Are Customers Ready to use Mobile Technology for Banking Transactions? An investigation
Bibliographic record
Abstract
Universally, a new business model is taking over the decisions in the banking industry incorporating mobile applications on smartphones. It is slated to organically change the business value proposition. Mobile devices, it is reported, are poised even to subtly replace the traditional banking operations and processes. Gartner’s Hype Cycle for mobile applications already in 2008 predicted mass institutionalization for Mobile Banking as late in 2015-16 (Gartner, 2008). A few thinkers estimated tech-savvy consumers and wireless technology to create markets ripe for Mobile Banking (Laukkanen and Lauronen, 2005). However, Mobile Banking’s wide adoption still remains low even within the established markets. This is contrary to the lusty dust that was raised within research and practice for long. Regarding the US market primarily, only 9 percent of the consumers in 2012 used mobile financial services (TNS Infratest Global and Kantar, 2012).
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".