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035 a| (OCoLC)1570278124;

049 a| EREE;

100 1 a| Bharath, Jeya Shivantie| author?| UNAUTHORIZED;

245 10 a| Understanding Employee Turnover Intentions Using Machine Learning: A Multi-Factor Approach;

264 1 a| [Greenville, N.C.] :b| [East Carolina Universityc| 2025.];

300 a| 106 pages;

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337 a| computerb| c2| rdamedia;

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538 a| System requirements: Adobe Reader.;

538 a| Mode of access: World Wide Web.;

502 c| East Carolina Universityd| 2025.;

504 a| Includes bibliographical references.;

520 3 a| Employee turnover continues to challenge organizations and understanding the psychological and workplace factors that shape turnover intention is essential for effective retention efforts. This study integrates Industrial/Organizational Psychology with modern Data Science to examine how attitudes such as job satisfaction, organizational commitment, work engagement, perceived organizational support, perceived organizational justice, work environment, work life balance and employee net promoter score (eNPS) influence employees' intentions to leave.Using survey data, four modeling approaches were compared, logistic regression, random forests, and a Bayesian Additive Regression Tree (BART) model, alongside an exploratory Automated Machine Learning (AutoML) pipeline that tested additional algorithmic configurations. Variable importance rankings from the machine-learning models were used to identify the most influential predictors contributing to turnover intention. Results show that organizational commitment, perceived support, and engagement are consistently the strongest drivers of turnover intention, and that machine-learning models, supported by AutoML exploration, uncover patterns that traditional methods often overlook.By combining psychological theory with interpretable machine learning, this thesis provides a clearer understanding of turnover behavior and offers HR leaders practical, data-informed guidance for designing targeted retention strategies.;

653 a| employee turnover intention; retention; job satisfaction; organizational commitment; work engagement; perceived organizational support; organizational justice; people analytics; machine learning; automated machine learning (AutoML); logistic regression; random forest; Bayesian additive regression trees;

700 1 a| Bowler, Dr. Marke| degree supervisor.?| UNAUTHORIZED;

710 2 a| East Carolina University=| ^A637090;

856 40 z| Access via ScholarShipu| http://hdl.handle.net/10342/14418;

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999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 6699113-1001l| JNETm| JOYNERr| Ys| Yt| JNE3ETDu| 1/27/2026x| ETDz| JERESOURCE;

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Understanding Employee Turnover Intentions Using Machine Learning: A Multi-Factor Approach

Author/creator	Bharath, Jeya Shivanti author
Other author	Bowler, Dr. Mark degree supervisor.
Other author	East Carolina University
Format	Theses and dissertations
Publication	[Greenville, N.C.] : [East Carolina University 2025.]
Description	106 pages
Supplemental Content	Access via ScholarShip

Summary	Employee turnover continues to challenge organizations and understanding the psychological and workplace factors that shape turnover intention is essential for effective retention efforts. This study integrates Industrial/Organizational Psychology with modern Data Science to examine how attitudes such as job satisfaction, organizational commitment, work engagement, perceived organizational support, perceived organizational justice, work environment, work life balance and employee net promoter score (eNPS) influence employees' intentions to leave.Using survey data, four modeling approaches were compared, logistic regression, random forests, and a Bayesian Additive Regression Tree (BART) model, alongside an exploratory Automated Machine Learning (AutoML) pipeline that tested additional algorithmic configurations. Variable importance rankings from the machine-learning models were used to identify the most influential predictors contributing to turnover intention. Results show that organizational commitment, perceived support, and engagement are consistently the strongest drivers of turnover intention, and that machine-learning models, supported by AutoML exploration, uncover patterns that traditional methods often overlook.By combining psychological theory with interpretable machine learning, this thesis provides a clearer understanding of turnover behavior and offers HR leaders practical, data-informed guidance for designing targeted retention strategies.
Dissertation note	East Carolina University 2025.
Bibliography note	Includes bibliographical references.
Technical details	System requirements: Adobe Reader.
Technical details	Mode of access: World Wide Web.

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Understanding Employee Turnover Intentions Using Machine Learning: A Multi-Factor Approach

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