mksc-contribution-framing

Contribution Framing (mksc-contribution-framing)

When to trigger

• The results exist but the "so what" for marketing is thin or implicit
• You cannot say in one sentence what the field learns
• A reviewer calls the contribution "incremental" or "a methods exercise"
• You are deciding the Frontiers track, which rewards one dominant dimension

Name the primary dimension

Marketing Science contributions fall on identifiable dimensions. State which one is primary, and let the others support it:

• Substantive — a new marketing insight or counterfactual answer (how a pricing/advertising/channel/platform decision should change, and why).
• Modeling — a new or sharper model that captures a marketing mechanism prior models missed.
• Methodological — a new estimator, identification strategy, or computational method others can reuse.
• Data — a novel dataset enabling questions that were previously infeasible (the Database track).
• Practice — a deployed solution with measurable impact (Practice Paper / Practice Prize, with a 500–1,000-word Impact Statement, single-blind).

Most regular papers lead on substantive or modeling and use methodology as support. Frontiers papers must make a major contribution on one primary dimension while meeting (relaxed) thresholds on the others — "different, but equal."

Write the contribution explicitly

• Intro contribution paragraph: state the marketing question, the model, the answer (including the key counterfactual magnitude), and what the field now knows that it did not.
• Discussion: generalize beyond the specific data — boundary conditions, what the mechanism implies for other settings, and where the model would break.
• Managerial implications: translate the counterfactual into a decision a manager could act on, with honest scope. The INFORMS lineage rewards decision-relevant, quantified takeaways.

Avoid the common downgrades

• A sophisticated model that answers no important marketing question reads as a technique demo.
• A correct estimate with no counterfactual leaves the "so what" unstated.
• Listing five "contributions" of equal weight dilutes the primary one.

Checklist

• Primary dimension named (substantive / modeling / methodological / data / practice)
• One-sentence statement of what the field learns
• Intro paragraph ties question → model → answer → counterfactual magnitude
• Discussion gives boundary conditions and generalization
• Managerial implication is decision-relevant and quantified, with scope
• If Frontiers: one dominant dimension, others at relaxed threshold

Anti-patterns

• "We contribute to the literature on X" with no specific learning.
• Equal-weight contribution lists that hide the real one.
• Method showcased with no marketing payoff.
• Managerial implications that restate results instead of prescribing a decision.

Contribution pass for Marketing Science

Treat this skill as an executable review pass, not a prose hint. First lock the demand/supply mechanism, fit evidence, and counterfactual decision margin; then judge whether the current manuscript answers the venue's real reader: quantitative marketing reviewers who read the model through the managerial counterfactual it makes possible.

• Do the pass: Translate the result into who learns what, which mechanism changes, and which alternative explanation is ruled out; keep the contribution narrower than the evidence.
• Return a ledger: give claim / evidence / risk / manuscript location rows, so the next agent can edit rather than rediscover the issue.
• Sibling guard: 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 a sibling owns the contribution, recommend re-routing before polishing format.
• Stop condition: do not give submission-ready advice until the pack's resources/official-source-map.md has been checked for volatile rules and the manuscript has one concrete fix for the largest venue-specific risk.

Output format

【Primary dimension】substantive / modeling / methodological / data / practice
【What the field learns】one sentence
【Intro contribution】question → model → answer → counterfactual magnitude
【Generalization】boundary conditions / scope
【Managerial implication】decision + quantified payoff
【Track】regular / Frontiers (one dominant dimension)
【Next step】mksc-tables-figures