FinTech Adoption of Financial Services Industry: Exploring the Impact of Creative and Innovative Leadership
Bibliographic record
Abstract
This paper examines the link between creative and innovative leadership and FinTech adoption through the transmission mechanisms of perceived ease of use (PEOU) and perceived usefulness (PU). This study used a questionnaire survey method to collect data from a sample of 721 employees working in the Indian financial services sector. The data were analyzed using structural equation modelling. The study results revealed a significant and positive influence of creative and innovative leadership, PEOU, and PU on FinTech adoption. Moreover, PEOU and PU mediated the link between creative and innovative leadership and FinTech adoption. This study proposes a new vision for managerial procedures to understand the critical aspects regarding FinTech adoption. The study advises that engineering managers should offer simple and user-friendly technology to enhance the adoption rate. Additionally, the results suggest the importance of creative and innovative leadership for competitively exploiting novel technologies. Given India’s digital revolution and huge market potential, the FinTech sector could prove a game-changer, especially in generating employment for the young and technologically qualified population. Tech-driven organizations could use the study findings strategically in this digital era.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".