Tugas3 Komas

 

The digital divide shifts to

differences in usage

 

Alexander JAM van Deursen

and Jan AGM van Dijk
University of Twente, The Netherlands

 

Prepublication Draft; definitive publishing:

 

New Media & Society

2014, Vol. 16(3) 507–526

 

© The Author(s) 2013

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DOI: 10.1177/1461444813487959

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From url  :

https://www.researchgate.net/publication/283144603_The_evolution_of_the_digital_divide_The_digital_divide_turns_to_inequality_of_skills_and_usage

Usage classifications

Proper observation of differences in usage requires a classification of Internet usage types derived from the most important contemporary Internet activities. There are several candidates for such a classification. Some are based on a particular theory, while others use a descriptive and inductive approach deriving classifications from factor analyses of the steadily growing list of Internet activities. Most theoretical classifications take the uses-and-gratifications approach (Katz, Blumler, and Gureitch, 1974) as a starting point. The first step of this approach is an examination of a medium to derive a list of motivations and gratifications inherent in its use. The uses and gratifications approach and the related Expectancy-Value Model (Palmgreen and Rayburn, 1979) explain the way people adopt and use communication media as a function of their psychological needs. For example, some gratifications found are problem solving, persuading others, relationship maintenance, status seeking, and personal insight (Flanagin and Metzger, 2001).

Other potential theoretical backgrounds include the Technology Acceptance Model (Davis, 1989, Davis and Venkatesh, 1996) and Social Cognitive Theory, the latter of which has, among others, produced the Model of Media Attendance (LaRose and Eastin, 2004). The first model posits perceived usefulness as an important explanatory variable for use but has not yet produced a list of perceived useful Internet applications. The second claims that expected outcomes are a direct cause of web usage: activity outcomes (playing games, entertainment, cheering-up, monetary outcomes (shopping and prizes), novel outcomes (news and information), social outcomes (talk and support), self-reactive outcomes (pass time and relaxation), and status outcomes (improve life prospects and familiarize oneself with new technology) (LaRose and Eastin, 2004).

Then, there are studies that account for differences in usage by grouping Internet users into use typologies (e.g., Brandtzæg, 2010; Livingstone and Helsper, 2007; Ortega Egea, Menéndez, and González, 2007). These studies utilize descriptive and inductive research to identify categories of usage types (Kalmus, Realo, and Siibak, 2011). The result is a variety of classifications that can be advanced to plot Internet usage. Kalmus et al. (2011) suggest that classifications can be used to differentiate between the use of online social, leisure, and information services (Amichai-Hamburger and Ben-Artzi 2000), between social, leisure, and academic Internet use (Landers and Lounsbury 2006), between technical, information exchange, and leisure motives (Swickert et al. 2002) or between ritualized and instrumental use (Papacharissi and Rubin 2000). Kalmus et al. (2011) evaluated the number of motives for Internet use from a list of Internet applications using exploratory factor analysis. They clustered their motivational items in two groups: social media and entertainment, as well as work and information. These researchers correlated these clusters not only with socio-demographic variables but also with personality traits and indicators of habitus and lifestyle, trying to explain Internet use at large. Their aim was broader than ours, as we focus solely on socio-economic variables and on differences in usage. Furthermore, we take an approach in which we clarify the distinction between motives and actual use, which are two different concepts. We use theoretical accomplishments in uses and gratifications research to propose classifications of usage activities. This is further explained in Section 3.3. The purpose of this operation is to relate validated usage clusters with socio-demographic variables to investigate whether differences in usage exist. 

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Understanding the digital divide: A literature survey and ways forward

 Chalita Srinuan and Erik Bohlin

 Department of Technology Management and Economics 

Chalmers University of Technology 

Gothenburg, Sweden 

E-mail address: chalita@chalmers.se

Link url : https://www.researchgate.net/publication/254460217_Understanding_the_digital_divide_A_literature_survey_and_ways_forward

Research methodology Due to its nature, research on the digital divide is difficult to confine to specific disciplines, and so the relevant material is scattered across various journals. Based on the frameworks of Norris (2001) and van Dijk (2003), work on the digital divide can be found in three types of journals: (1) Information technology and information systems (2) Economics and business and management and (3) Social science (see Figure 1). Consequently, the following online journal databases were searched to provide a comprehensive bibliography of the digital divide literature: the ABI/INFORM database, the ACM Digital Library, the Emerald Library and Science Direct