Can Collective Offers Drive Farmer Adoption?

Last registered on September 21, 2026

Pre-Trial

Trial Information

General Information

Title
Can Collective Offers Drive Farmer Adoption?
RCT ID
AEARCTR-0019549
Initial registration date
September 09, 2026

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
September 21, 2026, 8:10 AM EDT

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

Locations

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Primary Investigator

Affiliation
INSEAD

Other Primary Investigator(s)

PI Affiliation
Singapore Management University
PI Affiliation
INSEAD

Additional Trial Information

Status
In development
Start date
2026-09-10
End date
2027-02-28
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
The key idea of our research project is to test how social proof and coordination cost shape collective framing of technology adoption among smallholder farmers. In many agricultural technology adoption settings, an individual farmer's willingness to try a new product depends not only on her own information, but also on what she believes her peers are doing and on the extent to which successful adoption may depend on others' participation (Conley and Udry 2010). This distinguishes the mechanism we study from existing social-learning studies of technology diffusion, which are typically sequential, with early adopters acting first and neighbors subsequently learning by observing their outcomes (Foster and Rosenzweig 1995), rather than involving a simultaneous coordination problem that engineers simultaneous adoption of newer technology at scale.
The study also moves beyond digital advisory platforms that operate primarily as information channels (Fábregas et al. 2019; Cole et al. 2025) by examining whether a platform can actively structure the social environment surrounding an adoption decision. We study this in the context of a sustainable crop-care campaign promoting agricultural products that are relatively unfamiliar to many smallholder farmers, where uncertainty about product efficacy may make peer behavior particularly relevant.
We test these ideas using a four-condition cluster-randomized field experiment on an agricultural digital platform in India. Farmers receive either an Individual Offer, in which access to a 50% discount depends only on the farmer's own decision, or one of three Group Offer conditions, in which the discount is unlocked only if a required number of community members sign up for the offer. Within the Group Offer conditions, we vary the peer sign-up information farmers receive and the coordination cost associated with reaching the threshold for such sign-ups. We operationalize coordination cost as the number of additional farmer sign-ups required for the group to reach the threshold and unlock the discount.
The primary comparison tests whether introducing a group-contingent offer increases sign-up relative to an otherwise individual offer. Additional comparisons examine whether favorable peer social proof under low coordination costs increases sign-up, and whether increasing coordination costs versus social proof reduces sign-up while holding the displayed number of prior farmer sign-ups fixed. We further examine whether initial offer sign-up translates into subsequent self -expressed interest in adoption as well as purchase conversion. The experiment therefore studies two stages of the adoption process: farmers' initial willingness to join the offer and their subsequent completion of a product purchase.
The study has implications for how digital platforms in emerging markets can not only reduce informational barriers to agricultural technology adoption amongst resource-constrained smallholder farmers but also utilize social and coordination barriers surrounding the adoption decision to promote adoption of sustainable or unfamiliar products at scale.
External Link(s)

Registration Citation

Citation
Ghosh, Sukti, Habin Jung and Rupali Kaul. 2026. "Can Collective Offers Drive Farmer Adoption?." AEA RCT Registry. September 21. https://doi.org/10.1257/rct.19549-1.0
Experimental Details

Interventions

Intervention(s)
Farmers on the Krishify platform are exposed to a sustainable crop-care campaign offering a 50% discount on three agricultural products promoted under a common campaign theme.
Depending on random assignment, the discount is presented either as:
1. an Individual Offer, in which the discount is available based solely on the individual farmer's decision to sign up; or
2. a Group Offer, in which the discount is unlocked only if a required number of farmers from the same digital community sign up for the offer.
All farmers within the same randomized community (created at the geographic sub-district level) receive the same experimental condition.
In the Group Offer conditions, once the required sign-up threshold is reached, all farmers in that community who signed up for the offer become eligible for the 50% discount. The discount is therefore not limited to the farmers whose sign-ups complete the threshold. If the required threshold is not reached, the group discount is not unlocked.
The discount depth is fixed at 50% across all experimental conditions.
Intervention Start Date
2026-09-10
Intervention End Date
2026-10-10

Primary Outcomes

Primary Outcomes (end points)
Offer sign-up, measured at the individual farmer level as a binary outcome.
A farmer is coded as having signed up if the farmer clicks at least one eligible sign-up call-to-action (CTA) on the campaign offer page during the four-week campaign period.
Primary Outcomes (explanation)
Offer sign-up captures the farmer's immediate behavioral decision to participate in the campaign offer and is the outcome most directly targeted by the experimental manipulation.

Secondary Outcomes

Secondary Outcomes (end points)
Purchase intention/conversion (individual farmer level)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
This study is a four-condition cluster-randomized field experiment.
Randomization occurs at the geographic sub-district-level (as officially categorized by the Government of India) digital-community level. All eligible farmers within a randomized community receive the same experimental condition.
The four conditions are:
• T1: Individual Offer
• T2a: Group Offer – No Peer Sign-up Information
• T2b: Group Offer [4 of 5 farmers have joined]
• T2c: Group Offer [4 of 15 farmers have joined]
The Group Offer conditions share the same basic collective contingency: access to the discount depends on the community reaching a required sign-up threshold. They differ in the peer sign-up information displayed and in the remaining coordination cost required to reach that threshold.
Experimental Design Details
Not available
Randomization Method
Randomization will be conducted in an office using a computer program (e.g., R).
Digital communities will be randomly assigned to one of the four experimental conditions, with randomization stratified by state and pre-experimental community allocation status.
The planned allocation is approximately equal across the four experimental conditions.
Randomization Unit
Geographic sub-district-level (as officially categorized by the government of India) digital-community level
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
953 communities in sub district level, across 10 states
Sample size: planned number of observations
farmers that arrive on the app across the 953 communities in our sample
Sample size (or number of clusters) by treatment arms
• T1 – Individual Offer: 238 communities
• T2a – Group Offer, No Peer Sign-up Information: 238 communities
• T2b – Group Offer [4 of 5 joined] : 239 communities
• T2c – Group Offer [4 of 15 joined] : 238 communities
Total: 953 communities
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
Europe Campus – Boulevard de Constance 77305 Fontainebleau Cedex, France Tel: +33 (0)1 60 72 40 00 www.insead.edu Institut privé d’enseignement supérieur Association loi 1901 APE 8542Z SIRET 775 703 390 000 10 TVA FR60 775 703 390 INSEAD Institutional Review Board
IRB Approval Date
2026-07-15
IRB Approval Number
2026-51