Information Technology Mediated Customer Service: A Functional Perspective
Bibliographic record
Abstract
Companies can use information technology not only to conduct transactions with their customers but also to provide them with valuable functionalities before, during, and after those transactions, such as recommendations on what products to buy or to track product delivery. The e-Business environment provides a particularly fertile ground for leveraging IT resources to provide functionality. Although the general perceptions of online service quality and self-service technologies have been extensively studied, there has not yet been a through investigation into the specific functionality that IT can provide for delivering services that supplement a core product offering. This paper examines the role of IT as a service delivery mechanism. Drawing on a customer service life cycle framework and the concept of supplementary service, we propose and operationalize the construct of functionality: the broad array of IT applications that can enhance a customer’s experience with a company beyond just the core offering. Functionality focuses on specific IT-based service tools as opposed to broad perceptions of service (e.g., service quality) and so targets a key IT artifact. A crosssectional survey of current e-Business customers was used to validate the multidimensional functionality construct as well as to test a theoretical model relating functionality to the consequences of satisfaction, perceived website usefulness, and continued website usage. The results support that functionality is not only a conceptually valid construct but also one that is highly regarded by e-Business customers.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".