Toward a Provider-Based View on the Design and Delivery of Quality E-Service Encounters
Bibliographic record
Abstract
The advent of electronic-based service (e-service) transactions has resulted in numerous operational challenges for service providers. Extending previous service management insights, this article offers a provider-based framework identifying four overarching types of online interactions useful for advancing understanding on the design and delivery of quality e-service encounters. This framework allows for examination of the amount of service intervention, the degree of user participation, and the type of user connection underlying online interactions for both Web 1.0 and Web 2.0 applications and platforms. The article discusses how the quality of each e-service encounter type—informational, self-directive, intervenient, and intensive— requires, from a systems quality and operational standpoint, the management of three elements (i.e., target market, concept, and delivery system) underlying the firm’s e-service operations strategy. The article proposes promising areas in e-service encounter quality research where further investigation of design and delivery issues is urgently needed. One immediate implication stemming from this framework is that there is likely no single best strategy or approach to designing and delivering effective (i.e., quality) online moments of truth. What is required is the apt configuration of strategic e-service elements underlying each distinct e-service encounter type vis-à-vis critical e-service system quality dimensions (e.g., manageability, reliability, 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 | 0.017 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.021 | 0.017 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| 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".