The Effects of Service and Consumer Product Knowledge on Online Customer Loyalty
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
Customer loyalty is a key driver of financial performance for online firms. The effect of service quality on customer loyalty has been well established. Yet, there is a paucity of research that has studied the cost of obtaining service quality during the service process and the service outcome influenced by such cost. We extend previous research and propose the 3S Customer Loyalty Model by integrating sacrifice and service outcome as additional important service dimensions together with service quality when predicting online customer loyalty, and examining how their influences on loyalty vary across customers with different degrees of product knowledge. Further, we theorize that service quality and sacrifice -- as service process dimensions -- influence service outcome, and we theorize how “live help” technology improves customer perceptions of service quality and sacrifice. The empirical results indicate that 1) customer loyalty increases with higher perceived service quality, lower perceived sacrifice, and better perceived service outcome, 2) service quality and sacrifice influence service outcome, 3) customer product knowledge negatively moderates the relationship between service quality and online customer loyalty and positively moderates the relationship between sacrifice and customer loyalty, and 4) live help technology enhances service quality and reduces sacrifice. These findings support the theoretical importance of including sacrifice and service outcome (parallel with service quality) as antecedents of online customer loyalty. Our study also advances the theoretical understanding of what service process consists of and how the service process (i.e. service quality and sacrifice) influences service outcome.
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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 | 0.001 |
| 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 | 0.001 |
| 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