Predicting Consumer Behavior: An Extension of Technology Acceptance Model
Bibliographic record
Abstract
The purpose of this study is to examine the predictive power of the technology acceptance model (TAM) on customer’s intention to participate in the social customer relationship management (sCRM) program. Three additional constructs, perceived risk, user satisfaction, and perceived enjoyment were added to the original TAM. The collected data (n=264) were subject to statistical analysis of structural equation modeling, exploratory and confirmatory factor analysis. The study reveals that TAM by itself is not a robust model to predict customer’s intention to participate in the sCRM program. Among the original constructs of TAM, attitude is the only determinant of intention. The impact of perceived usefulness and perceived ease of use was not significant on intention. Among the extended variables, perceived risk is the only variable that significantly influenced intention; perceived enjoyment and user satisfaction did not have any impact on intention. By applying the TAM to the sCRM, this study extends the overall body of the theoretical knowledge surrounding technology acceptance.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.000 |
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".