Conflicting social influences regarding controversial information systems: the case of online dating
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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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".