دانلود مقاله ISI انگلیسی شماره 42144
ترجمه فارسی عنوان مقاله

درک کاربران آنلاین چینی و بازدیدکننده وب سایت هایشان: استفاده از قانون زیف

عنوان انگلیسی
Understanding Chinese online users and their visits to websites: Application of Zipf's law
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
42144 2013 12 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : International Journal of Information Management, Volume 33, Issue 5, October 2013, Pages 752–763

ترجمه کلمات کلیدی
قانون زیف - الگوی استفاده از اینترنت - جمعیت کاربران - بازار اینترنت چین
کلمات کلیدی انگلیسی
Zipf's law; Internet usage pattern; User demographics; Chinese Internet market
پیش نمایش مقاله
پیش نمایش مقاله  درک کاربران آنلاین چینی و بازدیدکننده وب سایت هایشان: استفاده از قانون زیف

چکیده انگلیسی

Competition for consumers to visit company websites has intensified in recent years. An important indicator of website popularity (and consequent survival) is the extent to which the website can draw consumer visits vis-à-vis other websites. A majority of the current understanding on consumer visits is limited to a single website, and leaves little knowledge on the performance of one website compared with others. In tracking the Internet usage behavior of 200 individuals in Mainland China for 30 consecutive days, we applied Zipf's law to identify the divergence points separating popular websites from non-popular ones. Two measurements were used, namely, visit traffic (number) and visit engagement (time spent). We observed that 94.87% of the entire visit traffic is devoted to 15.08% of all visited websites, whereas 84.63% of engagements are on the top 6.16% visited websites. These findings suggest that few websites accounted for the bulk of online traffic and time. Further, we segmented the dataset based on two key proxy variables of user demographics, which are gender and occupation. The findings on visit traffic remained salient after considering user segments, but the findings on website engagement varied across different user segments. Our further analysis, which categorized the visited websites by their main service, revealed the type of Internet users attracted to popular websites.