A talent management, organisational commitment and employee turnover intention framework for a government department in South Africa

Mokoena, Wasnaar; Schultz, Cecile; Dachapalli, Leigh-Anne Paul

doi:10.4102/sajhrm.v20i0.1920

Cited by 6 publications

(12 citation statements)

References 37 publications

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“…The study highlighted the importance of employer branding in attracting and retaining talent in the hospitality industry. Additionally, Mokoena et al (2022) developed a talent management framework for a government department in South Africa, emphasizing the mediating role of talent management in enhancing organizational commitment and reducing turnover intentions. The study underscored the importance of strategic talent management practices in the public sector to foster employee retention and organizational performance.…”

Section: Machine Learning Modelsmentioning

confidence: 99%

“…Through a comprehensive analysis of the dataset and rigorous model evaluation, this study aims to provide actionable insights into employee turnover prediction, empowering organizations to develop targeted retention strategies and cultivate a more engaged and stable workforce in an increasingly competitive landscape. By synthesizing findings from prior research and leveraging advanced analytics methodologies, this study endeavors to advance the understanding of turnover dynamics and offer practical guidance to organizations seeking to mitigate turnover risks and foster a culture of retention and engagement (Mokoena, Schultz, & Dachapalli, 2022;Olubiyi et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Identifying Patterns and Predicting Employee Turnover Using Machine Learning Approaches

Kanuto

2024

IJSB

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Employee turnover poses significant challenges for organizations, impacting productivity, morale, and financial stability. Identifying patterns and predicting employee turnover using machine learning approaches can help organizations proactively address retention issues and optimize workforce management strategies. The current study analyzed a dataset comprising 4653 valid respondent records sourced from Kaggle, containing diverse attributes related to employees' educational backgrounds, work history, demographics, and employment-related factors. Through exploratory data analysis and feature selection, the study identifies key predictors of employee turnover, including factors such as education, joining year, city, payment tier, age, gender, ever benched status, and experience in the current domain. The researcher employs three machine learning algorithms—K-Nearest Neighbors (KNN), Decision Tree, and Support Vector Machine (SVM)—to predict employee turnover based on these factors. Evaluation metrics such as accuracy, precision, recall, and F1-score were utilized to assess the performance of each model. Additionally, techniques such as the Synthetic Minority Over-sampling Technique (SMOTE) were applied to handle class imbalance in the dataset. The findings reveal distinct characteristics and performance of each model, with the Decision Tree model exhibiting the highest accuracy and predictive capability. Through comprehensive analysis and model evaluation, this study contributes valuable insights into employee turnover prediction, enabling organizations to develop targeted retention strategies and foster a more engaged and stable workforce.

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Section: Machine Learning Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identifying Patterns and Predicting Employee Turnover Using Machine Learning Approaches

Kanuto

2024

IJSB

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“…More specifically, in a positivist paradigm, causal research deals with cause-and-effect relationships among variables (Akter et al, 2022). An online questionnaire was used to collect numerical data from respondents with inferences drawn based on a statistical analysis of quantitative data (Mokoena et al, 2022).…”

Section: Research Approachmentioning

confidence: 99%

“…A questionnaire is a written, structured document comprising items designed to collect data from respondents in support of a primary research purpose (Setsena et al, 2021) A principal component exploratory and confirmatory factor analysis was performed to ensure construct validity and to identify the factors integral to each variable (Mokoena et al, 2022). Results indicated that the data were factorable, because the Kaiser-Meyer-Olkin (KMO) test for sampling adequacy returned values above 0.55 for most factors, and the Bartlett's test of sphericity reverted a statistically significant value on the 99th percentile.…”

Section: Measuring Instrumentmentioning

confidence: 99%

Impact of public service motivation on work evaluation and counterproductive work behaviour

Masukela,

Jonck,

Botha

2023

SA Journal of Human Resource Management

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Orientation: Public service motivation has increased in prominence in recent years, even though the influences on employee behaviour and attitudes have not received as much emphasis.Research purpose: This research investigates the impact of public service motivation on job satisfaction, organisational commitment, work engagement and counterproductive work behaviour.Motivation for the study: Counterproductive work behaviour of public service employees results in the citizenry developing negative perceptions about the government. Notwithstanding, the behaviour and attitudes of public service employees and the motivation that drives them are less explicit.Research approach/design and method: A positivist correlational research approach was implemented by administering a questionnaire to a sample of 1031 public service employees in the North West Province using a simple stratified sampling method. Statistical analyses included structural and regression modelling.Main findings: Results reveal that public service motivation statistically significantly predicts job satisfaction, organisational commitment and work engagement. The results show that 28.9% of the variance in counterproductive work behaviour could be attributed to public service motivation. Thus, as public service motivation increases, counterproductive work behaviour decreases.Practical or managerial implications: Strategies to improve public service motivation could putatively have an impact on the reduction of counterproductive work behaviour. Public service motivation could also improve service delivery to the citizenry by significantly impacting the public service employees’ work engagement.Contribution/value-add: Empirical evidence shows the influence of public service motivation on core work evaluation and counterproductive work behaviour, contributing to the corpus of knowledge with practical applicability.

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“…Every organization increases employee satisfaction and competitive advantage by investing in talent that aligns with its business strategy as organizational commitment [1].…”

Section: Introductionmentioning

confidence: 99%

Talent Management Policy and Meritocracy to Accelerate Bureaucratic Reform of Local Government: Comparative Study of Pontianak and Pekanbaru Cities

Dwiputrianti,

Nugraha,

Wirjatmi T.L.

et al. 2024

KSS

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The Indonesian government prioritizes implementing talent management for bureaucratic reform in civil service (ASN) management. The merit system index measures the implementation of human resource management based on meritocracy values in government agencies with a minimal score of 250. However, data shows that only 12.8%of district and city local government agencies are included in the good and excellent categories, which is still far from the target set. At the end of 2022, based on data from the Indonesian Civil Service Commission (KASN) assessment results, the Pekanbaru City Government (Riau Province) got an outstanding or “Good” category for the merit system index. On the other hand, Pontianak City (West Kalimantan Province) is also in the excellent or “Very Good” category. This study aims to analyze the efforts made by the Pontianak City Government and Pekanbaru City in implementing the ASN management merit system and identify strategies to implement talent management. Studies are based on the results of qualitative research through collection of data and information from various sources, such as interviews, observations, secondary data, and documentation. Purposive sampling is applied with primary data sources obtained directly from the location through documentary evidence, focus group discussion (FGD), and interviews. The results of the study found that the governments of Pontianak City and Pekanbaru City have prepared several policies and initiatives for merit-based ASN management, as well as the implementation of talent management. Political support encourages the implementation of good talent management to be the key to success, because of the understanding of the importance of human resources to improve public services and encourage economic growth and quality of life in Pekanbaru City and Pontianak City. The absence of in-depth studies of talent management applications in city/regency local governments, especially outside Java, can contribute significantly to both the application practice and the science of administration and public policy. Keywords: talent management policy, civil service management, meritocracy, local government, bureaucratic reform

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A talent management, organisational commitment and employee turnover intention framework for a government department in South Africa

Cited by 6 publications

References 37 publications

Identifying Patterns and Predicting Employee Turnover Using Machine Learning Approaches

Identifying Patterns and Predicting Employee Turnover Using Machine Learning Approaches

Impact of public service motivation on work evaluation and counterproductive work behaviour

Talent Management Policy and Meritocracy to Accelerate Bureaucratic Reform of Local Government: Comparative Study of Pontianak and Pekanbaru Cities

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