Investigating the Efficacy of App Training in Mitigating Complexity: A Randomized Trial in Shrimp Farming

Last registered on May 08, 2024

View Trial History

Pre-Trial

Trial Information

General Information

Title

Investigating the Efficacy of App Training in Mitigating Complexity: A Randomized Trial in Shrimp Farming

RCT ID

AEARCTR-0013506

Initial registration date

April 26, 2024

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

May 08, 2024, 4:55 PM EDT

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

Locations

Country

India

Region

Odisha

Primary Investigator

Name

Shree Saha

Affiliation

Cornell University

Contact Primary Investigator

Other Primary Investigator(s)

Additional Trial Information

Status

Completed

Start date

2024-01-11

End date

2024-04-30

Keywords

Agriculture, Behavior

Additional Keywords

Machine Learning, Perceived Complexity, Shrimp Farming, Digital Argriculture

JEL code(s)

Secondary IDs

Prior work

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

Abstract

This randomized trial investigates the impact of app training interventions on technology adoption in shrimp farming communities. The study focuses on addressing perceived complexity barriers by providing digital training to farmers. Findings will contribute insights into effective strategies for promoting technology uptake in agriculture, with implications for enhancing sustainability and productivity in shrimp farming communities.

External Link(s)

Registration Citation

Citation

Saha, Shree. 2024. "Investigating the Efficacy of App Training in Mitigating Complexity: A Randomized Trial in Shrimp Farming." AEA RCT Registry. May 08. https://doi.org/10.1257/rct.13506-1.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

The intervention consists of providing training to shrimp farmers on the use of a digital app for feed management in shrimp farming. Farmers are instructed on how to input data such as stocking volume, shrimp weight, and feed usage into the app. Additionally, they are taught to interpret the app's recommendations for feed quantity based on these inputs. The training involves hands-on demonstrations, active participation, and guidance from facilitators to ensure farmers understand and can effectively utilize the app. This intervention aims to empower farmers with digital tools to improve feed management practices and ultimately enhance shrimp farming productivity.

Intervention Start Date

2024-01-24

Intervention End Date

2024-02-15

Primary Outcomes

Primary Outcomes (end points)

Measuring perceived complexity, ability to use application and adoption of digital technology

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

The experimental design involves a randomized controlled trial conducted among shrimp farmers. Participants are randomly assigned to one of three groups, some of which receives some form of training in using digital technology, the control group receives no training. Outcome measures, including technology adoption, perceived complexity are assessed at baseline and follow-up to evaluate the impact of the interventions.

Experimental Design Details

Randomization Method

We compile the list of farmers in our survey village, and randomization is performed using a computer in our office. Farmers are then assigned to one of the three treatment groups based on the randomization results.

Randomization Unit

The randomization unit in this study is individual farmers within the surveyed village. We compile the list of farmers in our survey village, and randomization is performed using a computer in our office. Farmers are then assigned to one of the three treatment groups based on the randomization results.

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

We do not cluster treatment in our analysis.

Sample size: planned number of observations

Sample size (or number of clusters) by treatment arms

We aim to conduct interviews with 600 farmers across five districts in Odessa. This panel survey involves baseline and endline assessments, requiring re-interviews with the same 600 farmers at the endline stage.

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

Supporting Documents and Materials

IRB