Generating and Assessing Consumer’s Innovation Adoption through Consumer Innovativeness, Innovation Characteristics and Perceived Brand Innovativeness
Bibliographic record
Abstract
Concerning the digitalism, globalization and competition; offering innovative products or services starts to become a critical issue for banks. Banks adapt its technological infrastructure for mobile banking applications to serve in more efficient and valuable way to their existing and potential customers. Unlike with the western countries, mobile technologies usage in developing countries have skipped lots of steps and become so popular. So for, one of the emerging market was chosen for the data collection and data were collected via internet survey with 451 participants. The effects of innovation characteristics (compatibility, ease of use, perceived usefulness, observability, perceived risk), consumer innovativeness and consumer perceived brand innovativeness on consumer innovation adoption were investigated in a holistic model. The results suggested that innovation characteristics had the most impact on consumer innovation adoption. Among these characteristics, compatibility and observability were most influential factors. Consumer innovativeness also affected adoption of innovations. However, contrary to the expectations, consumer perceived brand innovativeness’ effect on adoption was not supported. Future research directions and managerial implications were also given.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".