Reducing perceived quality risk in fresh fruit and vegetables purchases: tasting as informational cue

Last registered on June 23, 2025

View Trial History

Pre-Trial

Trial Information

General Information

Title

Reducing perceived quality risk in fresh fruit and vegetables purchases: tasting as informational cue

RCT ID

AEARCTR-0016270

Initial registration date

June 23, 2025

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published

June 23, 2025, 3:11 PM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

Country

France

Region

paris

Primary Investigator

Name

Daniela Lima Rente

Affiliation

EconomiX (UMR 7235), Paris Nanterre University and CNRS ; Interprofessional Technical Center for Fruit and Vegetables (CTIFL), France

Contact Primary Investigator

Other Primary Investigator(s)

Additional Trial Information

Status

In development

Start date

2025-06-24

End date

2025-07-11

Keywords

Behavior

Additional Keywords

Experimental economics, Risk aversion, Fruit and vegetables

JEL code(s)

D12, D81, M31, L66

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

Insufficient consumption of fruits and vegetables remains a significant public health concern. Identifying effective levers to promote healthier dietary behaviors is therefore essential. A key barrier to purchasing fresh produce lies in the perceived uncertainty surrounding product quality, driven by the intrinsic variability of these products and the difficulty consumers face in assessing quality prior to consumption. Concurrently, shifting consumer expectations have elevated the importance of the shopping experience in shaping food choices.

This field study examines the role of tasting—an experiential and informational cue—in reducing perceived quality risk and influencing purchasing behavior. It focuses on the impact of melon tasting on both melon purchases and purchases of other products. For these additional purchases, two psychological mechanisms are considered: a halo effect, in which positive perceptions of the sampled product extend to nearby items, and an emotional effect, whereby the pleasurable experience of tasting improves mood and increases overall spending.

The study seeks to evaluate the potential of experiential cues such as tasting to reduce perceived purchase risk and encourage greater consumption of fruits and vegetables.

External Link(s)

Registration Citation

Citation

Lima Rente, Daniela. 2025. "Reducing perceived quality risk in fresh fruit and vegetables purchases: tasting as informational cue." AEA RCT Registry. June 23. https://doi.org/10.1257/rct.16270-1.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

The intervention is implemented in the context of a field experiment conducted in a fresh produce store located in Paris. It consists of a melon tasting setup designed to provide both sensory and informational cues to consumers. The tasting arrangement is standardized across all treatment sessions in terms of the presentation format, tray positioning, and quantity of melon offered. Melon pieces are made available in a self-service format, displayed on a tray placed directly within the melon section of the store.

The intervention aims to assess whether providing consumers with the opportunity to taste the product prior to purchase reduces perceived quality uncertainty and modifies purchasing behavior. By offering a direct sensory experience, the tasting is hypothesized to act as a risk-reducing cue, potentially increasing both the likelihood and quantity of melon purchases.

Intervention (Hidden)

Intervention Start Date

2025-06-24

Intervention End Date

2025-07-11

Primary Outcomes

Primary Outcomes (end points)

Observed behavioral outcomes (melon and other product purchases):
• Purchase incidence (binary: purchase vs. no purchase)
• Quantity purchased (measured in units or weight)
• Expenditure (total amount spent, in euros)
Self-reported outcomes (from post-purchase questionnaire):
• Reasons and barriers to purchasing
• Risk aversion level
• Confidence in product quality and perceived risk associated with the purchase

These outcome variables aim to assess the impact of the tasting intervention on consumer behavior, with particular attention to how it may reduce perceived quality risk and influence purchasing decisions.

Primary Outcomes (explanation)

The primary outcome variables include directly observable measures—purchase incidence, quantity purchased, and expenditure— based on actual purchase records collected. Additionally, more subjective constructs such as perceived risk and confidence in product quality are assessed through post-purchase questionnaire responses. These constructs are measured using Likert-type scales based on statements related to trust and perceived risk associated with the product. An aggregate risk perception score will be constructed either by calculating the mean of relevant questionnaire items or by applying dimensionality reduction techniques such as Principal Component Analysis (PCA), depending on the internal consistency and statistical properties of the items. Risk aversion levels are measured using an experimental lottery task.

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

This study employs a randomized controlled design conducted in a real-world field setting: a fresh produce store in Paris. Customers are exposed to one of two experimental conditions. In the treatment condition, melon pieces are offered in a self-service tasting format. In the control condition, melons are sold in the same self-service setup, but without tasting.

The tasting intervention is strictly standardized in terms of presentation format, tray placement, and quantity offered. Randomization is implemented at the session level, with alternating hourly time slots assigned to either the tasting or control condition. This approach is intended to minimize contextual biases related to time of day, customer flow, or other temporal effects.

Experimental Design Details

Randomization Method

All store customers were invited to participate in our study after completing their purchases. Assignment to experimental conditions was based on a pre-randomized schedule of tasting and non-tasting sessions (on a daily basis), in order to minimize contextual biases.

Randomization Unit

Randomization is conducted at the session level.
Each session corresponds to a two/three-hour time slot within the store's operating hours. Sessions are randomly assigned to either the tasting (treatment) or no-tasting (control) condition according to a pre-randomized schedule. This cluster-level randomization helps control for potential confounding factors such as time of day or variation in customer flow.

Was the treatment clustered?

Yes

Experiment Characteristics

Sample size: planned number of clusters

36 sessions (3 sessions per day alternating between control and treatment conditions, over a period of 12 days)

Sample size: planned number of observations

The objective is to recruit a total of 200 customers.

Sample size (or number of clusters) by treatment arms

100 customers in control, 100 customers in treatment (tasting).

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Based on the effect size observed in the prior laboratory study, the minimum detectable effect size for the main binary outcomes (Cohen’s w) is estimated at approximately 0.051. A power analysis using a chi-square test with 1 degree of freedom, a significance level of 0.05, and a target power of 0.8 suggests that a total sample size of around 2,998 observations would be required to detect such an effect. However, due to budgetary and logistical constraints related to participant recruitment in the field setting, the planned sample size has been limited to approximately 180–200 participants. With this reduced sample size, the statistical power to detect an effect of the same magnitude is around 11%.

Supporting Documents and Materials

IRB