Fintech – stick or carrot – in innovating and transforming a financial ecosystem: toward a typology of comfort zoning
Bibliographic record
Abstract
Purpose Fintech is an “untilled field” in which the relation between Fintechs and incumbents is yet to be understood. This paper aims to explore this relationship and advance its theoretical and practical understanding. It further contributes toward Fintech paradigm and research domain emergence that both to date remain yet elusive. Design/methodology/approach This paper adopted a multiple-case study strategy for the purpose of theory building. Seven players from the Fintech ecosystem in Quebec (Canada) were selected, representing financial institutions, Fintech start-ups and Quebec’s financial cluster. Primary data was collected via in-depth interviews with ten respondents at the level of vice presidents, Managers, directors, chief executive officers and founders, and unobtrusive data – in the form of running records, mass-media news reports, presentations and proceedings from Fintech events. Data analysis was informed by grounded theory methods and techniques. Findings Grounded in data, this paper puts forward a typology of “comfort zoning” and its four types: nimbling, imperiling, cocooning and discomforting. Research limitations/implications Following the tenets of the grounded theory, four criteria are used to evaluate the emergent theory: fit, relevance, workability and modifiability. It is expected the interpretation and adoption of comfort zoning typology will be challenged, modified and enhanced by Fintech researchers and practitioners. Practical implications The comfort zoning typology would aid practitioners in their efforts to define and refine the domain of Fintech, problematize it and eventually enhance the relationship between Fintechs. Originality/value This paper fulfills an identified need to explore the relationship between Fintechs and incumbents and advance the theoretical and practical understanding of this relationship.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".