A Design Theory Approach to Building Strategic Network‐Based Customer Service Systems*
Bibliographic record
Abstract
ABSTRACT Customer service is a key component of a firm's value proposition and a fundamental driver of differentiation and competitive advantage in nearly every industry. Moreover, the relentless coevolution of service opportunities with novel and more powerful information technologies has made this area exciting for academic researchers who can contribute to shaping the design and management of future customer service systems. We engage in interdisciplinary research—across information systems, marketing, and computer science—in order to contribute to the service design and service management literature. Grounded in the design‐science perspective, our study leverages marketing theory on the service‐dominant logic and recent findings pertaining to the evolution of customer service systems. Our theorizing culminates with the articulation of four design principles. These design principles underlie the emerging class of customer service systems that, we believe, will enable firms to better compete in an environment characterized by an increase in customer centricity and in customers' ability to self‐serve and dynamically assemble the components of solutions that fit their needs. In this environment, customers retain control over their transactional data, as well as the timing and mode of their interactions with firms, as they increasingly gravitate toward integrated complete customer solutions rather than single products or services. Guided by these design principles, we iterated through, and evaluated, two instantiations of the class of systems we propose, before outlining implications and directions for further cross‐disciplinary scholarly research.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".