Search Results (485)

Mental stress is a psychological or emotional strain that typically occurs because of threatening, challenging, and overwhelming conditions and affects human behavior. Various factors, such as professional, environmental, and personal pressures, often trigger it. In recent years, various deep learning (DL)-based schemes using electroencephalograms (EEGs) have been proposed. However, the effectiveness of DL-based schemes is challenging because of the intricate DL structure, class imbalance problems, poor feature representation, low-frequency resolution problems, and complexity of multi-channel signal processing. This paper presents a novel hybrid DL framework, BDDNet, which combines a deep convolutional neural network (DCNN), bidirectional long short-term memory (BiLSTM), and deep belief network (DBN). BDDNet provides superior spectral–temporal feature depiction and better long-term dependency on the local and global features of EEGs. BDDNet accepts multiple EEG features (MEFs) that provide the spectral and time-domain features of EEGs. A novel improved crow search algorithm (ICSA) was presented for channel selection to minimize the computational complexity of multichannel stress detection. Further, the novel employee optimization algorithm (EOA) is utilized for the hyper-parameter optimization of hybrid BDDNet to enhance the training performance. The outcomes of the novel BDDNet were assessed using a public DEAP dataset. The BDDNet-ICSA offers improved recall of 97.6%, precision of 97.6%, F1-score of 97.6%, selectivity of 96.9%, negative predictive value NPV of 96.9%, and accuracy of 97.3% to traditional techniques. Full article

Sustainability has emerged as a critical issue in development. Digital transformation functions as both an enabler and an effective tool for promoting sustainability. Strategy plays a pivotal role in the process of digital transformation. However, there is a paucity of existing research focused on strategic renewal in digital transformation within the context of China. This study employs organizational learning theory to examine the antecedents and consequences of strategic renewal in digital transformation. Data were collected from 389 government employees through a questionnaire survey and a quantitative analysis was performed to evaluate four hypotheses using structural equation modeling (SEM). The results indicate that knowledge acquisition and organizational memory significantly influence strategic renewal, which in turn affects government performance. The findings of this study could serve as a guide and provide concrete practical approaches for successful digital transformation among governments, thereby laying a foundation for sustainable development. Full article

As artificial intelligence (AI) technology becomes increasingly widespread in organizations, its impact on individual employees’ psychology and behavior has garnered growing attention. Existing research primarily focuses on AI’s effects on organizational performance and job design, with limited exploration of its mechanisms influencing individual employees, particularly in the critical area of risk-taking behavior, which is essential to organizational innovation. This research develops a moderated mediation model grounded in social cognitive theory (SCT) to explore how AI usage affects the willingness to take risks. A three-wave longitudinal study collected and statistically analyzed data from 442 participants. The findings reveal that (1) AI usage significantly enhances employees’ willingness to take risks; (2) self-efficacy serves as a partial mediator in the connection between AI usage and the willingness to take risks; and (3) learning goal orientation moderates both the relationship between AI usage and self-efficacy, as well as the mediating effect. This research enhances our understanding of AI’s impact on organizational behavior and provides valuable insights for human resource management in the AI era. Full article

Background: Hearing loss is increasingly prevalent and poses a significant public health concern. While both aging and occupational noise exposure are recognized contributors, their interactive effects and gender-specific patterns remain underexplored. Methods: This cross-sectional study analyzed data from 135,251 employees in Jiangsu Province, China. Demographic information, noise exposure metrics, and hearing thresholds were obtained through field measurements, questionnaires, and audiometric testing. Multivariate logistic regression, restricted cubic spline modeling, and interaction analyses were conducted. Machine learning models were employed to assess feature importance. Results: A nonlinear relationship between age and high-frequency hearing loss (HFHL) was identified, with a critical inflection point at 37.8 years. Noise exposure significantly amplified HFHL risk, particularly in older adults (OR = 2.564; 95% CI: 2.456–2.677, p < 0.001), with consistent findings across genders. Men exhibited greater susceptibility at high frequencies, even after adjusting for age and co-exposures. Aging and noise exposure have a joint association with hearing loss (OR = 2.564; 95% CI: 2.456–2.677, p < 0.001) and an interactive association (additive interaction: RERI = 2.075, AP = 0.502, SI = 2.967; multiplicative interaction: OR = 1.265; 95% CI: 1.176–1.36, p < 0.001). And machine learning also confirmed age, gender, and noise exposure as key predictors. Conclusions: Aging and occupational noise exert synergistic effects on auditory decline, with distinct gender disparities. These findings highlight the need for integrated, demographically tailored occupational health strategies. Machine learning approaches further validate key risk factors and support targeted screening for hearing loss prevention. Full article

Workaholism and perfectionism have increasingly been identified as significant obstacles to effective lifelong learning and skills development, ultimately undermining long-term career adaptability and organizational resilience. This study explores the predictive role of perfectionism and professional workaholism, with a particular focus on their implications for continuous education and occupational sustainability—defined as employees’ ability to remain adaptable and resilient over time. Using a cross-sectional quantitative design, data were collected from 105 participants (54 IT professionals and 51 nurses) who completed standardized measures of perfectionism and workaholism and reported their cognitive–emotional readiness for further training. Four regression models were employed to assess the impact of the three perfectionism dimensions and profession on overall workaholism and its subcomponents (excessive work, compulsive work, supplementary work). Socially prescribed perfectionism emerged as a strong predictor, accounting for over one-third of the variance in workaholism (β = 0.37; R²_adj = 0.368; p < 0.001), while self-oriented perfectionism significantly predicted excessive work (β = 0.25; p = 0.015). Professional domain had no significant effect, indicating the trans-professional nature of these psychological barriers. Additionally, workaholism was associated with reduced cognitive–emotional availability for ongoing training, highlighting its detrimental effects on lifelong learning. Limitations include the cross-sectional design and reliance on convenience sampling. From a practical perspective, the findings support interventions targeting maladaptive perfectionism, aiming to enhance engagement in continuous professional education and foster sustainable work environments, in line with the United Nations Sustainable Development Goals (SDG 4 and SDG 8). Full article

Background: Employee turnover poses a multi-faceted challenge to organizations by undermining productivity, morale, and financial stability while rendering recruitment, onboarding, and training investments wasteful. Traditional machine learning approaches often struggle with class imbalance and lack transparency, limiting actionable insights. This study introduces an Explainable AI (XAI) framework to achieve both high predictive accuracy and interpretability in turnover forecasting. Methods: Two publicly available HR datasets (IBM HR Analytics, Kaggle HR Analytics) were preprocessed with label encoding and MinMax scaling. Class imbalance was addressed via GAN-based synthetic data generation. A three-layer Transformer encoder performed binary classification, and SHapley Additive exPlanations (SHAP) analysis provided both global and local feature attributions. Model performance was evaluated using accuracy, precision, recall, F1 score, and ROC AUC metrics. Results: On the IBM dataset, the Generative Adversarial Network (GAN) Transformer model achieved 92.00% accuracy, 96.67% precision, 87.00% recall, 91.58% F1, and 96.32% ROC AUC. On the Kaggle dataset, it reached 96.95% accuracy, 97.28% precision, 96.60% recall, 96.94% F1, and 99.15% ROC AUC, substantially outperforming classical resampling methods (ROS, SMOTE, ADASYN) and recent literature benchmarks. SHAP explanations highlighted JobSatisfaction, Age, and YearsWithCurrManager as top predictors in IBM and number project, satisfaction level, and time spend company in Kaggle. Conclusion: The proposed GAN Transformer SHAP pipeline delivers state-of-the-art turnover prediction while furnishing transparent, actionable insights for HR decision-makers. Future work should validate generalizability across diverse industries and develop lightweight, real-time implementations. Full article

Employees’ green voice behavior (GVB), as a specific category of extra-role green behavior, plays a vital role in promoting a firm’s sustainable development. However, its underlying mechanism has not been sufficiently explored. Drawing on social learning theory (SLT), this study proposes a research model that examines the indirect influence of environmentally-specific transformational leadership (ESTFL) on GVB via psychological empowerment (PE) and ecological reflexivity (ER) as well as the moderating role of person-supervisor value congruence (PSVC). To achieve the research goals, we conducted a two-wave online survey via the convenience sampling method to collect data from 530 employees and 106 direct supervisors working in the manufacturing, hospitality and service, energy production, construction, transportation, information and communication, and finance industries in China. Regression analyses and CFA based on SPSS and Mplus were employed to test and validate the research model. Our findings show that PE and ER both partially mediated the positive association between ESTFL and GVB. Moreover, PSVC moderated the mediating effects of ESTFL on GVB via PE and ER. This study advances empirical research regarding how leadership impacts GVB by revealing dual cognitive mechanisms and identifying its boundary condition. It also offers managerial implications for leaders and enterprises in China to promote employees’ GVB and improve sustainable management. Full article

This paper explores the concept of ‘competency’ in modern organisations. It emphasises the strategic importance of aligning organisational values, strategic goals, and employee competencies. It introduces competency mapping as a framework for ensuring such an alignment, as well as for developing a culture of continuous learning and development, where the emotions and feelings of the interactants are also taken into account based on intrapersonal and interpersonal aspects of human behaviour. The article also elucidates the interconnection among diverse human ‘intelligences’ that are of paramount importance in shaping human knowledge and guiding us in navigating through life more smoothly and efficiently. Thus, through an interdisciplinary scope, we have attempted to analyse the intrinsic value of competency mapping as a knowledge driver in modern organisational and educational settings. Full article

This study explores how Human Resource Management (HRM) can help organizations to face the challenges of digital transformation, focusing on reducing digital inequalities and improving employee performance. As digital tools become more important in workplaces, many employees still experience digital exclusion, which affects not only their productivity but also their sense of fairness and inclusion, as well. To investigate these issues, quantitative research was conducted using a structured questionnaire distributed online to employees across EU-based companies. The data were analyzed through PLS-SEM, including IPMA and mediation analysis, to understand the relations between HRM practices, digital skills, and perceptions of organizational justice. The findings show that HRM strategies have a significant impact on bridging the digital divide, especially by promoting digital adaptability and supporting inclusive work environments. Inclusion was also found to mediate the relation between HRM and employee performance. This research offers practical suggestions, like using Key Performance Indicators (KPIs) to monitor digital participation and encouraging continuous learning. The study adds value by connecting digital empowerment with HRM policies in a way that supports both organizational efficiency and equality. Future research could focus on specific sectors or use longitudinal data to better capture how digital inclusion develops over time. 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

Leaders are critical players in determining how their employees behave in the workplace. Particularly in higher education, teachers are required to utilize psychological, social, and physical resources to perform their tasks. This, along with institutional limitations, renders the role of ethical leaders more critical for driving positive performance outcomes. In this context, the current study investigates the role of ethical leadership on innovative work behavior of university teachers in Turkey. To provide a better understanding, mediating effect of work engagement and the moderating impact of perceived organizational support are also analyzed. With a total of 211 surveys gathered in a cross-sectional manner and using partial least squares—structural equation modeling with Smart-PLS software—the hypotheses were tested. By embedding social exchange, self-determination, and organizational support theories, the current study highlights the importance of the unique characteristics of ethical leaders in academia as antecedents of innovation for teachers, implementing long-term positive changes in the faculty. When institutional support systems exist, faculty deans can trigger engagement by leveraging the facilities and initiatives of the university, ultimately enhancing the learning environment of students while tending to the wellbeing of academic staff. Full article

Continuous improvement (CI) is vital for Saudi manufacturing firms to remain competitive in the global market. However, cultural factors significantly influence CI adoption. This qualitative study, involving 28 interviews and focus groups with employees from five local manufacturing firms, explored these factors. Seven key cultural themes emerged, including communication, employee wellbeing, talent management, ethics, top management support, organisational learning, and compliance. A conceptual framework was developed to assess a firm’s cultural proximity to an ideal CI state. This framework integrates a diagnostic tool to guide firms in evaluating their cultural landscape and implementing targeted interventions for successful CI adoption. Future research should explore the long-term impacts of cultural shifts on performance and competitiveness. Full article

Knowledge extraction and sharing is one of the biggest challenges organizations face to ensure successful and long-lasting knowledge repositories. The North Carolina Department of Transportation (NCDOT) commissioned a web-based knowledge management program called Communicate Lessons, Exchange Advice, Record (CLEAR) for end-users to promote employee-generated innovation and to institutionalize organizational knowledge. Reusing knowledge from an improperly managed database is problematic and potentially causes substantial financial loss and reduced productivity for an organization. Poorly managed databases can hinder effective knowledge dissemination across the organization. Data-driven dashboards offer a promising solution by facilitating evidence-driven decision-making through increased information access to disseminate, understand and interpret datasets. This paper describes an effort to create data visualizations in Tableau for CLEAR’s gatekeeper to monitor content within the knowledge repository. Through the three web-based strategic dashboards relating to lessons learned and best practices, innovation culture index, and website analytics, the information displays will aid in disseminating useful information to facilitate decision-making and execute appropriate time-critical interventions. Particular emphasis is placed on utility-related issues, as data from the NCDOT indicate that approximately 90% of projects involving utility claims experienced one or two such incidents. These claims contributed to an average increase in project costs of approximately 2.4% and schedule delays averaging 70 days. The data dashboards provide key insights into all 14 NCDOT divisions, supporting the gatekeeper in effectively managing the CLEAR program, especially relating to project performance, cost savings, and schedule improvements. The chronological analysis of the CLEAR program trends demonstrates sustained progress, validating the effectiveness of the dashboard framework. Ultimately, these data dashboards will promote organizational innovation in the long run by encouraging end-user participation in the CLEAR program. Full article

Sharing is an important part of an organization’s culture, consisting of learning, innovation, and performance through the promotion of expertise, ideas, and best practices among employees. This study aimed to analyze the relationships between transactional leadership, human capital, and knowledge sharing. This study used a quantitative approach by using 355 responses from employees who work at customs offices throughout Sulaymaniyah Governorate, located in the Kurdistan region of Iraq. PLS (SEM) was used as a method of estimation in the study to test the hypotheses. The findings show that although transactional leadership greatly improves the development of human capital, it does not directly affect knowledge sharing. Moreover, knowledge and experience engaging in activities involving knowledge sharing and participating in human capital are quite important. The findings also show that the increase in human capital corresponds to the degree of effectiveness of leadership in knowledge management. Therefore, the findings provide practical implications for companies to increase employee capacities by improving transactional leadership within organizations. Full article

The recent transformation in Libya’s banking industry, driven largely by the Central Bank of Libya, has led to increased financial inclusion, enhanced banking services, and the adoption of digital banking technologies. While most banks have rapidly transitioned from traditional data analysis methods to using Artificial Intelligence (AI) for daily transaction analysis, the impact of AI on sustainable innovation performance and organizational learning remains underexplored. This study, grounded in dynamic capabilities theory, investigates the mediating role of organizational learning in the relationship between AI adoption in the banking sector and sustainable innovation performance. Data were collected from 401 employees across Libya’s conventional and Islamic banking sectors using a judgmental sampling technique. Partial Least Squares Structural Equation Modeling (PLS–SEM) was used to analyze the data and assess the relationships among the variables. The findings indicate that AI adoption significantly and positively influences sustainable innovation performance and organizational learning. Additionally, organizational learning was found to have a significant positive effect on sustainable innovation performance and to partially mediate the relationship between AI adoption and innovation performance. The study recommends that bank management teams implement training programs to enhance employees’ understanding of AI applications, sustainability objectives, and innovative financial services to improve overall efficiency. Full article