Corporate Image Aspect of Corporate Management in Healthcare Industry: Definition, Measurement and an Empirical Investigation
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
The concept of corporate image has increasingly been attracting interest in business and management fields, as identifying how they are perceived by the public, and re-formulating their strategies accordingly is of utmost importance for the efficient and effective executability of the business functions, enhancement of corporate performance, and for the sustainability of corporations. Specifically, in healthcare, corporate management executives are more and more aware of the importance of corporate image and its implications for their corporations’ life prospects. This important concept, however, has been approached and defined by authors in multiple different fashions, and due to its largely overlapping attributes, it has also commonly been associated with other related concepts such as corporate reputation and corporate identity. In this paper, in an effort to establish an all-encompassing, clear and consistent conceptual definition for corporate image, the authors first attempt to provide a definitional statement based on a substantial theoretical research, then present a new model for capturing the corporate image for the corporations operating in the healthcare industry, and finally execute its initial application on a chain hospital through surveying 710 people in Turkey.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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