Intelligent PITB Trust Blockchain Model of Sentiment Analysis for the Decision-Making of Taverns Dynamic Recommendation System in China

Authors

  • Wei Zhou Azman Hashim International Business School, University Teknologi Malaysia, Skudai, 81310 Johor, Malaysia
  • Nor Zafir Md Salleh Azman Hashim International Business School, University Teknologi Malaysia, Skudai, 81310 Johor, Malaysia
  • Bolin Wang Faculty of Hospitality Management, School of Hospitality Management, Shanghai Business School, Fengxian District, 201400, Shanghai,China
  • Zhihan Jia Faculty of Hospitality Management, School of Hospitality Management, Shanghai Business School, Fengxian District, 201400, Shanghai,China
  • Yanfang Ding School of Education, University Teknologi Malaysia, Skudai, 81310, Johor, Malaysia

Keywords:

Dynamic Recommendation, Security, Sentimental Analysis, Trust, Blockchain, psychometric indices

Abstract

Dynamic recommendations refer to personalized and real-time suggestions provided to users based on their current context, behavior, and preferences. One prominent concern is the potential invasion of user privacy. The need to collect and analyse vast amounts of user data for effective personalization raises ethical questions regarding the storage, security, and responsible use of sensitive information. This paper proposed a framework of the Psychometric Index Trust Blockchain (PiTB) model for the secure dynamic recommendation system for the hotel industry. The PiTB performs the decision-making process through the review of customers for the model prediction. With customer reviews, the Psychometric Index is computed for the reviews of the customer in the hotel with the uses of sentimental analysis. The PiTB model uses the trust mechanism for tanalyzehe security improvement in blockchain data for the application of sentimental analysis with use of psychometric indices. These indices serve as the foundation for a computer dynamic recommendation system, enabling real-time suggestions for hotel choices tailored to individual consumer preferences. Finally, a trust-based blockchain model is implemented for secure data processing in the online consumer in the hotel orders. The trusted blockchain model focused on the hotels dynamic recommendation system in China. Simulation analysis demonstrated that the proposed PiTB model achieves higher data security with effective dynamic recommendations to the customers of the hotel.

Downloads

Download data is not yet available.

References

Song, H., Ruan, W. J., & Jeon, Y. J. J. (2021). An integrated approach to the purchase decision making process of food-delivery apps: Focusing on the TAM and AIDA models. International Journal of Hospitality Management, 95, 102943.

Chen, L., Cai, W., Yan, D., & Berkovsky, S. (2023). Eye-tracking-based personality prediction with recommendation interfaces. User Modeling and User-Adapted Interaction, 33(1), 121-157.

Tkalcic, M., & Chen, L. (2022). Personality and recommender systems. Recommender Systems Handbook; Ricci, F., Rokach, L., Shapira, B., Eds, 757-787.

Pappas, N., Caputo, A., Pellegrini, M. M., Marzi, G., & Michopoulou, E. (2021). The complexity of decision-making processes and IoT adoption in accommodation SMEs. Journal of Business Research, 131, 573-583.

Berne Manero, M. D. C., Moretta Tartaglione, A., Russo, G., & Cavacece, Y. (2023). The impact of electronic word-of-mouth management in hotel ecosystem: insights about managers' decision-making process. Journal of Intellectual Capital, 24(1), 227-256.

Shamim, S., Yang, Y., Zia, N. U., & Shah, M. H. (2021). Big data management capabilities in the hospitality sector: Service innovation and customer generated online quality ratings. Computers in Human Behavior, 121, 106777.

Seth, A., Jiang, Y., Gyamfi, S. A., Emmanuel, D., & Amankwa, E. (2022). Development Of Measurement Scale For Personalized Recommended Product Acceptance (Prpa-Scale). Malaysian E Commerce Journal (MECJ), 6(2), 76-85.

Karami, M., Crick, D., & Crick, J. M. (2023). Non-predictive decision-making, market-oriented behaviours, and smaller-sized firms’ performance. Journal of Strategic Marketing, 31(5), 1107-1131.

Stray, J., Halevy, A., Assar, P., Hadfield-Menell, D., Boutilier, C., Ashar, A., ... & Vasan, N. (2022). Building human values into recommender systems: An interdisciplinary synthesis. ACM Transactions on Recommender Systems.

Lee, S. G., Jo, H. J., Koo, D. W., & Lee, S. M. (2022). Conceptual Similarities and Empirical Differences in Theoretical Approaches to Personal Values and Cultural Values Predicting Pro-Environmental Behavior in Hospitality and Tourism. Sustainability, 14(23), 15811.

Huifeng, P., & Ha, H. Y. (2023). Relationship dynamics of review skepticism using latent growth curve modeling in the hospitality industry. Current Issues in Tourism, 26(3), 496-510.

Kim, H. (2021). Use of mobile grocery shopping application: motivation and decision-making process among South Korean consumers. Journal of Theoretical and Applied Electronic Commerce Research, 16(7), 2672-2693.

Lai, V., Chen, C., Liao, Q. V., Smith-Renner, A., & Tan, C. (2021). Towards a science of human-ai decision making: a survey of empirical studies. arXiv preprint arXiv:2112.11471.

Guo, B., Zhang, B., & Li, Y. (2022). Modeling and Simulation of Consumer Preference Decision for Commercial Complex Location Based on System Dynamics. Scientific Programming, 2022, 1-10.

Roy, S. K., Singh, G., Hope, M., Nguyen, B., & Harrigan, P. (2022). The rise of smart consumers: role of smart servicescape and smart consumer experience co-creation. In The Role of Smart Technologies in Decision Making (pp. 114-147). Routledge.

Balaji, M. S., Jiang, Y., Roy, S. K., & Lee, J. (2022). To or not to adopt P2P accommodation: The traveler’s ethical decision-making. International Journal of Hospitality Management, 100, 103085.

Rathi, S., Verma, J. P., Jain, R., Nayyar, A., & Thakur, N. (2022). Psychometric profiling of individuals using Twitter profiles: A psychological Natural Language Processing based approach. Concurrency and Computation: Practice and Experience, 34(19), e7029.

Kim, N., Lee, S., Lee, C. K., & Suess, C. (2022). Predicting preventive travel behaviors under the COVID-19 pandemic through an integration of Health Belief Model and Value-Belief-Norm. Tourism Management Perspectives, 43, 100981.

Zhu, J. J., Chang, Y. C., Ku, C. H., Li, S. Y., & Chen, C. J. (2021). Online critical review classification in response strategy and service provider rating: Algorithms from heuristic processing, sentiment analysis to deep learning. Journal of Business Research, 129, 860-877.

Liang, W. Y., Huang, C. C., & Shih, B. R. (2022). The adaptation of the information system success model in recommender systems. The validation of the dual-coding theory.

Sangwan, S., & Sharma, S. K. (2022). Social media communication and consumer decision making: an empirical perspective. International Journal of Electronic Marketing and Retailing, 13(4), 391-410.

Li, S., & Wei, M. (2021). Hotel servicescape and customer citizenship behaviors: mediating role of customer engagement and moderating role of gender. International Journal of Contemporary Hospitality Management, 33(2), 587-603.

Mensah, K., & Amenuvor, F. E. (2021). The influence of marketing communications strategy on consumer purchasing behaviour in the financial services industry in an emerging economy. Journal of Financial Services Marketing, 1-16.

Elsharnouby, T. H., & Elbanna, S. (2021). Change or perish: Examining the role of human capital and dynamic marketing capabilities in the hospitality sector. Tourism Management, 82, 104184.

Pramod, D. (2023). Privacy-preserving techniques in recommender systems: state-of-the-art review and future research agenda. Data Technologies and Applications, 57(1), 32-55.

Wen, H., & Liu-Lastres, B. (2022). Consumers' dining behaviors during the COVID-19 pandemic: An Application of the Protection Motivation Theory and the Safety Signal Framework. Journal of Hospitality and Tourism Management, 51, 187-195.

Farrukh, M., Ansari, N. Y., Raza, A., Meng, F., & Wang, H. (2022). High-performance work practices do much, but HERO does more: an empirical investigation of employees' innovative behavior from the hospitality industry. European Journal of Innovation Management, 25(3), 791-812.

Downloads

Published

10.12.2023

How to Cite

Zhou , W. ., Salleh , N. Z. M. ., Wang , B. ., Jia, Z. ., & Ding, Y. . (2023). Intelligent PITB Trust Blockchain Model of Sentiment Analysis for the Decision-Making of Taverns Dynamic Recommendation System in China. International Journal of Intelligent Systems and Applications in Engineering, 12(8s), 499–514. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4157

Issue

Vol. 12 No. 8s (2024)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.