Conflicting social influences regarding controversial information systems: the case of online dating
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
Purpose Controversial information systems (IS) represent a unique context in which certain members of a user's social circle may endorse the use of a system while others object to it. The purpose of this paper is to explore the simultaneous and often conflicting roles of such positive and negative social influences through social learning and ambivalence theories in shaping user adoption intention of a representative case of controversial IS, namely online dating services (ODS). Design/methodology/approach The model was tested with two empirical studies using structural equation modeling techniques. The data of these studies were collected from 451 (Study 1) and 510 (Study 2) single individuals (i.e. not in a relationship). Findings (1) Positive social influence has a stronger impact on perceived benefits and adoption intention, while negative social influence exerts a greater impact on perceived risks; (2) positive and negative social influences affect adoption intention toward ODS differently, through benefit and risk assessments; and (3) ambivalence significantly negatively moderates the effects of social influences on adoption. Originality/value This study enriches and extends the IS use, ambivalence theory, prospect theory, and social learning theory research streams. Furthermore, this study suggests that it is necessary to focus on not only the oft-considered positive but also negative social influences in IS research.
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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 | 0.001 | 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 | 0.002 | 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