Big Data and Cloud Computing-Integrated Tourism Decision-Making in Smart Logistics Technologies
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Abstract
Since technology allows tourist companies to replace expensive human labor with electronic labor, labor expenses are reduced, yet customer service concerns are often avoided. Companies and organizations face new challenges daily. Increased consumer demands and global competition result in significant adjustments in the industrialized world. On the other hand, technology can bring forth entirely new types of unintended effects. There are new prospects for the tourist sector with the rise of big data. Data mining and cloud computing are widely used in the tourist sector to extract useful information from vast quantities of data. A new tourism marketing management model based on big data can be developed with this function. This research thus presents a big data and cloud computing-integrated tourism decision-making (BC2TDM) paradigm to analyze the behavior of travel consumers. This model uses the deep learning model to forecast the travel consumer behavior to ensure a personalized tourism experience.
References
[1]
Abraham, V., Bremser, K., Carreno, M., Crowley-Cyr, L., & Moreno, M. (2020). Exploring the consequences of COVID-19 on tourist behaviors: Perceived travel risk, animosity and intentions to travel. Tourism Review.
[2]
Albayrak, T., Karasakal, S., Kocabulut, Ö., & Dursun, A. (2020). Customer loyalty towards travel agency websites: The role of trust and hedonic value. Journal of Quality Assurance in Hospitality & Tourism, 21(1), 50–77.
[3]
Amudha, G. (2021). Dilated Transaction Access and Retrieval: Improving the Information Retrieval of Blockchain-Assimilated Internet of Things Transactions. Wireless Personal Communications, 1–21.
[4]
Arora, N., Charm, T., Grimmelt, A., Ortega, M., Robinson, K., Sexauer, C., & Yamakawa, N. (2020). A global view of how consumer behavior is changing amid COVID-19. Mcknsey and Company. https://www. mckinsey. Com /~/ media / McKinsey /.
[5]
Brune, S., Knollenberg, W., Stevenson, K. T., Barbieri, C., & Schroeder-Moreno, M. (2021). The influence of agritourism experiences on consumer behavior toward local food. Journal of Travel Research, 60(6), 1318–1332.
[6]
Chen, F., Yin, Z., Ye, Y., & Sun, D. J. (2020). Taxi-hailing choice behavior and economic benefit analysis of emission reduction based on multi-mode travel big data. Transport Policy, 97, 73–84.
[7]
Cheng, Y., Wei, W., & Zhang, L. (2020). Seeing destinations through vlogs: Implications for leveraging customer engagement behavior to increase travel intention. International Journal of Contemporary Hospitality Management, 32(10), 3227–3248.
[8]
Chi, H. K., Huang, K. C., & Nguyen, H. M. (2020). Elements of destination brand equity and destination familiarity regarding travel intention. Journal of Retailing and Consumer Services, 52, 101728.
[9]
Chu, S. C., Deng, T., & Cheng, H. (2020). The role of social media advertising in hospitality, tourism and travel: A literature review and research agenda. International Journal of Contemporary Hospitality Management, 32(11), 3419–3438.
[10]
Chua, B. L., Al-Ansi, A., Lee, M. J., & Han, H. (2020). Tourists’ outbound travel behavior in the aftermath of the COVID-19: Role of corporate social responsibility, response effort, and health prevention. Journal of Sustainable Tourism, 29(6), 879–906.
[11]
El-Manstrly, D., Ali, F., & Steedman, C. (2020). Virtual travel community members’ stickiness behavior: How and when it develops. International Journal of Hospitality Management, 88, 102535.
[12]
Fuchs, M., Höpken, W., & Lexhagen, M. (2014). Big data analytics for knowledge generation in tourism destinations–A case from Sweden. Journal of Destination Marketing & Management, 3(4), 198–209.
[13]
Gao, J., Wang, H., & Shen, H. (2020). Task failure prediction in cloud data centers using deep learning. IEEE Transactions on Services Computing.
[14]
Gao, J., Wang, H., & Shen, H. (2020, August). Machine learning-based workload prediction in cloud computing. In 29th international conference on computer communications and networks (ICCCN) (pp. 1-9). IEEE. 10.1109/ICCCN49398.2020.9209730
[15]
Han, Y., Zhang, T., & Wang, M. (2020). Holiday travel behavior analysis and empirical study with Integrated Travel Reservation Information usage. Transportation Research Part A, Policy and Practice, 134, 130–151.
[16]
Hashim, N. A. A. N., Bakar, N. A., Remeli, M. R., Samengon, H., Omar, R. N. R., Nawi, N. M. M., Razali, N. A. M., & Mahshar, M. (2020, December). Travel Mobile Technology Applications and Domestic Tourist Behavior: Analyzing the Reliability and Validity of Instruments IOP Conference Series. Materials Science and Engineering, 993(1), 012095.
[17]
Huifeng, W., Kadry, S. N., & Raj, E. D. (2020). Continuous health monitoring of sportsperson using IoT devices-based wearable technology. Computer Communications, 160, 588–595.
[18]
Huifeng, W., Shankar, A., & Vivekananda, G. N. (2020). Modeling and simulation of sprinters’ health promotion strategy based on sports biomechanics. Connection Science, 1–19.
[19]
Ioannou, A., Tussyadiah, I., & Lu, Y. (2020). Privacy concerns and disclosure of biometric and behavioral data for travel. International Journal of Information Management, 54, 102122.
[20]
Issaoui, Y., Khiat, A., Bahnasse, A., & Ouajji, H. (2021). Toward smart logistics: Engineering insights and emerging trends. Archives of Computational Methods in Engineering, 28(4), 3183–3210.
[21]
Jiao, J., Bhat, M., & Azimian, A. (2021). Measuring travel behavior in Houston, Texas, with mobility data during the 2020 COVID-19 outbreak. Transportation Letters, 1–12.
[22]
Kim, M. J., Lee, C. K., & Jung, T. (2020). Exploring consumer behavior in virtual reality tourism using an extended stimulus-organism-response model. Journal of Travel Research, 59(1), 69–89.
[23]
Lei, K., Wen, C., & Wang, X. (2021). Research on the coordinated development of the tourism economy based on embedded dynamic data. Microprocessors and Microsystems, 82, 103933.
[24]
Liu, B. H., Pham, V. T., Nguyen, T. N., & Luo, Y. S. (2019). A heuristic for maximizing the lifetime of data aggregation in wireless sensor networks.
[25]
Liu, X., Mehraliyev, F., Liu, C., & Schuckert, M. (2020). The roles of social media in tourists’ choices of travel components. Tourist Studies, 20(1), 27–48.
[26]
Lojo, A. (2020). Young Chinese in Europe: Travel behavior and new trends based on evidence from Spain. Tourism . An International Interdisciplinary Journal, 68(1), 7–20.
[27]
Manogaran, G., Rawal, B. S., Saravanan, V., Kumar, P. M., Martínez, O. S., Crespo, R. G., Montenegro-Marin, C. E., & Krishnamoorthy, S. (2020). Blockchain-based integrated security measure for reliable service delegation in 6G communication environment. Computer Communications, 161, 248–256.
[28]
Manogaran, G., Varatharajan, R., Lopez, D., Kumar, P. M., Sundarasekar, R., & Thota, C. (2018). A new architecture of the Internet of Things and big data ecosystem for secured smart healthcare monitoring and alerting system. Future Generation Computer Systems, 82, 375–387.
[29]
Nguyen, T. N., Liu, B. H., Nguyen, N. P., & Chou, J. T. (2020, June). Cyber security of smart grid: attacks and defenses. In ICC 2020-2020 IEEE International Conference on Communications (ICC) (pp. 1-6). IEEE. 10.1109/ICC40277.2020.9148850
[30]
Pencarelli, T. (2020). The digital revolution in the travel and tourism industry. Information Technology & Tourism, 22(3), 455–476.
[31]
RamprasadL.AmudhaG. (2014, February). Spammer detection and tagging based user-generated video search system—A survey. In International Conference on Information Communication and Embedded Systems (ICICES2014) (pp. 1-5). IEEE. 10.1109/ICICES.2014.7033826
[32]
Shakeel, P. M., Baskar, S., Fouad, H., Manogaran, G., Saravanan, V., & Montenegro-Marin, C. E. (2021). Internet of things forensic data analysis using machine learning to identify roots of data scavenging. Future Generation Computer Systems, 115, 756–768.
[33]
Shen, H., Wu, L., Yi, S., & Xue, L. (2020). The effect of online interaction and trust on consumers’ value co-creation behavior in the online travel community. Journal of Travel & Tourism Marketing, 37(4), 418–428.
[34]
Thota, C., Sundarasekar, R., Manogaran, G., Varatharajan, R., & Priyan, M. K. (2018). Centralized fog computing security platform for IoT and cloud in the healthcare system. In Fog computing: Breakthroughs in research and practice (pp. 365–378). IGI global.
[35]
Thota, C., Sundarasekar, R., Manogaran, G., Varatharajan, R., & Priyan, M. K. (2018). Centralized fog computing security platform for IoT and cloud in the healthcare system. In Fog computing: Breakthroughs in research and practice (pp. 365–378). IGI global.
[36]
Xiang, Z., Fesenmaier, D. R., & Werthner, H. (2021). Knowledge creation in information technology and tourism: A critical reflection and an outlook for the future. Journal of Travel Research, 60(6), 1371–1376.
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Published: 26 June 2023
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