Human Characteristics in Chatbots Cause an Impact on Customer Experience in The Banking Sector

Gonzalo Alva; Martin Caceres

doi:10.4067/s0718-27242025000200052

Authors

Gonzalo Alva Universidad de Lima https://orcid.org/0009-0007-2900-1321
Martin Caceres Universidad de Lima https://orcid.org/0009-0006-7379-9548

DOI:

https://doi.org/10.4067/s0718-27242025000200052

Keywords:

Artificial Intelligence, chatbots, customer experience, Banking Industry, perceived personalisation

Abstract

This study investigates how the human characteristics of chatbots, such as warmth and competence, impact customer experience in the banking sector of Lima, Peru. Given the increasing adoption of artificial intelligence in the financial sector, this analysis is warranted by the need to understand its influence on user satisfaction and loyalty. Through a non-experimental quantitative methodology and virtual surveys, we evaluated whether the personalization and social presence of chatbots enhance user perception, particularly among younger generations. The findings reveal that, while users value these characteristics, neutral responses persist, indicating a lack of emotional connection in certain cases. This underscores the importance of optimizing chatbot implementation to maintain competitiveness in the digital age. This study highlights the relevance of tailoring AI solutions to customer expectations to foster a more positive and personalized experience. During the course of this study, we found no similar research conducted within the Peruvian market designed to measure the impact of relevant variables on consumer experience in the banking sector when interacting with a chatbot.

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References

Adam, M., Wessel, M., & Benlian, A. (2020). AI-based chatbots in customer service and their effects on user compliance. Electronic Markets, 31(2), 427–445. https://doi.org/10.1007/s12525-020-00414-7

Ameen, N., Cheah, J.-H., & Kumar, S. (2022). It's all part of the customer journey: The impact of augmented reality, chatbots, and social media on the body image and self-esteem of Generation Z female consumers. Psychology & Marketing, 39(11), 2110–2129. https://doi.org/10.1002/mar.21715

Aprenda. (2018). El progreso es para todos: La campaña que visibiliza al emprendedor con talleres de organización ciudadana. https://www.aprenda.com.pe/elprogresoesparatodos-la-campana-que-visibiliza-al-emprendedor-con-talleres-de-organizacion-ciudadana/

Asociación Peruana de Empresas de Investigación de Mercados (APEIM). (2021). Presentación del Comité: Análisis de los NSE. https://apeim.com.pe/wp-content/uploads/2022/08/2021-APEIM-NSE-Presentacion_Comite-Vfinal2.pdf

Autoridad Bancaria Europea. (2022). Annual risk assessment of the European banking system (2022). https://www.eba.europa.eu/risk-analysis-and-data/risk-assessment-reports

Bach, T., Khan, A., Hallock, H., Beltrao, G., & Sousa, S. (2023). A systematic literature review of user trust in AI-enabled systems: An HCI perspective. International Journal of Human–Computer Interaction, 39(12), 2425–2451. https://doi.org/10.1080/10447318.2022.2138826

Bargueño, M. Á. (2023, 14 de junio). Inteligencia artificial: ¿por qué asusta el valle inquietante?. National Geographic. https://www.nationalgeographic.es/ciencia/2023/06/inteligencia-artificial-por-que-asusta-valle-inquietante

Belanche, D., Casaló, L. V., Schepers, J., & Flavián, C. (2021). Examining the effects of robots’ physical appearance, warmth, and competence in frontline services: The humanness-value-loyalty model. Psychology & Marketing, 38(12), 2357–2376. https://doi.org/10.1002/mar.21571

Blut, M., Wang, C., Wünderlich, N. V., & Brock, C. (2021). Understanding anthropomorphism in service provision: A meta-analysis of physical robots, chatbots, and other AI. Journal of the Academy of Marketing Science, 49(4), 632–658. https://doi.org/10.1007/s11747-020-00762-y

Chen, C. J., Tsai, P. H., & Tang, J. W. (2022). How informational-based readiness and social influence affect usage intentions of self-service stores through different routes: An elaboration likelihood model perspective. Asia Pacific Business Review, 28(3), 380–409. https://doi.org/10.1080/13602381.2021.1969299

Compañía Peruana de Estudios de Mercado y Opinión Pública (CPI). (2024). Perú: Población (2023). https://cpi.pe/banco/estadisticas-poblacionales.html

Credicorp. (2022). Informe de inclusión financiera: Digitalización 2022. https://grupocredicorp.com/indice-inclusion-financiera/IIF/IIF-Informe-Incusion-Financiera-Digitalizacion-2022.pdf

DatosMacro. (s. f.). Perú - PIB. Recuperado el 17 de julio de 2025, de https://datosmacro.expansion.com/paises/peru

De Cicco, R., e Silva, S. C., & Alparone, F. R. (2020). Millennials’ attitude toward chatbots: An experimental study in a social relationship perspective. International Journal of Retail & Distribution Management, 48(11), 1195–1211. https://doi.org/10.1108/IJRDM-12-2019-0406

Dwivedi, Y. K., Balakrishnan, J., Baabdullah, A. M., & Das, R. (2023). Do chatbots establish “humanness” in the customer purchase journey? An investigation through explanatory sequential design. Psychology & Marketing, 40(11), 2207–2234. https://doi.org/10.1002/mar.21888

Forbes Perú. (2023, 13 de julio). IA generativa podría aportar hasta US$4,4 billones a la economía global, según McKinsey. https://forbes.pe/economia-y-finanzas/2023-07-13/ia-generativa-podria-aportar-hasta-us44-billones-a-la-economia-global-segun-mckinsey

Guerreiro, J., & Loureiro, S. M. C. (2023). I am attracted to my cool smart assistant! Analyzing attachment-aversion in AI-human relationships. Journal of Business Research, 161, 113863. https://doi.org/10.1016/j.jbusres.2023.113863

Hassenzahl, M., & Tractinsky, N. (2006). User experience—a research agenda. Behaviour & Information Technology, 25(2), 91–97. https://doi.org/10.1080/01449290500330331

Hernández Sampieri, R. (2018). Metodología de la investigación: Las rutas cuantitativa, cualitativa y mixta. McGraw-Hill Interamericana.

Hoffman, D. L., & Novak, T. P. (1996). Marketing in hypermedia computer-mediated environments: Conceptual foundations. Journal of Marketing, 60(3), 50–65. https://doi.org/10.2307/1251841

Infobae. (2022, 13 de septiembre). Clase media en el Perú llega al 62% de la población y se mantiene desde 2019. https://www.infobae.com/america/peru/2022/09/13/clase-media-en-el-peru-llega-al-62-de-la-poblacion-y-se-mantiene-desde-2019/

Instituto Nacional de Estadística e Informática (INEI). (s. f.). Estadísticas - Índice Temático. Recuperado el 17 de julio de 2025, de https://www.inei.gob.pe/estadisticas-indice-tematico/

Instituto Nacional de Estadística e Informática (INEI). (2023, 20 de julio). El 57.4% de la población tiene una cuenta en el sistema financiero en el primer trimestre del año 2023. https://www.gob.pe/institucion/inei/noticias/780703-el-57-4-de-la-poblacion-tiene-una-cuenta-en-el-sistema-financiero-en-el-primer-trimestre-del-ano-2023

Ipsos Perú. (2022). Generaciones en el Perú 2022. https://www.ipsos.com/es-pe/generaciones-en-el-peru-2022

Jelahut, F., Jelahut, Y., & Jehamat, L. (2021). Theoretical review: Social responses to communication technologies theory (SRCT theory). Jurnalisa, 7(1), 45–56. https://doi.org/10.24252/JURNALISA.V7I1.18818

Kaushal, V., & Yadav, R. (2023). Learning successful implementation of chatbots in businesses from B2B customer experience perspective. Concurrency and Computation: Practice and Experience, 35(7), e7450. https://doi.org/10.1002/cpe.7450

Kearns, J. (2023, diciembre). AI reverberations across finance. Finanzas & Desarrollo. https://www.imf.org/es/Publications/fandd/issues/2023/12/AI-reverberations-across-finance-Kearns

King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43(6), 740–755. https://doi.org/10.1016/j.im.2006.05.003

Li, B., Qi, P., Liu, B., Di, S., Liu, J., Pei, J., Yi, J., & Zhou, B. (2023). Trustworthy AI: From principles to practices. ACM Computing Surveys, 55(9), 1–46. https://doi.org/10.1145/3555803

Li, C. Y., Fang, Y. H., & Chiang, Y. H. (2023). Can AI chatbots help retain customers? An integrative perspective using affordance theory and service-domain logic. Technological Forecasting and Social Change, 197, 122921. https://doi.org/10.1016/j.techfore.2023.122921

Liu, B., & Sundar, S. S. (2018). Should machines express sympathy and empathy? Experiments with a health advice chatbot. Cyberpsychology, Behavior, and Social Networking, 21(10), 625–636. https://doi.org/10.1089/cyber.2018.0110

Liu, M., Yang, Y., Ren, Y., Jia, Y. H., Luo, J., Fang, S., Qi, M., & Zhang, L. (2024). What influences consumer AI chatbot use intention? An application of the extended technology acceptance model. Journal of Hospitality and Tourism Technology. Publicación anticipada en línea. https://doi.org/10.1108/jhtt-03-2023-0057

Lu, Z. L., & Dosher, B. A. (2000). Spatial attention: Different mechanisms for central and peripheral temporal precues? Journal of Experimental Psychology: Human Perception and Performance, 26(5), 1534–1548. https://doi.org/10.1037/0096-1523.26.5.1534

Maar, D., Besson, E., & Kefi, H. (2023). Fostering positive customer attitudes and usage intentions for scheduling services via chatbots. Journal of Service Management, 34(2), 208–230. https://doi.org/10.1108/JOSM-05-2021-0164

Mishra, R., Singh, R. K., & Koles, B. (2021). Consumer decision-making in omnichannel retailing: Literature review and future research agenda. International Journal of Consumer Studies, 45(2), 147–174. https://doi.org/10.1111/ijcs.12617

Muguira, A. (2024, 6 de junio). ¿Qué es la escala de Likert y cómo utilizarla? QuestionPro. https://www.questionpro.com/blog/es/que-es-la-escala-de-likert-y-como-utilizarla/

Nguyen, M., Casper Ferm, L. E., Quach, S., Pontes, N., & Thaichon, P. (2023). Chatbots in frontline services and customer experience: An anthropomorphism perspective. Psychology & Marketing, 40(11), 2135–2149. https://doi.org/10.1002/mar.21882

Nguyen, Q. N., Sidorova, A., & Torres, R. (2022). User interactions with chatbot interfaces vs. menu-based interfaces: An empirical study. Computers in Human Behavior, 128, 107093. https://doi.org/10.1016/j.chb.2021.107093

Park, Y., Kim, J., Jiang, Q., & Kim, K. (2024). Impact of artificial intelligence (AI) chatbot characteristics on customer experience and customer satisfaction. Journal of Global Scholars of Marketing Science, 34(3), 439–457. https://doi.org/10.1080/21639159.2024.2362654

Petty, R. E., & Cacioppo, J. T. (1986). The elaboration likelihood model of persuasion. En R. E. Petty & J. T. Cacioppo (Eds.), Communication and persuasion: Central and peripheral routes to attitude change (pp. 1–24). Springer.

Pizzi, G., Vannucci, V., Mazzoli, V., & Donvito, R. (2023). I, chatbot! The impact of anthropomorphism and gaze direction on willingness to disclose personal information and behavioral intentions. Psychology & Marketing, 40(7), 1287–1301. https://doi.org/10.1002/mar.21813

Ranieri, A., Di Bernardo, I., & Mele, C. (2024). Serving customers through chatbots: Positive and negative effects on customer experience. Journal of Service Theory and Practice, 34(2), 157–181. https://doi.org/10.1108/JSTP-01-2023-0015

Rizomyliotis, I., Kastanakis, M. N., Giovanis, A., Konstantoulaki, K., & Kostopoulos, I. (2022). “How may I help you today?” The use of AI chatbots in small family businesses and the moderating role of customer affective commitment. Journal of Business Research, 153, 515–526. https://doi.org/10.1016/j.jbusres.2022.08.035

Roy, R., & Naidoo, V. (2021). Enhancing chatbot effectiveness: The role of anthropomorphic conversational styles and time orientation. Journal of Business Research, 126, 355–365. https://doi.org/10.1016/j.jbusres.2020.12.051

Saklani, S., & Kala, D. (2024). Perception of Gen Z customers towards chatbots as service agents. Journal of Telecommunications and the Digital Economy, 12(1), 356–376. https://doi.org/10.18080/jtde.v12n1.781

Salesforce. (2024, 14 de mayo). State of the Connected Customer report outlines changing standards for customer engagement. Salesforce News. https://www.salesforce.com/news/stories/state-of-the-connected-customer-report-outlines-changing-standards-for-customer-engagement/

Sari, H., & Adinda, R. (2023). Examining customer experience in using a chatbot. International Journal of Asian Business and Information Management, 14(1), 1–18. https://doi.org/10.4018/IJABIM.322438

Schmid, D., Staehelin, D., Bucher, A., Dolata, M., & Schwabe, G. (2022). Does social presence increase perceived competence? Proceedings of the ACM on Human-Computer Interaction, 6(CSCW1), Article 28, 1–22. https://doi.org/10.1145/3492845

Shin, H., Bunosso, I., & Levine, L. R. (2023). The influence of chatbot humour on consumer evaluations of services. International Journal of Consumer Studies, 47(2), 546–561. https://doi.org/10.1111/ijcs.12849

Shumanov, M., & Johnson, L. (2021). Making conversations with chatbots more personalized. Computers in Human Behavior, 117, 106627. https://doi.org/10.1016/j.chb.2020.106627

Song, M., Du, J., Xing, X., & Mou, J. (2022). Should the chatbot “save itself” or “be helped by others”? The influence of service recovery types on consumer perceptions of recovery satisfaction. Electronic Commerce Research and Applications, 55, 101199. https://doi.org/10.1016/j.elerap.2022.101199

Superintendencia de Banca, Seguros y AFP (SBS). (s. f.). Información estadística de banca múltiple. Recuperado el 17 de julio de 2025, de https://www.sbs.gob.pe/app/stats_net/stats/EstadisticaBoletinEstadistico.aspx?p=1