The Mobile Internet Underclass: Reality or Hyperbole?

Transcription

1 The Mobile Internet Underclass: Reality or Hyperbole? Lian Wang Southwest Petroleum University, China Chun Liu Southwest Jiaotong University, China

2 Introduction Two sides of one story: Mobile technology has the potential to bridge the digital divide. Mobile Internet has created a mobile Internet underclass. Our primary objective is to explore whether mobile-only users' mobile Internet usage patterns are inferior to those of people with multiple channels of access. We contribute to the debate about the mobile underclass from the perspective of a developing country.

3 Analytical framework Dimensions of Internet use Internet usage in general has been measured in different units or dimensions, including frequency, length of time spent online, data volume, types of online activities and, in rare cases, revenues. Amount use of Type use of Variety use of

4 Research questions In what way does mobile Internet usage differ depending on whether users are mobile-only or mobile- fixedbundled subscribers? In what way does the skewness of the distribution of mobile Internet usage differ depending on whether users are mobile-only or mobile-fixed- bundled subscribers?

5 Data and sample This study utilizes objective system-generated data from a major telecommunications service company in China. The sample was drawn from the company's mobile users in a mid-sized city in southwest China. Unlike previous studies that rely on survey or interview data, we empirically observed and extracted records of mobile Internet users' behaviors from their Internet service provider (carrier). Identification of mobile-only users.

6 Data and sample In our data, 44.3% of mobile users with mobile-fixedbundled access generate at least one record of App or website usage, while only 26.6% of mobile-only users generate at least one record of App or website usage. A logit model is estimated to model the dichotomous outcome variable of whether a mobile user engages in mobile Internet activities, using predictors of Internet access channel, gender, age, number of voice calls and SMSs, monthly bill and past mobile Internet experience. The final dataset includes data records from 210,218 mobile users.

7 Measurement Level of Analysis Amount of use Variety of use Type of use Sub-level of Analysis Data volume App Website App Website App Website Definition Volume of up- and downloaded IP-switched data traffic via an MNO s network Frequency of using an App on a mobile phone Frequency of visiting a URL when using a browser on a mobile phone Total number of different Apps that are used by a user at least once in a month Total number of different categories of websites that are visited by a user at least once in a month Share of different types of App usage Share of different types of website visits

8 Measurement Three conceptual types of use are defined for both App usage and website visits. "knowledge-intensive" activities, including finance, commercial transactions, information, news, education, reading, etc. "leisure-related activities", including games, sports, entertainment, video, social interaction, etc. The first type offers users more opportunities and resources for moving forward in their careers, work, education and societal positions than does the second type, which are mainly consumptive or entertaining. These three types are mutually exclusive, although a user may engage in mobile Internet activities of more than one type.

9 Descriptive statistics Variable Mean Std. dev. Mobile Internet usage Amount of use Data volume (MBs) Frequency of App usage Frequency of website visits Variety of use Variety of App usage Variety of website visits Type of use Share of knowledge-intensive App usage Share of leisure-related App usage Share of knowledge-intensive website visits Share of leisure-related website visits

10 Descriptive statistics Variable Mean Std. dev. Access channel Internet access (1=mobile Internet only, 0= mobile-fixedbundled Internet) Demographics Sex (1=male, 0=female) Age Other variables Number of calls made Number of SMS sent Charge (RMB Yuan) Mobile Internet experience (months)

11 Correlations Data volume Frequency of App usage Frequency of website visits Variety of App usage Variety of website visits Data volume Frequency of App usage Frequency of website visits Variety of App usage Variety of website visits

12 Mobile-fixed-bundled vs. mobile-only Statistics Mobile-only Data volume Frequency of App usage Frequency of website visits Mobile-fixed-bundled Data volume Frequency of App usage Frequency of website visits Mean Skewness cv*

13 Mobile-fixed-bundled vs. mobile-only Variety of use Mobile-fixed-bundled Mobile-only mean cv* skewness mean cv* skewness Variety of App usage Variety of website visits Type of use Share of knowledge-intensive App usage Share of leisure-related App usage Share of knowledge-intensive website visits Share of leisure-related website visits

14 Mobile-only breakdown Male Female Amount of use Data volume Frequency of App usage Frequency of website visits Variety of use Variety of App usage Variety of website visits Type of use Share of knowledge-intensive App usage Share of leisure-related App usage Share of knowledge-intensive website visits Share of leisure-related website visits

15 Mobile-only breakdown

16 Conclusions Generally speaking, in terms of online usage, our analysis seems to hardly support the mobile underclass claim that has been of concern to digital divide scholars since mobile broadband first emerged. Mobile-only users outperform mobile-fixed-bundled users in each of the variables of amount of use. Within the mobile-only user group, there are some noticeable differences between male and female users. It appears that users tend to first fulfill their utilitarian needs, and they do so with limited data usage. The primary concern about the mobile underclass remains the first order digital divide access issue. Making mobile Internet access more affordable is still the primary concern for policy makers and businesses.