14Psychology & user research · Read 13 min

When the average user hides opposite reactions

I connect traits, psychological needs and variants to explain why the same interface can produce very different experiences.

IN ONE SENTENCE

Personality traits can help explain why users respond differently to the same design, while psychological needs clarify what the experience should support.

CONTEXT

I connect three bodies of work used in my research—Big Five traits, trait–behavior relationships and psychological needs—to show how an unhelpful average can become a testable UX hypothesis.

SUMMARY

A global average describes a heterogeneous population imperfectly. Big Five traits can explain part of the variation in preferences and behavior. Lallemand’s UX Cards propose seven needs relevant to experience—relatedness, competence, autonomy, security, pleasure, meaning and influence. Connecting these levels allows researchers to test how a design supports particular needs and for which users the effect is strongest.

Key diagram

From individual differences to a testable UX hypothesis

01 · BIG FIVEIndividual differences

OOpennessCConscientiousnessEExtraversionAAgreeablenessNNeuroticism

→HYPOTHESES

02 · UX NEEDSNeeds supported by experience

AutonomyCompetenceRelatednessSecurityStimulationMeaningInfluence

→TEST

03 · VARIANT TESTCompare effects

Outcome = Variant + Trait + Need + Variant×Trait + Context

PreferenceBehaviorExperience

∿

The trait frames a variability hypothesis; the need defines the experiential quality to measure.

The Big Five describes continuous tendencies. UX needs define the target experience. The interaction between trait, variant and context helps explain differences in preference and behavior.

A research-based explainer grounded in the sources listed below. Some mechanisms are simplified without changing the overall conclusions.

Product application14

Does the winning variant support the same psychological needs for every user?

Measure traits as continuous scores, then test the trait-by-variant interaction. Add a short measure of the needs supported by the experience—autonomy, competence, security, stimulation or relatedness—to explain why the same design produces different effects.

What to measure in the experiment

Trait × variant interaction

Target-need satisfaction

Observed behavior and preference

Key idea

In UX research, measure traits as continuous variables, define the needs targeted by the design, then test how responses to each variant change across users and contexts.

01 — Variation between users

One variant can produce several experiences.

An A/B test randomly assigns visitors to two variants and compares an outcome such as conversion, time spent, click-through rate or retention. Its average effect may combine opposing reactions. A recommendation experience rich in novelty may stimulate some visitors and demand more effort from others; a highly guided journey may reassure some users while reducing autonomy for others.

Age, expertise, device and visit intent already explain part of this variation. Personality adds relatively stable tendencies in novelty seeking, planning, social interaction and responses to uncertainty. It becomes useful when the protocol tests a precise interaction between those tendencies and an interface property.

02 — What personality predicts

Probabilities of preferences and behaviors.

The Big Five places each person on five continuous dimensions: Openness, Conscientiousness, Extraversion, Agreeableness and Neuroticism. Many studies connect these scores with preferences, choices and behavior. Youyou et al., for example, showed that Facebook Likes contain statistical information about self-reported personality scores.

My own work also examines visual exploration: some relationships between personality and eye movements persist across changes in task, stimulus and time. These results support probabilistic population-level hypotheses. Effect size, stability and contextual dependence determine their value for user research.

03 — Lallemand’s approach

Seven needs describe experiential quality.

The UX Cards developed by Carine Lallemand are a design and evaluation tool grounded in psychological needs. They retain seven families: relatedness, competence, autonomy, security, pleasure and stimulation, meaning and self-actualization, and influence or popularity.

These needs give product teams a vocabulary for intended design effects. Discovery features may support stimulation; a clear and reversible history may strengthen security; meaningful controls may support autonomy; visible progress may foster competence. Evaluation then asks whether the lived experience actually supports the intended need.

04 — Building the bridge

Connect traits, needs and design in a testable hypothesis.

Traits and needs operate at complementary levels. A trait describes an individual tendency; need satisfaction describes a quality of experience in a situation. A protocol can therefore test whether one variant better supports a need and whether that effect changes progressively with a personality trait.

Consider a music interface. One variant emphasizes open discovery; another offers a guided and predictable path. The team can test whether Openness moderates preference for the first, whether security or autonomy explains that preference, and whether the relationships remain after controlling for age, expertise and session goal.

05 — Quantitative analysis of A/B tests

Measure the interaction with continuous variables.

The analysis includes the variant effect, trait score, their interaction and contextual variables. An interaction means the variant’s effect changes with the trait. A need-satisfaction measure can then document the proposed mechanism: does variant B perform better because it increases perceived control, stimulation or competence?

This approach requires larger samples than a simple A/B comparison, hypotheses specified before analysis and continuous scores from a validated questionnaire. Results include uncertainty intervals and context-specific analyses. Replication in a new population measures robustness.

06 — Responsible personalization

Offer direct control over the adaptive experience.

Teams can often ask for a useful preference directly: desired discovery level, information density, notification pace or degree of guidance. This gives immediate control and creates interpretable data. Personality is especially valuable for understanding variability, improving a study or testing an experience theory.

Any use for personalization requires clear consent, proportionate purpose and error measurement across populations. Sensitive traits remain separate from high-impact decisions. Adaptive interfaces should also let people inspect, correct and disable the proposed adaptation.

Primary source

Big Five, Gaze-based Signature of Personality and UX Cards

A synthesis of my doctoral research on personality and individual differences, connected with Carine Lallemand’s psychological-needs approach to UX.

Selected references

McCrae & John (1992), An Introduction to the Five-Factor Model and Its Applications
Lallemand, Koenig & Gronier (2014), Nordic Conference on Human–Computer Interaction — doi:10.1145/2639189.2639214
Lallemand (2015), Towards Consolidated Methods for the Design and Evaluation of User Experience
Hassenzahl et al. (2010), Needs, affect, and interactive products
Youyou, Kosinski & Stillwell (2015), Proceedings of the National Academy of Sciences — doi:10.1073/pnas.1418680112
Le Bras, Allibe & Doré-Mazars (2024), Scientific Reports — doi:10.1038/s41598-024-62210-z