Exploring the determinants of Internet continuance intention and the negative impact of Internet addiction on students’ academic performance
Bibliographic record
Abstract
This study aims to investigate the impact of integrating essential factors on Internet usage continuance intention in students’ context. The proposed model examines the influence of perceived enjoyment, perceived image, satisfaction, information value, and emotional value on Internet continuance intention. Additionally, it investigates the role of Internet addiction, satisfaction, and continuance intention on academic performance among university students. A survey questionnaire method was adopted to collect data from university students in Jordan. Data was collected from 450 voluntary participants, and the analysis was conducted using SPSS and AMOS. The analysis results show that perceived enjoyment, perceived image, information value, and emotional value have a significant positive influence on continuance intention of Internet use. Besides, the results show that continuance intention has a positive impact on satisfaction and Internet addiction. While continuance intention has a significant positive impact on students’ academic performance, and Internet addiction has a significant negative impact on students’ academic performance, the impact of satisfaction on academic performance was not supported. This study is the first to examine integrating of perceived enjoyment, perceived image, information value, and emotional value on Internet continuance usage. Furthermore, this study is also distinguished from other studies by investigating the negative impact of Internet addiction on students’ academic performance gap.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".