Supaporn Chai-Arayalert

Handwoven textiles constitute a form of intangible craft heritage whose cultural value resides not only in finished artifacts but also in the embodied skills, motif knowledge, practitioner narratives, and community-based meanings through which they are produced and transmitted. However, many digital heritage platforms remain oriented toward visual presentation, product cataloguing, and archival access, offering limited support for communicating the interpretive, practice-based, and relational dimensions of craft knowledge. Addressing this gap, this study designed, developed, and evaluated a four-room digital showroom for Nakhon Si Thammarat Brocade, a traditional brocade textile of southern Thailand. Guided by Design Science Research methodology and heritage interpretation theory, the digital showroom was conceptualized as a cultural communication interface rather than as an online product display. Knowledge elicitation with master weavers and a user-needs survey informed the development of four connected spaces: the Historical Room, Aesthetic Room, Livelihood and Craft Value Room, and Learning-by-Doing Room. Together, these rooms organize user engagement through a progressive interpretive pathway, moving from cultural orientation and motif understanding to artisan-value recognition and guided creative participation. An end-user evaluation with 100 participants showed positive perceptions across functionality and usability (M = 4.13, SD = 0.72), content structure and organization (M = 4.06, SD = 0.79), cultural content quality (M = 4.34, SD = 0.73), and interactive learning-by-doing experience (M = 4.17, SD = 0.81). Users particularly valued the culturally grounded content, practitioner-centered narratives, and opportunities to apply cultural knowledge through virtual design activities. The findings suggest that a practitioner-informed and theoretically grounded digital showroom can extend digital heritage practice beyond passive product representation by supporting contextualized, interpretive, and participatory engagement with intangible craft heritage.

The preservation of tacit knowledge embedded in Southern Thai textiles remains a significant challenge because much of this knowledge is dispersed across private households and is inadequately supported by conventional object-centered documentation systems. This study developed a crowdsourcing-based digital storytelling platform that enabled communities to document, organize, and disseminate knowledge related to Southern Thai textile heritage. The platform integrated community participation, structured narrative authoring, and knowledge organization within a socio-technical system designed for cultural heritage documentation. To guide its development, the study proposed the Crowdsourced-Storytelling Intangible Cultural Heritage Framework (CS-ICH Framework) and operationalizes it through requirements analysis, iterative prototyping, and empirical user evaluation. The evaluation results indicated high levels of user satisfaction and positive user perceptions regarding knowledge accessibility, content organization, and the platform’s support for heritage preservation. These findings suggested that participatory digital platforms can effectively facilitate the documentation and dissemination of locally embedded cultural knowledge that is difficult to capture through conventional institutional systems. This study contributed to digital heritage research by providing a potentially transferable framework and design principles for integrating crowdsourcing and digital storytelling within platforms designed to preserve and transmit intangible and community-held cultural heritage.

The increasing availability of tourism information on digital platforms has improved tourists’ access to destination-related data. However, existing tourism information systems often lack effective integration between user preference information and geospatial data, limiting their ability to provide personalized and context-aware recommendations. This study proposes a personalized tourism recommendation system by integrating Geographic Information System (GIS) technology with association rule mining to analyze relationships between user preferences and spatial characteristics of tourist destinations. The proposed system provides map-based visualization, calculates distances between users and destinations, and generates personalized recommendations based on both user interests and spatial proximity. The implementation results demonstrate that the system can generate location-aware and personalized tourism recommendations, supporting users in identifying suitable destinations within their surrounding geographic context. The integration of geospatial processing with association rule mining improves recommendation relevance by incorporating both preference patterns and spatial proximity. Furthermore, the proposed framework has the potential to support more balanced spatial distribution of tourism activities by recommending geographically appropriate destinations rather than concentrating suggestions on highly popular locations. These findings highlight the value of combining geospatial technologies with data mining techniques to support tourism recommendation systems and spatially informed tourism planning.

Small local museums play a crucial role in safeguarding cultural heritage, but often lack the necessary resources and digital capabilities to engage younger visitors effectively. This study examines whether a mobile serious game can enhance engagement, intrinsic motivation, and cultural knowledge among Generation Z museum visitors. This study introduces Thai-Craft-To-Go, a mobile serious game that mediates intangible cultural heritage—specifically Thai textiles and handicrafts—for Generation Z. Grounded in Self-Determination Theory (SDT) and Flow Theory and operationalized through the Mechanics–Dynamics–Aesthetics (MDA) framework, the game translates cultural content into interactive play. We conducted an exploratory evaluation with 30 Generation Z participants using the Game Engagement Questionnaire (GEQ), the Intrinsic Motivation Inventory (IMI), and a 10-item knowledge test administered before and after gameplay. Results indicated high engagement—particularly Presence and Absorption on the GEQ—strong intrinsic motivation on the IMI (notably perceived competence and value), and significant knowledge gains (mean scores increased from 4.40 to 8.03; t(29) = 8.39, p < 0.001, d = 1.53). These findings suggest that a well-designed serious game can align museum learning with the digital habits of younger audiences, enhancing engagement, motivation, and cultural understanding. For small local museums, serious games provide a feasible and cost-conscious pathway to revitalize visitor experiences and support the intergenerational transmission of intangible cultural heritage in the digital age.

Floods, as a form of disaster, significantly affect individuals and farmers in impacted areas, particularly through crop damage and the inability to harvest due to prolonged and extensive flooding. Among the most severely affected agricultural sectors are oil palm plantations, which regularly experience such disruptions annually. Current methods of assistance and relief during flooding rely on field surveys conducted manually by personnel, a process constrained by its time-intensive nature. Moreover, existing applications or platforms do not support the classification and inspection of oil palm plantations affected by floods during harvesting. This research aims to develop a method and application for inspecting oil palm plantations impacted by floods during harvesting. The approach utilizes deep learning and geographic information systems (GIS) to classify and analyze flood-affected areas and determine the ripeness of oil palm bunches on trees, enabling accurate and rapid identification of flood-affected areas. The study results demonstrate that the proposed method achieves a flood classification accuracy ranging from 96.80% to 98.29% and ripeness classification accuracy for oil palm bunches on trees ranging from 97.60% to 99.75%. These findings indicate that the proposed model effectively and efficiently monitors flood-affected areas. Additionally, the developed application serves as a valuable tool for flood management, facilitating timely assistance and relief for farmers impacted by flooding.

This study introduces a comprehensive phygital framework tailored for small local museums, addressing the unique challenges of textile exhibitions. By seamlessly integrating physical artifacts with advanced digital tools through a user-centered design–thinking approach, the platform transforms traditional museum visits into hybrid experiences. The research addresses challenges faced by small museums, such as limited interactivity, static information presentation, and resource constraints. The findings demonstrate that the phygital platform significantly enhances visitor satisfaction, usability, and engagement. Features like mobile applications, chatbots, and gamification foster dynamic interactions, increasing interest in historical textile collections. The evaluation highlights positive impacts on visitor learning and accessibility, with high usability scores and favorable feedback confirming the platform’s success. By bridging physical and digital realms, the platform empowers small local museums to modernize their exhibition experience offerings while preserving their authenticity and cultural significance. This study contributes to the growing literature on phygital strategies in museum contexts, offering practical recommendations for implementing such platforms in resource-constrained settings. The findings underscore the potential of phygital approaches to foster deeper connections with cultural heritage, ensure broader accessibility, and support sustainable visitor engagement.

This research focuses on developing an innovative platform to manage carbon footprint data related to office activities and monitor green spaces, integrating geoinformatics and machine learning technologies. The platform addresses the lack of automated systems for tracking carbon emissions, particularly in high paper consumption environments, such as those involving printing and photocopying. Additionally, it monitors green spaces within corporate settings, an aspect often overlooked in existing systems. The study demonstrates the platform’s capability to automate carbon footprint calculations and provide accurate assessments of green areas, achieving a high accuracy rate of 96.22% and a Kappa coefficient of 0.92. The results confirm the platform’s ability to deliver both comprehensive and granular environmental insights, supporting decision making for more sustainable office environments. The key novelty of this study lies in the integration of real-time data capture with geoinformatics and machine learning to efficiently track both carbon footprints and green spaces. This approach offers a practical solution to a gap in environmental data management in office settings, enabling organizations to align their practices with sustainability goals. The platform’s precise, automated system contributes significantly to the development of eco-friendly workplaces, highlighting its academic and practical value in the field of environmental sustainability.

Oil palm cultivation thrives as a prominent agricultural endeavor within the southern region of Thailand, where the country ranks third globally in production, following Malaysia and Indonesia. The assessment of oil palm bunch ripeness serves various purposes, notably in determining purchasing prices, pre-harvest evaluations, and evaluating the impacts of disasters or low market prices. Presently, two predominant methods are employed for this assessment, namely human evaluation, and machine learning for ripeness classification. Human assessment, while boasting high accuracy, necessitates the involvement of farmers or experts, resulting in prolonged processing times, especially when dealing with extensive datasets or dispersed fields. Conversely, machine learning, although capable of accurately classifying harvested oil palm bunches, faces limitations concerning its inability to process images of oil palm bunches on trees and the absence of a platform for on-tree ripeness classification. Considering these challenges, this study introduces the development of a classification platform leveraging machine learning (deep learning) in conjunction with geospatial analysis and visualization to ascertain the ripeness of oil palm bunches while they are still on the tree. The research outcomes demonstrate that oil palm bunch ripeness can be accurately and efficiently classified using a mobile device, achieving an impressive accuracy rate of 99.89% with a training dataset comprising 8779 images and a validation accuracy of 96.12% with 1160 images. Furthermore, the proposed platform facilitates the management and processing of spatial data by comparing coordinates derived from images with oil palm plantation data obtained through crowdsourcing and the analysis of cloud or satellite images of oil palm plantations. This comprehensive platform not only provides a robust model for ripeness assessment but also offers potential applications in government management contexts, particularly in scenarios necessitating real-time information on harvesting status and oil palm plantation conditions.