Product Meaning in Digital Product Innovation
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
Digital product innovation involves a meaning-making process. Designers of digital innovations often challenge established product meanings as they digitize physical products, such as cars, toothbrushes, and water bottles. A significant problem for product designers, however, is striking the right balance between the newness and comprehensibility of product meanings. Failure to do so may result in a digital product innovation that is too conventional or difficult to relate to or understand. Yet, the extant digital product innovation literature pays little, if any, attention to product meaning. To fill this void, this study examines a digital product innovation project in which product designers created a digital theater with product meanings beyond those of the traditional movie theater. Our theory, grounded in in-depth data collection and analysis, explains how product designers attribute meanings to their products in the process of digital innovation by enacting two meaning-making loops: a reinforcing loop that makes the product meaning comprehensible, and a differentiating loop that captures emerging product meanings. The two loops come together via meaning sedimentation, through which a new core product meaning is created. Our study contributes to the digital product innovation literature by shedding light on the essential role of meaning-making in innovation and offers an explanatory process theory.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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