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Enhancing Traveller Experience In Integrated Mobility Services Via Big Social Data Analytics

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  • Cuomo, Maria Teresa
  • Colosimo, Ivan
  • Celsi, Lorenzo Ricciardi
  • Ferulano, Roberto
  • Festa, Giuseppe
  • La Rocca, Michele

Abstract

The research intends to propose a data-driven approach to boost the tourist experience in integrated mobility services and discuss how the experience may be improved. In particular, the data-driven approach, owing to the design of a recommendation system based on a big-data analytics engine, makes it possible to: i) rank the tourist preferences for the most attractive Italian destinations on Google; ii) rank the main attractions – leisure, entertainment, culture, etc. – associated with single tourist destinations, obtained from the analysis of relevant thematic websites such as Tripadvisor, Minube, and Travel365. This study is dependent on the support of big social data for the concept of tourism experience co-design, with a focus on integrated mobility services. From a technological viewpoint, analytics on big social data is enabled by relying on a cloud-based data platform, such as Amazon web services (AWS), Microsoft Azure, or Google cloud platform (GCP). This has proved to be the key to regularly collecting, updating, and processing data from several heterogeneous sources such as Google search queries accessible via Google Trends, or any social data scraped from websites, as well as extracting relevant insights that can meet the business needs expressed by mobility companies.

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  • Cuomo, Maria Teresa & Colosimo, Ivan & Celsi, Lorenzo Ricciardi & Ferulano, Roberto & Festa, Giuseppe & La Rocca, Michele, 2022. "Enhancing Traveller Experience In Integrated Mobility Services Via Big Social Data Analytics," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    • Handle: RePEc:eee:tefoso:v:176:y:2022:i:c:s0040162521008957
    DOI: 10.1016/j.techfore.2021.121460
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    References listed on IDEAS

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    1. Vecchio, Pasquale Del & Secundo, Giustina & Maruccia, Ylenia & Passiante, Giuseppina, 2019. "A system dynamic approach for the smart mobility of people: Implications in the age of big data," Technological Forecasting and Social Change, Elsevier, vol. 149(C).
    2. Merz, Michael A. & Zarantonello, Lia & Grappi, Silvia, 2018. "How valuable are your customers in the brand value co-creation process? The development of a Customer Co-Creation Value (CCCV) scale," Journal of Business Research, Elsevier, vol. 82(C), pages 79-89.
    3. Erevelles, Sunil & Fukawa, Nobuyuki & Swayne, Linda, 2016. "Big Data consumer analytics and the transformation of marketing," Journal of Business Research, Elsevier, vol. 69(2), pages 897-904.
    4. Fosso Wamba, Samuel & Akter, Shahriar & Edwards, Andrew & Chopin, Geoffrey & Gnanzou, Denis, 2015. "How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study," International Journal of Production Economics, Elsevier, vol. 165(C), pages 234-246.
    5. Susilo, Yusak O. & Cats, Oded, 2014. "Exploring key determinants of travel satisfaction for multi-modal trips by different traveler groups," Transportation Research Part A: Policy and Practice, Elsevier, vol. 67(C), pages 366-380.
    6. Wang, Xia & Li, Xiang (Robert) & Zhen, Feng & Zhang, JinHe, 2016. "How smart is your tourist attraction?: Measuring tourist preferences of smart tourism attractions via a FCEM-AHP and IPA approach," Tourism Management, Elsevier, vol. 54(C), pages 309-320.
    7. Sigala, Marianna, 2020. "Tourism and COVID-19: Impacts and implications for advancing and resetting industry and research," Journal of Business Research, Elsevier, vol. 117(C), pages 312-321.
    8. Cuomo, Maria Teresa & Tortora, Debora & Foroudi, Pantea & Giordano, Alex & Festa, Giuseppe & Metallo, Gerardino, 2021. "Digital transformation and tourist experience co-design: Big social data for planning cultural tourism," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    9. de Oliveira, Daniel Thomé & Cortimiglia, Marcelo Nogueira, 2017. "Value co-creation in web-based multisided platforms: A conceptual framework and implications for business model design," Business Horizons, Elsevier, vol. 60(6), pages 747-758.
    10. Lalicic, Lidija & Dickinger, Astrid, 2019. "An assessment of user-driven innovativeness in a mobile computing travel platform," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 233-241.
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