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Our proposed machine learning model contributes to improving the quality of Hire by providing a more nuanced and comprehensive analysis of candidate attributes. Instead of focusing solely on obvious factors, such as qualifications and experience, our model also considers various dimensions of fit, including person-job fit and person-organization fit. By integrating these dimensions of fit into the model, we can better predict a candidate’s potential contribution to the organization, hence enhancing the Quality of Hire.

Within the scope of the investigation, the competencies of the personnel working in the IT department of one in the largest state banks of the country were used. The entire data collection includes information on 1,850 individual employees as well as 13 different characteristics. For analysis, Python’s “keras” and “seaborn” modules were used. The Gower coefficient was used to determine the distance between different records.

The K-NN method resulted in the formation of five clusters, represented as a scatter plot. The axis illustrates the cohesion that exists between things (employees) that are similar to one another and the separateness that exists between things that have their own individual identities. This shows that the clustering process is effective in improving both the degree of similarity within each cluster and the degree of dissimilarity between clusters.

Employee competencies were evaluated within the scope of the investigation. Additionally, other criteria requested from the employee were not included in the application.

This study will be beneficial for academics, professionals, and researchers in their attempts to overcome the ongoing obstacles and challenges related to the securing the proper talent for an organization. In addition to creating a mechanism to use big data in the form of structured and unstructured data from multiple sources and deriving insights using ML algorithms, it contributes to the debates on the quality of hire in an entire organization. This is done in addition to developing a mechanism for using big data in the form of structured and unstructured data from multiple sources.

In recent years, technological advancement has radically changed the landscape of work by creating an “always-on” workplace. As a result, many employees use their information and communication technologies (ICTs) to perform work-related tasks during personal times. This constant availability then paves the way for harming the employee’s wellbeing. Nevertheless, research on ICTs usage after-hours has received limited consideration in organizational scholarship. To address this gap in the literature, current study aims to examine the relationships between workplace telepressure and employee wellbeing with the role of work-related email (WRE) use after-hours and subjective norm of connectivity.

Data were collected from 421 employees from different sectors. The hypotheses were tested using structural equation modeling and PROCESS macro for SPSS.

The results showed that (1) workplace telepressure positively relates to work-related email use after-hours; (2) work-related email use after-hours negatively relates to employee wellbeing; (3) the subjective norm of connectivity moderates the link between workplace telepressure and work-related email use after-hours and (4) work-related email use during nonwork hours mediates the link between workplace telepressure and employee wellbeing.

This study contributes to overcoming the lack of studies by investigating underlying mechanisms and boundary conditions for ICTs during nonwork hours. In this expanded sense, this study broadens our understanding of WRE use after-hours by delineating the impact of workplace telepressure, subjective norm of connectivity and employee wellbeing.