The project is open to the international scientific research community.

To participate, at least one member of the research team must have an active affiliation with a university or research institution or be enrolled as a PhD student.

A research team consists of three or four researchers who jointly analyze the provided dataset and address the predefined research questions on the green gap.

Each team submits one independent set of analyses and results.

Participating research teams are required to:

• Analyze the provided dataset independently
• Address the predefined study
• Submit a complete description of their conceptualization, analytical strategy, and results
• Provide fully reproducible code used for all analyses
• Comply with the project’s multiple-blind and non-collaboration rules

Some research teams will additionally be asked to participate in interviews, as specified in the study plan.

All research teams answer the same two core questions:

1. How can we conceptually link consumers’ actual purchases of organic products with their attitude towards sustainable consumption?
2. What is the average marginal effect (percentage points) of a one-standard deviation increase in the pro-sustainability attitude score on consumers’ organic purchase share in each country?

Teams are free to choose their conceptual definitions, statistical models, and estimation strategies, within the provided data constraints.

We aim to recruit approximately 140 research teams, anticipating a dropout rate of about 30%, resulting in around 100 active teams contributing final analyses.

No. To ensure independence of analytical decisions, research teams must work fully independently and are not allowed to discuss analyses, interim results, or methodological choices with members of other teams.

Yes. Participation requires the ability to work with complex observational data and to conduct quantitative statistical analyses relevant to consumer behavior and marketing research.

Yes. All teams must submit the complete analysis code used to generate their results (e.g., R, Python, Stata, SPSS, Matlab). Code transparency and reproducibility are central to the project.

Yes. The project has been reviewed and approved by the ethics review board of the University of Innsbruck.

Questions can be directed to the project coordination team via the official project contact email or directly to the coordinators.

By participating, you contribute to:

• Advancing knowledge on the robustness and variability of evidence on sustainable consumption
• Improving understanding of researcher’s degrees of freedom and biases

• Developing best practices for many-analyst and open science research designs

All research teams that successfully complete the study requirements will be listed as co-authors on the resulting scientific publication.

The manuscript will be submitted to a leading peer-reviewed journal and will undergo the standard review process.

No. The dataset is provided exclusively for use within the Green Gap Project.

• Participants may only use the data to address the predefined research questions
• The coordination team will archive the dataset for verification and replication purposes
• Research teams must delete all local copies of the data after submitting their final
• Any additional use of the data beyond this project is strictly prohibited.

The dataset includes consumer purchase and survey data from five European countries: Germany, Italy Netherlands, Poland, and Romania.

The exact country coverage is disclosed to participating teams upon acceptance into the project and is identical for all teams.

The empirical focus of the project is the green gap, defined as the relationship between:

• Consumers’ attitudes toward sustainable consumption, and
• Their actual purchasing behavior, operationalized through observed purchases of organic products

The data allow teams to link attitudinal measures to real-world consumer behavior at the individual level.

The primary behavioral outcome is consumers’ actual organic purchasing behavior, typically operationalized as:

• The share of organic products in total purchases, or
• Related transformations derived from observed purchase records

Research teams may propose alternative operationalizations, provided they are theoretically justified and clearly documented.

The dataset contains standardized measures capturing attitudes toward sustainable consumption. Teams are free to:

• Select specific items or scales
• Combine measures into indices
• Apply transformations or scaling decisions

All choices must be reported transparently and reproducibly.

The raw data are provided at the individual consumer level.

All teams are required to estimate the average marginal effect (percentage points) of a one-standard deviation increase in the pro-sustainability attitude score on consumers’ organic purchase share in each country.

The precise functional form, statistical model, and estimation strategy are chosen by each team, subject to the constraints of the provided data.

Yes. Bayesian approaches are explicitly allowed. If you use Bayesian estimation, please report:

• The posterior mean (as the point estimate)
• The posterior standard deviation (as an analogue to the standard error)
• An interpretable Bayesian analogue to a p-value (e.g., posterior mass on the opposite side of zero

Allowing teams to make independent choices regarding:

• Conceptualization
• Variable construction
• Statistical modeling
• Estimation techniques

is a core feature of the project, enabling systematic examination of researchers degrees of freedom, analytical variability, and potential biases in green gap research.

Accepted research teams are required to:

·       Sign a data use and confidentiality agreement

• Complete an initial survey (e.g., background, theoretical beliefs)
• Conduct independent analyses addressing the predefined research questions

• Submit a full description of conceptual, methodological, and analytical choices
• Upload all results and fully reproducible analysis code
• Complete a final survey reflecting on the research process

Some teams will additionally be asked to participate in interviews, as specified in the study protocol.

The required time depends on the team’s experience, modeling choices, and analytical depth.

As a rough guideline, participating teams should expect to spend approximately 20–40 hours on the project.

Once accepted, research teams will receive the agreement by email.

The document can be signed electronically and must be returned before data access is granted.

For administrative and ethical compliance, participants may be asked to provide a copy of a valid passport or official ID.

How do I access the data?

After the signed agreement is received, teams will be provided with:

• A link to a short questionnaire
• Access to the data repository and detailed documentation

Data access may depend on the team’s assigned experimental condition (e.g., preregistration vs. immediate access).

Each team will receive a unique submission link prior to the submission deadline. Through this link, teams will be asked to:

(a)  Upload required files:

1. Results file containing effect estimates linking attitudes to organic purchasing behavior
2. Methodological description detailing model specifications, variable definitions, and key assumptions
3. A replication package (zipped), including:
   • All code used to process the data and generate results (with clear comments)
   • A README explaining how to reproduce the analyses, required software, and execution order

Teams are expected to follow established data and code documentation standards to the best of their ability.

Optional:

• Additional materials the team wishes to share (e.g., robustness checks, alternative specifications)

(b) Answer a final survey:

Teams will respond to questions regarding:

• Their analytical process
• Key modeling choices
• Interpretation of results

If a team has multiple members, the survey should be completed once per team, jointly.

All submitted analyses, estimates, and survey responses are treated confidentially during the analysis phase.

In project reporting, individual results cannot be traced back to specific research teams.