User Perception of Chatbots in a Mobile Commerce Environment
Palabras clave:
Chatbots; Artificial Intelligence; Mobile Commerce; Consumer BehaviorResumen
En el panorama actual, la inteligencia artificial (IA) está creciendo rápidamente y las organizaciones adoptan continuamente tecnologías inteligentes. Junto con los chatbots, la IA se considera un elemento clave en la transformación digital y tiene el poder de remodelar por completo la forma en que los clientes se comunican con las organizaciones. Sin embargo, existen pocas investigaciones empíricas sobre el impacto que los chatbots tienen en sus usuarios.
En este contexto, este artículo investiga la implementación de heurísticas de interacción, lenguaje y antropomorfismo en aplicaciones de chatbots, utilizando un diseño experimental 2x3 con videos de comercio móvil. El estudio explora hasta qué punto las heurísticas relacionadas con los chatbots pueden influir en las percepciones de confianza, satisfacción e intención de compra.
Los hallazgos de esta investigación confirmaron que el uso de imágenes y lenguaje similares a los humanos aumenta la satisfacción del usuario. La presencia social en el entorno en línea a través de rasgos antropomórficos también puede generar una mayor satisfacción. Por lo tanto, las organizaciones, desarrolladores, diseñadores y especialistas en marketing deben centrarse en elegir una apariencia humana, así como en señales lingüísticas antropomórficas, para mejorar la satisfacción y ofrecer una mejor experiencia general del usuario.
Citas
Abdul-Kader, S. A., & Woods, J. (2015). Survey on chatbot design techniques in speech conversation systems. International Journal of Advanced Computer Science and Applications, 6(7), 72-80.
Burgoon, J. K., Bonito, J. A., Bengtsson, B., Cederberg, C., Lundeberg, M., & Allspach, L. (2000). Interactivity in human–computer interaction: A study of credibility, understanding, and influence. Computers in Human Behavior, 16(6), 553-574. https://doi.org/10.1016/S0747-5632(00)00029-7
Capgemini. (2019, September). Conversational interfaces: The next big thing in customer interaction. Capgemini. https://www.capgemini.com/wp-content/uploads/2019/09/Report-%E2%80%93-Conversational-Interfaces_Web-Final.pdf
Corti, K., & Gillespie, A. (2019). A truly human interface: Interacting face-to-face with someone whose words are determined by a computer program. Journal of Interaction Science, 7(1), 5. https://doi.org/10.3389/fpsyg.2015.00634
Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2019). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science. Advance online publication. https://doi.org/10.1007/s11747-019-00696-0
Epley, N., Waytz, A., & Cacioppo, J. T. (2007). On seeing human: A three-factor theory of anthropomorphism. Psychological Review, 114(4), 864-886. https://doi.org/10.1037/0033-295X.114.4.864
Fávero, L. P., & Belfiore, P. (2017). Manual de análise de dados: Estatística e modelagem multivariada com Excel®, SPSS® e Stata®. Elsevier Brasil.
Fan, L., Scheutz, M., Lohani, M., McCoy, M., & Stokes, C. (2017). Do we need emotionally intelligent artificial agents? First results of human perceptions of emotional intelligence in humans compared to robots. In Intelligent Virtual Agents: 17th International Conference, IVA 2017, Stockholm, Sweden, August 27-30, 2017, Proceedings (pp. 129-141). Springer International Publishing. https://doi.org/10.1007/978-3-319-67401-8_15
Gartner. (2023, August 30). Gartner reveals three technologies that will transform customer service and support by 2028. Gartner. https://www.gartner.com/en/newsroom/press-releases/2023-08-30-gartner-reveals-three-technologies-that-will-transform-customer-service-and-support-by-2028
Haas, C., & Moussawi, S. (2020). Are anthropomorphic intelligent agents more intelligent? AIS Electronic Library (AISeL). https://aisel.aisnet.org/
Hofacker, C. F., Malthouse, E. C., & Sultan, F. (2016). Big data and consumer behavior: Imminent opportunities. Journal of Consumer Marketing, 33(2), 89-97. https://doi.org/10.1108/JCM-04-2015-1399
Jain, M., Kota, R., Kumar, P., & Patel, S. N. (2018). Convey: Exploring the use of a context view for chatbots. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (p. 468). ACM. https://doi.org/10.1145/3173574.3174042
Jain, M., Kumar, P., Kota, R., & Patel, S. N. (2018). Evaluating and informing the design of chatbots. In Proceedings of the 2018 on Designing Interactive Systems Conference 2018 (pp. 895-906). ACM. https://doi.org/10.1145/3196709.3196735
Jonke, A. W., & Volkwein, J. B. (2018). From tweet to chatbot–Content management as a core competency for the digital evolution. In Digital Marketplaces Unleashed (pp. 275-285). Springer. https://doi.org/10.1007/978-3-662-49275-8_28
Krasodomski, A., Gwagwa, A., Jackson, B., Jones, E., King, S., Lane, M., ... & Tarkowski, A. (2024). Artificial intelligence and the challenge for global governance. https://doi.org/10.55317/9781784136086
King, K. (2019). Using artificial intelligence in marketing: How to harness AI and maintain the competitive edge. Kogan Page Publishers.
Klaus, P., & Zaichkowsky, J. (2020). AI voice bots: A services marketing research agenda. Journal of Services Marketing, 34(3), 389-398. https://doi.org/10.1108/JSM-01-2019-0043
Kumar, V., Dixit, A., Javalgi, R. R. G., & Dass, M. (2016). Research framework, strategies, and applications of intelligent agent technologies (IATs) in marketing. Journal of the Academy of Marketing Science, 44(1), 24-45. https://doi.org/10.1007/s11747-015-0426-9
Li, J., Monroe, W., Ritter, A., Galley, M., Gao, J., & Jurafsky, D. (2016). Deep reinforcement learning for dialogue generation. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (pp. 1192-1202). Association for Computational Linguistics. https://doi.org/10.18653/v1/D16-1127
Luo, X., Tong, S., Fang, Z., & Qu, Z. (2019). Frontiers: Machines vs. humans: The impact of artificial intelligence chatbot disclosure on customer purchases. Marketing Science. https://doi.org/10.1287/mksc.2019.1192
Malhotra, N. K. (2015). Pesquisa de mercado. Pearson Ed.
Marôco, J. (2018). Análise estatística com o SPSS Statistics: 7ª edição. ReportNumber, Lda.
Murphy, M. C., & Dweck, C. S. (2016). Mindsets shape consumer behavior. Journal of Consumer Psychology, 26(1), 127-136. https://doi.org/10.1016/j.jcps.2015.06.005
Oke, A. O., Kamolshotiros, P., Popoola, O. Y., Ajagbe, M. A., & Olujobi, O. J. (2016). Consumer behavior towards decision making and loyalty to particular brands. International Review of Management and Marketing, 6(4S), 43-52.
Shankar, V., Kleijnen, M., Ramanathan, S., Rizley, R., Holland, S., & Morrissey, S. (2016). Mobile shopper marketing: Key issues, current insights, and future research avenues. Journal of Interactive Marketing, 34, 37-48. https://doi.org/10.1016/j.intmar.2016.03.002
Shareef, M. A., Dwivedi, Y. K., & Kumar, V. (2016). Online consumer behavior and marketing. In Mobile Marketing Channel (pp. 1-24). Springer. https://doi.org/10.1007/978-3-319-31287-3_1
Solomon, M. R., Dahl, D. W., White, K., Zaichkowsky, J. L., & Polegato, R. (2014). Consumer behavior: Buying, having, and being (Vol. 10). Pearson.
Sterne, J. (2017). Artificial intelligence for marketing: Practical applications. John Wiley & Sons.
Tong, S., Luo, X., & Xu, B. (2019). Personalized mobile marketing strategies. Journal of the Academy of Marketing Science. https://doi.org/10.1007/s11747-019-00693-3
Van den Broeck, E., Zarouali, B., & Poels, K. (2019). Chatbot advertising effectiveness: When does the message get through? Computers in Human Behavior, 98, 150-157. https://doi.org/10.1016/j.chb.2019.04.009
Wierenga, B., & Van der Lans, R. (Eds.). (2017). Handbook of marketing decision models (Vol. 254). Springer.
Zarouali, B., Van den Broeck, E., Walrave, M., & Poels, K. (2018). Predicting consumer responses to a chatbot on Facebook. Cyberpsychology, Behavior, and Social Networking, 21(8), 491-497. https://doi.org/10.1089/cyber.2017.0518
Mobile Marketing Association's Brazil. (2019, August 23). Retrieved from https://www.mmaglobal.com/research
Statista. (2024, April 27). Retail mobile commerce revenue worldwide. Retrieved from https://www.statista.com/statistics/1449284/retail-mobile-commerce-revenue-worldwide/
Voicebot.ai. (2021, April 14). U.S. smart speaker growth flat-lined in 2020. Retrieved from https://voicebot.ai/2021/04/14/u-s-smart-speaker-growth-flat-lined-in-2020/
Descargas
Publicado
Cómo citar
Número
Sección
Licencia
Derechos de autor 2026 Norberto Almeida de Andrade
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
Declaro que o presente artigo é original, não tendo sido submetido à publicação, quer em sua totalidade quer em parte, em qualquer outro periódico nacional ou internacional. Declaro, ainda, que, uma vez publicado no International Journal of Business & Marketing (IJBMKT), o mesmo jamais será submetido por mim ou por qualquer um dos demais coautores a qualquer outro periódico. Através deste instrumento, em meu nome e em nome dos demais coautores, porventura existentes, cedo os direitos autorais do referido artigo ao International Journal of Business & Marketing (IJBMKT) e declaro estar ciente de que a não observância deste compromisso submeterá o infrator a sanções e penas previstas na Lei de Proteção de Direitos Autorais (nº 9609, de 19/02/98).
