Consumer & Market Research on Sustainable Vehicle Adoption
From an honours bachelor thesis to a peer-reviewed academic chapter: self-directed learning, advanced statistical methods, and consumer insights applied to EV/HEV/PHEV adoption.
Type
Individual research project / published research
Area
Consumer Insights · Quantitative Research
Tools
Google Forms · R · SPSS · AMOS · Excel
Techniques
Survey research · SEM · CFA · MANOVA · Logistic regression · Consumer profiling
Output
Research + academic publication
Value
Individual project where I analysed motivations, barriers, and adoption profiles for electric and hybrid vehicles using primary survey data, SPSS/AMOS, SEM/CFA, MANOVA, and regression to extract transferable consumer insights.
Highlight
I did not stop at delivering a strong thesis. I used it as a platform to learn more advanced methods, raise the analytical bar, and turn the work into a peer-reviewed publication.
Honours bachelor thesis
Primary survey responses
Sustainable-vehicle cases for comparison
Age odds ratio in logistic model
Executive summary
The value of this project does not lie only in the applied sector. It lies in the way the work evolved: recognising that the initial brief was not enough, learning new techniques when the problem required them, and turning an academic project into a higher-standard outcome.
Business context
The adoption of sustainable vehicles does not depend only on technology, price, or incentives. It also depends on perceived barriers, social norms, perceived control, purchase intention, and consumer behaviour. For companies, institutions, and brands, understanding who adopts and why is key to designing communication, product, infrastructure, and incentives.
My role
Individual research project that started as a bachelor thesis and evolved into a peer-reviewed publication. It received 10/10 with honours, was nominated for Best Thesis in the Economics Department, and I proactively expanded the methodological scope by learning advanced statistical techniques to strengthen the work.
Data & methods
- Primary research with a 358-response survey.
- Non-probabilistic sample design with intentional recruitment of sustainable-vehicle users to enable group comparison.
- Data cleaning and variable recoding in R: income, age, vehicle age, and Coche_Eco dummy.
- CADM model as the conceptual base.
- SEM and CFA to validate relationships and measurement.
- Logistic and linear regression to profile adoption and vehicle renewal.
- Non-parametric tests and chi-square tests for group comparison.
- MANOVA to compare behavioural differences.
- R/RStudio, SPSS, AMOS, and Excel.
Research questions
- Which psychological factors explain the decision to acquire a sustainable vehicle?
- Who are current adopters in sociodemographic terms?
- Are there psychological and behavioural differences between adopters and non-adopters?
Why this project matters
This case shows initiative, rigour, and learning velocity. Automotive appears as the applied context, but the transferable capability is consumer insight, advanced analysis, and translation into decisions.
Self-directed learning
To go beyond descriptive analysis and basic regression, I independently studied Structural Equation Modelling, Confirmatory Factor Analysis, and MANOVA. The goal was not to add complexity for its own sake, but to choose methods that better answered the research questions.
- SEM to analyse relationships between psychological adoption factors.
- CFA to evaluate the validity of the measurement model.
- Logistic regression to profile sustainable-vehicle adopters.
- MANOVA to compare behavioural differences between adopters and non-adopters.
This experience taught me how to learn technical methods independently, assess whether they were appropriate, and translate statistical results into practical recommendations.
Process
- 01Define the adoption problem from a consumer-behaviour perspective.
- 02Review literature on TPB, NAM, CADM, and sustainable mobility.
- 03Design the conceptual model.
- 04Collect and prepare the data.
- 05Validate the psychological model with SEM.
- 06Refine the measurement through CFA.
- 07Build the adopter profile through logistic regression.
- 08Compare profiles with MANOVA.
- 09Translate results into implications for companies and the public sector.
Key findings
- Adoption cannot be explained only by environmental awareness.
- Perceived control and perceived feasibility are key.
- Infrastructure, price, uncertainty, and trust shape intention.
- Adopters show differentiated patterns that enable segmentation.
- In the socioeconomic analysis, income and age are associated with adoption/renewal; habitat is not significant after controlling for those variables.
- The sample was designed to compare profiles, not to estimate the real share of sustainable vehicles in Spain.
- The challenge is not only to sell sustainable vehicles, but to make the alternative feel viable, tangible, and socially normalised.
Business implications
- For manufacturers: communicate ease, trust, range, total cost, and uncertainty reduction.
- For dealerships: sales arguments based on real consumer barriers.
- For public institutions: incentives and infrastructure as psychological-friction reducers.
- For marketing: segment by motivations and barriers, not only demographics.
Although the case focuses on mobility, the analytical logic is transferable to any category where adoption depends on price, perceived value, consumer barriers, trust, and positioning.
Limitations
- Academic context.
- Non-probabilistic sample limited to a specific population.
- Intentional oversampling of sustainable-vehicle users; not representative of the Spanish market.
- Variables such as income, age, and vehicle age are recoded from ranges using class marks.
- Results are subject to the survey design.
- The publication validates rigour, but it is not equivalent to professional automotive experience.
What this project proves
I learn independently when the problem requires it.
I do not settle for the minimum deliverable.
I can raise an academic project to a publishable standard.
I connect consumer behaviour, statistics, and business decisions.
I can translate complex analysis into understandable implications.
What I would do next
- Replicate the study with a nationally representative sample.
- Incorporate actual sales or configurator data.
- Create actionable segments for campaigns.
- Test messaging through A/B experiments.
- Use machine learning to model adoption propensity.
- Develop a barrier-by-segment dashboard.
Assets
Suggested visuals
CADM model diagram.
Methodology flow.
Survey sample card.
SEM/CFA conceptual diagram.
Adopter profile card.
Business implications grid.
From thesis to peer-reviewed chapter timeline.
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