Reputation for Product Innovation: Its Impact on Consumers
Bibliographic record
Abstract
Just as firms compete for customers, they also vie for reputational status across their relevant constituent groups. To many firms, a reputation as an innovative company is something that is both prized and actively sought after. Despite an abundance of anecdotal evidence pointing to several firms' active pursuit of an innovative reputation, there is little empirical evidence to evaluate the soundness of this pursuit. On a general level, this research recognizes that firms compete for competitive advantage via their tangible and intangible resources. Much of the innovation literature centers on the tangible impact that new product development initiatives have on outcomes of innovation. Yet research investigations of the less tangible facets of innovation, such as a reputation, remain relatively uninvestigated despite their promise as a source of sustainable competitive advantage. This study investigates the effects of a corporate reputation for product innovation (RPI) and its impact on consumers. Consumer involvement levels are proposed to mediate the relationship between RPI and consumer outcomes. Empirical results indicate that a high consumer perceived RPI, via the involvement construct, leads to excitement toward and heightened loyalty to the innovative firm. A more positive overall corporate image and tolerance for occasional product failures are also positive outcomes noted in the results. Contrary to expectations, a high perceived RPI does not lead to a consumer propensity to pay price premiums.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".