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The study aims to explore the mediating role of organizational culture in the relationship between on-the-job training (OJT), induction training and employee job performance. This study is conceptually grounded in Albert Bandura’s Social Cognitive Theory. The function that organizational culture plays as a mediator in the links between employee performance and on-the-job training and induction is a unique aspect of this study.

An industry-wide representation was ensured in the study by using a stratified random sampling technique to choose participants. The main characteristics pertaining to organizational culture, training initiatives and worker job performance were measured by using validated scales from earlier studies. For the purpose of validating the measurement model, factor loadings, internal consistency reliability and discriminant validity were evaluated through the use of partial least squares structural equation modelling in SmartPLS.

In support of Bandura’s Social Cognitive Theory, the study’s results show a strong association between work performance, organizational culture, on-the-job training and induction training. The study highlights the positive synergistic effect that supportive organizational culture and well-designed training programmes have on improving employee job performance. The unique contribution of this study is the provision of empirical support for these correlations across a wide range of industries, highlighting the crucial roles that organizational culture plays in promoting employee success.

Authors’ knowledge of how organizations may create environments that maximize worker productivity and potential is expanded by the study’s practical insights.

Motivated by recent research indicating the significant challenges posed by imbalanced datasets in industrial settings, this paper presents a novel framework for Industrial Data-driven Modeling for Imbalanced Fault Diagnosis, aiming to improve fault detection accuracy and reliability.

This study addressing the challenge of imbalanced datasets in predicting hard drive failures is both innovative and comprehensive. By integrating data enhancement techniques with cost-sensitive methods, the research pioneers a solution that directly targets the intrinsic issues posed by imbalanced data, a common obstacle in predictive maintenance and reliability analysis.

In real industrial environments, there is a critical demand for addressing the issue of imbalanced datasets. When faced with limited data for rare events or a heavily skewed distribution of categories, it becomes essential for models to effectively mine insights from the original imbalanced dataset. This involves employing techniques like data augmentation to generate new insights and rules, enhancing the model’s ability to accurately identify and predict failures.

Previous research has highlighted the complexity of diagnosing faults within imbalanced industrial datasets, often leading to suboptimal predictive accuracy. This paper bridges this gap by introducing a robust framework for Industrial Data-driven Modeling for Imbalanced Fault Diagnosis. It combines data enhancement and cost-sensitive methods to effectively manage the challenges posed by imbalanced datasets, further innovating with a bagging method to refine model optimization. The validation of the proposed approach demonstrates superior accuracy compared to existing methods, showcasing its potential to significantly improve fault diagnosis in industrial applications.

This paper aims to investigate the factors influencing trade artisans’ choice of skills selection as apprentice’s trainee in the Ghanaian construction sector and to identify and address the challenges associated with traditional apprenticeship. Trade artisans with technical know-how in construction and general workplace skills from the traditional apprenticeship training (TAT) in the area of construction were selected from selected sites and training centers.

This paper adopted the purposive sampling technique with the aim of gathering knowledge from individuals with expertise in the research area, particularly trade craftsmen who have been trained through the TAT system and are directly involved in construction-related works. Partial least square structure equation modelling (PLS-SEM) analytical approach and principal component analysis were used to reduce the dimensionality of the data set and preserve as much information as possible.

Three major components, namely, personal and social interest, job assessment and stability and family and faith were identified as the variables that influence an artisan's choice of a skill trade. These influenced the choice of apprenticeship training by young trainees in choosing apprenticeship as a mode of training. Personal interest, living situation of artisans and parents’ educational attainment are the most influencing factors that determine artisans’ choice of selected trades. Moreover, the study also shed light on the challenges inherent in traditional apprenticeship systems, such as the lack of formal technical education, limited access to modern technology and information and poor working conditions.

The study underscores the imperative for stakeholders to enhance apprenticeship programmes within the construction sector. This involves providing more stable job opportunities, improving working conditions and offering access to modern technology and information. Such enhancements not only attract more young individuals to apprenticeship training but also ensure the sustainability and relevance of the workforce in meeting industry demands.

The study finally developed a model that could be used as a foundation for future PLS-SEM evaluation and identified the factors that influence the selection of apprenticeship training by trade artisans.

The purpose of this study is to investigate the potential of using computer vision and deep learning (DL) techniques for improving safety on construction sites. It provides an overview of the current state of research in the field of construction site safety (CSS) management using these technologies. Specifically, the study focuses on identifying hazards and monitoring the usage of personal protective equipment (PPE) on construction sites. The findings highlight the potential of computer vision and DL to enhance safety management in the construction industry.

The study involves a scientometric analysis of the current direction for using computer vision and DL for CSS management. The analysis reviews relevant studies, their methods, results and limitations, providing insights into the state of research in this area.

The study finds that computer vision and DL techniques can be effective for enhancing safety management in the construction industry. The potential of these technologies is specifically highlighted for identifying hazards and monitoring PPE usage on construction sites. The findings suggest that the use of these technologies can significantly reduce accidents and injuries on construction sites.

This study provides valuable insights into the potential of computer vision and DL techniques for improving safety management in the construction industry. The findings can help construction companies adopt innovative technologies to reduce the number of accidents and injuries on construction sites. The study also identifies areas for future research in this field, highlighting the need for further investigation into the use of these technologies for CSS management.

This study aims to examine the relationships among structured-on-the job training (ST), mentoring, job rotation and the work environment factors on tacit knowledge transfer from training.

This study used quantitative research techniques to examine the causal relationships among the key study variables. A questionnaire-based survey has developed to evaluate the research model by drawing a convenience sample includes 239 employees working in the Arab Potash Company located in Jordan. Surveyed data were examined following the structural equation modeling procedures.

The results revealed that adapting of the ST, mentoring and job rotation in industrial firms had direct effect on the employees’ abilities to learn and transfer tacit knowledge from training to the actual work, and how these learning strategies strengthen employees’ abilities in solving work problems, improving customers’ satisfaction and quality of products and services. As well as, it affirmed the strong direct effect of work environment factors such as supervisor and peer support on the employees’ abilities to learning and transferring tacit knowledge to their jobs. However, this study showed that work environment factors have no significant mediating role on the relationship among ST, mentoring, job rotation and the employees’ abilities to learn and transfer tacit knowledge to their jobs.

The study results are opening the doors for future studies to examine the relationships among the methods of training and learning in the workplace, the work environment factors and tacit knowledge transfer from training to the jobs as prerequisites for improving the employees and organization performance. These results would be validated by conducting future research, examining larger samples of industrial companies to give more accurate data and clear explanations to the relationships among the study variables. It also suggests to replace the characteristics of work environment (supervisor support and peer support) by trainees’ characteristics (self-efficacy and career commitment) to give a better understanding to the relationships among the key study variables.

With regard to improving the employees’ competency while doing their jobs, this study developed a conceptual framework that guides managers to recognize the importance of ST, mentoring and job rotation in increasing the employees’ learning together; and giving them the chance to use the new learned experiences and knowledge to improve the organization performance and its competitive advantage. This study helps managers build a positive work environment that encourages social interaction, respect and mutual interest among employees, and increases their sense of responsibility for learning and transferring skills and knowledge to the jobs.

The training methods in the workplace go beyond immediate work performance to act as a promising tool make employees’ learning more easily and faster, and help them to transfer and retain new skills and knowledge, adapt with changing environments, build stronger relationships with stakeholders and at the same time, make the organizations ensure that employees comply with their societal goals.

The authors have noticed that large portions of the studies on training and human resources development neglected the role effect of (ST, mentoring and job rotation) on the tacit knowledge transfer from training to the jobs. Hence, these gaps in researches have motivated to develop a theoretical model that helps to examine the relationship between the two constructs. This study also suggests to examine the mediating role effects of work environment factors on the relationships among (ST, mentoring and job rotation) and tacit knowledge transfer, as well as it extends to examine the mediating role of work environment factors on transferring knowledge to jobs, attributed to the demographic variables such as gender, age, work experience and education level.

Crack detection of pavement is a critical task in the periodic survey. Efficient, effective and consistent tracking of the road conditions by identifying and locating crack contributes to establishing an appropriate road maintenance and repair strategy from the promptly informed managers but still remaining a significant challenge. This research seeks to propose practical solutions for targeting the automatic crack detection from images with efficient productivity and cost-effectiveness, thereby improving the pavement performance.

This research applies a novel deep learning method named TransUnet for crack detection, which is structured based on Transformer, combined with convolutional neural networks as encoder by leveraging a global self-attention mechanism to better extract features for enhancing automatic identification. Afterward, the detected cracks are used to quantify morphological features from five indicators, such as length, mean width, maximum width, area and ratio. Those analyses can provide valuable information for engineers to assess the pavement condition with efficient productivity.

In the training process, the TransUnet is fed by a crack dataset generated by the data augmentation with a resolution of 224 × 224 pixels. Subsequently, a test set containing 80 new images is used for crack detection task based on the best selected TransUnet with a learning rate of 0.01 and a batch size of 1, achieving an accuracy of 0.8927, a precision of 0.8813, a recall of 0.8904, an F1-measure and dice of 0.8813, and a Mean Intersection over Union of 0.8082, respectively. Comparisons with several state-of-the-art methods indicate that the developed approach in this research outperforms with greater efficiency and higher reliability.

The developed approach combines TransUnet with an integrated quantification algorithm for crack detection and quantification, performing excellently in terms of comparisons and evaluation metrics, which can provide solutions with potentially serving as the basis for an automated, cost-effective pavement condition assessment scheme.

Amid the growing global emphasis on sustainable agriculture, organizations and governments face a pressing need to equip farmers with the knowledge and tools necessary for the adoption of sustainable farming practices, aligning with the Sustainable Development Goals (SDGs). However, understanding the complex relationship between training programs and the adoption of sustainable practices among small-scale farmers remains a critical challenge. Taking a human resource approach, this paper attempts to understand the interrelationships between training effectiveness, farmers’ psychological and demographic characteristics in explaining the adoption of sustainable farming practices.

We employed a multi-stage random sampling method and administered a structured questionnaire to collect data from 331 small farmers who were part of a government-led, large-scale intervention aimed at training them in sustainable farming practices.

Our research findings not only emphasize the critical role of HR approach through training but also underscore its importance in the broader mission of aligning with the SDGs. Specifically, we demonstrate that sustained exposure to training, intrinsic motivation to acquire knowledge, and the innovative capacity of farmers collectively enhance the effectiveness of training programs, thereby contributing significantly to the widespread adoption of sustainable farming practices in line with SDGs.

Drawing from self-determination theory, training effectiveness literature, and the call for improved alignment with the SDGs, this study presents a model that explains how psychological characteristics, combined with the quality and quantity of training influence the adoption of sustainable farming practices among small-scale farmers.

The growth of the internet, access to technology and rapid digital transformations have paved the way for developing attack surfaces for individuals and organizations. There is a dire need to provide cybersecurity awareness most effectively. Gamification-based platforms have evolved to make cybersecurity education more engaging and effective. This study explores the gamification platforms available for cybersecurity training and awareness, the extent to which they are used and their benefits and challenges.

PRISMA 2020 was used to conduct the systematic literature review.

The study comprehends the game design elements and their role in the effectiveness of cybersecurity training and awareness. The study unveils that traditional education methodologies are insignificant in cybersecurity awareness, and gamification-based platforms are more beneficial. The paper summarizes the implications of the findings and further postulates future research directions.

This work comprehends the various forms of gamification platforms and frameworks available for cybersecurity training and will motivate further development of gamification platforms. This paper will help academia, private and public organizations and game designers enhance their gamification-based cybersecurity education interventions.

Prisons can utilize effective business process, operations management and continuous improvement techniques to provide an opportunity to support the rehabilitation of inmates. The purpose of this paper is to analyze the Iowa Prison Industries (IPI) work training program offered for incarcerated individuals under the supervision of the Iowa Department of Corrections (IDOC).

The IPI developed an Operational Excellence (OpEx) apprenticeship program that has worked with approximately 800 incarcerated “associates” to help them acquire skills necessary to obtain gainful employment upon release. This includes skills and belt level assessment in Lean Six Sigma as well as 6S/5S events and training. These techniques have been deployed by 22 teams at seven locations across the state of Iowa (USA). These sites have established objectives and goals and provided significant security-related benefits in the correctional industries environment.

Findings and success outcomes have been encouraging as those who completed the IPI apprenticeship program had lower recidivism, higher wages and more employability compared to the general prison population.

The rehabilitation of incarcerated individuals is critical to society. This novel and unique case study reviews one approach to providing work and life skills (such as esteem-building professional and personal skill sets) to support this process within the prison system.

This OpEx program has provided numerous benefits to both IPI and the incarcerated individuals. We feel that these findings could be beneficial to other correctional institutions and better prepare more inmates for a successful return to the workforce and to society.

This study proposes a collaborative federated learning (CFL) framework to address personal data transmission and retention issues for artificial intelligence (AI)-enabled video surveillance in public spaces.

This study examines specific challenges for long-term people monitoring in public spaces and defines AI-enabled video surveillance requirements. Based on the requirements, this study proposes a CFL framework to gradually adapt AI models’ knowledge while reducing personal data transmission and retention. The framework uses three different federated learning strategies to rapidly learn from different new data sources while minimizing personal data transmission and retention to a central machine.

The findings confirm that the proposed CFL framework can help minimize the use of personal data without compromising the AI model's performance. The gradual learning strategies help develop AI-enabled video surveillance that continuously adapts for long-term deployment in public spaces.

This study makes two specific contributions to advance the development of AI-enabled video surveillance in public spaces. First, it examines specific challenges for long-term people monitoring in public spaces and defines AI-enabled video surveillance requirements. Second, it proposes a CFL framework to minimize data transmission and retention for AI-enabled video surveillance. The study provides comprehensive experimental results to evaluate the effectiveness of the proposed framework in the context of facial expression recognition (FER) which involves large-scale datasets.