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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.

The spread of technology usage and the increasing popularity of network technology have formed a basis for alternative working systems as virtual teams and teleworking in the working environment. However, social relations have weakened in these working systems and loneliness has become a common emotion in the workplace. Hence, the concept of loneliness has attracted significant interest in recent years. Yet, despite this interest, research examining loneliness in the workplace is limited in the literature. This study aims to investigate the role of workplace loneliness on intrinsic motivation and the effect of intrinsic motivation on employee well-being through the moderating role of organizational nostalgia.

Data were collected from 380 respondents working in different sectors. The hypotheses were tested by using structural equation modeling and PROCESS macro for SPSS.

Results empirically showed that workplace loneliness negatively relates to intrinsic motivation, intrinsic motivation positively relates to employee well-being, intrinsic motivation mediates the link between workplace loneliness and employee well-being and organizational nostalgia moderates the link between workplace loneliness and intrinsic motivation. Furthermore, organizational nostalgia also moderates the indirect effect of workplace loneliness on employee well-being through intrinsic motivation.

To the best of the authors’ knowledge, this study contributes to the existing literature on workplace loneliness by investigating underlying mechanisms and boundary conditions for loneliness in the workplace.

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

Firms can ensure that the right candidates are hired for the right jobs by identifying close person-environment fit. Use of machine learning algorithms enables HR professionals to make these decisions based on swift, efficient analysis of large volumes of relevant data.

The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.