Internet Connectedness and its Social Origins: An Ecological Approach to Postaccess Digital Divides

Abstract

This study examines the influence of sociocultural factors on the level of “Internet connectedness.” The Internet Connectedness Index (ICI), composed of five items, is modified and applied to measure disparities in the ways in which people use the Internet. With a dataset of 384 randomly selected telephone survey respondents, the ICI is regressed on various social indicators. The result indicates that technological environments, social environments, and the scope and intensity of Internet-related goals significantly influence individuals' Internet connectedness. This finding highlights that even after people gain access to the Internet, the ways they incorporate the Internet into their everyday lives differ, and that the differences reflect disparities in the multiple dimensions of the social context in which individuals are situated.

Keywords:

• Communication Technology
• Digital Divide
• Internet Connectedness
• Internet Use
• Media System Dependency Theory
• Social Context
• Social Environment

This manuscript is based on the author's doctoral dissertation titled, “Internet Connectedness and its Social Origins: An Ecological Approach to Communication Media and Social Inequality.” The author would like to thank her dissertation advisor Sandra Ball-Rokeach for her guidance, and also two anonymous reviewers for their helpful comments.

Notes

N = 330.

*p < 0.5. **p < .01. ***p < .001.

See Jung and Ball-Rokeach (2004) for an in-depth examination of media system dependency theory and communication infrastructure theory.

The well-known RDD sample advantages of bias reduction are particularly evident in the Los Angeles area. It is estimated that 50% of Los Angeles household phone numbers are unlisted. Thus, the financial and procedural advantages of employing phone (or other) directories, while considerable, are outweighed by the advantages of the RDD procedure. This is particularly the case when the research design objective is to gain access to representative samples of geographically located area residents.

Eligible phone numbers were calculated by examining the total number of study phone numbers excluding phone numbers for which eligibility could not be determined, inappropriate/duplicate phone numbers, nonqualified household phone numbers (e.g., outside the study area) and the estimated number of initial refusals not likely to qualify for the study.

Principal Component Analysis (PCA) was chosen over Principal Factor Analysis (PFA) because the purpose of the analysis here was to derive an Internet Connectedness Index factor by reducing five variables into one factor. PCA is usually used to reduce the number of variables while PFA is used to detect structure in the relationships between the variables (Pedhazur & Schmelkin, 1991).

The formula for deriving BIC for multiple regression is BIC = n ln (1−R ²) + p ln n, where n is the sample size, R ² is the value of R ² for the model of interest and p is the number of independent variables in the model.