Salary Prediction Using Regression Techniques

Das, Sayan; Barik, Rupashri; Mukherjee, Ayush

doi:10.2139/ssrn.3526707

Cited by 17 publications

(11 citation statements)

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“…Mart ın et al (2018) analyzed the Spanish IT job market using high-dimensional samples to anticipate employee turnover, with recall and F-measure as evaluation criteria. Das et al (2020) identified experience and job position as crucial factors that impact compensation. Khongchai and Songmuang (2016) experimented with several MLM such as naive Bayes, K-nearest neighbor, support vector machines and neural networks, eventually employing random forests to develop a salary prediction system aimed at enhancing student motivation.…”

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%

“…(2018) analyzed the Spanish IT job market using high-dimensional samples to anticipate employee turnover, with recall and F-measure as evaluation criteria. Das et al. (2020) identified experience and job position as crucial factors that impact compensation.…”

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

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“…This program offers the manager suggestions on how much the company should pay. However, the final decision belongs to the manager (Das et al, 2020).…”

Section: Remuneration and Reward Systemmentioning

confidence: 99%

Untitled

2023

SPSUTOM

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The aim of this article is to identify attributes related to the quality of the hospitalization process, categorized from absolutely necessary to those whose presence does not significantly impact this quality (according to the Kano methodology). The identified attributes also simultaneously contribute to shaping patient satisfaction. The primary objective of this article is to indicate at what level in the satisfaction assessment factors determine the quality of the hospitalization process and to what extent they align with the expectations of hospitalized patients. Design/methodology/approach: The literature review will focus on identifying factors determining the quality of the hospitalization process. The empirical part of the article will revolve around classifying these attributes according to the Kano methodology through survey research. The next stage of the research process will involve assessing the satisfaction of hospitalized patients in areas corresponding to the Kano attributes -through survey researchand identifying discrepancies between expectations and satisfaction levels. Findings: Among the attributes that particularly determine the attractiveness of a hospital unit are those related to reducing time. Shortening the admission time to the ward, waiting for discharge, and the time it takes to conduct prescribed tests are emphasized. Additionally, the importance of adhering to the treatment stages according to the planned schedule is highlighted. Therefore, it is recommended as a priority to simplify procedures for both admission to the ward and discharge, as well as to strictly adhere to the treatment schedule while optimizing it to reduce the waiting time for prescribed tests during hospitalization.Research limitations/implications: The article presents the results of literature research, which can be complemented in further studies on this topic. One limitation of the research is the relatively narrow group of patients who completed the satisfaction assessment survey, both in terms of the number of patients, geographical area, and the number of hospital facilities. Additionally, the study only presented attributes related to the organization and the quality of the hospitalization process, without delving into broader topics related to personnel or material conditions, which likely also influence the level of patient satisfaction. These limitations point towards directions for future research. Practical implications: The results of the conducted research can have practical applications in the business field. Satisfaction assessment surveys completed by hospitalized patients from various hospitals will identify areas where patient satisfaction is the lowest. Consequently, 10 N. Baskiewicz, M. Daroń, A. Pachura, A. Sanojca they can serve as a basis for developing guidelines for efforts aimed at improving the quality of the hospitalization process. Additionally, the Kano methodology will indicate which attributes that determine the quality of the hospitalization process are most significant to pati...

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“…Bansal et al compared simple linear regression and multiple linear regression to predict personnel salary and they showed that multiple linear regression was obtained better accuracy results [12]. Das et al designed a system to estimate personnel salary after specific time and they shared the visual results of that system [13]. Li et al predict the salary of employees using support vector regression, nearest geometric center, linear regression, logistic regression, k nearest regression and random forest regression on dataset that generated using job posting in England and they obtained 0.184% mean absolute error [14].…”

Section: Literature Reviewmentioning

confidence: 99%

SALDA-ML: Machine Learning Based System Design to Predict Salary In-crease

Görmez

Arslan

Sari³

et al. 2022

Advances in Artificial Intelligence Research

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Number of employees are increases with growing in companies. Firms basically make salary increases for their employees in order not to lose their talents and moreover to increase them. Although there is not much problem in how to in-crease the salary in small organizations, this process should be carried out carefully in terms of many parameters in large organizations and should not result in negativities that may disrupt employee motivation. For companies with a large number of employees, creating a model in which the market conditions are determined correctly and all economic parameters are taken into account reveals the need for a process that needs to be worked on for months. In this context, a machine learning-based salary increase prediction system was designed with the study. Specific attributes were determined and a specific scale was developed for performance score for this study.

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Salary Prediction Using Regression Techniques

Cited by 17 publications

References 0 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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SALDA-ML: Machine Learning Based System Design to Predict Salary In-crease

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