Public value of using fintech services’ mobile applications: Citizens’ perspective in a Jordan setting
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it