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Abstract
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Before
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.
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After
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.
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Trial Start Date
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July 01, 2019
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July 16, 2019
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JEL Code(s)
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L26, D9
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L26, D03, D9, D21, D22
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Last Published
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July 03, 2019 03:21 PM
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July 15, 2019 09:52 PM
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Intervention (Public)
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Before
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 that integrates psychology, economics, and strategy research, 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.
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After
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.
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Intervention Start Date
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Before
July 08, 2019
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July 16, 2019
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Primary Outcomes (End Points)
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Before
We measure entrepreneurs' stated willingness to take each of the hypothetical strategic decisions described in the "Interventions" section, and the difference between primed and non-primed entrepreneurs. Each scenario is a 5-point Likert item, with possible responses as "Very Unlikely" (1), "Unlikely" (2), "Neutral" (3), "Likely" (4), "Very Likely" (5). We define an outcome variable "Strategic Commitment Score," as the average of each subject's answers, and study this variable in several ways:
(a) First, we run a simple regression showing whether treatment (a binary variable representing those that are primed) predicts a higher level of Strategic Commitment 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 Strategic Commitment Scoreis determined by differences in the underlying characteristics of subjects. To do so, we run interaction models where we interact our treatment with the pre-treatment characteristics and then estimate flexible functions of pre-treatment characteristics with the estimated effects from (b).
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After
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).
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Experimental Design (Public)
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Before
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 that integrates psychology, economics, and strategy research, 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.
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After
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.
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Randomization Method
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Before
Study 1: 45 primed and 45 control. Random assignment through Qualtrics in their outreach approach.
Study 2: 250 primed and 250 control. Block randomization, the values of which will be chosen based on the characteristics provided by the (class) pre-registration of the subjects. Potential candidates in this randomization are gender, marital status, and age, but we would like to see the distribution of this variables in the data first before randomizing.
We will amend our pre-registration when we have the class subjects identified to we can better state our randomization approach before study 2 has taken place.
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After
We will use block randomization on variables chosen based on the characteristics of subjects registered in the class. Potential candidates in this randomization are gender, marital status, and age, but we would like to see the distribution of this variables in the data first before randomizing. We will amend our trial registration when we have the class subjects registered so we can better state our randomization approach.
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Randomization Unit
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Before
We will amend our pre-registration when we have the class subjects identified to we can better state our randomization approach before study 2 has taken place.
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After
We will use block randomization on variables chosen based on the characteristics of subjects registered in the class. Potential candidates in this randomization are gender, marital status, and age, but we would like to see the distribution of this variables in the data first before randomizing. We will amend our trial registration when we have the class subjects registered so we can better state our randomization approach.
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Planned Number of Clusters
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Before
We will amend our pre-registration when we have the class subjects identified to we can better state our randomization approach before study 2 has taken place.
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After
We will use block randomization on variables chosen based on the characteristics of subjects registered in the class. Potential candidates in this randomization are gender, marital status, and age, but we would like to see the distribution of this variables in the data first before randomizing. We will amend our trial registration when we have the class subjects registered so we can better state our randomization approach.
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Planned Number of Observations
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Study 1: We recruit 90 entrepreneurs registered with Qualtrics Panels.
Study 2: We hope to register 1000 individuals in our class, but after screening and attrition our target is to have about 500 subjects for analysis.
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We plan to recruit 1000 class participants during a span of two weeks. If we do not meet our target of by the end of that period, we will continue to recruit for another week, then close enrollment. Before the class begins, we will invite class registrants to participate in the experimental surveys. Overall, we aim to have at least 250 subjects per treatment group after 20-50% attrition.
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Sample size (or number of clusters) by treatment arms
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Before
Study 1: 45 primed and 45 control.
Study 2: 250 primed and 250 control.
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250 primed and 250 control
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Power calculation: Minimum Detectable Effect Size for Main Outcomes
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Before
We do not have good priors on the amount of power necessary to identify our effects, as there is no established value in the literature that can help guide our analysis. We believe that doing any power calculations at this stage would simply not be useful.
We do note that we can take advantage of the fact that we have two different studies to assess the validity and necessary power from one to the other, and hope to do so accordingly. We also intend in updating our pre-registration by the end of study 1, with our measure of power for Study 2.
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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.
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Intervention (Hidden)
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Before
Research in psychology posits that individuals have multiple identities centered around the different social roles they occupy, and that their behavior at a given time may depend on which identity is dominant (Akerlof and Kranton, 2000). Priming interventions can cause a particular identity to become more salient, altering an individual's values and desires and, thus, his or her economic decisions (Benjamin et al, 2010). This study examines how the profit-maximizing strategic decisions of startups are constrained by the personal motivations of their utility-maximizing founders. In a novel experimental approach that integrates psychology, economics, and strategy research, we apply priming techniques to study entrepreneurial decision-making. In two experiments, we prime entrepreneurs' "entrepreneurial identity" through a survey mechanism. Entrepreneurs are randomly distributed either the priming or the control version of the survey. The priming questionnaire asks four questions about their startup strategy (e.g., "What is your exit strategy?"), while the control questionnaire asks four questions about their work-life balance (e.g., "Whom do you typically spend leisure time with?").
Experiment 1 (Manipulation Check): The purpose of the first experiment is to verify that our priming intervention does increase "entrepreneurial identity." Our sample for the first experiment consists of entrepreneurs recruited through Qualtrics Online Samples, and we screen out those who have companies older than 5 years. After answering the priming or control questions, both priming and control groups complete a word-choice test to measure the dominance of their entrepreneurial identities. Subjects are asked to choose the word that best describes them from a series of ten word pairs. In each pair, one word is entrepreneurial (i.e., "innovative") and one is not (i.e., "generous"). We expect that the priming group will have a higher average of entrepreneurial words selected. We also use this first experiment as an opportunity to test the hypothetical scenario questions described below, and we intend to use the experimental results from this study for our power calculations.
Experiment 2 (Main Experiment): Our main sample is drawn from a population of entrepreneurs who have registered for a free online class that will be taught by one of us. Entrepreneurs are asked to complete a 10-minute registration form with questions about their personal and startup backgrounds. We screen our registrants to select growth-driven startups that are not more than 5 years old. Several weeks after registering for the class, subjects are requested to complete a pre-class survey. The pre-class survey is again either the priming or control condition, consisting of the same priming or control questions, followed by questions about how the subject would behave in the following hypothetical scenarios. We designed these questions to specify a trade-off between startup growth and personal utility:
A. A major Silicon Valley venture capital firm has contacted you to express interest in investing in your startup. The firm has a history of partnerships with numerous highly-successful companies, and Series A investments in its partners are typically around $5 million. However, the firm only invests in startups that are located in Silicon Valley. How likely would you be to decide to move to Silicon Valley?
B. For the last month, you have been employing a close relative to do part-time work for your startup. However, the quality of their work is below expectations, often requiring you to spend extra time correcting their errors. You have spoken to the relative regarding your concerns, but the errors still continue. How likely would you be to fire your relative?
C. You have planned a long vacation to spend some quality time with your family and/or your closest friends, but you have just been invited to a special event where you will have the opportunity to network with numerous potential investors. This event is going to be held during the time you had planned to be on vacation. How likely would you be to cancel your vacation?
D. Your startup has the opportunity to make an investment that may greatly increase growth in the future. In order to finance the investment, you would have to nearly drain your own retirement savings. How likely would you be to make the investment?
We examine the discrepancy between the priming and control groups to elucidate how personal costs constrain strategic decision-making.
Prior to this trial registration, we conducted a pilot on a sample of 12 entrepreneurs recruited through Qualtrics Online Samples (a different sample than the one that will be used in the manipulation check described above). The data and R scripts used are attached to this trial registration.
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After
Research in psychology and economics posits that individuals have multiple identities centered around the different social roles they occupy, and that their behavior at a given time may depend on which identity is dominant (Akerlof and Kranton, 2000). Priming interventions can cause a particular identity to become more salient, altering an individual's values and desires and, thus, his or her economic decisions (Benjamin et al, 2010). This study examines how the profit-maximizing strategic decisions of startups are constrained by the personal motivations of their utility-maximizing founders.
We develop a novel experimental approach that applies priming techniques to study entrepreneurial decision-making and, in turn, how identity limits startup strategy. Our experiment primes entrepreneurs' "entrepreneurial identity" through a survey mechanism, where entrepreneurs are randomly distributed one of two versions of a survey, priming or control. The priming version asks subjects four questions about their startup strategy (e.g., "What is your exit strategy?"), while the control version asks four questions about their work-life balance (e.g., "Whom do you typically spend leisure time with?"). In both surveys, the priming or control questions are followed by questions about how the subject would behave in the following hypothetical scenarios. We designed these questions to specify a trade-off between startup growth and personal utility:
A. A major Silicon Valley venture capital firm has contacted you to express interest in investing in your startup. The firm has a history of partnerships with numerous highly-successful companies, and Series A investments in its partners are typically around $5 million. However, the firm only invests in startups that are located in Silicon Valley. How likely would you be to decide to move to Silicon Valley?
B. For the last month, you have been employing a close relative to do part-time work for your startup. However, the quality of their work is below expectations, often requiring you to spend extra time correcting their errors. You have spoken to the relative regarding your concerns, but the errors still continue. How likely would you be to fire your relative?
C. You have planned a long vacation to spend some quality time with your family and/or your closest friends, but you have just been invited to a special event where you will have the opportunity to network with numerous potential investors. This event is going to be held during the time you had planned to be on vacation. How likely would you be to cancel your vacation?
D. Your startup has the opportunity to make an investment that may greatly increase growth in the future. In order to finance the investment, you would have to nearly drain your own retirement savings. How likely would you be to make the investment?
The questionnaire also includes a word-choice test to measure the dominance of subjects' entrepreneurial identities. Subjects are asked to choose the word that best describes them from a series of ten word pairs. In each pair, one word is entrepreneurial (i.e., "innovative") and one is not (i.e., "generous"). We study variation in the number of entrepreneurial words chosen across treatment conditions as a manipulation check. Finally, the questionnaire asks respondents the minimum value of their startup to measure whether priming affects startup valuation as well as personal preferences.
Our main sample is drawn from a population of entrepreneurs who have registered for a free online class that will be taught by one of us (Guzman). Entrepreneurs register by completing a 10-minute registration form with questions about their personal and startup backgrounds. Several weeks after registering for the class, subjects are asked to fill out a pre-class survey, which constitutes the experiment. We screen our registrants to select young growth-driven startups, selecting only those who answer "Yes" to the question "Are you a startup founder?".
We have already conducted a two-phase pilot through Qualtrics Online Samples, and we are now updating our trial registration to include improvements made after this pilot. Our first pilot phase was a soft launch of our surveys on a sample of 12 self-identified entrepreneurs, and our second phase was a full launch on a sample of 100. In our initial trial registration, we had hoped to present the second phase as an experiment in its own right. As of this update after pilot completion, we have found strong evidence for the effect of priming on entrepreneurial identity and strategic decision-making, and have attached our data and R code for analysis. However, we are concerned about the quality of our data: our survey respondents recruited by Qualtrics included, for instance, a contract driver and a teenager who mows lawns. In addition, although survey distribution was randomized, we also found evidence that entrepreneurs with less valuable companies were less likely to complete our priming survey. We also did not collect statistics on attrition and survey drop-out. Therefore, we believe this first experiment does not meet scientific standards, and we intend to present these results not in our main paper but in an appendix. Nevertheless, the results of the pilot provide strong motivation for launching our experiment on our main sample, and were also useful for power calculations.
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Secondary Outcomes (End Points)
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Before
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) Only for Study 1, the impact of being more 'entrepreneurial' (as defined through the word choice tests) on the Strategic Commitment Score as well as the impact of treatment across this distribution.
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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.
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