Integrating Technology Addiction and Use: An Empirical Investigation of Facebook Users
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
The purpose of this study was to conceptually replicate the model proposed by Turel, Serenko, and Giles (2011) in the new context of social networking websites. For this, the original instrument was adapted, data from 186 social networking website users were collected, and the model was analyzed by means of Partial Least Squares (PLS). The results supported the ideas advanced in the original study and show that addiction distorts user perceptions of usefulness and enjoyment attributed to the system, which in turn, influence behavioral usage intentions. In contrast to study 2 in the original paper, and in line with study 1 in the original paper, no relationship between addiction and perceived ease of use was observed. Comparing central tendencies across studies, it seems that users of social networking websites are more likely to exhibit technology addiction symptoms than users of online auction websites. The results ultimately imply that context matters in technology addiction research since it can alter some aspects of the measurement model, nomological network, and construct means.
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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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