Bibliographic record
Abstract
As customers gain Web site-specific skills they come to perceive the Web site differently and more favourably than inexperienced customers. This is not only due to familiarity, emotional attachment, liking, trust, etc. Often, it is the result of an objective change in the utility of the interface as a result of skill acquisition. This chapter reviews recent work on the link between skill acquisition and loyalty in electronic environments, and extends this work by investigating the impact that learning has on consumers’ perceptions of electronic interfaces. I report the results of an experiment, which demonstrates that with increasing task experience the probability that participants will choose an incumbent Web site, over an objectively equivalent competitor, increases. In addition the data indicate that with increasing experience participants’ perceptions of product quality also increase. Although the two interfaces (i.e., incumbent and competitor) are not perceived to be any different when each has been used only one time, there is a significant difference in quality perceptions between the interfaces when the incumbent has been used six times and the competitor has only been used once. These findings are important, because perceptions of quality have an impact on the choices that customers make when shopping online. Therefore, changes in perception that occur with increasing exposure to the incumbent are meaningful and can have an impact on a Web site’s market share. The data presented in this chapter provide strong evidence that perceptions of interface quality are affected by experience with an interface in a way that gives an incumbent an advantage over competitors.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".