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Multiple Approaches to Entrepreneurial Decision-Making: Evidence from India
Last registered on September 07, 2020


Trial Information
General Information
Multiple Approaches to Entrepreneurial Decision-Making: Evidence from India
Initial registration date
July 13, 2020
Last updated
September 07, 2020 1:58 AM EDT

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Primary Investigator
Other Primary Investigator(s)
PI Affiliation
Indian School of Business
PI Affiliation
Indian School of Business
PI Affiliation
Bocconi University
PI Affiliation
Bocconi University
PI Affiliation
Bocconi University
Additional Trial Information
In development
Start date
End date
Secondary IDs
An increasingly complex socio-economic landscape has recently highlighted the importance of decision-making under uncertainty. Making decisions under conditions of uncertainty is crucial to technological innovation, social change, and the creation of new ideas, and it is at the core of entrepreneurship. However, evidence suggests that entrepreneurs lack a method to make decisions under conditions of uncertainty. This project extends prior work that shows that entrepreneurs can improve their ability to make these decisions by adopting a scientific approach based on the formulation of models tested with data, such as scientists do. It proposes to conduct a randomized controlled trial (RCT) to provide solid evidence and test the effects of the adoption of this approach vis-a-vis another approach to decision-making, effectuation. By scientific approach to entrepreneurial decision making, we mean that entrepreneurs should behave like scientists, accurately framing and identifying the problem they wish to address, articulating theories, defining clear hypotheses, conducting rigorous tests to prove or disprove these hypotheses, measuring the results of the tests, and making decisions based on these tools. By effectuation, we mean that entrepreneurs use a non-predictive approach whereby they identify the next, best step by assessing the resources 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 800 entrepreneurs to understand the conditions under which different approaches to decision-making sustain entrepreneurial action.
External Link(s)
Registration Citation
Camuffo, Arnaldo et al. 2020. "Multiple Approaches to Entrepreneurial Decision-Making: Evidence from India ." AEA RCT Registry. September 07. https://doi.org/10.1257/rct.6020-1.1.
Experimental Details
Startups 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
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
1. Revenue: our main dependent variable is the amount of revenue
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
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
The RCT focuses on nascent startups. 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 online. 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. 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. The second block of applicants who took longer to complete the baseline survey and interview might be also randomly assigned to one of the arms of the experiment. There will be separate randomization (pure randomization) with balance checks for this second set of applicants.
Randomization Unit
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
not clustered
Sample size: planned number of observations
800 startups X 12 (points in time) = 9600 observations
Sample size (or number of clusters) by treatment arms
800 startups (200 in the scientific group, 200 in the effectuation group, 200 in the control group, 200 in the pure control group)

An additional 200 start-ups might be randomized into the experimental arms (50 in the scientific group, 50 in the effectuation group, 50 in the control group, 50 in the pure control group)
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: Sensitivity: Compute required effect size Input: α err prob = 0.05 Power (1-β err prob) = 0.95 Total sample size = 600 Number of groups = 4 Number of measurements = 12 Corr among rep measures = 0.5 Nonsphericity correction ε = 1 Output: Noncentrality parameter λ = 36.9219551 Critical F = 1.4381562 Numerator df = 33.0000000 Denominator df = 6556 Effect size f = 0.0506362
IRB Name
Indian School of Business
IRB Approval Date
IRB Approval Number
ISB-IRB 2019-10