A Comparison of the Influencing Factors of Using a Mobile Phone: Atlantic Canada vs. Cameroon Africa
Bibliographic record
Abstract
INTRODUCTION Since numerous years, mobile phone is used for different professional purposes, particularly by senior managers in the workplace. And this technology is more and more used in the workplace since mobile applications have been integrated to actual enterprise business strategies. Individual adoption of technology has been studied extensively in the workplace (Brown & Venkatesh, 2005). 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 the latest research from Strategy Analytics, global mobile phone shipments grew from about 1% annually to reach 362 million units in the second quarter of 2012 (Business Wire, 2012), that is, more than 1.5 billion units were sold in 2012. The International Telecommunication Union (ITU) inventoried 4.6 billion subscriptions in 2010, from which 57% come from the developing countries. In addition, according to Cisco, one of the greatest global networking companies, there will be 5.2 billion mobile phone users in the world by 2017 while the population will be reaching 7.6 billion people (Ferland, 2013). So the purpose of this paper is then to pursue the investigation on what make such people around the world are so using the mobile phone. In fact, on the basis of two recent studies already conducted on the influencing factors of using a mobile phone, a first study made in Atlantic Canada involving 327 respondents (Fillion & Booto Ekionea, 2010) and a second study performed in Cameroon Africa involving 505 respondents (Fillion et al., in press), this paper establishes a comparison of the different factors influencing mobile phone usage in these two countries located at about 10 000 miles of distance one from the other. Few studies have been conducted until now which investigate the intention to adopt a mobile phone by people in household (in the case of those who do not yet own a mobile phone) or the use of mobile phone in the everyday life of people in household (in the case of those who own a mobile phone). Yet we can easily see that the mobile phone is actually completely transforming the ways of communication of people around the world. It is therefore crucial to more deeply examine the determining factors in the use of mobile phone by people in household as well as the differences in the determining factors between different countries in the world. So this is the aim of the present paper. The related literature on the actual research area of mobile phone is summarized in Table 1. In addition to the summary of literature on the actual research area of mobile phone presented in Table 1, other researchers have identified some factors which may increase the use of mobile phone by people in household. For example, in a large study conducted in 43 countries of the world, Kauffman and Techatassanasoontorn (2005) noted a faster increase in the use of mobile phone in countries having a more developed telecommunications infrastructure, being more competitive on the wireless market, and having lower wireless network access costs and less standards regarding the wireless technology. Another study involving 208 users by Wei (2007) showed that different motivations predict diverse uses of mobile phone. According to the Wei's findings, mobile phone establishes a bridge between interpersonal communication and mass communication. And a large study conducted by Abu and Tsuji (2010) in 51 countries classified by the Banque Mondiale revealed that, in general, income is a very important factor to adopt a mobile phone in the countries having a fix telephone infrastructure. As we can see in the summary of literature related to mobile phone presented above, few studies until now examined the determining factors in the use of mobile phone by people in household. And, at our knowledge, no study until now tried to establish a comparison of the determining factors of using a mobile phone between people of different countries around the world. …
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How this classification was reachedexpand
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 | 0.006 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".