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Abstract

Technology is radically redefining the customer service landscape. Chatbots have marked a quick expansion and high penetration with rapid technological development. With the advent of AI-enabled chatbots, organisations are adopting them to provide better customer services. However, the end user of chatbots may not always be a sophisticated information system user. Hence, the current study has two goals: to comprehensively understand the role of user characteristics, system characteristics and perceived anthropomorphism on chatbot user experience. Secondly, the study provides a design guide for the developers to provide a seamless chatbot user experience. The study has proposed a model using the IS success model and cognitive load theory to understand human-computer interaction comprehensively. The study is conducted with 400 participants interacting with chatbots exhibiting different levels of anthropomorphism. The study results indicated that information quality, service quality and personal innovation in the domain of information technology play an essential role in practical user experience.

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Introduction

Artificial intelligence plays a vital role in customer-based interactions; even studies have confirmed that the use of chatbots will have increased by 136% by 2021 (Balakrishnan and Dwivedi, 2021). Baabdullah et al. (2022) defined chatbots as “machine conversation system [s] [that] interacts with human users via natural conversational language”. The web is now considered an essential tool for accessing information and communication. Chatbots have tremendously improved customer service areas in terms of communication and customer contacts (Kushwaha, Kumar and Kar, 2021). This study develops a theoretical framework illustrating the chatbot's user and systems characteristics moderated by perceived anthropomorphism. This study proposes and empirically tests a parsimonious model assessing the critical components of chatbot user experience. Secondly, the characteristics can help marketers to tap into the rich vein of communication effectively study contributes to information systems practice by delineating human-computer interaction. Therefore, the characteristics will help develop a parsimonious model usable in practice while designing chatbots.

In particular, we attempt to address the following research questions:

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(1) How do user characteristics and system characteristics impact user experience?

(2) Does the perceived anthropomorphism moderate the relationship proposed in RQ1?

Materials and Methods

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To understand perceived anthropomorphism’s effect on the cognitive absorption of chatbot users, we developed two chatbot versions designed for this study to help users shop for apparel and shoes. They represented the fictitious name “Luxury Indiano”. We built the chatbots by using an online platform, Collect.Chat. where version one is the base chatbot lacking personalisation and humanisation techniques, and the second version, i.e., interactive chatbot, includes the techniques. The techniques used are as follows 1) chatbot self-introduction, 2) engaging with the users by their name, 3) use of emoticons, 4) varied responses, 5) display of appreciation, and 6) personalised recommendation.

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References

Agarwal, R. and Karahanna, E. (2000) ‘Time flies when you’re having fun: Cognitive absorption and beliefs about information technology usage’, MIS Quarterly: Management Information Systems, 24(4), pp. 665–694. doi: 10.2307/3250951.

Akter, S. et al. (2019) ‘Analytics-based decision-making for service systems: A qualitative study and agenda for future research’, International Journal of Information Management, 48, pp. 85–95. doi: 10.1016/j.ijinfomgt.2019.01.020.

Bolander, W. et al. (2015) ‘Social networks within sales organizations: Their development and importance for salesperson performance’, Journal of Marketing, 79(6), pp. 1–16. doi: 10.1509/jm.14.0444.

Brady, M. K. and Cronin, J. J. (2001) ‘Some New Thoughts on Conceptualizing Perceived Service Quality: A Hierarchical Approach’:, Journal of marketing, 65(3), pp. 34–49. doi: 10.1509/JMKG.65.3.34.18334.

Chavez, R. et al. (2015) ‘Customer integration and operational performance: The mediating role of information quality’, Decision Support Systems, 80, pp. 83–95. doi: 10.1016/j.dss.2015.10.001.

Chen, Q. et al. (2022) ‘Classifying and measuring the service quality of AI chatbot in frontline service’, Journal of Business Research, 145, pp. 552–568. doi: 10.1016/j.jbusres.2022.02.088.

Compeau, D. R. and Higgins, C. A. (1995) ‘Computer self-efficacy: Development of a measure and initial test’, MIS Quarterly: Management Information Systems, 19(2), pp. 189–210. doi: 10.2307/249688.

DeLone, W. H. and McLean, E. R. (1992) ‘Information systems success: The quest for the dependent variable’, Information Systems Research, 3(1), pp. 60–95. doi: 10.1287/isre.3.1.60.

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Conclusions

The study unfolds the understanding of chatbot user experience, which is essential for organisations for the technological edge in the market. Specifically, we examined the humanisation impact on user and system characteristics, subsequently impacting cognitive absorption, which gauges a user's holistic experience with the technology. The research offers inputs toward the foundation of the chatbot user experience through the perspective of anthropomorphism.

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Bharti School of Telecommunications Technology & Management

Industrial Significance

The study helps in developing the characteristics that can help marketers to tap into the rich vein of communication effectively study contributes to information systems practice by delineating human-computer interaction.  

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What creates better UX for Chatbots – Findings from Design thinking

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Shagun Sarraf and Arpan Kumar Kar^*

Result

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Conceptual Model

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Industry Day Theme # Communication Technologies

Conceptual Model

Structural Model

Moderating effect of perceived anthropomorphism in the relation of information quality and cognitive absorption

Moderating effect of perceived anthropomorphism in the relation of system quality and cognitive absorption