Customer Loyalty as Measure of Competitiveness
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
Years after the publication of our work on the analysis of customer loyalty concepts (Montinaro & Sciascia, 2011), I still dwell on these aspects, taking up a paper that we did not publish in those years and which attempted to describe an application example of integration. Market share and relative price are two indicators that businesses often use to measure their market success. In this study we propose to consider an alternative and innovative indicator of innovation success that takes into account the views of clients, true protagonists of the purchase decision making. Customer loyalty is the construct measured in this work that join customer satisfaction and market segmentation. We propose a generalized model where the customer loyalty is a function of customer satisfaction relieved in time and a more complex smoothing model that introduces in the function the influence of the market segmentation adopted by the company. On a simulated dataset are then calculated values of customer loyalty comparing it with a worst case and best case scenarios.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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