JCR fully supports research integrity and the ethical conduct of consumer research. JCR‘s Policy Board is responsible for the development and implementation of editorial direction and policy.
The 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 the policies and practices detailed below, prior to submitting their manuscript:
- Data Collection Paragraph
- Open Science and Research Transparency Guidelines
- Providing Data and Study Materials during the Review Process and Archiving
- Guidelines for Methodological Reporting
- Recommendations for Statistical Reporting
- Data Maintenance Policy
- Plagiarism Check
- Author Misconduct Policy
Additionally, the Committee on Publication Ethics has guidelines and information on research integrity that may be useful to authors.
Data Collection Paragraph
(revised October 1, 2020)
When submitting manuscripts for review, authors must include a data collection paragraph in the relevant field during Step 6 of the submission process. 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 paragraphs and review the following general guidance for adherence to this requirement:
- Do not include the data collection paragraph 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 paragraph 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 (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), briefly state why you cannot provide this information.
- State the repository, university server, or private directory where the data are stored.
- 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 paper and no data was collected.”).
- This information must be provided with each revision and must also be included in the final accepted version of the manuscript for publication.
Sample Data Collection Paragraphs
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. 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. 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 project folder at the Qualitative Data Repository under the management of the first author.
Open Science and Research Transparency Guidelines
(revised March 1, 2022)
The Journal of Consumer Research embraces the principles of open science by encouraging the collaborative sharing of research materials, methods, data, and code for data collection, data preparation, and/or data analysis. Upon submission, authors are required to include in the paper a full disclosure and explanation of the methods used in the reported work. Upon acceptance, this entails providing the information noted below in the manuscript or a web appendix.
Data Collection Instruments: 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. If data were non-questionnaire measures (e.g., response latencies, eye tracking responses, or brain scans), authors must describe how they were collected. For qualitative data: authors must describe fieldwork, observation, or interview procedures (e.g., the field guide that was used and the interview questions that were asked), as well as a description of how data were captured (field notes, audio recording, photographs, etc.).
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, ethnicity, other factors relevant to the research study context); 2) the method of recruitment (e.g., participant pool, online panel, snowball, compensation) and sampling method (e.g., convenience, probability); 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.
Providing Data and Study Materials during the Review Process and Archiving
(revised March 1, 2022)
The journal strongly encourages the anonymized public posting of data and study materials for all submitted manuscripts when possible. At least, the authors must meet the following data requirements at submission.
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. When there is a request, authors must provide them in a timely manner using a repository associated with an established third-party organization. The journal requests the use of one of the following: Open Science Framework, Harvard Dataverse, 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. Access to the data should be provided through anonymized posting on one of the data repositories listed in the above subsection (or an approved alternative). The data will not be made publicly available. In the event that a reviewer wishes to have access to the data, 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. If the request is granted, the editor will work with the authors to identify the specific data to be shared with the reviewer. The editor will ensure that any data 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:
- 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).
- Certify that the study materials are available in the third-party data repository or a web appendix.
- Provide a working URL(s) to where the data are stored (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 data files 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 data and study materials have been translated into English.
- Attest that the archived data include all original data and interim techniques (such as transformations and analysis procedures) or explanations that would allow the authors or independent researchers to replicate the analyses described in the manuscript.
- 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 it 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 all authors of a manuscript to comply with the above policy upon resubmission will constitute grounds for desk rejection of the revised manuscript.
JCR recognizes that consumer data take many forms, and not all data can be digitally stored or stored in ways that allow for respondent de-identification. In addition, some data may have proprietary or institutional (e.g., IRB-specific) restrictions that limit their ability to be shared with the journal and/or all authors. In such cases, the authors must very clearly indicate that not all of the reported data are shareable due to proprietary or other restrictions and provide an explanation. The editor assigned to the manuscript will decide if the explanation is acceptable. The editor reserves the right to contact the authors to request more information before deciding if the requested exception will be granted. The editor may also discuss this with other members of the editorial team. If the editor does not grant an exception, the review process will not commence until the authors have provided access to their data.
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.
Guidelines for Methodological Reporting
(revised March 1, 2022)
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 (e.g., study protocol) 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.
Study Materials and Context: For lab studies: provide study materials (e.g., original surveys) and describe all experimental conditions/manipulations, scenarios, vignettes. 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.
Post-Data 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.
Data Description: 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 in terms of the number of images. 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 amount of missing data should be reported.
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.
Recommendations for Statistical Reporting
(revised March 1, 2022)
Authors must provide the information noted below in the manuscript or a web appendix.
ANOVA: Describe the study design, factors, factor levels, whether factors are between or within-subjects, cell sizes, covariates and their significance, and results if covariates are not included. Report full ANOVA table and effect sizes.
Regression: Indicate which variables are included and in which order. Report regression coefficients (with confidence limits) or standard errors. Specify whether coefficients are standardized or not. In moderated regressions, note which variables are continuous, if variables are centered or standardized, and which values are used to define high and low levels (e.g., +/-1 SD).
Exploratory Factor Analysis: Clarify use of EFA or PCA, the method of rotation, eigenvalues/percentage of variance accounted for by each factor, and 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 Modelling 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).
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).
Estimation Details: Identify the algorithms used (e.g., GMM, 2SLS, ML, EM, MCMC, HMC, VB), estimator characteristics, convergence criteria, run-times, machine learning packages employed (e.g., R or Python packages, their web locations, access versions).
Simulation Studies: For papers with custom programming or models, describe various scenarios with parameters both similar to and different from the ones estimated in the paper. Report full details regarding parameter recovery and code correctness.
Analytical Models: Describe robustness checks: assumptions, models explored, distributions.
Computer Science or Machine Learning Methods: Describe the exact model(s) and/or algorithm(s) used, researcher choices in setting parameters for the models/algorithms, packages employed (e.g., R or Python packages, their web locations, access versions), other methodological choices made, and robustness checks run. If using natural language processing or computer vision algorithms, include details on the libraries used and/or training datasets used and their sources.
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, etc.).
Data Maintenance Policy
(revised October 1, 2020)
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 (7) years.
Exceptions will be made for identifiable or proprietary data. Authors must request such an exception and state the basis for it in the first version of a submission in which the relevant data appear. This request must normally be made at the time of the initial submission, although a request pertaining to data added in revision is allowable when the relevant revision is submitted. Should the manuscript be accepted or offered revision, the editor will address the request in the decision letter.
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
(revised September 1, 2020)
Please direct all comments regarding JCR‘s research ethics policies to the Policy Board.