(revised May 26, 2023)
The Journal of Consumer Research requires research integrity and the ethical conduct of research. The journal embraces the principles of research transparency and open science by requiring detailed information about data collection and analysis processes and encouraging the collaborative sharing of research materials, methods, data, and code for data collection, data preparation, and/or data analysis.
JCR‘s Policy Board and editors have implemented the following policies and practices to promote research integrity and professional ethics as specified in each of the sections below. Authors should review these policies and practices, prior to submitting their manuscript:
- Research Method Transparency Guidelines and Reporting Requirements
- Sharing Data and Study Materials during the Review Process and Archiving
- Data and Study Materials Archiving Requirements
- Data Ownership and Public Access
- Institutional Review Board (IRB) and Grant/Foundation Requirements
- Exemptions to Sharing Data during the Review Process and Data Archiving
- How to Obtain an Exemption for Data Sharing and Archiving
- Data and Study Materials Maintenance Policy
- Plagiarism Check
- Author Misconduct Policy
These policies and guidelines are informed by the international organization COPE (Committee on Publication Ethics). See the COPE website for more information on research integrity.
Research Method Transparency Guidelines and Reporting Requirements
The text of the manuscript must fully disclose and explain the methods for data collection and analysis used in the project. In addition, in step 6 of the submission process, authors must provide a detailed data collection statement that includes information about who collected the data, and how, when, and where data collection occurred, and how the data are stored. This information is not included in the manuscript and is only visible to the editors. It will be published with the manuscript if it is accepted. Details about these research transparency guidelines and requirements are provided below:
Guidelines for Data Collection and Research Method Reporting
Data Collection Procedures: Authors must provide details about the types of data collected and offer a data collection rationale. Details about participants’/researchers’ activities in the process of data collection must be provided. All experimental conditions, including the control groups and factors that were part of the original design, should be described. Pertinent details about the procedure (e.g., session size, task sequence, filler task) should be provided. If secondary data sources are used, the source(s) and time periods involved must be indicated. If automated digital data capture is employed, procedures should be rendered as transparent as possible. Qualitative data should describe the research approach, evolution of the data collection, and specific details about how the data was collected and used in the study. This should include, as applicable, how field sites and participants were selected, the time periods that were considered, and a list of all archival documents considered in interpretation.
Data Collection Instruments/Study Materials: For questionnaires: either in a web appendix or an open-access electronic repository, authors must provide all questionnaires presented to respondents in the original form as viewed by participants. For third party data: describe the nature of the larger survey that the data was from (if applicable) and provide details (e.g., purpose; who administered it, when and how; the types of questions; length, etc). For lab studies: provide study materials (e.g., original surveys) and describe all experimental conditions/manipulations, scenarios, vignettes. If data were non-questionnaire measures (e.g., response latencies, eye-tracking, neural activity), authors must describe how they were collected.
Context: For field studies: describe the consumer setting, context rationale, and relevant contextual factors. For ethnography/cultural approaches: explain the choice of context, relevant contextual details, and the theoretical rationale for selecting this context.
Sample (of respondents, data, studies, documents, events): For primary research involving human participants, authors must indicate: 1) demographic characteristics of samples (e.g., gender, age, education, ethnicity, other factors relevant to the research study context such as country where the data was collected); 2) the method of recruitment (e.g., participant pool, online panel, snowball, compensation, direct outreach) and sampling method (e.g., convenience, probability, purposive, theoretical); 3) sample sizes used in all studies and how sample sizes were determined; and 4) the number of excluded participants, the exclusion criteria, and their justification (if relevant).
Data Description: The authors should include all relevant information about the data in the manuscript or in a web appendix referenced in the manuscript. As appropriate to the study, report descriptive characteristics (e.g., N’s, means and proportions, standard deviations), transformations, correlations, intercoder reliabilities, scale reliabilities, final items/items deleted. The number and length of depth interviews should be reported. If formal field notes exist, the size of the corpus should be mentioned. Similarly, photographic evidence should be described at least in terms of the number of images, possibly along with a sample of images (to the extent that the sharing of such images is not precluded). The nature and number of websites, message boards, gaming sites, message threads, and social media units should be reported. How any missing data were handled, with an explanation for substantial amounts of missing data.
Post-Data Collection Screening: Describe the method used to screen data after collection (e.g., elimination of outliers, attention screens, comprehension screens, content analysis to determine if directions were followed on a writing task, time spent on an item), cut-offs for screening measures, distribution of eliminated individuals across conditions.
Maintaining Participants’ Rights: For primary research, indicate how participants’ rights were safeguarded (i.e., by IRB/ethics committee approval or national policy for safeguarding participants’ rights). Describe procedures for managing/archiving data, anonymization and de-identification of data, and procedures for ensuring data security.
Guidelines for Reporting on Statistical, Analytical, and/or Interpretive Techniques
The following are guidelines for rigorous reporting of statistical, analytical, and/or interpretive techniques in the manuscript or in a web appendix.
Experimental Designs/ANOVA: Describe the properties of the design, including factors, levels, whether factors are between or within-subjects, and other relevant technical features (alias structures) in sufficient detail that would allow it to be reproduced by other researchers. For the analysis, report, as appropriate, the full ANOVA table with treatment means, tests of relevant contrasts, and effect sizes. If covariates are measured, report findings with and without covariates.
Correlational Designs/Regression: Describe the variables used in the analysis and in sufficient detail to allow the analysis to be reproduced by other researchers, including whether variables are standardized or mean-centered, coding schemes if categorical variables are used, and the full correlation matrix among predictors. For analyses using mixed models indicate relevant nesting structure and the assumed error structure. For all analyses, report the full model results table with coefficients, standard errors (and/or confidence intervals) as well as effect sizes.
Exploratory Factor Analysis: Indicate the method of decomposition (e.g., principle components), the method of rotation, eigenvalues/percentage of variance accounted for by each factor. As appropriate, report standardized factor loadings, correlation matrix of all final scale items, factor correlations (if an oblique method of rotation is used) and items removed through purification.
Structural Equation Modeling and Confirmatory Factor Analysis: Describe the model, estimation method (e.g., maximum likelihood), omnibus fit statistics (e.g., Chi-square, df, RMSEA, CFI, Tucker-Lewis Index or Bentler-Bonett Non-normed Fit Index, Standardized RMR), parameter estimates, standard errors (z values) for all (including nonsignificant) paths (in Figure or Table). Describe model modifications made to achieve satisfactory fit.
Meta-Analysis: Indicate how variables were chosen for inclusion and exclusion. Report effect sizes and bases (e.g., means, binary data, correlations, risk ratios). Describe whether fixed vs. random effects models are used. Describe procedures for identifying and quantifying heterogeneity. Report confidence or credibility intervals, procedures used to account for small samples or unequal group numbers, methods for weighting study results, description of transformations, and model fit (if using Bayesian analysis).
Machine Learning Methods: Describe the algorithms used, researcher choices in setting parameters. If using natural language processing or computer vision algorithms, include details on the libraries used and/or training datasets used and their sources.
Estimation Details: For all statistical analyses, indicate the statistical packages used in the the analysis (e.g., SPSS, SAS, R or Python packages and versions). In addition, sufficient details should be provided about the method of estimation to allow replication by other researchers (e.g., burn-in given MCMC estimation, details of HB estimation.
Qualitative Interpretation: Describe the analytical procedures used (the exact process by which themes, interpretations, and/or frameworks were developed through a particular interpretive paradigm or approach — e.g., grounded theory, phenomenology, discourse analysis, abduction, extended-case method, hermeneutic analysis, analytic case method, analytic framework). Describe your unit of analysis or types of cases. Describe how your data interpretation has evolved over time and why. Describe procedures used to ensure trustworthiness, credibility, verisimilitude, and theoretical generalizability of interpretation (e.g., member checks, negative cases, triangulation, immersion in context).
Data Collected via APIs or Web Scraping: Describe the exact steps used to collect the data, including complete details on researchers’ choices regarding all aspects of the data collection process (e.g., sampling choices). Provide the code and (if accessing data via an API) full details on the API and a link to the API’s documentation.
Other Analyses: A similar level of detail should be provided for methods not explicitly mentioned here (such as multidimensional scaling or unfolding; correspondence analysis, cluster analysis, analyses of neural data, multi-level (random effects) models, etc.).
Data Collection Statement
When submitting manuscripts for review, authors must include a more specific data collection statement for the editors in the relevant field during Step 6 of the submission process. This information is not visible to the reviewers during the anonymous review process but will be included in the final publication should the manuscript be accepted. Provide the following information for each study:
- Where the data were collected
- When the data were collected
- Who collected the data
- Who analyzed the data
- Where the data are currently stored
See our sample data collection statements and review the following general guidance for adherence to this requirement:
- Do not include the data collection statement in the manuscript file during the review process.
- Be as specific as possible:
- Include university and place names and other identifying details (the data collection statement will not be shared with reviewers).
- If a research assistant or lab manager collected data under the supervision of one of the authors, state this (although you are not required to provide their names).
- If data was not obtained in a physical location such as a university lab or field interviews, state where it was obtained (e.g., MTurk, Prolific, public source, private company, web crawler).
- If data was obtained from a third-party source, state the source.
- If you are unable to reveal the source of your data, describe it (e.g., a large grocery retailer) and state the reason why you are unable to share the name of the source (e.g., the data is proprietary and you have signed an NDA).
- If you are unable to specify the location of data collection (e.g., you conducted qualitative interviews and your IRB prohibits you from disclosing this or it would violate the ethics of confidentiality), briefly state why you cannot provide this information.
- State the repository, university server, or private directory where the data are stored, if applicable (see data archiving requirements and exemptions).
- Write in the third person (e.g., “The authors jointly analyzed the data.”)
- If no data was collected, state this in the text field (e.g., “This is a conceptual manuscript and no data was collected.”).
- This information must be provided with each revision (and updated if necessary) and must also be included in the final accepted version of the manuscript for publication.
Sample Data Collection Statements
The first author supervised the collection of data for the first study by research assistants at the University of Chicago Decision Research Lab in the autumn of 2011. The first and second authors jointly analyzed these data. The first and second authors jointly managed the collection of data for study 2 using the Qualtrics panel described in the methods section in the spring of 2012. These data were analyzed jointly by all three authors with support of a statistical staff member at the University of Chicago. The data are currently stored in a project directory on the Open Science Framework.
The first author conducted all of the in-person fieldwork herself from autumn of 2007 until spring of 2009 in collective exercise communities in a large city in France. The second author acted as confidante throughout the process and visited the field site twice. Both authors conducted the online fieldwork independently and equally as active social media participants on Facebook and Reddit groups that focused on communal exercise. Data were discussed and analyzed on multiple occasions by both authors using the first author’s field notes, photographs, video, and artifacts, and both authors’ online notes, screen captures, and text files. The final ethnography was jointly authored. All notes, images, and data are currently stored in a password-protected project folder on a server at the first author’s university.
Sharing Data and Study Materials during the Review Process and Archiving
In the spirit of open science, the journal strongly encourages the anonymized public posting of data and study materials for submitted manuscripts when possible. At least, the authors must meet the following data sharing and archiving requirements to allow the review team and research community to assess the integrity of the data and analysis, replicate findings in future research if appropriate, and learn from the processes used by published authors.
It is recognized that some types of data should not be shared and/or archived. See the exemptions section for data that cannot be de-identified (often, this includes depth interviews, field notes, and photos), confidential data, proprietary data, and protected data.
Data and Study Materials Archiving Requirements
Initial Manuscript Submissions: For initial submissions of a manuscript, the data and study materials used in the research do not need to be made accessible to the editors. However, the editors may request these data and study materials at any time (see exemptions section for proprietary and restricted data, data that cannot be de-identified, and data that cannot be shared). When there is a request, authors must provide them in a timely manner using a repository associated with an approved third-party organization. The journal requests the use of one of the following: Open Science Framework, Harvard Dataverse (cannot be used for sensitive or identifiable data), Qualitative Data Repository, or ResearchBox. If a different third-party public repository is used, justification must be communicated to the editors. Storage on private directories such as Google Drive or Dropbox will not be accepted.
Revised Manuscript Submissions: If a revision of the manuscript is invited, the data and study materials used in the research must be made accessible to the editors when submitting the revision unless the data are exempt (see the exemptions section for details). Access to the data and study materials should be provided through anonymized posting on one of the data repositories listed in the above subsection (or an approved alternative). The data and study materials will not be made publicly available. In the event that a reviewer wishes to have access to the data and study materials, they must submit a written request to the editor, who will decide on the merits of the reviewer’s request and convey restrictions on their use of the data and study materials. If the request is granted, the editor will work with the authors to identify the specific data and study materials to be shared with the reviewer. The editor will ensure that any data and study materials for which access is granted will be handled confidentially. Links to the data and study materials must be updated/revised for each subsequent stage of the review process and maintained by the authors for a minimum of seven (7) years after publication.
Specifically, at each stage in the review process of the resubmitted manuscript, each author must do the following:
- Certify their agreement with JCR’s data policy.
- Certify that the data have been archived in one of the above third-party data repositories (or an approved alternative) unless the data are exempt from third party archiving (see below).
- Certify that the study materials are available in the third-party data repository, in the manuscript, or in a web appendix.
- Provide a working URL(s) to where the study materials and/or data are stored if applicable (the URL must be directly accessible by the editors, i.e., it may be password-protected, but the editors must be able to view the data files without requesting access).
- Ensure that the study materials and data files archived in the accessible repository are completely anonymized, and authors’ names (or other identifying information, such as the names of universities) do not appear in the URL or within the uploaded files.
- Ensure that the study materials have been translated into English, and provide materials in both English and in the original language.
- Attest that the archived data include all original data and interim techniques (such as transformations and analysis procedures).
- Certify that if the archived data are modified from an original primary source (e.g., an Amazon Mechanical Turk or Qualtrics data file), each author has access rights to the original form of the data.
The above steps are required with each resubmission and after the manuscript is accepted. Authors of rejected resubmissions are not required to provide continued access to their data files and study materials and may discontinue the URL provided to the journal.
Failure by authors of a manuscript to comply with the above policy upon resubmission will constitute grounds for desk rejection of the revised manuscript.
For information about exemptions to third party data archiving for proprietary and restricted data, data that cannot be de-identified, and data that cannot be shared, see sections below.
Data Ownership and Public Access
Archiving and providing the journal with access to data do not imply that the authors give up ownership of their data. Rather, the authors are granting temporary, fair usage of their data to limited parties. Authors may respond to queries about their data by sharing specific results instead of raw data. Authors who do choose to share archived data must ensure that the data are treated according to the institutional policies under which the data were collected (e.g., they have been de-identified to protect the rights of human subjects).
Institutional Review Board (IRB) and Grant/Foundation Requirements
Authors must check with their respective Institutional Review Boards or comparable university ethics committees on data archiving as well as with any granting institutions or foundations that have supported their research. Different institutions may have their own data archiving requirements. In cases when these are in conflict with the JCR Policy, contact the editors.
Exemptions to Sharing Data during the Review Process and Data Archiving
Not all data should be shared or archived. Exemptions to the data sharing and archiving requirement may be granted in the following situations:
- Data cannot be de-identified. Data where it is virtually impossible or overly burdensome to require authors to de-identify the data prior to sharing is exempt from data storage and archiving requirements. Typically, de-identifying this type of data would change the context of the material itself and alter the interpretability of the data. This often includes, but it is not limited to, ethnographic field notes, photographs, depth interviews, video material, and archives of online engagement in communities and associated data analysis files that cannot easily be de-identified.
- Data are restricted by ethics boards and/or confidentiality agreements (verbal or written) with informants.
- Proprietary/Restricted Data. Data sharing is restricted by the government or corporations that provide access to it.
How to Obtain a Data Sharing and Archiving Exemption
When authors believe that some or all data qualify for an exemption from the sharing and archiving policy, they must do the following during step 6 of the submission process:
- Enter into the text box a list of the categories of exempt items not included. For example, 22 interviews, 300 PDFs of websites from xyz types of web communities from which netnographic data was collected, 203 photos of people from a field site, etc.
- For each exempted item, briefly explain why it is exempt, e.g., “cannot be de-identified” for field notes and interviews or “restricted local government data that cannot be used outside of data center.”
- For studies that use multiple kinds of data, list what data are being archived, if any. For example, if a study uses anonymous experiments and in-depth interviews that cannot be de-identified, authors will be required to archive in a third party repository their experimental data but not their interviews that cannot be de-identified.
- Upload all data collection materials to the third-party data repository, unless all data collection materials appear in the manuscript or in a web appendix.
- In the case of public data that cannot be archived, provide a link to the data source and the programing code for statistical replication.
- In cases where an author has developed or purchased proprietary code for studies that they are unable to share, they should provide a detailed written description of the steps in the code. This information should be provided in a web appendix.
The editor assigned to the (re)submitted manuscript will then review the information provided about the exempted data. Upon submission, the editor reserves the right to contact the authors to request more information about the exempted data. If there is disagreement between authors and editors about exemptions, the Policy Board will adjudicate.
Appeals about data storage/archiving exemption denials or concerns about exemption processes should be submitted to the Policy Board.
Data and Study Materials Maintenance Policy
Authors of manuscripts that report data-dependent results will make available, upon request only, exact information regarding their procedures, stimuli, and data for seven (7) years after the date of publication for the benefit of researchers interested in replicating or extending these results. However, JCR encourages authors to make their information available beyond the mandatory seven years.
In cases where data has been exempted from third party archiving, data must still be secured for at least seven years after publication. That is, all archived data and study materials must be maintained for seven years after publication and all exempted data must be secured privately by the authors for seven years after publication.
The editorial office runs a plagiarism check on every submission using iThenticate. JCR considers “self-plagiarism” (instances in which authors borrow from their own previously published work without the proper citation) a form of plagiarism.
Author Misconduct Policy
Should there be suspicion of author misconduct, the Journal will follow the procedures outlined in our Policy and Procedures Regarding Author Misconduct. All submitting authors must acknowledge that they have read the policy and must agree to the terms.
Please direct all comments regarding JCR‘s research ethics policies to the Policy Board.