To improve customer experience and achieve sustainable development, many industries, especially banking, have leveraged artificial intelligence to implement a chatbot into their customer service. By integrating DeLone and McLean’s information systems success (D&M ISS) model and the expectation confirmation model (ECM) with the factor of trust, the aim of this study was to investigate the determinants of users’ continuance intentions towards chatbot services in the context of banking in Vietnam. A total of 359 questionnaire surveys were collected from a real bank’s chatbot users and analyzed using structural equation modeling. The findings revealed that users’ continuance intentions towards the banks’ chatbot services were influenced by satisfaction, trust, and perceived usefulness, of which trust had the strongest effect. The results also indicate that information quality, system quality, service quality, and confirmation of expectations had significant effects on three drivers of continuance intention in different ways. Our study contributes to the literature by providing a more comprehensive viewpoint to understand the perceptions and reactions of chatbot users in the post-adoption stage. The results of this study also yield several key suggestions for banking service providers on how to increase their customers’ intentions to continue using chatbot services, serving as a basis for long-term and sustainable development strategies in the current digital era.

https://www.mdpi.com/2071-1050/13/14/7625/pdf

Determinants of Continuance Intention towards Banks’ Chatbot Services in Vietnam: A Necessity for Sustainable Development

Understanding the user satisfaction and loyalty of customer service chatbots

As a rapidly emerging generative AI chatbot, ChatGPT has garnered unprecedented global attention for its advanced AI-based text generation capabilities. However, the issue of ChatGPT acceptance requires further investigation. Prior studies on chatbot acceptance primarily focused on traditional technology acceptance models (TAMs) and did not consider the intelligence features of AI technology. Based on the AI device use acceptance (AIDUA) model and cognitive appraisal theory (CAT), this study proposed a research model to investigate the acceptance of ChatGPT. Participants with experience using ChatGPT were invited to take part in the survey. A total of 500 valid questionnaires were collected through the Credamo survey platform. Our findings reveal compelling associations: social influence, novelty value, and humanness positively correlate with performance expectations, while hedonic motivation, novelty value, and humanness negatively correlate with effort expectations. Both performance and effort expectations contribute to cognitive attitudes. Age, as a control variable, exhibits a significant negative impact on the willingness to reject ChatGPT. Notably, this study expands the current AIDUA framework within chatbot contexts by incorporating perspectives on novelty value, perceived humanness, and cognitive attitudes to examine chatbot acceptance. These insights offer practical implications for the design and development of AI-based chatbots, contributing to the evolving landscape of AI technology acceptance. 

Are users willing to embrace ChatGPT? Exploring the factors on the acceptance of chatbots from the perspective of AIDUA framework

The role of meta-UTAUT factors, perceived anthropomorphism, perceived intelligence, and social self-efficacy in chatbot-based services?

Artificial intelligence (AI)-based chatbots have received considerable attention during the last few years. However, little is known concerning what affects their use for educational purposes. This research, therefore, develops a theoretical model based on extracting constructs from the expectation confirmation model (ECM) (expectation confirmation, perceived usefulness, and satisfaction), combined with the knowledge management (KM) factors (knowledge sharing, knowledge acquisition, and knowledge application) to understand the sustainable use of chatbots. The developed model was then tested based on data collected through an online survey from 448 university students who used chatbots for learning purposes. Contrary to the prior literature that mainly relied on structural equation modeling (SEM) techniques, the empirical data were analyzed using a hybrid SEM-artificial neural network (SEM-ANN) approach. The hypotheses testing results reinforced all the suggested hypotheses in the developed model. The sensitivity analysis results revealed that knowledge application has the most considerable effect on the sustainable use of chatbots with 96.9% normalized importance, followed by perceived usefulness (70.7%), knowledge acquisition (69.3%), satisfaction (61%), and knowledge sharing (19.6%). Deriving from these results, the study highlighted a number of practical implications that benefit developers, designers, service providers, and instructors. 

Understanding the impact of knowledge management factors on the sustainable use of AI-based chatbots for educational purposes using a hybrid SEM-ANN approach

This study aims to investigates customer satisfaction from the use of bank chatbots and the effect of perceived trust in chatbots and banks' reputation on customer satisfaction.,A survey is conducted in Turkey involving 240 customers who experienced banking transactions using a chatbot. Partial least squares structural equation modeling (PLS-SEM) is used to investigate the relationships between the variables. The data were analyzed using SPSS 21 and SmartPLS programs.,Perceived performance, perceived trust and corporate reputation significantly affect customer satisfaction with chatbot use. Customer expectations and confirmation of customer expectations have no direct impact on customer satisfaction, but customer expectations positively affect perceived performance. Customer expectations exert an indirect influence on customer satisfaction through perceived performance. Perceived performance has a positive impact on the confirmation of customer expectations, but customer expectations do not significantly impact the confirmation of customer expectations.,This study relies on a limited number of participants. Moreover, its sample is not representative of the target population due to the convenience sampling technique. Even if the results may not be generalized to the entire population of Turkey, they reflect the reality of emerging markets with relatively high technology sensitivity and a young population.,The results provide new insights regarding banking service delivery channels, which may be of interest to professionals, academics, banks' top management, product development teams, design teams and customer satisfaction units.,This study is believed to help the community make their lives easier by providing them with knowledge and awareness about chatbots.,This study extends expectations confirmation theory's predictions to chatbot use in banking.

Determinants of customer satisfaction in chatbot use: evidence from a banking application in Turkey

This study aimed to examine the effect of the quality dimensions of information system success model (ISSM), perceived risk, perceived transaction speed, and optimistic perspective on Quick Response (QR) code mobile payment (m-payment) experience. For this purpose, a survey was conducted in Turkey involving 207 adults who had experienced the QR code m-payment method. Partial least squares structural equation modeling (PLS-SEM) was used to investigate the relations between the variables. Research results showed that information quality, system quality, perceived transaction speed, and optimism significantly affected customer experience. However, service quality and perceived risk regarding QR code m-payment had no impact on customer experience. This study tried to reveal behavioral intentions of users in future by bringing together the concepts of ISSM and marketing, examining the QR code m-payment customer experience in the pandemic period with an integrative approach. In this context, the research has theoretical implications that extend ISSM toward improving technology-based financial services and omnichannel marketing. 

/pdf/qr-code-m-payment-from-a-customer-experience-perspective-1k8jdmmf.pdf

QR code m-payment from a customer experience perspective

Robo-advisor is one of the most up-to-date innovations in the financial world. So, the number of experienced users is also very limited, yet. The study investigates to reveal the components that determine the usage intention for robo-advisor by private pension investors who have not yet experienced the product in order to determine the real potential of robo-advisor. This study assimilated elements of the UTAUT “performance expectancy, effort expectancy, facilitating conditions, and social influence” and extended the model by adding in three vital elements “a need for interaction with service employees, financial risk tolerance, and trust.” A survey was conducted involving 265 investors in Turkey who have private pension investments and have experienced digital banking. The outcomes of this study indicate that the factors affecting robo-advisor usage intention in private pension investments are performance expectancy, social impact, facilitating conditions, financial risk tolerance, and trust. Also, trust positively affects financial risk tolerance. However, effort expectancy and the need for interaction with service employees have no effect on the intention to use a robo-advisor. This research can support robo-advisor service providers and regulators in designing services and improving the adoption of robo-advisors. The study should also shed light on the future of distance services. 

https://link.springer.com/content/pdf/10.1057/s41264-023-00229-5.pdf

Antecedents of robo-advisor use intention in private pension investments: an emerging market country example

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Determinants of customer satisfaction in chatbot use: evidence from a banking application in Turkey

QR code m-payment from a customer experience perspective

Antecedents of robo-advisor use intention in private pension investments: an emerging market country example