A Scientific Approach to Innovation Management – SAIM

Last registered on May 02, 2023


Trial Information

General Information

A Scientific Approach to Innovation Management – SAIM
Initial registration date
March 10, 2022

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
March 10, 2022, 9:10 PM EST

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Last updated
May 02, 2023, 9:31 AM EDT

Last updated is the most recent time when changes to the trial's registration were published.



Primary Investigator

ICRIOS, Bocconi University

Other Primary Investigator(s)

PI Affiliation
Bocconi University
PI Affiliation
Rotterdam School of Management
PI Affiliation
PI Affiliation
Bocconi University
PI Affiliation
Bocconi University
PI Affiliation
Bayes Business School
PI Affiliation
Universidad de los Andes School of Management

Additional Trial Information

Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
There is strong evidence that managers and entrepreneurs have poor methods to make decisions under uncertainty. This is a serious problem that discourages innovation and the success of many start-ups. At the aggregate level it sties economic growth and the returns from many public and private incentives to innovation or entrepreneurship. This project studies whether managers and 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. The project develops a framework that explains the mechanisms and implications of this approach and tests them through a large-scale RCT in six international sites. The framework shows that a scientific approach improves performance by pursuing valuable innovations and by terminating unsuccessful projects earlier. It also helps decision-makers to interpret signals: in particular, they understand better how to change a project in response to negative signals. The RCT tests mechanisms and performance of the scientific approach against current practice in innovation management, and its coverage ensures a good assessment of its validity across contexts and conditions. The results of this RCT are potentially ground-breaking because they can: (i) change the way we think innovation management and entrepreneurship; (ii) encourage the practical application of many economic and managerial theories ignored by managers or entrepreneurs in spite of their practical prescriptions; (iii) help to rethink the curricula of business schools by making a scientific approach to management more prominent and by revamping the application of economic and managerial theories; (iv) improve the mentorship of the many public and private initiatives that support innovation and entrepreneurship worldwide.
External Link(s)

Registration Citation

Berchicci, Luca et al. 2023. "A Scientific Approach to Innovation Management – SAIM." AEA RCT Registry. May 02. https://doi.org/10.1257/rct.9082-3.0
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 six sessions of in-person training. The sessions will take place every other week for about three 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-based’ 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 ‘evidence-based’ treatment group.

Our expectations before conducting the study were that both versions would have been beneficial for entrepreneurs in different ways. We expected entrepreneurs who follow an evidence-based approach to engage in a fast-iterative decision-making process and improve their business idea through data collection and analysis. We expected entrepreneurs who follow a theory-based approach to be slower and more selective, taking more time to pivot to adapted, but more defined and effective, business idea.

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. Participation in the surveys, interviews and all the other activities of the present study is voluntary. Participants will not be paid for their participation, but they will receive free training and free access to all events and activities.

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 events held during the project.
The performance of participants in all three groups will be monitored over the course of one year and a half.
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 considering the number of employees and related changes over time.
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 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 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.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
The research team plans to also examine additional categories of outcomes of interest:

DOMAINS: Studying how broad domains and scientific thinking impact entrepreneurial idea quantity (number of alternative frameworks devised for the same idea), breadth (number of contexts of application for the same idea, distance among the contexts of application), and quality (performance and startup size).
Within one of the RCTs (Barcelona, Spain), these relationships will be studied through a 2x2 manipulation entailing a domains treatment (manipulating the breadth of domains) in addition to the main treatment. Main outcome variables:
- Number of alternative frameworks for the same idea
- Idea breadth
- Number of application contexts
- Distance among application contexts
- Startup size.

ENTREPRENEURIAL TEAMS: analyzing whether our interventions are associated with a higher likelihood and number of team changes in treated startups and with a higher adoption of a dual team formation strategy rather than resource-seeking or interpersonal attraction strategy alone. Main survey measures related to this outcome:
- Team formation strategies
- Team member joining/leaving and reasons
- Transactive Memory System.

GOAL ORIENTATION: investigating whether exposure to different types of entrepreneurship training changes entrepreneurs’ goal orientation, and whether different goal orientations affect the relationship between the training received (treatment) and the strategic decisions entrepreneurs make (outcome). Goal orientation is a multi-dimensional construct that includes three related dimensions:
- Performance-approach goal orientation
- Performance-avoid goal orientation
- Learning goal orientation.

MOTIVATIONS: studying whether entrepreneurs who are classified as belonging to different categories based on their motivations and growth aspirations have differing outcomes in terms of method application, pivoting and termination. The four categories will be created according to a 2x2 matrix generated from the following survey measures:
- Intrinsic or Extrinsic motivation to entrepreneurship
- High or Low growth aspiration.

PARSIMONY: analyzing the effect of the treatments on the parsimony of the entrepreneurs’ reasoning and formulation of business models, as well as studying the effects of parsimony as a mediator towards performance, pivoting and termination outcomes. The expectation is that a scientific treatment will be positively related to parsimony. Main measures related to this outcome:
- Output of a computer simulation task where we measure additive and subtractive changes performed by entrepreneurs to a 10x10 digital grid pattern
- Survey measure of self-reported additive or subtractive changes to each entrepreneur’s business idea
- Analysis of the business idea descriptions submitted by entrepreneurs to identify additive or subtractive changes to their business models over time.

PERCEPTION OF CHALLENGES: analyzing the effect of the intervention on entrepreneurs’ perceptions of challenges to entrepreneurship and their perceived ability to respond to these challenges. The expectation is towards a higher perceived ability for the theory-driven intervention, especially in relation to challenges related to the business development rather than on the external environment. We ask entrepreneurs in the survey to:
- Indicate the top-3 challenges among a list of proposed challenges
- Indicate, for each of the proposed challenges, their perceived ability to deal with them (Likert scale)
- Open-ended question about challenges and obstacles to business goals in the phone interview, to both validate results from the questionnaire and get richer information.
The analysis will also look at potential moderation effects and correlation with overconfidence traits, specifically using a battery of questions capturing the “illusion of control” bias.

PITCH DEVELOPMENT: study whether training entrepreneurs to think like scientists leads them to produce narratives of their ideas that elicit more positive evaluations of their written ‘pitch’ (i.e., description of their business idea) from external audiences. Main measures related to this outcome (coded by external raters on 1-5 scales):
- Perceived idea novelty
- Perceived cognitive legitimacy
- Perceived business viability.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The RCT focuses on the entrepreneurial individual who intend to start a new business. 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 structured interviews.

Participants will have to complete an initial survey after applying to the program. Data collected in the survey includes personal information about the participants, such as age, gender, education, but excludes any special data categories (religion, political affiliation, sexual orientation, etc.) or biometric data.
The survey will also include questions about the entrepreneurial project pursued by the participants, their goals and expectations regarding the project, the resources, and competencies they can access, and cognitive and decision-making aspects that are relevant to the research.
Participants will also be asked to provide a textual description of their business idea, akin to an "elevator pitch", and to perform a brief computer task to measure their approach to changing a digital 10x10 grid pattern.

Other surveys will be administered in subsequent steps of the project, according to the needs of the research, and following the same procedure and content type described for the initial survey.

Interviews will be conducted by research assistants, who we will contact participants to provide a periodical update on their entrepreneurial project for up to 15 months after the completion of the training program.
Individual-level measures include participant demographic information, education and work background, prior experience with entrepreneurship, and some cognitive and psychological traits. Project-level measures include past and current performance, team composition and perceptions of future performance. 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 them.
Experimental Design Details
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 subgroups of participants will be created and randomly matched with the instructors/coaches so that each instructor/coach teaches two subgroups. Randomization checks: mean values comparison, t-tests across groups, F-tests of joint orthogonality.
Randomization Unit
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
Not clustered.
Sample size: planned number of observations
The same research design will be deployed in six different locations (Milan, Barcelona, London, Rotterdam, Bogotá, and Chennai) For each location, we plan to collect about 225 participants X 10 (points in time) = 2250 observations As a whole, we plan to collect 2250 observations X 6 locations = 13500 observations.
Sample size (or number of clusters) by treatment arms
About 225 startups (75 in the theory-driven group, 75 in the experimentation-driven group, and 75 in the pure control group) in each location
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.

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