Product innovativeness from the firm's perspective: Its dimensions and their relation with project selection and performance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There has recently been tremendous interest in product innovativeness. However, it seems that we need a better understanding of exactly what product innovativeness means. This article presents a conceptual framework to clarify its meaning. The framework first distinguishes customer and firm perspectives on product innovativeness. From the customer's perspective, innovation attributes, adoption risks, and levels of change in established behavior patterns are regarded as forms of product newness. Within the firm's perspective, environmental familiarity and project‐firm fit, and technological and marketing aspects are proposed as dimensions of product innovativeness. Next, the article offers a tentative empirical test of the proposed dimensions of product innovativeness from the firm's perspective. A well‐known dataset of 262 industrial new product projects is used to: I) clarify the product innovativeness construct and examine its underlying dimensions, 2) examine the relation of product innovativeness with the decision to pursue or kill the project, and 3) examine the relationship between product innovativeness and product performance. Five dimensions of product innovativeness are found which have distinct relations with the Go/No Go decision and product performance: market familiarity, technological familiarity, marketing fit, technological fit, and new marketing activities. Most strikingly, measures of fit are related to product performance, whereas measures of familiarity are not. The article concludes that researchers need to be careful about which definitions and measures of product innovativeness they employ, because depending on their choice they may arrive at different findings. New product practitioners are encouraged to evaluate new product opportunities primarily in terms of their fit with their firm's resources and skills rather than the extent to which they are “close to home”.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| 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 it