Digital Technology Adoption

Last registered on June 14, 2023

Pre-Trial

Trial Information

General Information

Title
Digital Technology Adoption
RCT ID
AEARCTR-0008104
Initial registration date
September 01, 2021

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 02, 2021, 11:08 AM EDT

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

Last updated
June 14, 2023, 11:17 AM EDT

Last updated is the most recent time when changes to the trial's registration were published.

Locations

Region

Primary Investigator

Affiliation
University of Oxford

Other Primary Investigator(s)

Additional Trial Information

Status
In development
Start date
2022-06-20
End date
2025-01-01
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Adoption of technology can enable growth and increase productivity in firms in low and middle-income countries. However, adoption remains low for seemingly profitable technologies across varied settings. In this project I offer one specific explanation: that high learning costs at onset leads firms to make sub-optimal adoption choices. To study adoption choice, I offer firms access to a digital technology that helps them manage cash flow and maintain records of business transactions. In a field experiment in India, I randomize firms to treatment arms where learning costs are subsidized to varying degrees. In a setting where learning costs are salient and adoption choice is observable, I examine whether a temporary intervention that can subsidize learning costs can move firms from a low adoption equilibrium to a high adoption one. To guide the key predictions of the paper, I set up a model that describes technology adoption choices made by a firm that optimizes on an infinite horizon in discrete time. I then apply the model to shed light on the mechanisms through which temporarily subsidizing learning costs can enable to a firm to move to a high-adoption equilibrium.
External Link(s)

Registration Citation

Citation
Mukherjee, Sanghamitra. 2023. "Digital Technology Adoption." AEA RCT Registry. June 14. https://doi.org/10.1257/rct.8104-2.1
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Experimental Details

Interventions

Intervention(s)
In this project, I investigate the role of learning costs on digital technology adoption by women microentrepreneurs in the state of Karnataka in India. Over the last decade, digitalisation has slowly transformed how informal micro-firms operate in rural areas. This transformation involves a sharp rise in new and emerging digital technologies, however, the adoption still remains low. To help rationalize the low adoption of digital technology, I argue that high learning costs faced by a firm limits adoption of digital technologies. This study examines whether a temporary intervention that can subsidize learning costs can move firms from a low adoption equilibrium to a high adoption one. To guide the key predictions of the paper, I set up model that describes technology adoption choices made by a firm that optimizes on an infinite horizon in discrete time. I complement the model with a field experiment where I randomize firms to treatment arms where learning costs are subsidized to varying degrees, and study how this drives adoption choice. I also present findings on the firm level effects of sustained adoption. Further, to understand the comparative advantage of various firms from digital technology adoption, I study the heterogeneity in treatment effects by size of firm, level of human capital and behavioral parameters.
Intervention (Hidden)
Intervention Start Date
2022-06-20
Intervention End Date
2024-01-01

Primary Outcomes

Primary Outcomes (end points)
The two primary outcome variables are - (i) a binary measure of adoption and (ii) the extent of adoption. Data for the primary outcomes is collected using the MeraBills dashboard which collects real time data on when and how the app is used.
Primary Outcomes (explanation)
For the binary adoption dummy, real time data from the app is aggregated to the daily level. To study the extent of adoption, the real time data is aggregated to calculate the number of transactions entered weekly on the digital platform. Using this data, I create weekly measures that are then pooled for the study period.

Secondary Outcomes

Secondary Outcomes (end points)
The secondary outcomes of interest are under two broad categories: business practices and firm outcomes. Under business practices, I look at outcomes such as, managing trade credit, managing inventories, managing customers etc. For firm outcomes, I look at profits, revenues, costs, probability of shutting down, probability of starting new business, number of employees and time use. The study has other auxiliary outcomes of interest such as Women’s Economic Empowerment and time use.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The sample is comprised of 320 women-led microenterprises that are located across the Kolar, Tumkur, Chikkabalapura, and Ramanagar districts on Karnataka. The design randomizes participants into three groups: one control and two treatment groups corresponding to the three experimental treatments described below. All three groups are offered access to the app. To study whether learning costs drive adoption decisions, I vary the extent to which subsidize learning costs across the three arms.
Experimental Design Details
The sample is comprised of 320 women-led microenterprises that are located across the Bangalore, Tumkur, Chikkabalapura, and Ramanagar districts on Karnataka. The design randomizes participants into three groups: one control and two treatment groups corresponding to the three experimental treatments described below. All three groups are offered access to the app. To study whether learning costs drive adoption decisions, I vary the extent to which subsidize learning costs across the three arms.

The control group (C) will be given minimal help in overcoming learning costs. As is common in traditional training sessions, entrepreneurs will be provided guidance on why the MeraBills app is helpful and how to use it. They will be provided assistance on how to download it. This is done to ensure that the control group has basic knowledge of how to enter business transactions on the app. However, the learning costs will not been subsidized for this group.

In addition to what is provided to the control group, treatment group T1 is provided additional hand holding in entering transactions on the app. For this treatment I subsidize the cost of learning by hand holding the entrepreneurs on using the app. As entrepreneurs learn by doing, this reduces the learning cost they will bear while using the app after the training sessions. I hypothesize that firms will be more likely to adopt the digital technology in group T1 as compared to group C, as T1 has been moved further along the learning curve.

For treatment group T2 I intensify the extent to which I subsidize learning costs. For this treatment arm, trained project staff will visit the entrepreneurs on a weekly basis for one month and hand hold them to enter all the transactions on the app. I hypothesize that this group is group is moved further along the learning curve as compared to groups T1 and C.

The sample is selected for this study based on three criteria - they are actively running a business, they are involved in decision making in the business and have regular access to a smartphone. From pilot work in September 2021, I note that it is key to check that the entrepreneurs are currently actively running a business. During the pandemic a lot of entrepreneurs stopped running their business (temporarily or permanently). Further, as I work with women entrepreneurs I check that they are involved in decision making. In LMIC settings it is quite for women to be involved in a family business, but not have much agency in the decision making, Since I want to study decisions around adoption of a new technology, it is important that the women are closely involved in running the business. As for the last criteria, I ensure that women have a smartphone they regularly use. Driven by the low cost of phone data and widespread use of apps like WhatsApp, I find that 25-40\% women in the region have a basic smartphone. However, there in our setting there is a distinction between ``owning" a phone and regularly using a phone. Quite often, smartphones are shared by members of the household. In the pilot, I find that 70-80\% of women who own a smart said that they share the phone with other members. To be able to adopt this digital technology, it is key that the participants have regular access to the phone. Therefore, instead of asking if participants own a phone, I ask whether they have a regular access to a phone.

Based on the criteria discussed above, I select sixteen field officers from Buzz Women who are tasked with selecting 20 entrepreneurs each from their respective panchayats. A panchayat is a geographical area within a district. I ask the field officers to choose the women eligible for this study as they have local knowledge. The survey team will then call the 320 participants and double-check that they meet the criteria for our study. After baseline surveys are conducted, there will be an induction session where entrepreneurs will be informed about the study and their relevant treatments. Following \cite{Drexler2014}, I begin the induction session a rule-of-thumb training that focuses on very simple heuristics or routines for business and financial practices. In line with COVID precautions entrepreneurs will be invited to the induction session in groups of 40.

The randomization will be done after stratifying on district, age, and frequency of business transactions. As I choose firms across four districts, I stratify on this as there is likely to be some regional variation in the firms. I stratify on age as digital literacy and experience using digital technologies varies by age. Younger entrepreneurs are more likely to have used similar technologies in the past. Lastly, firms in the sample vary in their frequency of transactions.
Randomization Method
Randomization done in office by a computer.
Randomization Unit
Individual level randomization
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
320 women microentrepreneurs
Sample size: planned number of observations
320 women microentrepreneurs
Sample size (or number of clusters) by treatment arms
~100 microentrepreneurs in the Control group ; ~110 microentrepreneurs in the T1 group; ~110 microentrepreneurs in the T2 group.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
For power calculations, I assume 2\% of the control group will use the app, 15\% of T1 will use the app and 30\% of T2 will use the app. I estimate the sample size for two-sample comparison of proportions (for T2 vs. T1 and T1 vs. C), where the proportion in each group are given by the proportion that will use the app. This shows that a sample of 288 participants is enough to detect the above change in proportions with a power of 85\%. In this project, I set the sample size 320 (slightly greater than 288). I also run power calculations by simulating the experiment data and obtain similar estimates. \cite{mckenzie2012beyond} provides useful insights on optimal design. He shows that using multiple rounds of data collection and averaging outcomes over these rounds, leads to substantial increases in power, especially when outcomes are poorly correlated over time (which is often the case for business outcomes). Using the app, I collect weekly data on outcome variables and pool the data over the rounds.
IRB

Institutional Review Boards (IRBs)

IRB Name
CENTRAL UNIVERSITY RESEARCH ETHICS COMMITTEE (CUREC), University of Oxford
IRB Approval Date
2021-02-02
IRB Approval Number
ECONCIA21-22-07
Analysis Plan

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Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials