A Model of Two-Sided Costly Communication for Building New Product Category Demand
Bibliographic record
Abstract
When a firm introduces a radical innovation, consumers are unaware of the product’s uses and benefits. Moreover, consumers are unsure of whether they even need the product. In this situation, we consider the role of marketing communication as generating consumers’ need recognition and thus market demand for a novel product. In particular, we model marketing communication as a two-sided process that involves both firms’ and consumers’ costly efforts to transmit and assimilate a novel product concept. When the marketing communication takes on a two-sided process, we study a firm’s different information disclosure strategies for its radical innovation. We find that sharing innovation, instead of extracting a higher rent by keeping the idea secret, can be optimal. A firm may benefit from the presence of a competitor and its communication effort. The innovator can share its innovation so that competitors can also benefit, which encourages rivals to enter the market. The presence of such competition guarantees a higher surplus for consumers, which can induce greater consumer effort in a two-sided communication process. Moreover, the increased consumer effort, in turn, prompts complementarity in the communication process and lessens the potential free-riding effect in communication between firms. Additionally, it encourages the rival firm to exert more effort, especially when the role of consumers becomes more important. Sharing innovation with a rival serves as a mechanism to induce more efforts in a two-sided communication process. The online appendix is available at https://doi.org/10.1287/mksc.2017.1071 .
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.012 | 0.004 |
| Insufficient payload (model declined to judge) | 0.080 | 0.008 |
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".