Definition of innovation problems in organizations using data analysis tasks from hybrid sources: social networks and organizational databases
Bibliographic record
Abstract
Nowadays, we can express the experience we have lived with the products we use. Most of the time, we interact with brands and let our likes and dislikes be seen on digital platforms, either by interacting with social networks, filling out satisfaction surveys, or registering requests, complaints, and claims. To get the value of all available customer data of the companies, in this article, we propose to study the data from different sources using an autonomic cycle of data analysis tasks to define innovation problems in an organization. The tasks of the autonomic cycle are filter the customer comments from different sources (e.g., from social networks, PCCS (petitions, complaints, claims, suggestions) systems of organizations, etc.), obtain their keywords, and analyze the patterns of the users to answer the questions of the 5W methodology (what, who, where, when and why?), to define innovation problems. Finally, this article analyzes a case study of a fashion company using its PCCS system and comments of Twitter, to identify useful information. Part of the information discovered was the reasons for customer returns, merchandise delivery problems, shipment failures, failure to respond timely to customers, among other things. With this information, the autonomic cycle is able to define customer and organization-oriented innovation problems, to respond to these identified problems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".