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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.

High performance of new hires is of imminent interest to organizations in the hospitality sector. Yet, limited studies have focused on the relevant traits of new hires that improve on-job performance. This study aims to identify and understand a few critical traits that predict high performance across multilevel roles within the hospitality sector.

Drawing on the human capital theory, through a time-lagged field study spanning 16 months, this study used multisource data for 540 active job opportunities and 205 new hires within the hospitality industry. This study used partial least squares-based structural equation modeling and analyzed the various traits that predict high on-job performance.

This study finds that humility is a significant predictor of job performance and wholly mediates the effect of interpersonal understanding, self-confidence and flexibility on new hires’ performance.

This study enhances talent management research for the hospitality sector by determining the critical traits of new hires that can predict superior on-job performance.

This study aims to develop a model of learning-by-hiring in which knowledge gains may occur at the time of recruitment but also after recruitment when other incumbent organizational members assimilate a recruit’s knowledge. The author’s model predicts that experienced recruits are more likely to catalyze change to their organization’s core technological capabilities.

The continuous-time parametric hazard rate regressions predict core technological change in a long panel (1970–2017) of US biotechnology industry patent data. The author uses over 140,000 patents to model the evolution of knowledge of over 52,000 scientists and over 4,450 firms. To address endogeneity concerns, the author uses the Heckman selection method and does robustness tests using a difference-in-difference analysis.

The author finds that a hire’s prior research and development (R&D) experience helps overcome inertia arising from her or his new-to-an-organization “distant” knowledge to increase the likelihood of core technological change. In addition, while the author finds that incumbent organizational members resist technological change, experienced hires may effectively induce them to adopt new ways of doing things. This is particularly the case when hires collaborate with incumbents in R&D projects. Understanding the effects of hiring on core technological change, therefore, benefits from an assessment of hire R&D experience and its effects on incumbent inertia in an organization.

First, the author does not recommend managers to hire scientists with considerable distant knowledge only as this may be detrimental to core technological change. Second, the author recommends organizations striving to effectuate technological change to hire people with considerable prior R&D experience as this confers them with the ability to influence other members and socialize incumbent members. Third, the author recommends that managers hire people with both significant levels of prior experience and distant knowledge as they are complements. Finally, the author recommends managers to encourage collaboration between highly experienced hired scientists and long-tenured incumbent organizational members to facilitate incumbent learning, socialization and adoption of new ways of doing things.

This study develops a model of learning-by-hiring, which, to the best of the authors’ knowledge, is the first to propose, test and advance KM literature by showing the effectiveness of experienced hires to stimulate knowledge diffusion and core technological change over time after being hired. This study contributes to innovation, organizational learning and strategy literatures.

Early research on overqualification suggested that overqualification is primarily associated with negative attitudes and behavior. As a consequence, hiring practitioners were advised against hiring overqualified job applicants. However, recent studies have revealed that there are several potential positive consequences of overqualification. Given this change in perspective on overqualification, we examine how hiring practitioners nowadays look at overqualified job applicants, and what their considerations are for hiring an overqualified job applicant or not.

We have interviewed 33 hiring practitioners to examine their attitudes and considerations toward hiring overqualified job applicants.

Results show that hiring practitioners are aware of potential positive as well as negative consequences of overqualification and consider a variety of factors to assess how beneficial hiring an overqualified candidate will be. These factors fall under three categories: Individual considerations, interpersonal considerations and contextual considerations.

We show that overqualification is not a stigma anymore and that the decision to hire an overqualified job applicant or not depends on a mixture of factors that are carefully considered. Two of these three considerations transcend the individual level (i.e. the overqualified person), whereas most research and theories on the consequences of overqualification do not go beyond the individual level. As such, our findings call for more theory and research on interpersonal and contextual factors shaping the consequences of overqualification.

This study aims to investigate the implementation of lean human resource management (HRM) practices in manufacturing small- and medium-sized enterprises (SMEs) and explore how various factors interact to influence their successful adoption. By exploring the interplay among these factors, the research seeks to identify key drivers affecting the adoption of lean HRM in manufacturing SMEs. Ultimately, the research intends to provide insights that can guide organisations, practitioners and policymakers in effectively implementing lean HRM practices to enhance operational efficiency, workforce engagement and competitiveness within the manufacturing SME sector.

The study combined total interpretive structural modelling (TISM) and Matrice d'Impacts Croisés Multiplication Appliquée à un Classement (MICMAC) analysis. TISM helped in understanding the hierarchical relationship among different factors influencing lean HRM implementation, whereas MICMAC analysis provided insights into the level of influence and dependence of each factor on others.

The research revealed that “top management support” emerged as the most independent factor, indicating that strong support from top management is crucial for initiating and sustaining lean HRM practices in manufacturing SMEs. On the other hand, “employee involvement and empowerment” was identified as the most dependent factor, suggesting that fostering a culture of employee engagement and empowerment greatly relies on the successful implementation of lean HRM practices.

While the study provided valuable insights, it has certain limitations. The research was conducted within the specific context of manufacturing SMEs, which might limit the generalizability of the findings to other industries. Expert opinions introduce subjectivity in data collection. Additionally, the study may not cover all critical factors, allowing room for further exploration in future research.

The findings have practical implications for manufacturing SMEs aiming to implement lean HRM practices. Recognising the pivotal role of top management support, organisations should invest in cultivating a strong leadership commitment to lean HRM initiatives. Furthermore, enhancing employee involvement and empowerment can lead to better adoption of lean HRM practices, resulting in improved operational efficiency and overall competitiveness.

This research contributes to the field by offering a comprehensive exploration of the interplay among factors influencing lean HRM implementation. The use of TISM and MICMAC analysis provides a unique perspective on the relationship dynamics between these factors, allowing for a nuanced understanding of their roles in the adoption of lean HRM practices in manufacturing SMEs. The identification of “top management support” as the most independent and “employee involvement and empowerment” as the most dependent factors adds original insights to the existing literature.

This study aims to compare the expectations of non-collaborating professionals and the actual opinions of collaborating professionals regarding success factors of horizontal logistics collaboration (HLC) and investigates the reasons behind the observed differences.

This study employs a mixed-method approach. First, a survey is conducted to collect data from two samples representing collaborating and non-collaborating industry professionals. Second, confirmatory factor analysis (CFA) is used to compare the measurement models from the two samples and identify their similarities and differences. Third, a Delphi study is conducted to identify factors limiting collaborative behavior.

The results show that collaborating professionals exhibit lower levels of joint relationship efforts and trust than expected. This is primarily due to inadequate information sharing, poor collaboration formalization and the absence of a clear costs and benefits allocation mechanism.

The findings indicate that, in HLC, managers should give high importance to facilitating timely and complete information exchange, putting in place an acceptable costs/benefits allocation mechanism, formalizing the collaboration and prioritizing integrity over competency when selecting partners.

To the best of the authors’ knowledge, this is the first study that shows the existence of differences between industry professionals' pre-collaboration expectations and the actual experiences in HLC. This is also the first study that points to the exact HLC enablers that fail in practice and the barriers responsible for it.

The purposes of this paper are to delve into the “liability of foreignness” among immigrants and to explore factors that may enhance or moderate such liability while obtaining jobs in host countries. We explore the competition for jobs in a host country among foreign-born individuals from various backgrounds and local residents, by examining such factors as their human capital, as well as, for the foreign-born, their duration of residence in the host country.

Applying configurational theorizing, we propose that the presence of specific human capital can help reduce the challenges associated with the “liability of foreignness” for migrants who have shorter durations of stay in the host country, and, to a lesser extent, for female migrants. Our study draws upon extensive career data spanning several decades and involving 249 employees within a Danish multinational enterprise.

We find that specific human capital helps established immigrants in general, although female immigrants are more vulnerable. We furthermore find a strong “gender liability” in the industry even for local females, including returnees in the host countries. Our findings suggest that for immigrants, including returnees, career building requires a mix of right human capital and tenure in the host country, and that career building is especially challenging for female immigrants.

While the concept of “liability of foreignness” – focussing on discrimination faced by immigrants in the labour market – has been brought to the fore, a notable gap exists in empirical research pertaining to studies aiming at disentangling potential means to overcome such liability, as well as in studies seeking to explore this issue from a stance of gendered experience.

This study aims to explore the career transition process of women professionals when they return to the workplace after a break and re-integrate with their career aspirations.

This research used a qualitative approach with semi-structured interviews for data collection. All 20 women participants have returned to their careers after a break.

The data analysis reveals women’s career transition and re-integration process into the workplace after a career break. Supervisory support, work–life balance practices, role models, coworker support and career success emerged as factors enabling successful transition. The employing organisations’ diversity, equity and inclusion (DEI) hiring strategy emerged as an important mechanism for re-entry.

Although previous research has studied women professionals returning after a break, the “transition process” that enables re-entry is missing.

The purpose of this paper is to test the impact of selected characteristics of jobseekers on employers’ decisions regarding potential hires (direct and probabilistic signals). The main focus of the study is to test the impact of jobseekers’ participation in selected active labour market programmes on employers’ hiring decisions for three positions: unskilled worker, skilled worker and administrative employee. Other characteristics tested include age, gender, presence of children in the household, state of health, experience of short- and long-term unemployment and indebtedness.

This study analyses data from a representative survey of employers with five or more employees in the Czech Republic. The survey was conducted in December 2020 using stratified random sampling, combining online questionnaires and personal interviews. The study includes 1,040 employers and uses the factorial survey experiment (FSE) design.

The results of the FSE suggest that the perceived positive impact of completing one of the activation programmes depends on the position for which the candidate is being recruited. While for the unskilled job category, the completion of any of the tested schemes (training, subsidised jobs or public works) had a positive effect; for the skilled job category, only the training and subsidised jobs schemes had a positive effect; and for the administrative job category, public works programme even had a negative effect.

A somewhat limiting factor in the context of this study seems to be the definitions of the positions tested (unskilled and skilled workers and administrative staff). The decision-making of the respondents was somewhat restricted by such broadly defined categories. Typically, studies with FSE designs have a focus on a specific sector of the economy, which allows for a better definition of the positions or jobs under test. The relationship between position and the impact of individual characteristics is clearly a matter for further research.

The results of the study confirm that completion of the activation programme, as well as other candidate characteristics, constitute differentiating signals for employers that influence their hiring decisions. At the same time, there is evidence that the training programme and the subsidised jobs programme are effective in terms of increasing participants’ chances of employment.

The demand side should be included in the evaluation of activation policies. The design of the FSE provides an appropriate way to test the impact of activation measures on the decision-making of employers.