Multiple Approaches to Entrepreneurial Decision-Making: Evidence from India

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

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

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

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

2020-08-22

Intervention End Date

2020-12-05

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

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

Before starting the training program we ask all the applicant startups to send a pitch of their business idea and the vitae of the founders. On the basis of this information, we categorized them across stages of development. Out of the 1000+ applicants to the program, we selected roughly 800 startups early stage that complete a baseline survey and a brief call to assess the levels of scientific decision-making and effectual decision-making pre-intervention. All participants that complete the baseline data collection process are admitted to the training program and will be randomly assigned to one of the conditions. We might randomize a second block of applicants into the experimental arms should they also complete the survey and telephone interview prior to the program start date. The randomization of the second block of applicants will ensure balance checks across arms. In order to avoid contamination, treated and control startups are taught/coached in different time slots of the same day (morning and afternoon). All communications to the three groups of startups are distinct. Individual-level, team level, and performance data are gathered periodically by a team of 30 research assistants following a purposely defined research protocol. Data gathering also includes startups’ learning and behavioral monitoring (level of adoption of a scientific approach to entrepreneurial decision making and effectuation), conducted via phone interviews every four weeks.

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

Start-ups

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

Supporting Documents and Materials

IRB