Relationship benefits in an internet environment
Bibliographic record
Abstract
Purpose The notion of relationships has been shown to be a worthwhile strategy in many service industries. This coupled with the rapid development of the internet means that it is now possible (and even beneficial) to implement internet based relationship management programs. Given the importance of this issue this paper seeks to understand the relational benefits that consumers receive in an internet environment relative to the benefits consumers receive in a traditional environment i.e. face‐to‐face. Design/methodology/approach Results are derived from 15 in‐depth interviews (10 from the internet context and 5 from the traditional context) and over 200 quantitative surveys. Findings The relationship benefit of “history” appears in both samples which was missing from the original study on relationship benefits. Findings also show that there are differences between the internet group of customers and the traditional customers in respect to the perceived relational benefits. In particular internet customers appear to receive lower levels of the confidence benefit. Research limitations/implications We must be careful as these results may be context specific – one company from one industry. Future research must further investigate the ability of the internet to create and sustain relationships. The concept of history seems to be a potent one – how can firms use this newly discovered relationship benefit? Practical implications Ultimately internet based relationships are sufficiently different from traditional relationships to require specialized management attention. Managers must pay particular attention to the results which indicate loss of confidence and the need for the personal touch. Originality/value First piece of research to look at relationship benefits in the internet context.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".