New Service Development: Ares for Exploitation and Exploration
Bibliographic record
Abstract
The management of new service development (NSD) has become an important competitive concern in many service industries. However, NSD remains among the least studied and understood topics in the service management literature. As a result, our current understanding of the critical resources and activities to develop new services is inadequate given NSD’s importance as a service competitiveness driver. Until recently, the generally accepted principle behind NSD was that “new services happen” rather than occurring through formal development processes. Recent efforts to address this debate have been inconclusive. Thus, additional research is needed to validate or discredit the belief that new services happen as a result of intuition, flair, and luck. Relying upon the general distinctions between research exploitation and exploration, this paper describes areas in NSD research that deserve further leveraging and refinement (i.e. exploitation) and identifies areas requiring discovery or new study (i.e. exploration). We discuss the critical substantive and research design issues facing NSD scholars such as defining new services, choice in focusing on the NSD process or performance (or both), and specification of unit of analysis. We also examine what can be exploited from the study of new product development to further understanding of NSD. Finally, we explore one important area for future NSD research exploration: the impact of the Internet on the design and development of services. We offer research opportunities and research challenges in the study of NSD throughout the paper.
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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 | 0.075 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.010 | 0.060 |
| Scholarly communication | 0.042 | 0.067 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".