JOURNAL OF THE ACADEMY Summers / OF RESEARCH MARKETING IN MARKETING SCIENCE FALL 2001 Guidelines for Conducting Research and Publishing in Marketing: From Conceptualization Through the Review Process
Bibliographic record
Abstract
A primary mission of institutions of higher learning is the generation and dissemination of knowledge. The low acceptance rates at the leading research journals in marketing, typically in the single digits to low teens, suggests the need to increase the quality of the research manuscripts produced. This article presents a set of guidelines for researchers aspiring to do scholarly research in marketing. Discussed are issues such as developing the necessary research skills, conceptualizing the study, constructing the research design, writing the manuscript, and responding to reviewers. Also presented are the author’s personal observations concerning the current state of research in marketing. This article is intended for doctoral students and those researchers who are beginning or are early in their careers and would like to increase their journal acceptance rates. The experienced author with several major publications and years of reviewing experience will find little, if anything, “new ” to them. What follows are the author’s reflections on more than a quarter century of guiding doctoral students and reviewing for, and publishing in, some of the leading journals in marketing. The author’s remarks primarily relate to research that involves the collection and analysis of primary data (e.g., case studies, surveys, and experiments). Not addressed are such things as review papers, theory development not based on empirical research, and quantitative marketing models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.044 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.046 | 0.032 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".