Technologies for Stakeholder Management in Sustainable Tourism in Manabí, Ecuador

Maritza Sandra Pibaque Pionce; Jose Jorge Tualombo Tituaña; Martha Lorena Figueroa Soledispa; Gloria Pascuala Chiquito Tigua

doi:10.56294/saludcyt20251509

Authors

Maritza Sandra Pibaque Pionce Universidad Estatal del Sur de Manabí (UNESUM). Jipijapa, Ecuador Author https://orcid.org/0000-0001-9873-9134
Jose Jorge Tualombo Tituaña Universidad Estatal del Sur de Manabí (UNESUM). Jipijapa, Ecuador Author https://orcid.org/0000-0002-0420-7274
Martha Lorena Figueroa Soledispa Universidad Estatal del Sur de Manabí (UNESUM). Jipijapa, Ecuador Author https://orcid.org/0000-0001-9083-8680
Gloria Pascuala Chiquito Tigua Universidad Estatal del Sur de Manabí (UNESUM). Jipijapa, Ecuador Author https://orcid.org/0000-0002-3593-2049

DOI:

https://doi.org/10.56294/saludcyt20251509

Keywords:

Tourism, Stakeholders, Technological Innovation, Artificial Intelligence, Big Data, Sustainability

Abstract

Introduction: Sustainable tourism faces multiple challenges in the efficient management of its stakeholders, requiring the adoption of innovative solutions. In this context, artificial intelligence (AI) and Big Data analytics have proven to be strategic tools for optimizing decision-making and tourism planning.

Objective: To analyze the impact of artificial intelligence and Big Data on stakeholder management in the province of Manabí, Ecuador, through the implementation of a predictive analysis and recommendation system.

Methods: An AI and Big Data-based system integrating machine learning algorithms and real-time data analysis was developed. A case study was conducted in Manabí, applying inferential statistical analysis tools to assess the system’s effectiveness in stakeholder coordination, resource utilization, and tourist experience enhancement.

Results: The integration of AI and Big Data improves stakeholder coordination, optimizes resource utilization, and enhances the tourist experience. Statistical analyses reveal significant differences in response time reduction, resource savings, and stakeholder satisfaction after system implementation.

Conclusions: These technologies represent a viable strategy to promote sustainability and efficiency in the tourism sector. Investment in digital infrastructure and training programs is recommended to facilitate adoption.

References

1. Organización Mundial del Turismo. Panorama del turismo internacional 2019. Madrid: OMT; 2019.

2. Buhalis D, Leung D, Law R. eTourism: critical perspectives on big data and artificial intelligence. J Tour Futur. 2021;7(1):73-88. https://doi.org/10.1108/JTF-12-2020-0185.

3. Gretzel U, Sigala M, Xiang Z, Koo C. Smart tourism: foundations and developments. Electron Mark. 2015;25(3):179-88. https://doi.org/10.1007/s12525-015-0196-8. DOI: https://doi.org/10.1007/s12525-015-0196-8

4. Neuhofer B, Buhalis D, Ladkin A. Experience economy in tourism: a critical review of research. Tour Manag. 2014;44:243-53. https://doi.org/10.1016/j.tourman.2014.01.002. DOI: https://doi.org/10.1016/j.tourman.2014.01.002

5. Xiang Z, Fesenmaier DR. Big data analytics in tourism: challenges and opportunities. J Travel Res. 2017;56(3):287-98. https://doi.org/10.1177/0047287516629913.

6. Kose U, Sert S. Artificial intelligence applications in tourism and hospitality. J Hosp Tour Technol. 2020;11(1):5-20. https://doi.org/10.1108/JHTT-08-2019-0095.

7. Gursoy D, Chi CG, Lu L. AI in tourism: the influence of chatbots on tourist experience. J Dest Mark Manag. 2022;23:100652. https://doi.org/10.1016/j.jdmm.2022.100652.

8. Li Y, Xu W, Tang Z, Wang M. Predictive analytics in tourism: a case study on tourist flow forecasting. Ann Tour Res. 2021;90:103267. https://doi.org/10.1016/j.annals.2021.103267. DOI: https://doi.org/10.1016/j.annals.2021.103267

9. Law R, Buhalis D, Cobanoglu C. Progress on information and communication technologies in hospitality and tourism. Int J Contemp Hosp Manag. 2014;26(5):727-50. https://doi.org/10.1108/IJCHM-02-2013-0076. DOI: https://doi.org/10.1108/IJCHM-08-2013-0367

10. Chen C, Schwartz Z. Revenue management and customer behavior in online bookings. Int J Contemp Hosp Manag. 2013;25(1):81-98. https://doi.org/10.1108/09596111311290250.

11. Sigala M. The role of AI and big data in tourism and hospitality: prospects and challenges. Tour Rev. 2020;75(1):1-16. https://doi.org/10.1108/TR-08-2019-0351.

12. Del Vecchio P, Mele G, Ndou V, Secundo G. Open innovation and social big data for sustainability: evidence from the tourism industry. Sustainability. 2018;10(9):3215. https://doi.org/10.3390/su10093215. DOI: https://doi.org/10.3390/su10093215

13. Jeong M, Shin H. The role of artificial intelligence in personalized customer services. J Hosp Mark Manag. 2020;29(6):639-57. https://doi.org/10.1080/19368623.2020.1723038.

14. Choi TM, Wallace SW, Wang Y. Big data analytics in operations management. Prod Oper Manag. 2018;27(10):1868-89. https://doi.org/10.1111/poms.12838. DOI: https://doi.org/10.1111/poms.12838

15. Kim J, Kim J, Han H. Understanding the role of AI chatbots in improving customer experience. J Retail Consum Serv. 2021;57:102257. https://doi.org/10.1016/j.jretconser.2020.102257. DOI: https://doi.org/10.1016/j.jretconser.2020.102257

Salud, Ciencia y Tecnología. 2025; 5:1509 6

ISSN: 2796-9711

16. Martínez C, Rodríguez J, García J. Smart destinations: a review of key elements and trends. J Hosp Tour Technol. 2019;10(4):1-20. https://doi.org/10.1108/JHTT-08-2018-0072.

17. Pencarelli T. The digital revolution in the travel and tourism industry. Inf Technol Tour. 2020;22(3):455-74. https://doi.org/10.1007/s40558-019-00160-3. DOI: https://doi.org/10.1007/s40558-019-00160-3

18. Mariani MM, Baggio R, Fuchs M, Höpken W. Business intelligence and big data in hospitality and tourism: a systematic literature review. Int J Inf Manag. 2018;54:38-50. https://doi.org/10.1016/j.ijinfomgt.2020.102134. DOI: https://doi.org/10.1016/j.ijinfomgt.2020.102134

19. Femenia-Serra F, Neuhofer B. Smart tourism experiences: conceptualisation, key dimensions and research agenda. J Hosp Tour Technol. 2018;9(1):60-83. https://doi.org/10.1108/JHTT-12-2017-0131.

20. Hernández-Mogollón JM, Campón-Cerro AM, Alves HC. Innovation in hospitality and tourism companies: innovative behavior, management practices, and entrepreneurial creativity. J Hosp Tour Manag. 2019;41:123-34. https://doi.org/10.1016/j.jhtm.2019.10.001. DOI: https://doi.org/10.1016/j.jhtm.2019.10.001

21. Tussyadiah IP, Park S, Kachel M. The influence of artificial intelligence on consumer perceptions of hotel service quality. Int J Hosp Manag. 2021;99:103062. https://doi.org/10.1016/j.ijhm.2021.103062. DOI: https://doi.org/10.1016/j.ijhm.2021.103062

22. Li L, Suomi R. The role of artificial intelligence in digital marketing for tourism. J Inf Technol Tour. 2020;22(3):329-47. https://doi.org/10.1007/s40558-019-00161-2. DOI: https://doi.org/10.1007/s40558-020-00188-w

23. Fang Y, Ye Q, Kucukusta D, Law R. Analysis of the perceived value of online hotel reviews: influence of big data. Tour Manag. 2016;53:1-13. https://doi.org/10.1016/j.tourman.2015.09.009. DOI: https://doi.org/10.1016/j.tourman.2015.09.009

24. Wang D, Xiang Z, Fesenmaier DR. Assessing travel-related search engines for tourist information needs. J Travel Res. 2016;55(3):298-312. https://doi.org/10.1177/0047287516633554

25. Gartner W, Crouch GI. The future of tourism: AI and machine learning applications. J Dest Mark Manag. 2022;23:100667. https://doi.org/10.1016/j.jdmm.2022.100667.

Technologies for Stakeholder Management in Sustainable Tourism in Manabí, Ecuador

Authors

DOI:

Keywords:

Abstract

References

Downloads

Published

Issue

Section

License

How to Cite