Browsing by Author "Chompunuch Jittithavorn"

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    Blockchain of things (BoT) innovation for smart tourism
    (John Wiley and Sons Ltd, 2024) Pannee Suanpang; Pattanaphong Pothipassa; Chompunuch Jittithavorn; C. Jittithavorn; College of Management, University of Phayao, Bangkok, 10330, Thailand; email: chompunuch.ji@up.ac.th
    This study aims to (a) develop the innovation of BoT prototype; and (b) provide an effective platform to recommend tourists activity, implement and trials blockchain prototype for booking travel activities, whether booking travel programs, air ticket booking hotel stay visits to attractions and payment of goods and services, and evaluate tourist intention to use BoT. The developed architecture enables the integration of blockchain technology capabilities into IoT technology based on high performance of usability, stability, accuracy, and completeness. The BoT prototype is evaluated by 428 users to support smart tourism. This support is significant and the level includes the BoT functional benefit (security, process, and availability) that is positively related to the intention to adopt BoT, and user benefit (trust, usability) is also positive related with intention to adopt BoT. This study significantly contributes to revolutionizing the tourism industry by implementing BOT in smart tourism destinations to gain competitive advantage. © 2024 John Wiley & Sons Ltd.
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    Can Optimized Genetic Algorithms Improve the Effectiveness of Homestay Recommendation Systems in Smart Villages? A Case of Thailand
    (John Wiley and Sons Ltd, 2024) Pannee Suanpang; Pitchaya Jamjuntr; Arunee Lertkornkitja; Chompunuch Jittithavorn; C. Jittithavorn; College of Management, University of Phayao, Bangkok, Thailand; email: chompunuchj@gmail.com
    This paper introduces a novel approach to optimize genetic algorithms (GAs) for homestay recommendation systems, specifically designed for smart village tourism destinations. Researchers developed an advanced GA focused on maximizing user satisfaction, the main quality metric. The algorithm was tailored to address the dynamic nature of homestay offerings and the varied preferences of travelers, using users' reviews, listing attributes, and historical booking data. The GA framework included a custom encoding scheme, fitness function, and parameters. Validation occurred through a case study in a smart village, with the algorithm's effectiveness tested via user surveys and ratings. Results showed that GA-driven recommendations surpassed traditional methods, enhancing user satisfaction, trust, and booking rates while benefiting hosts with positive reviews. The optimized GA improved recommendation accuracy and efficiency, boosting economic benefits for local communities and contributing significantly to recommendation system research. © 2024 John Wiley & Sons Ltd.