Assessing Unobserved Heterogeneity in SEM Using REBUS-PLS: A Case of the Application of TAM to Social Media Adoption
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The present study applies the REBUS-PLS algorithm to handle unobserved heterogeneity in the context of the application of the technology acceptance model (TAM) to social media adoption and use within a workplace environment. Using data collected from 2556 social media users within their workplace from UK, US, Canada, India and Australia, the REBUS-PLS algorithm automatically detects three groups of social media users, each of them being characterized by different values for model parameters and manifest variable means. A post-hoc analysis of each group shows that metropolitan geographic location, postgraduate education level, country and ages range from 18 to 24 & 25 to 34 are the places where we can find main differences that depict the three discovered social media users groups. Finally, implications for research and practice are discussed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it