Entrepreneurial strategies and domains: evidence from a simulation-based lab experiment

The experiment is based on a Qualtrics-based business simulation where participants will first be asked to complete a baseline survey, collecting both demographic variables and variables assessing the level of each participant’s absorption of the theory-based approach.

Next, participants will be shown a training video (including the “domain” intervention or a placebo video). In particular:

1) Treated participants will receive an 8-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 their decision-making; the “treatment video” will also talk about scaling a business, and focusing on how start-ups move from theory formulation to execution.

2) Non-treated participants will receive an 8-minutes training (placebo) video talking about scaling a business, and focusing on how start-ups move from theory formulation to execution.

Next, participants will be provided with 3 business models in the form of “theories of value” (Agarwal et al., 2023) developed by 3 early-stage start-ups in the process of defining their first strategic configurations.

At this point, we will ask participants whether they want to explore further or whether they want to launch the start-ups with the presented business models. In case they decide to engage in further exploration, we will ask them to propose elements (i.e., attributes) that they would explore on before going to the market, as well as any additional connections that they would draw among those elements.

Throughout the simulation, we will collect a number of measures, including participants’ beliefs about the final business models, and whether they would have considered additional attributes (and links) or not. Moreover, we will collect the time that it takes each participant to complete relevant steps (i.e., questions related to exploration and to additional attributes / links).

2024-10-13

2024-12-31

• Level of exploration effort (decision to explore/ not, time spent deciding to explore)
• Business model (theory) components: number of additional attributes and links considered

- Confidence measure of final business models (theories)

In this laboratory experiment, we rely on a sample of 75 MSc students (enrolled in the entrepreneurship MSc program at Bocconi University) who have been trained on the theory-based approach to decision-making (the “scientific” or “theory” intervention), and we expose them to a second intervention, the “domains” intervention. In subsequent waves, we plan to involve more students from different courses.

The domains intervention, which is at the core of this pre-analysis plan, 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, and subsequent strategic alternatives.

Participants to the study, who have already received the theory intervention, are randomly allocated to two groups for the domains intervention (treated, untreated). We adopt a simple randomization routine.

The activity is presented as one of the requirements of a curricular course on strategic decision-making, and will take place during one of the course’s sessions (after participants have received the “theory” intervention). Students are rewarded with up to a bonus point (1/10) based on their performance during the experiment. Students who participate in this experiment are unaware of the goals of the experiment.
The experiment is based on a Qualtrics-based business simulation where participants will first be asked to complete a baseline survey, collecting both demographic variables and variables assessing the level of each participant’s absorption of the theory-based approach.
Next, participants will be shown a training video (including the “domain” intervention or a placebo video). In particular:

1) Treated participants will receive an 8-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 their decision-making; the “treatment video” will also talk about scaling a business, and focusing on how start-ups move from theory formulation to execution.

2) Non-treated participants will receive an 8-minutes training (placebo) video talking about scaling a business, and focusing on how start-ups move from theory formulation to execution.

Next, they will be provided with 3 business models in the form of “theories of value” (Agarwal et al., 2023) developed by 3 early-stage start-ups in the process of defining their first strategic configurations.

At this point, we will ask them whether they want to explore further or whether they want to launch the start-ups with the presented business models. In case they decide to engage in further exploration, we will ask them to propose elements (i.e., attributes) that they would explore on before going to the market, as well as any additional connections that they would draw among those elements.

Throughout the simulation, we will collect a number of measures, including participants’ beliefs about the final business models, whether they would have considered additional attributes (and links) or not. Moreover, we will collect the time that it takes each participant to complete relevant steps (i.e., questions related to exploration and to additional attributes / links).

Not available

Participants are randomly assigned to the 2 treatment groups by simple randomization. Randomization is performed using STATA 18.0.

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 half of the ranking to the domains condition, and the second half to the control condition.

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

Individual

No

75 individuals (wave 1) + subsequent waves

75 individuals, 50-50 split between treatment and control. Moreover, we collect our key dependent variable of interest for 3 cases (per each respondent), ending with 225 observations. Moreover, subsequent waves may concur to increasing the number of observations.

40 in domains (treated group), 35 in control group.
Subsequent waves will replicate this breakdown.

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). Given a small (f=0.15-0.2) effect size, assuming standard type I and type II errors (α err prob = 0.05 Power (1-β err prob) = 0.95), given the existence of 2 experimental cells, about 3 observations per individual on key outcome variables (exploration), and relatively high correlation among repeated measures (0.5) and correction for non-sphericity (ε = 1), we obtain a target sample size of 66 to 116 participants. We believe that our sample is in line with the effect that we expect to record.