The Impact of AI-Powered Try-On Technology on Online Consumers’ Impulsive Buying Intention: The Moderating Role of Brand Trust

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Overall, the paper presents an interesting and well-structured study on AI-powered try-on technology and impulse buying. The theoretical framework and empirical methodology are sound, and the findings provide useful insights for e-commerce and digital marketing. However, the paper could be significantly improved by clarifying its unique research contribution, reducing redundancy, providing more actionable managerial implications, and offering more precise future research directions. Below I offer some suggestions for refining the paper.

Intro:

The introduction provides a broad discussion of AI in digital marketing but fails to articulate a clear problem statement early on, making it unclear what the core research focus is. The authors should establish the research problem within the first few paragraphs to ensure clarity. Furthermore, the introduction discusses AI, AR, VR, and other emerging technologies interchangeably, yet the study focuses specifically on AI-powered try-on technology. The discussion should be more focused on virtual try-on technology rather than a broad overview of AI in digital marketing. The claim that existing studies mainly focus on consumer satisfaction rather than impulsive buying behavior lacks sufficient direct citations or examples to support this assertion. Similarly, the statement that “there is a notable lack of research examining how AI-powered integration technologies influence impulsive online buying behavior” is misleading, as existing studies on AI’s role in impulsive buying do exist. The authors should strengthen their justification by explicitly citing gaps in prior research rather than making general claims. Furthermore, while the study extends the Stimulus-Organism-Response (S-O-R) model, it does not adequately justify why this model is appropriate for analyzing AI-powered try-on technology. The relevance of the four key features of AI-powered try-ons (visual vividness, interactive control, personalized configuration, ease of use) is introduced but not clearly explained in relation to impulsive buying. The authors could provide definitions and their direct connection to the research problem to enhance clarity. Also, the introduction claims that the study makes contributions by extending the S-O-R model, shifting focus to young Chinese consumers, and providing strategic insights for e-commerce platforms. However, extending a model and focusing on a specific consumer segment are not necessarily novel contributions unless framed in a way that highlights their unique value. The research questions are broad and could be reframed for precision. For example, instead of “How does AI-powered try-on technology stimulate impulsive buying by shaping consumers’ perceived value?” a more specific approach would be: “Which aspects of perceived value (utilitarian, hedonic, immersion) mediate the relationship between AI-powered try-on technology and impulsive buying behavior?” Finally, the mention of chapters (e.g., “Chapter 2 provides a theoretical overview...”) suggests a thesis or dissertation format rather than a journal article, which should be revised for coherence with journal publication standards.

 

Lit review

The literature review assumes that the SOR model is the most appropriate framework but does not critically evaluate its strengths and limitations in the context of AI-powered try-on technology. While the literature review presents multiple sources, it does not compare them critically. For instance, it cites several studies on VR, AR, and AI-driven personalization but does not highlight differences in findings, contradictions, or areas needing further exploration. The literature review briefly states that prior studies have used the SOR model to examine social presence, omnichannel retailing, and VR shopping but does not clearly justify why it is ideal for AI-powered try-ons. Instead of simply stating that the SOR model has been used in similar studies, the authors should explain why it is particularly relevant to AI-driven try-ons and provide a brief comparison with alternative frameworks (e.g., Technology Acceptance Model, Uses and Gratifications Theory) to justify its selection. The discussion on perceived value describes utilitarian, hedonic, and immersion factors but does not clearly link these to AI-powered try-ons. The cognitive and emotional pathways through which AI-powered try-ons influence impulsive buying are not fully explained. The authors should clarify how AI-powered try-ons create utilitarian value, such as reducing size uncertainty, providing better product visualization, and decreasing return rates. Similarly, the impulse buying behavior section describes general factors affecting impulse buying but does not sufficiently focus on AI-driven try-ons. The examples provided (IKEA, Sephora, ZARA) discuss technological advancements but do not directly explain how AI enhances impulsive purchasing beyond traditional e-commerce tactics. Moreover, there is repetition between sections, particularly when discussing AI-powered try-ons and their impact on impulsive buying. Some concepts, such as interactivity and immersion, appear in multiple sections (e.g., Sections 2.2 and 2.3), creating redundancy. To improve flow and readability, the authors could consolidate overlapping discussions.

 

Hypotheses

The hypotheses claim that AI-powered try-on features increase impulse buying but do not explain why these specific features trigger impulsive purchases. The discussion assumes a direct relationship between each feature and impulse buying without incorporating consumer psychology theories, such as loss of self-control, fear of missing out (FOMO), or immediate gratification (e.g., Baumeister, R. F. (2002). Yielding to temptation: Self-control failure, impulsive purchasing, and consumer behavior. Journal of Consumer Research, 28(4), 670-676.; Przybylski, A. K., Murayama, K., DeHaan, C. R., & Gladwell, V. (2013). Motivational, emotional, and behavioral correlates of fear of missing out. Computers in Human Behavior, 29(4), 1841-1848. Dhar, R., & Wertenbroch, K. (2000). Consumer choice between hedonic and utilitarian goods. Journal of Marketing Research, 37(1), 60-71.) The authors should clarify why each feature specifically drives impulsive behavior rather than merely improving decision-making efficiency. Also, the hypotheses H3a–H3h (mediators) and H4a–H4c (moderators) are interrelated, but their distinctions are not well explained. While H3a–H3h focus on how AI-powered try-on features shape perceived value, leading to impulse buying, H4a–H4c examine brand trust as a moderator. However, the hypotheses do not specify how brand trust changes the strength of these relationships: whether it amplifies or weakens them. The authors should clearly articulate whether brand trust strengthens or weakens the influence of perceived value on impulse buying and whether its effect differs for utilitarian, hedonic, or immersive experiences.

Method:

The on-site trial involving 20 participants is mentioned briefly but lacks a clear explanation of its purpose: whether it was conducted to validate the realism of AI-powered try-ons or to refine the experimental design. In addition, there is no discussion on how insights from this trial influenced the main study structure. Participants were asked to use the app for 15 minutes and submit three screenshots, but it is unclear what specific instructions they were given. Were they free to explore the application on their own, or did they follow a structured task, such as trying on specific categories of clothing? Furthermore, the study does not clarify whether researchers verified participant engagement beyond the submission of screenshots. It would strengthen the methodology section to provide more details on the try-on process, including how participant interactions were monitored. The study also cites university students’ purchasing power as a justification for their selection as participants, but it does not explain why impulse buying is particularly relevant for this demographic. Are students more prone to impulsive purchases compared to other consumer groups? Addressing these questions would enhance the rationale for participant selection and the study’s overall validity.

 

Discussion:

The discussion does not clearly position the study as addressing a specific research gap. While it asserts that AI-powered try-on technology influences impulse buying, it fails to articulate what has been previously overlooked in the literature. To strengthen the contribution, the authors should explicitly state what this study does differently from prior research and how it advances existing knowledge.

The managerial implications section lacks specificity and could be more actionable. For example, “Fashion brands should therefore prioritize optimizing these elements in virtual try-on applications to strengthen purchase motivation,” is too general as it does not specify how brands should optimize these elements or what concrete strategies they should implement.

Similarly, the future research directions are too vague. The paper suggests cross-cultural studies, demographic variations, and longitudinal research, but it does not explain why these aspects are relevant. For instance, the suggestion that “Future research could explore cross-cultural variations to enhance the generalizability of the findings” would benefit from more specificity. What cultural differences are likely to influence consumer reactions to AI-powered try-ons? How might adoption differ between Western and Asian markets? Offering more concrete and theory-driven ideas for future research would strengthen this section and provide clearer guidance for scholars interested in building on this work.

Comments on the Quality of English Language

Typos:

You need a space here: Table3.Results of construct validity and reliability analysis.

Cronbachα" appears multiple times without a space after

t- value should be "t-value" (remove extra space)

Some citations are inconsistent in format. Ensure spacing, capitalization, and ordering follow the journal’s reference style.

Author Response

See annex for details.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

I have read the manuscript carefully and believe it can be accepted after major revisions. Please see my comments below:

1. The abstract is well-written.
2. Line 41: It would be better to avoid discussing COVID-19 again, as it may not be central to the study's current focus.
3. It would improve the flow if the research hypotheses were presented after the explanation of the SOR model, rather than in the methodology section.
4. The target demographic of Chinese university students raises a question: How can the authors generalize the findings of this study to other populations?
5. The description of how data collection was conducted is not clear and needs improvement.
6. The descriptive results should include tests for the normality of the data. Checks for skewness and kurtosis are necessary. For reference, consider the paper: “Applying the UTAUT Model to Understand Factors Affecting Micro-Lecture Usage by Mathematics Teachers in China,” Mathematics, vol. 10, no. 7, pp. 1–20, 2022. https://doi.org/10.3390/math10071008.
7. It would be beneficial if the discussion and conclusion sections were separated to enhance clarity.

Author Response

See annex for details

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

dear editor, dear author, i have read the revised manuscript carefully and find that authors has been revised the manuscript well. now, manuscript ready to published.

well done.