured constructs.

Table 2 reveals that the mean score for perceived competition perception was 2.97 (maximum = 5; standard deviation [SD] = 0.38) with a median of 3. Thus, participants rated the level of competition in the manufacturing industry as moderate. In terms of COVID-19 impact, the mean was 22.03 (maximum = 39; SD = 1.78), with a median of 21. As a result, participants perceived the COVID-19 impact to be moderately positive. Furthermore, the mean of talent management was 26.26 (maximum = 35; SD = 4.70) with a median of 26.00, indicating that participants deemed talent management important in the organisation. Considering the SMME size, the overall mean was 1.40 (maximum = 3; SD = 0.66) with a median of 1.00, indicating that most SMMEs were between micro- and small-enterprises. In terms of the enterprise age, the mean was 3.76 (maximum = 4; SD = 0.47), reflecting that most of the enterprises were established between late 1990 and 2023. Moreover, the mean for talent management practices was 118.07 (maximum = 162; SD = 19.22) with a median of 117, indicative of participants concurring that these practices are being implemented in the SMMEs. Lastly, the mean for organisation performance was 46.27 (maximum = 67; SD = 8.27) with a median of 47, indicating the participants deemed the SMMEs performance to be moderate. The skewness and kurtosis results are within the set threshold values (skewness ≤ 3; kurtosis ≤ 10) except for the perceived competition perception kurtosis score; thus, the supposition of univariate normality was met and supported. As a result, the maximum likelihood method utilised to evaluate the fitness of the model was justified (Fitong Ketchiwou et al., 2023; Masukela et al., 2023).

Bivariate correlation analysis

Under the assumption that univariate normality was met, Pearson product-moment correlation was performed to investigate the strength and direction of the relationship between the measured variables with results depicted in Table 3.

 

 

A closer look at the results in Table 3 revealed that talent management had a statistically significant large correlation with organisational performance (r = 0.66**). The association was positive; thus, as talent management increases, there would be a concomitant increase in organisational performance. Competition perception reverted a small statistically significant correlation with organisational performance on the 95th percentile (r = -0.12*). The association was negative, indicating an inverse relationship, viz. if competition perception increases, there would be a decrease in organisational performance. A similar result was found between competition perception and COVID-19 impact (r = -0.16**). Perceived importance of talent management reverted a small statistically significant association with talent management on the 99th percentile (r = 0.16**). Similarly, a small statistically significant association on the 99th percentile was reported between perceived talent management importance and organisational performance (r = 0.18**), as was the case with COVID-19 impact (r = 0.16**).

Nature of services reverted a small negative correlation on the 99th percentile with talent management (r = -0.14**), organisational performance (r = -0.13**) and perceived talent management importance (r = -0.15**). Statistically significant medium positive associations were revealed between SMME size and talent management (r = 0.44**), organisational performance (r = 0.30**) and perceived talent management importance (r = 0.31**). A small statistically significant association was found between SMME size and COVID-19 impact (r = 0.16**) and a negative small statistically significant association was reported between SMME size and nature of services (r = -0.25**). Enterprise age had statistically significant small and negative correlations with talent management (r = -0.28**), organisational performance (r = -0.16**), COVID-19 impact (r = -0.12**), and perceived talent management importance (r = -0.21**). A small positive correlation on the 99th percentile was reported between enterprise age and the nature of services rendered (r = 0.21**). Lastly, a medium negative association was reported between enterprise age and SMME size on the 99th percentile (r = -0.40**).

Structural equation modelling

To provide statistical evidence for the direct effects, viz. H1 to H13, SEM was performed with results summarised in Table 4.

 

 

In terms of the direct effect, Table 4 indicates that competition perception, COVID-19 impact and perceived talent management importance statistically significantly influenced organisational performance on the 95th percentile. COVID-19 impact, SMME size and enterprise age statistically significantly influenced talent management on the 99th percentile. Furthermore, talent management statistically significantly influenced organisational performance on the 99th percentile. Based on the results displayed, H3, H5, H6, H7, H9, H10 and H13 were accepted. H1, H2, H4, H8, H11 and H12 were rejected.

Mediation analysis

Six independent simple mediation models were tested by means of PROCESS version 4.3 macro in SPSS (model 4) by Hayes (2018). Organisational performance was the outcome variable in each model, with talent management practices as the mediator. The predictors subsume exogenous and endogenous contextual factors. The results of the mediation analysis are reported in Table 5.

As can be deduced from Table 5, model 4 and model 5 demonstrate adequate results to consider talent management as a mediator in the relationship between predictor variables and the outcome, viz. organisational performance. As a result, H17 and H18 were accepted, while hypotheses 14, 15, 16 and 19 were rejected. More specifically, considering model 4, the first regression between perceived talent management importance and talent management (path a) was statistically significant (β = 3.60; t = 0.14; p ≤ 0.01). Talent management was a statistically significant predictor of organisational performance (path b) (β = 0.26; t = 9.48; p ≤ 0.01). Moreover, a 95% bias-corrected confidence interval indicated that the indirect effect through talent management (path ab) (β = 0.92; SE = 0.10; LLCI = 0.72; ULCI = 1.13) was above zero providing evidence of a mediating effect. The results further indicated that approximately 44% of the variance in organisational performance was accounted for by the variation in both talent management and the importance thereof (R² = 0.44; f = 154.02; p ≤ 0.01; large practical effect). In addition, the mentioned pathways (a, b and c) are statistically significant and positive; hence, the mediation model is deemed a complementary mediation model, as per Zhao et al. (2010). Results for model 4 are graphically illustrated in Figure 2.

 

 

Results displayed in Table 5 showed that SMME size was a significant predictor of talent management (path a) (β = 12.96; t = 9.45; p ≤ 0.01**), and talent management subsequently predicted organisational performance (path b) (β = 0.28; t = 15.43; p ≤ 0.01**). Also, a 95% bias-corrected confidence interval indicated that the indirect effect through talent management (path ab) (β = 3.68; SE = 0.43; LLCI = 2.84; ULCI = 4.56) was above zero, providing evidence of a mediating effect. Moreover, approximately 9% of the variance in organisational performance was accounted for by the variation in both talent management and SMME size (R² = 0.09; f = 38.46; p ≤ 0.01; small practical effect). Lastly, the three pathways are statistically significant and positive, indicating a complementary mediation model (Zhao et al., 2010). Results for model 5 are graphically depicted in Figure 3.

 

 

Discussion and implications

Evidence from the study indicated that exogenous contextual factors, namely, COVID-19 impact and competition perception, had a statistically significant direct impact on organisational performance. At the same time, perceived talent management importance (viz. an endogenous factor) had a statistically significant impact on stated performance. The mentioned results support the assertion that organisational performance is subject to the business environment consisting of exogenous and endogenous contextual factors (Jankowić et al., 2016). Moreover, the results partially verify research by Olawale and Garwe (2016) that exogenous contextual factors subsuming competition perception influence organisational performance. Other exogenous factors, inter alia, social anomalies, for example, COVID-19, impact organisational performance (Jankowić et al., 2016), which was confirmed in the research reported on. Endogenous factors that influence organisational performance include human capital (Irfan et al., 2023) of which perceived talent management importance can be categorised, which was verified in this study.

COVID-2019 impact, an exogenous contextual factor, statistically significantly influenced talent management, while, endogenous contextual factors, inter alia, SMME size and enterprise age similarly impacted talent management. These findings support research by Muratbekova-Touron et al. (2018) in that the macro environment (viz. exogenous factors) and organisational environment (endogenous factors) interrelate to influence talent management. The mentioned findings also confirm research by Gallardo-Gallardo et al. (2020), which explained that both external and internal organisational contexts influence talent management. Results presented also support the notion put forward by Paauwe (2004) as well as Paauwe and Farndale (2017) relating to contextual-based models, which argue that internal and external factors influence human resources and, by extension, talent management systems. The mentioned result partially confirms research findings by Newman and Sheikh (2014) in that firm size and age had a statistically significant correlation with human resource management adoption and growth orientation.

Furthermore, talent management was found to predict organisational performance. This result verifies research by Irfan et al. (2023), which found that talent development, a facet of talent management, statistically significantly influenced organisational performance. Similar findings were reported by Al Aina and Atan (2020). The reported research revealed that talent management, as the main construct, statistically significantly influenced organisational performance, which confirms research reported by Cizmić and Ahmić (2021) in a developing country context. More specifically, Cizmić and Ahmić (2021) research reported that talent management had a positive statistically significant influence on organisational performance in a developing country context, which corresponds to that of South Africa.

Considering the indirect effect, results indicate that talent management mediates the relationship between endogenous contextual factors (viz. perceived talent management importance and SMME size) and organisational performance. Ascribed to a lacuna in the corpus of knowledge, the mentioned finding could not be verified in the existing literature. However, research reported by Chadee and Raman (2012) partially verified the result presented; however, the antecedent in the mentioned author's study was external knowledge and not the measured predictors. Despite this, talent management has been found to mediate a relationship with organisational performance as an outcome.

Managerial implications based on the findings presented would suggest that SMMEs in the manufacturing sector need to be cognisant of exogenous and endogenous factors that might impede organisational performance. Thus, SMMEs in the manufacturing sector need to put contingency plans in place to curb negative perceptions related to competition and counter any lingering impact of the global pandemic on the organisation to ensure sustainable organisational performance. Furthermore, talent management should be an organisational strategic objective because it mediates the relationship with organisational performance. The strategic role of talent management should be emphasised, specifically perceptions of the importance of talent management and talent management as a human resource management strategy in pursuit of organisational excellence. Training and awareness initiatives could contribute towards improved understanding of the importance of talent management in the pursuit of organisational performance, specifically for SMMEs in the manufacturing sector.

 

Limitations and future research

Caution is advised when interpreting the results because a non-probability sampling technique was used to generate the sample that could adversely influence the external validity of the results. In addition, the research was limited to the Gauteng province and to the manufacturing sector. It is recommended that future research endeavours expand research nationally to have a national perspective. It is also recommended that the research be extended to other business sectors. Furthermore, it is recommended that future studies verify the research reported on as there is limited empirical evidence on the impact of exogenous and endogenous contextual factors within the South African context. Future studies could, for example, focus on economic instability and infrastructure (viz. loadshedding) as exogenous factors influencing organisational performance. While talent management, conceptually, might not be a significant mediator in a possible relationship between infrastructure and organisational performance, it has the potential to be a mediator in the relationship between economic instability and organisational performance.

 

Conclusion

The research attempted to address a lacuna in the corpus of knowledge relating to strategic human resource management research, notably talent management in various SMME types, specifically focusing on contextual factors and characteristics. Furthermore, the research explored the mediating effect of talent management, which speaks to the organisational effectiveness of the latter. Based on the objectives of this study and the empirical findings, it can be concluded that talent management remains an important aspect that can have a positive impact on organisational performance if implemented and practised by SMMEs in the manufacturing sector. It is also pivotal to pay careful attention to exogenous and endogenous contextual factors ascribed to the impact thereof on organisational performance.

 

Acknowledgements

Competing interests

The authors declare that they have no financial or personal relationships that may have inappropriately influenced them in writing this article.

Authors' contributions

G.B.Z., a PhD student, was responsible for conceptualising the article, development of the questionnaire, data gathering and write-up. P.J., the main promotor, was responsible for data analysis, methodological rigour and article editing. A.P. was the critical reader and co-promotor of the PhD student.

Funding information

This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

Data availability

Data sharing is not applicable as no new data were generated or analysed in this study.

Disclaimer

The views and opinions expressed in this article are those of the authors and are the product of professional research. It does not necessarily reflect the official policy or position of any affiliated institution, funder, agency or that of the publisher. The authors are responsible for this study's results, findings and content.

 

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Correspondence:
Petronella Jonck
petronellajonck@hotmail.com

Received: 29 Jan. 2024
Accepted: 25 June 2024
Published: 14 Aug. 2024