The outcomes and the mediating role of the functional triad: The users' perspective
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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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".