Determinants of Customer Loyalty and Financial Performance
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
Recent research in accounting advocates nonfinancial measures of company performance, such as customer satisfaction and loyalty, as useful indicators of aspects of firm performance. But what are the drivers of customer satisfaction and loyalty? We provide an integrated causal model of company performance in the personal computer (PC) industry that simultaneously tests links between product value attributes resulting from business process performance, customer loyalty, and financial outcomes. Our results extend prior accounting research (e.g., Banker et al. 2000; Ittner and Larcker 1998) in two directions: (1) by explaining the determinants of customer loyalty, and (2) by clarifying the relation between customer loyalty and measures of financial performance. We report that product value attributes directly and differentially impact levels of customer loyalty as well as prevailing average selling prices. Furthermore, measures of customer loyalty explain levels of relative revenue growth and profitability, and relatively high customer loyalty engenders a competitive advantage in the PC industry.
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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