Send-for-review decisions, brand equity, and pricing
Bibliographic record
Abstract
Purpose – The purpose of this paper is to study firms’ decisions of voluntary disclosure of high product quality by sending their products to intermediaries for review, and how the nature of the reviews subsequently substitutes the brand names in terms of affecting the price of the product. Design/methodology/approach – Using data on camcorders and point & shoot digital cameras collected from multiple intermediary sources, this paper empirically tests the relationships among brand equity, send for review decisions, the nature of reviews and pricing, controlling for endogeneity. Findings – This paper finds that firms are likely to send their high-quality products to the intermediaries for review, but such likelihood varies across different brands. Relatively weak brands are more likely to send their products for review than relatively strong brands. By doing so, weak brands receive two benefits: first, intermediary reviews help eliminate price differentials between weak brands and strong brands. The more positive is the review, the higher is the price. When intermediary reviews are not obtained, some strong brands effectively charge a price premium over weak brands. Second, intermediary reviews subsequently attract more consumer word of mouth (WOM). The more positive is the review, the more consumer WOM is attracted. Researchlimitations/implications – One limitation is that this paper does not account for the influence of intra-brand competition. The second limitation is related to the assumption that intermediary reviews are accurate. Practicalimplications – This paper offers managerial implications to brand managers concerning send-for-review and pricing decisions. It proposes how managers leverage third-party endorsement in launching a new product. Originality/value – This paper is one of the few papers empirically studying the interaction of two communication approaches: disclosure and brand signaling. It modifies the commonly assumed relationships among brand, quality and price by demonstrating the substitute effect of intermediary reviews and brand names on price. This paper is also the first research that empirically examines the impact of firms’ “send-for-review” decisions on generating consumer WOM. Managerially, this paper substantiates Godes et al. ’s (2005) framework on how firms should manage social interactions upon the release of new products.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".