Determinants of Customersâ Adoption of Mobile Banking:An Empirical Study by Integrating Diffusion of Innovation withAttitude
Bibliographic record
Abstract
Adoption of mobile technology as an alternate distribution channel in delivering the banking services to customer’s shows prospective in the newly developed banking model all over world. Mobile banking is a new radical innovation in the excellence of service delivery to banks. Banks are mining this technology to empower the society containing both banked and un-banked customers as well as bringing profits to mobile network operators and reducing the operational cost for the banks. This research attempted to integrate the customer’s attitude and social environmental factor i.e. mimetic force with Diffusion of Innovation (DOI) model by Roger’s in widening the applicability to mobile banking in India. It explains the customers’ attitude towards mobile banking in terms of innovation attributes i.e. Relative Advantage, Compatibility, Trialbility, Observability and Institution theory i.e. mimetic pressure which leads to the formation of attitude towards adoption of mobile banking. It was found compatibility; trialability and mimetic force are the good predictors for attitude towards adoption of mobile banking in Indian context. The research has enhanced knowledge base on customer adoption of mobile banking and has identified the innovation attributes and mimetic force in explaining the customers’ attitude in better understanding of the commercial likelihood of distribution channel.
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".