A contingency model of employees’ turnover intent in young ventures

Abstract

A defining characteristic of young ventures is that they are more likely to experience periods of change (of both a positive and negative nature) than are established organizations. This could result in a misalignment between employees’ expectations when hired and actual work experiences. Based on met expectations theory, we argue that employees’ experiences in young ventures result in greater turnover intent over time. We further theorize that the relationship between time and turnover intent is contingent on employees’ prior work experience at a start-up and venture growth rates. Using a unique longitudinal data set containing 1,151 survey responses from 458 employees of 67 ventures, we find that employees’ turnover intent increases over time and that this effect is particularly strong for employees with little prior start-up work experience and employees working in low-growth ventures. We discuss implications for the literature on employees in entrepreneurial firms.

Identifying Patterns and Predicting Employee Turnover Using Machine Learning Approaches

“…Recent advancements in machine learning () have opened up new opportunities for predicting employee turnover. Studies have demonstrated the efficacy of machine learning algorithms such as logistic regression, decision trees, and support vector machines in predicting turnover intentions based on diverse sets of predictors (Lazzari et al, 2022;Domurath et al, 2023). For instance, Lazzari et al (2022) compared various classification models and found logistic regression and LightGBM to be the top-performing algorithms for predicting turnover intentions.…”

“…For instance, Lazzari et al (2022) compared various classification models and found logistic regression and LightGBM to be the top-performing algorithms for predicting turnover intentions. Similarly, Domurath et al (2023) developed a contingency model of turnover intent in young ventures using longitudinal data and highlighted the predictive power of machine learning in understanding turnover dynamics in entrepreneurial firms. Moreover, the literature on employee turnover provides valuable insights into the determinants, consequences, and predictive models of turnover.…”

“…Through exploratory data analysis and feature selection techniques, the researcher aims to identify key predictors of employee turnover, shedding light on the factors that significantly influence individuals' decisions to leave an organization. Building upon prior research that has highlighted the predictive efficacy of machine learning algorithms in various domains, the researcher employs three distinct methodologies-K-Nearest Neighbors (KNN), Decision Tree, and Support Vector Machine (SVM)-to model and predict employee turnover based on the identified predictors (Domurath, Taggar, & Patzelt, 2023). By evaluating the performance of each model using established metrics such as accuracy, precision, recall, and F1-score, the researcher seek to ascertain the comparative effectiveness of different machine learning algorithms in predicting turnover.…”

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“…It is therefore desirable not only to define a suitable applicant profile in terms of the position to be filled, but also to assess the risk of the applicant quitting his or her job. According to Domurath et al (2023), the desire to change jobs grows stronger over time, and this effect is particularly strong for employees working in low-growth ventures. Moreover, people under the age of 30 often change their jobs as they are still figuring out what they want to do in life (Arnett, 2006).…”

Why do young workers quit their first job? Identification of the risk factors using the Cox model and survival trees

“…It is therefore desirable not only to define a suitable applicant profile in terms of the position to be filled, but also to assess the risk of the applicant quitting his or her job. According to Domurath et al (2023), the desire to change jobs grows stronger over time, and this effect is particularly strong for employees working in low-growth ventures. Moreover, people under the age of 30 often change their jobs as they are still figuring out what they want to do in life (Arnett, 2006).…”