x

Please fill out this short user survey of only 3 questions in order to help us improve the site. We appreciate your feedback!
Different Approaches to Entrepreneurial Decision-Making: A Field Experiment in Italy
Last registered on September 07, 2020

Pre-Trial

Trial Information
General Information
Title
Different Approaches to Entrepreneurial Decision-Making: A Field Experiment in Italy
RCT ID
AEARCTR-0006263
Initial registration date
September 04, 2020
Last updated
September 07, 2020 2:00 AM EDT
Location(s)

This section is unavailable to the public. Use the button below to request access to this information.

Request Information
Primary Investigator
Affiliation
INSEAD
Other Primary Investigator(s)
PI Affiliation
Politecnico di Milano
PI Affiliation
Politecnico di Torino
PI Affiliation
Bocconi University
PI Affiliation
Politecnico di Torino
PI Affiliation
Politecnico di Milano
PI Affiliation
Bocconi University
PI Affiliation
Politecnico di Torino
PI Affiliation
Politecnico di Milano
PI Affiliation
Bocconi University
PI Affiliation
Bocconi University
PI Affiliation
Politecnico di Torino
PI Affiliation
Politecnico di Torino
PI Affiliation
Politecnico di Milano
PI Affiliation
Politecnico di Torino
Additional Trial Information
Status
In development
Start date
2020-05-01
End date
2022-02-28
Secondary IDs
M13
Abstract
This project focuses on how entrepreneurs can make decisions under conditions of uncertainty. The goal is to extend prior work that shows that entrepreneurs can improve their ability to make key decisions for the development of their business by adopting a set of practices labelled 'scientific approach'. A scientific approach is a set of routines based on the formulation of theories and predictions tested with data, resembling what scientists do. When entrepreneurs use this approach, they accurately frame and identify the problem they face, while articulating theories, defining testable hypotheses, conducting rigorous tests, and making decisions based on the results of these tests. We conduct a randomized controlled trial (RCT) to obtain rigurous evidence and test the impact of the scientific approach vis-a-vis another popular approach to decision-making, effectuation. When using effectuation, entrepreneurs use a non-predictive approach through which they identify the next steps by gauging what resources they have available, while continuously balancing the interests of self-selected stakeholders with their resources.
Building on two pilot studies (RCTs) conducted in Italy and involving 116 and 250 founders of new startups, we are conducting another RCT with 500 entrepreneurs to understand the conditions under which different approaches to decision-making enable or constrain entrepreneurial action.
External Link(s)
Registration Citation
Citation
Bacco, Francesca et al. 2020. "Different Approaches to Entrepreneurial Decision-Making: A Field Experiment in Italy." AEA RCT Registry. September 07. https://doi.org/10.1257/rct.6263-1.1.
Experimental Details
Interventions
Intervention(s)
Entrepreneurs will receive 8 sessions of online training. These sessions will include interactive lectures and coaching by qualified mentors/instructors each working with a subset of the startup sample. Treated and control startups will receive the same amount of training on entrepreneurial decision making (on topics like business model canvas, customers' interviews, minimum viable products/services, concierge/prototype, etc.). One set of treated startups will be taught how to make entrepreneurial decisions according to a scientific approach. Another set of treated startups will be taught how to make entrepreneurial decisions according to an effectuation approach. The third set of entrepreneurs will receive equivalent training without any specific approach. A final set of entrepreneurs will not receive any training.
Intervention Start Date
2020-10-16
Intervention End Date
2021-02-13
Primary Outcomes
Primary Outcomes (end points)
1. Revenue: our main dependent variable is the amount of revenue (euros)
2. Dropout: this is a binary variable that takes value 0 until the firm drops out (they abandon the program and cease the startup), 1 in the time period in which the firm drops out, and it is a missing value thereafter. In order to avoid attrition biases, we will check that the entrepreneurs that leave the program will actually cease any entrepreneurial activity.
3. Pivot: this is the cumulative number of times in which a startup makes a major change to its business model. We defined a change to be major by analyzing whether the entrepreneur moved from the original idea to another idea that changed the core value proposition of the product or service.

The research team will also examine potential mechanisms behind these outcomes by collecting variables related to how and when entrepreneurs make decisions. The key mechanism being tested is precision and improvement in the precision of predictions.
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
The research team is also examining the interaction of the treatments with a number of covariates of interest, including:
a) Gender
b) Psychological traits
c) Perception of competition and information sharing
d) Management practices adopted by the entrepreneurial teams
e) Well-being
f) Accumulation of knowledge
g) Passion
h) Communication
i) Knowledge sharing among team members
j) Influence of external mentors
k) Role of experience and background in adopting different approaches
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
The RCT focuses on nascent entrepreneurs (defined as those in the process of starting a new business). We do not restrict participation to startups operating in one or a few industries. We advertise the program through digital channels as a general course overseeing all the important aspects of new venture creation. The course is free of charge in order to ensure the participation of firms with limited financial resources. Startups receive training sessions remotely through an online platform. The same amount of training is offered to all three groups in order to ensure comparability. Multiple instructors/coaches will be trained to deliver the training using standard materials, purposely designed for this study. Each instructor/coach teaches three classes (one subgroup of startups treated with the scientific approach, one subgroup of startups treated with an effectuation approach, and one subgroup of control startups). The research team designs, coordinates and oversees all the activities, ensuring that the learning modules and coaching activities are appropriately carried out by the instructors/coaches.
Experimental Design Details
Not available
Randomization Method
Pure parallel randomization, done in office by a computer using STATA. After being randomly assigned into one of the four groups (pure control, control, effectuation or scientific), multiple subgroups of startups (of about 35 startups) will be created and randomly matched with the instructors/coaches so that each instructor/coach teaches three subgroups - of approximately 35 startups. Randomization checks: mean values comparison and t-tests across groups.
Randomization Unit
Start-up
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
Not clustered
Sample size: planned number of observations
500 startups X 12 (points in time) = 6000 observations
Sample size (or number of clusters) by treatment arms
500 startups (125 in the scientific group, 125 in the effectuation group, 125 in the control group, 125 in the pure control group)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering) Power calculations were conducted with GPower, assuming a very small effect (0.1) F tests - ANOVA: Repeated measures, within-between interaction Analysis: Post hoc: Compute achieved power Input: Effect size f = 0.1 α err prob = 0.05 Total sample size = 500 Number of groups = 2 Number of measurements = 4 Corr among rep measures = 0.5 Nonsphericity correction ε = 1 Output: Noncentrality parameter λ = 40.0000000 Critical F = 2.6108586 Numerator df = 3.0000000 Denominator df = 1494 Power (1-β err prob) = 0.9999133
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Bocconi University
IRB Approval Date
2020-05-22
IRB Approval Number
SA000112