Assessment of Personal Values for Data-Driven Human Resource Management

Abstract

Business organizations have introduced data analytics to human resource management (HRM) to predict employee behavior in recent years. This practice is called HR analytics, and uses the various properties of employees, including their personal values, as inputs. Personal values are conceptualized as stable personal traits that represent the relevant employee. Previous studies in the area have shown that the personal values of employees influence their attitudes and behaviors as well as the performance of their group. Assessing these values is thus important in HRM. Although most previous studies have measured personal values based on self-reported questionnaires, this method encounters many problems, such as the social desirability bias. Accordingly, more recent studies have proposed alternative methods that apply machine learning algorithms to linguistic or visual data. This study reviews research on personal values by focusing on the methods used to measure them, and discusses the usefulness of and the challenges to the measurement of personal values. We also discuss future directions of research on the assessment of personal values.

Fusing talent horizons: the transformative role of data integration in modern talent management

“…Turning our attention to machine learning and predictive analytics, these technological advances are revolutionizing traditional HR practices [66]. The strength of machine learning resides in its capability to dissect vast datasets, unveiling patterns crucial for predicting employee behavior and enhancing talent management strategies [67,68]. In this vein, Shet and Nair [68] underscore the prowess of clustering algorithms in assessing the person-environment fit, thus optimizing candidate selection processes.…”

“…In this vein, Shet and Nair [68] underscore the prowess of clustering algorithms in assessing the person-environment fit, thus optimizing candidate selection processes. The incorporation of natural language processing (NLP) into HR analytics offers a novel approach, particularly in personality assessment, reducing inherent biases in conventional methods [67]. Nonetheless, the reliability of these predictive models is contingent upon the quality and diversity of the data, emphasizing the need for a holistic view of employees [69].…”