Human-Like Chatbot : A quantitative study of the emotional response toward human-to-machine interaction

Abstract: Problem formulation: The problem that the thesis research relates to is the limitations of artificially intelligent chatbots as interlocutors. The emotional component of communication plays an essential role in the customer experience, but many users have a negative attitude toward chatbots due to their lack of humanity and empathy. The potential of the new ChatGPT in changing user attitudes toward chatbots is also being explored. However, the limited data available on recent versions of ChatGPT presents an additional challenge for research in this area.  Purpose: Our study aims to study people's emotional responses to human-like chatbots and their impact on user satisfaction. We also explore whether human likeness is a crucial driver of chatbot preference and how the new ChatGPT can change user attitudes toward them in a positive way.  Theoretical framework: The study's theoretical framework considers various aspects of using chatbots based on artificial intelligence (AI) in marketing. In this context, we observe ChatGPT as a revolutionary breakthrough in customer service, capable of improving customer experience and interaction with customer. We emphasise the emotional component of human-chatbot interactions, investigating customer emotions, attitudes, and trust, as well as the chatbot's capacity for empathy and human-like characteristics. Drawing from this theoretical exploration, we formulate four hypotheses to guide our research. Methodology: This quantitative study involves 79 respondents aged 18 years and over. The online survey was conducted using social media for dissemination. The empirical data obtained were coded and analysed using the SPSS program.  Empirical findings: Our study confirms the hypothesis of diverse emotional responses (H4) and a generally neutral emotional response during chatbot interactions (H3). We also find partial support for the presence of negative emotions (H2), but not for consistent positive emotions (H1). The data indicate a range of emotional responses, highlighting the complexity of human reactions to chatbots. Conclusion: Our research provides an overall picture of users' emotional responses to interactions with chatbots. Users show a variety of emotions, mostly neutral, which can change depending on the interaction. We also discovered the potential of the new ChatGPT in changing user attitudes towards chatbots in a more positive or neutral direction. The study also uncovers factors influencing users' emotional responses, such as age, attitudes, and past experiences. The results can be used to develop more effective marketing and business strategies for interacting with chatbots.