Bibliographic record
Abstract
In this article, we introduce the concept of the service delivery network (SDN) defined as two or more organizations that, in the eyes of the customer, are responsible for the provision of a connected overall service experience. This responds to calls for frameworks recognizing that dyadic service encounters are embedded in the series of experiences customers have with complementary providers as part of the journey to achieve their desired goals. Adopting an SDN perspective presents a dramatically different set of challenges for managers and provides research opportunities challenging the current view of established service concepts. Managers must recognize that to better serve the customer they need to understand the role that they play in the customer-defined service journey and be prepared to coordinate their activities with complementary providers. Participating in helping build and manage the SDN for the customer, or understanding how they fit into customer’s self-designed SDN, becomes a central challenge, often requiring firms to develop a new set of capabilities. The SDN also challenges the way we view many of the core concepts in service research, which are anchored in the dyadic view. This provides considerable opportunity for future inquiry. We present a series of research questions, inspired by the SDN, organized into categories including building cooperative and collaborative networks, customer cocreation, systems thinking, customer relationship management, managing service failure and recovery, building capabilities, and customer-to-customer interactions.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".