This study explores how demographic factors shape perceptions of celebrity and influencer marketing in the context of promoting cryptocurrencies, particularly in the tourism sector. It evaluates whether such marketing strategies effectively promote cryptocurrencies and how their impact varies across demographic groups. By analyzing responses from a sample of 161 predominantly young and educated respondents, the study uses statistical methods to identify differences in perceived marketing effectiveness based on age, gender, and other demographics. Findings reveal no significant demographic differences in effectiveness; instead, the study underscores the importance of universal marketing qualities, such as authenticity, credibility, and relevance. These results suggest the need for inclusive marketing strategies that foster trust and transparency. Additionally, the study highlights avenues for future research, including cultural and ethical considerations, to refine marketing approaches and develop innovative campaigns that drive cryptocurrency adoption and trust in the tourism industry.

The effectiveness and efficiency of e-learning system in industry significantly depend on users’ acceptance and adoption. This is specifically determined by external and internal factors represented by subjective norms (SN) and experience (XP), both believed to affect users’ perceived usefulness (PU) and perceived ease of use (PEOU). Users’ acceptance of e-learning system is influenced by the immensity of region, often hampered by inadequate infrastructure support. Therefore, this study aimed to investigate behavioral intention to use e-learning in the Indonesian insurance industry by applying Technology Acceptance Model (TAM). To achieve this objective, Jabotabek and Non-Jabotabek regions were used as moderating variables in all related hypotheses. An online survey was conducted to obtain data from 800 respondents who were Indonesian insurance industry employees. Subsequently, Structural Equation Model (SEM) was used to evaluate the hypotheses, and Multi-Group Analysis (MGA) to examine the role of region. The results showed that out of the seven hypotheses tested, only one was rejected. Furthermore, XP had no significant effect on PU, and the most significant correlation was found between PEOU and PU. In each relationship path model, the role of region (Jabodetabek and Non Jabodetabek) had no significant differences. These results were expected to provide valuable insights into the components of e-learning acceptability for the development of a user-friendly system in the insurance industry.

This research examines intangible assets or intellectual capital (IC) performance of tourism-related industries in an underexplored area which is a tourism intensively-dependent country. In this study, VAIC which is a monetary valuation method and also the most widely applied measurement method, is utilized as the performance measurement method for quantifying IC performance to monetary values. Moreover, to better understand performance, the standard efficiency levels are further applied for classifying the performance levels of tourism industries. The sample sizes of study are 20 companies operating in the tourism-related industries in the world top travel destination or Thailand, and the companies’ data are collected from 2012 to 2021. Therefore, finally, there are 187 firm-year observations. The utilization of VAIC could assess IC performance of tourism firms and industries, and the standard efficiency levels further support the uniform interpretation of IC efficiency levels. The obtained results show the strong performance of both human and structural capital of the focused tourism dependent country especially in the logistics industry that directly supports and connects to the tourism attractions. Moreover, the finding also highlights the significance of human capital which plays as a major contributor for overall IC performance in this tourism dependent economy. This study contributes the new exploration of IC in the high impact industries and also specifically in the top significant tourism country. Moreover, the application of VAIC also confirms a practical application for management. The limited number of studied countries is a limitation of study. However, these new obtained data and information could be further applied for making comparisons or in-depth or statistical analysis in the future works.

This research explores the advancement of Artificial Intelligence (AI) in Occupational Health and Safety (OHS) across high-risk industries, highlighting its pivotal role in mitigating the global incidence of occupational incidents and diseases, which result in approximately 2.3 million fatalities annually. Traditional OHS practices often fall short in completely preventing workplace incidents, primarily due to limitations in human-operated risk assessments and management. The integration of AI technologies has been instrumental in automating hazardous tasks, enhancing real-time monitoring, and improving decision-making through comprehensive data analysis. Specific AI applications discussed include drones and robots for risky operations, computer vision for environmental monitoring, and predictive analytics to pre-empt potential hazards. Additionally, AI-driven simulations are enhancing training protocols, significantly improving both the safety and efficiency of workers. Various studies supporting the effectiveness of these AI applications indicate marked improvements in risk management and incident prevention. By transitioning from reactive to proactive safety measures, the implementation of AI in OHS represents a transformative approach, aiming to substantially reduce the global burden of occupational injuries and fatalities in high-risk sectors.

In the fast-paced modern society, enhancing employees’ professional qualities through training has become crucial for enterprise development. However, training satisfaction remains under-studied, particularly in specialized sectors such as the coal industry. Purpose: This study aims to investigate the impact of personal characteristics, organizational characteristics, and training design on training satisfaction, utilizing Baldwin and Ford’s transfer of training model as the theoretical framework. The study identifies how these factors influence training satisfaction and provides actionable insights for improving training effectiveness in China’s coal industry. Design/Methodology/Approach: A cross-sectional design that allowed the study to capture data at one point in time from a large sample of employees was employed to conduct an online survey involving 251 employees from the Huaibei Mining Group in Anhui Province, China. The survey was administered over three months, capturing a diverse sample with nearly equal gender distribution (51% male, 49% female) and a majority aged between 21 and 40. The participants represented various educational backgrounds, with 52.19% holding an undergraduate degree and most occupying entry-level positions (74.9%), providing a broad workforce representation. Findings: The research indicated that personal traits were the chief predictor of training satisfaction, showing a beta coefficient of 0.585 (95% CI: [0.423, 0.747]). Linear regression modeling indicates that training satisfaction is strongly related to organizational attributes (β = 0.276 with a confidence interval of 95% [0.109, 0.443]). In contrast, training design did not appear to be a strong predictor (β = 0.094, 95% CI: [−0.012, 0.200]). Employee training satisfaction was the principal outcome measure, measured with a 5-point Likert scale. The independent variables covered personal characteristics, organizational characteristics, and training design, all measured through validated items taken from former research. The consistency of the questionnaire from the inside was strong, as Cronbach’s alpha values stood between 0.891 and 0.936. We completed statistical testing using SPSS 27.0, complemented by multiple linear regression, to study the interactions between the variables. Practical implications: This research contributes to the literature by emphasizing the necessity for context-specific training approaches within the coal industry. It highlights the importance of considering personal and organizational characteristics when designing training programs to enhance employee satisfaction. The study suggests further exploration of the multifaceted factors influencing training satisfaction, reinforcing the relevance of Baldwin and Ford’s theoretical model in understanding training effectiveness. Ultimately, the findings provide valuable insights for organizations seeking to improve training outcomes and foster a more engaged workforce. Conclusion: The study concluded that personal and organizational characteristics significantly impact employee training satisfaction in the coal industry, with personal characteristics being the strongest predictor. The beta coefficient for personal characteristics was 0.585, indicating a strong positive relationship. Organizational characteristics also had a positive effect, with a beta coefficient of 0.276. However, training design did not show a significant impact on training satisfaction. These findings highlight the need for coal companies to focus on personal and organizational factors when designing training programs to enhance satisfaction and improve training outcomes.