How important is the “social” in social networking? A perceived value empirical investigation
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 – The purpose of this paper is to report on a value-based empirical investigation of the adoption of Twitter social networking application. The unprecedented popularity of social networking applications in a short time period warrants exploring theory-based reasons of their success. Design/methodology/approach – A cross-sectional survey-based study to elicit user views on Twitter was conducted with participants recruited through the web site of a North-American university. Findings – All facets of perceived value considered in the study (utilitarian, hedonic and social) had a significant and relatively strong influence on consumer intent to use Twitter. Quite surprisingly for a social networking application, though, the social value facet had comparatively the weakest contribution in the use equation. Research limitations/implications – User value perception might have been influenced by the features of the actual social networking application under scrutiny (i.e. Twitter in this case). Practical implications – To maximize the chances of success of new social networking applications, developers and marketers of these media should focus on the hedonic and utilitarian sides of their perceived value. Social implications – Additional efforts are necessary to better understand the reasons and factors leading to a comparatively lower social value perception of a social networking application, compared to its hedonic and utilitarian values. Originality/value – Overall, the study opens the door for investigating user perceptions on popular social networking applications in an effort to understand the unparalleled success of these services in a short time period.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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