Addressing the <i>What</i> and <i>How</i> of Online Services: Positioning Supporting-Services Functionality and Service Quality for Business-to-Consumer Success
Bibliographic record
Abstract
With the continued growth of business-to-consumer (B2C) e-business, online vendors are providing an increasing array of services that support and enhance their core products or services. For example, Amazon.com does not just sell books; it also enhances that core product with automated product recommendations, “wish list” tracking, order status updates, customer reviews, and many other valuable supporting services. These supporting services are made possible exclusively through the design and deployment of information technology (IT) to provide website supporting services functionality (SSF). In this paper, we define and develop the concept of B2C SSF and investigate how IT can support core products or services. We theorize the role that SSF plays in an environment where individuals who visit B2C websites are not only customers but also technology users. Given the unique online environment that amalgamates vendor services with information systems (IS), our model integrates theories from both services marketing and technology acceptance to help explain the behavior of these customers/users. In doing so, we investigate the role of the extensively researched concept of service quality in relation to SSF. Although service quality provides guidance for how supporting services should be provided (e.g., responsively and reliably), it does not address what those services are (e.g., product recommendations). SSF addresses this deficiency, thus providing both theoretical and practical benefits through a focus on IT design and deployment. The results of a field study support that SSF is an important predictor of customer beliefs and behavior, beyond that predicted by service quality alone. SSF is an important concept to consider—theoretically and practically—in IT-mediated B2C service.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.017 | 0.028 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| 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".