Overview of the Policy
The five journals published by the American Marketing Association (Journal of Interactive Marketing, Journal of International Marketing, Journal of Marketing, Journal of Marketing Research, Journal of Public Policy & Marketing) contribute to the development of knowledge in the field of marketing by ensuring that the claims made by authors publishing their work in the journals are novel, credible, and reliable. Authors are asked to disclose potential conflicts of interest and funding sources and describe their methods clearly and transparently so that reviewers and readers can effectively evaluate the evidence provided by the authors for their claims. Consistent with Sage’s Research Data Sharing Policy, the AMA journals encourage authors to share, cite, and link to their research data and methodological materials, subject to ethical and legal restrictions, to increase research transparency and reproducibility.
Prior to the final acceptance of their manuscript for publication in one of the AMA journals, authors must provide to the journal the data and materials required to replicate and validate their results (or propose an alternative disclosure plan at the time of initial submission) and include a data availability statement in their final manuscript.
Implementation of the Policy
With each submission to one of the AMA journals, authors will confirm their understanding that the journal requires submission of materials required to replicate and validate their results or an alternative disclosure plan prior to the final acceptance of a paper. In addition, authors should ensure that enough information is provided in their submission files for the manuscript to be effectively evaluated by the reviewers.
Although submission of data and methodological materials is not required until conditional acceptance, authors should provide detailed methodological materials with each submission, such as surveys and stimuli, sample construction, and analysis procedures (see AMA Journals Editorial Policies & Procedures). With each submission, authors may provide additional materials such as survey files (e.g., .qsf if using Qualtrics, .crd if using Credamo), code, and raw data in a secure location (e.g., ResearchBox, OSF), with settings such that the materials are accessible to the journal’s editors and review teams.
Upon conditional acceptance, authors are required to provide the complete stimuli, data, well commented code, and other computational details that permit replication and validation of all analyses reported or referred to in the paper or materials consistent with their alternative disclosure plan. Authors will upload these materials to the journal’s Dataverse collection (see Dataverse Submission Instructions), to which the journal’s current Editor in Chief and AMA staff will have permanent access and the Coeditor for the paper will have access while the paper is under review. If data and methodological materials are publicly available on another trusted repository (e.g., Harvard Dataverse, ResearchBox, OSF), authors are permitted to provide links to these repositories rather than uploading another copy of the data and methodological materials. Authors of accepted papers are encouraged to make their data and methodological materials accessible to all readers via external repositories, but this is not required.
In the final version of their manuscript, authors must provide a data availability statement, indicating whether data are available and shared, consistent with Sage’s Research Data Sharing Policy.
In addition to requiring submission of data and methodological materials for each submission prior to final acceptance, individual journals may elect to add a verification step. For example, journals may assign Data Editors to verify the data and materials submitted and complete a Data Editor report for empirical submissions prior to final acceptance.
In addition to making the data and methodological materials available to the Editor in Chief, the Coeditor handling the paper, and AMA staff, journals may elect to make these materials available to Associate Editors. If journals make the materials available to others, they will clarify this policy to the authors.
Submission Guidelines
Guidelines for All Submissions
With each submission to an AMA journal, authors should ensure that enough information is provided in their submission files for the manuscript to be effectively evaluated by the reviewers. This includes disclosing potential conflicts of interest and funding sources to the editors.
- When primary data have been collected (e.g., surveys, experiments, field studies), authors should provide:
- A clear description of how data were collected, including recruitment procedures, instructions, and stimuli presented to research participants. Sufficient information to evaluate the evidence should be provided in the text of the manuscript, and full materials should be provided in the web appendix that accompanies the submission.
- Indication that the authors followed Institutional Review Board or Institutional Ethics Committee policies appropriate for the location in which the research was conducted.
- If the protocol was preregistered, an anonymized link to the preregistration source should be included. Authors are strongly encouraged to preregister their studies.
- A clear description of how data were analyzed, including any data exclusions and identification of the models estimated in the analysis.
- When data collected by other parties have been analyzed (e.g., licensed databases, company-provided data), authors should provide:
- A clear description of how data were obtained and accessed and how the researchers attempted to validate the data.
- Indication that the researchers adhered to confidentiality and usage policies appropriate for the data (see AMA’s Data Scraping Policy).
- A clear description of the steps that were taken to arrive at the final dataset that was analyzed, including any data exclusions and identification of the models estimated in the analysis. When using multiple secondary datasets, it is important for reviewers to have a clear understanding of how the author(s) arrived at the final sample that was used to test the hypotheses or key relationships.
Guidelines for Conditionally Accepted Manuscripts
In addition to the above requirements for all submissions, the authors will be asked to supply more detailed methodological materials to the journal’s Dataverse collection prior to the paper’s acceptance (see Dataverse Submission Instructions):
- A README file identifying and describing the files uploaded to the journal’s Dataverse and instructions for replicating the results.
- Any scripts or code used to collect or analyze the data. These should be summarized as appropriate in the submitted manuscript and provided in full prior to publication. If no scripts or code were used, the authors should identify the analysis tools and procedures used so that an informed reader can replicate the analysis.
- When primary data have been collected for the research (e.g., surveys, experiments, field studies, web scraping), authors should provide:
- The raw data analyzed for all quantitative results reported in the paper. These data must be provided as .qsf, Excel, ASCII, or text files prior to final acceptance, with sufficient explanation provided to allow a reasonably adept user to replicate the data analysis and results using standard analysis programs.
- For papers based on qualitative data from interviews, participant observation, textual datasets, or other cultural, archival, or historical material, authors should supply the following:
- A listing of all material consulted such as a table of interview participants (with pseudonyms), places where observation took place, or a list of all articles or documents considered in interpretation.
- An interview guide or other data collection protocol, if applicable. Please provide, either in the methods section of the paper or in an appendix, a full description of how participants or documents were selected and how data were collected.
- A table with examples from the data of key themes or categories from the findings, augmented with a sufficient number of transcribed interviews annotated to illustrate the process by which themes or categories were identified.
- Links for all preregistered data collection. Although preregistration is not required, authors are strongly encouraged to preregister their studies, especially those conducted during the review process.
- When the research relies on data sourced from licensed databases (e.g., Census Bureau, Compustat, CRSP, Factset, WRDS), the authors should provide detailed instructions for accessing and linking to licensed data, along with code, sufficient for replication by others. If multiple sources of data were combined, the authors must provide a description of how intermediate datasets were combined to create the final dataset.
- If the research relies on proprietary data covered by a nondisclosure agreement, sensitive human-subject data, or unique datasets that required an extensive time or monetary investment to compile, authors may propose an alternative disclosure plan that is in keeping with the spirit of replicability while respecting the specific situation faced by the authors. Authors should propose an alternative disclosure plan at the time of initial submission to the journal. For instance, the authors might propose to:
- Disguise the data to protect sensitive information but allow replication of the main results. For instance, the authors could add noise or apply multipliers to the variables (e.g., Acimovic et al. 2019), or the authors could normalize the data to provide limited precision (limited decimal places). If the authors propose to share disguised data, the authors should disclose to the editor the details of the process or method for creating the transformed dataset.
- Provide all necessary summary and distributional statistics to populate the model reported so that others can replicate the study (e.g., Shi et al. 2016).
- Provide a randomly drawn subset of the data that could be used to replicate the paper’s results, albeit with the expectation of larger standard errors.
- Provide a synthetic dataset that is representative of the actual data, and evidence that the synthetic dataset is a valid surrogate for the actual data.
- Provide sufficient details so that other researchers could readily generate their own dataset comparable to that used in the research (e.g., Gallino and Moreno 2014).
Acceptance of a proposed alternative disclosure plan is at the discretion of the journal’s editors. When considering an authors’ plan, the editors will evaluate the benefits of enforcing the policy against the costs of being unable to publish important findings. Authors who have questions about the appropriateness of alternative disclosure plans may contact the Editor in Chief of the journal prior to submission.
When an alternative disclosure plan has been approved for a paper, authors should note this in their data availability statement.
Acknowledgments
To develop the AMA Journals Research Transparency Policy, a subcommittee was convened by the VP Publications Committee in February 2025. The subcommittee consulted Sage’s Research Data Sharing Policy, research transparency policies adopted by the Journal of Marketing and Journal of Marketing Research, policies adopted by Management Science, American Economic Association, Journal of Finance, and Marketing Science. The subcommittee benefited from discussions with AMA journal editors and AMA staff.
The AMA Journals Research Transparency Policy was approved by the VP Publications of the American Marketing Association, Roland Rust, on June 11, 2025 and will take effect August 1, 2025.
References
Acimovic, Jason, Francisco Erize, Kejia Hu, Douglas J. Thomas, and Jan A. Van Mieghem (2019), “Product Life Cycle Data Set: Raw and Cleaned Data of Weekly Orders for Personal Computers,” Manufacturing & Service Operations Management, 21 (1), 171–76.
Gallino, Santiago and Antonio Moreno (2014), “Integration of Online and Offline Channels in Retail: The Impact of Sharing Reliable Inventory Availability Information,” Management Science, 60 (6), 1434–51.
Shi, Pengyi, Mabel C. Chou, J.G. Dai, Ding Ding, and Joe Sim (2016), “Models and Insights for Hospital Inpatient Operations: Time-Dependent ED Boarding Time,” Management Science, 62 (1), 1–28.
Journal-Specific Policies
Journal of Marketing Research: Verification Step
All conditionally accepted manuscripts go through a verification step before final acceptance in the Journal of Marketing Research. Coeditors assign the Data Editor to review the data and materials submitted to JMR‘s Dataverse and submit a Data Editor report as a review on ScholarOne. For more information about JMR‘s process, see JMR‘s Policy for Research Transparency.
Journal of Marketing: Verification Step
Some conditionally accepted manuscripts go through a verification step before final acceptance in the Journal of Marketing. Coeditors may assign a Data Editor to review the data and materials submitted to JM‘s Dataverse and submit a Data Editor report as a review on ScholarOne.