Prediction of Print and Online Newspaper Readership from

Prediction of Print and Online Newspaper Readership from Indices of News Quality

Esther Thorson

Acting Dean and

Hans Meyer

Doctoral Student

University of Missouri-Columbia

Frank Denton

Vice President for Journalism and

Jim Smith

Vice President of Research

Morris Publishing Group

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Abstract

This secondary analysis of a representative sample from respondents in 27

American cities shows that people’s perceptions of local news quality and their preferences for new media features (represented by cell phones and access to broadband) is highly predictive on readership of local print newspapers. Only the new media features are predictive of readership of those newspapers’ web sites, except in one city where a concerted effort has been made to develop the web site and drive traffic to it. In that city, web site readership is strongly predicted by the news quality variables. The Media

Choice Model, which combines demographics, news quality perceptions, and preference for new media features helps explain why quality explains newspaper readership.

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The central question of this study concerns what variables predict use of print and/or online local newspapers. This question has theoretical value because it provides insight into the ever more complex question of how people choose their news media. It has practical value in that local newspapers can use the findings to help improve use of their print and online products.

Today’s statistics about the health of local newspapers are intriguing—and have a decidedly negative flavor. We start with them because they are the backstory for the research reported here. The decrease in print newspaper circulation has accelerated in the last few years (Crosbie, 2004); weekday circulation of U.S. daily papers as dropped from

62.8 million in in 1985 to 55.2 million in 2002. Young people are far less likely to read newspapers than those older than 40 (Crosbie, 2004). Online local newspapers showed a slight loss in traffic between 2006 and 2007 (Patterson, 2007), while national newspapers’ traffic was up slightly and aggregator news sites (e.g., Google, Yahoo) were up greatly. On average, local newspapers make most of their revenue from the print product (90%) and only a small portion (usually less than 10%) from the online product

This means that newspapers cannot sustain their revenue streams (or their very high profit margins (e.g., Lacy & Thorson, 2004) without the print product. Although print revenues continue to climb, the growth has significantly slowed (e.g., from 33% increase in 2006 to probably 19% in 2007; Fitzgerald & Saba, 2007). Overall revenues for local newspapers have plummeted in the last two years (Fitzgerald & Saba, 2007; in August,

2007, advertising revenue at McClatchy fell 9.2%, 4.6% at the New York Times Co.,

7.2% at Tribune, and 9.6% at the Journal Register Co.). To maintain profit margins,

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newspapers have increasingly reduced costs, which almost always involves reducing professional personnel in newsrooms (Baker, 2007). Indeed, the number of professionals in newsrooms continues to fall (see Farhi, 2006 for details of recent losses of news professionals from the newspaper business). Studies of time spent with online newspapers show very short visits, usually less than eight minutes a day (Crosbie, 2004).

Stock prices for newspapers companies have tended downward. Firesales of newspapers are common. In the most egregious example, Knight-Ridder was sold to McClatchy, which then sold most of its newspapers to various buyers. Then to get the tax break,

McClatchy sold the Minneapolis Star Tribune for $530 million, while they paid $1.2 billion (Serres, 2006) for it seven years ago.

The story of these statistics is that the newspaper business is in trouble in many dimensions: loss of support for its business model, loss of audience, and loss of quality because its newsrooms are being gutted. Embedded in this crisis is a central unanswered question about newspaper readership: Is it affected by news quality? Or is the current crisis unaffected by the fact that newspaper companies continue to lay off newsroom staff, reduce local coverage, and hire increasingly young reporters who know little or nothing about the community in which they are reporting?

Behind that question is an even more perplexing and controversial one: what is news quality? Adding to the complexity, there are many different measures of

“readership.” Does once in a while picking up a paper count as readership? Should a

“reader” read both weekdays and Sunday? Is an eight-minute a day reader equivalent to a 40 minute a day reader? What, if anything, does circulation tell us about readership?

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This study integrates a large and diverse literature about news quality and its impact on readership. This literature generates some important hypotheses about whether and how people choose to get news, and how this affects their use of a local newspaper and its web site. The most important question here is whether some generally agreed upon indicators of news quality affect how much local newspapers and their websites are read.

Readership

The simplest index of newspaper readership is “do you read a daily newspaper?” but it’s not particularly useful given the lack of variation in its dichotomous answers. A slightly more elaborated question is “how many days a week do you read a daily newspaper?” The Readership Institute at Northwestern has developed a more elaborate index of readership called the Reader Behavior Score (RBS)—a measure that has seen millions of dollars of investment by newspaper companies through the efforts of the

Association of Newspaper Editors. Unfortunately, there are virtually no scholarly studies based of the various data sets generated by this investment. The RBS score is the average of the number of weekday and weekend issues looked at, time spent with each issue, and the percent of the whole paper read. Thus it indexes not just frequency but also time investment and thoroughness of the reading. It is not clear, however, that people are able to answer these detailed questions in a way that reflects reality.

The measure of readership we use in this study falls between how many days a week and the RBS. It is the combination of “how many of the past five weekday issues have you read or looked into, and have you read or looked into any part of the Sunday newspaper in the past four weeks?”

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News Quality

Research on news quality has approached the concept with several areas of operational definitions: perceptions of news professionals (Bogart, 2004), perceptions of news consumers (Bogart, 1989), and in terms of the resources invested in news content

(Litman & Bridges, 1986). Most relevant here, however, is quality measurement in terms of news content. Content of news has been examined for a variety of features in quantitative content analyses. News has been sorted by topic (e.g., government, crime and judiciary, disasters, education, sports, weather, fashion, business and finance); by geography (local, state, national, international), type of writing (e.g., inverted pyramid vs narrative styles), and type of “voice” (e.g., straight news, opinion, analysis, columnists, citizen journalism). It has been sorted by advertising versus editorial content and photos/illustrations/other visuals versus text, as well as what kind of sources (e.g., gender, societal role, anonymous, non-anonymous, and highly elaborated).

Unfortunately, understanding how news quality influences readership is still in its infancy. Indeed, the newspaper industry has not been inclined to support and execute research on its critical questions (e.g., Meyer, 2004). With the advent of the Internet and the explosion of other news channels and sources of news, the question of readership, both print and online have become even more crucial.

It has also meant that “media choice” has become much more critical as a central theoretical question to be answered. For a long time, the main media choice question of interest to those concerned about “news” was how people decided to use television or print news. With the rapid diffusion of cable stations and high speed Internet, not to mention cell phone news delivery the question of media choice has exploded.

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Newspapers and local television programs can be enjoyed via television, print, or online.

Cable provides news 24/7. The variety of online news is almost endless. People can read newspapers from anywhere in the world, including national newspapers like USA Today ,

The New York Times , and the Wall Street Journal . They can read Yahoo or Google news, both of which aggregate everyone else’s content. They can access sites where people have rated what news is most interesting (e.g., Digg). They can find news with a point of view (e.g., National Rifle Association News and other “branded” news), or they can read interpretations about the news from any of a vast array of “bloggers.” Media choice has become vastly more complex, and for traditional news media, the heightened competition threatens their very existence.

Theories of media choice

There have been a number of theories applied to the question of how and why people make news media choices. A commonly applied theory is diffusion of innovation

(Rogers, 1995). The central tenets of the theory are the features that would determine the extent to which an innovation takes hold. The features include the relative advantage of the innovation, its compatibility with current and past preferences of the chooser, the complexity (or understandability) of the innovation, its trialability , that is, how easy it is to sample how the new medium works, and the observability , that is, how salient the new medium is to a person. As McQuail and Windahl (1993) point out, however, diffusion of innovations does not easily incorporate the impact of “features” that media channels have (for example, immediacy, interactivity, mobility) that are clearly important determinants of choice.

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A better approach to dealing with how people prefer media “features” and how those preferences influence their choice is uses and gratifications theory. First conceptualized by Katz (1959), uses and gratifications theory suggests that all media use can be understood in terms of people’s efforts to fill needs they have in a way that satisfies or “gratifies” those needs. Katz, Blumler and Gurevitch (1974, p. 20) suggested that uses and gratifications research should focus on a handful of central questions, including a) the social and psychological origins of b) needs that generate c) expectations of d) the mass media or other sources, which lead to e) different media choices , resulting in f) fulfillment of the needs . Rosengren (1974) added two important additional variables: g) individual differences like demographics and lifestyles, and h) particular contexts in which the needs must be filled. Unlike many competing theories of media use, this approach assumes that media users are active, picking and choosing to maximize satisfaction of their needs.

The uses and gratifications approach has led to the development of many taxonomies of communication needs. Excellent summaries can be found in Rubin (1983;

1994) and in Ruggiero (2000). Uses and gratifications theory has been applied to aid understanding of use of every medium, including newspapers (Elliott & Rosenberg,

1987), television (Poindexter & Conway, 2003; Babrow, 1987; Conway & Rubin, 1991), cable television (Heeter & Greenberg, 1985), email (Dimmick, Kline, & Stafford, 2000), and most recently the Internet (Beaudoin & Thorson, 2004; Kaye & Johnson, 2002;

Papcharissi & Rubin, 2000; Perse & Dunn, 1998; Rodgers & Thorson, 2001).

Uses and gratifications theory has also been used in models that attempt to identify how people choose among media. A good example is Lacy (2000), who

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suggested that five communication needs (surveillance, diversion, social-cultural interaction, decision making, and self understanding) combine with other variables like quality of news and media features such as cost to determine how much time people will spend with various media. Denton (1999) suggested that need states are crucial for understanding how people choose sources of local news.

Thorson and Duffy (2005) suggested a number of developments in a uses and gratifications model that would make it more applicable to understanding how people choose news in the digital landscape. In their Media Choice Model, there is still a basic set of “needs” that drive media choice. They include connectivity, information, entertainment, and shopping. Connectivity refers to communication with others.

Information and entertainment were self-defined by individuals. For example, if a person is asked what medium they use most for information in the evening before going to bed, no matter what kind of television programming the person watches, if she categorizes it as “informational” use, then that is the definition of information. Shopping refers to any activity that involves acquiring goods and services.

Importantly, the Media Choice Model identifies additional classes of variables considered significant to choice. The one relevant to the present study is “features.”

Papyrus scrolls were good communication devices if you could read, but they were not handy is you needed mobility. Cell phones, on the other hand, allow you to communicate with others even if you are illiterate; they are convenient to carry around, and increasingly they connect you to the internet, which then provides immediacy, interactivity, and customizability. Likewise, newspapers are a good communication medium if you are a proficient reader; they are mobile and you can scan them, but you

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cannot search them, there is no immediacy, interactivity or customizability. As electronic media grow, more and more communication features become available and for those who value these features, the legacy media are less likely to be chosen. Digital media offer the same features as television (sound and moving images), but add even more features including immediacy, mobility, participation, search, customization, and time-shifting.

The news media choice model suggests these features essentially change the nature of news (Duffy & Thorson, 2006).

The Media Choice Model can be straightforwardly modified to consider news quality in terms of the content that people are attracted to. It then clearly suggests that the more quality content features people perceive in a news source, and the more it fits with their preference for new media features, the greater their readership of that source will be.

News Quality Measures That Predict Readership

We look at two taxonomies of what news quality variables lead to higher levels of readership. We then select a subset of these items to test in the current study, favoring variables that seem consistently to be considered important.

The first taxonomy is from the Readership Institute (2001). That research program identified content areas of local news that were associated with higher Reader

Behavior Scores (Those with a positive relationship to RBS were focus on local people, lifestyle news (health, fitness, home, garden, food, fashion, beauty), politics/government,

Movies/TV/weather, business/economics/personal finance, science/technology/ environment, and sports. Those with a negative relationship with RBS were disasters/accidents and police/crime/judicial system. This approach to thinking about local news, however, seems less useful because there are so many ways to cover each of

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these topics, and there was no indication of which might be preferable. For example, research has shown that people respond to crime/violence news better when it is embedded in context that explains why and how there can be prevention (e.g., Thorson,

Dorfman, & Stevens, 2003; Coleman & Thorson, 2002).

A second approach is from an ASNE report written by Frank Denton (1999) on dimensions of high quality local news. This list includes the following:

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Proximity : That which is closest is most important.

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Safety : Reporting crime in a way that puts it in a public health perspective that focuses on prevention and amelioration rather on scaring people and/or making them think crime is out of control and nothing can be done.

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Utility : information that helps people with everyday problems they experience.

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Government : reporting on government in a way that does not make people feel pushed out of the decision making; and that makes the important aspects of government interesting to people (Kovach & Rosenstiel, 2007)

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Education : Not only is local education important in terms of what it does for youth and in terms of taxation, it is a core component of most communities— everything from high school football to school fundraisers to average SAT scores in different schools.

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Spirituality : A majority of Americans believe in God or a higher power and religious community activity and interest is high.

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Support from the community : Studies report that Americans are involved in various kinds of community support groups (Wuthrow, 1994; Leerhsen, 1990).

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Identification with others and the community : Many studies report how extensively Americans desire and seek a sense of community by a variety of participations (e.g., Berry, Portney & Thomson, 1991; Davidson & Cotter, 1997).

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Recognition for achievements of self and others

: Whether it’s the high school honor roll or winning a local golf tournament, people desire what Janowitz (1952) called “democratizing prestige.”

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Empowerment : a sense that people can participate in community and help ameliorate problems and change things for the better.

These items are consistent with many of the Readership categories but are better specified in terms of how they are presented, and therefore we chose these to organize the analysis here.

One other crucial area of aspect of quality news is its believability or credibility.

Reader Behavior Scores are highly correlated with perceptions of media credibility

(Readership Institute, 2001), and it makes sense that people will migrate toward choosing credible sources for their news. We look briefly at research on credibility before returning to define a set of variables to be examined in the current research.

Credibility

Definitions of media credibility abound in the literature. Generally, credibility is defined as a multidimensional construct that measures the perceived believability of a message (article), source (journalist or media company) or medium (newspaper, Web site, radio station, etc.). Gaziano and McGrath (1986) created a 12-item scale that included questions measuring fairness and community concern and that loaded onto a

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single factor, credibility. Meyer (1988) found their results indicated two factors, believability and community concern, and created a scale reflecting both.

Putting it all together

Access to a survey of 400 people in each of 27 cities allowed us to use secondary analysis to test the notion that the 10 local news dimensions plus credibility, and new media features would predict the number of days people read their local newspaper, and perhaps even how often they looked at the local newspaper’s website. Of course, it has been clear for many years that demographics are large predictors of readership, so we controlled out the primary demographics before testing the impact of the other variables.

Yan and Patwardhan (2004) showed, for example, that demographics, internet credibility, and internet dependency could be combined in a structural equation model that successfully predicted use of the web for political, national, and international news.

The Media Choice model suggested that how much people perceived the presence of the local news quality features and how their use of digital features (cell phone and internet access) would predict readership. The survey did not have a great set of measures of preferred “new media features” like customizability, mobility, and interactivity. The closest we could come was access to broadband internet at home, work, or elsewhere and the pattern of cell phone use.

The relationship between print and online newspaper use

Since the earliest days of online news, there has been a consistent finding that online news use is positively, not negatively related to print news use. In one early study,

Bromely and Bowle (1995) showed a positive correlation. Chyi and Lasorsa (1999) in a survey in Austin, TX, showed a positive correlation as well, but also noted that while

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readership of local newspapers dominated in print, the national newspapers did better online.

Hypotheses

Based on the Media Choice Model’s combining of news quality features and digital features, the following hypotheses were articulated.

H1: Demographics will account for a large percent of variance in readership, both online and print.

Higher income, more education, being male were all expected to be positive predictors of the dependent variables.

H2: Perception of the presence of the 10 dimensions of local news should all be positively correlated with readership except for government and crime, given the negative tinge of how these topics were asked in the surveys used here (see Appendix 1).

H3: Access to the Internet should be a positive predictor of print newspaper use and predictor of online newspaper use, given that use of online and print newspapers are generally positively correlated.

RQ1: Is prediction of print readership more accurate than prediction of online readership? Online readership will produce a much smaller number of respondents, and the research on which our predictive variables were developed came from print-based work. The crucial question is how they predict online news use.

One of the cities tested here had an unusual history of connecting very closely its printed newspaper with the online site. We also asked whether predictability of the online news site in this city would be better than for the other cities in the total sample.

RQ2: Are there differences in what predicts readership in metros and non-metros?

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Local media choices in smaller communities are fewer and there may be more loyalty to the local newspaper. Smaller communities may also have lower levels of access to broadband, a situation associated with lower use of online news (Horrigan &

Rainie, 2002).

Method

To examine the hypotheses and research questions, this study relied upon secondary analysis of a market analysis the research firm Wilkerson & Associates conducted for a medium-sized U.S. newspaper chain. The research firm telephone surveyed approximately 400 people in each of the chain’s 27 markets, which included both large cities and small towns. The telephone survey was nearly identical in every market, only differentiating whether the newspaper in that city had a weekend or Sunday newspaper or whether certain businesses operated in that community. The 40-question survey focused on weekday and Sunday newspaper readership and use of the newspaperoperated Web site in each community while exploring such predictive factors as demographic information – age, gender, income, and education - and purchasing decisions in such areas as grocery, entertainment, clothes, and furniture shopping. The survey also asked participants to rate which of the newspaper’s sections they read most frequently and which were most important to them.

No information was available about the survey response rates, but all together the research firm generated nearly 12,000 responses. The fewest responses in any one city was 393, while the largest number was 598. Within the responses, the survey firm sought diversity by asking for pre-determined family members in each home. In total, respondents represented a wide variety of ages, educational levels and incomes. More

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then 58 % of the survey respondents were women. The most common age of respondents was 75 and over (13 %), but there were also large percentages of people aged 50 to 54

(11 %), 55 to 59 (10 %) and 44 to 49 (10%). The most frequently cited educational level was high school graduate (29 %), but there were also almost equally high percentages for those with some college without completing a four-year degree (26%) and those who completed a four-year degree (17%). The most frequently cited income level was between $50,000 and $74,999 annually (22%), but the next two highest percentages were between $35,000 and $49,999 (21 %) and less than $19,999 annually (18%). Most of the respondents were married (56 %), but there were nearly equal numbers of respondents who were separated (15%), divorced (13%) and never married (11%).

Based on the literature, we decided to use age, income, marital status, education and gender as control variables for predicting weekday and Sunday newspaper readership and newspaper Web site use. News quality was indexed with 17 questions that asked respondents to use a five-point Likert-type scale to rate their level of agreement (see

Appendix 1). Respondents could choose to strongly disagree, disagree, agree or strongly agree with statements such as “I am interested in reading about people who live in my city or town,” “I think that information in newspapers is pretty accurate,” and “I have a lot in common with the people who live near me.” These items served as operationalizations of Denton’s 10 local news quality indicators. We subjected these responses to an exploratory factor analysis to see if it would be useful to simplify the number of quality features.

Preference for media features was operationalized first with a measure of Internet access. Internet access came from combining questions about access at home, work,

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school and other, with a question about whether respondents had used the Internet for news yesterday, creating a continuous scale from 0 to 6. More than 31 % said they did not have access to the Internet. Most respondents scored in the 2 to 3 range, meaning they had used accessed the Internet yesterday from either work or home or both.

Media features preferences were also indexed with cell phone use. This was a combination of whether the respondent had a cell phone and whether they use it to access news and information to connect to the Internet, creating a scale from 0 to 2. Once again,

31% reported they did not have a cell phone. More than 65 % reported owning a cell phone, but only 4 % said they used it to access the Internet. The extremely low level of internet access makes this index problematic.

We constructed print and newspaper web site from multiple survey questions.

First, we added whether respondents had read or looked at a weekday newspaper this week with how many weekdays they had looked at or read the newspaper to create a continuous newspaper readership variable with a range from 0 to 6. The mean weekday readership score was 3.0. We computed readership including Sunday by adding the weekday readership score to question on whether they had read the Sunday or weekend paper at all during the last four weeks and how may of the past four issues they had read.

This created a range from 0 to 12, with the mean score of 5.3. This analysis eliminated about 2,800 respondents because seven of the 27 newspapers in the survey did not publish Sunday editions. Newspaper web site use was computed by adding if they knew the paper had a Web site with the last time they had visited the site. The range was 0 to 3 with a mean score of 1.2.

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Based on the circulation sizes of the 27 newspapers, we divided them into metro and nonmetro. Seven newspapers in the chain qualified as metro newspapers with circulations greater than 100,000. There were 3,578 metro respondents because some of the metro newspapers had more than 400 participants each. (For a full description of all variables, see the Appendices 1 and 2.)

Results

Prior to predicting readership, we needed to determine whether respondents’ level of agreement with the 17 statements about their communities and the newspaper’s role therein factored as predicted by Denton’s 10 features, or if there was a more simple solution. An exploratory factor using principle component analysis with varimax rotation suggested five factors. (For all the factors and their loading scores, see Table 1). The first included five statements that seemed to suggest a focus on local news. The first two statements – I am interested in reading about people who live in my city of town and I am interested in reading about people who live in my neighborhood – equally loaded onto a second factor with a third statement, I have a lot in common with the people who live near me. No other factors loaded with these statements and logically, having a lot I common with neighbors relates to the same local news focus as the other statements. We decided to combine the two factors, and in reliability testing, we obtained a Cronbach’s alpha score of .75.

The second factor included five statements, four of which related to newspapers helping respondents in their daily life. The fifth addressed spirituality and had a factor loading score of less than .5, so we decided to eliminate it from the second factor. With the four remaining statements, we obtained a Cronbach’s alpha score of .71. The

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spirituality statement became a factor on its own because it was the only question to specifically address Denton’s idea of the importance of religion and faith.

The third factor also contained three statements. The first two that both addressed the accuracy of news had factor loading scores above .7. The third – News that is reported from people who are really experiencing it is something I can rely on – had a .41 loading coefficient, but did not have higher scores on any of the other factors. A fourth statement – I like news that supports my point of view on important issues - loaded better on the first factor about local news, but also addressed issues of credibility and believability, so we added it to the third factor. The Cronbach’s alpha reliability score for these four statements taken together was .60.

The final factor contained just two statements, which questioned how much coverage the newspaper devoted to institutional news. The factor loading score for each was above .70, and the correlation between the two was .25, which was statistically significant at the .01 level.

Once we determined the factors to include the analysis, we executed hierarchical linear regressions for each of our dependent variables. The first level of each of these analyses were demographic variables, including age, marital status, income, education and gender. The second level included the news quality measures derived from the factor analysis. The final level included the two indices of preference for new media features, cell phone use and Internet access.

Hypothesis 1 suggested that demographics would account for a large percent of the variance in print and online readership. This was true for all but metro newspaper web site usage. For the whole sample, we found the usual pattern of older, more

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educated and higher income people reading more. This was true for both weekday and 7 day readership.

For the whole sample, we found that younger male respondents read the online newspaper more often. Our one unusual non-metro with an emphasis on its web site showed greater readership by married males.

In comparing metros to smaller papers, there were interesting differences in the impact of demographics. For print readership, the smaller papers were predicted by age, income and gender. The metros, however, were predicted by age, being married, education and income. In the smaller newspapers, online readership was predicated by younger males. There were no significant demographic predictors for metro online site readership. Thus, as expected, demographics did account for significant portions of readership variance, for both online and print newspapers.

Hypothesis 2 suggested the indicators of quality news would have positive impact on readership, except for the government and crime indicators, which would be negative.

For weekday newspaper readership, local news coverage was the strongest predictor after age (See Table 2). Other variables that were statistically significant at the .001 level were income, the helps me in my daily life factor and coverage of crime and local government.

As predicted, crime and government coverage was a negative predictor, meaning the more respondents thought the newspaper covered those arenas too much, the less likely they were to read. The predictors in the full regression model accounted for more than

20% of the variance after adjusting for sample size. The new media variables – Internet access and cell phone use – were not statistically significant and added nothing to the explained variance. This same general pattern held when we looked at readership

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including Sundays. Thus there was significant support for the impact of the quality news indicators.

Hypothesis 3 suggested that access to the internet and use of cell phones would be a positive predictor of readership. This was unsupported for print readership where neither variable was ever significant. But it was correct for prediction of web site use for the whole sample and for both metros and non-metros analyzed separately. Thus preference for new media features, at least at this point in time, predicts online but not print newspaper readership.

R1 suggested whether prediction of print readership would be more accurate that prediction of online. This was clearly the case. For weekday (20% of the variance) and seven-day print readership (21% of the variance) fully one-fifth of the variance in readership scores was accounted for. But web site use had only 8% of its variance accounted for. The patterns were about the same for non-metros (19% of the variance vs.

9%) and for metros (26% and 7%). The only place where readership of the newspaper web site was strongly predicted was in the unusual non-metro where web site use is unusually emphasized. Here an impressive 20% of the variance in online readership was accounted for. This has important implications for our future understanding of what drives online newspaper readership.

R2 asked about what differences would be observed in metros and non-metros.

As noted above, web site readership in the metros was better accounted for (26% of the variance) than for the smaller papers (19% of the variance). There were a handful of differences in the impact of demographics. In non-metros, males were more likely to read the print addition, along with older higher income respondents. In metros, there

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were no gender differences but in addition to older higher income respondents, married and more highly educated respondents were more likely to read. And for online readership, there were no demographic predictors for the metros, but younger males showed higher readership of the smaller online newspapers.

We also calculated the correlation between Web site usage and local newspaper readership. While statistically significant at the .001 level due to the study’s large sample size, the correlation between site use and readership on weekdays and Sundays was less than .05 and .06 respectively, meaning these correlations had little practical significance.

Discussion

This study adds to a growing body of evidence that shows that news quality is a primary driver of print newspaper readership (e.g., see the reviews in a special issue of

Newspaper Research Journal called Good Journalism is Good Business (2004) and

Meyer, 2004). After controlling for the effects of demographics, the five indicators of news quality account for approximately 10% of the variance in print readership.

Coverage of local news and helps me in my daily life account for the lion’s share of that variance. The impact of lots of coverage of crimes and stories about government meetings has a lesser effect, but as expected, it is always a negative effect. Credibility has a small effect, but it is consistently a positive one. Spirituality tends not to have a significant effect, although for weekday readership of the web sites it is negative.

The powerful variable “coverage of local news” includes three of Denton’s quality indicators: proximity, recognition, and education. The other powerful variable helps me in daily life also includes three quality variables: utility, support and

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empowerment. Given that we had only one or two items for each of these individual quality indictors, it is not surprising that the items loaded into the more simple two-factor structure. We would expect that a more detailed questionnaire would restore the individual items. In either case, these data strongly support Denton’s taxonomy of what content variables are important drivers of newspaper readership.

As noted, the theoretical approach employed here was not as successful in predicting readership of newspaper web sites. Clearly a primary problem is that so few respondents were reading those sites. Encouraging, however, is the regression analysis for the single unusual newspaper where there has been a concerted effort to drive both print and online readership. For the whole sample of newspaper web sites, overall predictability of readership is low (i.e., around 8% of the variance). But for the concerted effort city, nearly 20% of the variance is accounted for. In the whole sample, the small variance accounted for can be largely attributed to demographics (3%) and having internet access (5%), neither of which can be controlled by the newspapers themselves.

The quality indicators only account for 1% of the variance in readership (negative effect of spirituality and crime/local government and a positive effect of coverage of local news). But in the concerted effort city, the quality variables, specifically helps me in my daily life, account for an impressive 7% of the variance. (The effects of demographics are also much higher than in the other cities.) This means that when a newspaper exerts great effort in building its web site, quality also counts. In fact, in the single city, neither of the feature preference variables, internet access and cell phone use, is significant. An important next test would be to build this kind of web-centric emphasis in other

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newspapers and then determine whether the quality variables similarly start to exhibit their impact.

As in all secondary analyses, the measurement items do not fit those specified by the model as closely as would be desired. Nevertheless, the predictive strength shown here is as high or perhaps higher than many other studies of newspaper readership (e.g.,

Readership Institute, 2001). This study also joins a series of other studies that show high predictive strength of the Media Choice Model (Duffy & Thorson, 2006; Hamman &

Thorson, 2006; Thorson & Thorson, 2006).

As the statistics for newspaper health in the U.S. continue to head south, it is crucial that theoretical and practical approaches to figuring out what drives readership of print and online products are executed and shared with an industry in need of all the help it can get.

24

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27

Factors

Table 1 Exploratory factor analysis using principle component analysis with varimax rotation of level of agreement with community and newspaper’s role in the community statements.

1 2 3 4 5

I am intereted in reading about people who live in my city of town

I am interested in reading about people who live in my neighborhood.

I am interested in what is going on inside local classrooms.

I think the newspaper should play a role in building a sense of community in my town.

I think it’s important for the newspaper to get local names and faces in the newspaper.

I have a lot in common with the people who live near me.

The newspaper offers solutions to problems in the community.

The newspaper includes a lot of information that is helpful in my daily life.

I look to the newspaper for information about support groups or clubs I might be interested in.

Reading the newspaper makes me want to become more involved in the community.

I am interested in reading about spirituality and values in the newspaper

I think that the information in newspapers is pretty accurate.

I think that information in TV news programs is pretty accurate.

News that is reported from people who are really experiencing it is something I can rely on.

I like news that supports my point of view on important issues.

.553

.458

.625

.663

.660

.063

.506

.526

.544

.741

.587

.635

.713

.674

.485

.761

.798

.412

.222

The newspaper provides too much coverage of crime.

The newspaper includes too many stories about what goes on in government meetings.

.774

.783

Variance Explained 14.461 8.536 13.775

Table 2

1

2

Age

Marital Status

Education

Income

Gender

Age

Marital Status

Education

Income

Hierarchical linear regression of demographic variables, newspaper quality variables and new media measures on weekday newspaper readership for all markets.

R 2 Change Total R 2 Adjusted R 2 Beta

.317

c

.002

.045

c

.148

c

.055

c

.307

c

.017

.034

b

.131

c

.113 .113 .113

3

Gender

Spirituality

Coverage of crime and local government

Credibility / Believability

Coverage helps me in my daily life

Coverage of local news

Age

Marital Status

Education

Income

Gender

.011

.015

-.059

c

.035

b

.145

c

.165

c

.313

c

.018

.031

b

.124

c

.011

.090 .203 .202

Spirituality





Has Internet access

Has a Cell Phone

.015

-.058

c

.036

b

.146

c

.164

c

.009

.015 .000 .204 .202

28

a.

p < .05 b.

p < .01 c.

p < .001

Table 3 Hierarchical linear regression of demographic variables, newspaper quality variables and new media measures on newspaper readership including Sunday for all markets with a Sunday newspaper (20 of 27).

1 Age

Marital Status

Education

Income

Gender

2 Age

Marital Status

Education

Income

Gender

Spirituality





3 Age

Marital Status

Education

Income

Gender

Spirituality





Has Internet access

Has a Cell Phone a.

p < .05 b.

p < .01 c.

p < .001

.320

c

.021

.029

a

-.064

c

.025

.021

.028

a

-.063

c

.010

.021

Beta

.008

.048

c

.155

c

.068

c

.309

c

.024

.042

c

.139

c

.175

c

.145

c

.317

c

.025

.039

b

.130

c

.026

.175

c

.144

c

R 2

.118

.096

.001

Change Total R

.118

.214

.215

2 Adjusted R 2

.213

.213

.117

29

Table 4 Hierarchical linear regression of demographic variables, newspaper quality variables and new media measures on newspaper web site use for all markets.

R 2 Change Total R 2

1

2

Age

Marital Status

Education

Income

Gender

Age

Marital Status

Education

Beta

-.138

c

.000

.067

c

.049

c

.036

b

-.139

c

.028 .028

3

Income

Gender

Spirituality





Age

.005

.053

c

.042

b

.026

-.038

a

-.035

b

-.019

.022

.100

c

-.052

c

.011 .039

Adjusted R

.027

.037

2

Marital Status

Education

Income

Gender

Spirituality





Has Internet access

Has a Cell Phone

.001

.008

.012

.032

a

-.037

a

-.032

a

-.009

.027

.082

c

.239

c

-.017 .045 .083 .081

Table 5 Hierarchical linear regression of demographic variables, newspaper quality variables and new media measures on affiliated Web site use for a single market with a strong local news focus on its site.

1 a.

p < .05 b.

p < .01 c.

p < .001

Age

Marital Status

Beta R 2 Change Total R 2 Adjusted R 2

-.336

c

.148

a

.167 .167 .146

Education

Income

Gender

2 Age

Marital Status

Education

.003

.028

.057

-.275

c

.17

b

-.017

Income

Gender

.018

.045

Spirituality -.024

Coverage of crime and local government -.066




3 Age

Marital Status

-.114

.312

c

.031

-.227

b

.180

a

Education

Income

Gender

Spirituality

-.016

.004

.043

-.009

Coverage of crime and local government -.068

.070 .237 .198

30




Has Internet access

Has a Cell Phone

-.103

.310

c

.034

.050

.079 .009 .246 .199 a.

p < .05 b.

p < .01 c.

p < .001

Table 6 Hierarchical linear regression of demographic variables, newspaper quality variables and new media measures on newspaper readership including Sunday for small town markets.

R 2 Change Total R 2 Adjusted R 2

1 Age

2

Marital Status

Education

Income

Gender

Age

Marital Status

Education

Income

Beta

.271

c

-.018

.028

.127

c

.094

c

.268

c

.000

.012

.106

c

.039

a

.093 .093 .092

3

Gender

Spirituality





Age

.021

-.066

c

.027

.162

c

.166

c

.274

c

.097 .190 .188

Marital Status

Education

Income

Gender

Spirituality





Has Internet access

Has a Cell Phone

.001

.010

.100

c

.039

a

.020

-.064

c

.028

.162

c

.166

c

.010

.013 .000 .191 .188 a.

p < .05 b.

p < .01 c.

p < .001

31

Table 7 Hierarchical linear regression of demographic variables, newspaper quality variables and new media measures on affiliated Web site use for small town markets.

R 2 Change

2

3

Age

Marital Status

Education

Income

Gender

Age

Marital Status

Education

Income

Gender

Spirituality





Age

Beta

-.146

c

-.016

.059

c

.033

.044

b

-.149

c

-.014

.039

a

.024

.034

a

-.066

c

-.035

a

-.044

a

.062

b

.107

c

-.061

c

.027

.017

Marital Status

Education

Income

Gender

Spirituality

-.017

-.003

-.008

.041

a

-.062

c





Has Internet access

Has a Cell Phone

-.030

-.032

.065

c

.086

c

.252

c

-.036 .050 a.

p < .05 b.

p < .01 c.

p < .001

Total R 2

.044

.027

.094

Adjusted R 2

.041

.026

.091

32

Table 8 Hierarchical linear regression of demographic variables, newspaper quality variables and new media measures on newspaper readership including Sunday for seven metropolitan markets.

R 2 Change Total R 2 Adjusted R 2

1 Age

Marital Status

Education

Income

Beta

.393

c

.049

.078

c

.195

c

Gender

2 Age

Marital Status

Education

Income

Gender

Spirituality


.029

.372

c

.061

b

.085

c

.189

c

-.006

.038

-.059

c

.169 .169 .167




3 Age

Marital Status

Education

Income

Gender

Spirituality





Has Internet access

Has a Cell Phone

.022

.204

c

.106

c

.383

c

.061

b

.080

c

.176

c

-.008

.037

-.059

c

.023

.206

c

.104

c

.013

.035

.097

.001

.266

.267

.262

.263 a.

p < .05 b.

p < .01 c.

p < .001

33

Table 9 Hierarchical linear regression of demographic variables, newspaper quality variables and new media measures on affiliated Web site use for seven metropolitan markets.

1 Age

Marital Status

Education

Income

Gender

2 Age

Marital Status

Education

Income

Gender

Spirituality





3 Age

Marital Status

Education

Income

Gender

Spirituality





Has Internet access

Has a Cell Phone a.

p < .05 b.

p < .01 c.

p < .001

Beta

-.121

c

.036

.082

c

.083

b

.018

-.117

c

.044

.077

b

.077

b

.010

.014

-.040

.044

-.067

a

.086

b

-.036

.035

.032

.051

.014

.010

-.040

.051

-.058

.071

a

.202

c

.024

R 2 Change

.010

.032

.034

Total R 2

.042

.032

.076

Adjusted R 2

.036

.029

.069

34

Appendix 1: Operationalizations of the Dimensions of Local News

(These were the original expectations prior to the factor analysis.)

DIMENSIONS OF LOCAL NEWS

Crime

The (NEWSPAPER) provides too much coverage of crime.

Government

The (NEWSPAPER) includes too many stories about what goes on in government meetings.

Credibility

I think that information in newspapers is pretty accurate.

Proximity

I am interested in reading about people who live in my city or town.


Recognition

I think it's important for the (NEWSPAPER) to get local names and faces in the newspaper.

Support

Reading the (NEWSPAPER) makes me want to become more involved in the community.

I look to the (NEWSPAPER) for information about support groups or clubs that I might be interested in.

Education


Utility

The (NEWSPAPER) includes a lot of information that is helpful in my daily life.

Empowerment

The (NEWSPAPER) offers solutions to problems in the community.

Reading the (NEWSPAPER) makes me want to become more involved in the community.

Spirituality

I am interested in reading about spirituality and values in (NEWSPAPER).

35

Appendix 2: Operationalization of All Variables

Dependent Variables

Newspaper Readership



Have you read or looked into any part of the weekday newspaper in the last seven days? (0 or 1)



How many of the past five weekday issues have you read or looked at? (0 to 5)

Newspapers Readership with Sunday



Q1: Have you read or looked into any part of the weekday newspaper in the last seven days? (0 or 1)



Q4: How many of the past five weekday issues have you read or looked at? (Range from 1 to 5)



Q5: Have you read or looked into any part of the Sunday / Weekend newspaper in the past four weeks? (0 or 1)



Q8: How many of the past four Sunday / Weekend issues of the newspaper have you read or looked into? (0 to 4)

Net Use



Q28: Did you know the paper has a Web site? (1 – Yes, 2 – No)



Q29: When was the last time you visited the site?



Today



Yesterday



In the past 7 days

5

4

3



In the past 8 to 30 days

 All others

2

0

Independent Variables

Demographics

Age



1 – 18 to 20



2 – 21 to 24



3 – 25 to 29



4 – 20 to 24



5 – 35 to 29



6 – 40 to 44



7 – 45 to 49



8 – 50 to 54



9 – 55 to 59



10 – 60 to 64



11 – 65 to 69



12 – 70 to 74



13 – 75 or older

36

Marital Status



1 – Married



2 – Widowed



3 – Divorced



4 – Separated



5 – Living in a Partnered Relationship



6 – Never Married

Education



1 – less than high school graduate



2 – graduated high school (includes GED)



3 – Trade, technical or vocational school



4 – Some college



5 – 4-year degree



6 – Working on post-graduate degree



7 – Completed a post-graduate degree

Income



1 – Less than $19,999



2 – $20,000 and $24,999



3 – $25,000 and $29,999



4 – $30,000 and $34,999



5 – $35,000 and $49,999



6 – $50,000 and $74,999



7 – $75,000 and $99,999



8 – $100,000 and $149,999



9 – $150,000 or more

Gender



1 – Male



2 – Female

Spirituality Do you agree with the following statement? (1 strongly disagree to

5 strongly agree)



I am interested in reading about spirituality and values in the newspaper?

Coverage of Crime and Local Government

Do you agree with the following statements? (1 strongly disagree to 5 strongly agree)



The newspaper provides too much coverage of crime.



The newspaper includes too many stories about what goes on in government meetings.


37




I think that the information in newspapers is pretty accurate.



I think that information in TV news programs is pretty accurate.



I like news that supports my point of view on important issues.



News that is reported from people who are really experiencing it is something I can rely on.





The newspaper offers solutions to problems in the community.



The newspaper includes a lot of information that is helpful in my daily life.



I look to the newspaper for information about support groups or clubs that I might be interested in.



Reading the newspaper makes me want to become more involved in the community.





I am interested in reading about people who live I my city or town.









I think the newspaper should play a role in building a sense of community in my town.

 I think it’s important for the newspaper to get local names and faces in the newspaper.



I have a lot in common with the people who live near me.

Has Internet Access

Do you have Internet access at … (1 – Yes, 0 – No)

At home?

At your place of employment?

At school?

At some other place I haven’t mentioned?

Does the computer you use at HOME connect to the Internet through …

Dial-up (0)?

38

Has Cell Phone

High Speed (1)?

Did you get news online through the Internet yesterday?

Yes (1)

No (0)

Do you personally have a cell phone?

Yes (1)

No (0)

Do you use your cell phone to connect to the Internet to search for things like movie listings, weather, news, or other information?

Yes (1)

No (0)

39

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