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The purpose of this study is to determine the competency characteristics required for the employment of master’s degree students in educational technology.

A combined qualitative and quantitative method was used to consult multiple experts through a modified Delphi method. Competency characteristics were extracted from Chinese recruitment apps, national recruitment websites and university training programs. Ten senior teacher experts who teach educational technology master’s students were consulted through a questionnaire consultation to validate the proposed competency model. The weights of competency characteristics were determined through a combination of the analytic hierarchy process and entropy method.

The results show that when recruiting educational technology master’s students, more emphasis is placed on operational skills. The majority of companies tend to assess practical abilities rather than theoretical knowledge. Relevant knowledge of educational technology, psychology, computer science and education is considered to be the basic knowledge components of educational technology master’s students, while professional skills are the core skills required for their positions. Therefore, universities need to focus on training, educational technology graduate students in these areas of competence. The study also found that professional qualities (such as physical and mental fitness) and personality traits (interpersonal communication and interaction) receive more attention from companies and are essential competencies for educational technology master’s students.

A competence model for educational technology master’s students is proposed, which includes aspects such as knowledge, personal skills/abilities, professional qualities and personality traits. The competence elements included in this model can serve as reference indicators for universities to cultivate the competence of educational technology master’s students, as well as reference points for recruiting units to help them select talents. This represents a new dimension in research related to the employment of educational technology master’s students. The study enriches the research objects and competence dictionary in the field of competence research.

This study aims to establish an evaluation method for cross-regional major infrastructure project (CRMIP) supportability. The focus is to identify evaluation indicators from a complexity perspective and develop an evaluation model using qualitative and quantitative methods. Case studies are carried out to verify the reliability of the evaluation model, thereby providing theoretical and practical guidance for CRMIP operations and maintenance (O&M).

Guided by the idea of complexity management, the evaluation indicators of CRMIP supportability are determined through literature analysis, actual O&M experience and expert interviews. A combination of qualitative and quantitative methods, consisting of sequential relationship analysis, entropy weighting, game theory and cloud model, is developed to determine the indicator weights. Finally, the evaluation model is used to evaluate the supportability of the Hong Kong–Zhuhai–Macao Bridge (HZMB), which tests the rationality of the model and reveals its supportability level.

The results demonstrate that CRMIPs' supportability is influenced by 6 guideline-level and 18 indicator-level indicators, and the priority of the influencing factors includes “organization,” “technology,” “system,” “human resources,” “material system,” and “funding.” As for specific indicators, “organizational objectives,” “organizational structure and synergy mechanism,” and “technical systems and procedures” are critical to CRMIPs' O&M supportability. The results also indicate that the supportability level of the HZMB falls between good and excellent.

Under the guidance of complexity management thinking, this study proposes a supportability evaluation framework based on the combined weights of game theory and the cloud model. This study provides a valuable reference and scientific judgment for the health and safety of CRMIPs' O&M.

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 purpose of the paper is to afford a comprehensive conceptualization and operationalization of the construct of talent management through an inclusive exploration of conceptual clarifications for existing confusions while developing a complete measuring instrument.

The archival method was adopted together with a systematic review based on Khan et al.’s (2003) five steps of systematic literature review. The systematic review has encircled published research articles between 1982 and 2023 in the human resource management (HRM) arena. A total of 130 articles were initially scrutinized, and 106 were systematically reviewed to conceptualize, operationalize and explore clarifications for confusions and instrument development for talent management.

This study explored conceptual clarifications for existing confusions towards talent management while recognizing definitions that come under the main philosophical schools for the underlying concept of talent. A novel practical definition has been established for talent management while recognizing dimensions, and then certain elements. A comprehensive instrument has been developed to measure talent management.

This study is limited to instrument development in measuring talent management; nevertheless, there is an enormous scope for using the instrument to empirically measure talent management through organizational and employees perspectives linked to diverse global contexts in future studies.

The developed comprehensive instrument is a vibrant contribution to future investigations related to empirically measuring talent management associated with organizational and employee perspectives related to diverse global contexts in winning “war for talent.” This study endows a significant input to the whole frame of HRM knowledge as it resolves existing conceptual ambiguities towards talent management while defining and operationalizing it.

Based on the JD-R model and process-focused HRM perspective, this research paper aims to investigate the processes underlying the relationship between AI-enabled HR analytics and employee well-being outcomes (resilience) that received less attention in the AI-driven HRM literature. Specifically, this study aims to examine the indirect effect between AI-enabled HR analytics and employee resilience via job crafting, moderated by HRM system strength to highlight the contextual stimulus of AI-enabled HR analytics.

The authors adopted a time-lagged research design (one-month interval) to test the proposed hypotheses. The authors used two-wave surveys to collect data from 175 full-time hotel employees in China.

The findings indicated that employees' perceptions of AI-enabled HR analytics enhance their resilience. This study also found the mediation role of job crafting in the mentioned relationship. Moreover, the positive effects of AI-enabled HR analytics on employee resilience amplify in the presence of a strong HRM system.

Organizations that aim to utilize AI-enabled HR analytics to achieve organizational missions should also dedicate attention to its associated employee well-being outcomes.

This study enriched the literature with regard to AI-driven HRM in that it identifies the mediating role of job crafting and the moderating role of HRM system strength in the relationship between AI-enabled HR analytics and employee resilience.

To contribute to achieving the Sustainable Development Goals, this study aims to explore how to encourage innovative green behaviors among college students and the mechanisms behind the formation of green innovation behavior. Specifically, this study examines the influences of schools, mentors and college students themselves.

A multilevel, multisource study involving 261 students from 51 groups generally supported this study’s predictions.

Proenvironmental and responsible mentors significantly predicted innovative green behavior among college students. In addition, creative motivation mediated the logical chain among green intellectual capital, emotional intelligence and green innovation behavior.

The study findings offer new insights into the conditions required for college students to engage in green innovation. In addition, they provide practical implications for cultivating green innovation among college students.

The authors proposed and tested a multilevel theory based on the ability–motivation–opportunity framework. In this model, proenvironmental and responsible mentors, green intellectual capital and emotional intelligence triggered innovative green behavior among college students through creative motivation.

Organizations and markets are constantly developing and changing, impacting how organizations create value and manage supply chains. This paper aims to identify the motor themes and subthemes of academic research that relate supply chain management (SCM) maturity and business models (BMs).

The Science Mapping Analysis Tool (SciMAT) was used to conduct a seven-step bibliometric analysis of 889 documents indexed in the Scopus database clustering by relatedness of works and keywords. The methodological approach included content analysis of the literature, longitudinal analysis and strategic diagram analysis.

The thematic evolution analysis revealed that three themes drive the studies on SCM maturity and BM: industry 4.0 (motor theme), environmental management (transversal theme) and product management (highly developed and isolated theme). The strategic diagrams and co-word networks allowed to graphically identify the main topics connecting SCM maturity and BM. Considering this connection, the scientific mapping emphasizes the significance and strength of the link between digital technologies, technology management and manufacturing management in Industry 4.0. In addition, the connections between the subthemes revealed that circular economy and green human resource management are important concepts to advance theory and practice on the connection between SCM maturity and BM.

This research extends the knowledge base by providing an analysis of the key themes and the links with subthemes in the literature that relate to SCM maturity and BM. Key studies are analyzed and linked with the key topics identified using the SciMAT software. Future research avenues are outlined, providing new insights to advance theory and practice.

This meta-analytical review aims to clarify the relationships between three bundles of human resource management (HRM) practices—competency-enhancing, motivation-enhancing and opportunity-enhancing—and organizational innovation by addressing two questions: (a) Which types of HRM bundles are most strongly related to different forms of innovation (i.e. process and product innovation)? And (b) Which mechanism provides a stronger explanation for the positive effects of HRM bundles on innovation?

Based on data from 103 studies, a meta-analysis was conducted to quantitatively summarize existing HRM–innovation studies at the organizational level.

The results showed that the competency-enhancing bundle was more positively related to product innovation than the motivation-enhancing and opportunity-enhancing bundles. The opportunity-enhancing bundle was most strongly associated with process innovation. The authors further found that knowledge management capability (KMC) and employee motivation mediated the positive relationship between the three HRM bundles and innovation outcomes. In comparing the two mechanisms, this review suggests that KMC better explains both the impact of the competency-enhancing HRM bundle on product innovation and the effect of the opportunity-enhancing bundle on process innovation.

Based on behavioral and knowledge management perspectives, this study takes a sub-bundle approach to providing an integrative review by comparing the direct effects and mediating paths of HRM bundles on product and process innovation.

The purpose of this study is to explore the potential contribution of the metaverse to improve training and development as a function of human resource development (HRD) perspective. The authors explore the benefits and challenges of the metaverse and introduce cases of companies using the metaverse in training.

A narrative literature review was conducted to collect information on the metaverse in training. The authors reviewed peer- and non-peer-reviewed articles, book chapters, white papers, corporate websites and blogs and business magazines.

A total of 75 articles were reviewed, including 14 cases, which were summarized to demonstrate how companies are applying metaverse technology in training contexts. For a more in-depth review, three cases were selected and summarized in terms of context, process and outcomes.

The metaverse is an emergent topic in HRD. It has the potential to revolutionize the functions of training and development through the combination of advanced technologies, including virtual reality, augmented reality and mixed reality. This article is the foundational attempt to provide a comprehensive summary of existing literature and case studies that highlight the potential of the metaverse in training within the context of HRD.

The success of the organization depends on its sustainability and growth in the competitive market. Retention and attraction of talent in the organization are strongly associated with organizational performance. Employer branding is an outcome activity that helps organizations show their strength to attract and retain talent. Talent management practices are mostly essential in the current context for retaining talent. This study aims to explore and identify the contributing factors in efficient talent management and to examine whether the factors contributing to employer branding differ concerning different demographic profiles of the employees for information technology organizations.

Data were collected from 617 adult participants using an 85-item questionnaire on talent management comprising 25 domains, developed for the present study.

Principal component analysis of the data indicated that 20 different factors make an impact in developing strong talent management practices. Three broad areas were identified, namely, personal benefits and growth, transparent organizational culture and social commitment of the organizations.

Present research has not taken care of few other factors associated with the organization where employees’ retention gets adversely effected such as evaluation of performance and compensation management, training and development, etc. So, future research can be conducted these areas. These aspects are also required to be incorporated in future research.

Several implications of the present research can be presented in the following areas. It is found in the present research that the effectiveness of the talent management system mostly depends on personal benefits and growth, organizational culture and climate and the organizational out. Apart from the academic implications of the present research, practical implications of the present study cannot be ignored. The components and elements of the talent management in the perspectives employer branding can also appropriately applied by the organizations.

The contribution of the study lies in exploring and identifying three important aspects of the organization in talent management. Findings will have implications for different organizations in understanding, developing and implementing policies related to employer branding and talent management.