Offensive and Defensive Marketing in Spatial Competition
Bibliographic record
Abstract
While it is well established that travel costs impact on customer preference toward local service providers, research about how this situation affects competitive marketing strategies remains sparse. This article investigates, in a local market with two competing service providers, whether service providers should undertake defensive marketing (DM), targeted at the nearest customers who typically prefer their offering for convenience and/or offensive marketing, directed to relatively remote customers who favor the rival as the closest alternative. We find that the service providers can exclusively undertake either DM or offensive marketing or combine the two in a full differentiated strategy at the equilibrium. We compare the outcomes of these three strategic options to identify the conditions under which they are worth implementing. Main findings suggest that service providers are better off undertaking offensive marketing alone when their rival’s retaliatory offensive capacity is weak and customers incur small travel costs. Otherwise, service providers may exclusively undertake DM or combine it with offensive marketing when travel costs become significant. Also, service providers should not invest in any marketing activity when they have no market power, like in the case of two adjacent outlets in a mall. Finally, the implications of these findings are discussed.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.000 |
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".