Chatbot Service Research Articles

The determinant factors of continuance use of customer service chatbot in Indonesia e-commerce: extended expectation confirmation theory

Purpose The implementation of customer service chatbots in various industries is increasingly accepted globally. Previous research has not extensively explored the relationship between chatbot disclosure, technology anxiety, chatbot quality, customer experience and customer satisfaction derived from using chatbot customer service in e-commerce. Therefore, this paper aims to examine the determinant factors of customer service chatbot continuance intention by extending the expectation confirmation theory (ECT). The researchers integrate chatbot quality, technology anxiety and disclosure into ECT to comprehensively understand the phenomena. Design/methodology/approach The quantitative study uses the partial least square structural equation model disjoint two-stage approach with a sample of 310 respondents collected using purposive sampling. Findings The study reveals that perceived usefulness, confirmation and satisfaction positively affect customer service chatbot continuance intentions. Moreover, chatbot disclosure can enhance chatbot quality. However, technology anxiety negatively affects chatbot quality. Originality/value This research contributed to adapting customer service chatbots in Indonesian e-commerce, focusing on chatbot quality, technological anxiety and transparency. Furthermore, it underscores the need for clarity, addresses transaction-specific concerns and artificial intelligence-driven customer assistance in the Indonesian market.

Revolutionizing Customer Service : The Impact of Large Language Models on Chatbot Performance

This article examines the transformative impact of Large Language Models (LLMs) on customer service chatbots across various industries. Through a comprehensive analysis of case studies, the article explores the implementation strategies, performance metrics, and technological advantages of LLM-powered chatbots in sectors including e-commerce, telecommunications, financial services, healthcare, and travel. The article highlights the advanced capabilities of LLMs in natural language understanding, response generation, and continuous learning, demonstrating their potential to significantly enhance customer interactions and operational efficiency. The article also addresses key technical challenges in implementing LLM chatbots, such as data privacy, response accuracy, and scalability, offering potential solutions for each. Finally, it discusses future directions for LLM-powered chatbots, including multimodal capabilities, emotion recognition, and predictive customer service, providing insights into the evolving landscape of AI-driven customer engagement.

AniBalloons: Animated chat balloons as affective augmentation for social messaging and chatbot interaction

Despite being prominent and ubiquitous, message-based communication is limited in nonverbally conveying emotions. Besides emoticons or stickers, messaging users continue seeking richer options for affective communication. Recent research explored using chat-balloons’ shape and color to communicate emotional states. However, little work explored whether and how chat-balloon animations could be designed to convey emotions. We present the design of AniBalloons, 30 chat-balloon animations conveying Joy, Anger, Sadness, Surprise, Fear, and Calmness. Using AniBalloons as a research means, we conducted three studies to assess the animations’ affect recognizability and emotional properties (N=40), and probe how animated chat-balloons would influence communication experience in typical scenarios including instant messaging (N=72) and chatbot service (N=70). Our exploration contributes a set of chat-balloon animations to complement nonverbal affective communication for a range of text-message interfaces, and empirical insights into how animated chat-balloons might mediate particular conversation experiences (e.g., perceived interpersonal closeness, or chatbot personality).

Navigating the Customer Experience Landscape: Unraveling the Dynamics of AI-driven Chatbot Services for FMCG Retailers

This paper evaluates the impact of AI-based chatbot services on the FMCG retail sector’s customer satisfaction. The data of 615 respondents was collected from FMCG customers in organized retail shops in and around the Albaha region of Saudi Arabia. The study acquired data from consumers who used chatbot services for the purchase of FMCS products. Seven hypotheses were formulated and tested to explain the relationships between these variables. It was found that responsiveness is related to customer satisfaction and affects the conversational tone. Further, the adopted conversational tone also influenced customer satisfaction, and the customers were able to process it better. Privacy concerns have an important role in how customers communicate online. This study contributes to the choice of FMCG retailers as far as customizing the chatbot services to ensure that their conversational tone is responsive and mindful of privacy matters in an attempt to increase satisfaction. Further this study can be generalized in other geographic regions with similar setup in order to understand the consumer behavior.

Conversational Breakdown in a Customer Service Chatbot: Impact of Task Order and Criticality on User Trust and Emotion

While chatbots are increasingly used for customer service, there is a knowledge gap concerning the impact of Conversational Breakdown in such chatbot interactions. In a 2x4 factorial design online experiment, we studied how Conversational Breakdown impacts user emotion and trust in a chatbot for customer service, given variations in task criticality and breakdown task order. Here, 257 participants were randomly assigned to complete high- or low-criticality tasks with a prototype chatbot for customer service, experiencing Conversational Breakdown for the first, second, third, or none of their tasks. The task set was decided from a 63-participant pre-study. We found significant impact of Conversational Breakdown, including a marked order effect on overall trust, as well as a bounce-back effect on task-specific trust and emotion after subsequent successful task completion. We found no post-interaction effect of Task Criticality. Based on our findings, we discuss theoretical and practical implications and suggest future research.

Impact of Chatbot Service on Bank Performance Based on a Case Study of IBM Corporation

Exploring the impact of chatbot on bank performance helps to identify the potential of this technology to revolutionize the bank's business model and enhance customer experience, while it can improve employee efficiency and provide a scientific basis and guidance for bankers to effectively respond to the increasing competitive market and achieve bank performance growth and sustainable development. This paper delineates the definitions of chatbot and business performance, provides an overview of current research, and examines the influence of chatbots on bank performance. Furthermore, it utilizes IBM's chatbot as a case study to investigate its impact on banking performance, focusing primarily on cost reduction, enhanced customer satisfaction, and improved employee efficiency. At the same time, the banking industry also faces the negative risk impact of chatbot, which mainly includes perceived risk, security threat and moral risk. The paper also concludes with recommendations on the negative risk impacts of smart customer service. This study links chatbot with bank performance to fill the research gap. It also provides an outlook and suggestions for the future development prospects of chatbot in the banking industry.

The Impact of Default Options on Tourist Intention Post Tourism Chatbot Failure: The Role of Service Recovery and Emoticon

Chatbot service failures are inevitable in tourism and hospitality, how chatbot service recovery retains tourists is an issue that cannot be ignored. The nudging effect of default options on consumers' behavior has been demonstrated in marketing. Whether default options can be as a powerful tool for service failure recovery is underexplored. Thus, this study aims to explore whether the default option can improve tourists' willingness to continue using the chatbot after service failure. Hypotheses were tested using three experiments, results showed that the opt-out default option can increase higher tourists' continuous use intention by decreasing tourists' affective effort. This effect is moderated by service recovery and emoticon. Specifically, opt-out default option is more effective to improve continuous usage intention when using informational help and pleading emoticon. These findings enriched theories on chatbot service failure and recovery and provide insight for default options using in chatbot under tourism and hospitality industry.

Emotional expressions of care and concern by customer service chatbots: Improved customer attitudes despite perceived inauthenticity

In customer service, emotional expressions by chatbots are considered a promising direction to improve customer experience. However, there is a lack of comprehensive understanding of how and when chatbots' emotional expressions improve customer attitudes. Although chatbots' emotional expressions of care and concern may feel inauthentic to customers in the inferential path, which can negatively affects customer attitudes, we propose that the positive effect of the affective reactions path can result in a positive effect on customer attitude based on the dual-path view of Emotions as Social Information (EASI). The relative strengths of the two EASI paths can be moderated, and we explored the moderating effects of rational thinking styles (information processing in EASI) and beliefs in computer emotion (perceived appropriateness in EASI). According to EASI, situation can affect the meaning of emotions, so we conducted experiments in two situations. With chatbot identity disclosure, we found that the chatbot's emotional expressions reduce customers' perceived authenticity (reflecting the inferential path in EASI) but ultimately improve customer attitudes. Belief in computer emotions and rational thinking style moderated the negative relationship between emotional expressions and authenticity. With chatbot identity non-disclosure, the chatbot's emotional expressions still improve customer attitudes but with no effect on authenticity. Because there is high likelihood of chatbot identities being discovered by customers, this finding of the moderating effect of perceived humanness on authenticity is highly relevant. Our findings make important contributions to research on computer emotion and service authenticity.

Rethinking feminized service bots: user responses to abstract and gender-ambiguous chatbot avatars in a large-scale interaction study

AbstractCompanies increasingly rely on chatbots to enable efficient and engaging communication with customers. Previous research has highlighted a trend towards female-gendered designs of customer service chatbots, adding to concerns about the reinforcement of outdated gender stereotypes in human-computer interactions. Against this background, the present study explores design alternatives to traditionally gendered chatbot avatars. In an online experiment, N = 1064 participants interacted with a bank service chatbot, where one half saw a gender-ambiguous anthropomorphic face as the chatbot’s default avatar, and the other half an abstract non-anthropomorphic icon. Contrary to earlier studies, which linked anthropomorphism to higher user acceptance, our manipulation of avatars did not significantly alter intentions to use the chatbot. After the interaction, participants could select their preferred avatar image from a set of six, including non-anthropomorphic icons (speech bubbles) and anthropomorphic faces (female, male, gender-ambiguous). While many adhered to their initially viewed image, a clear majority opted for abstract non-anthropomorphic icons. This overall preference was consistent across all user genders, although men were more likely than women to favor a traditionally female-looking avatar. Notably, less than a quarter of participants recognized the gender-ambiguous avatar as such. In accordance with traditional gender binaries, most identified it as either male or female. Those who perceived it as female reported higher intentions to use the chatbot. As a practical implication, our findings advocate for the adoption of more abstract and gender-neutral chatbot designs, as they not only help to avoid problematic stereotypes but also seem to align with customer preferences for non-gendered chatbot interactions.

Customer service chatbot enhancement with attention-based transfer learning

Customer service is an important and expensive aspect of business, often being the largest department in most companies. With growing societal acceptance and increasing cost efficiency due to mass production, service robots are beginning to cross from the industrial domain to the social domain. Currently, customer service robots tend to be digital and emulate social interactions through on-screen text, but state-of-the-art research points towards physical robots soon providing customer service in person. This article explores the feasibility of Transfer Learning different customer service domains to improve chatbot models. In our proposed approach, transfer learning-based chatbot models are initially assigned to learn one domain from an initial random weight distribution. Each model is then tasked with learning another domain by transferring knowledge from the previous domains. To evaluate our approach, a range of 19 companies from domains such as e-Commerce, telecommunications, and technology are selected through social interaction with X (formerly Twitter) customer support accounts. The results show that the majority of models are improved when transferring knowledge from at least one other domain, particularly those more data-scarce than others. General language transfer learning is observed, as well as higher-level transfer of similar domain knowledge. For each of the 19 domains, the Wilcoxon signed-rank test suggests that 16 have statistically significant distributions between transfer and non-transfer learning. Finally, feasibility is explored for the deployment of chatbot models to physical robot platforms including “Pepper”, a semi-humanoid robot manufactured by SoftBank Robotics, and “Temi”, a personal assistant robot.

Optimization Chatbot Services Based on DNN-Bert for Mental Health of University Students

Optimization Chatbot Services Based on DNN-Bert for Mental Health of University Students

Impact of artificial intelligence (AI) chatbot characteristics on customer experience and customer satisfaction

ABSTRACT This study analyzed the characteristics affecting the consumer’s intention to use the chatbot service and customer satisfaction, which is a representative artificial intelligence (AI) technology in the banking industry. The theoretical model was based on the technology acceptance model (TAM). The anthropomorphism and personalization of chatbots were proposed as important characteristics that lead to the intention to use the chatbot service. A research model was constructed and demonstrated whether the characteristics of these chatbots had a significant effect on product knowledge, perceived ease of use, perceived usefulness, and customer satisfaction. The empirical study was conducted on customers with experience in using chatbots. The data were analyzed using Smart PLS 3.0 structural equation modeling (PLS-SEM). As a result of the analysis, the anthropomorphism and personalization characteristics of the chatbot had a significant direct or indirect influence on the consumer’s intention to use and customer satisfaction. This study is expected to provide meaningful insights in discovering innovative services that provide new customer value experiences by artificial intelligence (AI) chatbot services, which are evaluated as innovative technologies in the next stage of online banking and mobile banking.

Impact of System-Level Indicators of Chatbots on Perceived Usefulness and Intention to use for Banking Services

Technology is rapidly mounting, and AI has transformed the financial industry rapidly. The primary objective of the research is to identify factors (like Design, Information Quality, Security, and Facilitating Conditions) that influence a customer to employ chatbot technology in the banking industry. A survey of 250 respondents was carried out utilizing an online questionnaire. The study’s target respondents are customers who use banking services and chatbots through a random sampling method. The structured questionnaire employed in this study comprised two modules: one detailing the attributes of respondents and enterprises and the other containing questions on research constructs. The Stimulus Theoretical Framework and Technology Acceptance Model were tested throughout the study. The model helps to analyze the factors responsible for customer propensity that influence the use of chatbots. The research first conducted an exploratory factor analysis (EFA) to determine the measured data’s structure. The internal consistency of data was tested using Cronbach’s alpha values. Finally, the research hypothesis was assessed using Structural Equation Modeling (SEM). The research findings can help banks to meet the needs of their diverse customer base. The study would help the providers of chatbot services to reduce the complexity of communication and services for the users and make the customers adopt the technology services without any intervention. This develops trust among the consumers, thus making it a valuable contribution

Perception of Tourists on Chatbot Service Usage on Tourism Destination: A Study of Kainji Lake National Park, Nigeria

Quest for satisfying tourists and customers’ expectations across the globe led to the development and application of various information communication technologies (ICT) in the hospitality and tourism industry. These information communication technologies have been adopted by tourism enterprises and managers such as Email, Mobile Phone, Social Media Networking (Facebook, WhatsApp, Zoom, Artificial Intelligence). Also, the quest for providing friendly interaction among tourists and destination managers further led to the development of one of the artificial intelligence algorithms known as chatbot. Chatbots are Artificial Intelligence (AI) test and/or voice-based virtual assistants that aid customers to get what they ask for quickly and accurately through a conversation in natural human language. Chatbots have been reported to be the technology for hotels, travel agencies, and airlines, however, there is no evidence-based research on the factors that explain consumers’ willingness to interact with chatbots. It is on this basis that this research is carried out. The research examined tourists’ usage of chatbots, focusing on the Kainji Lake Park (Nigeria). It is an exploratory research that utilized descriptive statistics to analyze data collected. The findings reveal that tourists are aware of the existence of chatbots as deployed to tourism industry. Also, it found that chatbots enhance destination information search. As an emerging market, stakeholders and management in the tourism industry should integrate chatbots into their main information communication technologies to enhance customer patronage.

Development of AI-generated medical responses using the ChatGPT for cancer patients

Background and objectiveTo develop a healthcare chatbot service (AI-guided bot) that conducts real-time conversations using large language models to provide accurate health information to patients. MethodsTo provide accurate and specialized medical responses, we integrated several cancer practice guidelines. The size of the integrated meta-dataset was 1.17 million tokens. The integrated and classified metadata were extracted, transformed into text, segmented to specific character lengths, and vectorized using the embedding model. The AI-guide bot was implemented using Python 3.9. To enhance the scalability and incorporate the integrated dataset, we combined the AI-guide bot with OpenAI and the LangChain framework. To generate user-friendly conversations, a language model was developed based on Chat-Generative Pretrained Transformer (ChatGPT), an interactive conversational chatbot powered by GPT-3.5. The AI-guide bot was implemented using ChatGPT3.5 from Sep. 2023 to Jan. 2024. ResultsThe AI-guide bot allowed users to select their desired cancer type and language for conversational interactions. The AI-guided bot was designed to expand its capabilities to encompass multiple major cancer types. The performance of the AI-guide bot responses was 90.98 ± 4.02 (obtained by summing up the Likert scores). ConclusionsThe AI-guide bot can provide medical information quickly and accurately to patients with cancer who are concerned about their health.

The Influence of AI Chatbots in Fintech Services on Customer Loyalty within the Banking Industry

The convergence of Artificial Intelligence (AI) and FinTech services has brought about a transformative impact on customer engagement in institutions. AI-powered chatbots have emerged as indispensable tools for enhancing customer experiences within the banking sector. In this era of digital transformation, financial institutions have recognized the vital importance of exceptional service quality and the utilization of chatbots in attracting and retaining customers. Consequently, there is a growing interest in comprehending the role of chatbot service quality in shaping customer loyalty. This study adopts an empirical and descriptive approach, employing a quantitative research method. Data were obtained from 729 respondents through an online survey employing judgemental nonprobability sampling. The gathered data were analysed using Smart PLS- Structural Equation Model (SEM). The study delineates the attributes of AI chatbot service quality and identifies their significant influence on customer loyalty, taking into account the mediating role of customer satisfaction. The findings of this study contribute to the understanding of the relationship between chatbots, customer satisfaction, and loyalty. The study validates the significance of customer satisfaction, enhances the understanding of chatbot effectiveness, explores the unique characteristics of chatbot interactions, and demonstrates how technology-driven customer service can be integrated into loyalty models. Moreover, this study offers practical implications that can guide businesses in optimizing their chatbot strategies. By prioritizing the enhancement of chatbot performance, personalizing interactions, integrating across channels, and monitoring customer satisfaction and loyalty metrics, organizations can elevate customer satisfaction, cultivate loyalty, and achieve positive business outcomes.

Implementation of Anxiety UK's Ask Anxia Chatbot Service: Lessons Learned.

Chatbots are increasingly being applied in the context of health care, providing access to services when there are constraints on human resources. Simple, rule-based chatbots are suited to high-volume, repetitive tasks and can therefore be used effectively in providing users with important health information. In this Viewpoint paper, we report on the implementation of a chatbot service called Ask Anxia as part of a wider provision of information and support services offered by the UK national charity, Anxiety UK. We reflect on the changes made to the chatbot over the course of approximately 18 months as the Anxiety UK team monitored its performance and responded to recurrent themes in user queries by developing further information and services. We demonstrate how corpus linguistics can contribute to the evaluation of user queries and the optimization of responses. On the basis of these observations of how Anxiety UK has developed its own chatbot service, we offer recommendations for organizations looking to add automated conversational interfaces to their services.

It’s better than nothing: The influence of service failures on user reusage intention in AI chatbot

Artificial intelligence (AI) chatbot have become increasingly popular as a tool for improving employee productivity over the last few years. In the early stages of AI chatbot development, exploring the impact of AI chatbot service failures on user reusage intention is useful for coordinating human–computer interaction and optimizing AI chatbot service mechanisms. The extant literature on AI service failures focuses on service recovery and anthropomorphism. There is less literature comparing different types of service failures and their effects. The article includes three studies. First, a randomized group experiment was conducted with 120 respondents. The results showed significant differences in the impact of different AI chatbot service failures on user reusage intentions. Second, an online questionnaire was completed by 386 respondents, the results found specific impact mechanisms of service failures on user reusage intentions. Third, an interview survey was conducted with 15 customers using AI chatbots to verify the findings of Study 1 and Study 2. Furthermore determine the boundary conditions for the unsupported hypotheses through meta-inference. The research enriches the literature on relationship marketing and expands the attribution theory of service failures. In addition, which provides theoretical basis and practical support for companies to reduce adverse effects of service failures.

Chatbot symbolic recovery and customer forgiveness: a moderated mediation model

AR眼镜在遗产旅游中使用时遇到的阻碍因素：个人能力的调节效应通过SEM-PLS过程研究目的系统评估阻碍因素作为技术接受模型（TAM）的先驱方面存在一定的空白。本研究调查了三个阻碍因素, 即不适感、不安全感和风险。此外, 本研究提出了调节变量 - 个人能力（PC）, 并测试其对感知有用性（PU）、感知易用性（PEU）和行为意图（BI）之间关系的影响。研究方法使用量化数据分析验证了通过Smart PLS4使用的调整模型。对327名有效受访者的数据进行了分析。研究发现不适感是影响PU和PEU的显著先驱因素。不安全感和风险分别是PEU和PU的抑制因素。本研究在稀缺文献中贡献了个人能力的调节效应, 积极调节PU和BI之间影响的研究。研究创新本研究通过纳入阻碍因素并探索个人能力在AR眼镜方面的调节作用, 为TAM提供了一种新的拓展。此外, 该研究还使创新公司能够通过用户的反馈来增强其产品和服务的设计。

P‐130: PixelTalk: Knowledge‐Integrated Chatbot Service from Samsung Display

PixelTalk, designed for Samsung Display, enhances security and accuracy in Large Language Models‐based services. It integrates natural language processing, offers knowledge‐integrated responses, and a code assistant. Its architecture uses department‐specific databases and entity vector space for efficient information retrieval, setting a precedent for LLM applications in corporate environments.