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Trust Gap

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Research

Research home / Trust Gap

Research home / Trust Gap

Flagship research programme

Flagship research programme

A world trading
with confidence.

Research towards a world trading with confidence—where shoppers can trust what they read and merchants can earn the trust they deserve.

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THE GUIDING QUESTION

What does it take to build a world trading with confidence?

PROGRAMME RECORD

Partnership

Knowledge Transfer Partnership

Planned duration

36 months

Status

Funding awarded

Research outputs

Forthcoming

THE QUESTION BEHIND THE PROGRAMME

What does it mean to
measure trust in commerce?

At Judge.me, our vision is a world trading with confidence. Shoppers should be able to make informed choices, and merchants should be able to build a reputation that fairly reflects the experience they deliver.

But incomplete, manipulated or malicious reviews can undermine confidence on both sides. Through the Trust Gap research programme, Judge.me and UCL are investigating how review information relates to real customer experiences—and how better evidence can support more reliable reviews, fairer reputations and better decisions.

Research position

Research position

These are open research questions. The planned framework will be evaluated for data quality, predictive value and robustness; components may be excluded if the evidence does not support them.

These are open research questions. The planned framework will be evaluated for data quality, predictive value and robustness; components may be excluded if the evidence does not support them.

THREE CONNECTED LINES OF INQUIRY

The research agenda

01

01

THE MANIPULATION GAP

THE MANIPULATION GAP

How does selective review visibility shape the information available before purchase?

Planned approach

Planned approach

Examine ratings, emotional language and patterns of selection or suppression to develop and validate merchant-level measures.

Examine ratings, emotional language and patterns of selection or suppression to develop and validate merchant-level measures.

Why it matters

Why it matters

For commerce operators, this asks whether the visible review set gives shoppers a representative picture. The challenge is to distinguish selection effects from legitimate differences in products and customer experiences.

02

02

THE EXPERIENCE GAP

THE EXPERIENCE GAP

Where do actual transaction outcomes diverge from the signals available before purchase?

Planned approach

Planned approach

Compare review signals with delivery, returns and other transaction outcomes, examining variation across merchants, categories, geographies and order values.

Compare review signals with delivery, returns and other transaction outcomes, examining variation across merchants, categories, geographies and order values.

Why it matters

Why it matters

This connects the promise made before purchase with what happens afterwards. The research is intended to identify where reviews are informative and where important aspects of the experience remain difficult to judge.

This connects the promise made before purchase with what happens afterwards. The research is intended to identify where reviews are informative and where important aspects of the experience remain difficult to judge.

03

03

THE ATTRIBUTE SALIENCE GAP

THE ATTRIBUTE SALIENCE GAP

Do the product attributes most discussed in reviews predict a satisfactory purchase?

Planned approach

Planned approach

Extract themes such as fit, packaging and durability, then compare their prominence with their ability to predict outcomes such as repeat purchase or no return.

Extract themes such as fit, packaging and durability, then compare their prominence with their ability to predict outcomes such as repeat purchase or no return.

Why it matters

Why it matters

An attribute can be frequently discussed without being a reliable indicator of satisfaction. The research asks which signals merit attention and how their relevance changes across products and contexts.

An attribute can be frequently discussed without being a reliable indicator of satisfaction. The research asks which signals merit attention and how their relevance changes across products and contexts.

PROGRAMME OUTPUTS

Publications will appear here.

The programme is intended to produce research papers, preprints and other openly shared findings. The first publications are forthcoming.

The programme is intended to produce research papers, preprints and other openly shared findings. The first publications are forthcoming.

Research forthcoming

Research forthcoming

A growing record of the work

A growing record of the work

As findings become available, each publication will bring together its research question, methodology, limitations and source document.

As findings become available, each publication will bring together its research question, methodology, limitations and source document.

ACADEMIC AND BUSINESS COLLABORATION

The programme team

Judge.me

Judge.me

Peter-Jan Celis

Peter-Jan Celis

Senior business lead

Senior business lead

Strategic direction, resources and chair of the Local Management Committee.

Strategic direction, resources and chair of the Local Management Committee.

Judge.me

Judge.me

Fabrizio Assabese

Fabrizio Assabese

Business supervisor

Business supervisor

Day-to-day business supervision, research relevance and coordination with product and engineering.

Day-to-day business supervision, research relevance and coordination with product and engineering.

University College London

University College London

Giuseppe Destefanis

Giuseppe Destefanis

Academic lead

Academic lead

Research strategy, empirical methods and academic supervision.

Research strategy, empirical methods and academic supervision.

University College London

University College London

Silvia Bartolucci

Silvia Bartolucci

Academic supervisor

Academic supervisor

Statistical modelling, robustness and experimental design.

Statistical modelling, robustness and experimental design.

A Knowledge Transfer Partnership between Judge.me and UCL. Further details of the programme timeline will be added as the work develops.

A Knowledge Transfer Partnership between Judge.me and UCL. Further details of the programme timeline will be added as the work develops.