I am writing this editorial to guide authors interested in submitting their work to the Journal of Sustainable Marketing (JSM) on how to maximize the likelihood of successful publication. Before I begin, I want to emphasize that I am very proud of the progress our Editorial Team has made toward establishing the journal as the leading academic marketing journal focused on environmental and social equity research. Our submission rate has grown dramatically, as has the number of articles published. Our reviewer database has also become significantly larger and continues to grow.
Our entire team – Editorial Review Board, Associate Editors, Managing Editor, Production Editor, and, of course, our beloved Reviewers – should be very pleased with their success in bringing quality research and conceptual thinking to this critical stream of academic endeavor. The need has never been greater for a journal that provides educators, managers, and policymakers with rigorous, cutting-edge, and innovative research to combat global heating, multiple forms of pollution (e.g., microplastics), biodiversity loss, and supply chain inequities. For updates on our journal's growth and impact, please visit the JSM LinkedIn page: https://www.linkedin.com/company/jsustmark/.
That said, content changes reflected in some of the manuscripts submitted to JSM over the past two years are less positive. To begin, we are receiving numerous papers that are suspiciously alike in the following ways. First, they use similar approaches, as reflected in their research focus and analytical methods. For example, these submissions propose tests of well-researched theories such as the Theory of Planned Behavior or Stimulus-Organism-Response Theory. They propose to ”test” a minor contribution to the theory stream using cross-sectional data and partial least squares SEM (PLS-SEM). They frequently include a moderator and/or a mediator. More often than not, the outcome measure gauges intentions rather than actual behavior. Such submissions frequently do not include measurement item tables, appropriate tests for common method bias, state-of-the-art mediation analyses, or measures of multivariate normality.
Please be advised that our editorial policy prefers SEM-based submissions to use covariance-based SEM (CB-SEM) rather than PLS-SEM. We follow this policy because PLS-SEM is suitable for initial exploratory research as well as research that only seeks to assess the predictive value of a set of antecedents for one or more outcomes. It is less suitable for investigating theory–based causal relationships grounded in prior research.
This is because PLS–SEM provides limited guidance on how well the specified structural model fits the covariance matrix data. CB–SEM, by contrast, provides multiple model–fit measures (e.g., RMSEA, CFI, SRMR, etc.) that enable a more accurate assessment of the specified model's theoretical fit to the data. In addition, PLS–SEM tends to inflate path coefficients and to lead to acceptance of models for analysis that do not meet the standard fit criteria used in CB–SEM. Thus, this method suits preliminary exploratory investigations or models that seek to predict one or more outcomes, rather than ”testing” potential causal theory. Related to this is the tendency for such studies to omit confirmatory factor analyses (CFA) using CB-SEM, which would more reliably assess the measurement model's discriminant validity, convergent validity, and fit.
Another common approach in these submissions involves the use of common method bias tests that are no longer considered adequate alone or, by some, in combination with other tests. One such example is Harman's Single Factor Test. Authors who have used Harman's test in the past are advised to read Podsakoff et al. (2003) and search forward to ensure use of appropriate tests for this important threat to internal validity. PLS-SEM offers approaches to test for CMB, but given this journal's tendency to desk-reject studies that use this method, authors are advised to employ other methods before data collection (e.g., marker variables or variations in question format) and, after data collection, various methods that employ CB-SEM.
Also frequently missing are tests of the assumption of multivariate normality (MVN) for SEM analyses and other linear models. If the MVN test indicates a violation of multivariate normality, bootstrapping is required. In addition, to ensure high-quality mediation analyses, please read Zhao et al. (2010) to more hypothesize, test, and report mediation results. Although their paper does not specifically address SEM models, its logic and terminology can be and are frequently applied to structural equation modeling.
Another challenge that often leads to desk rejection is that latent construct items do not reflect the factor or are related to other factors in the proposed model. These issues seriously threaten internal validity. Unfortunately, some authors do not include their construct items in the first submission, and then reviewers or the editors request them as part of the revision. At that point, the editorial team discovers the threat to internal validity posed by non-reflective or confounded items, and this leads to rejection. Authors can save themselves time by always including the tables with all items listed in full, regardless of the study design.
Related to this problem, many authors ”modify” scales that have been peer-reviewed and published, or they create their own scales. Each approach threatens internal validity. Using all or a subset of author-modified items from the original scale can lead to inaccurate conclusions about relationships in the analyzed model. To avoid this, authors are recommended to modify previously published scale items only if their study's context differs from the context in which the scale was developed and tested, e.g., a study about EVs sent to JSM that uses a satisfaction scale that was developed for use in a PC or laptop purchase context. Modifying scale items beyond this can lead to serious misrepresentation of the latent construct.
Using scales expressly developed for the author's study that are not psychometrically validated can pose even more serious threats to internal validity. Thus, authors are advised to use scales shown in repeated studies to have predictive, convergent, and discriminant validity. They are also advised to include all items in the original scale. It is possible, post-hoc, to select a smaller number of reflective items for latent constructs based on various analytical methods such as factor analysis. For CB-SEM, this is sometimes required in order to obtain adequate fit between the proposed model and the covariance matrix. If this is done, it is important to show that the use of fewer items does not significantly reduced the variance explained by the factor and to provide the data that shows this.
This editorial's emphasis on SEM may leave the reader with the impression that the Editorial Team does not welcome the use of other linear models, experimental designs in the lab or field, abductive qualitative investigations, or conceptual papers. This is not the case. However, each approach has its own issues that can lead to negative editorial decisions. For example, the use of regression assumes perfect measurement of sum-scaled or averaged items on latent constructs. This should be acknowledged as a limitation. In addition, many regression papers fail to include standard diagnostics such as measures of autocorrelation and heteroskedasticity. As another example, in experimental studies, failure to adequately pre-test manipulations and/or to include adequate manipulation checks can lead to serious internal validity issues.
Turning to qualitative investigations, JSM welcomes such studies. However, a clear discussion of the inductive method employed (e.g., constructivist grounded theory) must be included. In addition, while large samples are typically inappropriate for such studies, researchers should continue interviewing respondents until themes begin to repeat and exhaust a probable set of thoughts related to the exploratory topic. In my experience, this rarely happens before 20 interviews. Also, in many cases, this type of investigation would be better positioned as Study 1 followed by either a cross-sectional or experimental study or both. A major mistake I have observed occurs when authors go far beyond the preliminary, exploratory findings from their qualitative study and present strong implications in the discussion section. Exploratory studies should not be used to provide strong theoretical contributions or recommendations to managers and policymakers. Instead, they can generate research questions and/or hypotheses, combined with pre-existing research, for future research to enable empirical testing of the findings.
Similarly, conceptual papers with no data other than what the authors report in their literature review should not be used to provide extensive recommendations to managers and policymakers. One can suggest applied implications based on consistent findings from a systematic literature review, but these should be limited. Again, I recommend that the authors focus on important theoretical issues and research questions/hypotheses that subsequent empirical research can test. Including a conceptual model would also be helpful. In some cases, the submitted conceptual paper is limited in its contribution and would be better positioned as the front end of an empirical investigation that tests hypotheses generated in the review. Meta-analyses, of course, are an exception to this.
A related approach involves bibliometric analyses. JSM receives many of these manuscripts. With the software available today, such submissions often look and feel the same. We do publish such analyses, but only when they address a sustainable marketing topic that does not already have a recent literature review, meta-analysis, or bibliometric analysis on the same or a closely related topic.
Formatting of submitted manuscripts is also an issue. While we receive quality manuscripts that are not properly formatted and send these to our Editorial Team, I am concerned that non-standard formats may lead reviewers to question the paper's quality. At the very least, such formats increase the work that our reviewers have to do to carefully read the submission and offer constructive advice. Thus, please place your Title and Abstract on a single first page, single-spaced. Then, use double-spacing throughout the body of the paper and use single-spacing for references (APA style, alphabetically listed, not numbered) and figures/tables. For figures/tables, do not use output from statistical packages. Instead, create your own figures and minimize information; e.g., for SEM models, include only the path coefficients in the proposed model. Do not include the items.
In today's AI environment, many low-cost copyediting systems are available. JSM encourages authors to use these systems, e.g., Grammarly. Such tools can help authors identify grammar, punctuation, and word-choice errors and improve writing style, for example, by encouraging active rather than passive voice. Many journals discourage the use of ”we” or ”the research team” in manuscripts. However, this can lead to excessive passive voice. Thus, JSM is not opposed to a blend of active sentence format that uses ”we” or the ”research team.” However, one should always avoid ”I.” Although I am a native English speaker, I rely on Grammarly to help with the issues noted above.
The growing prevalence of bot respondents is a concern with submissions that use online panel data for cross-sectional or experimental studies. While many panel companies have attempted to employ safeguards to reduce bots' influence on survey outcomes, it is very important for authors to work closely with the panel company to do their best to ensure such respondents are eliminated prior to data analysis. During data collection, consider including multiple attention checks and negotiating with the panel company so that payment for those who fail (as bots may do) is not required. Please note however that some believe that AI bots can pass attention checks so this is not a foolproof method for bot detection/avoidance.
After data collection, authors should also run their own checks to ensure respondents took a reasonable amount of time to complete the study, did not railroad, did not respond extremely consistently, failed attention checks, or left significant portions of the questionnaire unanswered. Statements that these issues have been addressed when panel companies are used are highly recommended.
Three other points that should be included in submissions to JSM are: 1) certification of the ethical review and approval by an internal human studies committee affiliated with the research institution of each study in the manuscript prior to data collection; 2) a statement describing how AI was used in producing the manuscript, e.g., for copyediting only; and 3) a statement that all authors significantly contributed to the development and completion of the manuscript.
Last, in quality social science journals such as the Journal of Sustainable Marketing, a single cross–sectional study using self–reported consumer perception measures is generally not considered sufficient to warrant publication. This is particularly true in sustainable marketing, where social desirability reporting bias is likely to be high. Thus, I recommend adding a second study that employs an experimental design and one or more behavioral measures. However, JSM is not opposed to publishing single studies if they are rigorous and make significant contributions to the current state of sustainable marketing theory and application.
Starting with all acceptances after 1 January 2027, JSM will follow some of the Center for Open Science (https://www.cos.io/initiatives/top-guidelines) disclosure guidelines. While the journal will not yet require pre-registration of studies for empirical papers, it will require that all accepted papers disclose within the manuscript whether the materials (e.g., questionnaires, experimental treatments) and data sets are available and, if so, where.
Accepted article authors will also be asked to submit a 750-word max clinical abstract of their paper for inclusion on the RISM World website (the journal's affiliated organization). The clinical abstract will also be distributed to online content providers such as Science Daily, CleanTechnica, and Sustainable Brands to increase the authors' impact on real-world managers and policymakers.
In addition, a 5-10 slide presentation summarizing major points in the study will be prepared. These presentations will also be available on RISM World (https://rism.world/). Links to RISM World will be included on the journal's website. The editorial team will be available to assist authors with the preparation of these valuable diffusion tools.
Beyond sharing top-notch research with our colleagues, JSM believes that impacting managers, policymakers, educators, and students is critical to ensuring successful achievement of the journal’s sustainable marketing mission. That is why our Managing Editor, Dr. Ahmad Aljarah, works tirelessly to disseminate the journal's findings through multiple social media channels and other outlets. Thus, by publishing in JSM, you can rest assured that your contributions to our critical field will be widely read and cited.
One final recommendation is to carefully read the journal's “Aims and Scope” criteria prior to submitting your manuscript. Ensuring your work fits the editorial team's topical guidelines and reflects our readers' expertise and interests will save everyone time. Here is a link to the journal's “Aims and Scope” page: https://luminousinsights.net/journals/jsm/aims_and_scope
Please join us on this important journey to safeguard the planet for future generations by addressing existential issues that threaten the environment and fair trade equity. Submit your work to the Journal of Sustainable Marketing. Our review process turnaround is very reasonable, and the feedback from our expert reviewers is always collegial and helpful. If you have questions, you are welcome to email me: dalden@hawaii.edu.
References
- . Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879 CrossRef | Google Scholar
- . Reconsidering Baron and Kenny: Myths and truths about mediation analysis. Journal of Consumer Research, 37(2), 197–206. https://doi.org/10.1086/651257 CrossRef | Google Scholar
About contributors
Dana L. Alden
Department of Marketing, Shidler College of Business, University of Hawai’i at Manoa, Hawaii, United States
United States
