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
- Data Analysis Transparency Guidelines
- Providing Data upon Submission and Archiving
- Data Maintenance Policy
- Plagiarism Check
- Author Misconduct Policy
Please see the current editorial team’s post explaining how JCR’s data policy works in practice. 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. This information is updated with each revision and included in the final accepted version of the manuscript.
Enter your data collection paragraph in the relevant field during Step 6 of the submission process (do not include the data collection paragraph in the manuscript file during the review process).
Include university names and other identifying details; the data collection paragraph will be accessible to the editor and associate editor but will not be shared with the reviewers.
Write in the third person (e.g., “The authors jointly analyzed the data.”) and 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
If a research assistant or lab manager collected data under the supervision of one of the authors, this should be stated in the data collection paragraph. However, authors are not required to provide the names of research assistants or lab managers.
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 Dropbox folder under the management of the first author.
Open Science and Research Transparency Guidelines
(revised October 1, 2020)
The Journal of Consumer Research embraces the principles of open science by encouraging the collaborative sharing of research materials, methods, and data. Toward this end, upon submission and after publication authors are required to include in the paper a full disclosure of the methods used in the reported work. This entails providing the information noted below. Information can be presented in the manuscript or in a web appendix.
Data Collection Instruments: For questionnaires: authors must provide either in a web appendix or an open-access electronic repository all original questionnaires presented to respondents. If questionnaires were generated by an online survey platform (e.g., Qualtrics), the electronic repositories should include the original generating file (e.g., the .qsf file). For qualitative data: describe fieldwork, observation or interview procedures (e.g., types of questions) and how these evolved, as well as description of how data were captured (field notes, audio recording, photographs, etc.). If automated web crawling algorithms or other automated procedures of digital data capture are employed, details should be provided in an appendix.
Sample (of respondents, data, studies, documents, events): For primary research involving human subjects, authors must indicate: 1) the method of recruitment/selection (e.g., subject pool, online panel, snowball, compensation; 2) the sample sizes used in all studies and how sample sizes were determined; and 3) the number of excluded participants, the exclusion criteria, and their justification.
For meta-analyses, identify databases used, journals searched, publication date ranges, procedures used to contact researchers regarding unpublished studies and evidence of publication bias).
For all types of research, data inclusion criteria, stop rules (which may include theoretical saturation) should be described, along with the criteria used to select sample or research sites. In addition, sample characteristics and other sample-related factors relevant to the research context should be provided.
Data Collection Procedures: Provide details about the types of data collected and rationale. Provide details about participants’/researchers’ activities in the process of data collection (e.g., study protocol). Describe data collection context and location, stimuli shown to respondents, and the order in which measures/data were collected. If secondary data- sources are used, indicate source(s) and time-periods involved. If automated digital data capture is employed, procedures should be rendered as transparent as possible.
Context: For lab studies: describe all experimental conditions/manipulations, scenarios, vignettes. For field studies: describe the consumer setting, context rationale, and relevant contextual factors. For ethnography/cultural approaches: describe 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., outliers, attention screens, comprehension screens), cutoffs for screening measures, distribution of eliminated individuals across conditions.
Data Description: Report descriptive characteristics (e.g., N’s, means, 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 numbers of images. The nature and number of websites, message boards, gaming sites, message threads, and units of social media should be reported. Treatment of missing data should be reported as well.
Maintaining Participants’ Rights: For primary research, indicate how participants’ rights were safeguarded (i.e., by IRB approval or national policy for safeguarding participant rights). Describe procedures for managing/archiving data, anonymization and de-identification of data, and procedures for ensuring data security.
Data Analysis Transparency Guidelines
Authors must provide the information noted below. Information can be presented in the manuscript or in 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, 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/% variance accounted for, 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 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 insignificant) 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 heretogeneity. 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 (process by which themes, interpretations, and/or frameworks were developed, usually referencing a particular paradigm or approach — e.g., grounded theory, phenomenology, discourse analysis, abduction, extended-case method, analytic case method, analytic framework, etc.). Describe unit of analysis or types of cases. Describe how data interpretation evolved over time. Describe procedures used to ensure trustworthiness, credibility, verisimilitude, and theoretical generalizability of interpretation (e.g., member checks, negative cases, triangulation, immersion in context, etc.).
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.
Providing Data upon Submission and Archiving
(revised October 1, 2020)
Upon submission and after acceptance, the data used in the research must be made accessible to both the editors of the journal (see exceptions below) and all authors. The policy allows scientific advancement by:
- Allowing resolution of questions about data and analyses that may arise during the peer-review process;
- After publication, ensuring that authors can access their original data if it is pertinent to a new research project of their own; and
- Ensuring that authors can respond to queries about their data or results about which non-authors may have an interest.
Links to data must be revised after each stage of the review process, and maintained after publication for a minimum of seven (7) years.
At each stage in the review process, each author must certify:
- Their agreement with JCR’s data policy;
- That data have been archived in an off-site repository of the authors’ choice, and the URL(s) for where the data are stored. Suggested repositories are noted here.
- That archived data includes all original data and interim techniques (transformations, analysis procedures) or explanations that would allow the authors or independent researchers to replicate the analyses contained in the manuscript.
- If the archived data are modified from an original primary source (e.g., an Amazon Mechanical Turk or Qualtrics data file), that each of the authors have access rights to the original form of the data.
This is required when the manuscript is first submitted, with each resubmission, and after it is accepted.
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 restrictions that limit their ability to be shared with the journal and/or all authors. In such cases, the authors must indicate that not all of the reported data are shareable due to proprietary or other restrictions and provide an explanation.
Data Ownership and Public Access
Archiving and providing the journal with access to data does not imply that authors give up ownership of their data or that they have agreed to share their data in part or in full with others. Authors may respond to queries about their data by sharing specific results as opposed to raw data. Authors who do choose to share archived data must ensure that 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 should also check with their respective Institutional Review Boards or comparable institutions 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.
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)