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

Last registered on September 12, 2024
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Trial Information

General Information

Title
Entrepreneurial domains, theories, and new ideas: evidence from a simulation-based lab experiment
RCT ID
AEARCTR-0014300
Initial registration date
September 05, 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
September 12, 2024, 5:49 PM EDT

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

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Primary Investigator

Name
Claudia Frosi
Affiliation
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Other Primary Investigator(s)

PI Name
George Chondrakis
PI Affiliation
ESADE Business School
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PI Name
Luisa Gagliardi
PI Affiliation
Bocconi University
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PI Name
Myriam Mariani
PI Affiliation
Bocconi University
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Additional Trial Information

Status
In development
Start date
2024-09-12
End date
2025-06-30
Keywords
Behavior, Education, Firms & Productivity
Additional Keywords
Entrepreneurship, Decision-making
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any 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.

The goal of this project is to understand whether making entrepreneurs aware of the impact exerted by their domains on their entrepreneurial ideas triggers a change in experimentation and search, beyond initial domains. We want to understand under which circumstances exploration past initial domains can be beneficial in terms of key entrepreneurial outcomes such as idea generation and performance.

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).

To this aim, we investigate the impact that domain awareness (which shall trigger increased exploration) has in addition 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 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 administers a similar manipulation, but aims to uncover how the domains intervention, together with the scientific (or theory-based) intervention, impacts theory framing and decision-making. Moreover, this study aims to uncover the mechanisms underlying the main effects of receiving the domains intervention and the scientific (or theory-based) intervention (i.e., more 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 results of the companion study (Frosi, Chondrakis, Gagliardi, and Mariani, 2024) that finds that domain awareness and theory-based experimentation have a complementary effect on the number of ideas generated by entrepreneurs.

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).

In this laboratory experiment, we leverage on a sample of approximately 200 MSc students (enrolled in different MSc programs at Bocconi University). Participants will be randomly allocated to 4 experimental conditions (domains intervention: treated and control X theory intervention: treated and control). Participants are 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 three 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 manipulation will take place through a business simulation. Both interventions will be delivered via training videos embedded within teh 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 (depending on their allocation):

1) Theory-Treated participants will receive a 9-minutes training video on the theory-based approach, emphasizing the role of forming theories and thoroughly framing problems prior to experimentation;

2) Theory-Control participants will receive an 9-minutes training video centered on the lean startup approach, and focusing on the business model canvas as a tool to conceputalize business ideas.

Following the first video, participants will be shown a second video (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. September 12. https://doi.org/10.1257/rct.14300-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
In terms of experimental flow, participants will engage with 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 theory-based decision making intensity). 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).

Following the first theory framing attempt, all participants will be shown a video (depending on their allocation):
1) Theory-Treated participants will receive a 9-minutes training video on the theory-based approach, emphasizing the role of forming theories and thoroughly framing problems prior to experimentation;
2) Theory-Control participants will receive a 9-minutes training video centered on the lean startup approach, and focusing on the business model canvas as a tool to conceputalize business ideas.

Following the first video, participants will be shown a second video (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.

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 Bocconi University facilities. We will collect their informed consents prior to the experiment.
Intervention Start Date
2024-09-12
Intervention End Date
2025-06-30

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

Experimental Design
In this laboratory experiment, we leverage on a sample of 200 MSc students (enrolled at two MSc programs at Bocconi University), who, through a business simulation will receive two interventions ("theory" intervention and "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 will adopt a stratified randomization methodology, using the 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 (embedded within the simulation and delivered via a video) 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 (embedded within the simulation and delivered via a video), 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 two curricular courses, and will take place during one of the courses' sessions.

The experiment is based on a business simulation (within which both the theory and the domains intervention are 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.


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).

Following the first theory framing attempt, all participants will be shown a video (depending on their allocation):
1) Theory-Treated participants will receive an 8.30-minutes training video on the theory-based approach, emphasizing the role of forming theories and thoroughly framing problems prior to experimentation;
2) Theory-Control participants will receive an 8.30-minutes training video on a the lean startup approach, emphasizing the role of business idea conceputalization via the business model canvas;

Following the first video, participants will be shown a second video (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 videos, we will run a manipulation check, and we will ask participants in all 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 across all groups will be asked to come up with two more theory attempts, regardless of whether they decided to engage 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 (both in map- and in text-based form), 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
Not available
Randomization Method
We adopt stratified randomization, using the student cohorts that we leverage on as strata.

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

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 fourth 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?
No

Experiment Characteristics

Sample size: planned number of clusters
3 cohorts
Sample size: planned number of observations
200 MSc students
Sample size (or number of clusters) by treatment arms
200 individuals, 0.25:0.25:0.25:0.25 split: 50 individuals in cell 1 (control-control), 50 individuals in cell 2 (control-treated), 50 individuals in cell 3 (treated-control), 50 individuals in cell 4 (treated-treated). Numbers may slightly vary depending on the number of 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 (0.5) and correction for non-sphericity (ε = 1), we obtain a required total sample size of 160 participants. Given a targeted sample size of 200 students, we believe that our sample is in line with the effect that we expect to record.
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Bocconi Research Ethics Committee
IRB Approval Date
2024-09-05
IRB Approval Number
EA000729.02
Analysis Plan

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