Evaluating Corporate Identity in the Office of Production Business Group in Bangkok through Automotive Business and Real Estate Business
Bibliographic record
Abstract
The objective of this research is to (1) evaluate the differences of physical factors and (2) evaluate the level of Building users' perception of the physical factors in the office which convey the meaning of Corporate Identity between the automotive business and the real estate business. The samples chosen for this research include 10 automotive business offices and real estate offices located in Bangkok area with a total number of 324 persons, using questionnaire to collect relevant data; using Likert Scale to evaluate the internal environment in the office; using t-test / ANOVA to analyze perceptual level data and differences in 8 physical factors which reflect the corporate identity under this research, i.e. (1) Colors in decoration; (2) Lighting in decoration; (3) Furniture style; (4) Decorative style; (5) The shape of space; (6) Continuity of space; (7) Wall decoration and graphic works and (8) Material in decoration. Based on the results of the research, it was found that there were physical factors which reflected the corporate identity of the organization and affected the perception level of those who were building users at a high level. The said 4 physical factors consisted of (1) Colors in decoration; (2) Interior decorative style; (3) Lighting in decoration, and lastly (4) Material in decoration. However, the individual characteristics of building users such as gender, age, and duration of work also affected the recognition of corporate identity significantly.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".