Can chatbots satisfy me? A mixed-method comparative study of satisfaction with task-oriented chatbots in mainland China and Hong Kong

Liu, Yuli; Hu, Bo; Yan, Wenjia; Zhi, Lin

doi:10.1016/j.chb.2023.107716

Cited by 22 publications

(19 citation statements)

References 82 publications

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“…This outcome could be explained in several ways. In the context of information systems, higher levels of user trust lead to more positive attitudes [ 29 , [105] , [106] , [107] , [108] , 112 ]. While assessing the level of satisfaction among users, their trust in banks plays a positive role.…”

Section: Discussionmentioning

confidence: 99%

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Exploring chatbot trust: Antecedents and behavioural outcomes

Alagarsamy¹,

Mehrolia

2023

Heliyon

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Section: Discussionmentioning

confidence: 99%

“…While assessing the level of satisfaction among users, their trust in banks plays a positive role. Users who trust banking chatbots continue to utilize them because they feel that banks would not exhibit any opportunistic behaviour [ 4 , 104 , 112 ].…”

Section: Discussionmentioning

confidence: 99%

Exploring chatbot trust: Antecedents and behavioural outcomes

Alagarsamy¹,

Mehrolia

2023

Heliyon

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“…The present research combined qualitative and quantitative paradigms, adopting a mixed methods approach commonly employed by researchers studying technology adoption and usage (Lim et al, 2022;Liu et al, 2023;Pillai & Sivathanu, 2020;Udeozor et al, 2023). A mixed-method approach delivers high-quality results from a study (Mariani & Baggio, 2020).…”

Section: Research Questionsmentioning

confidence: 99%

AI Chatbot Adoption in Academia: Task Fit, Usefulness and Collegial Ties

Soodan,

Rana,

Jain

et al. 2024

JITE:IIP

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Aim/Purpose: This mixed-methods study aims to examine factors influencing academicians’ intentions to continue using AI-based chatbots by integrating the Task-Technology Fit (TTF) model and social network characteristics. Background: AI-powered chatbots are gaining popularity across industries, including academia. However, empirical research on academicians’ adoption behavior is limited. This study proposes an integrated model incorporating TTF factors and social network characteristics like density, homophily, and connectedness to understand academics’ continuance intentions. Methodology: A qualitative study involving 31 interviews of academics from India examined attitudes and the potential role of social network characteristics like density, homophily, and connectedness in adoption. Results showed positive sentiment towards chatbots and themes on how peer groups accelerate diffusion. In the second phase, a survey of 448 faculty members from prominent Indian universities was conducted to test the proposed research model. Contribution: The study proposes and validates an integrated model of TTF and social network factors that influence academics’ continued usage intentions toward AI chatbots. It highlights the nuanced role of peer networks in shaping adoption. Findings: Task and technology characteristics positively affected academics’ intentions to continue AI chatbot usage. Among network factors, density showed the strongest effect on TTF and perceived usefulness, while homophily and connectedness had partial effects. The study provides insights into designing appropriate AI tools for the academic context. Recommendations for Practitioners: AI chatbot designers should focus on aligning features to academics’ task needs and preferences. Compatibility with academic work culture is critical. Given peer network influences, training and demonstrations to user groups can enhance adoption. Platforms should have capabilities for collaborative use. Targeted messaging customized to disciplines can resonate better with academic subgroups. Multidisciplinary influencers should be engaged. Concerns like plagiarism risks, privacy, and job impacts should be transparently addressed. Recommendation for Researchers: More studies are needed across academic subfields to understand nuanced requirements and barriers. Further studies are recommended to investigate differences across disciplines and demographics, relative effects of specific network factors like size, proximity, and frequency of interaction, the role of academic leadership and institutional policies in enabling chatbot adoption, and how AI training biases impact usefulness perceptions and ethical issues. Impact on Society: Increased productivity in academia through the appropriate and ethical use of AI can enhance quality, access, and equity in education. AI can assist in mundane tasks, freeing academics’ time for higher-order objectives like critical thinking development. Responsible AI design and policies considering socio-cultural aspects will benefit sustainable growth. With careful implementation, it can make positive impacts on student engagement, learning support, and research efficiency. Future Research: Conduct longitudinal studies to examine the long-term impacts of AI chatbot usage in academia. Track usage behaviors over time as familiarity develops. Investigate differences across academic disciplines and roles. Requirements may vary for humanities versus STEM faculty or undergraduate versus graduate students. Assess user trust in AI and how it evolves with repeated usage, and examine trust-building strategies. Develop frameworks to assess pedagogical effectiveness and ethical risks of conversational agents in academic contexts.

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“…Based on assessment theory and grounded in consumers' psychological assessment and psychological behavior towards service failure in different service failure type contexts, this paper examines the impact of AI service failure types on customers' recovery expectations through three contextual experiments and draws the following conclusions: (1) Robot non-functional failures reduce consumer recovery expectation compared to robot functional failures. As online platforms mostly use task-oriented bots in providing AI services, the main purpose of bot services is to be able to help customers solve their core problems in real time (Liu et al, 2023). Consumers generally believe that the occurrence of functional service failure will lead to their core needs not being responded to, and thus the more eager they are to get service remedies.…”

Section: Research Conclusionmentioning

confidence: 99%

I can feel AI failure: the impact of service failure type and failure assessment on customer recovery expectation

Song,

Xing,

Duan

et al. 2023

IMDS

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PurposeBased on appraisal theory and social response theory, this study aims to explore the mechanism of AI failure types on consumer recovery expectation from the perspective of service failure assessment and validate the moderate role of anthropomorphism level.Design/methodology/approachThree scenario-based experiments were conducted to validate the research model. First, to test the effect of robot service failure types on customer recovery expectation; second, to further test the mediating role of perceived controllability, perceived stability and perceived severity; finally, to verify the moderating effect of anthropomorphic level.FindingsNon-functional failures reduce consumer recovery expectation compared to functional failures; perceived controllability and perceived severity play a mediating role in the impact of service failure types on recovery expectation; the influence of service failure types on perceived controllability and perceived severity is moderated by the anthropomorphism level.Originality/valueThe findings enrich the influence mechanism and boundary conditions of service failure types, and have implications for online enterprise follow-up service recovery and improvement of anthropomorphic design.

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Can chatbots satisfy me? A mixed-method comparative study of satisfaction with task-oriented chatbots in mainland China and Hong Kong

Cited by 22 publications

References 82 publications

Exploring chatbot trust: Antecedents and behavioural outcomes

Exploring chatbot trust: Antecedents and behavioural outcomes

AI Chatbot Adoption in Academia: Task Fit, Usefulness and Collegial Ties

I can feel AI failure: the impact of service failure type and failure assessment on customer recovery expectation

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