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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.

This research aims to explore whether or how organizations adopt people analytics (PA), its value and potential socio-technical factors that can enable or hinder PA projects by disrupting and reshaping human resource management. We do this by focusing on the Italian context.

We conduct a scoping review of data collected between 2018 and 2022 via Google Alerts (GA), a content change detection and notification service that is gaining popularity in scholarly research.

Our findings suggest that the diffusion of PA applications in Italy, especially those of a descriptive nature, is growing. Most of the existing PA applications are positioned in a positive technocratic light, envisioning the value of PA for both employees and organizations. The value for the latter appears to be direct, while the value for employees is realized through organizational initiatives. The findings also suggest that while enablers can vary between PA application types, the barriers, especially technological and environmental, are generic for both descriptive and predictive/prescriptive PA applications.

Theoretically, we propose a framework for analyzing PA applications, their values, enablers and barriers. Methodologically, we present and describe in detail a novel approach, drawing on GA that can be used to study PA in specific contexts. Practically, our study serves as a helpful point of reference for managers planning or implementing PA in Italy, for benchmarking PA in Italy over time and for comparative international studies.

This study aims to investigate the effects of HRM practices on administrative and faculty members’ work engagement and organizational commitment at Minhaj University Lahore, Pakistan. The primary objectives of the research are to investigate how HRM practices – such as selection and recruitment, training and development, rewards and compensation, job security, employee, participation and performance appraisal, directly and indirectly, influence organizational commitment via work engagement.

The study uses a quantitative research methodology using a survey questionnaire given to academic and administrative staff at Minhaj University Lahore. For descriptive statistics, SPSS was used, and Smart PLS 3.3.3 was used for structural equation modelling (SEM).

The results indicate that while selection and recruitment, rewards and compensation and job security do not show a significant direct impact, training and development, employee participation and performance appraisal significantly influence work engagement. Moreover, work engagement also influenced organizational commitment. In addition, work engagement significantly mediates the relationship between training and development, employee participation, performance appraisal and organizational commitment. However, selection and recruitment, rewards and compensation and job security do not show significant mediation effects.

To enhance work engagement in Pakistani HEIs, focus on tailored training, active faculty participation, effective appraisals and competitive compensation while fostering a supportive environment and recognizing accomplishments to increase commitment and institutional performance.

This study emphasizes the importance of particular tactics catered to the regional academic context for institutional performance and sustainability, providing novel insights into enhancing work engagement and organizational commitment in Pakistani higher education.

The purpose of this study is to explore the association between select human resource management practices and employee performance in academic libraries in India.

The current study uses the quantitative method of research. Partial least squares-structural equation modelling (PLS-SEM) was used to analyse the results. The current study uses a cross-sectional approach by using a convenient sampling method. The sample size of the study was 163, which was adequate for conducting PLS-SEM analysis.

The study found a significant positive correlation between human resource management practices and employee performance in academic libraries. This suggests that the implementation of effective human resource management practices has a beneficial impact on various aspects of employee performance.

The study’s cross-sectional design may limit the ability to establish causality or determine the direction of the relationship between human resource management practices and employee performance. The study may have limitations regarding the sample size and its representativeness. If the sample is small or limited to specific academic libraries in India, it may not be possible to generalise the findings to a broader population of academic libraries in the country or to libraries in other regions or countries.

The study has practical implications for academic libraries in India. By recognising the significant correlation between human resource management practices and employee performance, libraries can prioritise the implementation of effective human resource management strategies. This includes aligning human resources practices with organisational goals, focusing on employee development and engagement and adopting best practices in recruitment, training and performance management.

This study can have broader social implications by promoting a supportive and productive work culture that positively impacts the academic library community.

The paper focuses on a quite important and largely unexplored area of human resource management within the academic libraries sector.

This research aims to investigate the influence of green human resource management (HRM), with analysis and description of job position, recruitment, selection, training, performance assessment and rewards on sustainability with the resource-based view (RBV) theory as underlying theory. The extent to which absorptive capacity strengthened the “green HRM-sustainability” link as a buffering mechanism was also examined.

The study model was tested with empirical data gathered from 253 Indonesian oil and gas firms. The elicited data were analysed using structural equation modelling using partial least squares (PLS).

Resultantly, the (i) analysis and description of job position and (ii) recruitment positively influenced sustainability. Absorptive capacity also influenced the strength of the moderated relationship between (i) recruitment and (ii) training and sustainability.

As far as we know, this is the first study which assigned the moderator role of absorptive capacity in a relationship between green HRM and sustainability in oil and gas firms in Indonesia. Notably, the theoretical and practical implications of applying the empirical outcomes to the oil and gas sector were extensively discussed.

The study investigates the mediating effect of HR flexibility in the relationship between capacity-enhancing HR practices and job performance in small and medium-sized enterprises (SMEs) operating in Egypt.

On the basis of the literature review, the results imply a quantitatively tested conceptual model. The model is empirically validated using the partial least squares method to structural equation modelling (PLS-SEM) with survey data from 270 SME owners and managers in Egypt. The sample was selected using a quota sampling approach for small and medium-sized businesses and a proportionate stratification sampling method for the industry and region.

Findings for the sample revealed that capacity-enhancing HR practices affected job performance positively and significantly. The findings also revealed a direct, positive and significant impact of capacity-enhancing HR practices on HR flexibility and HR flexibility on job performance. Functional flexibility was identified as a significant mediator of the capacity-enhancing HR practices-job performance link, whereas behavioural and skill flexibility were not significant mediators for such a relationship.

The study's cross-sectional design is an evident weakness. All variables were self-reported; this may raise issues regarding method bias. Other limitations include the generalisability of the study's findings outside the setting in which it was conducted. The accuracy of the field study results would have been enhanced if they had not been limited exclusively to the geographical confines of Egypt.

The paper proposes many implications emphasising the role of HR flexibility in enhancing the performance of SMEs. The study developed a mediation model to understand how SMEs boost the performance of human resources by focusing on flexibility dimensions. Accordingly, companies may strategically employ flexible practices and provide an environment that encourages skill and behavioural development.

This study aims to investigate the configurational effects of five rules – artificial intelligence (AI)-based hiring decision transparency, consistency, voice, explainability and human involvement – on applicants' procedural justice perception (APJP) and applicants' interactional justice perception (AIJP). In addition, this study examines whether the identified configurations could further enhance applicants' organisational commitment (OC).

Drawing on the justice model of applicants' reactions, the authors conducted a longitudinal survey of 254 newly recruited employees from 36 Chinese companies that utilise AI in their hiring. The authors employed fuzzy-set qualitative comparative analysis (fsQCA) to determine which configurations could improve APJP and AIJP, and the authors used propensity score matching (PSM) to analyse the effects of these configurations on OC.

The fsQCA generates three patterns involving five configurations that could improve APJP and AIJP. For pattern 1, when AI-based recruitment with high interpersonal rule (AI human involvement) aims for applicants' justice perception (AJP) through the combination of high informational rule (AI explainability) and high procedural rule (AI voice), there must be high levels of AI consistency and AI voice to complement AI explainability, and only this pattern of configurations can further enhance OC. In pattern 2, for the combination of high informational rule (AI explainability) and low procedural rule (absent AI voice), AI recruitment with high interpersonal rule (AI human involvement) should focus on AI transparency and AI explainability rather than the implementation of AI voice. In pattern 3, a mere combination of procedural rules could sufficiently improve AIJP.

This study, which involved real applicants, is one of the few empirical studies to explore the mechanisms behind the impact of AI hiring decisions on AJP and OC, and the findings may inform researchers and managers on how to best utilise AI to make hiring decisions.

The study aims to explore the organizational benefits of positive employee experience and to provide a framework for measuring it. The positive employee experience has a profound impact on employees’ attitudes; thus, it is particularly important to what extent an organization can create the conditions supporting this.

The study is based on literature review and the framework needs to be empirically tested to draw final conclusions.

Organizational performance and success are influenced by employees’ well-being, commitment, job satisfaction and the high level of individual performance. However, this grouping of variables is not exhaustive, but in practice, it is often not necessary to fully understand the complex and complicated relationships among the organizational variables. However, a positive employee experience has an impact on all of these variables. According to our understanding and experience, the task of management is not to strengthen the variables describing employee attitudes individually, based on the knowledge of specific relations presented in the management literature and selected for the sake of a single research, but to create an acceptable level of the positive employee experience, which is able to strengthen these variables in a way that is useful for the organization.

In this study, the authors introduce the concept of the positive employee experience and the ways and steps to measure it. The authors review the methodology of predictive analytics, the main principles of data collection and the types of data with their possible applications. Finally, the limitations of the framework and the risks of enhancing the positive employee experience are also discussed.

The paper analyzes and develops Chiva's (2014) proposal on the common welfare HRM system and uncovers its relationship with innovativeness, using altruism as a mediator.

The common welfare HRM system implies a certain human and organizational development of the classic control and commitment HRM systems, and its main goal is to promote innovation through a prosocial approach. To this end, the authors investigated its HRM practices, developed a measurement instrument and provided initial illustrative evidence of some of its main implications for innovativeness and altruism. They tested these relationships on a sample of 269 Spanish firms using structural equations and bootstrapping to confirm the significance of the mediated effect.

Results confirm the study’s hypotheses, thus supporting the common welfare HRM system as a relevant tool for developing innovativeness through the power of altruism. This paper therefore provides empirical evidence of these relationships.

This study has implications that can help managers to increase innovativeness through a specific HRM system. The findings reveal that a coherent set of HRM practices based on common welfare principles and a high level of consciousness creates a climate of altruism that results in innovativeness.

This research shows that humanistic HRM practices also have an impact on performance variables such as innovativeness, through altruistic employees' behaviors. It also develops a measurement instrument for the common welfare HRM system and provides some initial illustrative evidence of some of its main implications.

本文分析並發展Chiva (2014) 關於共同福祉人力資源管理系統的建議;同時，擬以利他主義為中介變量、揭示共同福祉人力資源管理系統與創新意念之間的關係。

共同福祉人力資源管理系統暗示了典型的管制和承諾型人力資源管理系統的人類與組織發展;而共同福祉人力資源管理系統的主要目的是以親社會理念去鼓勵創新。為此，我們探討了共同福祉人力資源管理措施，制訂了測量儀器，並為共同福祉人力資源管理可幫助帶來創新意念和利他主義的啟示，提供了初步的例證。研究的樣本為269間西班牙公司;我們以結構方程去檢測共同福祉人力資源管理與創新意念和利他主義之間的關係，並創造環境，俾能確認有關之中介效應的重要性。

研究結果證實了我們的假設，就是說，研究結果確認了透過利他主義的影響力，共同福祉人力資源管理系統是可以成為促進創新意念的工具的。就此而言，本文提供了關於這些關係的經驗證據。

本研究顯示了人文主義的人力資源管理措施，亦可透過僱員無私的行為，影響著各個績效變量，像是創新意念。研究亦為共同福祉人力資源管理系統制訂了測量儀器，並提供了初步的例證，確認了該人力資源管理系統所給予我們的主要啟示。

本研究為管理人員提供了啟示，協助他們透過特定的人力資源管理系統去增強創新意念。研究結果顯示、建基於共同福祉的原則和高度意識水平的一整套連貫的人力資源管理措施，能創造一個引來創新意念的利他主義氣氛。

Starting from the relevance of ethics to the application of artificial intelligence (AI) in the context of employee recruitment and selection (R&S), in this article, we aim to provide a comprehensive review of the literature in light of the main ethical theories (utilitarian theories, theories of justice, and theories of rights) to identify a future research agenda and practical implications.

On the basis of the best-quality and most influential journals, we conducted a systematic review of 120 articles from two databases (Web of Science and Scopus) to provide descriptive results and adopt a framework for deductive classification of the main topics.

Inspired by the three ethical theories, we identified three thematic lines of enquiry for the debate on AI in R&S: (1) the utilitarian view: the efficient optimisation of R&S through AI; (2) the justice view: the perceptions of justice and fairness related to AI techniques; and (3) the rights view: the respect for legal and human rights requirements when AI is applied.

This article provides a detailed assessment of the adoption of AI in the R&S process from the standpoint of traditional ethics theories and offers an integrative theoretical framework for future research on AI in the broader field of HRM.