Bibliographic record
Abstract
The present chapter discusses the nature of e-loyalty in B2C e-commerce. Based on previous theoretical work on loyalty in traditional commercial settings, we argue that highly affective forms of loyalty are unlikely to develop in online environments. Rather, we suggest that the e-commerce environment promotes self-sufficiency and mitigate the need for customer/employee interaction which represents a primary source of affect. Research on relationships in both social psychology and marketing suggests that this loss may not affect all customers equally. We distinguish between customers who value establishing interpersonal relationships with company employees (communally oriented) from customers who see customer/employee interactions as utilitarian and who derive little social benefits from such encounters (exchange oriented). The chapter suggests that online environments may be seen as useful by communally-oriented customers but relationally unsatisfying. Conversely, there exists a good “fit” between what exchange oriented customers value in their relationships with companies and what online environments offer. Consequently, online companies should not be surprised to find that e-loyal customers may be predominantly exchange oriented. Finally, we argue that because e-loyalty lacks a strong affective foundation, it may be less enduring than “traditional” customer loyalty. Implications of our analysis and areas for future research are discussed.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".