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The purpose of this study is to investigate the impacts of Chinese young consumers’ traits (fashion leadership, quality consciousness, price consciousness, environmental consciousness and social media usage) as the external factors on their behavioral intention toward using clothing subscription rental services.

Quantitative research was conducted, and empirical data were collected from 255 Chinese college students. Structural equation modeling was conducted to test the proposed hypotheses.

The study offered convincing evidence that fashion leadership, price consciousness and social media usage are the three main drivers of Chinese young consumers’ favorable attitudes toward clothing subscription rental services, which together with social norms further lead to their intention to use clothing subscription rental services. Contrary to expectations, the study showed that consumers’ quality consciousness and environmental consciousness have no significant impact on consumer attitudes toward clothing subscription rental services. Moreover, the findings of the study demonstrated the impact of attitude and subjective norms on Chinese young consumers’ intention to use clothing subscription rental services.

This study contributes to the literature by integrating additional factors (fashion leadership, quality consciousness, price consciousness, consumer environmental knowledge, social media usage) into the traditional theory of reasoned action model to investigate how Chinese young consumers’ characteristics impact their attitudes and how their attitudes and subjective norms impact their intention toward using clothing subscription rental services.

Driven by Davis’s (1989) technology acceptance model (TAM) and Westaby’s (2005) behavioral reasoning theory (BRT), the purpose of this study is to develop and test a conceptual model and examine consumers’ acceptance of artificial intelligence (AI) chatbots for apparel shopping.

Data from 353 eligible US respondents was collected through a self-administered questionnaire distributed on Amazon Mechanical Turk, an online panel. Confirmatory factor analysis and path analysis were used to test all hypothesized relationships using the structural equation model.

The results show that optimism and relative advantage of “reasons for” dimensions have a positive and significant influence on perceived ease of use (PEU), while innovativeness and relative advantage have a positive and significant influence on perceived usefulness (PUF). Discomfort and insecurity have no significant impact on PEU and PUF. However, complexity has a negative and significant impact on PEU but not on PUF. Additionally, PEU has a positive influence on PUF. Both PEU and PUF have a positive and significant influence on consumers’ attitudes toward using AI chatbots, which, in turn, affects the intention to use AI chatbots for apparel shopping. Overall, this study identifies that optimism, innovativeness and relative advantage are enablers and good reasons to adopt AI chatbots. Complexity is a prohibitor, making it the only reason against adopting AI chatbots for apparel shopping.

This study contributes to the literature by integrating TAM and BRT to develop a research model to understand what “reasons for” and “reasons against” factors are enablers or prohibitors that significantly impact consumers’ attitude and intention to use AI chatbots for apparel shopping through PEU and PUF.