Bibliographic record
Abstract
The notion of ‘user satisfaction’ plays a prominent role in HCI, yet it remains evasive. This exploratory study reports three experiments from an ongoing research program. In this program we aim to uncover (1) what user satisfaction is, (2) whether it is primarily determined by user expectations or by the interactive experience, (3) how user satisfaction may be related to perceived usability, and (4) the extent to which satisfaction rating scales capture the same interface qualities as uncovered in self-reports of interactive experiences. In all three experiments reported here user satisfaction was found to be a complex construct comprising several concepts, the distribution of which varied with the nature of the experience. Expectations were found to play an important role in the way users approached a browsing task. Satisfaction and perceived usability was assessed using two methods: scores derived from unstructured interviews and from the Web site Analysis MeasureMent Inventory (WAMMI) rating scales. Scores on these two instruments were somewhat similar, but conclusions drawn across all three experiments differed in terms of satisfaction ratings, suggesting that rating scales and interview statements may tap different interface qualities. Recent research suggests that ‘beauty’, or ‘appeal’ is linked to perceived usability so that what is ‘beautiful’ is also perceived to be usable [Interacting with Computers 13 (2000) 127]. This was true in one experiment here using a web site high in perceived usability and appeal. However, using a site with high appeal but very low in perceived usability yielded very high satisfaction, but low perceived usability scores, suggesting that what is ‘beautiful’ need not also be perceived to be usable. The results suggest that web designers may need to pay attention to both visual appeal and usability.
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 | 0.014 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".