Abstract
The study sought to investigate the service quality of chatbots on telecom service providers' websites and apps. The research model was based on an integration of the expectation confirmation model of IS, the E-S-Qual model, and the technology anxiety construct. The study used a quantitative research design. Data were collected from 263 chatbot users; however, after data cleaning, 255 responses were retained. Data analysis was done using structural equation modelling. The results show significant relationships between confirmation and perceived usefulness, as well as confirmation and satisfaction. Additionally, efficiency and privacy constructs of the E-S-Qual model have significant effects on satisfaction. Post-use confirmation, efficiency, and privacy have the most significant impacts on chatbot usage satisfaction of customers on telecom service providers' websites and apps. Technology anxiety could not significantly mediate the relationship between the E-S-Qual model constructs and post-use confirmation.
TopIntroduction
Advancements in artificial intelligence (AI) have created significant interest in chatbot (Chhabria, 2022). A chatbot can be described as software that interacts with people using AI (Albayrak et al., 2018). The Chatbot has emerged as an effective tool to address the user queries in an automated, most appropriate and accurate way (Kumar et al., 2022). Technologies that connect customers with a company are characterized by their ability to serve the personalized needs of customers (Venkatesan, 2017; Heavin and Power, 2018). Moreover, these chatbots to interact with humans through text or voice messages Alan (Ramaditiya et al., 2021), answer customers’ questions Alan (Kohli et al., 2018), and stimulate interactions with humans (Banu & Patil, 2020). a recent surge in interest in human-chatbot relationships in academia, industry, and public sectors (Aoki, 2020; Barnett et al., 2021). Chatbots have gained popularity in various fields (Akhtar and Neidhardt, 2019; Liu, Li and Xiang, 2022). They help to automate customer service, reduce response time, and provide personalized recommendations (Bhardwaz & Kumar, 2023).
Prior research has investigated how various communication aspects of AI chatbots, including empathy, assertiveness, and customizing content, affect how believable and trustworthy users find them. For example, a study by Lee & Chan (2023) established credibility in AI Chatbots importance of customization, communication competency and user satisfaction. Also, in education studies have been conducted on use of AI chatbots in the context of self-directed learning (Esiyok et al., 2024). This study found that individuals' comfort with technology (ICT self-efficacy) only predicts how easy they find chatbots use. Further, Joshi (2024) studied the role of AI-powered chatbots in project stakeholder engagement as critical aspect of successful project management. The study identified that chatbots enhance communication by offering stakeholders immediate, personalized responses, thereby reducing response times, and improve the efficiency of information exchange.
Jin and Youn (2023) examined the associations among AI-powered chatbots’ anthropomorphism (human-likeness, animacy, and intelligence), social presence, imagery processing, psychological ownership, and continuance intention in the context of Human-AI-Interaction. Their results from a path analysis using LISREL 8.54 showed that consumers’ perceived human-likeness of AI-powered chatbots is a positive predictor of social presence and imagery processing. The empirical findings entail practical implications for AI-powered chatbot developers and managerial implications for commercial brands such that (1) increasing anthropomorphism of chatbots and inducing the sense of being co-present with the chatbots are important factors AI-chatbot designers and developers need to consider and (2) inducing vivid visualization of the products endorsed by the chatbots is an important feature to understand.
Key Terms in this Chapter
Ethical Considerations: Bias, transparency, and responsible use of chatbots in the telecommunication industry.
Machine Learning (ML): A field in computer science that focuses on developing algorithms that can learn and improve from data without being explicitly programmed (Mahesh, 2020 AU133: The in-text citation "Mahesh, 2020" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ; Bertolini et al., 2021 AU134: The in-text citation "Bertolini et al., 2021" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ). The aim is to create intelligent machines that can learn from experience and make predictions or decisions.
Artificial Intelligence (AI): The simulation of human intelligence in machines that are programmed to think and learn like humans (Dwivedi et al., 2019 AU120: The in-text citation "Dwivedi et al., 2019" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ; Fjelland, 2020 AU121: The in-text citation "Fjelland, 2020" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ; Gkinko, 2022 AU122: The in-text citation "Gkinko, 2022" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ; Kasneci et al., 2023 AU123: The in-text citation "Kasneci et al., 2023" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ). It involves the development of algorithms and computational models that enable computers to perform tasks that typically require human intelligence, such as problem-solving, pattern recognition, speech recognition, decision-making, and language understanding (Chan, 2023 AU124: The in-text citation "Chan, 2023" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ; Sarker et al., 2021 AU125: The in-text citation "Sarker et al., 2021" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ).
E-S-Qual Model: Evaluates the service quality of chatbots based on efficiency, fulfillment, system availability, and privacy (potentially including empathy).
Natural Language Processing (NLP): Enables chatbots to understand and respond to user queries in natural language (Chowdhary & Chowdhary, 2020 AU135: The in-text citation "Chowdhary & Chowdhary, 2020" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ; Khurana et al., 2023 AU136: The in-text citation "Khurana et al., 2023" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ).
Empathy: The ability of the chatbot to understand and respond to user emotions in a supportive manner.
Dialogue Management: A component of spoken dialogue systems and chatbots, responsible for guiding the conversation and determining the system's responses (Carfora et al., 2020 AU131: The in-text citation "Carfora et al., 2020" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ). It essentially acts as the brain behind the conversation, controlling the flow, understanding the user's intent, and generating appropriate responses (Reimann et al., 2023 AU132: The in-text citation "Reimann et al., 2023" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ).
User Experience (UX): A broad term that encompasses all aspects of a user's interaction with a product, service, or system (Luther & Tiberius, 2020 AU139: The in-text citation "Luther & Tiberius, 2020" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ). It focuses on how users feel while interacting with the chatbot, aiming for a smooth and satisfying experience (Alomari et al., 2020 AU140: The in-text citation "Alomari et al., 2020" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ).
Confirmatory Factor Analysis (CFA): A statistical technique used in research to test whether a pre-defined model of latent variables and observed variables fits the collected data (Marsh et al., 2020 AU128: The in-text citation "Marsh et al., 2020" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ). It helps confirm if the relationships expected between different groups of variables actually exist.
Customer Service Chatbots: Specific type of chatbot designed to interact with customers through text or voice, providing automated support and assistance (Sheehan et al., 2020 AU129: The in-text citation "Sheehan et al., 2020" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ; Nicolescu & Tudorache, 2022 AU130: The in-text citation "Nicolescu & Tudorache, 2022" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ). They are becoming increasingly popular tools for businesses to improve customer service efficiency, availability, and personalization.
Service Quality Constructs: Fundamental building blocks used to assess and understand the perceived quality of a service(Nunkoo et al., 2020 AU137: The in-text citation "Nunkoo et al., 2020" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ; Lin et al., 2021 AU138: The in-text citation "Lin et al., 2021" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ) . They represent specific aspects of the service experience that customers evaluate when forming their overall impression. By identifying and measuring these constructs, organizations can gain valuable insights into how they can improve their service offerings and deliver greater satisfaction to their customers.
ChatBot: Computer program designed to simulate conversation with human users through text or voice interaction (Adamopoulou & Moussiades, 2020 AU126: The in-text citation "Adamopoulou & Moussiades, 2020" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ; Følstad et al., 2021 AU127: The in-text citation "Følstad et al., 2021" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. )
User Interface (UI): The visual and interactive elements that users directly engage with when using a digital product or service (Alomari et al., 2020 AU141: The in-text citation "Alomari et al., 2020" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ). It's essentially the “look and feel” that enables users to control, navigate, and interact with the system. Defines the way users interact with the chatbot, including text, voice, or graphical interfaces.