بررسی قصد استفاده از چت‌بات‌های مبتنی بر هوش مصنوعی توسط مشتریان با رویکرد پذیرش تکنولوژی

نوع مقاله : مقاله پژوهشی

نویسندگان
گروه مدیریت، دانشکده ادبیات و علوم انسانی، دانشگاه ملایر، ملایر، ایران.
چکیده
هدف: چت‌بات‌ها به‌عنوان یکی از دستاوردهای هوش مصنوعی، توانسته‌اند شیوه ارائه خدمات مشتری را دگرگون سازند. با گسترش این فناوری در سازمان‌ها، شناخت عواملی که بر قصد استفاده مشتریان از چت‌بات‌های هوشمند تأثیرگذارند، به ضرورتی اجتناب‌ناپذیر تبدیل‌شده است. پژوهش حاضر با رویکرد مدل‌های پذیرش فناوری، در پی تبیین عوامل مؤثر بر تمایل مشتریان به استفاده از چت‌بات‌ها در تعاملات خدماتی است.

روش پژوهش: این پژوهش از نوع کمی، کاربردی و به روش توصیفی– پیمایشی انجام‌شده است. در این پژوهش، جامعه آماری شامل کلیه افرادی است که به‌عنوان مشتری، تجربه استفاده از خدمات چت‌بات‌های مبتنی بر هوش مصنوعی را داشته‌اند. نمونه آماری این تحقیق شامل ۲۳۰ نفر از این افراد است. به‌منظور تحلیل داده‌های گردآوری‌شده، پس از تأیید شاخص‌های اندازه‌گیری، مدل ساختاری پژوهش، با بهره‌گیری از نرم‌افزار SmartPLS ارائه و آزمون شده است.

یافته‌ها: این تحقیق به بررسی درک چگونگی و چرایی تصمیم کاربران برای استفاده از چت‌بات‌های مبتنی بر هوش مصنوعی پرداخت. بر اساس اهداف تحقیق، 9 فرضیه به‌منظور بررسی و ارزیابی تعریف و طراحی شد و از میان 9 فرضیه مطرح‌شده، 7 فرضیه تأیید و 2 فرضیه رد شد.

نتیجه‌گیری: با ادغام ربات‌های چت در فرایندهای تجاری، سازمان‌ها می‌توانند کانالی نوآورانه برای ارتباط با مشتریان ایجاد کنند. چت‌بات‌ها به دلیل ارتباط مستقیم با کاربران و نقش آن‌ها در تسهیل فرایندهای مختلف، نیازمند طراحی با دقت و توجه به اصول کاربردپذیری هستند. یک چت‌بات کاربرپسند نه‌تنها موجب افزایش رضایت کاربران بلکه به پذیرش بیشتر این فناوری‌ها در میان عموم مردم کمک کند.
کلیدواژه‌ها

عنوان مقاله English

Investigating the Intention to Use Artificial Intelligence-Based Chatbots by Customers with a Technology Acceptance Approach

نویسندگان English

Soheila Shirezhian
Seyed mehdi Mirmehdi
Master of business management, Department of Management, Faculty of Literature and Humanities, Malayer University, Malayer, Iran .
چکیده English

Introduction

In recent years, advancements in technology, particularly in artificial intelligence, have significantly transformed how customers interact with businesses. One of the most prominent manifestations of this transformation is the emergence of chatbots as intelligent digital agents in marketing and customer service. Chatbots are AI-powered programs capable of responding to user inquiries through text or voice interactions, playing a crucial role in enhancing the efficiency of customer-organization communication. These tools enable companies to provide 24/7 services, reduce response times, increase customer loyalty, and save human resources. Unlike human agents, chatbots are unaffected by factors such as fatigue or holidays, ensuring constant availability. However, traditional customer service channels like email, websites, or phone calls remain popular among some customers.

In the retail sector, chatbots facilitate effective customer-brand interactions by offering convenience, flexibility, and easy access. They streamline the online shopping process by providing quick responses and guiding users, creating a seamless and satisfying experience while addressing the impersonal nature of e-commerce. Recent advancements in natural language processing have enabled chatbots to perform complex tasks, such as analyzing customer preferences and delivering personalized responses. These capabilities, combined with the widespread use of messaging platforms, have driven the growth of the chatbot industry. Nevertheless, concerns like data security and privacy pose significant barriers to widespread adoption, requiring careful consideration from system designers. This study, grounded in the Technology Acceptance Model, examines factors such as trust, personal innovativeness, ease of use, social influence, and hedonic motivation to understand the reasons behind users’ acceptance or rejection of chatbots.

Methods and Materoal

This study adopts a quantitative approach with an applied objective, utilizing a descriptive-survey design. The target population consists of Iranian users with experience using AI-based chatbots in online customer service platforms, such as websites, apps, or messaging services. Inclusion criteria required participants to have used at least one service-oriented chatbot and to be familiar with digital tools. Exclusion criteria included incomplete questionnaires, lack of actual chatbot experience, or use of chatbots for non-customer-service purposes (e.g., entertainment or language learning). To enhance accuracy and minimize bias, the influence of the chatbot’s application domain (e.g., retail, banking, education, or healthcare) was analyzed using variance analysis and control of contextual variables.

Data were collected through three primary methods: documentary studies, electronic resources, and field research. The data collection tool was a questionnaire based on a 5-point Likert scale (ranging from “strongly disagree” to “strongly agree”), measuring variables such as trust, hedonic motivation, social influence, personal innovativeness, perceived usefulness, ease of use, attitude, and intention to use. The questionnaire was designed based on standardized scales from prior research, and its content validity was confirmed by experts.

Resultss and Discussion

The findings indicate that trust, personal innovativeness, and ease of use significantly influence the perceived usefulness of chatbots. Trust enhances perceived usefulness by providing accurate and prompt responses. Personal innovativeness strengthens this perception by aligning chatbots with users’ needs, while ease of use, by simplifying interactions, positively affects both perceived usefulness and users’ attitudes. Both perceived usefulness and positive attitudes directly increase the intention to use chatbots. However, social influence and hedonic motivation did not show a significant impact on perceived usefulness, possibly due to customers’ preference for traditional channels or the functional focus of chatbots over entertainment.

Conclusion

This study reveals that trust, personal innovativeness, and ease of use are critical drivers of chatbot adoption. Trust, fostered through reliable and swift responses, enhances the perception of chatbots’ usefulness. Personal innovativeness aligns chatbot functionalities with users’ creative needs, further boosting this perception. Ease of use simplifies interactions, fostering positive attitudes and increasing the intention to use chatbots. The lack of significant impact from social influence may stem from customers’ preference for traditional channels like email or phone calls. Similarly, hedonic motivation’s limited effect could be attributed to the service-oriented nature of chatbots, which prioritizes efficiency over enjoyment.

Chatbots, by automating routine tasks, offering predictive analytics, and enhancing customer experiences, serve as innovative tools in digital services. However, challenges such as data security and privacy concerns remain barriers to broader adoption. Designing user-friendly and trustworthy chatbots can enhance their acceptance and improve the digital customer experience. This study recommends further research on non-users and environmental factors that may hinder the impact of social influence and hedonic motivation to better understand adoption barriers.

کلیدواژه‌ها English

Chatbots
Artificial intelligence
Attitude
Intention to use
Technology Acceptance Model
Adam, M., Wessel, M., & Benlian, A. (2021). AI-based chatbots in customer service and their effects on user compliance. Electronic Markets, 31(2), 427-445.
Al-Abdullatif, A. M. (2023). Modeling Students’ perceptions of chatbots in learning: Integrating technology acceptance with the value-based adoption model. Education Sciences, 13(11), 1151.
Ashfaq, M., Yun, J., Yu, S., & Loureiro, S. M. C. (2020). I, Chatbot: Modeling the determinants of users’ satisfaction and continuance intention of AI-powered service agents. Telematics and Informatics, 54, 101473.
Auer, I., Schlögl, S., & Glowka, G. (2024). Chatbots in Airport Customer Service—Exploring Use Cases and Technology Acceptance. Future Internet, 16(5), 175.
Bazadeh Khoramshahi, F. (2021). The impact of chatbot features on consumer trust and positive responses. Master's thesis, Al-Zahra University, Faculty of Social and Economic Sciences.
Bennett, C. C., & Hauser, K. (2013). Artificial intelligence framework for simulating clinical decision-making: A Markov decision process approach. Artificial intelligence in medicine, 57(1), 9-19.
Chen, J. S., Tran-Thien-Y, L., & Florence, D. (2021). Usability and responsiveness of artificial intelligence chatbot on online customer experience in e-retailing. International Journal of Retail & Distribution Management, 49(11), 1512-1531.
Chen, Q., Lu, Y., Gong, Y., & Xiong, J. (2023). Can AI chatbots help retain customers? Impact of AI service quality on customer loyalty. Internet Research, 33(6), 2205-2243.
Chen, S., Li, X., Liu, K., & Wang, X. (2023). Chatbot or human? The impact of online customer service on consumers' purchase intentions. Psychology & Marketing, 40(11), 2186-2200.
Cheng, X., Bao, Y., Zarifis, A., Gong, W., & Mou, J. (2021). Exploring consumers' response to text-based chatbots in e-commerce: the moderating role of task complexity and chatbot disclosure. Internet Research, 32(2), 496-517.
Cheriyan, A., Sharma, R. K., Joseph, A., & Kappil, S. R. (2022). Consumer acceptance towards AI-enabled chatbots; case of travel and tourism industries. Journal of Positive School Psychology, 6(3), 3880-3889.
Chung, M., Ko, E., Joung, H., & Kim, S. J. (2020). Chatbot e-service and customer satisfaction regarding luxury brands. Journal of Business Research, 117, 587-595.
Chung, T. S., Wedel, M., & Rust, R. T. (2016). Adaptive personalization using social networks. Journal of the Academy of Marketing Science, 44, 66-87.
Corti, K., & Gillespie, A. (2016). Co-constructing intersubjectivity with artificial conversational agents: People are more likely to initiate repairs of misunderstandings with agents represented as human. Computers in Human Behavior, 58, 431-442.
Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard business review, 96(1), 108-116.
Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48, 24-42.
De Cicco, R., Silva, S. C. L. D. C. E., & Alparone, F. R. (2021). “It’s on its way”: Chatbots applied for online food delivery services, social or task-oriented interaction style?. Journal of Foodservice Business Research, 24(2), 140-164.
Duarte, P., e Silva, S. C., & Ferreira, M. B. (2018). How convenient is it? Delivering online shopping convenience to enhance customer satisfaction and encourage e-WOM. Journal of Retailing and Consumer Services, 44, 161-169.
Hill, J., Ford, W. R., & Farreras, I. G. (2015). Real conversations with artificial intelligence: A comparison between human–human online conversations and human–chatbot conversations. Computers in human behavior, 49, 245-250.
Huang, M. H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of service research, 21(2), 155-172.
Jarrahi, M. H. (2019). In the age of the smart artificial intelligence: AI’s dual capacities for automating and informating work. Business Information Review, 36(4), 178-187.
Liu, M., Yang, Y., Ren, Y., Jia, Y., Ma, H., Luo, J., ... & Zhang, L. (2024). What influences consumer AI chatbot use intention? An application of the extended technology acceptance model. Journal of Hospitality and Tourism Technology, 15(4), 667-689.
Magner, N., Welker, R. B., & Campbell, T. L. (1996). Testing a model of cognitive budgetary participation processes in a latent variable structural equations framework. Accounting and Business Research, 27(1), 41-50.
Moradi Ganjeh, H. (2023). Examining the impact of AI chatbots on improving customer experience in online shopping. Master's thesis, Payame Noor University of Tehran Province, West Tehran Payame Noor Center.
Nair, K., & Gupta, R. (2021). Application of AI technology in modern digital marketing environment. World Journal of Entrepreneurship, Management and Sustainable Development, 17(3), 318-328.
Ng, M., Coopamootoo, K. P., Toreini, E., Aitken, M., Elliot, K., & van Moorsel, A. (2020, September). Simulating the effects of social presence on trust, privacy concerns & usage intentions in automated bots for finance. In 2020 IEEE European symposium on security and privacy workshops (EuroS&PW) (pp. 190-199). IEEE.
Przegalinska, A., Ciechanowski, L., Stroz, A., Gloor, P., & Mazurek, G. (2019). In bot we trust: A new methodology of chatbot performance measures. Business Horizons, 62(6), 785-797.
Rajman, M., Bui, T. H., Rajman, A., Seydoux, F., Trutnev, A., & Quarteroni, S. (2004). Assessing the usability of a dialogue management system designed in the framework of a rapid dialogue prototyping methodology. Acta acustica united with acustica, 90(6), 1096-1111.
Rana, J., Gaur, L., Singh, G., Awan, U., & Rasheed, M. I. (2022). Reinforcing customer journey through artificial intelligence: a review and research agenda. International Journal of Emerging Markets, 17(7), 1738-1758.
Rana, J., Jain, R., & Nehra, V. (2024). Utility and acceptability of AI-enabled Chatbots on the online customer journey. International Journal of Computing and Digital Systems, 15(1), 323-335.
Rose, S., Clark, M., Samouel, P., & Hair, N. (2012). Online customer experience in e-retailing: an empirical model of antecedents and outcomes. Journal of retailing, 88(2), 308-322.
Silva, F. A., Shojaei, A. S., & Barbosa, B. (2023). Chatbot-based services: a study on customers’ reuse intention. Journal of Theoretical and Applied Electronic Commerce Research, 18(1), 457-474.
Sivaramakrishnan, S., Wan, F., & Tang, Z. (2007). Giving an “e‐human touch” to e‐tailing: The moderating roles of static information quantity and consumption motive in the effectiveness of an anthropomorphic information agent. Journal of Interactive Marketing, 21(1), 60-75.
Spychalska, D. K. (2019). How chatbots influence marketing. Management, 23(1), 251-270.
Tang, J., Zhang, B., & Akram, U. (2020). User willingness to purchase applications on mobile intelligent devices: evidence from app store. Asia Pacific Journal of Marketing and Logistics, 32(8), 1629-1649.
Trappey, A. J. C., Trappey, C., Govindarajan, U. H., Sharma, A., & Yeh, L. C. (2018, November). Conversational service bot specifications for advanced manufacturing applications. In 2018 IEEE International Conference on Advanced Manufacturing, ICAM 2018.
Vlačić, B., Corbo, L., e Silva, S. C., & Dabić, M. (2021). The evolving role of artificial intelligence in marketing: A review and research agenda. Journal of Business Research, 128, 187-203.
Yun, J. J., Kim, D., & Yan, M. R. (2020). Open innovation engineering—Preliminary study on new entrance of technology to market. Electronics, 9(5), 791.
Zumstein, D., & Hundertmark, S. (2017). Chatbots—an Interactive Technology For Personalized Communication, Transation And Services. IADIS International Journal on WWW/Internet, 15(1), 96-105.