Service quality and satisfaction in business‐to‐business services
Bibliographic record
Abstract
Purpose The purpose of the paper is to investigate the effects of service quality and service satisfaction on intention in a business‐to‐business setting. Design/methodology/approach This research addresses three unanswered questions regarding satisfaction and service quality: the distinction between customer satisfaction and perceived service quality; their causal ordering; and their relative impact on intentions. The data were collected using a large survey of buyers in a business setting. Findings The data were analyzed using structural equation modeling. The results show that service quality has a larger impact on intentions than does customer satisfaction. The results also show that the effects of individual transactions on intentions are mediated by corresponding cumulative constructs. Research limitations/implications The primary implications for theory include demonstrating the distinction between satisfaction and service quality; specifying, based on theory and logic, the causal ordering between transaction constructs and cumulative constructs, and between service quality and satisfaction; and assessing their relative impact on behavioral intentions. Originality/value The results show that one negative transaction outcome may not be sufficient to cause the customer to switch if the cumulative levels are sufficiently positive. Thus, a negative outcome may be discounted by the user if it is seen as a unique occurrence. However, a series of successive negative transaction outcomes may cause the cumulative constructs to become less positive, resulting in lower intentions to repurchase from the same supplier.
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.
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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".