The outcomes and the mediating role of the functional triad: The users' perspective
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
Abstract B.J. Fogg's Functional Triad shows the manner in which computing technologies can persuade people by playing 3 different functional roles, namely, as tools, media, or social actors. However, the effects of user perceptions of these 3 functional roles are largely unknown. We advance Fogg's framework by developing a conceptual model to explain how a feature of a computing technology (ie, the trade‐off transparency feature of a recommendation agent [RA], which interactively demonstrates the trade‐offs among product attribute values) can result in certain outcomes by shaping the beliefs of individuals regarding the 3 functional roles. We examine the effects of the perceived Functional Triad on the following 3 outcomes: (1) persuading users to use an RA (intention to use), (2) persuading users to follow the advice of the RA (recommendation adherence), and (3) persuading users to recommend the RA to others (recommendation to friends). We conducted a laboratory experiment to manipulate 4 levels of trade‐off transparency, thereby creating an adequate amount of variations for the perceived Functional Triad. A total of 160 participants were recruited from a large university in North America. Although designers could control the technology design aspects, these designs may not accomplish the intended effects on users, who have their own perceptions. This study contributes to existing literature by simultaneously evaluating the 3 different outcomes of the Functional Triad from the perspective of users.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 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