Can Access to Smartphones Improve Adoption of Digital Agricultural Technologies?

Last registered on June 24, 2024


Trial Information

General Information

Can Access to Smartphones Improve Adoption of Digital Agricultural Technologies?
Initial registration date
June 02, 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
June 24, 2024, 11:51 AM EDT

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


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Primary Investigator

International Food Policy Research Institute & Wageningen University

Other Primary Investigator(s)

PI Affiliation
Wageningen University
PI Affiliation
International Food Policy Research Institute (IFPRI)

Additional Trial Information

On going
Start date
End date
Secondary IDs
Prior work
This trial is based on or builds upon one or more prior RCTs.
The landscape of digital innovations in Africa is characterized by significant “digital divide”: less than 40 percent of smallholder households have access to the internet (Mehrabi et al., 2021). Marginalized farmers and households have even less access to the internet and mobile phones, this differential access to digital innovations and associated complementary infrastructures may generate further inequality.
Taking Egypt as a case study, smallholder farmers can easily get excluded from the agricultural digital revolution, as the existing rural farming communities in less developed or remote regions in Egypt usually lack the infrastructure required and internet connectivity to navigate these tools (OBG, 2022; AGBI, 2023). The high cost of smartphones and internet services may be one reason why urban households are more digital than rural households.
Consistent with literature and evidence from experimental learning (e.g., Carter et al, 2014; Fischer et al., 2019), short run subsidies that allow users experience a technology may help potential users revise their expectations, and eventually adjust their beliefs on the relative costs and benefits of these technologies. In this study, we utilize the Egyptian context and aim to investigate how access to a shared smartphone, which allows smallholder farmers to operate and use agricultural digital tools free of charge over the internet for a short period of time would: (i) impact farmers’ use and adoption of digital agricultural tools, (ii) affect farmer’s willingness-to-pay (WTP), following a 6-month experimental learning period, to keep and invest in these smartphone devices for future adoption.

External Link(s)

Registration Citation

Abay, Kibrom, Fatma Abdelaziz and Erwin Hendricus Bulte. 2024. "Can Access to Smartphones Improve Adoption of Digital Agricultural Technologies? ." AEA RCT Registry. June 24.
Experimental Details


In this intervention we seek to promote the use of digital mobile apps to smallholder farmers who do not own smartphones and are likely excluded from the agriculture digital revolution. Accordingly, we provide 100 smartphones, with a pre-charged internet bundle to approximately 500 farmers in Minya and Benisuef. Farmers will be asked to form groups of 4-5s depending on their proximity to one another, and will be asked to share a smartphone together, whereby each member gets to keep the device for a week and then passes it on to the next. This intervention is meant to encourage and enable smallholders who do not own smartphones to access and use agricultural digital mobile applications for free for a 6-month experimental learning period. The objective is to evaluate the impact of access to smartphones on farmers’ adoption and investment in digital tools for agriculture using rigorous impact evaluation methods.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
We aim to evaluate the impact of access to smartphones on the following primary outcomes:
1. Awareness of digital agricultural tools
2. Uptake of digital tools
3. Utilization of digital agricultural tools for marketing purposes
4. Willingness-to-pay (WTP) for a digital technology.
5. Farmer cooperativeness and collective action
Primary Outcomes (explanation)
1. Awareness of digital agricultural tools. Respondents will be asked to report on their awareness of the three agricultural digital apps Mahsoly, and CROPSA, in the baseline (before they receive the smartphone intervention), and in the midline (after they receive the smartphone intervention) and later in the endline.

2. Farmer uptake of digital tools. This will be constructed based on downloads data available in the backend database of the three different digital apps Mahsoly, & CROPSA, this data will be shared by the mobile app developers. Self-reported downloads in the baseline, midline and endline data will also be compared.

3. Utilization of digital agricultural tools. This will be constructed using the midline and later, the endline survey data using respondents use of digital tools to support their agricultural activities, including selling of their crops and purchase an agricultural input. The reported number of attempts or actual transactions will be used to construct an adoption variable.

4. Willingness-to-pay (WTP): This will be constructed based on farmers’ bids, when asked to state how much they are willing to pay or “bid” to keep the smartphone with the digital tools activated; following the BDM auction method. As there will be two different bidding designs, the research team will also be able to construct and aggregate joint WTP.

5. Farmer cooperativeness and collective action: this will be constructed following the farmers responses to the public good game, whereby we will obtain (i) a measure of the preferences of individuals (selfish, or not) as well as (ii) a rough proxy of social capital in the group; both of which will be determinants of the group-level bidding

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
• Control Group: no intervention.
• Treatment Group: Farmers will be asked to self-select and formulate their own groups. The formulated groups will be provided with a shared smartphone device and will be assigned a trainer from their designated farmer organization to pass by every week to offer a refresher training when needed to farmers and to ensure that the farmers are complying to the smartphone sharing activity.
On each smartphone:
o The network operator (Vodafone) will provide 100 Internet SIM cards that can only function over specific agricultural digital tools/ apps (blocking unrelated services or apps that may consume internet or defuse the purpose of our intervention)
o The sim cards will be charged on monthly basis that grants internet for up to 10 GB
o After 6 months, farmers in the control and treatment arms will be invited to join one of two designed BDM auction bidding exercises, to measure their willingness to pay (WTP) to keep and invest in these smartphone devices for future agriculture digital tools adoption.
Experimental Design Details
Not available
Randomization Method
Randomization done via Stata software
Randomization Unit
Randomization Unit: Farmer
Randomization was stratified across farmer organizations
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
Planned number of observations: 950 farmers
Sample size: planned number of observations
Planned number of observations: 950 farmers
Sample size (or number of clusters) by treatment arms
• Control group: 520 Farmers
• Treatment group : 550 Farmers
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Our power calculations aim to achieve the standard and widely adopted 80 percent power at a significance level of 5 percent.

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number