Search Results (520)

Digital innovation is the core driving force to enhance the competitiveness of enterprises and promote sustainable development, and is a key enabler for achieving corporate ability goals. Executives with information technology (IT) backgrounds who have rich knowledge and skills in digital technology are the backbone of promoting the digital transformation of enterprises and optimizing the allocation of auditing resources. And they can lay the technological foundation for sustainable corporate development and play an important role in corporate audit decision-making. Based on the data of China’s A-share listed companies from 2015 to 2023, the impact of executives with IT backgrounds on auditor selection is empirically analyzed. The study shows that (1) the higher the proportion of executives with IT backgrounds in the executive team, the more the companies tend to choose high-quality auditors; (2) the degree of corporate digital innovation positively moderates the relationship between executives with an IT background and high-quality auditors; (3) the level of corporate internal control plays a mediating effect in the relationship between executives with an IT background and auditor selection; (4) for non-state-owned, large-scale, short executive tenures, and labor-intensive firms, executives with IT backgrounds exert a more significant influence on auditor selection. This study broadens previous research on corporate auditing behaviors from the perspective of executives with IT backgrounds, providing insights for companies to select suitable auditors, to make scientifically sound decisions regarding auditor selection in the context of digital innovation, further optimize internal management, enhance risk response capabilities, and thereby achieve sustainable corporate development. Full article

Artificial intelligence is rapidly reshaping skill expectations across media, marketing, and journalism, however, university curricula are not evolving at a comparable speed. To quantify the resulting curriculum–skill gap in communication-related programs, two synchronous corpora were assembled for the period July 2024–June 2025: 66 course descriptions from six leading UK universities and 107 graduate-to-mid-level job advertisements in communications, digital media, advertising, and public relations. Alignment around AI, datafication, and platform governance was assessed through a three-stage natural-language-processing workflow: a dual-tier AI-keyword index, comparative TF–IDF salience, and latent Dirichlet allocation topic modeling with bootstrap uncertainty. Curricula devoted 6.0% of their vocabulary to AI plus data/platform terms, whereas job ads allocated only 2.3% (χ² = 314.4, p < 0.001), indicating a conceptual-critical emphasis on ethics, power, and societal impact in the academy versus an operational focus on SEO, multichannel analytics, and campaign performance in recruitment discourse. Topic modeling corroborated this divergence: universities foregrounded themes labelled “Politics, Power & Governance”, while advertisers concentrated on “Campaign Execution & Performance”. Environmental and social externalities of AI—central to the Special Issue theme—were foregrounded in curricula but remained virtually absent from job advertisements. The findings are interpreted as an extension of technology-biased-skill-change theory to communication disciplines, and it is suggested that studio-based micro-credentials in automation workflows, dashboard visualization, and sustainable AI practice be embedded without relinquishing critical reflexivity, thereby narrowing the curriculum–skill gap and fostering environmentally, socially, and economically responsible media innovation. With respect to the novelty of this research, it constitutes the first large-scale, data-driven corpus analysis that empirically assessed the AI-related curriculum–skill gap in communication disciplines, thereby extending technology-biased-skill-change theory into this field. Full article

The result of accelerated development and technological progress is manifested through numerous changes in the labor market, primarily concerning the competencies of future employees. Many of those competencies have symmetrical character. The determinants that may influence the development of specific competencies are variable and dynamic, yet they share the characteristic of transcending temporal and spatial boundaries. In this paper we propose the use of a combination of Principal Component Analysis (PCA) and Fuzzy Analytic Hierarchy Process (FAHP) to rank 21st-century competencies that are developed independently of the formal educational process. Ability to organize and plan, appreciation of diversity and multiculturalism, and ability to solve problems appeared to be the highest-ranked competencies. The development of key competencies is symmetrical to the skills for the labor market. Also, the development of key competencies is symmetrical to the right selection of the quality of media content. The paper proves that the development of key competencies is symmetrical to the level of education of both parents. One of the key findings is that participants with higher levels of media literacy express more readiness for the contemporary labor market. Moreover, the family, particularly parents, exerts a highly significant positive influence on the development of 21st-century competencies. Parents with higher levels of education, in particular, provide a stimulating environment for learning, foster critical thinking, and encourage the exploration of diverse domains of knowledge. Full article

This paper examines the impact of import competition on skill premium and the moderating effect of labor market flexibility on it, using panel data from 30 provinces in China from 2010 to 2019. A dynamic panel threshold model with instrumental variables is employed to address the endogeneity problem and to identify the nonlinear moderating effect of labor market flexibility. The results show the following: (1) Import competition has a promoting effect on skill premium, and this effect declines from eastern to western regions in China. (2) The import competition increases the skill premium through the channels of enhancing regional innovation capacity and promoting industrial upgrading and rationalization. (3) There exists a significant threshold effect in the moderating effect of labor market flexibility. When labor market flexibility surpasses the threshold value of 1.330, the enhancing effect of import competition on the skill premium is alleviated, facilitating labor reallocation and wage adjustment. The integration of labor market flexibility into the globalization–inequality debate extends the existing literature for providing a new understanding of the mechanisms behind the skill premium. The policy implications are that targeted labor market reforms are essential for mitigating wage differentials between skilled and unskilled workers arising from intensified import competition. Full article

The emigration of highly skilled individuals has become a critical concern for many countries amid increasing global labor mobility. This study employs the Improved Fuzzy Step-Wise Weight Assessment Ratio Analysis (IF-SWARA) method within a fuzzy multi-criteria decision-making (FMCDM) framework to identify and prioritize the key drivers of skilled migration. Drawing on opinions from sixteen Turkish emigrants currently residing abroad, the study captures firsthand perspectives on the structural factors influencing their migration decisions. The results indicate that the most influential factors are workplace conditions, living standards, and academic standards. These findings underscore the multifaceted nature of brain drain and highlight the necessity for comprehensive policy approaches that address both push and pull dynamics. By systematically ranking these determinants, the study contributes to the growing body of evidence-based research on international human capital flows. Full article

In the context of advancing new quality productive forces (NQP), the optimization of factor allocation is of critical importance. This study empirically examines how inter-firm land allocation affects the development of NQP and explores the moderating roles of labor, capital, and data factors from a perspective of factor synergy. Combining theoretical analysis with empirical investigation, the findings are as follows: (1) optimizing land allocation across firms significantly enhances the level of urban NQP, and this result remains robust after accounting for endogeneity and a series of robustness checks; (2) capital expansion and the scaling of data resources substantially reinforce the positive effect of land allocation on NQP, whereas the interregional mobility of labor—particularly high-skilled workers—exerts a negative moderating influence. The results suggest that policymakers should promote the rational allocation of land resources while leveraging the synergistic effects of labor, capital, and data to accelerate the development of NQP at the local level. Full article

Work is historically evolving due to technological advancements, posing challenges for individuals and institutions. The actual Fourth Industrial Revolution, marked by AI, biotechnology, and nanotechnology, has rapidly increased these advancements, while the COVID-19 pandemic has anticipated future expected changes in the labor market. In this context, Higher Education Institutions must match their curricula with this dynamic labor market to equip graduates with relevant skills. However, the slow pace of educational transformation, partly due to a lack of data, hinders this alignment. This research introduces Future-oriented Higher Education Degree Employability (Fo-HECE) as an evaluation tool for the adherence of undergraduate degrees to the demands of the labor market in the next decade. We used a novel approach, combining the Operationalization of a social phenomenon with Multi-Criteria Decision-Making, with the participation of nine experts. As a use case, we applied the new approach to assess the future employability of ten undergraduate programs with the most enrolled students from one of the largest Brazilian universities. As a result, the indicators considered most relevant to measure Fo-HECE are Student-to-Job Ratio, Employment Balance, and Wage Premium. The degrees with the highest Fo-HECE grades were Law and Medicine, while Physical Education had the lowest grade. The Fo-HECE approach, as demonstrated in this case study, shows potential applicability to other HEIs and countries, provided that comparable labor market data are available. Full article

The burgeoning economic relationship between Türkiye and Poland, marked by a targeted $10 billion trade volume, has catalyzed significant Turkish engagement in the Polish construction sector. Ranked second globally in international contracting, Turkish firms are increasingly undertaking complex infrastructure projects in Poland, making it a critical European market to analyze. This study develops a comprehensive framework to identify and evaluate the sources of sustainable competitive advantage for Turkish contractors operating in this dynamic environment. The research adopts a qualitative, single-case study methodology, centered on the extensive project portfolio of a leading Turkish firm in Poland. The analytical approach is twofold. First, it employs Porter’s Diamond Framework to deconstruct the existing competitive advantages, revealing a shift from traditional low-cost models to a sophisticated synergy of superior labor management capabilities, strategic local partnerships, and expertise in complex project delivery. These strengths are shown to align directly with Poland’s critical needs, particularly its skilled labor shortage and ambitious infrastructure agenda. Second, a Foresight Analysis is conducted to map plausible future scenarios through 2035, addressing key uncertainties such as geopolitical shifts and the pace of technological adoption. The findings demonstrate that the sustained success of Turkish contractors hinges on their ability to deliver targeted value. The study concludes by proposing a set of “no-regrets” strategies—including accelerated ESG and digital up-skilling, forging deep local partnerships, and developing financial engineering capabilities—designed to secure and enhance their competitive positioning. The results provide an actionable roadmap for industry practitioners and valuable insights for policymakers fostering bilateral economic collaboration. Full article

Generative Artificial Intelligence (GenAI) is widely recognized for its profound impact on labor market demand, supply, and skill dynamics. However, due to its transformative nature, GenAI increasingly overlaps with traditional AI roles, blurring boundaries and intensifying the need to reassess workforce competencies. To address this challenge, this paper introduces KANVAS (Kolmogorov–Arnold Network Versatile Algorithmic Solution)—a framework based on Kolmogorov–Arnold Networks (KANs), which utilize B-spline-based, compact, and interpretable neural units—to distinguish between traditional AI roles and emerging GenAI-related positions. The aim of the study is to develop a reliable and interpretable labor market classification system that differentiates these roles using explainable machine learning. Unlike prior studies that emphasize predictive performance, our work is the first to employ KANs as an explanatory tool for labor classification, to reveal how GenAI-related and European Skills, Competences, Qualifications, and Occupations (ESCO)-aligned skills differentially contribute to distinguishing modern from traditional AI job roles. Using raw job vacancy data from two labor market platforms, KANVAS implements a hybrid pipeline combining a state-of-the-art Large Language Model (LLM) with Explainable AI (XAI) techniques, including Shapley Additive Explanations (SHAP), to enhance model transparency. The framework achieves approximately 80% classification consistency between traditional and GenAI-aligned roles, while also identifying the most influential skills contributing to each category. Our findings indicate that GenAI positions prioritize competencies such as prompt engineering and LLM integration, whereas traditional roles emphasize statistical modeling and legacy toolkits. By surfacing these distinctions, the framework offers actionable insights for curriculum design, targeted reskilling programs, and workforce policy development. Overall, KANVAS contributes a novel, interpretable approach to understanding how GenAI reshapes job roles and skill requirements in a rapidly evolving labor market. Finally, the open-source implementation of KANVAS is flexible and well-suited for HR managers and relevant stakeholders. Full article

The persistent issue of unemployment and the mismatch between graduate skills and labor market demands has drawn increasing attention from academics and educational institutions, especially in light of rapid advancements in technology. Emerging technologies such as Artificial Intelligence (AI) and Machine Learning (ML) offer valuable opportunities to analyze job market dynamics. In this work, we present a novel framework aimed at predicting graduate employability using current labor market data from Morocco. Our approach combines Multi-Criteria Decision-Making (MCDM) techniques with ML-based predictive models. AHP prioritizes employability factors and TOPSIS ranks skill demands—together forming input features for machine learning models. 2100 job listings obtained through web scraping, we trained and evaluated several ML models. Among them, the K-Nearest Neighbors (KNN) classifier demonstrated the highest accuracy, achieving 99.71% accuracy through 5-fold cross-validation. While the study is based on a limited dataset, it highlights the practical relevance of combining MCDM methods with ML for employability prediction. This study is among the first to integrate AHP–TOPSIS with KNN for employability prediction using real-time Moroccan labor market data. Full article

In confronting escalating economic uncertainty, achieving a win–win situation for low-carbon transition and improved employment structure will contribute to economic recovery and sustainable growth but also contribute to building a community with a shared future for mankind. A critical issue for China’s economy and societal welfare, as well as a core component of sustainable development, concerns whether low-carbon economic transition influences employment skill structure. This study utilizes data from 30 provinces (municipalities and autonomous regions) in China from 2006 to 2021. Employing the entropy method, a low-carbon economic development level indicator system was constructed from four aspects: low-carbon output, low-carbon consumption, low-carbon resources, and low-carbon environment to measure the low-carbon economy and explore its direct and indirect effects on employment skill structure and spatial effects. The research findings indicate that low-carbon economies not only directly and significantly promote employment skill structure optimization but also indirectly generate promotional effects through pathways such as industrial structure adjustment, green innovation’s innovative effects, and factor substitution effects of increased pollution control investment. Among these, the indirect impact of industrial structure adjustment contributes most substantially. Low-carbon economies’ influence on employment skill structures exhibits spatial spillover effects, with neighboring regions’ low-carbon economies exerting positive spillover effects on local skill structures. Additionally, significant negative interdependence exists among regional employment skill structures. Based on the aforementioned research conclusions, the following recommendations are proposed: accelerate low-carbon economy development and employment skill structure enhancement in central and western regions to diminish regional disparities; encourage green innovation and promote traditional industry upgrading and transformation; formulate regional coordinated development plans, thereby strengthening the low-carbon economy’s optimizing role upon employment skills structure; and increase educational investment and strengthen labor skill training. Full article

This research examines the impact of the project ‘Development of Science Experiential Education Programs and Science Experiential Centres’, implemented by the Mobilis Science Center in Győr between 2017 and 2021. The professional and societal relevance of the program and research lies in the growing importance of STEM disciplines and careers worldwide in recent decades, ensuring a long-term supply of skilled workers. A vital tool for this is the development of curricula that meet the needs of the 21st century, as well as the innovation of teaching methods in science subjects. The research involves a review of the literature on experiential science teaching and subject attitudes, the role of science centers, and relevant project documents. The present research, involving 592 students, focused on attitudes toward technology and science, openness to STEM careers, and the experiences and memories of participants in the student lab theme days. The results of the statistical data analyses confirm the effectiveness of the experiential education methods used in the theme day sessions, as the students’ openness to STEM careers is higher for those who participated in the sessions compared to the non-participants. There are significant differences in the attitudes of girls and boys participating in the program toward science subjects. The results suggest that the success in stimulating interest in science was mainly due to the experiential nature of the sessions. Moreover, the research found that the project led to the strengthening of the participants’ personal and social skills. This study is the first to look at the impact of the project. The results shed light on how teaching STEM subjects using experiential pedagogical methods can contribute to the long-term effectiveness of Széchenyi István University’s enrollment efforts and lead to the economic success of companies in a region facing a significant labor shortage in STEM careers. Full article

This study explores the innovation barriers and implementation strategies within small and medium-sized manufacturing enterprises (SMEs) in the Mazowieckie Voivodeship of Poland. Despite their crucial role in the regional economy, these enterprises face significant hurdles that impede their growth potential and innovation capabilities. Using a mixed-methods approach, the research analyzes both quantitative and qualitative data from 426 manufacturing enterprises. The findings reveal that the primary barriers include limited access to capital, outdated technologies, and a shortage of skilled labor. Furthermore, the study identifies that while company size and age do not significantly influence the type of innovations introduced, external factors such as market reach and capital availability play critical roles. The study underscores the need for tailored policy interventions to support SMEs in overcoming these barriers and fostering an environment conducive to innovation. Full article

The economic efficacy of logistics infrastructure is being reshaped by the dual forces of digitalization and the labor market. However, a new-era “investment return paradox” has emerged. Digitalization and an abundant labor force are theoretically positive forces, so why does their combination, when coupled with capital investment, paradoxically engender negative emergence that suppresses growth? Conceptualizing the regional economy as a Socio-Technical System (STS), this paper unravels this paradox by identifying and theorizing an “adaptive lag trap”. Using provincial panel data from China, we first provide empirical validation for this trap, identifying a significant negative three-way interaction involving labor quantity (coef. = −0.218, p < 0.05). We then demonstrate that high-skilled labor quality is the key to mitigating this trap. While its direct interactive effects are not statistically significant, our analysis uncovers a robust and theoretically potent pattern: a higher-skilled workforce systematically reverses the negative trend of the interaction effect. The split-sample test provides the clearest evidence of this pattern, showing the coefficient pivoting from negative (−0.0572) in the low-skill subsample to positive (+0.109) in its high-skill counterpart. Our findings establish that high-skill human capital is a necessary condition to circumvent the “adaptive lag trap”, underscoring the imperative for a policy shift from investing in the scale of labor to cultivating its skill structure within a co-evolutionary framework. Full article

Artificial intelligence (AI) soft skills have become increasingly vital in today’s technology-driven world, as they support decision-making systems, strengthen collaboration among stakeholders, and enable individuals to adapt to rapidly changing environments—factors that are fundamental for achieving the sustainability goals of smart cities. Even though AI soft skills are becoming more important, no scale specifically designed to identify and evaluate individuals’ AI soft skills has been found in the existing literature. Therefore, this paper aimed to develop a reliable and valid scale to identify the AI soft skills of individuals. A sample of 685 individuals who were employed in AI-active sectors, with a minimum of a bachelor’s degree, and at least one year of AI-related work experience, participated in the study. A sequential exploratory mixed-methods research design was utilized. Exploratory factor analysis (EFA) identified a five-factor structure that accounted for 67.37% of the total variation, including persuasion, collaboration, adaptability, emotional intelligence, and creativity. Factor loadings ranged from 0.621 to 0.893, and communalities ranged from 0.587 to 0.875. Confirmatory factor analysis (CFA) supported this structure, with strong model fit indices (GFI = 0.940, AGFI = 0.947, NFI = 0.949, PNFI = 0.833, PGFI = 0.823, TLI = 0.972, IFI = 0.975, CFI = 0.975, RMSEA = 0.052, SRMR = 0.035). Internal consistency for each factor was high, with Cronbach’s alpha values of dimensions ranging from 0.804 to 0.875, with a value of 0.921 for the overall scale. Convergent and discriminant validity analyses further confirmed the construct’s robustness. The finalized AI soft skills (AISS) scale, consisting of 24 items, offers a psychometrically valid and reliable tool for assessing essential AI soft skills in professional contexts. Ultimately, this developed scale enables the determination of the social and cognitive skills needed in the human-centered and participatory governance structures of smart cities, supporting the achievement of specific Sustainable Development Goals such as SDG 4, SDG 8, and SDG 11, and contributes to the design of policies and training programs to eliminate the deficiencies of individuals in these areas. Thus, it becomes possible to create qualified human resources that support sustainable development in smart cities, and for these individuals to take an active part in the labor market. Full article