This study investigates the influence of perceived value and perceived risk on consumer intentions to purchase counterfeit luxury goods, drawing upon an integrated theoretical framework encompassing perceived value theory, risk perception theory, and consumer behavior models. Through a quantitative research design involving a structured survey and Structural Equation Modeling (SEM), the study examines the relationships among perceived value dimensions (functional, emotional, social, economic), perceived risk factors (financial, social, performance), consumer attitudes, and purchase intentions. The findings reveal that perceived value positively influences purchase intentions, with consumer attitudes acting as a critical mediating mechanism. Conversely, perceived risk negatively impacts purchase intentions, with this relationship also mediated by consumer attitudes. Furthermore, Bayesian Network analysis uncovers the indirect pathways through which perceived risk shapes purchase intentions via its influence on consumer attitudes. By integrating these theoretical frameworks and employing advanced analytical techniques, this study contributes to a comprehensive understanding of the complex decision-making processes underlying counterfeit luxury goods consumption. The findings provide valuable insights for policymakers, luxury brand managers, and consumer protection agencies in devising targeted strategies to address consumer perceptions of value and risk, ultimately mitigating the proliferation of counterfeit luxury goods.

The debate on the effect of work environment on job satisfaction is very inconclusive. Most of the existing literature has focused on either the developed economy or job satisfaction and other variables other than the dimensions of the work environment. To fill the contextual and conceptual gap this study examined the effect of dimensions of work environment on job satisfaction among public sector workers in a developing economy. The study used the quantitative method and positivist philosophical viewpoint but specifically, the explanatory design was used to guide the study. A structured questionnaire was used for data collection and data analysis was done by partial least square modelling. The study found that the three dimensions of work environment such as physical, psychological and administrative work environment had a significant relationship with job satisfaction among public workers in a developing economy. It was recommended that the management of public sector organisations should improve upon the psychological, physical and administrative work environment to ensure job satisfaction among their workers. 

The digital era has ushered in significant advancements in Generative Artificial Intelligence (GAI), particularly through Generative Models and Large Language Models (LLMs) like ChatGPT, revolutionizing educational paradigms. This research, set against the backdrop of Society 5.0 and aimed at sustainable educational practices, utilizes qualitative analysis to explore the impact of Generative AI in various learning environments. It highlights the potential of LLMs to offer personalized learning experiences, democratize education, and enhance global educational outcomes. The study finds that Generative AI revitalizes learning methodologies and supports educational systems’ sustainability by catering to diverse learning needs and breaking down access barriers. In conclusion, the paper discusses the future educational strategies influenced by Generative AI, emphasizing the need for alignment with Society 5.0’s principles to foster adaptable and sustainable educational inclusion.

Edible cutlery is a safe alternative that, if adopted, can act as a panacea to plastic pollution. Consumers who believe in a lifestyle of health and sustainability (LOHAS) can motivate others by taking the lead in this direction. This study has explored the psychological variables associated with LOHAS consumers in conjunction with the product attributes of edible cutlery to check whether these variables can influence lifestyle of health and sustainability (LOHAS) consumers to adopt edible cutlery. An empirical study on 210 LOHAS consumers using Partial Least Squares Structure Equation Modelling (PLS-SEM) and Importance Performance Matrix Analyses (IPMA) showed that social consciousness and subjective norms motivate them to adopt edible cutlery in restaurants. This finding has an implication for hospitality businesses using edible cutlery that can target LOHAS consumers with strategies that affect their social consciousness and subjective norm belief for better adoption intentions.

This study aims to identify the causes of delays in public construction projects in Thailand, a developing country. Increasing construction durations lead to higher costs, making it essential to pinpoint the causes of these delays. The research analyzed 30 public construction projects that encountered delays. Delay causes were categorized into four groups: contractor-related, client-related, supervisor-related, and external factors. A questionnaire was used to survey these causes, and the Relative Importance Index (RII) method was employed to prioritize them. The findings revealed that the primary cause of delays was contractor-related financial issues, such as cash flow problems, with an RII of 0.777 and a weighted value of 84.44%. The second most significant cause was labor issues, such as a shortage of workers during the harvest season or festivals, with an RII of 0.773. Additionally, various algorithms were used to compare the Relative Importance Index (RII) and four machine learning methods: Decision Tree (DT), Deep Learning, Neural Network, and Naïve Bayes. The Deep Learning model proved to be the most effective baseline model, achieving a 90.79% accuracy rate in identifying contractor-related financial issues as a cause of construction delays. This was followed by the Neural Network model, which had an accuracy rate of 90.26%. The Decision Tree model had an accuracy rate of 85.26%. The RII values ranged from 68.68% for the Naïve Bayes model to 77.70% for the highest RII model. The research results indicate that contractor financial liquidity and costs significantly impact construction operations, which public agencies must consider. Additionally, the availability of contractor labor is crucial for the continuity of projects. The accuracy and reliability of the data obtained using advanced data mining techniques demonstrate the effectiveness of these results. This can be efficiently utilized by stakeholders involved in construction projects in Thailand to enhance construction project management.