Background: Research on the theoretical integration of attributes of wellness tourism in the form of the physical, mental, spiritual, and environmental (PMSE) attribute framework and the effect of cultural differences on the framework validation has not been conducted before. Objectives: This study developed and validated the key underlying attributes of the wellness tourism experience framework within the Thailand context. Methods: From reviewing the related literature to identify potential sub attributes under the four dimensions of the PMSE framework, themes were developed using a theory- and prior research-driven method within different cultural contexts. The fitness of the PMSE attribute framework model within the context of Thailand wellness tourism was confirmed through a survey design with a quantitative approach from the generalities of the sample groups. The entire dataset was statistically tested with descriptive statistics and confirmatory factor analysis (CFA). Results and Conclusions: After adjustments, the four-factors with a 28-item model substantiated the data. The results validated that the mental experience dimension accounted for the most variance, with an indicator value of 0.99 in the framework, predicting the effective wellness tourism experience better than the other attributes. Experience design should involve the participation of all stakeholders in the wellness tourism system in terms of managerial contribution. Recommendations for the planning of wellness experiences concerning each four-factor will be investigated in future studies.

This research aimed to examine the obstacles of classical practices for extracting tacit knowledge, and to propose a new method of self-extracting tacit knowledge in organisations. This study opted for an exploratory study using the mixed methods research covering both qualitative and quantitative studies. The focus group interview was used to obtain qualitative information from 24 executives and experts in knowledge management, in five organisations. ATLAS.ti was used to analyse focus group data. The quantitative data was gained from a survey of 26 executives and KM experts who participated in the KM seminar. The result from the analysis was then used to develop a model of tacit knowledge extraction. The key finding is that major challenges of extracting tacit knowledge are lack of certainty of corporate policy, lack of motivation, lack of continuity in knowledge management activities, lack of a support system for the learning environment, lack of participation, non-supportive culture and behaviour, lack of preparation for interviewing, discontinuity in interviews during extraction of tacit knowledge, misinterpreted questioning, and inexperienced interviewers has a lack of experience. Web-based applications were the key source for the questions, which were comprised of both primary and secondary questions.

With the continuous growth of Industry 4.0 (I4.0), the industrial sector has transformed into smart factories, enhancing business competitiveness while aiming for the sustainable development of organizations. Machinery is a critical component and key to the success of production in a smart industrial factory. Minimizing unplanned downtime (UPDT) poses a significant challenge in designing an effective maintenance system. In the era of Industry 4.0, the most widely adopted maintenance frameworks are intelligent maintenance systems (IMSs), which integrate predictive maintenance with computerized systems. IMSs are intelligent tools designed to efficiently plan maintenance cycles for each machine component in a smart factory. This research presents the application of a search algorithm named state space search (SSS) in conjunction with a newly designed IMS, aimed at optimizing maintenance routines by identifying the optimal timing for maintenance cycles. The design began with the development of a new IMS concept that incorporates three key elements: the automation pyramid standard, Industrial Internet of Things (IIoT) sensors, and a computerized maintenance management system (CMMS). The CMMS collects machine data from the maintenance database, while real-time parameters are gathered via IIoT sensors from the supervisory control and data acquisition (SCADA) system. The new IMS concept provides a summary of the total maintenance cost and the remaining lifetime of the equipment. By integrating with SSS algorithms, the IMS presents optimized maintenance cycle solutions to the maintenance manager, focusing on minimizing costs while maximizing the remaining lifetime of the equipment. Moreover, the SSS algorithms take into account the risks associated with maintenance routines, following factory standards such as failure mode and effects analysis (FMEA). This approach is well suited to smart factories and helps to reduce UPDT.