Analysis of Engineering Students Acceptance and Usage of 5G Technology: A Case Study of Gannon University
Bibliographic record
Abstract
It has been around 20-25 years since the internet was first developed for public use, and since then the number of users has been increasing exponentially. In a recent report, there are around 313 million internet users, out of which 276.8 million are mobile internet users and internet penetration is 90.3% (Johnson, 2021). With the development of wireless telecommunication technology and mobile devices, use of the internet access has been increasing. From the first generation (1G) of cellular networks to the current 5th Generation (5G), there has been a huge improvement in the data rate, coverage, and security that made it possible to have the internet on mobile phones. The internet in mobile devices has existed since 2G and was used for checking emails and browsing the web (Yamauchi et al., 2005). It is important that users should accept new technology. In this study, acceptance, and usage of the 5G technology that was investigated in a survey of students from the engineering discipline of Gannon University. This work describes the usage of a statistical technique called the technology acceptance model to determine the engineering students perception of the degree to which the 5G technology is accepted and useful. It aims to answer the research questions of whether perceived usefulness and perceived ease of use a ect the actual usage of 5G technology among engineering students, as they tend to bend towards new technology because of the high involvement of technology in engineering studies. This work aims to answer, to what extent perceived usefulness and perceived ease of use determine the usage of 5G technology among the selected group of participants.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".