The “Matthew Effect” and the Rise of Usage Gaps
(Fragment of Chapter 6, The Deepening Divide, pp. 125-129)

With all the types of access to the digital media and their social consequences discussed in this book, it appears that those who already have a large amount of resources at their disposal benefit first and most from the capacities and opportunities of these media. This phenomenon has been called the “Matthew effect” by the sociologist Robert Merton (1968), according to the Gospel of Matthew: “For to everyone who has, more shall be given” (Matt. 25:29, New American). A popular version of this might be “The rich get richer.” If we want to turn these popular and biblical expressions into a more precise statement, the Matthew effect could be framed as follows: Those who already have a head start in possessing particular resources benefit more from a new resource than those who are behind and already have some disadvantage. In the case of new media access, the existing possessions are the material, mental, temporal, social, and cultural resources discussed in this book, and the new resource is the potential value of having and using computers and networks.

A Matthew effect was clearly demonstrated in chapter 4, in which it was seen that most gaps of physical access appear to have widened, until very recently, in developed countries. In all cases, the category with the most resources at its disposal—the social class with high income, high education, high-level occupations, and full employment; the ethnic majority in a country; and the generation with the highest learning potential (the younger generation)—was the first to take advantage of the new technology. In this way, this category increased its advantage compared to the categories with lesser resources. The same was observed with regard to rich and poor nations and regions worldwide. At the start of the 21st century, the effect in terms of physical access starts to weaken in some developed countries, as the best resource categories are getting saturated. However, with every new innovation (e.g., current broadband diffusion), the process seems to start anew.

Regarding skills access, I also claimed that people of high social class, especially those with high levels of education, males, and young people were the first and the best in developing digital skills. This appeared to be the case for information and strategic digital skills in particular, which are primarily and almost exclusively mastered by people who already have a very high level of ability in using the old media.

Finally, there is usage access. Here the Matthew effect has the strongest and most lasting impact. Usage is the ultimate goal of the whole process of appropriation of the new media, of course. Here all inequalities in earlier types of access come together. Subsequently, they are mixed with all existing economic, social, cultural, and political inequalities in society. Inequalities of motivational, material, and skills access might partly disappear, as we have seen in previous chapters. Gradually, more segments of the population are convinced that they should participate in the information society and get access to computers and networks. Concerning physical access to new media that have been in existence longer, the higher social categories are getting saturated and the lower social categories are catching up. With the right educational policies, digital skills can be better disseminated among populations. However, inequalities of usage access will not disappear that easily, if they ever do. Instead, they may grow.

This conjecture is the hypothesis of the rising usage gap I have discussed in several previous publications (van Dijk, 1997, 1999, 2000, 2003c, 2004). It is similar to the classic knowledge gap hypothesis (Tichenor, Donohue, & Olien, 1970). Clearly, the knowledge gap thesis is based on the Matthew effect too: “As the diffusion of mass media information into a social system increases, segments of the population with a higher socio-economic status tend to acquire this information at a faster rate than the lower status segments” (Tichenor et al., 1970, p. 159). However, the knowledge gap is only about the differential diffusion and development of knowledge or information. The usage gap is broader, as it is about unequal practices and applications; that is, action or behavior in particular contexts. This includes knowledge and information.

Although the evidence in favor of the thesis of the knowledge gap has not been conclusive (Gaziano, 1987), it might get another chance in the broader context of the differential practices and applications characterizing the information or network society. In the following two chapters, I explain why. In this chapter, I first clarify which current tendencies combine to produce usage gaps in the appropriation of information and communication technology. Finally, I provide data indicating the rise of such gaps.

Causes and Characteristics of Usage Gaps

Several tendencies come together to produce the probability of usage gaps in contemporary society (van Dijk, 2000):

Social and cultural differentiation and individualization in (post)modern society

Rising social-economic inequalities of income, employment, and property worldwide

Commercialization (privatization and liberalization) of formerly public information and communication facilities that increase conditional access

These societal tendencies, further described in chapter 10, increase the unequal distribution of resources and the positional and personal inequalities related to digital media usage. These tendencies and their results merge with the characteristics of ICT, as discussed in earlier sections.

Complexity: Some advanced applications are difficult for average users to use; others are relatively simple.

Expense: Some applications require special hardware, software, and conditional access; others are “free”—that is, available after the purchase of the basic technology (computer and connection).

Multiple functions: The same basic and extended computer technology can be used for very different, simple, and advanced applications.

Biased content: The software and information services offered favor particular social and cultural interests, languages, cultures, and multimedia literacy skills over others.

Together, these tendencies and characteristics increase the probability that the usage of the new media will diverge among different categories, sections, and classes of the population and produce more or less structural usage gaps. Such gaps also have been observed by other investigators. I have referred to data from the United States and the Netherlands in previous publications (van Dijk, 2000, 2003c, 2004). These data indicated that people with high levels of education and income tend to use the applications of databases, spreadsheets, bookkeeping, and presentations significantly more than people with low levels of education and income, who favor simple consultations, games, and other entertainment. Han Park (2002) replicated these statistics for South Korea, revealing the same distribution between Koreans with high and low levels of education. Using the same Pew Internet and American Life Project 2000 data as Howard et al. (2001), Cho, de Zúñiga, Rojas, and Shah (2003) claimed that U.S. Internet users who are young and have high socioeconomic status used this medium in a very specific goal-oriented way; that is, to strategically satisfy their motivations and gratifications of connection, learning, and acquisition (products and services). In contrast, those U.S. users who were older and had low socioeconomic status employed the Internet in various general and superficial ways, primarily to satisfy consumptive needs and the gratifications of connection.

A 2000 survey in Switzerland referred to by Bonfadelli (2002) indicated that Swiss people with high levels of education use many more applications for information, communication, and services than fellow Swiss with low levels of education; the latter favor entertainment applications (see Table 6.5).

Table 6.5. Types of Applications Used on the Internet by Level of Education (%)

Type of Application

Total

Low Education

Middle Education

High Education

Communication

92

90

92

94

Information

59

53

58

64

Services (transactions, downloading)

41

31

41

45

Entertainment

42

72

42

35

SOURCE: Bonfadelli (2002).

The recurrent Pew Internet and American Life Tracking surveys provide more detailed information about Internet activities by U.S. users. When we look at education and gender differences, which indicate the most important background categories of usage next to age and race or ethnicity, as discussed in the previous section, we find conspicuous gaps. See Table 6.4 for the year 2002. It shows that Americans with higher levels of education use almost every application of information, communication, education, work, business, and shopping significantly more than do Americans with lower levels of education. Instead, the latter use the Internet more just for fun, although they are equal to the former in use of the Internet for hobby and sport information. Other data reveal that Americans with low levels of education also use the Internet a great deal more for playing games, chatting, gambling, and downloading music (Howard et al., 2001). In Table 6.4, clear gender differences are expressed. U.S. males use more applications for information (except when it comes to searching for health information), business, shopping, and entertainment than do U.S. females. Women use more applications of communication: e-mail, personal support, and medical support.

In my opinion, these are the first signs of usage gaps, in terms of applications, that will not narrow but widen with the further diffusion of the new media in society. Computer and Internet use increasingly reflects differences and inequalities in society and reinforces them because they are tools and because they are affected by all of the societal and technological tendencies discussed earlier. Unfortunately, I am not able to prove that usage gaps are growing, as we have no sufficient time series data available. Data such as the regular Pew Internet use tracking data can help, but what is needed are not only data about people who have ever used a particular application but about the time(s) of usage of applications. One-time use of applications will grow quickly among the “lowest” categories, and with the “highest” categories, it is approaching peak levels.

A more structural usage gap appears when some segments of the population systematically and permanently use and benefit from advanced computer and Internet applications for information, communication, work, business, and education, and others only use the basic or simple applications for information, communication, and shopping and enjoy more applications for entertainment.