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The effect of competitor information on firm strategy
Last registered on November 21, 2018

Pre-Trial

Trial Information
General Information
Title
The effect of competitor information on firm strategy
RCT ID
AEARCTR-0003013
Initial registration date
July 03, 2018
Last updated
November 21, 2018 10:01 PM EST
Location(s)

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Primary Investigator
Affiliation
Harvard University
Other Primary Investigator(s)
Additional Trial Information
Status
In development
Start date
2018-05-01
End date
2021-09-30
Secondary IDs
Abstract
This project aims to understand how increased access to competitor information enabled by digitization affects the strategic decisions and performance of firms, by running a field experiment across nail salons in four cities (New York, Chicago, San Francisco, and Los Angeles).
External Link(s)
Registration Citation
Citation
Kim, Hyunjin. 2018. "The effect of competitor information on firm strategy ." AEA RCT Registry. November 21. https://doi.org/10.1257/rct.3013-2.0.
Former Citation
Kim, Hyunjin. 2018. "The effect of competitor information on firm strategy ." AEA RCT Registry. November 21. http://www.socialscienceregistry.org/trials/3013/history/37887.
Sponsors & Partners

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Experimental Details
Interventions
Intervention(s)
The intervention consists of providing firms with competitor pricing information comparing their prices to 9 competitors located closest to them.
Intervention Start Date
2018-06-18
Intervention End Date
2019-01-01
Primary Outcomes
Primary Outcomes (end points)
Firms' pricing decisions
Primary Outcomes (explanation)
I will first examine (descriptively) the extent to which firms already know information on competitor pricing. Then, I will analyze whether treated firms change their service offering prices (binary indicator) and how they change these prices (absolute and real changes in prices).
Secondary Outcomes
Secondary Outcomes (end points)
I will also examine the extent to which treatments impact firms' performance, as well as some exploratory analyses on a variety of firm decisions outside of pricing.
Secondary Outcomes (explanation)
To measure performance, I will use proxies of performance as measured by Yelp, including the number of page views, directions and map views, calls to the business, number of reviews, and survival. When available, I will also examine a binary indicator of availability the next day, as well as the number of employees and customers at the time of salon visit. Finally, for a subset of salons in San Francisco, I will look at quarterly revenues using tax data from the City of San Francisco.

In terms of firm decisions, I will look at decisions of scope (in terms of its service offerings e.g. massage, hair, etc), quality (e.g. nail polish brands offered, Yelp rating and review text on quality, cleanliness and luxuriousness of interior, etc), and practices (e.g. hours open, promotions/signs, etc).
Experimental Design
Experimental Design
Firms in my sample are randomly assigned to one of two conditions, control or treatment.
Experimental Design Details
Not available
Randomization Method
Randomization done in office
Randomization Unit
I first stratify on city, business type (whether it is an advertiser at Yelp, has a claimed Yelp page, or has not claimed its Yelp page), and rounded Yelp rating, and then randomly assign firms in each strata.
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
A minimum of 3,948 businesses. If the research budget stretches further (in terms of how long canvassers take to visit businesses to deliver treatments), I will expand the sample to include up to 9,587 firms. While these are the number of observations planned, I expect some of these businesses to be a fake, closed, or duplicate listing at the time of the intervention, so the actual minimum number in the sample may be lower.
Sample size: planned number of observations
A minimum of 3,948 businesses. If the research budget stretches further (in terms of how long canvassers take to visit businesses to deliver treatments), I will expand the sample to include up to 9,587 firms. While these are the number of observations planned, I expect some of these businesses to be a fake, closed, or duplicate listing at the time of the intervention, so the actual minimum number in the sample may be lower.
Sample size (or number of clusters) by treatment arms
Firms are randomly assigned within each strata. In the minimum sample, 1,976 are in control, and 1,972 in treatment.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Harvard University Institutional Review Board
IRB Approval Date
2018-05-21
IRB Approval Number
IRB18-0882
Analysis Plan

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