A decision paradox: benefit vs risk and trust vs distrust for online dating adoption vs non-adoption
Bibliographic record
Abstract
Purpose Online dating services have been growing rapidly in recent years. However, adopting these services may involve high risk and trust issues among potential users toward both online dating services and the daters they introduce to users. The purpose of this paper is to investigate how perceived benefits vs risks, and trust vs distrust affect user adoption vs non-adoption intentions toward using this rather controversial information and communications technology in the context of online dating. Design/methodology/approach Structural equation modeling was used to evaluate the research model using data from a survey of 451 single individuals. Findings The results indicated that perceived benefits play more essential roles in adoption, while perceived risks affect non-adoption more. Individuals' trust in online dating service predicts a major portion of the variation in user benefit perceptions, while distrust in online dating service and in daters that users might select significantly influence perceived risks. Moreover, benefit and risk perceptions can mediate the impacts of trust and distrust on both adoption and non-adoption decisions. Originality/value This study extends theories of decision-making in the use of controversial information technologies such as in the case of online dating. It investigates the coexistence of various trust and distrust beliefs as well as benefit and risk perceptions, and their different impacts on adoption and non-adoption in online dating services.
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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 | 0.011 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".