Exploring the behavioral intentions to use AI-based chatbots for apparel shopping
- UNCG Author/Contributor (non-UNCG co-authors, if there are any, appear on document)
- Mon Thu A. Myin (Creator)
- Institution
- The University of North Carolina at Greensboro (UNCG )
- Web Site: http://library.uncg.edu/
- Advisor
- Kittichai (Tu) Watchravesringkan
Abstract: In the retail industry, AI chatbots have played a vital role by offering 24/7 customer services, enhancing sales through prompt and accurate responses to customers’ questions, and providing personalized product recommendations based on customers’ preferences (Ashfaq et al., 2020). Despite the significant impact of AI chatbot technology on the apparel retail industry, its coverage is still nascent in existing apparel and retail literature. Specifically, the lack of studies has hindered our understanding of consumers’ antecedents (reasons for and reasons against) and consequences (willingness to buy and eWOM) regarding attitudes toward using AI chatbots, along with the moderating effect of technology familiarity. To address this gap, this dissertation developed and tested a conceptual model of the potential antecedents and consequences of consumers’ attitudes toward using AI chatbots. Specifically, three primary objectives of the study are: (1) to examine relationships between reasons for (perceived chatbot service quality) factors, reasons against (perceived chatbot barriers) factors, and attitudes toward using AI chatbots; (2) to investigate relationships between consumers’ attitudes toward using AI chatbots and their behavioral intentions as measured in terms of willingness to buy apparel with the help of AI chatbots and eWOM; and (3) to examine the moderating role of technology familiarity on the relationship between reasons for (perceived chatbot service quality) factors, reasons against (perceived chatbot barriers) factors, and attitudes toward using AI chatbots. Data were collected from 717 participants through a self-administered questionnaire distributed on Amazon Mechanical Turk (MTurk), an online panel. After careful screening, the final sample consisted of 632 usable responses for statistical analysis. Among participants, 35.1% were female, 64.9% were male. The majority (32.1%) were aged between 26 and 30. In addition, the largest proportion of participants identified as White (90.2%). A total of 58 measurement items were adapted from previous studies and assessed using a 5-point Likert-type scale. The two-step approach, as outlined by Anderson and Gerbing (1988), was employed using Mplus version 8 to establish both measurement and structural models. The confirmatory factor analysis (CFA) was employed first. After the measurement model was established, the path analysis was performed to test all hypothesized relationships using the structural equation model (SEM). Results showed that reasons for factors, such as responsiveness, reliability, and assurance positively influenced attitudes toward using AI chatbots. Conversely, there were no positive relationships between most reasons against factors, such as usage barrier, risk barrier, value barrier, image barrier, and attitudes toward using AI chatbots. While this study identified a significant positive relationship between tradition barrier and attitudes toward using AI chatbots, the result did not align with the proposed hypothesis. Therefore, this study highlighted the significance of responsiveness, reliability, and assurance as important factors influencing consumers' adoption of AI chatbots for apparel shopping. In contrast, this study suggested that all five barriers, namely usage barrier, risk barrier, value barrier, image barrier, and tradition barrier, may not be important factors in either rejecting or accepting AI chatbots for apparel shopping. Furthermore, the results revealed that attitudes toward using AI chatbots positively influenced both willingness to buy apparel with the help of AI chatbots and eWOM. Thus, the study suggested that consumers with positive attitudes toward AI chatbots are more likely to use them and share favorable reviews and comments about AI chatbots on social media and other online platforms. Subsequently, consumers’ positive reviews may encourage other online shoppers to engage with AI chatbot services offered by apparel brands. The results also indicated a positive moderating effect of technology familiarity on the relationship between reliability and attitudes toward using AI chatbots. In addition, the moderating effect of technology familiarity on the relationship between tradition barrier and attitudes toward using AI chatbots was negatively significant. This dissertation provides significant contributions to the literature by developing and testing a research model that investigates the antecedents and consequences of attitudes toward using AI chatbots for apparel shopping. Moreover, the findings also provide empirical evidence for the moderating role of technology familiarity on attitudes toward using AI chatbots. Practical implications are also provided. Additionally, this dissertation addresses limitations and suggests future research directions.
Exploring the behavioral intentions to use AI-based chatbots for apparel shopping
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Created on 5/1/2024
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Additional Information
- Publication
- Dissertation
- Language: English
- Date: 2024
- Keywords
- AI Chatbots, Apparel Shopping, Behavorial Reasoning Theory, Consumer Behaviors
- Subjects
- Artificial intelligence $x Industrial applications
- Clothing trade $x Data processing
- Consumer behavior