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Various scholars suggest that there is a lack of research on the recruitment in small and medium-sized enterprises (SMEs) and also a scarcity of theoretical basis for the recruitment procedures used by these companies. As the vast majority of studies concentrate on larger organizations, they may not accurately reflect the challenges faced by smaller-sized entities to profoundly and accurately comprehend their recruitment procedures. In addition, the use of technology in recruitment has grown in importance in today’s quickly evolving business environment, particularly in light of the COVID-19 pandemic footprint. This study aims to examine the recruitment procedures used by SMEs and how they have been compelled to adjust to different extents to these technological improvements by the effects of the aforementioned epidemic.

With the aim to investigate the current recruitment practices in SMEs and the extent to which digital technologies are embraced by these companies within human resources (HR) procedures, this research relied on interviews with SMEs representatives. The qualitative methods used provided access to relevant data and insights, as they allowed close interactions with top managers and CEOs of ten companies from various sectors. Thus, the research results draw a vivid and reliable image of the procedures and practices used by small and medium-sized companies to attract, select and retain their staff.

This study’s findings are of increased interest to HR professionals, recruiters and managers in SMEs, who aim to attract and retain the best talent and optimize their recruitment strategies in a rapidly changing business environment, enabled by technological advancements. Effective HR recruitment procedures adapted to the specific needs of small and medium-sized companies can lead to several benefits for the organization, including improved employee selection, reduced turnover and increased organizational productivity.

Although the interviews examined here encompass recruitment techniques from SMEs in a variety of industries, the results’ generalizability is limited by the sample size and geography. Furthermore, the findings’ dependability is dependent on the accuracy of the data provided by the respondents.

This investigation confirms some of the theoretical underpinnings which point to the lack of formalized structures and procedures in the recruitment process in SMEs, which enjoy more flexibility in managing HR processes. In addition, the results reinforce the arguments indicating an adjustment between HR strategies or policies and organizational goals in smaller enterprises which adapt faster to changes in the market. Moreover, it becomes apparent that there is a relationship between the quality of job descriptions and the successful fit in attracting the right candidates for the open positions. Furthermore, digital technologies offer opportunities for expanding the recruiters’ reach to a wider audience and also support the selection stage, thus increasing the chances of finding suitable staff. As the need to shift from traditional recruitment to e-recruitment in SMEs has been highlighted in the literature, the qualitative research revealed that this need was driven on the one hand by the COVID-19 pandemic when these companies successfully adapted and implemented new online methods of recruiting, but also by the lack of skilled labor, leading to the expansion of recruitment to other parts of the country or even to other countries.

With regard to the proportion of men and women used in small and medium-sized companies, there is a clear need to involve and train more women in the predominantly male-dominated industrial and IT sectors. From this point of view, companies tend to devote more interest to integrating communities of women in these industries, as well as in key management positions. Another point of interest that the study highlights is the fact that SMEs have started to get creative with the benefits package they propose to candidates and focus on remote work, hybrid office–home working, or seasonal work to offer future employees a better work–life balance.

The added value of this investigation is filling the gaps in the current literature concerning recruitment procedures currently used by SMEs, the challenges they face and the solutions they advanced to solve them. Furthermore, SMEs often drive innovation and competition in the market and play a crucial role in the supply chain of larger companies, providing them with the goods and services they need to operate and supporting the availability and reliability of products from larger companies. They are often the driving force behind revitalizing local economies and creating new employment opportunities. Consequently, the underlying significance of this study is rooted in the need to modernize and simultaneously improve HR recruitment procedures through the integration of technology and a focus on innovation.

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.

Staffing is one of the most influential human resource (HR) activities and is the primary method of hiring and retaining human resources. Among staffing’s several activities, recruitment and selection are one of the most crucial activities. It is possible to rehire former firm employees using the talent management strategy known as “boomerang recruitment”. The boomerang recruitment trend has tremendously grown because many employees who believe they are qualified for the position now wish to return to their old employers. According to data, boomerang employees can be 50% less expensive than conventional ways of hiring. The purpose of this study is to identify the generic critical factors that play a role in the boomerang hiring process based on the literature review. Next, the objective is to determine the relative weight of each of these factors, rank the candidates, and develop a decision-making model for boomerang recruitment.

This paper focuses on the grey-based multicriteria decision-making (MCDM) methodology for recruiting some of the best candidates out of a few who worked for the organization earlier. The grey theory yields adequate findings despite sparse data or significant factor variability. Like MCDM, the grey methods also incorporate experts' opinions for evaluation. Furthermore, sensitivity analysis is also done to show the robustness of the suggested methodology.

Seven (7) recruitment criteria for boomerang employees were identified and validated based on the opinions of industry experts. Using these recruitment criteria, three candidates emerged as the top three and created a pool out of six. In addition, this study finds that Criteria 1 (C1), the employee's past performance, is the most significant predictor among all other criteria in boomerang hiring.

Since the weights and ratings of attributes and alternatives in MCDM methods are primarily based on expert opinion, a significant difference in expert opinions (caused by differences in their knowledge and qualifications) may impact the values of the grey possibility degree. However, enough attention was taken while selecting the experts for this study regarding their expertise and subject experience.

The proposed method provides the groundwork for HR management. Managers confronted with recruiting employees who want to rejoin may use this model. According to experts, each attribute is not only generic but also crucial. In addition, because these factors apply to all sectors, they are industry-neutral.

To the best of the authors’ knowledge, this is the first study to apply a grey-based MCDM methodology to the boomerang recruitment model. This study also uses an example to explain the computational intricacies associated with such methods. The proposed system may be reproduced for boomerang recruiting in any sector because the framework is universal and replicable. Furthermore, the framework is expandable to include new criteria for different work.

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.

With increasing globalization, the importance of referring to cross-cultural contexts is also amplifying in the contemporary era. The models, framework and dimensions devoted to such situations are increasingly mannered in practice today. With due consideration derivation to the input manner, an organization’s performance is also taken aside for its increasing marginality. The following study aims to examine the American multinational oil and gas company operating in Kuwait's oil sector to evaluate the respect. The branch has a total of 600 employees, which were all included in the data collection phase. Hence, the results derived with cumulative aspects of leadership, power distance and organizational value as their impact on human resource management (HRM) functions.

Through the quantitative approach of a self-adapted questionnaire, 101 responses were collected, and through SPSS Version 22, results were discreet.

The results reverted with the manual that the cultural dimension of Hofstede, i.e. power distance, did not have an apparent impact on the HRM functions. Whilst leadership and organizational values had their dissolved set of effects on HRM functions of the chosen firm. However, HRM functions were apparent enough to have its impact upon leadership, power distance (PD) and organizational value accumulated. Opening to the opportunities where in the future studies, a greater extent of population as well as variables could be considered for a better impact.

The Major limitation of the study is related to the number of respondents as the research was conducted solely for the chosen branch of this private oil and gas firm in Kuwait, which makes its results limited and narrowed in the analysis phase. Moreover, the methodology selection did not do justice to the resulting research’s agenda, which was restricted due to the pandemic’s situation. Where with a better and respective methodology approach, better results could be attached.

The rationale of research refers to the gap concerning theoretical aspects within a field of business. As for the resulting study, in a cross-cultural management setting, the relevancy and implication of HRM functions concerning their transparency have not been explored. Globally, in different industries, the, study of similar nature has been conducted but has not examined, the fossil fuel industry particularly, which would be the focal point of this research.

Employment exchanges have been playing a significant role in the worldwide labor market for more than a hundred years now. In India, since 1946, millions of aspiring Indian youths have registered themselves with the government-run employment exchanges to find a job. About four million job seekers got registered at 1,000 employment exchanges in India, it is important to analyze the placement statistics of these exchanges. In recent years, new methods of job search have evolved. This study examines whether employment exchanges are effective in the changed scenario?

Using state-level employment exchange data for the period 2010–2011 to 2019–2020, this study analyzes the determinants of job placement at employment exchanges in India. A critical analysis of the functioning of employment exchanges along with recommendations to improve the job search ecosystem in India is also presented in the study.

This study found that increased share of service sector in the state economy negatively impacts placement at employment exchanges.

The absence of focus on the service sector requires policy intervention if Indian employment exchanges are to remain relavant.

The government administration should rethink that ignoring service sector employment potential is unaffordable for an emerging economy and employment exchanges should be aligned accordingly.

About 30 million people are unemployed in India. If employment exchanges are transformed, it can have far-reaching socio-economic advantages.

This study is the first sub-country level study on the institution of employment exchanges. This study comprehensively maps the landscape of career services in India. Empirically establishing the impact of sectoral structure of economy on efficacy of employment exchanges, and makes the case for policy intervention that is needed to keep the employment exchanges relevant in India.

This research has been conducted to determine the core reasons for gender inequality and to evaluate the effectiveness of higher education as a sole solution to ensure gender equality.

This qualitative case study research explores the causes behind gender inequality, and interviews were conducted with 20 male and female graduates and five stakeholders. Liberal feminism theory has been employed to understand the phenomena.

The findings show that receiving a university degree cannot ensure gender equality and empowerment of women in Bangladesh. The key factors contributing to gender disparity are societal and a general deficiency of technical knowledge.

More policies should support women in every aspect of life, and the existing policies should be carefully followed. The government must ensure high-quality education, and people must show more significant concern by acquiring knowledge from quality education seeking gender equality.

This research fills the gap in the current literature by providing knowledge about the practical effectiveness of quality higher education in solving the problem of gender inequality, its core reasons and possible solutions in Bangladesh's context.

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 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.