Goals of the Policy
The Journal of Marketing Research (JMR), as an empirical research journal, contributes to the development of knowledge in the field of marketing by ensuring that the claims made by authors publishing their work in JMR are credible and reliable. As JMR Coeditors, we require authors 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. When manuscripts are conditionally accepted, authors must provide to the journal the materials required to replicate and validate their results so the editorial team can assess their credibility and reliability prior to final acceptance. In cases where submission of the raw data is not possible, authors should propose an alternative disclosure plan to the Coeditors at the time of initial submission to the journal.
With each submission to JMR, authors will confirm their understanding that JMR 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 we do not require submission of methodological materials until conditional acceptance, we encourage authors to provide detailed methodological materials with each submission, such as stimuli, sample construction and analysis procedures. Authors are encouraged to provide materials such as surveys (e.g., qsf if using Qualtrics), code and raw data in a secure location (e.g., https://dataverse.harvard.edu, https://osf.io/), with settings such that the materials are accessible to JMR’s Coeditors and review teams.
Upon conditional acceptance (prior to final acceptance of papers by JMR), authors will be asked 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. These materials will be uploaded to a repository managed by the American Marketing Association (AMA), to which the JMR Coeditors and AMA staff will have access. If data and methodological materials have already been uploaded to a trusted repository (e.g., dataverse.harvard.edu, https://osf.io/), authors will be permitted to provide links to these repositories rather than uploading another copy of the data and methodological materials.
Authors of accepted papers will be encouraged to make their methodological materials and data accessible to all readers via external repositories, but this is not required.
Guidelines for All Submissions
With each submission to JMR, 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.
1. When primary data have been collected (e.g., surveys, experiments, field studies), authors should provide:
a. 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.
b. Indication that the authors followed Institutional Review Board or Institutional Ethics Committee policies appropriate for the location in which the research was conducted.
c. If the protocol was preregistered, an anonymized link to the preregistration source should be included. Authors are strongly encouraged to preregister their studies, especially those studies conducted during the review process.
d. A clear description of how data were analyzed, including any data exclusions and identification of the models estimated in the analysis.
2. When data collected by other parties have been analyzed (e.g., licensed databases, company-provided data), authors should provide:
a. A clear description of how data were obtained and accessed and how the researchers attempted to validate the data.
c. A clear description of the steps that were taken to arrive at the final data set that was analyzed, including any data exclusions and identification of the models estimated in the analysis. When using multiple secondary data-sets, 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.
Guidelines for Submission of Methodological Materials Prior to Final Acceptance
In addition to the above requirements for all submissions, the authors will be asked to supply more detailed methodological materials prior to a paper’s acceptance by JMR:
1. 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.
2. When primary data has been collected for the research (e.g., surveys, experiments, field studies, web scraping), authors should provide:
a. 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.
b. For papers based on qualitative data from interviews, participant observation, textual datasets, or other cultural, archival, or historical material, authors should supply the following:
i. 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.
ii. 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.
iii. A table with examples from the data of key themes or categories from the findings, augmented with at least one transcribed interview annotated to illustrate the process by which themes or categories were identified.
c. 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.
3. 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 data set.
4. If the research relies on proprietary data covered by a Non-Disclosure Agreement, sensitive human-subject data, or unique data sets 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:
a. 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 data set.
b. 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).
c. 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.
d. Provide a synthetic data set that is representative of the actual data, and evidence that the synthetic data is a valid surrogate for the actual data.
e. Provide sufficient details so that other researchers could readily generate their own data set 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 JMR’s Coeditors. When considering an authors’ plan, the Coeditors will evaluate the benefits of enforcing the data disclosure 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 prior to submission.
When an alternative disclosure plan has been approved for a paper, this will be noted in the published paper.
To develop JMR’s Research Transparency Policy, the Coeditors of the Journal of Marketing Research relied extensively on research transparency policies adopted by other academic journals, especially the Journal of Marketing. We would like to thank the editors of the Journal of Marketing for extensive discussions and for their partnership in adopting shared policies across journals published by the American Marketing Association. Like the Journal of Marketing’s policy, our policy builds upon prior policies adopted by Management Science, the American Economic Association, the Journal of Finance, and Marketing Science.
JMR’s Research Transparency Policy was approved by the VP Publications of the American Marketing Association, Ron Hill, and incoming VP Publications, Roland Rust, on June 21, 2023 and will take effect on July 1, 2023.
Acimovic, Jason, Francisco Erize, Kejia Hu, Douglas J. Thomas, and Jan A. Van Meighem (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.