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Mass collaboration and reading citizen journalism.

1. Introduction

The Internet now can be described as user-centric. Many platforms allow users to interact, share, and cooperate. Citizen journalism has merged under such scenario. Citizen journalism is a type of grassroots reporting. With the Web 2.0, it shows more importance than before.

In many countries where journalism is not well developed, news items are often untrusted. Newspapers are full of paparazzi reports and gossips. Due to intense competition, media tend to focus on shocking news instead of quality. Media need to take care of mass market and then small groups' needs are usually ignored.

But the advent of Web 2.0 may change this. The term Web 2.0 is associated with web applications that facilitate participatory information sharing, interoperability, user-centered design, and collaboration on the World Wide Web (Wikipedia, 2011). One product of Web 2.0 is blogs. Following the concept, using blog has become all the rage. The trend of using blog for recording and sharing has gradually formed. Therefore, citizen journalism has been brought to a new level (Rosen, 1999).

Before Web 2.0, media content was solely created by professionals. Now, since different platforms offer users opportunity to make contributions online, any users can write news items. In earlier stage of Web 2.0, people simply used blogs to write their news reports. Later, media companies collect these reports and become 'one-shop-for-all' news center. The typical example is Huffington Post. Another type of citizen journalism, which is the focus of our research, is an existing news company or a voluntary organization creating a platform and allows everyone to contribute news contents into that platform. The contributors are called citizen journalists. Examples are Ohmynews in Korea, Ground Report in New York, Global Voices sponsored by Harvard Law School, BBC News Online, AgoraVOX in France, Nowpublic in Canada, Peopo, NEnews, and in Taiwan.

The success of citizen journalism relies on the journalists and the readers. More citizen journalists create more content which may attract more readers. When there are more readers and readers made comments or form communities, there will be more readers and more citizen journalists will contribute. This self-reinforcement makes citizen journalism successful.

The reciprocity of authors and readers can be explained by the concept of two-sided markets (Eisenmann, Parker, & Van Alstyne, 2006; Parker & Van Alstyne, 2005). A two-sided market has two groups and each forms its own network. The two groups interact through a platform. The utility of a participant in a network is determined by the size of the other network. The SNS users and the SNS games are an example. When Facebook collects users and becomes a giant social network, developers will come to provide their games. It shows that as long as one-side network is formed, the other will merge as long as the platform works well. The evolution from blogs to citizen journalism can be viewed as the evolution to two-sided markets where the platform plays a crucial role. There are numerous blogs on the Internet but most blogs are seldom visited. Readers usually do not care about what blogs they read news items or articles from. This can be a problem for bother writers and readers as writers do not know who their readers really are and readers may need to spend time searching to find the right articles they need. When a platform appears between the writers and the readers, this problem can be solved and the writer-reader relationship can be consolidated. A citizen news platform for citizen journalists thus is similar to a social network site such as Facebook for game developers. In such a two-sided market, the platform can get writers to contribute articles and the issue is how to get readers involved. This is our research question.

For example, the largest citizen news website issues 150 articles daily. There are 62,000 citizen journalists and 70 formal employees (Ohmynews, 2011). These show that they have no problem to establish the platform and form one of the participants of the two-sided market--the journalists. But, they need to form reader network too but they have no control of readers. One way to form the reader network is to know the factors which cause the intention and the behavior of the citizen news readers and then reinforce these factors. With more readers involved, citizen news platforms will increase their values. Our paper thus is to investigate the factors for readers to accept citizen journalism created by mass collaboration.

Another reason this issue requires further investigation is the profit decrease of traditional news media. The overall advertisement income of news media in 2009 reduced 26% compared to the previous year and the average loss of news industry was 43%. Since then the situation does not improve much (State of News Media 2010, 2010). Some news companies seek the solutions by providing more shocking news with undue methods. The recent reports about Murdoch's hacking the phones of private citizens are one example (Wikipedia, 2012). We believe a better way to face the profit reduction in news industry is not to create more paparazzi reports but to catch up the new wave of news production--mass collaboration.

The data were collected in Taiwan. The first significance of Taiwanese data is that Taiwan is the only Chinese society with absolute freedom of publication. Although Taiwan cannot represent China, studying Taiwan's citizen journalism today is studying the current status of citizen journalism in Chinese society. Second, Taiwan's news environment may foster citizen journalism. According to Reporters without Borders, Taiwan's "Press Freedom Index" was ranked 32 in 2007 and 36 in 2008(Press Freedom, 2007), but readers are concerned with the quality of current news reports. There is even a Wikipedia item to note Taiwan's media chaos and foundations were established to prevent such chaos (Wikipedia, 2009). Citizen journalism may be viewed as reaction to this phenomenon. In addition, since citizen journalism is mass collaborative, it will increase citizens' social participation.

This paper is divided into the following sections. Section 2 introduces the evolution of citizen journalism and its recent development. Section 3 is the research framework and the research hypotheses. Section 4 describes the survey and data analysis. Section 0 is the conclusion.

2. Definition of Citizen Journalism and its


Citizen journalism can trace its origin to public or civic journalism in the 80s in the US, after journalists themselves began to question the predictability of their coverage of the 1988 U.S. presidential election. Facing the vicious competition, newspapers were full of biased reports and reports of scandals. People's dissatisfaction was reflected the lowest voter turnout in history. To save the crisis, Washington Post journalist David Broder and Journalism scholar Jay Rosen proposed public or civic journalism (Cappella & Jamieson, 1996; Dzur, 2002; Merritt, 1998; Rosen & Merritt, 1994; Rosen, 1999).

Citizen journalism is also called participatory journalism, open-source journalism, public journalism, or grassroots reporting. In citizen journalism, news content is no longer provided by traditional professionals but citizen reports. Anyone can be a citizen reporter (Bowman & Willis, 2003; Lasica, 2003). The Internet gives citizen journalism a new phase. First, traditional news readers decreased but online news readers increased. In 2007, 37% readers are online readers, increased 7% from two years ago (Johnson & Wiedenbeck, 2009). In 2010, there were 34% American got the news from the Internet, increased 5% from 2009. It is the only increase among TV, Radio, Newspaper, and Internet (State of News Media 2011, 2011). Some media have thought about changing. Christian Science Monitor has stopped the paper edition in 2008. New York Times is considering following (Huffington Post, 2010).

Due to the emergence of Web 2.0, citizen journalist websites are increasing and many professionals joined citizen journalism. For example, the number reached 1,500 in 2007 in the US (Johnson & Wiedenbeck, 2009). Hellweg (2005) called this new type of citizen journalism collaborative citizen journalism (CCJ) for its nature of mass collaboration. He proposed that a new type of journalism is rising and it can improve the transparency of news media. Through the collaborative wisdom, this new type of news provides original news reports ignored by traditional media, and compensates their distorted reports. Social network websites then distribute these reports to the Internet users.

3. Research Framework and Hypotheses

To understand "the factors influencing readers accepting mass-collaborative citizen journalism", we chose Unified Theory of Acceptance and Use of Technology (UTAUT) (V. Venkatesh, Morris, Davis, & Davis, 2003). The reasons are: First, citizen journalism is a kind of human computer interface (HCI) and HCI is the research domain of UTAUT (Davis, 1989). Second, UTUAT studies web-based systems and citizen journalism is a web-based system. Third, UTAUT has been applied and tested with high explanatory power ([R.sup.2])(AbuShanab, Pearson, & Setterstrom, 2010; Al-Gahtani, Hubona, & Wang, 2007; Chan et al., 2010; Chen, Wu, & Yang, 2008; Chiu & Wang, 2008; Im, Hong, & Kang, 2011; Lu, Yu, & Liu, 2009; Yeow & Loo, 2009; Yuen, Yeow, Lim, & Saylani, 2010). The purpose of UTAUT is to improve technology acceptance and use which is the same as that of our study.

But UTAUT is not fully applicable to our study. First, when UTAUT was developed, the Internet is not participatory. Users accepted the content created by websites instead of creating their own content. The Internet today is participatory. Users can contribute content. Thus, we should ass "mass collaboration" into UTAUT. In addition, similar to online reviews, user-created content may get more credibility among the Internet users. This credibility can be enhanced due to the fact that hyperlinks can allow other readers to get the content creators' online personal profiles.

Second, since UTAUT is a unified model, there can be some constructs not applicable to citizen journalism. According to Venkatesh, parsimony is an important criterion to set up a research model (V. Venkatesh & Morris, 2000). Thus, we may need to delete some constructs. Our model is illustrated in Figure 1.

There are several features in this model:

1. We added mass collaboration as one construct. Mass-collaborative citizen journalism is defined as "news content which is created by professional or non-professional without pre-publication regulation and edited by volunteers. The fundamental nature of citizen journalism is news items are not monopolized by news media and everyone can contribute. The reason of its popularity is to provide different news channels and to save the overall news industry. According to Anthony et al.(2007), the quality of mass-collaborative content is no less than the content created by professionals.

2. The original moderators in UTAUT (gender, age, experience, and voluntariness of use) were deleted in our model: First, our research is not focused on these variables' influence. Second, the ANOVA showed that these variables put no effects on any constructs (See Table 6, Table 7 and Table 8).



5. Third, voluntariness is not applicable since all contributors to citizen journalism are voluntary.

6. We added two moderators--role and degree of involvement:

a. Role: Since writers, which are also readers, and pure readers are two roles on citizen journalist platforms, we want to see if different roles lead to different consequences. We assume that writers may be more active in citizen journalist activities and this may show different impact on the constructs.

b. Degree of Involvement: Jackson et al.(1997) and Swanson (Swanson, 1974) stated that user involvement may affect the use of information system. Their argument may hold true since citizen journalist websites are also information systems. Zaichkowsky (1985) defined involvement as "a person's needs or values and a general level of interest in or concern about an issue without reference to a specific position."

During the time we did the survey, reading Citizen journalism was new. To know its sustainability, we want to know if those already showed their involvement has interest in continuing reading citizen journalsm. Thus, we divided the respondents into two groups--higher involvement and lower involvement and check if higher involvement may generate higher interest.

7. The other UTAUT variables are applied to this study with some modifications:

a. Performance expectancy: It is the expectation of the citizen journalist readers that citizen journalism may provide utilities other news media cannot. Blumler (1979) argued that readers will pursue the media once the media contain helpful information or fit into their preference.

b. Effort expectancy: It means readers believe the operation of citizen journalism platform is easy. Different levels of effort readers need may have different impact on their intention to use citizen journalism (Smith, 1997).

c. Social influence: Readers' intention to use citizen journalism is affected by their relatives, friends, or other people. The case of the public journalism movement originated in 1988 affecting readers' attitude toward traditional and new media is an example (Brown, Broderick, & Lee, 2007; Cappella & Jamieson, 1996; Merritt, 1998; Rosen & Merritt, 1994; Rosen & Merritt, 1994).

d. Facilitating condition: The reason people want to read citizen journalism is the accessibility to citizen journalist platform the Internet provides and the convenience the platform offers. These include the news items a citizen can publicize to the citizen journalist websites (Lasica, 2003).

8. Two endogenous variables are behavior intention and use behavior. Their definitions are "the intention to use citizen journalism based on the readers' subjective judgment" and "the actual behavior the readers use the citizen journalism platforms."

Table 1 shows the measures. All measures are modified from Venkatesh (2003) with experts' opinions and consideration of the practical situation in citizen journalism.

Based on the above discussions, we have the following hypotheses:

H1. Performance expectancy positively affect the intention to use citizen journalist websites.

H2. Effort expectancy positively affects the intention to use citizen journalist websites.

H3. Social influence positively affects the intention to use citizen journalist websites.

H4. Mass collaboration positively affects the intention to use citizen journalist websites.

H5. Facilitating condition positively affects the intention to use citizen journalist websites.

H6. Intention to use citizen journalist websites positively affects the behavior to use them.

H7. The influence of performance expectancy on intention to use citizen journalist websites is moderated by roles, such that the effect is stronger for writers.

H8. The influence of effort expectancy on intention to use citizen journalist websites is moderated by roles, such that the effect is stronger for writers.

H9. The influence of social influence on intention to use citizen journalist websites is moderated by roles, such that the effect is stronger for writers.

H10. The influence of mass collaboration on intention to use citizen journalist websites is moderated by roles, such that the effect is stronger for writers.

H11. The influence of facilitating conditions on intention to use citizen journalist websites is moderated by roles, such that the effect is stronger for writers.

H12. The influence of performance expectancy on intention to use citizen journalist websites is moderated by degrees of involvement, such that the effect is stronger for users with higher involvement.

H13. The influence of effort expectancy on intention to use citizen journalist websites is moderated by degrees of involvement, such that the effect is stronger for users with higher involvement.

H14. The influence of social influence on intention to use citizen journalist websites is moderated by degrees of involvement, such that the effect is stronger for users with higher involvement.

H15. The influence of mass collaboration on intention to use citizen journalist websites is moderated by degrees of involvement, such that the effect is stronger for users with higher involvement.

H16. The influence of facilitating conditions on intention to use citizen journalist websites is moderated by degrees of involvement, such that the effect is stronger for users with higher involvement.

4. Survey and data analysis

The measures in Table 2 then are put into the questionnaire. The questionnaire has eight parts. The first part consists of items used to test for demographic differences. The second to sixth parts are for evaluating the factors affecting the attitude of reading citizen journalism. The seventh and eighth parts are for testing the actual attitude of reading citizen journalism. 7-point Likert scales are used in Part 2 to part 7.

4.1. Pre-test

The first step in developing the pre-test was to invite 10 scholars (two MIS professors, three Ph.D. candidates, and five professionals with domain knowledge and extensive experience with citizen journalism) to examine the above preliminary version of the questionnaire for internal validity. All 10 judges agreed that the questionnaire "can measure what it is supposed to measure" and that "all dimensions are essential to the evaluation of citizen journalsm." Thus, face validity and content validity were achieved.

Then the questionnaire was put on My3Q ( ml) and given to 42 people with experience in reading citizen journalism. 3respondents submitted invalid questionnaires. The sampling period was May 1 through May 6, 2010.

Cronbach's [alpha] was used to assess the reliability of the scales composing the questionnaire. Guilford suggested that an a value greater than 0.7 means that the reliability is adequate (Guilford, 1965). As shown in

Table 3, the [alpha] value for the facilitating conditions scale was lower than 0.7; to remedy the situation, I discarded items FC03.

To determine if the scales had adequate validity and were suitable for factor analysis, I applied the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy (Kaiser, 1974). A KMO score greater than 0.69 shows that the items have low partial correlations with the total scale of which they are a part. The KMO value was 0.685.

We then conducted an exploratory factor analysis. Table 4 shows the factor loadings after orthogonal rotation.

The table shows that effort expectancy and facilitating condition should be combined. This shows that these two factors are the same variable and thus we deleted FC.

4.2. Main test

The same questionnaire on the Internet was used for the main test. Tan and Teo (2000) believed that online survey has some merits that traditional surveys do not. The critical benefit is our study is focused on online users. The sampling period was two weeks. 422 responses were collected with 45invalid.

1. Demographic variables

Table 5 gives the demographic data.

I then conducted a series of one-way ANOVAs to test for interactions between demographic variables and psychological variables. The criterion for statistical significance was p <.05, two-tailed. The results are presented in through Table 8. The overall results show that the demographic factors did not affect the results.

2. Reliability

The procedure to test reliability is the same as in pre-test. The Cronbach's a of the social influence scale was 0.609. After removing SI01, it reached 0.732 as shown in Table 9.

3. Construct validity

To determine if the scales had adequate validity and were suitable for factor analysis, we applied the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy (Kaiser, 1974). A KMO score greater than 0.8 shows that the items have low partial correlations with the total scale of which they are a part. The KMO value was 0.952. We also applied Bartlett's sphericity test, which was significant (p < 0.05). Thus, the scales are factorable by both criteria.

The confirmatory factor analysis gave five psychological dimensions corresponding to the actual structure of the questionnaire (see Table 10). This means that the constructs are valid.

We then conducted convergent and discriminant validity tests. Convergent validity is the degree to which an operation is similar to (converges on) other theoretically similar operations. According to Fornell and Larcker (1981), convergent validity is achieved when all the standardized factor loadings exceed 0.5 in the same dimension. The factor loadings of PE04, PC03, and BI1 are lower than 0.5, so they are removed from the main test.

Discriminant validity is the degree to which the operationalization (construct) diverges from other operationalizations that it theoretically should not be similar to. A construct is said to have good discriminant validity when the factor loading of that construct and another construct is low, usually lower than 0.5. All measures show good discriminant validity.

4. Model fit

We next sought to determine whether our model is the best of the available choices--the question of model fit. Absolute fit is a measure of how well the model fits the sample data. It is measured by four indices: [chi square]/df, GFI (Goodness of Fit Index), AGFI (Adjusted Goodness of Fit Index), and RMSEA (Root Mean Square Error of Approximation).

Incremental fit is a measure of how well the test model fits the data compared to the baseline (null) model. It is measured by two indices: IFI (Incremental Fit Index) and CFI (Comparative Fit Index).

Parsimonious fit measures if the model applies a minimum number of scales and is free from overfitting. Two indices were used: PGFI (Parsimony Goodness-of-Fit Index) and PNFI (Parsimony Normed Fit Index).

As shown in

Table 11, the results meet the minimum acceptable levels for all the model fitting measures.

5. Hypothesis tests

Finally, we used maximum likelihood estimation to test the hypotheses listed in Section 3. Figure 2 and Table 12 show the structural equation model for the path analysis and the results of the hypothesis test.

Figure 2 Path analysis for direct variables

For the hypotheses about moderating variables, we also use maximum likelihood estimation. Moderating variables are arranged as dummy variables in the estimation. The results are listed Table 13.

5. Conclusion

Citizen journalist websites can be regarded as a two-sided market composed by writers, readers, and the platforms. For the reason mentioned in Section 1, we chose readers as our research topic. From Figure 2 and Table 12, we can tell all dimensions except social influence have positive impact on intention to read citizen journalism. In addition to traditional dimensions, our study shows that mass collaboration increase users' intention to read. Thus, news companies need to put effort on developing mass collaboration and even design new business models based on mass collaboration. For example, companies can design a 'top contributor' system similar to the one of Yahoo! Answers and allow user ratings and comments to encourage citizen journalists and to create a self-selection mechanism.

Table 13 shows that the influence of effort expectancy and mass collaboration on intention to read citizen journalism is positively moderated by degrees of involvement (H13 and H15). Their influence is also moderated by roles such that it is stronger for writers (H8 and H10). It is not difficult to understand the moderating effects of roles and degrees of involvement on effort expectancy. When a reader is also a writer, she would have better system knowledge over the websites and perceive it when the system is easy to use. It is easier for her transforming such perception into the intention to use the system. It's similar for degrees of involvement.

For mass collaboration, roles and degrees of involvement posit similar effects: double roles and higher involvement allow users easier to translate their perception of mass collaboration into the intention to use. Double roles also make it easier to translate the perception of citizen journalist sites' performance into the behavior intention (H7). However, similar conclusion does not hold true for involvement (H12). Roles and involvement do not show moderating effects on social influence or facilitating condition (H9 and H14). Combined with H3, This reveals that social influence does not affect users' intention to read citizen journalism, this impact still does not exist even when users take double roles or get more involved.

One conclusion is counter intuition: intention to read citizen journalism does not necessarily lead to the actual use. This can be explained by referencing to Table 5: 88.6% of the users read citizen journalism less than three hours each week. This may show that citizen journalism is not popular in Taiwan or readers do not realize they are reading citizen journalism. Combining the high scores of that mass collaboration, performance expectancy, and effort expectancy (see Table 6 through Table 8), we can tell that users have good opinions about citizen journalism but they do not have much news resource from limited citizen journalist websites. Thus, we should increase the number of citizen journalist websites. It is also possible that readers do not know the news they are reading are citizen journalism. In this case, news media need to inform readers well because literature shows that readers are disappointed by traditional media and our research shows that they want news to be collaborative work.

From the viewpoint of management, our research is also important. One feature of Web 2.0 is democratization of production tools. The Internet became production line and consumers have become producers with the production tools. For example, the Connect + Develop project by P&G and the world share patents. Canyon Bike allows consumers design their own bikes. Starbucks uses Internet to survey customers for new flavors. Nestle develops a toolkit for customers to make new coffee products. Lego develops new software to allow players to design new Legos. These new business models which employ Web 2.0 have drawn attention in business world, and news media also notice this trend. With Web 2.0 models, they can cover many topics not covered by traditional models, and create a platform for citizen journalists to write community news. More importantly, applying Web 2.0 models may increase long-tail effect (Erik Brynjolfsson, Yu Jeffrey Hu, Michael D.Smith, 2006): Contrary to traditional business models, where companies who want to satisfy mass market cannot satisfy local or elite market, the Internet allow any kind of products listed online without space or time restrictions and thus small groups can still find the right products they need. Similarly, the current news media which follow traditional models cannot provide news pertinent to specific groups. This can be changed with citizen journalism. Thus, we believe citizen journalism is not only a topic in journalism but also one in business.

Our research shows some limitations. Although we studied the reasons to read citizen news, there are reasons for readers not to read it. Knowledge is one of such reasons (Kaufhold, Valenzuela, & de Zuniga, 2010) concluded that those with higher political knowledge tend to consume professional journalism instead of user-generated journalism. Does it imply that when users realize citizen journalism does not bring political knowledge, they tend not to read it? If so, we may need to include such negative force into our model. A push-pull-mooring framework can be helpful to identify both positive and negative antecedents (Cheng, Yang, & Lim, 2009; Fu, 2011; Zhang, Cheung, Lee, & Chen, 2008). From online news to citizen news, the change is not only the appearance. The content, authorship, writing style, coverage, participation and so on are all different. Our next study will be focused on this paradigm shift.


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Wesley Shu, Lin, Hota Chia-Sheng

National Central University, Taiwan

Correspondence to:

Wesley Shu & Lin, Hota Chia-Sheng

Department: Information Management National Central University, Taoyuan County, Taiwan


Table 1 Constructs and their measures

Construct                Item   Measures

performance expectancy   PE01   Citizen journalistwebsites
                                help me obtain the news I

                         PE02   Citizen journalist websites
                                have good number of news

                         PE03   Reading citizen journalism is

                         PE04   Reading citizen journalism is

                         PE05   Reading citizen journalism is

                         PE06   Information on citizen
                                journalist website is useful.

effort expectancy        EE01   Citizen journalist websites
                                are easy to operate.

                         EE02   Using citizen journalist does
                                not take me much time.

                         EE03   The citizen journalist
                                websites are user friendly.

                         EE04   The functions at Citizen
                                journalist websites are easy
                                to use.

social influence         SI01   My family or friends affect my
                                attitude to using citizen
                                journalist websites.

                         SI02   The society affects my
                                attitude to using citizen
                                journalist website.

                         SI03   I believe citizen journalism
                                is a trend.

mass collaboration       MC01   People can share experience
                                and knowledge at citizen
                                journalism websites.

                         MC02   I believe the citizen
                                journalist websites are open
                                platforms for everyone to

                         MC03   Readers can also be
                                journalists on citizen
                                journalist websites.

                         MC04   I can provide content to
                                citizen journalist websites.

facilitating condition   FC01   I can use citizen journalist
                                websites even if I do not have
                                similar experience.

                         FC02   The technical support at
                                citizen journalist websites is
                                easy to obtain.

                         FC03   I am confused by the citizen
                                journalist website operations.

Behavior intention       BI01   I like to use citizen
                                journalist websites.

                         BI02   I like to recommend citizen
                                journalist websites to other

                         BI03   I will continue using citizen
                                journalist websites.

Table 3 Reliabilities of the pre-test scales

Scale                    Deleted items     Cronbach's   Cronbach's
                                           [alpha]      [alpha]
                                           before       after
                                           deletion     deletion

performance expectancy   None              0.891        0.891
effort expectancy        None              0.929        0.929
social influence         None              0.730        0.730
mass collaboration       None              0.909        0.909
facilitating condition   FC03              0.699        0.727
Behavior intention       None              0.882        0.882

Table 4. Pre-test factor loadings for the psychological scales

       Factor loading

       1       2      3       4       5

PE01   .136    .149   .209    .788    .119
PE02   .301    .410   .563    .727    .259
PE03   .120    .462   .403    .777    .165
PE04   .223    .318   .375    .638    -.136
PE05   .024    .203   .378    .733    .100
PE06   .004    .059   .100    .872    .092
EE01   .883    .179   .034    -.025   .193
EE02   .889    .051   .066    .135    .127
EE03   .892    .147   .015    .226    .009
EE04   .791    .341   .176    .044    .021
SI01   -.073   .031   .009    -.083   .648
SI02   .088    .370   .263    .011    .748
SI03   .071    .085   .204    .097    .866
FC01   .756    .372   -.171   -.090   .077
FC02   .652    .219   .129    .185    -.091
MC01   .121    .802   .298    .167    .224
MC02   .385    .810   -.003   .127    .110
MC03   .197    .783   .362    .185    .107
MC04   .317    .786   .087    .098    -.004
BI01   .198    .489   .671    .188    .290
BI02   -.068   .074   .756    .183    .231
BI03   .087    .257   .849    .231    .087

Table 5 Demographic variables

Variable       Category            N     %       Cumulative %

Most visited   Peopo               225   59.7%   59.7%
CJ sites       Wenews              56    14.9%   74.6%
               Newserr             18    4.8%    79.3%
               Other               78    20.7%   100.0%

Gender         Female              166   44.0%   44.0%
               Male                210   55.7%   100.0%

Age            15-18               11    2.9%    3.2%
               19-23               159   42.2%   45.4%
               24-28               164   43.5%   88.9%
               29-35               34    9.0%    97.9%
               36-41               2     1.5%    98.4%
               41 or above         6     1.6%    100.0%

Hours per      2 hours or below    18    4.8%    4.8%
day viewing    2~4hours            86    22.8%   27.6%
Internet       4~6hours            95    25.2%   52.8%
               6~8hours            85    22.5%   75.3%
               8~10hours           60    15.9%   91.2%
               10~12hours          22    5.8%    97.0%
               12 hours or above   11    2.9%    100.0%

CJ             0.5 or below        161   42.7%   42.7%
experience     0.5-1               99    26.3%   69.0%
in years       1-1.5               22    5.8%    74.9%
               1.5-2               43    11.4%   86.3%
               2-2.5               28    7.4%    93.7%
               2.5-3               2     0.5%    94.3%
               3 or above          21    5.6%    100.0%

Hours per      0.5 hour or below   108   28.7%   28.7%
day viewing    0.5-1 hour          83    22%     50.8%
CJ             1-1.5 hours         62    16.5%   67.3%
               1.5-2 hours         49    13%     80.3%
               2-2.5 hours         43    11.4%   91.6%
               2.5-3 hours         14    3.7%    95.5%
               3 or above          17    4.5%    100.0%

Table 6 Scores on the psychological dimensions
as a function of gender

Dimension                Mean              F       P

                         Male     Female

performance expectancy   5.1701   5.1558   0.032   0.857
effort expectancy        5.2500   5.4100   0.070   0.063
social influence         4.6794   4.7470   0.528   0.468
mass collaboration       5.2571   5.2892   0.152   0.696
Behavior intention       5.1595   5.1958   0.129   0.719
Use behavior             2.3190   2.2018   1.453   0.229

Table 7 Scores on the psychological dimensions as a
function of age

Dimension   Mean
            15-18   19-23   24-28   29-35   36-41   [greater than
                                                    or equal to] 42

PE          5.16    5.14    5.60    5.05    5.06    4.93
EE          5.36    5.31    5.67    5.08    5.04    5.00
SI          4.60    4.74    5.29    4.79    4.65    4.50
MC          5.25    5.34    5.63    5.19    5.12    5.00
BI          5.08    5.33    5.70    5.01    4.84    4.50

Dimension   F-Value   P-Value

PE          1.592     0.148
EE          2.325     0.032
SI          2.077     0.055
MC          1.314     0.250
BI          3.121     0.055

Table 8 Scores on the psychological dimensions as a function of CJ

Dimension                Mean                        F-Value   P-Value

                         Peopo    Wenews   Newserr

performance expectancy   5.1993   4.9748   4.3929    2.863     0.055
effort expectancy        5.3623   5.1250   4.4375    2.534     0.078
social influence         4.7236   4.6961   4.5000    0.089     0.994
mass collaboration       5.2914   5.1765   4.0833    3.029     0.061
facilitating condition   5.3278   5.2794   4.5000    2.171     0.057
Behavior intention       5.1811   5.2353   4.7500    0.984     0.427

Table 9 Cronbach's [alpha]

Dimension                Cronbach's [alpha]   N Questions
performance expectancy   0.890                6
effort expectancy        0.837                4
social influence         0.732                2
mass collaboration       0.829                4
Behavior intention       0.850                3

Table 10 Factor loadings for the psychological scales

       Factor loading

       1      2       3      4       5

EE01   .797   .145    .048   .050    .185
EE02   .686   .276    .152   .095    .326
EE03   .733   .201    .217   .208    .214
EE04   .699   .447    .076   .169    .252
PE1    .240   .687    .195   .299    .225
PE2    .278   .625    .110   -.019   .206
PE3    .167   .752    .137   .200    .140
PE5    .245   .769    .152   .207    .157
PE6    .273   .685    .249   .207    .086
MC1    .108   .444    .635   .108    .103
MC2    .469   .165    .665   .164    .037
MC4    .311   .219    .698   .167    .175
BI2    .158   .193    .171   .850    .231
BI3    .279   .442    .131   .601    .012
SI2    .239   .027    .301   .260    .682
SI3    .246   .099    .160   .294    .719

Table 11 Measurements of model fit

Statistic          Value   Threshold   Result

Absolute fit
[chi square]/df    2.01    < 3         Good
GFI                0.89    > 0.8       Good
AGFI               0.86    > 0.8       Good
RMSEA              0.069   < 0.1       Good
Incremental fit
IFI                0.98    > 0.9       Good
CFI                0.98    > 0.9       Good
Parsimonious fit
PGFI               0.68    > 0.5       Good
PNFI               0.81    > 0.5       Good

Table 12 Results of hypothesis tests for direct variables

     Path              t-statistic   p-value   Result

H1   performance       19.104        0.0001    accepted
     [right arrow]

H2   effort            12.366        0.0001    accepted
     [right arrow]

H3   social            -0.062        0.4753    Not accepted
     [right arrow]

H4   mass              14.455        0.0002    accepted
     [right arrow]


H6   Behavior          0.050         0.4801    Not accepted
     [right arrow]
     Use behavior

*** p<0.001 ** p< 0.01 * p< 0.05

Table 13 Results of hypothesis tests for moderators

      Path                                 t-statistic   p-value

      Author (T) or Non-author (F)

H7    performance expectancy   T   0.776   2.114         0.002
      [right arrow] Behavior   F   0.284

H8    effort expectancy        T   0.563   1.471         0.007
      [right arrow] Behavior   F   0.295

H9    social influence         T   0.638   0.081         0.502
      [right arrow] Behavior   F   0.621

H10   mass collaboration       T   0.659   2.317         0.012
      [right arrow] Behavior   F   0.388

H11   facilitating condition   T   0.725   0.521         0.321
      [right arrow] Behavior   F   0.702

      Higher involvement (H) or Lower involvement (L)

H12   performance expectancy   H   0.727   0.186         0.168
      [right arrow] Behavior   L   0.685

H13   effort expectancy        H   0.766   2.243         0.015
      [right arrow] Behavior   L   0.509

H14   social influence         H   0.758   0.559         0.355
      [right arrow] Behavior   L   0.692

H15   mass collaboration       H   0.671   2.121         0.002
      [right arrow] Behavior   L   0.365

H16   facilitating condition   H   0.706   0.499         0.313
      [right arrow] Behavior   L   0.702


      Author (T) or Non-author (F)

H7    Accepted

H8    Accepted

H9    Not accepted

H10   Accepted

H11   Not accepted

      Higher involvement (H) or Lower involvement (L)

H12   Not accepted

H13   Accepted

H14   Not accepted

H15   Accepted

H16   Not accepted

*** p<0.001 ** p< 0.01 * p< 0.05
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Author:Shu, Wesley; Lin; Chia-Sheng, Hota
Publication:China Media Research
Article Type:Report
Geographic Code:9CHIN
Date:Jan 1, 2015
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