Agility in Retail Banking: A Numerical Taxonomy of Strategic Service Groups
Bibliographic record
Abstract
This research demonstrates that operations agility—defined as the ability to excel simultaneously on operations capabilities of quality, delivery, flexibility, and cost in a coordinated fashion—is a viable option for retail banks encountering increasing environmental change. The question of whether there is empirical evidence that services, specifically retail banks, display the characteristics of agility like their manufacturing counterparts is open to debate. Conventional wisdom in operations management posits that most successful services trade off one capability for another. Drawing from the resource-based view of the firm, combinative capabilities view, and the cybernetics work of Ashby (1958), theoretical arguments suggest the contrary. The agility paradigm is viable in environments calling for a mix of strategic responses. Applying cluster analytic techniques to a sample of retail banks, using capabilities as taxons, we identify four strategic service groups: agile, traditionalists, niche, and straddlers. Our empirical results provide thematic explanations consistent with theory that account for how the agile strategic group offers a unique configuration of service concept, resource competencies, strategic choices, and business orientation. Profiles of the operations strategies of each strategic service group suggest that each group has found a fit between what certain segments of the market may want and what they have to offer. In particular, we found that the agile group exhibited greater resource competencies than its counterparts, requiring greater investments in infrastructure and technology. Consistent with theory, agile banks performed better over time on an absolute measure of return on assets.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".