CUSTOMER ORIENTATION AND OFFICE SPACE PERFORMANCE: ASSESSING THE MODERATING EFFECT OF BUILDING GRADE USING PLS-MGA
Bibliographic record
Abstract
This study presents a framework to measure and empirically validate the relationship between customer orientation and office space performance. The framework uses two types of customer orientation (i.e., responsive customer orientation and proactive customer orientation) and two types of office space performance metrics (i.e., tenant satisfaction and tenant loyalty). Moreover, the building grade (Grade A and Non-grade A) is incorporated into the framework to assess its moderating effect on the relationships. 380 usable responses were collected from building managers in Grade A and Non-grade A buildings using a questionnaire survey. Partial least squares structural equation modeling was utilized to perform latent variable and multi-group analyses. The findings indicate that proactive customer orientation enhances satisfaction to a level not reached by responsive customer orientation as well as suggesting the applicability of both customer orientations in different scenarios. While proactive customer orientation practices lead to higher satisfaction in Non-grade A office ten-ants, responsive customer orientation practices lead to greater satisfaction in grade A office tenants. The latter tend to be more satisfied with Grade A office and thus loyal. Theoretical and managerial implications are discussed.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".