Popularity is often a factor used in structuring Internet search results but popular information is not always most correct or representative of the breadth of knowledge and opinion on a topic.
While conducting commercial research fosters a deep concern with costs, and library research fosters a concern with access, Internet research fosters a deep concern for quality, managing the abundance of information and with avoiding unintended bias. Library and commercial research has many search tactics and strategies unavailable on the Internet and the library and commercial environments invest more deeply in organizing and vetting their information. The most popular search tools for finding information on the Internet include Web search engines , meta search engines , Web directories , and specialty search services.
A Web search engine uses software known as a Web crawler to follow the hyperlinks connecting the pages on the World Wide Web.
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The information on these Web pages is indexed and stored by the search engine. To access this information, a user enters keywords in a search form and the search engine queries its algorithms, which take into consideration the location and frequency of keywords on a Web page, along with the quality and number of external hyperlinks pointing at the Web page. A Meta search engine enables users to enter a search query once and it runs against multiple search engines simultaneously, creating a list of aggregated search results.
Since no single search engine covers the entire web, a meta search engine can produce a more comprehensive search of the web. Most meta search engines automatically eliminate duplicate search results. However, meta search engines have a significant limitation because the most popular search engines, such as Google , are not included because of legal restrictions. A Web directory organizes subjects in a hierarchical fashion that lets users investigate the breadth of a specific topic and drill down to find relevant links and content. Web directories can be assembled automatically by algorithms or handcrafted.
Human-edited Web directories have the distinct advantage of higher quality and reliability, while those produced by algorithms can offer more comprehensive coverage. Specialty search tools enable users to find information that conventional search engines and meta search engines cannot access because the content is stored in databases. In fact, the vast majority of information on the web is stored in databases that require users to go to a specific site and access it through a search form.
Often, the content is generated dynamically.
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As a consequence, Web crawlers are unable to index this information. In a sense, this content is "hidden" from search engines, leading to the term invisible or deep Web. Specialty search tools have evolved to provide users with the means to quickly and easily find deep Web content. These specialty tools rely on advanced bot and intelligent agent technologies to search the deep Web and automatically generate specialty Web directories, such as the Virtual Private Library.
When using the Internet for research, countless websites appear for whatever search query is entered.
Each of these sites has one or more authors or associated organizations. Who authored or sponsored a website is very important to the accuracy and reliability of the information presented on the website. For example, a website about civil rights that is authored by a member of an extremist group most likely will not contain accurate or unbiased information. The author or sponsoring organization of a website may be found in several ways.
Sometimes the author or organization can be found at the bottom of the website home page. It may be directly listed, determined from the email address, or by emailing and asking. Internet research software captures information while performing Internet research. This information can then be organized in various ways included tagging and hierarchical trees.
Dixit et al surveyed mobile phone dependency of medical college students in India and found However, those surveys allowed for the participants to evaluate themselves in terms of their internet usage experience, i. Therefore, each participant was allowed to choose based on his or her own subjective criteria. The survey presented in this study is designed to overcome the flaws of vague wording and subjective responses as seen in previous studies of similar context Young, ; Widyanto and McMurran, The wording of the questions is considered more specific and the participants are asked yes or no questions, which allows for more definite answers.
The main research question looks at the differences in mobile usage behaviours in two countries: the U. First of all, the author compares the mean value MIUI of the two countries. In addition, mean values of MIUI of each category, such as gender, student, age group, and usage pattern, are compared. The next two research questions look at which factors best explain the MIUI values and what the strength of the linear relationship between the MIUI and the factors is.
The next stage for Asia's mobile internet | Asia Business Development - Asia Business Consulting
Whang et al. They found that 3. Yoo et al.
Jang et al. In addition, the male gender and longer periods of internet usage were significantly associated with internet addiction. In hypothesis 1 H 1 , the author suggests that Korean mobile users will have more severe mobile internet dependency than U. H 1 A Korean mobile user shows stronger signs of mobile internet dependency than an American mobile user does.
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From a survey by SecureEnvoy, Kung argued that the younger generation received more stress from mobile phone addiction. In hypothesis 2 H 2 , the author suggests that the younger generation, that is, those under 30 years of age, will be more susceptible to mobile internet dependency.
H 2 A younger mobile user under 30 years of age shows stronger signs of mobile internet dependency than an older mobile user does. Leung argued that males within the younger generation showed lower mobile phone addiction rates than females. However, most of the recent studies in this area indicate that males are more severely affected by internet addiction than females White, Mythily, Qiu, and Winslow define criteria for excessive internet usage as more than 5 hours every day.
They found that young males in Singapore have a higher percentage of internet usage based on this criteria than females do. Zhang et al. They found that male college students had a higher rate of internet addiction than female college students in both the U. In hypothesis 3 H 3 , the author makes suggests that male mobile users have more severe mobile internet dependency than female users. H 3 A male user shows stronger signs of mobile internet dependency than a female does.
Most research articles about internet addiction have college student participants, as indicated by those referenced above. In hypothesis 4 H 4 , the author suggests that college student mobile users have more severe mobile internet dependency than non-student users. H 4 A student user shows stronger signs of mobile internet dependency than a non-student does.
There are no studies on whether job status affects internet dependency. In hypothesis 5 H 5 , author suggests that unemployed users have more time to use mobile internet and as such is more likely to show mobile dependency. H 5 An unemployed user shows stronger signs of mobile dependency than an employed user does. There is a significant relationship between usage hours and internet addiction.
In hypothesis 6 H 6 , the author suggests that mobile internet dependency is explained by age as well as usage hours and access frequency. H 6 There is a significant linear relationship between MIUI and mobile internet usage hours, mobile internet access time, and age together. The online survey site has been active for two months from March to May Two universities one in a metro area and the other in a small city in each country were selected and email invitations were sent to students of those four universities. Student participation was voluntary and anonymous, and the participants were also able to invite others to take part in the survey.
Via social networking sites such as www. This is the reason that the survey subject could be a student or a non-student. The author excluded participants under the age of The surveys for both Korea and the U. There were valid participants for the U. The total number of participants in this survey is , Of this number, The minimum score is 0, and the maximum score is Table 2 presents the distribution and statistics of MIUI.
The mean value is Standard deviation is 3. The percentage of severe mobile internet dependent users is 8. While the percentage of Korean dependent users is Korea has a much higher percentage than the U. Mobile internet usage pattern is composed of the average daily number of mobile internet access times and usage hours. While the typical Korean respondent is a female student in her twenties with no job, a typical U. Therefore, in the Korean data, college students and unemployed users are strongly associated with ages twenty to twenty-nine.
Table 3 shows the distribution of demographic and usage pattern data. These groups are categorised by 1 country, 2 age, 3 gender, 4 student status, and 5 employment status. The ANOVA tests were run twice: once with all data sets and secondly with the data sets divided by country of origin. When all data sets were used without country distinction, hypotheses H 1 through H 5 were tested. When country discriminate data sets were used, H 2 through H 5 were tested. Table 4 summarises the description of MIUI in each category. In each hypothesis, Korean mobile internet users, younger generation, students, and unemployed users have significantly higher MIUI mean values.
However, in the case of H 3 , the p-value was 0. The younger generation and student groups of the U. However, the author assumes that in the case of mobile internet, the number of access times due to the ease of access might be more influential to the MIUI than usage hours. The author estimated the following regression model:.
In the above regression model, the intercept dummy variable Country is introduced as an assumption that the Korean mobile internet users have a higher mobile internet usage than those in the U. Therefore, for the U. Table 7 presents the regression results. Because there are differences in units of measurement between the dependent variable MIUI and the independent variables Country, Access Times, Usage Hours, and Age , standardised coefficients are used.
In the standardised estimated regression model, Korea.