Impacts of Consumers’ Heterogeneity on Decision-Making in Electric Vehicle Adoption: An Integrated Model
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsDear Author(s),
Your work presents a novel and commendable approach to modelling consumer attitudes toward EV adoption using an integrated framework that combines ABM, SML, and multiple behavioural theories. The segmentation of consumers and incorporation of social interactions, environmental beliefs, and external events represents a significant advancement in the field. We particularly appreciate the empirical grounding of your model using UK ONS data, as well as your innovative efforts to validate it at the individual level.
However, while the manuscript offers significant contributions, we believe that certain revisions are necessary before it can be considered for publication. In particular:
- Abstract
The abstract would benefit from briefly summarizing key findings with more specificity. For instance, what distinct segments were found? Which socio-demographic factors were most predictive? This helps readers quickly grasp the main empirical contributions.
- Introduction
* The first paragraph could be more focused. It starts broadly but jumps quickly into complexity without a clear anchor. So, start with a more pointed problem statement about why modelling consumer decision-making in EV adoption is particularly challenging.
* The introduction refers to TPB, SIT, and DOI but treats them somewhat descriptively. More emphasis could be placed on how previous studies used these theories inadequately or in isolation. Include specific limitations of prior ABM or consumer behaviour models that the present study addresses.
* The distinction between how ABM and SML contribute to the model is not entirely clear. Do they serve complementary roles (e.g., ABM simulates and SML classifies), or are they integrated in a novel way? Consider briefly clarifying their individual contributions.
* Presenting key findings in the introduction is helpful, but it would be more impactful to list them under a distinct “This study contributes in three ways…” format for clarity and emphasis.
* “This complexity is especially apparent in innovation adoption, especially in electric vehicles (EVs) field…” → Repetitive use of “especially.”
- Research Gaps
* You mention reviewing 441 articles but give few details about inclusion/exclusion criteria beyond keyword relevance. So, briefly explain how the 16 articles were selected (e.g., screening criteria, timeframe, relevance to ABM + EV adoption).
* You rightly mention that theoretical foundations are often lacking, but this point deserves more emphasis and a sharper critique. Strengthen this point.
* The four-factor structure is excellent, but transitions between them are somewhat abrupt. Use a bridging sentence to introduce the four areas.
* Consider removing redundancy: Phrases like “consumer behaviours regarding EV adoption in ABM applications” are slightly repetitive.
* Close with a strong summary of the identified research gap.
- Construction of Agents’ Decision-Making Mechanisms Using Control Experiment
* You state: “The construction of the mechanisms are guided by the multi-theoretical framework integrating the theories of TPB, SIT, and DOI.” This is strong—but consider adding a sentence linking each theory to a specific functional module or phase in the model development process.
- Experimental Results
* The structure is excellent, with clear subsections and logical transitions. You successfully align empirical results with theory (DOI, TPB, SIT), which strengthens the conceptual underpinnings.
* There are occasional redundancies and dense phrasing that could be streamlined for better flow.
* The sentence “Post-2020, sustaining growth may require targeted interventions...” is important— list concrete examples.
* "4.3.1 – Increasing Positive Environmental Beliefs" Great setup of scenario-based experimentation. The phrase “highlighting a barrier to influencing these groups” could be followed by a brief recommendation.
* When referencing changes from 0.58 to 1.74 and 2.9, consider explaining what these scaled coefficients represent practically for decision-makers.
* "4.3.3 – Combined Influence:" The drop in early majority’s attitudes despite interventions is intriguing—consider emphasizing this anomaly and proposing potential reasons (e.g., overexposure, reactance).
- Conclusion
* The current conclusion is overly long and reads more like a continuation of the discussion section. A good conclusion should distill key insights, highlight the main contributions, and point to future work in a concise manner. so, consider trimming repetition and moving detailed explanations (e.g., policy recommendations and technical validation procedures) to the Discussion or Implications section. Aim for a clearer structure with a strong summary of findings, contributions, implications, limitations, and future directions.
* The section would benefit from a clearer structure. While the content is rich, the narrative occasionally drifts between empirical findings, methodological contributions, and practical applications without smooth transitions. Suggestion: Organize the conclusion using logical subsections or a structured paragraph sequence: summary of objectives and findings, theoretical contributions, methodological innovations, practical implications, limitations, and future research directions.
* The three highlighted contributions are valuable but can be stated more succinctly and explicitly. The phrasing is at times dense and requires unpacking by the reader. So, use bullet points or numbered items to emphasize each unique contribution clearly.
* The policy recommendations, while insightful, are too detailed for the conclusion. For instance, suggestions like “eco-rebates tied to emissions” or “EV awareness campaigns in neighbourhoods” are more suitable for the practical implications section. Suggestion: Retain one or two high-level strategic implications and move specific tactics to a prior section.
- The discussion on cultural limitations is appropriate and well-justified. However, the conclusion could more directly suggest how researchers might approach cross-cultural adaptation of the model.
- References
The manuscript would benefit from a stronger engagement with the most recent literature on EV adoption, particularly studies published between 2022 and 2025. While the theoretical framework is well-established, integrating recent findings would enhance the paper’s relevance and situate it more firmly within the current academic conversation. For example, the Journal of Sustainability has published several articles in the past few years exploring emerging factors influencing EV adoption—such as evolving consumer trust, infrastructure maturity, and region-specific policy shifts—which could support or contrast your model’s assumptions and findings.
We encourage you to revise the manuscript by addressing these points. With careful revision, this work has strong potential to make a meaningful impact in the study of innovation adoption and sustainable transportation.
Author Response
Please see the attachment.
Author Response File: 
Author Response.docx
Reviewer 2 Report
Comments and Suggestions for AuthorsThe paper presents a methodologically rigorous and theoretically grounded study of consumer behavior in EV adoption. It is well structured and well presented. I have just some minor comments and suggestions.
Methodological remarks:
The consumer segments (e.g., early adopters, laggards) are treated as static. As consumers can shift segments over time, have the authors thought about the possibility of developing a dynamic segmentation? How would that affect the data collection and the results?
I would suggest discussing this briefly in the conclusion.
Technical remarks:
There are some formatting issues regarding Figure 9.
DOI formatting is not consistent throughout the references, for example:
Ajzen, I. (1985). From Intentions to Actions: A Theory of Planned Behavior. In: Kuhl, J. and Beck-mann, J. eds. Action Control: From Cognition to Behavior. SSSP Springer Series in Social Psychol-ogy. Berlin, Heidelberg: Springer, pp. 11–39. Available at: https://doi.org/10.1007/978-3-642-69746-3_2 [Accessed: 16 January 2024].
de Assis, R.F., Guerrini, F.M., Santa-Eulalia, L.A. & de Paula Ferreira, W. (2023). An agent-based model for regional market penetration of electric vehicles in Brazil. Journal of Cleaner Production, 421. doi: 10.1016/j.jclepro.2023.138477.
Bharadwaj, S. & Menon, A. (2000). Making innovation happen in organizations: individual creativ-ity mechanisms, organizational creativity mechanisms or both? Journal of Product Innovation Man-agement, 17(6), 424–434. Doi: 10.1111/1540-5885.1760424.
Author Response
Please see the attachment.
Author Response File: Author Response.docx
Reviewer 3 Report
Comments and Suggestions for AuthorsReview of „Impacts of Consumers’ Heterogeneity on Decision-Making in Electric Vehicle Adoption: An Integrated Model” manuscript
The manuscript effectively points to critical and under-researched points in the consumer decision-making process for electric vehicles.
The authors' new approach is based on the integration of several theories, aiming to eliminate the weaknesses of other theories by not focusing only on one aspect of behaviour. In this way, the manuscript highlights the limitations of the theory of planned behaviour (TPB), the theory of social influence (SIT) and Rogers' theory of diffusion of innovations (DOI). However, it is not clear how these theories are synthesised in the new model. It would be good to see a framework for the new model that would clarify the operationalisation of the proposed model.
It is not clear what criteria were used to select the 16 articles that formed the basis of the review, as they are of great relevance to ABM applications for EV implementation.
The manuscript examines, among other things, the impact of social interactions and environmental beliefs on attitudes in the new model. However, the authors do not make clear exactly which interactions and influences are meant or how social interactions were simulated in the study. This is also an important point in terms of what strategies should be used for target groups where these social interactions have a strong influence.
The manuscript highlights the potential of ABM to describe behaviour. However, it could also address issues related to the implementation of ABM, such as data requirements and computational complexity.
The chapter on “Construction of Agents' Decision-Making Mechanisms Using Control Experiment” gives a good overview of the methodology, detailing the design, development and validation of the model. The integration of ABM and SML is one of the strengths of the test method used, it would be useful to justify the advantages of this combination over other methods. Furthermore, it could also explain how, despite the validity of the data sources, the dataset used is subject to potential bias.
Consumer segmentation based on the DOI theory is an interesting starting point and through this it focuses well on the heterogeneity of consumers. The question is how the consumer segments were operationalised in the analysis. It would also be useful to discuss how the assumption of staticity of the segments affects the applicability of the model.
Similarly, the use of the optimised model to predict attitudes towards EVs between 2014 and 2030 is a significant achievement. However, it would be worthwhile for the chapter to address the assumptions of the forecasts, as these can significantly affect the results.
The manuscript also discusses the impact of external factors on consumer attitudes. It is not clear how these factors were incorporated into the model. Lack of knowledge and purchase cost are also identified as barriers to adoption. It would be interesting to know how these factors vary across consumer segments.
Overall, targeting segment-specific behaviors is a strong point of the manuscript, as is examining how social interactions and environmental beliefs shape attitudes. The application of the integrated model and the new methodology and its results would provide an opportunity for further segment-specific inferences and strategic recommendations. Another exciting question is how the model can adapt to the continuous changes in the external environment.
The results of the study provide important insights into how environmental beliefs and social interactions influence consumer attitudes, especially among different segments of the population. This segmentation is key to developing targeted marketing strategies and policies to promote the uptake of electric vehicles. However, in addition to the comments described above, an important question is what limitations affect the generalisability of the results.
Author Response
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Author Response File: Author Response.docx
Reviewer 4 Report
Comments and Suggestions for AuthorsThis study proposed an innovative theory-driven Intelligent Body-Based Modeling (ABM) framework based on empirical data focusing on the differential impacts of psychological, social, and environmental factors on early adopters versus the mass population in the adoption process of electric vehicles (EVs). While the research topic is current hotspot, and the paper is innovative in research methodology and theoretical level, reasonable in structure, and in line with the specification for the publication of the paper, there are still some problems that need to be improved:
1. The research background of the Introduction and the relevance of the research topic are portrayed too broadly and lack of depth, for example,
the first two paragraphs of the Introduction mention “consumer characteristics, which add complexity to behavioral modeling, and this complexity is particularly evident in innovation adoption, especially in the field of electric vehicles (EVs),” but there is no further elaboration of the specific mechanisms and principles of the research
. But without further clarification of the exact mechanism and rationale.
2. “Research Gaps” is insufficient to systematically sort out the existing literature, and should systematically review the key findings and consensus of the existing research in the field, and discover the theoretical and methodological gaps in the research, so as to lead to the entry point of this study. In addition, the authors list four groups of key factors in consumers' EV choice decisions; are these four categories equally applicable to other decision-making scenarios?
3. Section 4.3 examines the influence of environmental beliefs on attitudes towards electric vehicles, but there is no introduction or explanation of the concept of “Environmental Beliefs” before this section, which prevents readers from understanding the content of the study.
4. There is the absence of model evaluation in the thesis, for example, the discriminative ability of the prediction model can be evaluated by the ROC curve.
5. Attention should be paid to the format of references
Author Response
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Author Response File: Author Response.docx
Round 2
Reviewer 1 Report
Comments and Suggestions for AuthorsDear Authors,
I would like to extend my sincere thanks and appreciation to you for the time and effort you have invested in revising your manuscript titled "Impacts of Consumers’ Heterogeneity on Decision-Making in Electric Vehicle Adoption: An Integrated Model."
It is clear that you took great care in addressing all of the concerns raised during the previous review. The revised version demonstrates thoughtful consideration of the feedback, resulting in a manuscript that is significantly strengthened in both clarity and rigor. Your work now offers a more robust and nuanced understanding of consumer heterogeneity in the context of electric vehicle adoption, which I believe will make a valuable contribution to the field.
Thank you again for your dedication and responsiveness throughout the revision process.
