Search Results (56)

Introduction: Poorly designed safety job descriptions may have a negative impact on occupational safety and health (OSH) performance. Firstly, they limit the chances of hiring highly qualified safety professionals who are vital to the success of OSH management systems in organizations. Secondly, the relationship between the presence of qualified safety professionals and the safety culture (and performance) in an organization is reciprocal. Thirdly, the low quality of job descriptions limits exploring the proper competencies needed by safety professionals before they are hired. The safety professional is thus uncertain of what level of education or training and which skills they should attain. Objectives: The main goal of the study is to integrate the analytic hierarchy process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) with importance–performance analysis (IPA) to evaluate job descriptions in multiple sectors. Results: The results of the study indicate that it is vital to clearly define job levels, the overall mission, key responsibilities, time-consuming tasks, required education/certifications, and necessary personal abilities in safety job descriptions. This clarity enhances recruitment, fairness, performance management, and succession planning. The organization can then attract and retain top talent, improve performance, foster a strong safety culture, create realistic job expectations, increase employee satisfaction and productivity, and ensure that competent individuals are hired, ultimately leading to a safer and more productive workplace. Conclusion: The outcomes of this study provide a robust framework that can and should be used as a guideline to professionalize job description development and enhance talent acquisition strategies. Full article

Against the backdrop of digital transformation, this study constructs an analytical framework for the influence mechanism of the data-driven dynamic capabilities of foreign trade SMEs from the perspective of digital intelligence immunity, aiming to clarify the complex relationships among influencing factors and multi-combination paths for capability improvement. The research employs the fuzzy AHP-DEMATEL method to quantify the complex influence relationships among factors and uses fsQCA to analyze the configuration paths of high-level data-driven dynamic capabilities. Results show that digital intelligence management and analysis, digital intelligence supervision and early warning, and digital intelligence ecosystem are key drivers of data-driven dynamic capabilities, with digital intelligence talents serving as a guarantee and digital foundation as a foundation. The study identifies the following two core paths for forming high-level capabilities: “management–talent–ecology collaboration” and “early warning–technology–mechanism enhancement.” It concludes that foreign trade SMEs should strengthen digital intelligence management and ecological construction, improve early warning mechanisms, and adopt multi-pronged approaches to build data-driven dynamic capabilities, providing a theoretical basis for their digital transformation and capability upgrading. Full article

This study responds to the call by some scholars to establish a framework for ignorance. It challenges the myth that ignorance is all bad and an utterly undesirable state in organizations and proposes a new framework for the application of ignorance analytics in organizations. It includes a taxonomy of deliberate and unconscious ignorance in decision-making and judgment as well as the drivers of personal and corporate deliberate ignorance and their behavioral implications. Ignorance plays a substantial role in competency development, scientific progress, innovation, and organizational strategic advantage. The proposed framework can help developers of talent, including management trainers, educators, and HR practitioners, to recognize the drivers of willful ignorance and help managers design effective interventions to move employees from unconscious incompetence to mastery. This paper suggests an agenda and identifies opportunities for future research. Full article

This study proposes a predictive framework that integrates machine learning techniques with Transactional Net Promoter Score (tNPS) data to enhance sustainable Human Resource management. A synthetically generated dataset, simulating real-world employee feedback across divisions and departments, was used to classify employee performance and engagement levels. Six machine learning models such as XGBoost, TabNet, Random Forest, Support Vector Machines, K-Nearest Neighbors, and Neural Architecture Search were applied to predict high-performing and at-risk employees. XGBoost achieved the highest accuracy and robustness across key performance metrics, including precision, recall, and F1-score. The findings demonstrate the potential of combining real-time sentiment data with predictive analytics to support proactive HR strategies. By enabling early intervention, data-driven workforce planning, and continuous performance monitoring, the proposed framework contributes to long-term employee satisfaction, talent retention, and organizational resilience, aligning with sustainable development goals in human capital management. Full article

The financial valuation of professional football players is influenced by multiple factors that evolve throughout a player’s career. This study examines these determinants using Gradient Boosting Machine Learning models, segmented by three age categories and three playing positions to capture the dynamic nature of player valuation. K-fold cross-validation is applied to measure accuracy, with results indicating that incorporating a player’s projected future potential improves model precision from an average of 74% to 84%. The findings reveal that the relevance of valuation factors diminishes with age, and the most influential features vary by position—shooting for attackers, passing for midfielders, and defensive skills for defenders. The study adopts a dynamic segmentation approach, providing financial insights relevant to club managers, investors, and stakeholders in sports finance. The results contribute to sports analytics and financial modeling in sports, with applications in contract negotiations, talent scouting, and transfer market decisions. Full article

In the world of academia, there is a great mobility of talented university professors with a high level of movement among different entities. This could be a major problem, as universities must retain a minimum level of talent to support their various academic programmes. In this sense, finding out what factors could increase the loyalty of such staff can be of great interest to human resource (HR) departments and the overall administrative management of an organisation. Thus, this area, also known as People Analytics (PA), has become very powerful in human resource management to strategically address challenges in talent management. This paper examines talent commitment within the university environment, focusing on identifying key factors that influence the loyalty of professors and researchers. To achieve this, machine learning (ML) techniques are employed, as Principal Component Analysis (PCA) for dimensionality reduction and clustering techniques for individual segmentation have been employed in such tasks. This methodological approach allowed us to identify such critical factors, which we have termed Quantitative Emotional Salary (QES), enabling us to identify those factors beyond those merely related to compensation. The findings offer a novel data-driven perspective to enhance talent management strategies in academia, promoting long-term engagement and loyalty. Full article

In today’s rapidly evolving global landscape, the need to cultivate innovation-ready, globally competent talent has become a strategic imperative. This study critically investigates how sustainable open innovation strategies—particularly within non-profit organizations and university alliances—can serve as a catalyst for global talent development. Responding to the growing demand for interdisciplinary, cross-sectoral collaboration, the research employs a robust mixed-methods approach, integrating the Analytic Hierarchy Process (AHP) and Fuzzy Analytic Hierarchy Process (FAHP) to evaluate and prioritize key strategic factors. The findings reveal that initiatives such as international internship programs, operational funding mechanisms, joint research ventures, and technology transfer are essential drivers in creating environments that nurture and scale global talent. Building on these insights, this study introduces a structured, sustainable innovation model that categorizes strategies into three tiers—collaborative, interactive, and foundational service-oriented actions—providing a practical roadmap for resource optimization and strategic planning. More than a theoretical exercise, this research offers actionable guidance for non-profit leaders, academic administrators, and corporate partners. It highlights the reciprocal value of multi-sector collaboration and contributes to a broader understanding of how mission-driven innovation ecosystems can foster resilient, future-ready workforces. By positioning non-profit–academic partnerships at the center of global talent strategies, the study sets a foundation for rethinking how institutions can co-create value in addressing pressing global challenges. Full article

In the digital economy era, firms pursue innovation while also considering their environmental impact to ensure alignment with sustainability. However, existing research offers limited insights into how corporate environmental ethics influence the relationship between big data analytics capabilities (BDACs) and green radical innovation (GRI). This study investigates the impact of BDACs, environmental ethics, and GRI, using a sample of 291 firms and integrating resource-based theory with an environmental ethics perspective. Empirical results indicate that environmental ethics positively moderate the positive relationships between the three dimensions of BDAC—managerial, technical, and talent capability—and GRI. Moreover, there are differences in the moderating effects on this relationship. This study enriches boundary condition research on how BDACs impact GRI. Additionally, it contributes to understanding the mechanisms through which environmental ethics affect GRI, highlighting the combined effect of environmental ethics and BDAC. Furthermore, this study advances research on the heterogeneous role of environmental ethics, emphasizing the importance of enhancing corporate environmental ethics in transforming BDA technical capability into GRI. This contribution offers a new perspective on how firms can more effectively leverage their BDAC toward sustainable development. Full article

This paper introduces a novel approach to job recommendation systems by incorporating personality traits evaluated through the Gallup CliftonStrengths assessment, aiming to enhance the traditional matching process beyond skills and qualifications. Unlike broad models like the Big Five, Gallup’s CliftonStrengths assesses 34 specific talents (e.g., ‘Analytical’, ‘Empathy’), enabling finer-grained, actionable job matches. While existing systems focus primarily on hard skills, this paper argues that personality traits—such as those measured by the Gallup test—play a crucial role in determining career satisfaction and long-term job retention. The proposed approach offers a more granular and actionable method for matching candidates with job opportunities that align with their natural strengths. Leveraging Gallup tests, we develop a job-matching approach that identifies personality traits and integrates them with recommendation algorithms to generate a list of the most suitable specializations for the user. By utilizing a GPT-4 model to process job descriptions and rank relevant personality traits, the system generates more personalized recommendations that account for both hard and soft skills. The empirical experiments demonstrate that this integration can improve the accuracy and relevance of job recommendations, leading to better career outcomes. The paper contributes to the field by offering a comprehensive framework for personality-based job matching and validating its effectiveness, paving the way for a more holistic approach to recruitment and talent management. Full article

With the rapid development of society and technology, the physical health of university students has become a critical concern, influencing both individual well-being and the national talent pool. This study employs an improved K-means algorithm integrated with machine learning models to analyze university students’ fitness data and develop personalized health intervention strategies. The enhanced K-means algorithm overcomes the limitations of traditional clustering approaches, leading to improved clustering accuracy and stability. Machine learning models—including Random Forest, decision trees, Gradient Boosting Trees, and logistic regression—were utilized to validate the clustering outcomes and to identify key health indicators associated with different student groups. Based on the clustering and model analysis, targeted intervention programs are proposed, such as strength training for groups with low muscular explosiveness, endurance training for those with stamina deficiencies, and flexibility exercises for groups exhibiting limited mobility. This integrated analytical framework provides a scientifically grounded tool for comprehensive health assessments and offers actionable, data-driven recommendations for student health management. Future research will focus on optimizing algorithmic performance, enhancing data diversity, and broadening the application scope to further improve the effectiveness and feasibility of health interventions. Full article

The innovative behavior of university teachers plays a vital, long-term role in advancing scientific and technological innovation and in nurturing high-level talent. Flow experience is influenced by flow antecedents such as the balance between challenge and skill, clear goals, immediate feedback, intrinsic motivation, and perceived risk. Moreover, flow experience, characterized by deep concentration and effective attention allocation, is essential in facilitating innovative behavior by enhancing problem-solving and analytical abilities. This study explores the relationship between flow experience and innovative behavior among university teachers, providing a fresh theoretical perspective for encouraging such behavior. To investigate this, the study developed the “University Teacher Flow Experience Scale” and the “University Teacher Innovative Behavior Scale”. A survey of 316 university teachers in China was conducted, with statistical analysis utilizing variance analysis and structural equation modeling. Results showed that both flow experience and innovative behavior were at moderate levels. Significant variations in innovation levels were noted across disciplines, professional titles, and positions, but no gender differences were found. Antecedents such as a balance between challenges and skills, clear goals, immediate feedback, and intrinsic motivation positively influenced flow experience, while perceived risk had a negative impact. Flow experience itself significantly enhanced innovative behavior among university teachers. The findings highlight the importance of optimizing the factors contributing to flow experience at institutional and individual levels to promote innovation in higher education. Full article

This paper presents a novel AI-driven conceptual smart model designed to help organizations enhance workforce professional development by upskilling and reskilling employees while fostering job satisfaction and staying competitive in their markets; this novel model is called Professional Development Analytics (PDA). The model’s main focus is to provide a new design model that concentrates on how artificial intelligence (AI) can optimize personalized training and how it can improve employees’ technical and soft skills, enabling companies to create their talent map at the same time. By compiling personnel data and their roles within the company, AI is able to create detailed and personalized profiles. In the next stage, this information is classified, analyzed, and used to enhance current skills while also predicting future training needs. These processes result in the creation of personalized learning paths, where AI recommends customized courses tailored to each employee’s unique needs. The system will be automatically fed and adjusted by means of the gathered data and continuous feedback from the employees and their supervisors. The proposed AI tools are powered by machine learning, deep learning, natural language processing, generative AI and data analytics. Our model aims to support learning and development departments by delivering precise, personalized training solutions that address employees’ unique needs, enabling skill development and professional growth through an automated and customized process. Full article

Talent cultivation is the fundamental mission of higher education institutions, and the key to improving the quality of talent cultivation lies in enhancing the quality of teaching. In this regard, the Joint Committee recommends that the United Nations Educational, Scientific and Cultural Organization (UNESCO) should be invited to participate in this conference, in accordance with their respective mandates. However, in China, research on course evaluation systems and mechanisms in application-oriented universities is relatively scarce, and the evaluation dimensions are often limited; therefore, the evaluation of graduate courses in universities faces challenges such as a lack of specialized assessment systems, limitation of evaluation methods, and an imbalance between emphasis on outcomes and neglect of the teaching process. In this study, a comprehensive evaluation system for the Advanced Structural Dynamics (ASD) course is constructed based on the context-input-process-product (CIPP) evaluation model. The evaluation was conducted from four perspectives: teaching objectives, teaching inputs, teaching processes, and teaching outcomes. The fuzzy analytic hierarchy process (AHP) and simulated annealing particle swarm algorithm (SAPSO) are employed to study evaluation indicators and weights at various levels for the ASD course, and the proposed method is validated through practical examples. This study combines qualitative and quantitative evaluation indicators to achieve comprehensive assessment and adopts more scientifically rational algorithms for weight calculation, aiming to improve the accuracy and efficiency of weight calculation. The research findings of this study can further enhance the evaluation level of teaching quality and talent cultivation in graduate courses at application-oriented universities. Full article

This study constructs an analytical framework to assess the effectiveness of collaborative governance in smart communities, focusing on six key elements: collaborative subjects, funding sources, community participants’ literacy, community-specific systems, community culture, and supporting facilities. Using fuzzy set qualitative comparative analysis (QCA) on 20 typical cases of community governance, the study identifies that collaborative subjects and supporting facilities are necessary conditions for achieving effective community governance. Community culture and community participants’ literacy are recognized as sufficient conditions for effective collaborative governance involving multiple subjects in smart communities. The study also identifies several pathways to enhance the effectiveness of collaborative governance in smart communities, including the subject-–culture-embedded pathway, technology–resource-driven pathway, and system–talent-led pathway. These pathways highlight the integration of community-specific cultural elements and the leveraging of modern technologies to foster stakeholder engagement, enhance decision-making processes, and improve service delivery. The findings suggest that robust community culture and literacy, combined with advanced technological infrastructure and diverse funding sources, significantly contribute to the success of collaborative governance initiatives. By providing a comprehensive analysis of the interplay between these factors, the study offers valuable insights into the construction of smart communities and proposes strategies for enhancing the effectiveness of collaborative governance. This research contributes to the broader discourse on sustainable urban development and the knowledge economy, emphasizing the crucial role of innovation, technology, and community engagement in shaping the future of smart cities. Full article

In the field of engineering management, teaching emphasizes the cultivation of applied talents within the context of new engineering disciplines. Evaluating the effectiveness of this cultivation is particularly necessary. The evaluation system for teaching effectiveness plays a crucial role in enhancing teaching quality, promoting students’ comprehensive development, and driving educational reforms. In recent years, there have been numerous research achievements on the evaluation system for practice-oriented teaching quality. However, compared with other disciplines, the field of engineering management remains in its infancy. Therefore, this study clarified the ability goals that talents in this major should possess by studying the evaluation indicators of teaching effectiveness in the field. Based on an emphasis on cultivating different abilities, a practical teaching effectiveness evaluation system was constructed. This study used the Delphi method and the analytic hierarchy process (AHP) to construct a teaching evaluation indicator system for engineering management majors. The system assigns weights to each indicator based on the “Four Abilities”, including professional competence, practical skills, innovation abilities, and employability. This results in the establishment of a relatively scientific and reasonable teaching effectiveness evaluation system. Furthermore, based on the research results, teaching reform studies related to ability cultivation in the “Building Structures” course were carried out. The teaching effectiveness was verified through post-class student feedback, and a student assessment method was established. This study contributes to a better understanding of the path of practical teaching reform and provides a reference value for teaching practice research in related majors. Full article