Public value of using fintech services’ mobile applications: Citizens’ perspective in a Jordan setting
Bibliographic record
Abstract
Measuring the performance of Fintech services on mobile apps (FSMA) is considered a major key to sustain, develop, and improve financial services and their processes, depending on users’ standpoints on digital platforms. Public value aims at enhancing the performance of government institutions services. Throughout the current research, authors have suggested a novel way to evaluate the performance and management of FSMA by theorizing a new conceptual framework entitled Public Value of Fintech Services’ Mobile Apps (PV-FSMA). A quantitative approach was chosen to measure several factors influencing the use of FSMA and evaluate the degree of public value of FSMA among Jordanians. The structural equation model was conducted based on the results of the PV-FSMA model hypotheses. The results confirmed that FSMA-intention to use (FSMA-ITU) and its predictors: FSMA-usefulness (FSMA-US), FSMA-awareness (FSMA-AR), FSMA-security (FSMA-SE), FSMA-social influence (FSMA-IS), and FSMA-system quality (FSMA-SQ) except FSMA-ease of use (FSMA-ES) are valuable determinants of PV-FSMA. The article presents theoretical implications regarding financial services and public value theories and practical implications regarding public institution leaders, managers, and information technology specialists in the Fintech domain to improve the quality and performance of FSMA in Jordan.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".