Application of Behavioral Theory in Predicting Consumers Adoption Behavior
Bibliographic record
Abstract
A society produces some values, ideas, intentions, and speculations about the human personality. These perceived psychological phenomena depend on rules, regulations, relationships, culture, tradition, etc. Depending on cultural factors, the behavioral intention to adopt online system operated through information and communication technology (ICT) can be affected vividly. Since adoption of ICT potentially depends on citizens’ beliefs and attitude toward technology, adoption behavior of users should be revealed considering citizens behavioral differences. Technology Acceptance Model (TAM) by Davis et al. (1989) is a strong information system theory that models how users come to accept and use a technology. However, the foundation of TAM including many other ICT adoption models has been developed from the deep insight of two popular and widely used behavioral theories named Theory of Reasoned Action (TRA) and the Theory of Planned Behavior (TPB). To understand ICT adoption behavior, these two theories can provide generalized concept of human behavioral attitude and different beliefs which ultimately lead to behavioral intention to adopt ICT. This study has set its first objective to explore TRA and TPB as the theoretical foundation of behavioral attitude toward ICT-based online adoption. Then, based on that theoretical paradigm, our second objective focuses on developing a theoretical framework of revealing generalized ICT adoption and diffusion behavior.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".