Consumer Based Modeling of Mobile Service Consumption Using Explanatory Factor Analysis and Analytic Network Process
Bibliographic record
Abstract
Purpose: This paper explores a model of the Consumer based mobile service consumption, combining the use of exploratory factor analysis (EFA) and Analytic network process (ANP). Design/methodology/approach: The research form is divided in to two phases. In the first phase the consumer based factors that mentioned in theoretical framework was presented to samples of customers that use online banking to determine their level of agreement, exploratory factor analysis was used to form a model. In the second phase ANP method was used for ranking discovered factors to determine priority of this factor to improvement. Findings: Findings present a model for online banking usage this model has 4 main factors: internal, external, usefulness and demographic factors that effect on online banking usage. There are 10 sub-factors. The priority of factors show the banking industry must focus on the models factors by this priority. This priority is: 1) Internal benefits 2) Government support 3) external benefits 4) attitude, 5) Level of customer education, 6) customer experience, 7) bank activity, 8) trust 9) technology support 10) ease of use. Practical implications: Banking consumers are not supposed to use an online banking if they found it unfit for their daily tasks and bring no improvement in their execution. Thus banks should focuses on this reality in their marketing mix. They can expand their financial market and profitability by pay attention on this research priority. Banks by focus on high educated potential customers can faster expand their online market. They can segment their online market by level of education and then implement appropriate market positioning strategy. Originality/value: The main contribution of this study is the light it sheds on how banking consumers think about mobile banking service. It develops a model incorporating motives for mobile banking service consumption and determining the priorities held by consumers of mobile banking services.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".