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A Scientific Approach to Entrepreneurship and Innovation: A Field Experiment in China
Last registered on May 14, 2021


Trial Information
General Information
A Scientific Approach to Entrepreneurship and Innovation: A Field Experiment in China
Initial registration date
May 13, 2021
Last updated
May 14, 2021 9:39 AM EDT
Primary Investigator
Shanghai University
Other Primary Investigator(s)
PI Affiliation
Bocconi University
PI Affiliation
Bocconi University
PI Affiliation
Bocconi University
PI Affiliation
Bocconi University
PI Affiliation
Bocconi University
PI Affiliation
Shanghai University
Additional Trial Information
In development
Start date
End date
Secondary IDs
Making decisions under uncertainty is at the core of entrepreneurship. However, evidence suggests that entrepreneurs lack a method to make decisions under uncertainty. Prior work shows that entrepreneurs can improve their ability to make these decisions by adopting a scientific approach, i.e., validating entrepreneurial ideas as scientists act. Specifically, when entrepreneurs frame and identify the problem, articulate theories, define clear hypotheses, conduct rigorous tests to confirm or disconfirm these hypotheses, and make decisions based on these tools, they make better performance. Building on prior research conducted in Italy, the United Kingdom, India, and Tanzania, this study conducts a randomized controlled trial (RCT) which provides training to entrepreneurs, and explores the treatment effects of different groups in China. The study setting will be entrepreneurial firms in Shanghai as it is an essential economic center in China with a vibrant entrepreneurial ecosystem. Moreover, this study also seeks to provide solid evidence and identify elements of most significant importance in absorbing and applying the scientific approach.
External Link(s)
Registration Citation
Bacco, Francesca et al. 2021. "A Scientific Approach to Entrepreneurship and Innovation: A Field Experiment in China." AEA RCT Registry. May 14. https://doi.org/10.1257/rct.7594-1.0.
Experimental Details
We recruit participants through an online call for application. Applicants should either be working for an existing small business and willing to develop an innovative project within it, or be aspiring entrepreneurs willing to develop a new venture. Selected participants will be randomly assigned to three groups.
Participants in two treatment groups will receive 6 sessions of in-person training. The sessions will take place every one or two weeks for two months. These sessions will include interactive lectures and coaching by qualified mentors/instructors each working with a subset of the participants. Both treatment groups will receive general training related to entrepreneurial decision making and will learn how to collect and evaluate information about their entrepreneurial ideas. The two treatment groups are identical in terms of the number of classes offered, the basic structure of the course, the examples used, and the topics covered in the course.
Content and instruction in one treatment group will emphasize both the theory and experimental elements within the scientific method. Specifically, this content includes the theory behind hypothesis development, rigorous test design processes, hypothesis testing and experimentation. This will be referred to as the ‘theory-driven’ treatment group.
For the second treatment group, greater emphasis will be devoted to experimentation and lesser attention devoted to the theory element. In particular, greater emphasis will be devoted to hypothesis testing and experimentation. This will be referred to as the ‘experimentation-driven’ treatment group.
A third set of participants, the ‘pure control’ group, will not receive any formal training. However, these participants will be provided written materials relevant to decision making and the opportunity to participate in virtual events held during the project. The performance of participants in all three groups will be monitored over the course of one year.
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
1. Performance: our main dependent variable is the amount of revenue/total sales generated by the businesses over time. We also record an alternative measure for performance by recording the number of employees and related changes over time.

2. Exit: 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 period in which the firm drops out, and it is a missing value thereafter. To avoid attrition biases, we will check that the firms which leave the program will actually cease entrepreneurial activity.

3. Pivot: this is the cumulative number of times in which a firm makes a major change to its business model. We defined a change to be major by analyzing whether the firm 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 decisions are made. The key mechanism being tested is precision and improvement in the precision of predictions. To capture such mechanism, the team will collect through survey instruments the participants’ own perceptions about future revenues and likelihood of failing.

The research team will also measure the level of absorption and application of the scientific approach taught in the intervention by using quizzes, surveys and a semi-structured interview protocol. The research team will use instruments to identify the effect of absorption and application. Such measures will allow the team to test not only the “intention-to-treat” of the intervention, but also the extent to which entrepreneurs absorb and apply the concepts they are taught, and ultimately the effect of the latter on entrepreneurial performance.

The research team want to understand how the absorption and application of the scientific approach affect entrepreneurial performance. This research will also explore how the treatment affects absorption and application and the direct effect of treatment on entrepreneurial performance. Moreover, the research team will test how individual level characteristics affect the absorption and application of the scientific approach and entrepreneurial performance.
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
The secondary outcomes of this study include:
1. Investor’s investment intention and amount of investment.
2. Quality of entrepreneurial opportunities: the quality of entrepreneurial ideas will include several dimensions, such as value, rarity, nonimitability and nonsubstitutability.
3. Quantity of entrepreneurial opportunities identified or pursued.
4. Psychological and physiological states of the participants.

The research team is also examining the interaction of the treatments with several covariates of interest, including:
a) Gender
b) Psychological and cognitive traits
c) Management practices adopted by the entrepreneur and the entrepreneurial team
d) Accumulation of knowledge
e) Educational and academic background
f) Managerial experience of the entrepreneur
g) Entrepreneurial experience of the entrepreneur
h) Scientific background, thinking and reasoning
j) Environmental factors (e.g., environmental uncertainty)

The research team will also analyze the effect of the intervention on entrepreneurs’ perceptions of internal and external challenges to entrepreneurship in emerging economies and their perceived ability to respond to these challenges. We expect the training to push entrepreneurs to focus more on internal challenges, with such focus shifting towards external ones over time. As for the perception on abilities, we have competing hypotheses about the direction of the effect of the intervention.
The research team will study both the moderation effects of cognitive and psychological traits including overconfidence and goal orientation, as well as the direct effects that the intervention could have on such traits.
The research team will also examine the moderation effects of gender, education background, scientific background, previous work experience (including managerial experience), and entrepreneurial experience on the absorption and application of the scientific approach.
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
The RCT focuses on entrepreneurial individual who intend to start a new business or pursue a new business idea within an established entity. We advertise the program through digital channels as a general course overseeing all the important aspects of entrepreneurship. The course is free of charge to ensure the participation of individuals with limited financial resources. The same amount of training is offered to both groups receiving training to ensure comparability. Multiple instructors/coaches will be trained to deliver the training using standard materials, purposely designed. Each instructor/coach teaches two classes (one class employing the theory-driven approach and the second class using the experimentation-driven approach). The research team designs, coordinates, and oversees all activities, ensuring that the learning modules and coaching activities are appropriately carried out by the instructors/coaches.

The research team will collect various measures related to both participants and their business projects before, during, and after the training using surveys and interviews. Individual-level measures include participant demographic information, education and work background, prior experience with entrepreneurship, scientific background, academic background, psychological and physiological states, and some cognitive and psychological traits. Project-level measures include past and current performance, team composition and perceptions of future performance. Participant’s project will be evaluated by evaluators such as investors, and the evaluation data will also be collected. In addition, we will collect the quality and quantity of entrepreneurial opportunities identified or pursued by participants. We will also collect specific measures related to participants’ perception of internal and external challenges to entrepreneurship as well as their perceived ability to respond to those challenges.

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 three groups (theory-driven, experimentation-driven and pure control), multiple random subgroups of participants (each with about 20-30 members) will be created and randomly matched with the instructors/coaches so that each instructor/coach teaches two subgroups. Randomization checks: mean values comparison and t-tests across groups and subgroups.
Randomization Unit
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
not clustered
Sample size: planned number of observations
225 participants X 10 (points in time) = 2250 observations
Sample size (or number of clusters) by treatment arms
225 participants (75 in the theory-driven group, 75 in the experimentation-driven group, and 75 in the pure control group)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We report here the results of a sensitivity analysis, given a fixed sample size, in a conservative scenario with no attrition and only 5 data points collected. Calculations were done using the free GPower software. We set the study power and alpha at standard levels (0.8 and 0.05). We assume the treatment has a small effect (f = 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) = 0.8; Total sample size=225; Number of groups=3; Number of measurements=5; Corr among rep measures=0.5;Nonsphericity correction ε=1; Output: Noncentrality parameter λ =15.1535971; Critical F=1.9488129;Numerator df=8.0000000;Denominator df=888;Effect size f=0.0820666 We also run other two simulated scenarios considering large attrition. In the first exercise, we consider a large 35% attrition (N = 146) and 5 collected datapoints; in the latter, we consider 6 repeated measurements. All the other settings (power, alpha, groups, correlation, nonsphericity correction) are kept as in the above simulation. The computed MDE for the first additional scenario is of 0.102 while it is of 0.09 for the latter. Thus, the experimental design should allow capturing a small treatment effect (f = 0.1), even in the case of large attrition.
Supporting Documents and Materials

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IRB Name
Ethics Committee of Shanghai University
IRB Approval Date
IRB Approval Number
ECSHU 2021-125