User Perception of EV Form: Studying Design Attributes with Surveys and Renders

The problem

Designers rely on intuition about how forms are perceived, and EV buyers may expect different cues from conventional vehicles. Systematic studies linking specific form attributes to user perceptions, particularly in the Indian market, are limited. Controlled variants and careful surveys can provide evidence to guide design.

Objectives

• Identify form attributes and perception dimensions from literature and pilot interviews.
• Create controlled variants in Alias that change one attribute at a time.
• Render variants consistently and design a survey with suitable scales.
• Collect and analyse responses with appropriate statistics.
• Report how attributes relate to perceptions and the limits of the study.

What you need

Key concepts

Semantic differential

A rating scale between opposite adjectives, such as safe and unsafe, used to measure perceptions.

Controlled variants

Design versions that differ only in one attribute, so differences in ratings can be linked to that attribute.

Kansei engineering

A method that links users' emotional responses to product design properties.

Mixed-effects model

A statistical model that accounts for repeated ratings by the same participants and differences between participants.

Step-by-step guide

1. 1

   Review literature and define dimensions

   Review studies on vehicle form perception, product semantics, Kansei engineering and semantic differential methods. Choose perception dimensions such as safety, modernity, robustness and value, and form attributes such as stance, beltline height, surface tension, front lighting graphic and roof line. Run a few pilot interviews to check that the dimensions mean something to Indian EV buyers. Record the source for each chosen dimension.

   Checkpoint: A list of attributes and dimensions supported by literature and pilot interviews.

2. 2

   Plan the experiment

   Decide the experimental design, such as a full or fractional factorial of three or four attributes with two or three levels each, and calculate the number of stimuli. Estimate a sample size for the effect sizes you expect, using pilot data or published studies. Write hypotheses and an analysis plan before collecting data, consider pre-registering them and obtain ethics approval.

   Checkpoint: An approved study plan with hypotheses, design and sample size.

3. 3

   Create the baseline

   Model a neutral baseline EV concept in Alias with no strong brand cues, using a clean curve network and a small number of well-structured surfaces. Check quality with Diagnostic Shading and continuity checks. Keep the attributes you will vary on dedicated, editable curves, for example a single beltline curve and a single roof curve, so later changes stay controlled. Save it as the master file for all variants.

   Checkpoint: A clean baseline concept with documented surface structure and editable attribute curves.

4. 4

   Create variants

   Change one attribute at a time, for example raising the beltline by a set amount or swapping the lamp graphic, by editing the controlling curves and rebuilding the affected surfaces. Measure each change with Evaluate tools, such as distance and deviation, to confirm the intended difference and to show that other attributes have not shifted. Record every variant in a stimulus table.

   Checkpoint: A stimulus table of variants with measured attribute changes and unchanged other features.

5. 5

   Render consistently

   Render every variant with identical lighting environment, camera position, focal length, paint colour and background in VRED, KeyShot or SOLIDWORKS Visualize, ideally from a saved scene file. Inspect the images side by side for unintended differences, such as a reflection that moves and changes how a surface reads. Re-render any variant whose image differs in more than the intended attribute.

   Checkpoint: A render set verified for consistency with a record of the scene settings used.

6. 6

   Build the survey

   Build the survey with randomised stimulus order, semantic differential scales for each dimension, attention checks and a few demographic questions such as age, region and whether the participant owns or intends to buy an EV. Pilot it with a small group, time how long it takes and revise items that participants found unclear. Keep the session short to reduce fatigue.

   Checkpoint: A piloted survey with revisions documented and a measured completion time.

7. 7

   Collect data

   Recruit participants who match the target population, such as potential EV buyers in a stated set of cities, through channels you can describe in the report. Collect consent, demographics and responses, and monitor data quality as it arrives by checking attention-check failures and very fast completions. Follow your approved data protection plan and store responses without names. Log recruitment channels and dates.

   Checkpoint: A dataset meeting the planned sample size with stated exclusion rules applied.

8. 8

   Analyse

   Analyse the ratings with mixed-effects models, with participants and stimuli as random effects, or with repeated-measures ANOVA if the design allows. Report effect sizes and confidence intervals, check model assumptions and run robustness checks, such as excluding fast responders. Explore interactions between attributes, for example whether a high beltline reads as safe only with a particular front graphic. Keep the analysis scripts with the dataset.

   Checkpoint: Analysis results with statistics, effect sizes and diagnostic checks.

9. 9

   Interpret and report

   Interpret how each attribute relates to each perception and what that means for EV designers in practical terms, such as which attribute moves perceived safety most. State limitations clearly: renders rather than real vehicles, a sample that may not represent all buyers and the attribute levels chosen. Recommend follow-up work in VR or with physical scale models to test whether effects hold.

   Checkpoint: A research report with findings, limitations and design implications.

Testing & validation

• Variants differ only in the intended attribute, as measured.
• Renders are consistent across variants.
• Survey is piloted and meets sample size.
• Statistical analysis follows the pre-planned approach with assumption checks.

Expected results

Evidence on how specific EV form attributes relate to user perceptions, based on controlled Alias variants, consistent renders and a statistically analysed survey.

Take it further

• Use VR or physical scale models instead of renders.
• Add eye-tracking to see where participants look.
• Compare perceptions across regions or age groups.
• Use conjoint analysis to estimate trade-offs including price.

Viva and review questions

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