The Influence of Chatbot Anthropomorphism on Trust, Intention, and Engagement of Indonesian State-Owned Bank Customers: Investigation Using the DOI Theory

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Bernardinus Joko Prakosta Santu Aji
Yolanda Masnita http://orcid.org/0000-0002-5758-8146
Kurniawati Kurniawati http://orcid.org/0000-0002-4339-0278

Keywords

Artificial Intelligence, Antropomorphism, Initial Chatbot Trust, Usage Intention, Customer Engagement

Abstract

Nowadays, artificial intelligence (AI) is growing rapidly, especially in Indonesia. Chatbot is one of the new forms of technology in AI that works like humans. Its great development is evidenced by the major adoption of chatbots in various industrial sectors, especially in the banking industry. This study intends to investigate the anthropomorphism of chatbots as one influencing factor of the trust, intention, and engagement of state-owned bank customers in Indonesia. The study employed the DOI theory since consumer behavior toward new technology is determined by his beliefs on that particular technology. The research used non-probability sampling with a total of 108 respondents who had or frequently used chatbots for their needs. The data was processed using the Partial Least Square method to analyze the measurement and structural model. The results show that all variables of chatbot anthropomorphism have a positive effect on trust, intention, and engagement of state-owned bank customers in Indonesia. This study provides insight for bank managers to continuously develop chatbots in order to get better quality and security so that customer trust, intention and engagement can increase.

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References

Abdulrahman Al Moosa, H., Mousa, M., Chaouali, W., Mjahed Hammami, S., McKnight, H., & Danks, N. P. (2022). Using humanness and design aesthetics to choose the “best” type of trust: a study of mobile banking in France. International Journal of Retail and Distribution Management, 50(2), 251–275. doi: 10.1108/IJRDM-04-2021-0159
Ahmad, A., AlMallah, M. M., & AbedRabbo, M. (2022). Does eWOM influence entrepreneurial firms’ rate of diffusion of innovation? Journal of Research in Marketing and Entrepreneurship, 24(1), 92–111. doi: 10.1108/JRME-01-2021-0012
Akca, Y., & Ozer, G. (2014). Diffusion of Innovation Theory and Animplementation on Enterprise Resource Planning Systems. International Journal of Business and Management, 9(4). doi: 10.5539/ijbm.v9n4p92
Alsmadi, D., Halawani, M., Prybutok, V., & Al-Smadi, R. (2022). Intention, trust and risks as core determinants of cloud computing usage behavior. Journal of Systems and Information Technology. doi: 10.1108/JSIT-09-2020-0180
Chen, Q. Q., & Park, H. J. (2021). How anthropomorphism affects trust in intelligent personal assistants. Industrial Management and Data Systems, 121(12), 2722–2737. doi: 10.1108/IMDS-12-2020-0761
Cheng, Y., & Jiang, H. (2022). Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. Journal of Product and Brand Management, 31(2), 252–264. doi: 10.1108/JPBM-05-2020-2907
Chua, P. Y., Rezaei, S., Gu, M. L., Oh, Y. M., & Jambulingam, M. (2018). Elucidating social networking apps decisions: Performance expectancy, effort expectancy and social influence. Nankai Business Review International, 9(2), 118–142. doi: 10.1108/NBRI-01-2017-0003
Davis, F. . (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology (Vol. 13 No). MIS Quarterly.
Dean, D., Suhartanto, D., & Kusdibyo, L. (2019). Predicting Destination Image in Creative Tourism: A Comparative between Tourists and Residents. International Journal of Applied Business Research, 1(01), 1–15. doi: 10.35313/ijabr.v1i01.36
Ewe, S. Y., Yap, S. F., & Lee, C. K. C. (2015). Network externalities and the perception of innovation characteristics: Mobile banking. Marketing Intelligence and Planning, 33(4), 592–611. doi: 10.1108/MIP-01-2014-0006
Fonseka, K., Jaharadak, A. A., & Raman, M. (2022). Impact of E-commerce adoption on business performance of SMEs in Sri Lanka; moderating role of artificial intelligence. International Journal of Social Economics. doi: 10.1108/IJSE-12-2021-0752
Gao, L., Li, G., Tsai, F., Gao, C., Zhu, M., & Qu, X. (2022). The impact of artificial intelligence stimuli on customer engagement and value co-creation: the moderating role of customer ability readiness. Journal of Research in Interactive Marketing. doi: 10.1108/JRIM-10-2021-0260
Hair, J. F., et al. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Thousand Oaks. Sage, 165.
IDX Channel. (2022). Marak Digunakan di Indonesia, Ini Manfaat Chatbot Bagi Pengembangan Bisnis. https://www.idxchannel.com/economics/marak-digunakan-di-indonesia-ini-manfaat-chatbot-bagi-pengembangan-bisnis
Jami Pour, M., Rafiei, K., Khani, M., & Sabrirazm, A. (2021). Gamification and customer experience: the mediating role of brand engagement in online grocery retailing. Nankai Business Review International, 12(3), 340–357. doi: 10.1108/NBRI-07-2020-0041
Jia, Q., Lei, Y., Guo, Y., & Li, X. (2021). Leveraging enterprise social network technology: understanding the roles of compatibility and intrinsic motivation. Journal of Enterprise Information Management, 72061127002. doi: 10.1108/JEIM-05-2021-0225
Karjaluoto, H., Shaikh, A. A., Leppäniemi, M., & Luomala, R. (2020). Examining consumers’ usage intention of contactless payment systems. International Journal of Bank Marketing, 38(2), 332–351. doi: 10.1108/IJBM-04-2019-0155
Kritzinger, R., & Petzer, D. J. (2020). Motivational factors, customer engagement and loyalty in the South African mobile instant messaging environment: moderating effect of application usage. European Business Review, 33(4), 642–666. doi: 10.1108/EBR-04-2020-0104
Lee, J. H., Jun, J., Park, J., Yoo, J. W., & Park, H. (2021). The role of characters featured on digital stickers in forming usage intention: internet-only banks in Korea. Asia Pacific Journal of Marketing and Logistics, 33(8), 1743–1757. doi: 10.1108/APJML-07-2020-0506
Lim, X. J., Cheah, J. H., Morrison, A. M., Ng, S. I., & Wang, S. (2022). Travel app shopping on smartphones: understanding the success factors influencing in-app travel purchase intentions. Tourism Review, 77(4), 1166–1185. doi: 10.1108/TR-11-2021-0497
Mckinsey. (2021). Global survey: The state of AI in 2021 | McKinsey. Www.mckinsey.com. https://www.mckinsey.com/capabilities/quantumblack/our-insights/global-survey-the-state-of-ai-in-2021
Michaelis, M., Woisetschläger, D. M., Backhaus, C., & Ahlert, D. (2008). The effects of country of origin and corporate reputation on initial trust: An experimental evaluation of the perception of Polish consumers. International Marketing Review, 25(4), 404–422. doi: 10.1108/02651330810887468
Mostafa, R. B., & Kasamani, T. (2022). Antecedents and consequences of chatbot initial trust. European Journal of Marketing, 56(6), 1748–1771. doi: 10.1108/EJM-02-2020-0084
Mozafari, N., Weiger, W. H., & Hammerschmidt, M. (2022). Trust me, I’m a bot – repercussions of chatbot disclosure in different service frontline settings. Journal of Service Management, 33(2), 221–245. doi: 10.1108/JOSM-10-2020-0380
Nayal, K., Raut, R., Priyadarshinee, P., Narkhede, B. E., Kazancoglu, Y., & Narwane, V. (2021). Exploring the role of artificial intelligence in managing agricultural supply chain risk to counter the impacts of the COVID-19 pandemic. International Journal of Logistics Management. doi: 10.1108/IJLM-12-2020-0493
Nyagadza, B., Muposhi, A., Mazuruse, G., Makoni, T., Chuchu, T., Maziriri, E. T., & Chare, A. (2022). Prognosticating anthropomorphic chatbots’ usage intention as an e-banking customer service gateway: cogitations from Zimbabwe. PSU Research Review. doi: 10.1108/prr-10-2021-0057
Rania Badr Mustofa dan Tamara Kasmani (2021). Antecedents and consequences of chatbot initial trust. Journal of marketing, doi 10.1108/EJM-02-2020-0084
Richad, R., Vivensius, V., Sfenrianto, S., & Kaburuan, E. R. (2019). Analysis of factors influencing millennial’s technology acceptance of chatbot in the banking industry in Indonesia. International Journal of Management, 10(3), 107–118. doi: 10.34218/IJM.10.3.2019.011
Rogers, E. (1995). Diffusion of Innovations. Free Press.
Rui-Hsin, K., & Lin, C. T. (2018). The usage intention of e-learning for police education and training. Policing, 41(1), 98–112. doi: 10.1108/PIJPSM-10-2016-0157
Samala, N., Katkam, B. S., Bellamkonda, R. S., & Rodriguez, R. V. (2020). Impact of AI and robotics in the tourism sector: a critical insight. Journal of Tourism Futures, 8(1), 73–87. doi: 10.1108/JTF-07-2019-0065
Shaikh, I. M., Bin Noordin, K., Arijo, S., Shaikh, F., & Alsharief, A. (2020). Predicting customers’ adoption towards family takaful scheme in Pakistan using diffusion theory of innovation. Journal of Islamic Marketing, 11(6), 1761–1776. doi: 10.1108/JIMA-02-2018-0037
Srivastava, M., & Sivaramakrishnan, S. (2021). Mapping the themes and intellectual structure of customer engagement: a bibliometric analysis. Marketing Intelligence and Planning, 39(5), 702–727. doi: 10.1108/MIP-11-2020-0483
Thakur, R., & Srivastava, M. (2014). Adoption readiness, personal innovativeness, perceived risk and usage intention across customer groups for mobile payment services in India. Internet Research, 24(3), 369–392. doi: 10.1108/IntR-12-2012-0244
Yussaivi, A. M., Lu, C. Y., Syarief, M. E., & Suhartanto, D. (2021). Millennial Experience with Mobile Banking and Mobile Banking Artificial Intelligence Evidence from Islamic Banking. International Journal of Applied Business Research, 3(1), 39–53. doi: 10.35313/ijabr.v3i1.121
Zhu, D. H., & Chang, Y. P. (2020). Robot with humanoid hands cooks food better?: Effect of robotic chef anthropomorphism on food quality prediction. International Journal of Contemporary Hospitality Management, 32(3), 1367–1383. doi: 10.1108/IJCHM-10-2019-0904