Entrepreneurial domains, theories, and new ideas: evidence from a simulation-based lab experiment

Last registered on August 28, 2024

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Pre-Trial

Trial Information

General Information

Title

Entrepreneurial domains, theories, and new ideas: evidence from a simulation-based lab experiment

RCT ID

AEARCTR-0014233

Initial registration date

August 23, 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

August 28, 2024, 3:15 PM EDT

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

Locations

Country

Spain

Region

Primary Investigator

Name

Claudia Frosi

Affiliation

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

George Chondrakis

PI Affiliation

ESADE Business School

Contact Investigator

PI Name

Luisa Gagliardi

PI Affiliation

Bocconi University

Contact Investigator

PI Name

Myriam Mariani

PI Affiliation

Bocconi University

Contact Investigator

Additional Trial Information

Status

In development

Start date

2024-08-28

End date

2024-12-31

Keywords

Education, Firms & Productivity

Additional Keywords

Entrepreneurship

JEL code(s)

Secondary IDs

Prior work

This trial is based on or builds upon one or more prior RCTs.

Abstract

Entrepreneurs are often depicted as boundedly rational and myopic decision-makers who rely on local search and readily available data (Moore, Oesch, and Zietsma, 2007; Simon, 1957). When searching and attempting to define or assess market opportunities, entrepreneurs are guided by their pre-existing experiences. Their knowledge sets coming from individual experiences, backgrounds, values, and beliefs, or domains, define what is salient when framing strategic and entrepreneurial problems (Camuffo, Gambardella, and Pignataro, 2023).

While there is a growing body of literature arguing that theory framing is essential in strategic and entrepreneurial decision making (Felin and Zenger, 2017), little is known about the relationship between entrepreneurial domains, theory framing, and entrepreneurial idea (and solution) generation.

Since exploration beyond what is known can expand the opportunity set at the expense of increased uncertainty and monetary and non-monetary (i.e., time, effort) costs, it is unclear whether exploration beyond initial domains can be beneficial or not. Given this trade-off, we argue that entrepreneurs who engage in “scientific entrepreneurship” (Zellweger and Zenger, 2023), adopting a theory-based experimentation approach, explore in a more structured way and are able to reduce the burden of expanded search (Camuffo, Cordova, Gambardella, and Spina, 2020).

The goal of this project is to understand whether making entrepreneurs aware of the impact exerted by their domains on their entrepreneurial ideas, and endowing them with a structured decision-making approach, triggers a change in experimentation, search, and idea formulation. To this aim, we investigate the impact that domain awareness (which shall trigger increased exploration) has in conjunction to scientific or theory-based experimentation (which shall reduce the cost of exploration).

A companion study finds that domain awareness and theory-based experimentation have a complementary effect on the number of high-quality ideas generated by entrepreneurs (Frosi, Chondrakis, Gagliardi, and Mariani, 2024), but it remains agnostic as to what mechanism underlies this increase in idea generation.

This study adopts a similar design, and it aims to uncover the mechanisms underlying the main effects of receiving the domains intervention and the scientific (or theory-based) intervention (i.e., more and better ideas), attempting to understand what drives the increased number of ideas found in the field experiment with entrepreneurs. Lastly, this study aims to replicate the main effects of the same study (Frosi, Chondrakis, Gagliardi, and Mariani, 2024).

This project designs and implements a laboratory experiment following up on a field experiment with early-stage entrepreneurs carried out at ESADE University in Spain (previously pre-registered on AEA under the code AEARCTR-0009325) and a pilot laboratory experiment with entrepreneurship graduate students at Bocconi University in Italy (previously pre-registered on AEA under the code AEARCTR-0013189).

Practically, in this laboratory experiment, we leverage on a sample of 115 prospective MSc students (enrolled in different MSc programs at ESADE Business School). Participants will be randomly allocated to 4 experimental conditions (domains intervention: treated and control X theory intervention: treated and control). Participants will be randomly allocated to groups for the "theory" intervention (focused on the application of the Scientific Approach to entrepreneurial decision making) and for the "domains" intervention (focused on highlighting the advantages and disadvantages of exploring beyond initial domains), following a 2x2 experimental design. We will adopt a simple randomization technique.

The theory intervention is a representative but shorter version of the intervention delivered in the field experiment (AEARCTR-0009325) and it provides participants with a decision-making methodology that focuses on thoroughly framing a problem and on theorizing prior to experimenting. The domains intervention, similarly, is a representative but shorter version of the domains intervention delivered in the same field experiment (AEARCTR-0009325). In terms of execution, for the domains intervention we leverage on a simulation platform, following the same protocol of a pilot laboratory experiment run with students at Bocconi University (AEARCTR-0013189). Through this intervention, we make participants aware of their domains (the set of knowledge they derive from their past experiences and background) and of the impact that domains exert on the framing of their entrepreneurial theories.

Trainers will deliver the theory intervention via a frontal lesson, while the domains intervention will be based on a business simulation. During the latter, participants will be provided with a problem identified by an early-stage start-up, and will be asked to frame a “theory of value” (Agarwal et al., 2023) that maps the identified problem to an entrepreneurial solution. Following the first theory framing attempt, all participants will be shown a video, which will vary depending on their allocation:

1) Domains-Treated participants will receive an 8.10-minutes training video emphasizing the existence of domain-defining factors, their impact on theory formulation, and the advantages and disadvantages derived from exploring beyond them. Moreover, they will be asked to reflect on their own individual domains and how these may be influencing the theory framing that they are coming up with for the business simulation; the “treatment video” will also talk about scaling a business, and focusing on how start-ups move from theory formulation to execution.

2) Domains-Control participants will receive an 8.10-minutes training (placebo) video talking about scaling a business, and focusing on how start-ups move from theory formulation to execution.

After the video, we will collect a number of self-reported and objective measures of performance, and we will compare them across different experimental cells to investigate the interaction between the domains and the theory-based interventions on exploration, theory formulation, and entrepreneurial solution generation.

External Link(s)

Registration Citation

Citation

Chondrakis, George et al. 2024. "Entrepreneurial domains, theories, and new ideas: evidence from a simulation-based lab experiment." AEA RCT Registry. August 28. https://doi.org/10.1257/rct.14233-1.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

In terms of experimental flow, participants will first be exposed to the theory-based approach (if randomly allocated to the theory treated group) or to the lean startup approach (if randomly allocated to the theory control group).

After this session, participants start a computer-based simulation entailing the generation of an entrepreneurial solution. First, we will collect some questions on the familiarity of decision and / or industry provided in the simulation for each participant, as well as on their level of "scientific intensity" (a measure of absorption of the theory intervention). Next, participants will be exposed to the case of an early-stage startup in the process of defining its first idea configuration. We will ask participants to propose a first idea configuration in the form of a theory or "business idea map", indicating what the key elements of this theory are (i.e., attributes) and how they are connected together through causal relationships (i.e., links) to reach an end solution.

After this first attempt, we will ask participants to indicate their confidence (from 0 to 100) that the theory they have developed is true (i.e., the likelihood that such configuration will be successful) and their perception on the completeness (from 0 to 100) of the configuration (i.e., whether the configuration includes all relevant elements).

Then, we will ask them to watch an 8.10-minutes training video (different for control and treatment conditions), followed by a manipulation check.

At this point, we will ask participants to decide whether they want to update the initial configuration by exploring (and “unlocking”) new attributes or not. Participants will then be shown more attributes (if they have decided to “explore”) or the same attributes as in the first attempt (if they have decided not to “explore”) and they will create a second theory causal map attempt. In this second attempt, they will be free to include the new attributes or not, and to connect initial and new attributes as they wish. Similarly to attempt 1, participants will be asked to report their level of confidence and of perceived completeness of the theory configuration. We will run this exploration-theory formulation cycle one more time.

Finally, participants will be asked to answer to a final survey, collecting the key outcome variables of this study.

Theory configuration tasks within the experimental flow will be timed to grasp a measure of cognitive effort. In total, the activity will take approximately 1 hour to complete.

Participants will receive the training and solve the provided prompt in ESADE facilities. We will collect their informed consents prior to the experiment.

Intervention (Hidden)

Intervention Start Date

2024-08-28

Intervention End Date

2024-09-03

Primary Outcomes

Primary Outcomes (end points)

The primary outcomes that we analyse in this study include:

• Level of exploration effort: a variable indicating whether individuals decide to explore (and “unlock” more attributes or not)
• Text-based theory measure
• Theory causal map components:
o Number of different theories generated (3 attempts)
o Number and type of attributes used
o Number and type of attribute categories used
o Number of links among attributes
o Breadth of theories (number of vertical links)
o Depth of theories (number of horizontal links)
o Confidence and completeness measures of theory causal maps: 0-100 indicators
o Quality of theory causal maps (vs. benchmark, vs. “true” causal map, external evaluation)
o Level of cognitive effort: self-reported measure (1-7 Likert scale) and platform-derived measure (time spent on theory configuration)

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

In this laboratory experiment, we leverage on a sample of 120 prospective MSc students (enrolled at the entrepreneurship MSc program at ESADE Business School) who will be trained on the theory-based approach to decision-making (“theory” intervention) and receive a second "domains" intervention. This experiment is a follow-up of a field experiment with early-stage entrepreneurs (AEARCTR-0009325) and of a (pilot) lab experiment with graduate students (AEARCTR-0013189).

Participants will be randomly allocated to 4 experimental conditions (domains intervention: treated and control X theory intervention: treated and control). In particular, participants will be randomly allocated to groups for the "theory" intervention (focused on the application of the Scientific Approach to entrepreneurial decision making) and for the "domains" intervention (focused on highlighting the advantages and disadvantages of exploring beyond initial domains), following a 2x2 experimental design. We adopt stratified randomization, using the two student cohorts that we leverage on as strata. This way, we ensure that each cohort has a similar split of participants among the 4 conditions.

The theory intervention is a representative but shorter version of the intervention delivered in the field experiment (AEARCTR-0009325) and it provides participants with a decision-making methodology that focuses on thoroughly framing a problem and theorizing prior to experimenting. The domains intervention, instead, is identical to the pilot laboratory experiment run with students at Bocconi University (AEARCTR-0013189) and makes participants aware of their domains (the set of knowledge they derive from their past experiences and background) and of the impact that domains exert on the framing of their entrepreneurial theories.

The activity is presented as one of the requirements of a curricular course on business, and will take place during one of the course’s sessions.

The experiment is based on a frontal session (theory-based intervention) and on a business simulation (within which the domain intervention is embedded) where participants will be provided with a problem identified by an early-stage start-up, and will be asked to frame a “theory of value” (Agarwal et al., 2023) that maps the identified problem to an entrepreneurial solution. Following the first theory framing attempt, treated and untreated participants are shown different videos:

1) Treated participants will receive an 8.10-minutes training video emphasizing the existence of domain-defining factors, their impact on theory formulation, and the advantages and disadvantages derived from exploring beyond them. Moreover, they will be asked to reflect on their own individual domains and how these may be influencing the theory framing that they are coming up with for the business simulation;

2) Non-treated entrepreneurs will receive an 8.10-minutes training (placebo) video talking about scaling a business, and focusing on how start-ups move from theory formulation to execution.

After the video, we will run a manipulation check, and we will ask participants in both groups whether they would like to engage in exploration, gaining access to new problem elements (i.e., attributes – Camuffo, Gambardella, and Pignataro, 2023) at the cost of time, or whether they would like to keep relying on initial elements. Participants in both groups will be asked to come up with two more theory attempts, regardless of whether they decided to explore in exploration or not.

Throughout the simulation, we will collect a number of self-reported measures, including participants’ beliefs about their theories (confidence, completeness), whether they have thought of alternative solutions for the problem provided, whether they would have considered additional attributes or not, and whether the activity entails high cognitive effort or not. Moreover, we will collect all theory causal map attempts made by participants, measuring changes from one attempt to the next, as well as measuring a number of indicators on the theory causal maps’ composition (in terms of number of attributes, number of attribute categories, number of links among attributes), on their configuration (breadth and depth of causal maps), as well as on their quality (deviation from an aggregated benchmark, deviation from a “true” solution, external evaluation). Lastly, we will measure exploration decisions made by participants throughout the simulation (e.g., engage in exploration vs. not) as well as the time spent in each theory framing step.

Experimental Design Details

Randomization Method

We adopt stratified randomization, using the two student cohorts that we leverage on as strata.

We run randomization procedures in the office. We first split the complete sample into the 2 cohorts. Then, we perform randomization by cohort, assigning students to 4 experimental conditions (first for one cohort, then for the other).

Practically, we start by assigning a random number (from 0 to 1) that follows a uniform distribution to each student. We then rank students by their random numbers, and assign the first quarter of the ranking to the domains control - theory control condition, the second quarter to the domains control - theory treated condition, the third quarter to the domains treated - theory control condition, and the third quarter to the domains treated - theory treated condition.

We run this procedure 10 times, using 10 different seeds, and select one of the seeds for each cohort. We then run balance checks on the selected seed and find no imbalances across the groups.

Randomization Unit

Individual

Was the treatment clustered?

Yes

Experiment Characteristics

Sample size: planned number of clusters

2 cohorts (one receiving the two interventions on 28/08/2024, the other on 03/09/2024).

Sample size: planned number of observations

120 prospective MSc students.

Sample size (or number of clusters) by treatment arms

120 individuals, 0.25:0.25:0.25:0.25 split: 30 individuals in cell 1 (control-control), 30 individuals in cell 2 (control-treated), 30 individuals in cell 3 (treated-control), 30 individuals in cell 4 (treated-treated). Numbers may slightly vary depending on students showing up to the session.

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

We justify the size of the sample based on experimental power calculations conducted using G*Power 3.1 (command F-tests, ANOVA: repeated measures, within-between interactions). We assume we detect a small effect size (f=0.15). We assume standard type I and type II errors (α err prob = 0.05 Power (1-β err prob) = 0.95). Given the 4 experimental cells, about 3 observations per individual on key outcome variables (theory causal maps), and relatively high correlation among repeated measures as provided by the pilot laboratory experiment (0.7) and correction for non-sphericity (ε = 1), we obtain a required total sample size of approximately 96 participants. Given a targeted sample size of 120 students, we believe that our sample is in line with the effect that we expect to record.

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