Interactive Features of Online Newspapers:
Identifying Patterns and Predicting Use of
Engaged Readers
Deborah S. Chung
School of Journalism and Telecommunications
University of Kentucky, Lexington
This study seeks to identify 1) categories of interactivity, which are promoted through
different types of interactive features, 2) patterns of online newspaper readers’ uses of
interactive features, and 3) factors, if any, that predict the use of different types of
interactive features. Based on an online survey of 542 respondents, four categories of
interactive features were identified. Findings show that interactive features are generally
used infrequently, especially the features that facilitate human-to-human communication
and the features that allow audiences to express their views. Regression analyses
show that different user characteristics and backgrounds predict the use of specific types
of interactive features. This study illustrates that news organizations need not worry
about applying all types of interactive features to engage their readers as the features
serve distinct functions. Instead, news organizations should focus on building credibility
and may seek to identify their online news audiences and then subsequently provide
interactive features accordingly. (152)
doi:10.1111/j.1083-6101.2008.00414.x
Interactive Features of Online Newspapers: Identifying Patterns and
Predicting Use of Engaged Readers
The potential of ‘‘interactivity’’ has fueled extraordinary anticipation over the adoption
of a two-way communication model in the news industry. Interactivity, a quality
of new media and the Internet in particular, fundamentally challenges the traditional
one-way directional flow of news by providing news audiences with increased choice
options and even allowing them to participate in the production of information.
Interactivity, thus, has the potential to transform the nature of traditional journalism
practice through online news. Journalism is at a crossroads with its content and form
evolving daily through multimedia platforms and numerous hyperlinks that easily
allow readers to select stories. Most importantly, online newspapers are encouraged
to share their control of news presentation with the audience by allowing increased
Journal of Computer-Mediated Communication
658 Journal of Computer-Mediated Communication 13 (2008) 658–679 ª 2008 International Communication Association
communication among readers, promoting back-and-forth conversations between
newsroom personnel and the audience, and providing opportunities for personalized
journalism.
While online news publications and their application of interactivity have been
scrutinized (Li, 1998; Massey & Levy, 1999; Schultz, 1999; Chan-Olmsted & Park,
2000; Chung, 2004), little has been done to analyze and categorize the various
interactive features that promote interactivity. Present studies also fail to illuminate
the extent to which news audiences engage in interactivity, and the characteristics
and backgrounds of online news audiences who engage themselves with online
newspapers are absent in the literature. With information gathered through an
online survey of readers from a local online newspaper, this study attempts to
identify 1) categories of interactivity, which are promoted through different types
of interactive features, 2) online newspaper readers’ uses of different types of interactive
features, and 3) factors, if any, that predict the use of different categories of
interactive features.
Literature Review
The (R)Evolution of News—From Print to Online
The newspaper is one of the oldest elements of contemporary media (Boczkowski,
2004) and has long been considered the primary model of information delivery
(McQuail, 1994). Today there exist over 1,400 newspapers representing a $55 billion
industry (Newspaper Association of America, 2007). Yet there has long been concern
with the top-down communication model of newspapers. Critics contend that the
mass media have imposed a one-way discussion and argue that they largely produce
messages independently from news audiences (Habermas, 1962; Schultz, 1999).
Critics also note the lack of intense political discussion and citizen dialogue opportunities
available through traditional media channels (Barber, 1984; Habermas,
1996). The civic journalism movement, which flourished in the 90s, is based on such
discontent with the news industry. This movement attempted to reconnect with
communities by actively communicating with the news audiences and favoring
issues important to ordinary citizens (Rosen, 1992; Charity, 1995; Merritt, 1998).
Civic journalism today has further grown into community or participatory journalism
that promotes interactive engagement between newsrooms and their communities
(Bowman & Willis, 2003).
The Internet is only the latest to challenge traditional news delivery methods.
However, the implications of its influences are profound. While traditional news
media have delivered information through a top-down, centralized model with
journalists functioning as gatekeepers of information, online news media present
greater opportunities for control and ownership as users assume more active roles in
their news consumption experiences.
The popularity of online newspapers (Annual Report on American Journalism,
2006) can be attributed to the interactive quality of the Internet. Journalists expect to
Journal of Computer-Mediated Communication 13 (2008) 658–679 ª 2008 International Communication Association 659
bring people ‘‘closer to the news’’ by adopting interactivity in their online presentation
of the news (Brown, 2000). The immediate back-and-forth communication is
a new quality in the relationship between news publications and their audiences, and
research suggests that online readers find this interaction valuable (Pew Research
Center for the People and the Press, 1999). The Digital Journalism Credibility Study
(2003) claims that the interactive nature of e-mail links, chats and message boards
are the essence of the medium and has the potential to recreate community. In
general, interactivity is considered to be a positive characteristic of new technologies.
Rafaeli (1988) writes that the consequences of interactivity are satisfaction, motivation,
sense of fun, cognition and learning.
Interaction, Interactivity and Interactive Features on Online News Publications
Interactivity stems from the sociological concept of interaction where it is defined
as the relationship between two or more people, who, in a given situation, mutually
adjust their behaviors and actions to each other (Jensen, 1998). Duncan
(1989) refers to interaction as the state of reciprocal awareness. In the last
10–15 years the concept of interaction has found its way into the discussion of
new communication technologies, such as the Internet. Communication scholars
have, thus, discussed interaction online as ‘‘interactivity.’’ Many communication
researchers use face-to-face communication as the standard of interactivity and
evaluate the interactivity offered by websites based on how closely it resembles
such communication (Walther & Burgoon, 1992). While interactivity itself is not
equivalent to interaction, the ideal of interactivity, however, has been discussed
through the framework of ‘‘the conversational ideal (Schudson, 1978).’’ Some
scholars have challenged this movement (Rafaeli, 1988), yet the numerous definitional
models offered for interactivity suggest that ‘‘the conversational ideal’’ is
still an ideal to embrace.
Thus far, interactivity has been discussed through various definitional models
(Bordewijk & van Kamm, 1986; Rogers, 1986; Rafaeli, 1988, Rafaeli & Sudweeks,
1997; Heeter, 1989; Steuer, 1995; Kiousis, 2002). While there are numerous definitions
of interactivity that cover considerable ground, a helpful way that scholars
have conceptualized interactivity is through the distinction of medium interactivity
from human interactivity (Outing, 1998; Lee, 2000; Stromer-Galley, 2000, 2004;
Bucy, 2004; Chung, 2007). Medium interactivity, also known as user-to-system/
document or content interactivity, is interactive communication between users
and technology that is based on the nature of the technology itself and what the
technology allows users to do. Human interactivity, also known as user-to-user or
interpersonal interactivity, on the other hand, is communication between two or
more users that takes place through a communication channel. Stromer-Galley
(2000) considers human interactivity to be more interactive than medium interactivity
because she finds this kind of interaction to be the foundation for public
deliberation.
660 Journal of Computer-Mediated Communication 13 (2008) 658–679 ª 2008 International Communication Association
In this study interactivity is defined as a multi-dimensional construct that is on
a continuum of medium to human interactivity. While interactivity is represented
on a continuum, the categories of interactivity are manifested through various
different forms of interactive features that fall on that continuum. For example,
features representing medium interactivity solely rely on the technology to allow
users to exert control and are considered as lower levels of interactivity. Interactive
features that characterize medium interactivity include send-article-to-friend
options, audio and video downloads and photo galleries.
Those features that utilize characteristics of medium interactivity and also allow
partial human-to-human communication are considered middle-ground interactive
features. This category exemplifies the medium-to-human interactivity continuum
and resides between the two extremes of medium interactivity and human interactivity.
These features, that allow customization options, provide the means for users
to tailor information to their liking and/or share and express their views, but these
features generally do not support the exchange of ideas. Thus, interactive features
that represent this category include information customization features—such as
weather and topic customization—and content submission features—such as news
tips, news stories and photo submissions—and polls.
Finally, features that promote human interactivity that facilitate user-to-user
mutual communication, or interpersonal communication, are considered as higher
levels of interactivity. These features can be characterized through e-mail links,
message boards and chat features. Using these features require more effort in that
individuals must do more than clicking or selecting in order to actively use them.
Human interactivity is considered to contribute to the key distinction between
traditional forms of news delivery and online news in that the audience, if desired,
can participate as active agents through interpersonal communication.
Deuze (2003) also translates interactivity dimensions for the design of news
websites into navigational interactivity, adaptive interactivity and functional interactivity.
Navigational interactivity, like medium interactivity, allows users to ‘‘navigate’’
a site with hyperlinks and menu bars. Adaptive interactivity, much like the
blending of medium and human interactivity, allows the users’ experiences to have
consequences on site content. Functional interactivity, much like human interactivity,
allows users to communicate with other individuals. The interactivity model
suggested for the current study, thus, builds on this conceptualization of interactivity
of online news publications.
In this study, the first aim is to examine whether indeed certain interactive
features offered by newspaper websites share common ground and if these interactive
features serve distinct functions. Thus, the following research question is posed:
RQ1: What categories of interactive features exist on online newspapers?
Interactivity on Online News Publications and Online News Audiences
Outing (1998) says for a news site to be truly interactive it must facilitate communication
between humans through human interactive features. He argues that the
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Internet is a two-way medium, and for sites to excel at ‘‘interactivity,’’ they must
bring people together and promote communication among Web users as well as
communication between Web users and Web staff members and managers.
A number of studies have thus attempted to examine online newspapers’ use of
both human and medium interactive features. However, the majority of these studies
have discovered that online news publications have failed to fully take advantage
of the unique characteristics of the Internet (Schultz, 1999; Massey & Levy, 1999;
Chan-Olmsted & Park, 2000; Kenney, Gorelik & Mwangi, 2000; Rosenberry, 2005; Ye
& Li, 2006). Positive accounts of news publications’ implementation of interactivity
with increasing sophistication in form and content over time exist (Chung, 2004;
Salwen, 2005; Greer & Mensing, 2006). Overall, however, this body of literature,
which is predominantly based on the U.S. media environment, overwhelmingly
continues to find that the use of various interactive features among online news sties
is largely limited. Because of these findings, online news publications have been
heavily criticized for the lack of exploitation of interactivity—especially human
interactivity that encourages dialogic communication.
While studies suggest that news consumers welcome the interactive nature of
online news and particularly value features that facilitate two-way communication
(Pew Research Center for the People and the Press, 1999), little is known about how
online news audiences make use of the various interactive features offered by news
publications. There exists a significant push for online publications to adopt interactivity,
but how news audiences are using interactive features has not been examined
in depth. What little we do know about online news audiences’ use of interactive
features comes from studies reported by the Pew Research Foundations. The Pew
Research Center for the People and the Press (2006) mentions online news audiences’
uses of search engines, news updates and alerts. Recent reports by the Pew
Internet & American Life Project also briefly inform on the uses of online news
videos (2007a) and posting comments to online news groups (2007b). Yet the extent
of what we know about online news readers and their uses of interactive features
stops here, and little effort has been made to further examine online newspaper
consumers’ experiences with interactivity.
Based on the dearth of information about online news audiences and their
experiences of interactivity with online newspapers, this study also assesses how
frequently online news audiences are making use of various types of interactive
features.
RQ2: To what extent are online newspaper audiences making use of different types of
interactive features as identified by RQ1?
User Characteristics and Backgrounds
Over the years, studies have offered an increasingly sophisticated portrait of the
Internet news audience— who they are, what uses they make of online news sites,
and why they visit online news sites (Jeffres & Atkin, 1996; Hwang & He, 1999;
662 Journal of Computer-Mediated Communication 13 (2008) 658–679 ª 2008 International Communication Association
Althaus & Tewksbury, 2000; Ferguson & Perse, 2000; Papacharissi & Rubin, 2000;
Pew Internet & American Life Project, 2003; Pew Research Center for the People and
the Press, 2004, 2006). However, a discussion of online news audiences’ uses of
interactive features and lifestyle associations are almost completely absent in the
literature. In this study, user characteristics, attitudes and behaviors of news consumers
are examined to find relationships between uses of various interactive
features.
RQ3: What user attributes, if any, are associated with the use of different types of interactive
features as identified by RQ1?
While audience characteristics related to uses of interactive features have not
been examined, audience characteristics related to general online use, such as length
of online experience, perceived Internet skill level and perceived media credibility,
have been studied by researchers.
Studies have suggested that those who have been online longer have greater
online know-how (UCLA Internet Report, 2003) and have developed better strategies
for finding information online (Peterson, 1999). The Stanford Institute Study
(Nie & Ebring, 2000) found that people who spent more years online engage in more
online activities. Johnson and Kaye (2004) found that number of years users have
been online is positively correlated with Internet proficiency. These findings suggest
that these individuals may feel more comfortable exploring newer ways to experience
news through various interactive features.
H1: Years online will be a positive predictor of use of interactive features as identified by RQ1.
Researchers have also found that one’s level of perceived online skill level can
influence how often the Internet is used (Ferguson & Perse, 2000; UCLA Internet
Report, 2003). Eastin and LaRose (2000) report that Internet self-efficacy, or ‘‘the
belief in one’s capabilities to organize and execute courses of Internet actions
required to produce given attainments,’’ was significantly related to Internet use.
These findings suggest that one’s level of perceived online skill level may further
influence various interactive feature usage.
H2: Perceived Internet skill level will be a positive predictor of use of interactive features as
identified by RQ1.
In addition, past credibility studies suggest that perceived credibility of a medium
is strongly related to how often one uses it (Wanta & Hu, 1994; Carter & Greenberg,
1965; American Society of Newspaper Editors, 1985) and thus may further influence
how one may engage in experiencing news through various forms of interactive
features.
H3: Perceived credibility of online news will be a positive predictor of use of interactive
features as identified by RQ1.
Furthermore, studies report connections between online and offline activity.
Some studies have found that a relationship exists between civic engagement and
Journal of Computer-Mediated Communication 13 (2008) 658–679 ª 2008 International Communication Association 663
political activity offline and Internet use. For example, recent studies have found that
Internet use was positively associated with community engagement (Katz, Rice &
Aspen, 2001; Shah, McLeod, & Yoon, 2001; Shah, Kwak, Holbert, 2001; Shah et al.,
2005). Specifically, the use of expressive features of the Internet contribute to greater
civic involvement (Shah et al, 2001; Shah et al., 2005).
Katz et al. (2001) found that Internet users were more likely than nonusers to
engage in traditional political activity in the 1996 general election. Weber and
Bergman (2001) also found that individuals who engaged in online activities, such
as using chat-rooms, were more likely to be engaged in a variety of political activities.
These associations seem likely in that politically engaged individuals are concerned
with the free exchange of ideas and dialogic discussion as expressed by the foundations
of a democratic society. Additionally, the Annual Report on American Journalism
(2005) found strong correlations that indicated heavy users of the Internet also
spend more time with newspapers, magazines and television than do medium or
light Internet users, which suggest that ‘‘People bring to the Internet the activities,
interests and behaviors that preoccupied them before the Web existed (Pew Internet
& American Life Project, 2005, p. 58).’’ Thus, it might be reasonable to suggest that
civic involvement and political engagement offline may have an impact on online
activity, and thus, interactive feature usage.
H4: Civic involvement will be the strongest positive predictor of use of interactive features that
facilitate the expression of ideas as identified by RQ1.
H5: Political engagement will be the strongest positive predictor of use of interactive features
that facilitate interpersonal communication as identified by RQ1.
Method
To answer the above research questions and hypotheses, a Web-based survey was
employed. Participants were recruited via online advertisements on an online newspaper
in a medium-sized Midwestern city in the U.S. The survey was linked to the
teaser, which was placed on the homepage of the participating online newspaper. The
average weekly circulation of the newspaper was 42,672. In an attempt to increase
response rate, the initial window after the link was clicked informed participants that
upon completion of the survey, they would be automatically entered into a drawing
for a prize. The link then led the survey participants to the Informed Consent form,
which then made the link to the actual survey available at the bottom of the page. The
survey was posted for roughly three weeks in the summer of 2005. E-mail and IP
address information were collected in order to avoid duplicate submissions. The
survey consisted of a self-administered questionnaire that asked respondents regarding
their frequency of use of specific interactive features that represent interactivity.
In addition to basic demographic questions, the questionnaire also assessed attitudinal
measures of respondents’ perceived Internet skill level and perceived level of
credibility of online news. Behavioral measures of community involvement and
664 Journal of Computer-Mediated Communication 13 (2008) 658–679 ª 2008 International Communication Association
political engagement activities were also measured. The final sample size was 542 in
which the survey completion rate was 77 percent.
Dependent Measures Scale Creation
Respondents were asked to indicate how frequently they use 22 unique interactive
features generally offered by online newspapers. Features were selected based on
prior interactivity studies that examined the use of distinct interactive features,
particularly on news sites (Massey & Levy, 1999; Schultz, 1999; Chan-Olmsted &
Park, 2000; Greer & Mensing, 2006) but also political candidate homepages
(Stromer-Galley, 2000, 2002), health communication sites (McMillan, 2002; Noar
et al., 2006) and business/marketing sites (Ha & James, 1998; Aikat, 2000). This
compilation of features offers a near exhaustive list of features that were offered at
the time in which the survey instrument was developed. It also covers a wide range of
interactivity, from features that encourage user control and choice options to features
that facilitate customization of content and interpersonal communication
based on the review of the literature. The response scale ranged from 1 (never) to
4 (frequently). These 22 items were then factor analyzed to identify specific interactivity
categories. After assessing reliability of the factors (Cronbach a ranging from
.78 to .91), they were summed and then averaged to create scales that represent the
interactivity continuum. The scores for the scales ranged from 1 to 4, with smaller
values indicating lower levels of use of interactivity.
Independent Measures Scale Creation
The ‘‘civic involvement scale’’ was constructed by adding five questions about
involvement in local service organizations, community projects, parent-teacher
organizations, church activities and organized sports activities (Cronbach a = .71).
The variable derived from this scale, produced by the same method as that used for
the dependent measures, had scores ranging from 1 to 4, with smaller values indicating
lower levels of civic involvement.
The ‘‘political engagement’’ scale consists of five items that asked respondents
about attending local government meetings, attending political rallies, making phone
calls on behalf of candidates, staying in contact with elected officials and donating
money to political campaigns (Cronbach a = .87). The variable derived from this
scale, produced by the same method as that used for civic involvement, had
scores ranging from 1 to 4, with smaller values indicating lower levels of political
engagement.
Other independent measures included perceived Internet skill level and perceived
credibility of online news. To assess perceived Internet skill level participants were
asked ‘‘How do you consider your Internet skill level?’’ on a four-point response scale
from not very skilled (1) to very skilled (4). To assess perceived credibility of online
news participants were asked to answer the question ‘‘I consider online news as
a credible source of information’’ on a five-point response scale from strongly disagree
(1) to strongly agree (5).
Journal of Computer-Mediated Communication 13 (2008) 658–679 ª 2008 International Communication Association 665
In the regression analyses reported below, the three non-attitudinal variables —
age, gender and online experience — were entered in the first block. The two
attitudinal measures — perceived Internet skill level and perceived credibility of
online news — were entered in the second block. The behavioral measures — ‘‘civic
involvement’’ and ‘‘political engagement’’ scales were entered in the third block.
Examination of the tolerance and VIF scores testing multicollinearity revealed that
there were no high correlations among the independent variables.
Results
The Sample
The characteristics of the sample are in accord with the user profile of the participating
online newspaper. The newspaper readership demographics (Belden Interactive,
2007) indicate that 92 percent of its site visitors are white and 60 percent are
female. The median income is $56,000, and 43 percent have college degrees or an
advanced degree. The median age is 45. Similarly, 94 percent of the survey respondents
were white and 62 percent were female. About 25 percent of the survey participants
earned more than $50,000 annual household income, and 55 percent of them
had college degrees or above. The mean age of the respondents was 40 years old
(SD = 14.36).
Categories of Interactive Features
To identify specific categories of interactive features (RQ1), a principal components
factor analysis was conducted using Varimax rotation. Items (interactive features)
that cross-loaded on two or more factors and those with factor loadings lower than
.50 were eliminated. In addition, the log-in feature did not fit conceptually, thus, it
was dropped from the analysis. The analysis yielded a reduced scale of 15 items that
loaded on four factors, and thus, seven of the original 22 items were not included in
any of the factors. The four factors accounted for 67 percent of the variance. As
shown in Table 1, the factors were then subsequently created as four interactivity
scales. The scales were labeled medium, medium/human, human/medium and
human interactivity, building on the literature. The four extracted categories of
interactive features are also in agreement with an interactivity continuum: medium
interactive features generally allow readers more control or choice options in experiencing
news stories; medium/human interactive features allow users to customize
news to their liking; human/medium interactive features allow users to express their
personal opinions; human interactive features facilitate interpersonal communication
online. The medium interactivity scale consists of two features: audio files and
video files. The medium/human interactivity scale consists of five features: customized
weather, customized topics, customized headlines, search features and e-mail
updates/alerts. The human/medium interactivity scale consists of five features: ‘‘submit
stories,’’ ‘‘submit photos,’’ ‘‘submit news tips,’’ letters-to-the-editor features and
reporter/editor e-mail links. The letters-to-the-editor feature and reporter/editor
666 Journal of Computer-Mediated Communication 13 (2008) 658–679 ª 2008 International Communication Association
e-mail links are generally considered to be features that facilitate interpersonal communication.
However, these two features may have loaded on this factor in that
human/medium interactive features allow the expression of ideas and may not result
in direct human-to-human communication, which may be the predominant case
when users submit letters-to-the-editor or write messages to reporters/editors of
their local online news site. The human interactivity scale consists of three features:
chat functions, message boards and Q&A features.
Use of Interactive Features
The second research question sought to examine the extent to which online news
audiences make use of different types of interactive features (RQ2). In order to access
patterns of use of different forms of interactive features, the scales for medium,
medium/human, human/medium and human interactive features were used to calculate
mean scores. Table 1 includes the overall means of the four groups of interactivity.
A repeated measures ANOVA was conducted to investigate whether
Table 1 Categories of Interactive Features
Factors Factor Loadings
1 2 3 4
Factor 1: Human/Medium Interactive
Features
‘‘Submit stories’’ function .90
‘‘Submit photos’’ function .81
‘‘Submit news tip’’ function .81
Reporter/editor e-mail links .73
Letters-to-editor .57
Factor 2: Medium/Human Interactive
Features
Customized weather .84
Customized topics .82
Customized headlines .62
Search feature .59
E-mail updates/alerts .56
Factor 3: Human Interactive Features
Message boards .79
Chat functions .78
Q&A (live chats) .75
Factor 4: Medium Interactive Features
Audio files .91
Video files .91
Eigenvalues 3.34 2.70 2.22 1.86
Variance Explained 22.28 17.98 14.80 12.38
Reliability (Cronbach a) .85 .78 .81 .91
Mean (S.D.) 1.41 (.57) 2.33 (.76) 1.57 (.70) 2.27 (.92)
Journal of Computer-Mediated Communication 13 (2008) 658–679 ª 2008 International Communication Association 667
respondents showed significantly different uses of interactive features on online
newspaper sites. The results indicate that there were indeed meaningful usage differences
based on type of interactive feature as identified above, Wilk’s l = .36, F (3,
472) = 274.29, p , .00. Specifically, the medium/human interactive features (M =
2.33, S.D. = .76) and medium interactive features (M = 2.27, S.D. = .92) scored
the highest usage mean scores followed by human interactive features (M = 1.57,
S.D. = .70) and human/medium interactive features (M = 1.41, S.D. = .57). Features
that facilitate two-way communication and features that allow the audience to
express their views were used least. Overall, the online newspaper audience was
not using the interactive features frequently.
Predictors of Use of Interactive Features
The summated scales were used in the following analyses to examine what factors
predict the use of the four distinct types of interactive features. Thus, the relationships
between user characteristics/backgrounds and usage frequencies of interactive
features are the focus of the third research question (RQ3). Four separate independent
hierarchical multiple regression analyses were conducted after entering demographic,
attitudinal and behavioral variables.
Use of Medium Interactive Features
Overall, this model accounted for about 8 percent of the variance in the dependent
measure (use of medium interactive features). In the first regression, gender surfaced
as a predictor (b = .19, p , .001) of use of medium interactive features. When the
two attitudinal variables were added to the regression equation, the model was
significantly improved, R2
= .067, R2
change = .031, p , .01. Gender remained
a significant predictor (b = .18, p , .001), and the attitudinal variables of perceived
Internet skill level (b = .16, p , .01) and perceived credibility of online news (b = .12,
p , .05) were also significant predictors of use of medium interactive features.
The addition of the behavioral measures did not improve the model. However,
gender (b = .16, p , .01), perceived Internet skill level (b = .15, p , .01) and
perceived credibility of online news (b = .11, p , .05) remained as significant
predictors of use of medium interactive features. Among the three predictors, standardized
beta coefficients indicate that gender was the strongest predictor of use of
medium interactive features. Gender was dummy coded (female = 0 and male =1), so
the results showed that male users are likely to use medium interactive features on an
online newspaper. Table 2 shows the regression model for use of medium interactive
features.
Use of Medium/Human Interactive Features
This model accounted for about 9 percent of the variance overall in the dependent
measure (use of medium/human interactive features). In the first regression, no
predictors emerged. The addition of the attitudinal measures, however, improved
the regression model (R2
= .038, R2
change = .007, p , .01) as perceived Internet skill
668 Journal of Computer-Mediated Communication 13 (2008) 658–679 ª 2008 International Communication Association
level (b = .15, p , .01) surfaced as a positive predictor. When the two behavioral
variables were added, the model was again significantly improved (R2
= .089, R2
change = .051, p , .001). While perceived Internet skill level remained a significant
positive predictor (b = .12, p , .05), political engagement (b = .18, p , .01) also
surfaced as a significant predictor of use of medium/human interactive features.
Standardized beta coefficients indicate that political engagement was the stronger
predictor of use of medium/human interactive features. Table 3 shows the regression
model for use of medium/human interactive features.
Use of Human/Medium Interactive Features
This model explained about 25 percent of the variance in the dependent measure
(use of human/medium interactive features). In the first regression, gender (b = .15,
p , .01) surfaced as a significant positive predictor of use of human/medium
interactive features. The addition of the two attitudinal variables significantly
improved the explanatory power of the overall model (R2
= .078, R2
change =
.049, p , .001). In this second regression, gender (b = .15, p , .01) remained
a significant positive predictor. In addition, perceived Internet skill level (b = .13,
p , .05) and perceived credibility of online news (b = .19, p , .001) also surfaced as
positive predictors. The addition of the behavioral measures greatly improved the
explanatory power of the model (R2
= .249, R2
change = .171, p , .001) and yielded
age (b = 2.17, p , .01), perceived credibility of online news (b = .18, p , .001), civic
involvement (b = .16, p , .01) and political engagement (b = .35, p , .001) as
positive predictors of use of human/medium interactive features. Gender and
Table 2 Hierarchical Regression Analysis of Factors Influencing Use of Medium Interactive
Features
Predictor variables Block I Block II Block III Cum R2
Age 2.07 2.01 2.04
Gender .19*** .18*** .16**
Years online 2.02 2.03 2.03 .036**
Perceived Internet skill level .16** .15**
Perceived credibility of online news .12* .11* .067**
Civic involvement .08
Political engagement .07 .081a
*p , .05. **p , .01. ***p , .001.
a
adjusted R2
is .07.
Gender: Dummy-coded with female = 0 and male = 1.
Perceived Internet skill level: four-point response scale from not very skilled (1) to very
skilled (4).
Perceived credibility of online news: five-point response scale from strongly disagree (1) to
strongly agree (5).
Civic involvement: four-point response scale from never (1) to frequently (4).
Political engagement: four-point response scale from never (1) to frequently (4).
Journal of Computer-Mediated Communication 13 (2008) 658–679 ª 2008 International Communication Association 669
perceived Internet skill level, however, disappeared as significant positive predictors
of the dependent measure. Among the four significant variables, standardized beta
coefficients reveal that political engagement was by far the strongest predictor of use
of human/medium interactive features. Table 4 shows the regression model for use
of human/medium interactive features.
Use of Human Interactive Features
The overall model explained about 11 percent of the variance in the dependent
measure (use of human interactive features). The first regression yielded gender
(b = .13, p , .05) as a significant positive predictor for use of human interactive
features. The addition of the attitudinal variables significantly improved the regression
model (R2
= .097, R2
change = .074, p , .001). Gender (b = .15, p , .01), remained
a significant predictor while perceived Internet skill level (b = .12, p , .05) and
perceived credibility of online news (b = .26, p , .001) surfaced as predictors. The
addition of the behavioral variables significantly improved the model (R2
= .123, R2
change = .026, p , .01). While perceived Internet skill level disappeared as a predictor,
both gender (b = .11, p , .05) and perceived credibility of online news (b = .26,
p , .001) remained as predictor variables. Political engagement (b = .11 p , .05)
also emerged as a positive predictor of use of human interactive features. Standardized
beta coefficients indicate that among the three predictor variables, perceived credibility
of online news was the strongest predictor of use of human interactive features.
Table 5 shows the regression model for use of human interactive features.
In sum, these findings show that hypothesis 1 was not supported as years online
was not a predictor for any of the four types of interactive features. Hypothesis 2 was
Table 3 Hierarchical Regression Analysis of Factors Influencing Use of Medium/Human
Interactive Features
Predictor variables Block I Block II Block III Cum R2
Age 2.01 .04 2.03
Gender .07 .06 .01
Years online .09 .09 .09 .031
Perceived Internet skill level .15** .12*
Perceived credibility of online news .09 .09 .038**
Civic involvement .10
Political engagement .18** .089***a
*p , .05. **p , .01. ***p , .001
a
adjusted R2
is .07.
Gender: Dummy-coded with female = 0 and male = 1.
Perceived Internet skill level: four-point response scale from not very skilled (1) to very skilled (4).
Perceived credibility of online news: five-point response scale from strongly disagree (1) to
strongly agree (5).
Civic involvement: four-point response scale from never (1) to frequently (4).
Political engagement: four-point response scale from never (1) to frequently (4).
670 Journal of Computer-Mediated Communication 13 (2008) 658–679 ª 2008 International Communication Association
partially supported in that perceived Internet skill level was a positive predictor for
use of medium interactive features, which provide extended choice options, and
medium/human interactive features, which allow for personalization of content.
Thus, Internet skill level was associated with use of interactive features that were
based on the technological application itself rather than on human communication.
Hypothesis 3 was also partially supported in that perceived credibility of online news
was a positive predictor for use of all types of interactive features with the exception
of medium/human interactive features. In addition, civic involvement was indeed
a positive predictor for use of human/medium interactive features that allow the
personalized expression of ideas. This was the only incident in which civic involvement
surfaced as a predictor variable, but it was not the strongest predictor of use of
human/medium interactive features. Therefore, hypothesis 4 was not supported.
Political engagement was also a positive predictor for use of human interactive
features, but it, too, was not the strongest predictor. Therefore, hypothesis 5 was
not supported. Based on these findings it is apparent that the four types of interactive
features are each used for different purposes by individuals with distinct characteristics,
and more extensive research is necessary to clearly identify factors that predict
uses of distinct types of interactive features.
Discussion and Conclusions
It appears that the potential of interactivity afforded through online news publications
was cast in a rosy light with exaggerated excitement over audience adoption of
Table 4 Hierarchical Regression Analysis of Factors Influencing Use of Human/Medium
Interactive Features
Predictor variables Block I Block II Block III Cum R2
Age 2.08 2.04 2.17**
Gender .15** .15** .06
Years online .06 .04 .05 .029*
Perceived Internet skill level .13* .08
Perceived credibility of online news .19*** .18*** .078***
Civic involvement .16**
Political engagement .35*** .249***a
*p , .05. **p , .01. ***p , .001.
a
adjusted R2
is .24.
Gender: Dummy-coded with female = 0 and male = 1.
Perceived Internet skill level: four-point response scale from not very skilled (1) to very
skilled (4).
Perceived credibility of online news: five-point response scale from strongly disagree (1) to
strongly agree (5).
Civic involvement: four-point response scale from never (1) to frequently (4).
Political engagement: four-point response scale from never (1) to frequently (4).
Journal of Computer-Mediated Communication 13 (2008) 658–679 ª 2008 International Communication Association 671
interactive features. The findings from this study suggest that online audiences are
not using interactive features extensively contrary to anticipation by media scholars
and the news industry. These findings indicate that online news producers need not
worry about adopting all types of interactivity that are promoted through various
interactive features.
A factor analysis extracted four distinct factors that identified four categories of
interactive features that promote interactivity—medium, medium/human, human/
medium, and human interactive features. It appears that the interactivity continuum
consists of four unique types of interactive features instead of the three that were
proposed previously. Two categories of interactive features — medium/human and
human/medium interactive features — fall between the medium and human interactivity
extremes. For example, interactive customization options allow users to
tailor their news consumption experiences to their liking by providing personal
information to the website. These medium/human interactive features can be deemed
first-order personalization options and are more a function of the technology. Here,
users input information about themselves through a medium in order to customize
news, such as local weather and news topics, to their own interests. In addition,
interactive features that allow users to submit their opinions or stories to the news
sites provide the audience with a sense of ownership. Sharing something personal
obliges the user to put that much more at stake. These human/medium interactive
features allow users to share their own perspectives and become personally involved in
the creation of content. These second-order personalization options share interpersonal
communication qualities but do not necessarily facilitate human-to-human
Table 5 Hierarchical Regression Analysis of Factors Influencing Use of Human Interactive
Features
Predictor variables Block I Block II Block III Cum R2
Age 2.06 2.04 2.08
Gender .13* .15** .11*
Years online .06 .04 .05 .023*
Perceived Internet skill level .12* .10
Perceived credibility of online news .26*** .26*** .097***
Civic involvement .09
Political engagement .11* .123**a
*p , .05. **p , .01. ***p , .001.
a
adjusted R2
is .11.
Gender: Dummy-coded with female = 0 and male = 1.
Perceived Internet skill level: four-point response scale from not very skilled (1) to very
skilled (4).
Perceived credibility of online news: five-point response scale from strongly disagree (1) to
strongly agree (5).
Civic involvement: four-point response scale from never (1) to frequently (4).
Political engagement: four-point response scale from never (1) to frequently (4).
672 Journal of Computer-Mediated Communication 13 (2008) 658–679 ª 2008 International Communication Association
communication. These two additional categories of interactivity complete the full
spectrum of interactivity as a continuum. Thus, the findings from this study expand
the model of interactivity promoted through interactive features on online newspapers.
Upon scale creation, a repeated measures ANOVA revealed that certain interactive
features were used significantly more, or less, than others. About half of the interactive
features assessed in this study had a mean use score of less than 2. Among the less
frequently used features are the human interactive features and the human/medium
interactive features. It appears that the news audience does not actively engage in various
uses of interactive features on news websites, especially the features that facilitate communication
and the expression of ideas—features that require more effort to be utilized.
Further analysis attempted to identify factors that predict the use of specific
interactive features. The findings show that men, those who perceived themselves
as having adept Internet skills, and those who perceived online news to be credible
were more likely to use medium interactive features. This makes intuitive sense in
that the medium interactive features scale consists of the use of audio and video files.
Individuals who were confident of their Internet skills would more likely attempt to
use these types of interactive features. The results of the perceived Internet skill level
variable, a self-efficacy measure, show that if news organizations are truly interested
in providing their audiences with various story telling options through multimedia,
perhaps, it is necessary for news organizations to educate their audiences about using
the Internet and accessing news on the World Wide Web through various technologies.
This will help individuals gain confidence in their Internet skills. While perceived
credibility of online news also surfaced as a positive predictor, the largest
predictor here was being male, and men may also be characterized as having qualities
that are linked to familiarity and ease with technology.
The analysis also found that those who were politically engaged were most likely
to use medium/human interactive features. Thus, it appears that individuals who
were politically active were likely to customize news headlines, topics and weather
information to their liking. Individuals who perceived themselves to have adept
Internet skills were also likely users of medium/human interactive features, but
political engagement was the stronger predictor.
The findings further revealed that younger individuals, those who perceived
online news to be a credible source of information and those who were involved
with their communities and are politically engaged are likely to use human/medium
interactive features. Here, the strongest predictor by far was political engagement.
Because these human/medium interactive features allow the audience to express their
views, it seems reasonable that those who are socially active would also be active online
participants. While most news audiences are not using interactive features extensively,
those who are taking advantage of the human/medium interactive features are individuals
who are the movers and shakers of their communities. They are also individuals
who are political activists who attend local government meetings and donate
money to political campaigns. Thus, news organizations should consider adopting
human/medium interactive features to provide a forum for those news audiences
Journal of Computer-Mediated Communication 13 (2008) 658–679 ª 2008 International Communication Association 673
who are interested in communicating their opinions. In addition, it is worth noting
that younger individuals are less shy in expressing their views online and making use of
human/medium interactive features that facilitate social expression.
Finally, this study revealed that those who perceived online news to be a credible
source of information, men and politically engaged individuals were most likely to
use human interactive features that facilitate two-way interpersonal communication.
Human interactive features are what make online news truly different from news
delivered through traditional media channels, but it appears that they are generally
used infrequently. Most interestingly, individuals who found online news to be
credible were most likely to engage in dynamic human-to-human interaction
online—even more so than politically engaged individuals. Overall, politically
engaged individuals and those who perceived online news to be credible were most
likely to use all forms of interactive features and were consequentially making the
most out of online news.
Not all news readers are politically engaged or have a positive perception of
online news credibility, however, and news organizations may seek to first understand
who their online audiences are and then subsequently provide interactive
features according to their audiences’ characteristics and backgrounds. For example,
an online newspaper serving readers who are mostly younger female audiences may
spend less time on adopting much of the technology driven medium interactive
features but place more focus on the human/medium interactive features that allow
for personal expression. Additionally, online newspapers may seek to educate their
online audiences about how to use various interactive features on their sites and also
make efforts to build their reputation for credible news. This study, thus, points to
the importance of quality news reporting that will in turn build credibility of the
news organization and subsequently encourage audiences to actively participate in
their online news consumption experiences.
While this study provides somewhat discouraging results to the initial enthusiasm
about online news and the application of interactivity through the adoption of
interactive features, news organizations that are sincerely interested in communicating
with their news audiences should not discard their efforts in applying interactivity.
On the other hand, it may be helpful to be aware that providing all forms of
interactive features may not be the most effective approach to engage online news
audiences as the interactive features serve distinct purposes in the news consumption
experiences of online audiences.
While this study provides critical findings toward audiences’ use of interactive
features, it also suffers from several shortcomings. The initial list of 22 interactive
features may not be an exhaustive list as new information communication technologies
(ICTs) are constantly developing, and several features did not load on the four
extracted factors. Therefore, the analysis was limited to the use of 15 interactive
features as rigorous scale construction was employed for the creation of the four
categories of interactivity scales. Future studies should further examine and identify
various other forms of interactive features to refine and solidify the interactive
674 Journal of Computer-Mediated Communication 13 (2008) 658–679 ª 2008 International Communication Association
feature scales. This process will contribute to the understanding of different categories
of interactive features and how online newspaper audiences use them.
The regression models accounted for about 8 to 25 percent of the variance. The
model for use of human/medium interactive features is somewhat effective in
explanatory power, but a large portion of the variance is still left unexplained—
especially in the regression models for use of medium and medium/human interactive
features. Future studies should further identify possible predictors of uses of
interactive features.
This study was conducted through the assistance of a medium-sized local newspaper
located in a Midwestern city in the U.S. Thus, the generalizability of the study
is limited to the geographic scope of the sample. In addition, the sample populations
consisted of predominantly white and female participants. Future studies should
make efforts to sample from more diverse populations, which may provide differing
results. The online survey method itself may also be cited as a weakness of the study.
Individuals who participated in this survey were self-selected, thus, those who felt
more comfortable online to begin with may have been more likely to participate in
the survey. Thus, the inherent nature of the online survey itself also contributes to the
limitation of the generalizability of the findings as it may be likely that individuals
who participated in this study represent a specialized subset of the online news
population.
More importantly, the unsupported hypotheses point to the need for much
extensive research in order to better identify factors that are associated with the uses
of distinct type of interactivity. The findings from this report provide an important
foundation about news audiences’ uses of interactive features. It may help online
news publications better target their online audiences with greater understanding of
how to engage them as active information consumers.
References
Aikat, D. (2000). A New Medium for Organizational Communication: Analyzing Web
Content Characteristics of Fortune 500 Companies. The Electronic Journal of
Communication, 10(1&2), Retrieved December 4, 2007 from http://shadow.cios.org:7979/
journals/EJC/010/1/010111.html
Althaus, S., & Tewksbury, D. (2000). Patterns of Internet and Traditional News Media Use in
a Networked Community. Political Communication, 17(January), 21–45.
American Society of Newspaper Editors, (1985). Newspaper Credibility: Building Reader
Trust. MORI Research Inc.
Annual Report on American Journalism. (2005). The State of the News Media 2005.
Journalism.org, Retrieved July 06, 2006 from http://www.stateofthemedia.org/2005/index.asp
Annual Report on American Journalism. (2006). The State of the News Media 2006.
Journalism. org, Retrieved September 13, 2006 from http://www.stateofthemedia.org/
2006/index.asp
Barber, B. (1984). Strong Democracy: Participatory Politics for a New Age. Berkeley:
University of California Press.
Journal of Computer-Mediated Communication 13 (2008) 658–679 ª 2008 International Communication Association 675
Belden Interactive (2007). Sales and Site Survey, PowerPoint slide presentation.
Boczkowski, P. (2004). Digitizing the News: Innovation in Online Newspapers. The MIT Press.
Bordewijk, J., & van Kaam, B. (1986). Towards a New Classification of Teleinformation
Services. Inter Media, 14(1), 16–21.
Bowman, S. & Willis, C. (2003). We Media: How Audiences are Shaping the Future of News and
Information. The Media Center at The American Press Institute.
Brown, M. (2000). Bringing People Closer to the News. Brandweek, 41(38), 26.
Bucy, E. (2004). Interactivity in Society: Locating an Elusive Concept. The Information Society,
Nov/Dec,. 20(5), 373–83.
Carter, R., & Greenberg, B. (1965). Newspaper or Television: Which Do You Believe?
Journalism Quarterly, 42(winter), 29–34.
Chan-Olmsted, S., & Park, J. (2000). From On-air to Online World: Examining the Content
and Structures of Broadcast TV Stations’ Web Sites. Journalism & Mass Communication
Quarterly, 77(2), 321–39.
Charity, A. (1995). Doing Public Journalism. New York: Guilford.
Chung, Deborah S. (2004). Into Interactivity? How News Websites Use Interactive Features.
Paper presented at the International Communication Association annual convention,
New Orleans, LA, May 27–31.
Chung, Deborah S. (2007). Profits and Perils: Online News Producers’ Perceptions of
Interactivity and Uses of Interactive Features. Convergence, The International Journal of
Research into New Media Technologies, 13(1), 43–61.
Deuze, M. (2003). The Web and Its Journalisms: Considering the Consequences of Different
Types of Newsmedia Online. New Media & Society, 5(2), 203–30.
Duncan, S. Jr. (1989). Interaction, Face-to-Face. In E. Barnouw (Ed.) International
Encyclopedia of Communications (pp. 325–28). New York: Oxford University Press.
Eastin, M., & LaRose, R. (2000). Internet Self-Efficacy and the Psychology of the Digital
Divide. Journal of Computer-Mediated Communication, 6(1), Retrieved November 2, 2005
from http://jcmc.indiana.edu/vol6/issue1/eastin.html
Ferguson, D. A., & Perse, E. M. (2000). The World Wide Web as a Functional Alternative to
Television. Journal of Broadcasting & Electronic Media, 44(2), 155–74.
Greer, J., & Mensing, D. (2006). Evolution of Online Newspapers: A Longitudinal Content
Analysis, 1997–2003. In X. Li (Ed.) Internet Newspapers: The Making of a Mainstream
Medium (pp. 13–32). Mahwah, New Jersey: Lawrence Erlbaum Associates, Publishers.
Ha, L., & James, L. (1998). Interactivity Reexamined: A Baseline Analysis of Early Business
Websites. Journal of Broadcasting & Electronic Media, 42(4), 457–74.
Habermas, J. (1962). The Structural Transformation of the Public Sphere: An Inquiry into
a Category of Bourgeois Society. The MIT Press.
Habermas, J. (1996). Between Facts and Norms: Contributions to a Discourse Theory of Law and
Democracy. The MIT Press.
Heeter, C. (1989). Implications of New Interactive Technologies for Conceptualizing
Communication. In J. L. Salvaggio & J. Bryant (Eds.) Media Use in the Information Age
(pp. 217–35). Hillsdale, NJ: Lawrence Erlbaum Associates.
Hwang B., & He, Z. (1999). Media Uses and Acculturation Among Chinese Immigrants in the
USA: A Uses and Gratifications Approach. Gazette, 61(February), 5–22.
Jeffres, L. & Atkin, D. (1996). Predicting Use of Technologies for Communication and
Consumer Needs. Journal of Broadcasting & Electronic Media, 40(Summer), 318–30.
676 Journal of Computer-Mediated Communication 13 (2008) 658–679 ª 2008 International Communication Association
Jensen, J. F. (1998). Interactivity: Tracking a New Concept in Media and Communication
Studies. Nordicom Review, 1, 185–204.
Johnson, T., & Kaye, B. (2004). For Whom the Web Toils: How Internet Experience Predicts
Web Reliance and Credibility. Atlantic Journal of Communication, 12(1), 19–45.
Katz, J., Rice, R., & Aspen, P. (2001). The Internet, 1995–2000: Access, Civic Involvement, and
Social Interaction. American Behavioral Scientist, 45, 405–19.
Kenney, K., Gorelik, A., & Mwangi, S. (2000). Interactive Features of Online Newspapers. First
Monday, 5(1), Retrieved July 6, 2006 from http://www.firstmonday.org/issues/issue5_1/
kenney/
Kiousis, S. (2002). Interactivity: A Concept Explication. New Media & Society, 4(3),
355–83.
Lee, J. S. (2000). Interactivity: A New Approach. Paper presented at the Association for
Education in Journalism and Mass Communication Conference, Phoenix, AZ., August
9–12.
Li, X. (1998). Web Page Design and Graphic Use of Three U.S. Newspapers. Journalism &
Mass Communication Quarterly, 75(Summer), 353–65.
Massey, B., & Levy, M. (1999). Interactivity, Online Journalism, and English-Language
Web Newspapers in Asia. Journalism & Mass Communication Quarterly, 76(1),
138–51.
McMillan, S. J. (2002). A Four-Part Model of Cyber-Interactivity: Some Cyber-Places are
More Interactive Than Others. New Media & Society, 4(2), 271–91.
McQuail, D. (1994). Mass Communication Theory: An Introduction. 3rd
Ed. Sage Publications.
Merritt, D. (1998). Public Journalism and Public Life: Why Telling the News is Not Enough.
Mahwah: Lawrence Erlbaum Associates.
Newspaper Association of America, (2007). Total Paid Circulation. NAA.org, Retrieved
December 2007 from http://www.naa.org/TrendsandNumbers/
Total-Paid-Circulation.aspx
Nie, N., & Ebring, L. (2000). Study Offers Early Look at How Internet is Changing Daily Life.
(February 16). Retrieved July 10, 2006 from http://stanford.edu/dept/news/pr/00/
000216internet.html
Noar, S. M., Clark, A., Cole, Christi and Lustria, M. L. (2006). Review of Interactive Safer Sex
Web Sites: Practice and Potential. Health Communication, 20(3), 233–41.
OnlineNewsAssociation.org, (2003). Digital Journalism Credibility Study.
OnlineNewsAssociation.org. Retrieved October 31, 2003 from http://www.journalists.org
Outing, S. (1998). What Exactly is ‘‘Interactivity?’’ E & P Interactive, (December 4).
Retrieved Jan. 1999 from http://www.mediainfo.com/ephome/news/newshtm/stop/
st120498.htm
Papacharissi, Z., & Rubin, A. M. (2000). Predictors of Internet Use. Journal of Broadcasting &
Electronic Media, 44(Spring), 175–96.
Peterson, A. (1999). Lost in the Maze. Wall Street Journal, (December 6), p. B6.
Pew Internet & American Life Project. (2003). The Ever-Shifting Internet Population: A New
Look at Internet Access and the Digital Divide. Retrieved January 30, 2006 from
http://www.pewinternet.org/pdfs/PIP_Shifting_Net_Pop_Report.pdf
Pew Internet & American Life Project (2005). Internet: The Mainstreaming of Online Life.
Retrieved September 18, 2006 from http://pewresearch.org/assets/files/
trends2005-internet.pdf
Journal of Computer-Mediated Communication 13 (2008) 658–679 ª 2008 International Communication Association 677
Pew Internet & American Life Project (2007a). A Typology of Information and
Communication Technology Users. Retrieved February 23, 2008 from
http://www.pewinternet.org/pdfs/PIP_ICT_Typology.pdf
Pew Internet & American Life Project (2007b). Online Video: 57% of Internet Users have
Watched Videos Online and Most of Them Share What They Find with Others. Retrieved
February 28, 2008 from http://www.pewinternet.org/pdfs/PIP_Online_Video_2007.pdf
Pew Research Center for the People and the Press (1999). Trends 2005 ‘‘The Internet News
Audience Goes Ordinary.’’ Retrieved June 28, 2004 from http://peoplepress.org/reports/
display.php3?ReportID=72
Pew Research Center for the People and the Press (2004). News Audiences Increasingly
Politicized: Online News Audience Larger, More Diverse. Retrieved January 30, 2006 from
http://people-press.org/reports/display.php3?ReportID=215
Pew Research Center for the People and the Press (2006). Online Papers Modestly Boost
Newspaper Readership: Maturing Internet News Audience Broader Than Deep. Retrieved
February 24, 2008 from http://people-press.org/reports/display.php3?ReportID=282
Rafaeli, S. (1988). Interactivity: From New Media to Communication. In R. Hawkins,
J. Wiemann, & S. Pingree (Eds.) Advancing Communication Science: Merging Mass and
Interpersonal Processes (pp. 110–34). Newbury Park, CA: Sage.
Rafaeli, S., & Sudweeks, F. (1997). Networked Interactivity. Journal of Computer Mediated
Communication, 2(4), Retrieved Dec. 2004 from http://www.usc.edu/dept/annenberg/
vol2/issue4/rafaeli.sudweeks.html
Rogers, E. M. (1986). Communication Technology: The New Media in Society. New York, NY:
The Free Press.
Rosen, J. (1992). Politics, Vision and the Press: Toward a Public Agenda for Journalism. In
J. Rosen (Ed.) The New News vs. The Old News: The Press and Politics in the 1990s
(pp. 3–37). New York: The Twentieth Century Fund Press.
Rosenberry, J. (2005). Few Papers Use Online Techniques to Improve Public
Communication. Newspaper Research Journal, 26(4), 61–73.
Salwen, M. B. (2005). Online News Trends. In M. B. Salwen, B. Garrison & P. D. Driscoll
(Eds.) Online News and the Public (pp. 47–79). Mahwah, NJ: Lawrence Erlbaum
Associates, Inc.
Schudson, M. (1978). The Ideal of Conversation in the Study of Mass Media. Communication
Research, 5(3), 320–29.
Schultz, T. (1999). Interactive Options in Online Journalism: A Content Analysis of 100 U.S.
Newspapers. Journal of Computer Mediated Communication, 5(1), http://www.ascusc.org/
jcmc/vol5/issue1/schultz.html
Shah, D. V., Cho, J., Eveland, W. P. Jr., & Kwak, N. (2005). Information and expression in
a digital age: Modeling Internet effects on civic participation. Communication Research,
32(5), 531–565.
Shah, D. V., Kwak N., & Holbert, R. L. (2001). ‘‘Connecting’’ and ‘‘disconnecting’’ with civic
life: Patterns of Internet use and the production of social capital. Political Communication,
18(2), 141–162.
Shah, D. V., McLeod, J. M., & Yoon, S. H. (2001). Communication, context, and community:
An exploration of print, broadcast and Internet influences. Communication Research,
28(4), 464–506.
678 Journal of Computer-Mediated Communication 13 (2008) 658–679 ª 2008 International Communication Association
Steuer, J. (1995). Defining Virtual Reality: Dimensions Determining Telepresence. In Biocca,
F., & Levy, M. R. (Eds.) Communication in the Age of Virtual Reality (pp. 33–56). Hillsdale,
NJ: Lawrence Erlbaum Associates.
Stromer-Galley, J. (2000). On-line Interaction and Why Candidates Avoid It. Journal of
Communication, 50(4), 111–32.
Stromer-Galley, J. & Foot, K. A. (2002). Citizen Perceptions of Online Interactivity and
Implications for Political Campaign Communication, Journal of Computer-Mediated
Communication, 8(1), Retrieved February 13, 2007 from http://jcmc.indiana.edu/vol8/
issue1/stromerandfoot.html
Stromer-Galley, J. (2004). Interactivity-as-Product and Interactivity-as-Process.
The Information Society, Nov/Dec., 20(5), 391–94.
UCLA Internet Report (2003, January). Surveying the Digital Future: Year Three. Retrieved
August 21, 2006 from http://www.digitalcenter.org/pdf/InternetReportYearThree.pdf#
search=%22surveying%20the%20digital%20future%20and%20ucla%20internet%
20report%202003%22
Walther, J. B., & Burgoon, J. K. (1992). Relational Communication in Computer-Mediated
Interaction. Human Communication Research, 19(1), 50–88.
Wanta, W., & Hu, Y. (1994). The Effects of Credibility, Reliance, and Exposure on Media
Agenda-Setting: A Path Analysis Model. Journalism Quarterly, 71(1), 90–8.
Weber, L. M., & Bergman, J. (2001). Who Participates and How? A Comparison of Citizens
‘‘Online’’ and the Mass Public. Paper presented at the Annual Meeting of the Western
Political Science Association, Las Vegas, NV, March 15–17.
Ye, X. & Li, X. (2006). Internet Newspapers’ Public Forum and User Involvement. In X. Li
(Ed.) Internet Newspapers: The Making of a Mainstream Medium (pp. 243–260). Mahwah,
New Jersey: Lawrence Erlbaum Associates, Publishers.
About the Author
Deborah S. Chung [dchung@uky.edu] is an assistant professor in the School of
Journalism and Telecommunications at the University of Kentucky. Her research
interests include the impact of information communication technologies on journalism
practice, culture and education and how ICTs potentially empower information
consumers.
Address: School of Journalism and Telecommunications, 215 Grehan Building,
University of Kentucky, Lexington, KY 40506-0042, USA
Journal of Computer-Mediated Communication 13 (2008) 658–679 ª 2008 International Communication Association 679
Interactive Features of Online Newspapers:
Identifying Patterns and Predicting Use of Engaged Readers
Abstract
This study seeks to identify 1) categories of interactivity, which are promoted
through different types of interactive features, 2) patterns of online newspaper readers’
uses of interactive features, and 3) factors, if any, that predict the use of different types of
interactive features. Based on an online survey of 542 respondents, four categories of
interactive features were identified. Findings show that interactive features are generally
used infrequently, especially the features that facilitate human-to-human communication
and the features that allow audiences to express their views. Regression analyses show
that different user characteristics and backgrounds predict the use of specific types of
interactive features. This study illustrates that news organizations need not worry about
applying all types of interactive features to engage their readers as the features serve
distinct functions. Instead, news organizations should focus on building credibility and
may seek to identify their online news audiences and then subsequently provide
interactive features accordingly. (152)
Interaktive Angebote in Online-Zeitungen: Die Identifikation von Mustern und die
Vorhersage der Nutzung durch engagierte Leser
Diese Studie versucht 1) Kategorien von Interaktivität zu identifizieren, welche durch
verschiedene Typen interaktiver Funktionen begünstigt werden, 2) Muster der Nutzung
dieser interaktiven Funktionen durch Online-Zeitungsleser herauszuarbeiten und 3)
Faktoren, wenn es sie denn gibt, zu extrahieren, die die Nutzung der verschiedenen Arten
interaktiver Funktionen vorhersagen. Basierend auf einer Onlinebefragung von 542
Befragten wurden vier Kategorien interaktiver Funktionen identifiziert. Die Ergebnisse
zeigen, dass interaktive Funktionen im Allgemeinen eher unregelmäßig genutzt werden.
Die gilt insbesondere für solche Angebote, die Mensch-zu-Mensch-Kontakt fördern und
Angebote, welche dem Leser erlauben, seine Meinung auszudrücken.
Regressionsanalysen zeigen, dass sich die Nutzung bestimmter interaktiver
Angebotstypen durch verschiedene Nutzercharakteristika und Hintergründe voraussagen
lässt. Die Studie zeigt außerdem, dass sich Nachrichtenorganisationen nicht damit
belasten müssen, alle Möglichkeiten interaktiver Angebote bereitzustellen, um ihre
Nutzer zu binden, da die Angebote jeweils spezifische Funktionen erfüllen. Besser ist es,
wenn sich Nachrichtenorganisationen darauf konzentrieren, ihre Glaubwürdigkeit
auszubauen und ihr Online-Nachrichtenpublikum zu identifizieren, um so passende
interaktive Angebote bereithalten zu können.
Las Características Interactivas de los Periódicos Online:
Identificando las Pautas y Prediciendo el Uso de los Lectores Comprometidos
Resumen
Este estudio busca identificar 1) las categorías de interactividad que son promovidas a
través de tipos diferentes de características interactivas, 2) las pautas de uso de las
características interactivas de los lectores de periódicos online, y 3) los factores, si los
hay, que predicen el uso de tipos de características interactivas diferentes. Basado en una
encuesta online de 542 participantes, cuatro categorías de características interactivas
fueron identificadas. Los resultados muestran que las características interactivas son
generalmente usadas no muy frecuentemente, en especial las características que facilitan
la comunicación de humano a humano y las características que permiten a las audiencias
expresar sus opiniones. Los análisis regresivos muestran que las características diferentes
de los usuarios y sus antecedentes predicen el uso de tipos específicos de características
interactivas. Este estudio ilustra que las organizaciones de noticias no necesitan
preocuparse por aplicar todos los tipos de características interactivas para comprometer a
sus lectores porque éstas sirven funciones diferentes. En cambio, las organizaciones de
noticias deben focalizarse en desarrollar la credibilidad y en buscar identificar las nuevas
audiencias online y luego subsecuentemente proveer de características interactivas como
corresponde (152).
Palabras claves: Interactividad, características interactivas, periódicos Online, nuevas
audiencias online
网络报纸的互动性特征：
界定模式并预测热心读者之使用
摘要
本研究寻求界定（1）由各种互动性功能推动的互动性的种类;（2）网络报纸读者
使用互动性功能的模式；以及（3）可预测各种互动性功能之使用的可能因素。在
网络调查 542 个用户的基础上，我们界定了四种互动性功能。发现表明：受众总体
使用互动性功能的次数不多，尤其是不经常使用那些促进人与人沟通以及允许受众
表达其观点的互动性功能。回归分析显示：不同的用户特征及背景可预示特定互动
性功能的使用情况。本研究证明新闻机构无需担心需要应用所有种类的互动性功能
来招徕读者，因为这些功能有不同的效果。然而，新闻机构应将重点放在信誉塑造
方面，并努力界定他们的网络新闻受众，然后提供相应的互动性的服务。
온라인 신문의 상호적 특징: 참여적인 독자의 형태 확인과 사용예측에 관한 연구
요약
본 연구는 1) 다양한 상호성 특징들의 형태에 따라 촉진되는 상호성의 범주들, 2)
온라인 신문 독자들의 상호성 특징의 사용형태, 그리고 3) 다양한 상호성 특징들의
형태사용을 예측할 수 있는 요소들을 확인하기 위한 것이다. 542 응답자들의 온라인
조사에 근거, 4 가지 형태의 온라인 상호성 특징들이 확인되었다. 연구 결과, 상호성
특징들은 일반적으로 자주 사용되지 않는 것으로 나타났는바, 특히 인간대 인간
커뮤니케이션을 수월하게 하는 특징들과 독자들이 그들의 견해를 표현하는 특징들에
있어서 더욱 그러했다. 회귀분석 결과, 다른 사용자 특징들과 배경들은 상호성 특징들의
특정한 형태의 사용을 예측하고 있다는 것을 증명하고 있다. 본 연구 결과, 뉴스
조직들은 그 특징들이 다양한 기능을 위해 사용되는바 모든 형태의 상호성 특징들을
독자들과 서로 연계하려고 걱정할 필요는 없다는 것을 보여주고 있다. 그 대신, 뉴스
조직들은 신뢰도를 발전시키는데 중점을 두어야 하며, 온라인 뉴스 독자를 확인한뒤
지속적으로 상호적 특징들을 제공하여야 한다는 것을 지적하고 있다.