Mobile Telephony: The Influencing Factors of Using A Mobile Phone by Undergraduate and Graduate Students in Three Global Countries
Bibliographic record
Abstract
Individual adoption of technology has been studied extensively in the workplace, but far less attention has been paid to adoption of technology in the household (Brown & Venkatesh, 2005). Obviously, mobile phone is now integrated into our daily life. Indeed, according to International Data Corporation (IDC), the market reached 1.472 billion mobile phones sold in the world in 2017 (ZDNet, 2018). In addition, according to Statista, there was 4.77 billion mobile phone users worldwide in 2017 while the population was reaching 7.6 billion people, and there will be 5.07 billion mobile phone users worldwide by 2019 (Statista, 2018). The purpose of this study is then to pursue the investigation on the determining factors that make such people around the world are so using the mobile phone. On the basis of the model of acceptance of technology in household (MATH) developed by Brown and Venkatesh (2005) to verify the determining factors in intention to adopt a computer in household by American people, this study extends this moderator-type research model to examine the determining factors in the use of mobile phone in household by undergraduate and graduate students from four universities within three countries over the world. Data were randomly gathered from 750 undergraduate and graduate students from Yaounde in Cameroon, Kinshasa in Congo, and New Brunswick in Canada who own a mobile phone. Data analysis was performed using the structural equation modeling software Partial Least Squares (PLS). The results revealed, among others, that two-third of the variables examined in the study, including the three new variables we added to the Brown and Venkatesh’s research model, showed to be determining factors in the use of mobile phone by undergraduate and graduate students.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".