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The Limits of Startup Strategy
Last registered on August 15, 2019

Pre-Trial

Trial Information
General Information
Title
The Limits of Startup Strategy
RCT ID
AEARCTR-0004302
Initial registration date
July 02, 2019
Last updated
August 15, 2019 7:36 PM EDT
Location(s)

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Primary Investigator
Affiliation
Columbia Business School
Other Primary Investigator(s)
PI Affiliation
Columbia Business School
Additional Trial Information
Status
In development
Start date
2019-07-16
End date
2019-09-30
Secondary IDs
Abstract
In contrast to the traditional model of a firm, a startup is fundamentally inseparable from its founder. This experiment aims to demonstrate that the strategic decisions of startups are determined by a tradeoff between the conflicting professional and personal motivations of their founders. We use priming to explore how the relative importance of entrepreneurial identity affects the behavior of founders who face a strategic choice that will increase startup growth at a personal cost.
External Link(s)
Registration Citation
Citation
Gong, Sara and Jorge Guzman. 2019. "The Limits of Startup Strategy." AEA RCT Registry. August 15. https://doi.org/10.1257/rct.4302-3.0
Former Citation
Gong, Sara and Jorge Guzman. 2019. "The Limits of Startup Strategy." AEA RCT Registry. August 15. https://www.socialscienceregistry.org/trials/4302/history/51813
Sponsors & Partners

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Experimental Details
Interventions
Intervention(s)
This study examines how the profit-maximizing strategic decisions of growth-driven startups are constrained by the personal motivations of their utility-maximizing founders. In a novel experimental approach integrating economics, psychology, and strategy, we apply priming techniques to study entrepreneurial decision-making. We prime the "entrepreneurial identity" of startup founders and then study how a variation in identity salience influences their willingness to make certain choices in hypothetical scenarios.
Intervention Start Date
2019-07-16
Intervention End Date
2019-09-09
Primary Outcomes
Primary Outcomes (end points)
There are two key outcome variables of interest: entrepreneurs' score on the word-choice test describing their entrepreneurial identity dominance (entrep_score), and entrepreneurs' stated willingness to take hypothetical strategic decisions (strat_score). We measure entrep_score by calculating the number of entrepreneurial words a subject selects from the ten word pairs, divided by ten, so that entrep_score is between 0 and 1. We measure strat_score as the average of a subject's responses to the hypothetical scenario questions, where each scenario is a 5-point Likert item, with possible responses as "Very Unlikely" (1), "Unlikely" (2), "Neutral" (3), "Likely" (4), and "Very Likely" (5).

In our analysis, we study these variables in several ways:

(a) First, we run simple regressions showing whether treatment (a binary variable representing those that are primed) predicts a higher entrep_score and/or strat_score.

(b) We also study the distribution of these effects by using machine learning methods that allow us to recover the shape of the effects based on observables. In particular, we apply the honest tree method of Athey and Wagner (2015) and the sorted effects method of Chernozukhov et al (2018).

(c) Finally, we study how the difference in strat_score is determined by differences in the underlying characteristics of subjects. To do so, we run interaction models where we interact our treatment with pre-treatment characteristics and then estimate flexible functions of pre-treatment characteristics with the estimated effects from (b).
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
We perform the analysis described in "Primary Outcomes" independently on each hypothetical scenario. We do not have any priors at this moment on how their shape or magnitudes will be different.

We also add the following analyses:
(a) Changing our main outcome variable to a binary measure that is equal to 1 if the subjects choose "Likely" or "Very Likely" and 0 otherwise to study LPM, and logit specifications on the odds (or probability change) of being likely choose.
(b) The impact of being more 'entrepreneurial' (as defined by entrep_score) on strat_score as well as the impact of treatment across this distribution.
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
This study examines how the profit-maximizing strategic decisions of growth-driven startups are constrained by the personal motivations of their utility-maximizing founders. In a novel experimental approach integrating economics, psychology, and strategy, we apply priming techniques to study entrepreneurial decision-making. We prime the "entrepreneurial identity" of startup founders and then study how a variation in identity salience influences their willingness to make certain choices in hypothetical scenarios.
Experimental Design Details
Not available
Randomization Method
We performed block randomization according to the background characteristics of subjects collected at registration. We partitioned the covariate space using the following variables: startup financing stage, startup value, startup location, satisfaction with local resources, satisfaction with local quality of life, number of founders, number of employees, having children, marital status, having a second job, age, and gender. We then grouped similar subjects together, in backwards order of the listed variables, until each block had at least 4 subjects, as recommended by Athey and Imbens (2016), resulting in 18 total blocks. Finally, we performed a balance test (attached) to ensure that treatment and control group means were not significantly different among every covariate.
Randomization Unit
Individual.
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
The experiment is run at the individual level and not clustered.
Sample size: planned number of observations
We recruited class participants over the course of three weeks. Our total enrollment was 278, and after dropping non-entrepreneurs and minors, we had a subject pool of self-identified entrepreneurs with a startup, to whom we will send out the priming and control surveys. We been able to recruit 121 entrepreneurs for our experiment.
Sample size (or number of clusters) by treatment arms
61 treated, 60 control.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
There is no established value in the literature that can help guide our analysis, so we use the results of our pilot study for power calculations. However, because the effect sizes may have been biased by issues with the quality of our Qualtrics data, we intend to recruit larger samples than suggested by these power calculations. In our pilot study, we found that the mean entrep_score was 0.45 (0.18 SD) for the treatment group and 0.36 (0.19 SD) for the control group. This implied a Cohen's d effect size of 0.51, and thus the minimum sample size for detection (at a significance level of 0.05 with a power of 0.8) was 62 per group. We also found that the mean strat_score was 3.44 (0.81 SD) for the treatment group and 3.14 (0.81 SD) for the control group. This implied a Cohen's d effect size of 0.37, and thus the minimum sample size for detection (at a significance level of 0.05 with a power of 0.8) was 119 per group. We have attached the R code and output for these calculations.
Supporting Documents and Materials

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IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Columbia University IRB
IRB Approval Date
2019-07-02
IRB Approval Number
AAAS5166