New Service Development Competence and Performance: An Empirical Investigation in Retail Banking
Bibliographic record
Abstract
What can service firms do to improve their ability to offer new services? In this paper we argue that new service development success results from building a competence in the management of service development resources and routines. We conceptualize new service development competence as a multidimensional, second‐order latent construct that is represented by a system of four interrelated and complementary dimensions: (1) formalized new service development processes, (2) market acuity, (3) new service development strategy, and (4) information technology use and experience. We hypothesize that the growth of new service development competence is related to improved new service development performance. Using structural equations modeling, we analyze survey data from 166 retail banks and report three key empirical findings. First, we show that the four hypothesized dimensions are statistically significant in defining new service development competence. Second, contrary to conventional wisdom in new product development, we find that formalized processes play a lesser role in the success of new service development compared with the other three dimensions. Instead, market acuity—which captures the firm's ability to see the competitive environment clearly and to anticipate and respond to customers' evolving needs and wants—was the most important new service development competence indicator. Finally, we demonstrate the positive effect of new service development competence on new service development performance and show that new service development competence is also significantly related to business‐level performance. Together, our empirical results suggest that complementary benefits arise from the adoption of a more holistic approach to the management of new service development at the program level.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".