New service development competence in retail banking: Construct development and measurement validation
Bibliographic record
Abstract
Abstract New service development (NSD) has emerged as an important area of research in service operations management. However, NSD empirical investigations have been hindered by the lack of psychometrically sound measurement items and scales. This paper reports a two‐stage approach for the development and validation of new multi‐item measurement scales reflecting a multidimensional construct called NSD competence. NSD competence reflects an organization's expertise in deploying resources and routines, usually in combination, to achieve a desired new service outcome. This competence is operationalized as a multidimensional construct reflected by five complementary dimensions: NSD process focus, market acuity, NSD strategy, NSD culture, and information technology experience. In the first stage of measure development, we analyse judgment‐based, nominal‐scaled data collected through an iterative item‐sorting process to assess the tentative reliability and validity of the proposed measurement items. Our results demonstrate that a reduced set of measurement items have reasonable psychometric properties and, therefore, are useful inputs for multi‐item measurement scale development. In the second stage of measurement development, we conduct a confirmatory factor analysis of the five NSD competence dimensions using survey data collected from a sample of retail bank key informants and confirm the unidimensionality, reliability, and validity of the proposed five multi‐item scales. The NSD competence scales developed in this research may be used to advance scholarly understanding and theory in NSD. Further, these NSD scales may provide a useful diagnostic and benchmarking tool for managers seeking to assess and/or improve their firm's service innovation expertise.
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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 | 0.028 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".