Skills Productivity Positioning Marketing Science Manuscripts

Positioning Marketing Science Manuscripts

v20260724
mksc-literature-positioning
This skill guides researchers on how to structurally position a Marketing Science manuscript within its academic literature. It requires defining the model's baseline (modeling lineage) and the specific business problem it addresses (substantive stream). Crucially, it teaches how to frame the contribution as a concrete field gain rather than merely pointing out an existing knowledge gap.
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Overview

Literature Positioning (mksc-literature-positioning)

When to trigger

  • The intro reads as "no one has modeled X" (gap-spotting) rather than joining a modeling conversation
  • You are unsure which precedent papers define your model's baseline
  • Reviewers may say "this is a small extension of an existing model"
  • You build on your own prior work and must disclose how this paper goes beyond it

Position on two axes at once

A Marketing Science paper sits at the intersection of a modeling lineage and a substantive stream. Make both explicit.

  1. Modeling lineage. Which model is your baseline — a BLP-style demand system, a dynamic discrete-choice model, a channel/Stackelberg pricing game, an auction or search model? State what you add to it (a new mechanism, richer dynamics, a novel identification strategy, a tractable closed form) and why prior models could not answer your question.
  2. Substantive stream. Which marketing problem — pricing, advertising/digital and attribution, branding, distribution channels and retailing, platforms/two-sided markets, or marketing analytics/ML — does the paper speak to? Tie the model's payoff to that stream's open questions.

From gap-spotting to a real contribution

Do not justify the paper by absence ("X has not been studied"). Justify it by what the field gains: a sharper mechanism, a counterfactual prior models could not compute, a relaxed assumption that overturns a known result, or a method others can reuse. The strongest framings show that a natural prior modeling choice gives the wrong answer, and your model corrects it.

Self-overlap disclosure (required)

Marketing Science requires that if the submission builds on the authors' own published or under-review work, you cite it and state how this paper's contribution goes beyond it. Because regular review is double-anonymous, cite your own prior work in the neutral third person and keep the manuscript blinded (no "in our earlier paper").

Checklist

  • Baseline model(s) named; your delta to each is explicit
  • Substantive stream identified and its open question stated
  • Contribution framed as field gain, not absence of prior work
  • Closest competing model addressed head-on (why it cannot answer this)
  • Author self-overlap cited and differentiated; manuscript stays blinded

Anti-patterns

  • "No paper has done X" with no engagement of the nearest model.
  • Citing a wall of references without naming the one baseline you extend.
  • Hiding a close prior paper (your own or others') the editor will know.
  • De-blinding via "our previous work" phrasing under double-anonymous review.

Positioning pass for Marketing Science

Use this as a second-pass capability check. First lock the demand/supply mechanism, fit evidence, and counterfactual decision margin; then test whether the manuscript addresses quantitative marketing reviewers who read the model through the managerial counterfactual it makes possible.

  • Primary move: Map incumbent conversation, unresolved tension, this manuscript's delta, and the sibling-venue omission a referee might notice.
  • Decision ledger: return claim / evidence / blocker / next edit rows so the next pass can patch the manuscript directly.
  • Neighbor test: compare against Journal of Marketing Research for empirical marketing breadth, Management Science for wider OR/MS reach, Quantitative Marketing and Economics for specialist modeling; if the neighboring outlet has the stronger audience claim, recommend re-routing before polishing.
  • Verification floor: before submission-ready advice, re-open resources/official-source-map.md for volatile rules and name the one unresolved fact that could change the recommendation.

Output format

【Modeling lineage】baseline model(s) + your delta
【Substantive stream】pricing/advertising/channels/platform/analytics + open question
【Contribution framing】field gain (not gap)
【Closest competitor】why it cannot answer this question
【Self-overlap】prior work cited + differentiated; blinding intact
【Next step】mksc-methods
Info
Category Productivity
Name mksc-literature-positioning
Version v20260724
Size 4.49KB
Updated At 2026-07-28
Language