Chatbots en la Industria Hotelera: Análisis Comparativo entre Europa y Sudamérica a través de la Inteligencia Artificial
Palabras clave:
chatbot, inteligencia artificial, análisis sentimental, modelo de clasificación, industria hotelera
Resumen
Este estudio analiza los sentimientos de los gerentes de hoteles en Sudamérica y Europa hacia un proveedor específico de chatbots, con el objetivo de determinar si es posible categorizar estos sentimientos en función del origen geográfico, aportando una perspectiva cultural y empresarial sobre el uso de la inteligencia artificial (IA) en la industria hotelera. La muestra incluyó 154 reseñas de Hotel Tech Review, 53 de Europa y 101 de Sudamérica, sobre el Asksuite Hotel Chatbot. Se utilizaron herramientas como Google Cloud Natural Language e IBM Watson Natural Language Understanding para realizar un análisis de sentimiento y de aspectos. Los resultados revelaron que los gerentes sudamericanos expresan una mayor apertura hacia los chatbots, mientras que los europeos muestran actitudes más críticas hacia capacidades complejas, como la creatividad y la inteligencia emocional. Usando un modelo de árbol de decisión C5.0, con una precisión del 89.52%, se identificaron diferencias culturales clave, destacando la necesidad de soluciones de IA adaptadas a contextos regionales.
Citas
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Alturaief, N., Aljamaan, H., & Baslyman, M. (2021). AWARE: Aspect-based sentiment analysis dataset of apps reviews for requirements elicitation. 2021 36th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW).
Ashraf, A. R., Thongpapanl, N. (tek), & Auh, S. (2014). The application of the technology acceptance model under different cultural contexts: The case of online shopping adoption. Journal of International Marketing, 22(3), 68–93. https://doi.org/10.1509/jim.14.0065
Bisoi, S., Roy, M., & Samal, A. (2020). Impact of Artificial Intelligence in the Hospitality Industry. International Journal of Advanced Science and Technology, 29(05), 4265 - 4276. http://sersc.org/journals/index.php/IJAST/article/view/13739
Brynjolfsson, E., Li, D., & Raymond, L. (2023). Generative AI at Work. National Bureau of Economic Research.
Buhalis, D., & Cheng, E. S. Y. (2020). Correction to: Exploring the use of chatbots in hotels: Technology providers’ perspective. En Information and Communication Technologies in Tourism 2020 (pp. C1–C1). Springer International Publishing.
Calvaresi, D., Ibrahim, A., Calbimonte, J.-P., Fragniere, E., Schegg, R., & Schumacher, M. I. (2021). Leveraging inter-tourists interactions via chatbots to bridge academia, tourism industries and future societies. Journal of Tourism Futures, ahead-of-print(ahead-of-print). https://doi.org/10.1108/jtf-01-2021-0009
Campbell, J., Ansell, K., & Stelzer, T. (2024). Evaluating IBM’s Watson natural language processing artificial intelligence as a short-answer categorization tool for physics education research. Physical Review Physics Education Research, 20(1). https://doi.org/10.1103/physrevphyseducres.20.010116
Car, T., Pilepić Stifanich, L., & Šimunić, M. (2019). Internet of things (iot) in tourism and hospitality: Opportunities and challenges. Tourism in Southern and Eastern Europe.
Castillo, D., Canhoto, A. I., & Said, E. (2021). The dark side of AI-powered service interactions: exploring the process of co-destruction from the customer perspective. Service Industries Journal, 41(13–14), 900–925. https://doi.org/10.1080/02642069.2020.1787993
Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20(1). https://doi.org/10.1186/s41239-023-00411-8
Chen, Q., Gong, Y., Lu, Y., & Tang, J. (2022). Classifying and measuring the service quality of AI chatbot in frontline service. Journal of Business Research, 145, 552–568. https://doi.org/10.1016/j.jbusres.2022.02.088
Coeurderoy, R., Guilmot, N., & Vas, A. (2014). Explaining factors affecting technological change adoption: A survival analysis of an information system implementation. Management Decision, 52(6), 1082–1100. https://doi.org/10.1108/md-10-2013-0540
Dash, M., & Bakshi, S. (2019). An exploratory study of customer perceptions of usage of chatbots in the hospitality industry. International Journal on Customer Relations, 7(2), 27–33. http://www.publishingindia.com/IJCR/55/an-exploratory-study-of-customer-perceptions-of-usage-of-chatbots-in-the-hospitality-industry/838/5833/
Dogru, T., (Dr. True), Line, N., Mody, M., Hanks, L., Abbott, J., Acikgoz, F., Assaf, A., Bakir, S., Berbekova, A., Bilgihan, A., Dalton, A., Erkmen, E., Geronasso, M., Gomez, D., Graves, S., Iskender, A., Ivanov, S., Kizildag, M., Lee, M., … Zhang, T. (2025). Generative artificial intelligence in the hospitality and tourism industry: Developing a framework for future research. Journal of Hospitality & Tourism Research, 49(2), 235–253. https://doi.org/10.1177/10963480231188663
Enholm, I. M., Papagiannidis, E., Mikalef, P., & Krogstie, J. (2022). Artificial intelligence and business value: A literature review. Information Systems Frontiers: A Journal of Research and Innovation, 24(5), 1709–1734. https://doi.org/10.1007/s10796-021-10186-w
Fan, H., Han, B., & Wang, W. (2024). Aligning (in)congruent chatbot–employee empathic responses with service-recovery contexts for customer retention. Journal of Travel Research, 63(8), 1870–1893. https://doi.org/10.1177/00472875231201505
Gandhi, A., Adhvaryu, K., Poria, S., Cambria, E., & Hussain, A. (2023). Multimodal sentiment analysis: A systematic review of history, datasets, multimodal fusion methods, applications, challenges and future directions. An International Journal on Information Fusion, 91, 424–444. https://doi.org/10.1016/j.inffus.2022.09.025
Guillen, N. K., & Turpo, H. (2025). Decoding emotions, unveiling origins: USA vs UK airline travelers in south America. En Studies in Systems, Decision and Control (pp. 291–300). Springer Nature Switzerland.
Gupta, S., Modgil, S., Lee, C.-K., Cho, M., & Park, Y. (2022). Artificial intelligence enabled robots for stay experience in the hospitality industry in a smart city. Industrial Management + Data Systems, 122(10), 2331–2350. https://doi.org/10.1108/imds-10-2021-0621
Hallin, C. A., & Marnburg, E. (2008). Knowledge management in the hospitality industry: A review of empirical research. Tourism Management, 29(2), 366–381. https://doi.org/10.1016/j.tourman.2007.02.019
He, W., Tian, X., Tao, R., Zhang, W., Yan, G., & Akula, V. (2017). Application of social media analytics: A case of analyzing online hotel reviews. Online Information Review, 41(7), 921-935. http://doi.org/10.1108/OIR-07-2016-0201
Hocutt, D., Ranade, N., & Verhulsdonck, G. (2022). Localizing content: The roles of technical & professional communicators and machine learning in personalized chatbot responses. Technical Communication, 69(4), 114–131. https://doi.org/10.55177/tc148396
Hotel Tech Report. (2024, julio). Hotel chatbots: Everything you need to know about hotel AI bots. Hotel Tech Report. https://hoteltechreport.com/marketing/hotel-chatbots
Ivanov, S. H., & Webster, C. (2017). Adoption of robots, artificial intelligence, and service automation by travel, tourism, and hospitality companies–a cost-benefit analysis. Artificial Intelligence and Service Automation by Travel, Tourism and Hospitality Companies–A Cost-Benefit Analysis.
Khan, M. T., Durrani, M., Ali, A., Inayat, I., Khalid, S., & Khan, K. H. (2016). Sentiment analysis and the complex natural language. Complex Adaptive Systems Modeling, 4(1). https://doi.org/10.1186/s40294-016-0016-9
Li, L., Lee, K. Y., Emokpae, E., & Yang, S.-B. (2021). What makes you continuously use chatbot services? Evidence from chinese online travel agencies. Electronic Markets, 31(3), 575–599. https://doi.org/10.1007/s12525-020-00454-z
Liu, B. (2022). Sentiment analysis and opinion mining. Springer Nature.
Lukanova, G., & Ilieva, G. (2019). Robots, Artificial Intelligence, and Service Automation in Hotels. En Robots, Artificial Intelligence, and Service Automation in Travel, Tourism and Hospitality (pp. 157–183). Emerald Publishing Limited.
Ma, E., Cheng, M., & Hsiao, A. (2018). Sentiment analysis–a review and agenda for future research in hospitality contexts. International Journal of Contemporary Hospitality Management, 30(11), 3287–3308. http://doi.org/10.1108/IJCHM-10-2017-0704
Machová, K., Szabóova, M., Paralič, J., & Mičko, J. (2023). Detection of emotion by text analysis using machine learning. Frontiers in Psychology, 14, 1190326. https://doi.org/10.3389/fpsyg.2023.1190326
McCoy, S., Galletta, D. F., & King, W. R. (2007). Applying TAM across cultures: the need for caution. European journal of information systems: an official journal of the Operational Research Society, 16(1), 81–90. https://doi.org/10.1057/palgrave.ejis.3000659
McTear, M., Callejas, Z., & Griol, D. (2016). The conversational interface: Talking to smart devices (1a ed.). Springer International Publishing.
Mehraliyev, F., Chan, I. C. C., & Kirilenko, A. P. (2022). Sentiment analysis in hospitality and tourism: a thematic and methodological review. International Journal of Contemporary Hospitality Management, 34(1), 46–77. https://doi.org/10.1108/ijchm-02-2021-0132
Mohamad Suhaili, S., Salim, N., & Jambli, M. N. (2021). Service chatbots: A systematic review. Expert Systems with Applications, 184(115461), 115461. https://doi.org/10.1016/j.eswa.2021.115461
Nam, K., Dutt, C. S., Chathoth, P., Daghfous, A., & Khan, M. S. (2021). The adoption of artificial intelligence and robotics in the hotel industry: prospects and challenges. Electronic Markets, 31(3), 553–574. https://doi.org/10.1007/s12525-020-00442-3
Organisation for Economic Co-operation and Development: Development Centre, & United Nations: Economic Commission for Latin America and the Caribbean - OECD (2020). Latin American economic outlook 2020: Digital transformation for building back better. Organization for Economic Co-operation and Development (OECD).
Organisation for Economic Co-operation and Development: Development Centre, & United Nations: Economic Commission for Latin America and the Caribbean. (2020). Latin American economic outlook 2020: Digital transformation for building back better. Organization for Economic Co-operation and Development (OECD).
Pillai, R., & Sivathanu, B. (2020). Adoption of AI-based chatbots for hospitality and tourism. International Journal of Contemporary Hospitality Management, 32(10), 3199–3226. https://doi.org/10.1108/ijchm-04-2020-0259
Pillai, S. G., Haldorai, K., Seo, W. S., & Kim, W. G. (2021). COVID-19 and hospitality 5.0: Redefining hospitality operations. International Journal of Hospitality Management, 94(102869), 102869. https://doi.org/10.1016/j.ijhm.2021.102869
Rane, N. (2023). Enhancing customer loyalty through artificial intelligence (AI), internet of things (IoT), and big data technologies: Improving customer satisfaction, engagement, relationship, and experience. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4616051
Rapp, A., Curti, L., & Boldi, A. (2021). The human side of human-chatbot interaction: A systematic literature review of ten years of research on text-based chatbots. International Journal of Human-Computer Studies, 151(102630), 102630. https://doi.org/10.1016/j.ijhcs.2021.102630
Samy, H., Helmy, A., & Ramadan, N. (2021). Aspect-based sentiment analysis of mobile apps reviews using class association rules and LDA. 2021 Tenth International Conference on Intelligent Computing and Information Systems (ICICIS).
Savastano, M., Biclesanu, I., Anagnoste, S., Laviola, F., & Cucari, N. (2024). Enterprise chatbots in managers’ perception: a strategic framework to implement successful chatbot applications for business decisions. Management Decision. https://doi.org/10.1108/md-10-2023-1967
Shin, D., Al-Imamy, S., & Hwang, Y. (2022a). Cross-cultural differences in information processing of chatbot journalism: chatbot news service as a cultural artifact. Cross Cultural & Strategic Management, 29(3), 618–638. http://doi.org/10.1108/ccsm-06-2020-0125
Shin, D., Chotiyaputta, V., & Zaid, B. (2022b). The effects of cultural dimensions on algorithmic news: How do cultural value orientations affect how people perceive algorithms? Computers in Human Behavior, 126, 107007. https://doi.org/10.1016/j.chb.2021.107007
Srite, M., & Karahanna, E. (2006). The Role of Espoused National Cultural Values in Technology Acceptance. MIS Quarterly, 30(3), 679–704. https://doi.org/10.2307/25148745
Tamrakar, S., Madhavi, B. K., & Mohan, V. (2022). Democratizing Sentiment Analysis of Twitter Data Using Google Cloud Platform and BigQuery. In Handbook of Intelligent Computing and Optimization for Sustainable Development (pp. 287-304). http://doi.org/10.1002/9781119792642
Tsai, W.-H. S., Liu, Y., & Chuan, C.-H. (2021). How chatbots’ social presence communication enhances consumer engagement: the mediating role of parasocial interaction and dialogue. Journal of Research in Interactive Marketing, 15(3), 460–482. https://doi.org/10.1108/jrim-12-2019-0200
Um, T., Kim, T., & Chung, N. (2020). How does an intelligence chatbot affect customers compared with self-service technology for sustainable services? Sustainability, 12(12), 5119. https://doi.org/10.3390/su12125119
Vaughn, L. (2019). Psychology and Culture: Thinking, Feeling and Behaving in a Global Context. (2nd ed.). Routledge.
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Alturaief, N., Aljamaan, H., & Baslyman, M. (2021). AWARE: Aspect-based sentiment analysis dataset of apps reviews for requirements elicitation. 2021 36th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW).
Ashraf, A. R., Thongpapanl, N. (tek), & Auh, S. (2014). The application of the technology acceptance model under different cultural contexts: The case of online shopping adoption. Journal of International Marketing, 22(3), 68–93. https://doi.org/10.1509/jim.14.0065
Bisoi, S., Roy, M., & Samal, A. (2020). Impact of Artificial Intelligence in the Hospitality Industry. International Journal of Advanced Science and Technology, 29(05), 4265 - 4276. http://sersc.org/journals/index.php/IJAST/article/view/13739
Brynjolfsson, E., Li, D., & Raymond, L. (2023). Generative AI at Work. National Bureau of Economic Research.
Buhalis, D., & Cheng, E. S. Y. (2020). Correction to: Exploring the use of chatbots in hotels: Technology providers’ perspective. En Information and Communication Technologies in Tourism 2020 (pp. C1–C1). Springer International Publishing.
Calvaresi, D., Ibrahim, A., Calbimonte, J.-P., Fragniere, E., Schegg, R., & Schumacher, M. I. (2021). Leveraging inter-tourists interactions via chatbots to bridge academia, tourism industries and future societies. Journal of Tourism Futures, ahead-of-print(ahead-of-print). https://doi.org/10.1108/jtf-01-2021-0009
Campbell, J., Ansell, K., & Stelzer, T. (2024). Evaluating IBM’s Watson natural language processing artificial intelligence as a short-answer categorization tool for physics education research. Physical Review Physics Education Research, 20(1). https://doi.org/10.1103/physrevphyseducres.20.010116
Car, T., Pilepić Stifanich, L., & Šimunić, M. (2019). Internet of things (iot) in tourism and hospitality: Opportunities and challenges. Tourism in Southern and Eastern Europe.
Castillo, D., Canhoto, A. I., & Said, E. (2021). The dark side of AI-powered service interactions: exploring the process of co-destruction from the customer perspective. Service Industries Journal, 41(13–14), 900–925. https://doi.org/10.1080/02642069.2020.1787993
Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20(1). https://doi.org/10.1186/s41239-023-00411-8
Chen, Q., Gong, Y., Lu, Y., & Tang, J. (2022). Classifying and measuring the service quality of AI chatbot in frontline service. Journal of Business Research, 145, 552–568. https://doi.org/10.1016/j.jbusres.2022.02.088
Coeurderoy, R., Guilmot, N., & Vas, A. (2014). Explaining factors affecting technological change adoption: A survival analysis of an information system implementation. Management Decision, 52(6), 1082–1100. https://doi.org/10.1108/md-10-2013-0540
Dash, M., & Bakshi, S. (2019). An exploratory study of customer perceptions of usage of chatbots in the hospitality industry. International Journal on Customer Relations, 7(2), 27–33. http://www.publishingindia.com/IJCR/55/an-exploratory-study-of-customer-perceptions-of-usage-of-chatbots-in-the-hospitality-industry/838/5833/
Dogru, T., (Dr. True), Line, N., Mody, M., Hanks, L., Abbott, J., Acikgoz, F., Assaf, A., Bakir, S., Berbekova, A., Bilgihan, A., Dalton, A., Erkmen, E., Geronasso, M., Gomez, D., Graves, S., Iskender, A., Ivanov, S., Kizildag, M., Lee, M., … Zhang, T. (2025). Generative artificial intelligence in the hospitality and tourism industry: Developing a framework for future research. Journal of Hospitality & Tourism Research, 49(2), 235–253. https://doi.org/10.1177/10963480231188663
Enholm, I. M., Papagiannidis, E., Mikalef, P., & Krogstie, J. (2022). Artificial intelligence and business value: A literature review. Information Systems Frontiers: A Journal of Research and Innovation, 24(5), 1709–1734. https://doi.org/10.1007/s10796-021-10186-w
Fan, H., Han, B., & Wang, W. (2024). Aligning (in)congruent chatbot–employee empathic responses with service-recovery contexts for customer retention. Journal of Travel Research, 63(8), 1870–1893. https://doi.org/10.1177/00472875231201505
Gandhi, A., Adhvaryu, K., Poria, S., Cambria, E., & Hussain, A. (2023). Multimodal sentiment analysis: A systematic review of history, datasets, multimodal fusion methods, applications, challenges and future directions. An International Journal on Information Fusion, 91, 424–444. https://doi.org/10.1016/j.inffus.2022.09.025
Guillen, N. K., & Turpo, H. (2025). Decoding emotions, unveiling origins: USA vs UK airline travelers in south America. En Studies in Systems, Decision and Control (pp. 291–300). Springer Nature Switzerland.
Gupta, S., Modgil, S., Lee, C.-K., Cho, M., & Park, Y. (2022). Artificial intelligence enabled robots for stay experience in the hospitality industry in a smart city. Industrial Management + Data Systems, 122(10), 2331–2350. https://doi.org/10.1108/imds-10-2021-0621
Hallin, C. A., & Marnburg, E. (2008). Knowledge management in the hospitality industry: A review of empirical research. Tourism Management, 29(2), 366–381. https://doi.org/10.1016/j.tourman.2007.02.019
He, W., Tian, X., Tao, R., Zhang, W., Yan, G., & Akula, V. (2017). Application of social media analytics: A case of analyzing online hotel reviews. Online Information Review, 41(7), 921-935. http://doi.org/10.1108/OIR-07-2016-0201
Hocutt, D., Ranade, N., & Verhulsdonck, G. (2022). Localizing content: The roles of technical & professional communicators and machine learning in personalized chatbot responses. Technical Communication, 69(4), 114–131. https://doi.org/10.55177/tc148396
Hotel Tech Report. (2024, julio). Hotel chatbots: Everything you need to know about hotel AI bots. Hotel Tech Report. https://hoteltechreport.com/marketing/hotel-chatbots
Ivanov, S. H., & Webster, C. (2017). Adoption of robots, artificial intelligence, and service automation by travel, tourism, and hospitality companies–a cost-benefit analysis. Artificial Intelligence and Service Automation by Travel, Tourism and Hospitality Companies–A Cost-Benefit Analysis.
Khan, M. T., Durrani, M., Ali, A., Inayat, I., Khalid, S., & Khan, K. H. (2016). Sentiment analysis and the complex natural language. Complex Adaptive Systems Modeling, 4(1). https://doi.org/10.1186/s40294-016-0016-9
Li, L., Lee, K. Y., Emokpae, E., & Yang, S.-B. (2021). What makes you continuously use chatbot services? Evidence from chinese online travel agencies. Electronic Markets, 31(3), 575–599. https://doi.org/10.1007/s12525-020-00454-z
Liu, B. (2022). Sentiment analysis and opinion mining. Springer Nature.
Lukanova, G., & Ilieva, G. (2019). Robots, Artificial Intelligence, and Service Automation in Hotels. En Robots, Artificial Intelligence, and Service Automation in Travel, Tourism and Hospitality (pp. 157–183). Emerald Publishing Limited.
Ma, E., Cheng, M., & Hsiao, A. (2018). Sentiment analysis–a review and agenda for future research in hospitality contexts. International Journal of Contemporary Hospitality Management, 30(11), 3287–3308. http://doi.org/10.1108/IJCHM-10-2017-0704
Machová, K., Szabóova, M., Paralič, J., & Mičko, J. (2023). Detection of emotion by text analysis using machine learning. Frontiers in Psychology, 14, 1190326. https://doi.org/10.3389/fpsyg.2023.1190326
McCoy, S., Galletta, D. F., & King, W. R. (2007). Applying TAM across cultures: the need for caution. European journal of information systems: an official journal of the Operational Research Society, 16(1), 81–90. https://doi.org/10.1057/palgrave.ejis.3000659
McTear, M., Callejas, Z., & Griol, D. (2016). The conversational interface: Talking to smart devices (1a ed.). Springer International Publishing.
Mehraliyev, F., Chan, I. C. C., & Kirilenko, A. P. (2022). Sentiment analysis in hospitality and tourism: a thematic and methodological review. International Journal of Contemporary Hospitality Management, 34(1), 46–77. https://doi.org/10.1108/ijchm-02-2021-0132
Mohamad Suhaili, S., Salim, N., & Jambli, M. N. (2021). Service chatbots: A systematic review. Expert Systems with Applications, 184(115461), 115461. https://doi.org/10.1016/j.eswa.2021.115461
Nam, K., Dutt, C. S., Chathoth, P., Daghfous, A., & Khan, M. S. (2021). The adoption of artificial intelligence and robotics in the hotel industry: prospects and challenges. Electronic Markets, 31(3), 553–574. https://doi.org/10.1007/s12525-020-00442-3
Organisation for Economic Co-operation and Development: Development Centre, & United Nations: Economic Commission for Latin America and the Caribbean - OECD (2020). Latin American economic outlook 2020: Digital transformation for building back better. Organization for Economic Co-operation and Development (OECD).
Organisation for Economic Co-operation and Development: Development Centre, & United Nations: Economic Commission for Latin America and the Caribbean. (2020). Latin American economic outlook 2020: Digital transformation for building back better. Organization for Economic Co-operation and Development (OECD).
Pillai, R., & Sivathanu, B. (2020). Adoption of AI-based chatbots for hospitality and tourism. International Journal of Contemporary Hospitality Management, 32(10), 3199–3226. https://doi.org/10.1108/ijchm-04-2020-0259
Pillai, S. G., Haldorai, K., Seo, W. S., & Kim, W. G. (2021). COVID-19 and hospitality 5.0: Redefining hospitality operations. International Journal of Hospitality Management, 94(102869), 102869. https://doi.org/10.1016/j.ijhm.2021.102869
Rane, N. (2023). Enhancing customer loyalty through artificial intelligence (AI), internet of things (IoT), and big data technologies: Improving customer satisfaction, engagement, relationship, and experience. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4616051
Rapp, A., Curti, L., & Boldi, A. (2021). The human side of human-chatbot interaction: A systematic literature review of ten years of research on text-based chatbots. International Journal of Human-Computer Studies, 151(102630), 102630. https://doi.org/10.1016/j.ijhcs.2021.102630
Samy, H., Helmy, A., & Ramadan, N. (2021). Aspect-based sentiment analysis of mobile apps reviews using class association rules and LDA. 2021 Tenth International Conference on Intelligent Computing and Information Systems (ICICIS).
Savastano, M., Biclesanu, I., Anagnoste, S., Laviola, F., & Cucari, N. (2024). Enterprise chatbots in managers’ perception: a strategic framework to implement successful chatbot applications for business decisions. Management Decision. https://doi.org/10.1108/md-10-2023-1967
Shin, D., Al-Imamy, S., & Hwang, Y. (2022a). Cross-cultural differences in information processing of chatbot journalism: chatbot news service as a cultural artifact. Cross Cultural & Strategic Management, 29(3), 618–638. http://doi.org/10.1108/ccsm-06-2020-0125
Shin, D., Chotiyaputta, V., & Zaid, B. (2022b). The effects of cultural dimensions on algorithmic news: How do cultural value orientations affect how people perceive algorithms? Computers in Human Behavior, 126, 107007. https://doi.org/10.1016/j.chb.2021.107007
Srite, M., & Karahanna, E. (2006). The Role of Espoused National Cultural Values in Technology Acceptance. MIS Quarterly, 30(3), 679–704. https://doi.org/10.2307/25148745
Tamrakar, S., Madhavi, B. K., & Mohan, V. (2022). Democratizing Sentiment Analysis of Twitter Data Using Google Cloud Platform and BigQuery. In Handbook of Intelligent Computing and Optimization for Sustainable Development (pp. 287-304). http://doi.org/10.1002/9781119792642
Tsai, W.-H. S., Liu, Y., & Chuan, C.-H. (2021). How chatbots’ social presence communication enhances consumer engagement: the mediating role of parasocial interaction and dialogue. Journal of Research in Interactive Marketing, 15(3), 460–482. https://doi.org/10.1108/jrim-12-2019-0200
Um, T., Kim, T., & Chung, N. (2020). How does an intelligence chatbot affect customers compared with self-service technology for sustainable services? Sustainability, 12(12), 5119. https://doi.org/10.3390/su12125119
Vaughn, L. (2019). Psychology and Culture: Thinking, Feeling and Behaving in a Global Context. (2nd ed.). Routledge.
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Publicado
2025-04-03
Cómo citar
Garayar, D., Ciriaco, S., Regalado, M., & Guillén, N. K. (2025). Chatbots en la Industria Hotelera: Análisis Comparativo entre Europa y Sudamérica a través de la Inteligencia Artificial. Revista Venezolana De Gerencia, 30(110), 977-993. https://doi.org/10.52080/rvgluz.30.110.13
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