Applying relationship theories to web site design: development and validation of a site‐communality scale
Bibliographic record
Abstract
Abstract Our study investigates whether relationship theories can be of help in designing web sites which foster greater customer loyalty. Based on literature reviews of Communal‐Relationship Theory from Social Psychology, communality from marketing research and related concepts (e.g. commercial friendships), we develop and refine a multidimensional measure of ‘site communality’ using a sample of 305 participants. Each visited one among several real web sites chosen across three industries (i.e. banking, pharmaceuticals and insurance). We define site communality as the extent to which web site content signals that a company's relationship with its customers goes beyond the formal, ‘tit for tat’ business dealings that are typically expected from purely commercial exchanges, and instead, more closely abide by the norms and behaviours evocative of friendships and/or family relations. Our results indicate that demonstration of caring, role spanning and authenticity/genuineness are its most important dimensions. Preliminary, findings also show that site communality is positively related to the benevolence dimension of online trust which is an important antecedent of loyalty. The practical implications of our study are discussed in the form of recommendations to help companies in designing web sites high in site communality.
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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 | 0.032 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".