Recruitment Data Analysis Using Machine Learning in R Studio

Devakunchari, R.; Anand, Niketha; Vedhanayaki, Anusha; Visishta, Y. J.

doi:10.1007/978-3-030-33846-6_87

Cited by 1 publication

(2 citation statements)

References 11 publications

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“…This system implemented the resampling technique of 10fold cross-validation to divide the data set into training and testing subsets. Devakunchari et al (2019) found that random forest was more effective than support vector machine for job classification using recruitment data. Viroonluecha and Kaewkiriya (2018) created a salary predictor system using random forest and gradient boosting algorithms to estimate the monthly salaries of employees in Thailand.…”

Section: Machine Learning For Compensation Predictionmentioning

confidence: 99%

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Dissecting the compensation conundrum: a machine learning-based prognostication of key determinants in a complex labor market

Jaiswal

Gupta²,

Tiwari

2023

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PurposeAmidst the turbulent tides of geopolitical uncertainty and pandemic-induced economic disruptions, the information technology industry grapples with alarming attrition and aggravating talent gaps, spurring a surge in demand for specialized digital proficiencies. Leveraging this imperative, firms seek to attract and retain top-tier talent through generous compensation packages. This study introduces a holistic, integrated theoretical framework integrating machine learning models to develop a compensation model, interrogating the multifaceted factors that shape pay determination.Design/methodology/approachDrawing upon a stratified sample of 2488 observations, this study determines whether compensation can be accurately predicted via constructs derived from the integrated theoretical framework, employing various cutting-edge machine learning models. This study culminates in discovering a random forest model, exhibiting 99.6% accuracy and 0.08° mean absolute error, following a series of comprehensive robustness checks.FindingsThe empirical findings of this study have revealed critical determinants of compensation, including but not limited to experience level, educational background, and specialized skill-set. The research also elucidates that gender does not play a role in pay disparity, while company size and type hold no consequential sway over individual compensation determination.Practical implicationsThe research underscores the importance of equitable compensation to foster technological innovation and encourage the retention of top talent, emphasizing the significance of human capital. Furthermore, the model presented in this study empowers individuals to negotiate their compensation more effectively and supports enterprises in crafting targeted compensation strategies, thereby facilitating sustainable economic growth and helping to attain various Sustainable Development Goals.Originality/valueThe cardinal contribution of this research lies in the inception of an inclusive theoretical framework that persuasively explicates the intricacies of a machine learning-driven remuneration model, ennobled by the synthesis of diverse management theories to capture the complexity of compensation determination. However, the generalizability of the findings to other sectors is constrained as this study is exclusively limited to the IT sector.

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Section: Machine Learning For Compensation Predictionmentioning

confidence: 99%

“…This system implemented the resampling technique of 10-fold cross-validation to divide the data set into training and testing subsets. Devakunchari et al. (2019) found that random forest was more effective than support vector machine for job classification using recruitment data.…”

Section: Introductionmentioning

confidence: 99%

Dissecting the compensation conundrum: a machine learning-based prognostication of key determinants in a complex labor market

Jaiswal

Gupta²,

Tiwari

2023

View full text Add to dashboard Cite

show abstract

Recruitment Data Analysis Using Machine Learning in R Studio

Cited by 1 publication

References 11 publications

Dissecting the compensation conundrum: a machine learning-based prognostication of key determinants in a complex labor market

Dissecting the compensation conundrum: a machine learning-based prognostication of key determinants in a complex labor market

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