The Influence of Cognitive Innovativeness on the Behavior and Style of Consumer Adoption: Implications for Electronic-Banking Service Adoption
Bibliographic record
Abstract
Cognitive Innovative consumers are an important market segment for marketers. Revenue from new products adopted by innovative consumers plays a pivotal role for many firms. Hence having a correct understanding from the behavior and style of their purchase helps the firms to create and implement effective marketing plans for the new products. The current study investigates the behavior and shopping style of cognitive innovative consumers in electronic banking services through a hierarchical perspective. This research is quantitative and is practical in terms of the purpose. Yet it is a field study in terms of data gathering. The statistical population of this research includes the students of Azad University of Qazvin in Iran and the sample size is 384 persons. The resulted findings from this research, verify the hierarchy perspective of consumer innovativeness, specially the fact that cognitive innovativeness and domain-specific innovativeness are the best combination of predicting the adoption of new product behavior. Moreover the adoption behavior of these consumers follows the quality consciousness style. Results show that banks should target the cognitive innovative customers in order to have a successful marketing in regards with attracting customers and increasing the revenues from selling the electronic 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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".