Product Meaning in Digital Product Innovation
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.
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 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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".