我們為何使用部落格?部落格之接受、使用、與應用研究

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國 立 交 通 大 學

經營管理研究所

博 士 論 文

No. 137

我們為何使用部落格?

部落格之接受、使用、與應用研究

Why Do We Blog?

The Blog Acceptance, Usage, and Application

研 究 生:張耀升

指導教授:楊 千 教授

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我們為何使用部落格?

部落格之接受、使用、與應用研究

Why Do We Blog?

The Blog Acceptance, Usage, and Application

研 究 生:張耀升

Student:

Yao-Sheng

Chang

指導教授:楊 千 Advisor: Chyan Yang

國 立 交 通 大 學

經 營 管 理 研 究 所

博 士 論 文

A Dissertation

Submitted to Institute of Business and Management College of Management

National Chiao Tung University in Partial Fulfillment of the Requirements

for the Degree of Doctor of Philosophy

in

Business and Management May, 2011

Taipei, Taiwan, Republic of China

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我們為何使用部落格?

部落格之接受、使用、與應用研究

研究生:張耀升

指導教授:楊 千

國立交通大學經營管理研究所博士班

部落格的使用在近幾年呈現爆炸性的成長。部落格被認為是一種運用網路資訊技術 的互動媒體,因為其容易使用與互動的特性,而吸引廣大的使用群,也創造出廣泛的應 用。例如企業用於行銷,教育用於促進學習等應用。然而,哪些因素吸引我們使用部落 格,以及部落格的應用能幫我們做什麼,都是值得探討的問題。因此,本文的研究目的 將探討部落格的接受與使用行為,以及部落格的實際應用效果。 本文由兩個研究所組成,第一個研究將從媒體選擇與科技接受因素來探討部落格的 接受與使用行為。媒體選擇因素包括:媒體豐富度、關鍵數量、媒體經驗、與社會影響。 科技接受因素包括:有用性、與易用性。我們提出一個媒體選擇與科技接受因素影響使 用者態度與使用意願的假設模型。使用線上調查法收集521個樣本資料,並使用線性結 構模型來檢定模型與假設。研究結果顯示,媒體選擇與科技接受因素正向影響使用者對 使用部落格的態度與使用意願。 第二個研究將探討部落格的應用效果。本研究架設兩個教學部落格平台,比較互動 式部落格與孤立式部落格對學生同儕互動、學習動機與學業成就的影響。孤立式部落格

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平台只是提供學生線上撰寫作業功能,互動式部落格平台則在學生線上撰寫作業後必須 進行彼此評論。此研究使用準實驗設計法,進行兩學期 154 個學生樣本的比較研究。研 究結果顯示,使用互動式部落格相對於使用孤立式部落格的組別,對於同儕互動與學習 動機都呈現較正向態度。而部落格平台提供同儕共同工作的功能,不論在互動式或是孤 立式的組別,都可以提高學習動機。最後,並提供相關的建議及意涵。 關鍵詞:部落格、媒體選擇因素、科技接受模型、同儕學習、網路學習。

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Why Do We Blog?

The Blog Acceptance, Usage, and Application

Student: Yao-Sheng Chang

Advisor: Dr. Chyan Yang

Institute of Business and Management

National Chiao Tung University

ABSTRACT

Blogs, or weblogs, have been experiencing a rapid growth rate in recent years. Blogs are easy to use and possess interactive features, thus attracting wide use and leading them to be regarded as communication media in web-based information technology. Therefore, blogs have become an increasingly popular form of communication on websites, and have been adopted by users for several applications in domains such as journalism, business, and education. However, why do so many people like to use blogs? What blog applications can do for us? The purpose of this thesis is to investigate the blog acceptance and usage behaviors, and its applications.

This thesis contains two studies. The first study incorporates the technology acceptance model with media choice factors to explain and predict the blog acceptance behaviors. The media choice factors include media richness, critical mass, social influence, and media experience. The technology acceptance factors include perceived usefulness and perceived ease-of-use. An online field survey was conducted and the structure equation modeling method was applied to investigate the empirical strength of the relationships in the proposed model. 521 experienced blog users were surveyed to examine this model. The results strongly support the proposed hypotheses indicating that technology acceptance and media choice

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factors influence the blog acceptance behaviors.

The second study explores the usage of blogs in education setting, and how student attitudes towards online peer interaction and peer learning, as well as motivation to learn from peers, may differ when using the blog comments feature, and when students are encouraged to read and comment on each other’s work. We contrast two ways blogs affect learning engagement: (a) solitary blogs as personal digital portfolios for writers; or (b) blogs used interactively to facilitate peer interaction by exposing blogging content and comments to peers. A quasi-experiment was conducted across two semesters, involving 154 graduate and undergraduate students. The result suggests that interactive blogs, compared to solitary blogs, are associated with positive attitudes towards academic achievement in course subjects and in online peer interaction. Students showed positive motivation to learn from peer work, regardless of whether blogs were interactive or solitary. The implications and recommendations resulting from the two studies are provided.

Keywords: Blog; Media choice factors; Technology acceptance model (TAM); Peer learning; Web-based learning.

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誌 謝

人生總是不斷充滿驚喜,期待打開一扇窗後,會是怎樣的景緻。攻讀

博士是我人生的一段精彩時光,那些以校為家、以桌為床的日子;多少次與

老師同學們研究學問的澎湃熱情;以及最後與期刊編審在學術文章上的攻

防進退,都是我永生難忘的經歷。

首先,非常感謝我的指導教授 楊千老師,楊老師是位令人景仰的經師

人師。在研究指導方面,楊老師總是展現如同沙場上將軍帶兵的睿智與謀

略,方向精準而切中要害。在為人處事方面,老師總能在幽默的談笑間,

告訴我人生的哲理。能在攻讀博士期間遇見楊老師,實在是一件最幸福的

事。

另外,一路上承蒙 丁承老師嚴謹的統計訓練,讓我在面對解決學術問

題時,有適當可用的工具。也感謝 胡均立老師在方法上的指導,使我不只

看到點也可以看到面。而幾位口試委員的學問素養深厚,口試時給我論文

精闢的見解,是我在博士班日子中最精彩的一堂課,其對我論文的結構與

完整性,提供相當大的助益。

最後,我要感謝我的太太,照顧家庭與可愛的孩子們,及給我最大的

支持。在無數次博士班上課的日子,容忍我天未亮就出門上課,孩子都睡

了我才下課回到家。也感謝爸媽給我健康的身體與向上的力量,我才有體

力及勇氣讀完博士班。

博士班旅程要感謝太多人,沒有這些一路相伴的老師、同學及家人的

支持與鼓勵,我無法完成我的論文。我對你們獻上最深的敬意。

張耀升 謹誌

民國 100 年 5 月 台中

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Table of content

摘 要 ...i

ABSTRACT ...iii

誌 謝 ...v

Table of content ...vi

List of tables ...viii

List of figures ...ix

Chapter 1. 0BIntroduction ...1

1.1. Research background...1

1.2. Scope and objectives ...4

1.2.1. Blog acceptance behaviors: From the perspectives of technology acceptance and media choice factors ...4

1.2.2. A blog application: Assessing the effects of interactive blogging in education setting ...4

1.3. Organization of the dissertation...5

Chapter 2. Blog acceptance and usage: From the perspectives of technology acceptance and media choice factors ...6

2.1. Research background...6

2.2. Literature review...8

2.2.1. Blog ...8

2.2.2. Technology acceptance model (TAM)...9

2.2.3. Media choice factors... 11

2.3. Research model and hypotheses...18

2.3.1. Research model ...18

2.3.2. Hypotheses ...19

2.4. Methodology...23

2.4.1. Measurement development...23

2.4.2. Data collection and analysis ...23

2.5. Results ...27

2.5.1. Measurement model ...27

2.5.2. Structure model...30

2.6. Discussions ...33

2.7. Limitations...34

Chapter 3. A blog application: Assessing the effects of interactive blogging on student attitudes towards peer interaction, learning motivation, and academic achievements...35

3.1. Research background...35

3.2. Theoretical background and hypothesis development...38

3.3. Methods ...41

3.3.1. Participants ...41

3.3.2. Setting...41

3.3.3. Platform ...43

3.3.4. Measures and data collection...43

3.4. Findings ...46

3.5. Discussions ...49

Chapter 4. Implications and conclusions ...51

4.1. Implications...51

4.1.1. First study implications ...51

4.1.2. Second study implications...53

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Reference...56 4BAppendix ...67

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List of tables

Table 2.1. Demographic profile...26

Table 2.2. Goodness-of-fit indices for measurement model...27

Table 2.3. Descriptive statistics and reliability...29

Table 2.4. Discriminant validity and composite reliability ...30

Table 2.5. Parameter estimates for hypothesized paths in structure equation model ...32

Table 3.1. Coding scheme for reflection...46

Table 3.2. t-test results of I-Group and S-Group in two consecutive semesters...47

Table 3.3. Summary of the results in connection with hypotheses...48

Table 3.4. Level of academic achievements versus perceived attitudes towards educational blogs ...49

Appendix A. Question constructs and items used in the first study ...67

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List of figures

Figure 2.1. The technology acceptance model ...10 Figure 2.2. The proposed research model...18 Figure 2.3. Results of structural modeling analysis...32

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Chapter 1.

0B

Introduction

1.1. Research background

Blogs, or weblogs, have been experiencing a rapid growth rate in recent years. According to Technorati (2009, 2008), the number of blogs doubled to 133 million between 2007 and 2008. A blog is a website comprising blog posts, or content written by the blogger, which is typically organized into categories and sorted in reverse chronological order (Wright 2006). Most of the blogs are similar to personal diaries, or are corporate marketing channels for engaging existing as well as potential customers. Because of their easy creation, blog pages have become the web-authoring tool for the novice, as well as the expert. Blogs create a context for dialogues between bloggers and readers. Through blogger-initiated conversations, blog platforms build a solid base of shared experiences and mutual relationships (Bausch et al. 2002).

Blogs are defined as “frequently modified web pages in which dated entries are listed in reverse chronological sequence” (Herring et al. 2005), and they could be considered a form of web-based information technology (Du and Wagner 2006). Blogs have some distinctive and attracting characteristics. First, blogs are usually managed by one author only, but many readers can leave a comment on posted entries and authors can answer it with another comment or by posting a subsequent or revised entry (Kaplan and Haenlein 2010). In other words, blogs are dynamic and are developed to facilitate and accommodate frequent changes in content, particularly by giving many readers the opportunity to comment on the primary messages that appear on them (Kim 2005). Besides, based on the previous researches, media have been conceptualized as transmission conduits (Axley 1984) or channels (Fisher 1978) through which information can be conveyed. Therefore, the blog could be considered as a form of media, making fully two-way online communication possible (Wright 2006, Kaplan

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and Haenlein 2010). Second, the creator of the message prepares the content without having to be familiar with special coding and uploads the message to blogs just by clicking on the “Publish” button. Therefore, blogs can be considered as easy to use communication media in web-based information technology. Blogs made it feasible for the communication process to be much larger, less technical, with a higher number of users.

In the previous few years, blogs have been adopted by users for several applications in domains such as journalism (Hall and Bavison 2007), business (Tikkanen et al. 2009), and education (Chang et al. 2008). For example, teachers use blogs as a tool for encouraging interaction between students to facilitate learning (Chang et al. 2008). Corporate established blogs act as marketing channels for engaging existing and potential customers (Tikkanen et al. 2009). Blogs have become popular social media for facilitating interaction in a variety of specific fields.

Blog use in educational sectors has grown extensively and considerable research has focused on the educational use of blogs (Oravec 2002, Williams & Jacobs 2004, Richardson 2006; Dailey 2006, Churchill 2009, Kerawalla et al. 2009, Yang 2009). Although blogs did not originate in education sectors, they have become useful in various educational levels and settings, and as an authoring tool. For example, blogs often serve as a digital portfolio of student assignments and achievements (Liu & Chang 2010). Most blog platforms provide a personal writing space, which is easy to publish, sharable, and automatically archived, empowering users to form learning communities through inter-linkages. Therefore, blogs can combine solitary reflection and peer interaction in learning processes (Richardson 2006, Yang 2009). Furthermore, classroom discussion is often teacher-student centered, rather than a student-student dialog. The blog is a vehicle to ensure that everyone has a voice and is a valued member of the learning community. The instructor has previously been the only person to access student work. Students turn in their work to instructors and have no way to learn

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how their peers scored on the same assignments, thereby losing the potential of appreciating student work. In contrast, peer learning is a form of cooperative learning that enhances the value of student-student interaction. Students using interactive blogs can read peer postings and leave comments, which enable them to learn from peers by creating dialogues with each other (Kay 2006). Therefore, blogs may have the potential to transform personal learning to collaborative learning.

Why do we blog? Blogs can be considered as easy to use communication media in web-based information technology. Blogs made it feasible for the communication process to be much larger, less technical, with a higher number of users. Moreover, what blog applications can do for us? Blogs have been adopted by users for several applications in domains such as journalism, business, and education. Many blog applications have been change our life.

This thesis tried to investigate why people accept blogs, how people use blogs, and what application people do on blogs. This thesis would conduct a field study to explore blog acceptance and usage behaviors, and furthermore take an experimental study to assess the effects of interactive blogging in higher education. The first study would incorporate the technology acceptance model with media choice factors to explain and predict the blog acceptance behaviors. The second study would explore the use of blogs in education setting, and how student attitudes towards online peer interaction and peer learning, as well as motivation to learn from peers, may differ when using the blog comments feature, and when students are encouraged to read and comment on each other’s work.

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1.2. Scope and objectives

1.2.1. Blog acceptance behaviors: From the perspectives of technology

acceptance and media choice factors

Blogs are not only web-based information technology, but also media (Kim 2005, Yates 2008, Kaplan and Haenlein 2010). The research objectives of the first study are to incorporate the technology acceptance model (TAM) with media choice factors to explain and predict the blog acceptance behaviors. TAM assumes that an individual’s attitude toward use affect behavioral intentions, and an individual’s attitude toward using IT, is determined by two beliefs: perceived usefulness and perceived ease-of-use. The media choice factors include media richness, critical mass, social influence, and media experience. In this study, we address a proposed model and conduct a structure equation model. An online field survey, involving 521 experienced blog users, was conducted to investigate the empirical strength of the relationships in the proposed model. Therefore, this study would lead to a better understanding of how the two perspectives influence blog acceptance behaviors.

1.2.2. A blog application: Assessing the effects of interactive blogging in

education setting

Blogs have been increasingly used to supplement traditional classroom lectures in higher education. This study would explore the use of blogs, and how student attitudes towards online peer interaction and peer learning, as well as motivation to learn from peers, may differ when using the blog comments feature, and when students are encouraged to read and comment on each other’s work. We contrast two ways blogs affect learning engagement: (a) solitary blogs as personal digital portfolios for writers; or (b) blogs used interactively to facilitate peer interaction by exposing blogging content and comments to peers. A quasi-experiment was conducted across two semesters, involving 154 graduate and

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undergraduate students, to investigate the effects of interactive blogging in higher education.

1.3. Organization of the dissertation

The dissertation is organized as the following order. Chapter 2 explores blog acceptance behaviors from the perspectives of technology acceptance and media choice factors, describing why bloggers accept and use blog. We addressed a proposed model from the literature review, and an online field survey was conducted to verify this model. Chapter 3 designs an empirical study of blog application. We employ blogs as interactive learning tools for communities of practice in higher education. Three hypotheses were suggested to verify the effects of interactive blogging in empirical setting. A quasi-experiment was conducted to assess the effects of interactive blogging on student attitudes towards peer interaction, learning motivation, and academic achievements. The implication and recommendation are provided at the end of each chapter.

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Chapter 2.

1B

Blog acceptance and usage: From the perspectives

of technology acceptance and media choice factors

2.1. Research background

Blogs, or weblogs, have been experiencing a rapid growth rate in recent years. Blogs are easy to use and possess interactive features, attracting wide use leading them to be regarded as communication media in web-based information technology (Du and Wagner 2006). However, they differ from other websites in two ways. First, websites tend to have static or rarely changing content. Blogs, on the other hand, are dynamic and are developed to facilitate and accommodate frequent changes in content, particularly by giving readers the opportunity to comment on the primary messages that appear on them (Kim 2005). In most instances, readers will be able to place their contributions on social media such as blogs without requiring authorization. Therefore, fully two-way online communication is made possible (Wright 2006, Kaplan and Haenlein 2010). A second difference is the user empowerment characterized by the ease with which content can be placed on blogs (Du and Wagner 2006). The creator of the message prepares the content without having to be familiar with special coding and uploads the message to blogs by clicking on the “Publish” button. Therefore, blogs can be considered as easy to use communication media in web-based information technology.

Over the previous two decades, the technology acceptance model (TAM) (Davis 1989, Davis et al. 1989), has been widely used to explain and predict the acceptance behaviors of information systems (e.g. Adams et al. 1992, Agarwal and Karahanna 2000, Karahanna et al. 2006, Venkatesh et al. 2008). The TAM suggests that both perception usefulness and ease of use are key determinants of the adoption of user technology. Although TAM is a well

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established model, many studies have extended TAM with other constructs in various web-based information technologies, such as trust in online shopping (Gefen et al. 2003), playfulness in WWW context (Moon and Kim 2001), perceived risk in online transactions (Pavlou 2003), perceived enjoyment in internet-based learning (Lee et al. 2005), and social influence in online gaming (Hsu and Lu 2004). Blogs have been an emerging web-based information technology (Boulos and Wheeler 2007); hence, TAM could be applied to explain and predict the acceptance behaviors of blog. Therefore, our study extends TAM with some other constructs to investigate blog acceptance behaviors.

Blogs are not only web-based information technology, but also media (Kim 2005, Yates 2008, Kaplan and Haenlein 2010). Yates et al. (2008) addressed the genre model (Yates and Orlikowski 1992) to evaluate media usage, including blogs as a form of media. Kaplan and Haenlein (2010) provided a classification of social media according to media richness and self-presentation, and included blogs. Media have been conceptualized as transmission conduits (Axley 1984) or channels (Fisher 1978) through which information can be conveyed. Media vary in their capacity to convey information, which can influence individual/organizational media choices (e. g. Daft et al. 1987, Carlson and Zmud 1999). Several media choice theories have been developed to study individual/organizational communication, including media richness (Daft and Lengel 1984, Rice 1992), critical mass (Markus 1987), social influence (Fulk 1993, Schmitz and Fulk 1991), and media experience (Sitkin et al. 1992). Consequently, the purpose of this study is to incorporate the perspectives of technology acceptance and media choice factors to investigate blog acceptance behavior. Therefore, this study may lead to a better understanding of how the two structures influence blog acceptance behavior. This study applied a structure equation model and conducted an online field survey to investigate the empirical strength of the relationships in the proposed model.

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2.2. Literature review

2.2.1. Blog

Weblogs, or blogs, are defined as “frequently modified web pages in which dated entries

are listed in reverse chronological sequence” (Herring et al. 2005). The original blogs were

used mostly for web pages with links to other sites or blogs of interest, providing blogger commentary for added value (Blood 2002). After mid-1999, when free and easy-to-use blogging software (e.g. Pitas, Blogger, and Groksoup) was released, the nature of blogs changed with numerous blogs becoming more like personal websites containing diverse types of content posted in reverse chronological order. According to Winer (www.scripting.com), a blogging pioneer, blogs have four characteristics: they are personalized, web-based, community-supported, and automated (meaning easy-to-use). Herring (2005) presented the results of the content analysis of 203 randomly-selected weblogs, and proposed that blogs have the following characteristics: they are frequently updated, have reverse chronological order, a personal journal, an asymmetrical exchange, and hyperlinks. Blogs made it feasible for the communication process to be much larger, less technical, with a higher number of users. Therefore, blogs create a platform for dialogues between bloggers and readers. Through conversations initiated by bloggers and engaged in by readers, blog platforms build a solid base of shared experiences and mutual relationships.

Blogs are often viewed as similar to other media such as e-mail, bulletin board systems, and web pages. Blogs are a form of internet media (Kim 2005) and the social media equivalent of personal web pages, coming in a multitude of different variations, from personal diaries describing the author’s life to summaries of all relevant information in one specific field (Kaplan and Haenlein 2010). In the previous few years, blogs have become an increasingly popular form of communication on websites, and have been adopted by users for several

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applications in domains such as journalism (Hall and Bavison 2007), business (Tikkanen et al. 2009), and education (Chang et al. 2008). For example, teachers use blogs as a tool for encouraging interaction between students to facilitate learning (Chang et al. 2008). Corporate established blogs act as marketing channels for engaging existing and potential customers (Tikkanen et al. 2009). Two famous business examples include Jonathan Schwartz, CEO of Sun Micro-systems, who maintains a personal blog to improve the transparency of his company, and the automotive giant General Motors. Blogs have become popular social media for facilitating interaction in a variety of specific fields. In addition, blogs are web-based information technology (Du and Wagner 2006). Blog acceptance behaviors can be explained in part by TAM. Consequently, this study will discuss blog acceptance behavior from the perspectives of technology acceptance and media choice.

2.2.2. Technology acceptance model (TAM)

TAM, adapted from the Theory of Reasoned Action (TRA) (Fishbein and Ajzen 1975) and originally proposed by Davis (1986), has become a widely accepted model in the field of information systems to explain and predict an individual’s acceptance of IT (Lee et al. 2004). TRA suggests that an individual’s behavior is determined by his or her intention to perform the behavior, which in turn is determined by the individual’s attitude concerning the behavior. TAM is based on TRA’s belief-attitude-intention relationship to explain an individual’s IT acceptance behaviors.

TAM assumes that an individual’s attitude toward use affect behavioral intentions, and an individual’s attitude toward using IT, is determined by two beliefs: perceived usefulness (PU) and perceived ease-of-use (PEOU) (Davis 1989). Davis defined PU as “the degree to which a

person believes that using a particular system would enhance his or her job performance”,

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free of effort”. Furthermore, PEOU is a determinant of PU because, ceteris paribus, users

consider IT more useful when it is requires less effort. Also, there is the belief that PU affects an individual’s behavioral intentions. Furthermore, both beliefs are influenced by external variables, such as development process, system characteristics, and social influences. Figure 2.1 shows the TAM model.

Figure 2.1. The technology acceptance model

Based on TAM, numerous studies have extended the TAM with other constructs to enhance the understanding of an individual’s IT acceptance behavior in a specific context. For example, Gefen et al. (2003) proposed trust as an extended variable of TAM for online shopping acceptance research. Agarwal and Karahanna (2000) addressed cognitive absorption as a structure reflecting an individual’s intrinsic belief in WWW acceptance. Cheng et al. (2006) extended the TAM with playfulness and risk to explain consumer acceptance of the internet as a channel of distribution. Moreover, other studies have shown that TAM is a robust model of technology acceptance behavior. The TAM has been successfully applied to predict technology acceptance behavior, across time (Venkatesh 2000, 2008), across settings (Vance

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not only been applied in job related IT acceptance (e.g. Gefen and Straub 1997, Lederer et al. 2000, Hernandez et al. 2008), but also revised in other IT applications such as entertainment (Hsu and Lu 2004), online consumer behavior (Koufaris 2003, Kwon et al. 2007, Shin 2009), and media technology (Lederer et al. 2000). Since blogs are not only an IT application, but also media, this research attempts to extend TAM using media choice factors to understand an individual’s IT blog acceptance behaviors.

Although the TAM has been widely applied in MIS research, many limitations of the TAM also were discussed (Venkatesh and Davis 2000, Agarwal and Karahanna 2000, Agarwal and Karahanna 2000, Karahanna and Straub 1999). Lee et al. (2003) investigated many TAM studies in a couple of decades and summarized its limitations as follows: The most commonly reported limitation is self-reported use, although 36 studies relied mainly on self-reported use, assuming that self-reported use successfully reflects actual usage. The second limitation is the generalization problem, examining only one information system with a homogeneous group of subjects performing a single task at a single point of time. Other suggested limitations of TAM studies included student samples, a single subject (or restricted subjects), a one-time cross sectional study, single measurement scales, self-selection bias of the subjects. Consequently, follow-up IT acceptance studies which apply TAM would avoid the limitations.

2.2.3. Media choice factors

Media have been conceptualized as transmission conduits (Axley 1984) or channels (Fisher 1978) through which information can be conveyed. Moreover, some researchers have considered the capacity of different media to convey data (Daft and Lengel 1984, Sitkin et al. 1992), while others have focused on the capacity of different media to convey symbolic meaning (Feldman and March 1981, Trevino et al. 1987). From the different perspectives of media, several interrelated theories and studies have examined a variety of contingencies that affect which media are chosen and how effective choices are likely to be. Several media

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choice theories have been developed to study individual/organizational communication, and how to affect individual/organizational media attitudes, behavioral intentions, and usage behaviors (Carlson & Zmud, 1994; Fulk et al., 1995; Webster, 1998; Cameron & Webster, 2005). Webster (1998) summarized the prior literature and outlined the media choice factors, including media richness (Daft and Lengel 1984, 1986, Daft et al. 1987, Rice 1992), critical mass (Markus 1987, Oliver 1988), social influence (Fulk 1993, Schmitz and Fulk 1991), individual characteristic --- media experience (Sitkin et al. 1992), situational factors (Trevino et al 1987, Rice 1992), and media symbolism (Trevino et al. 1990). This study selects media richness, critical mass, social influence, and media experience, which are suited to the context of a blog, while excluding situational factors and medium symbolism. The situational factors were excluded because blogs are a web-based application, that it is not limited by the distance between communication partners. Media symbolism was also excluded because a blog’s symbolism is not clear. Media choice factors are introduced in the following sections.

2.2.3.1. Media richness

Media richness theory (MRT), introduced by Daft and Lengel (1984), suggests that the use of communication media in an organization is a rational process that achieves a match between the information processing tasks and the media capacities. Daft and Lengel (1986) defined media richness as the capacity of media to evolve shared meaning, overcome different frames of reference, and clarify ambiguous issues to change understanding in a timely manner. Media richness could be measured by four criteria sets (Daft and Lengel 1986), including (1) capacity for immediate feedback, (2) multiple information cues, (3) personalization, and (4) language verity. MRT proposed that an organization processes information to reduce uncertainty and equivocality (Daft and Lengel 1986). The uncertainty was defined as the difference between the amount of information required to perform the task and the amount of

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information already possessed by the organization (Galbraith 1977). The equivocality means that multiple and conflicting interpretations of an organizational situation exist (Weick, 1979). Moreover, organizations can facilitate the amount of information to reduce uncertainty and the richness of information to reduce equivocality (Daft and Lengel 1986).

Rich media are thought to be best when communication is ambiguous. A richer medium can be seen as equally useful for unambiguous tasks as for ambiguous ones (Schmitz and Fluk, 1991). The capacity of process information is as diverse as the different communication media. Schmitz and Fulk (1991) ranked the order of media richness as face-to-face, telephone, e-mail, personal written text (letters, memos), formal written text (documents), and formal numeric text. Face-to-face communication would be the richest medium because it provides maximum immediate feedback, multiple cues via body language and tone of voice, and the message context is expressed in natural language.

Many blogs offer communication tools for supporting interactions with others, and the most distinctive characteristic are comments and “Trackback” (Miura and Yamashita 2007), a reverse hyperlink tracking the referrer weblogs. Blogs are usually managed by one author only, but readers can leave a comment on posted entries and authors can answer it with another comment or by posting a subsequent or revised entry (Kaplan and Haenlein 2010). Therefore, blogs provide the capacity for feedback through readers’ comments, which are managed by the author, allowing the author to maintain his own personal requirements. Also, the blog readers can maintain their personal requirements through “Trackback”. Moreover, multiple information cues are present on blogs, because the majority of blogs provide multimedia capability (such as pictures, music, and emoticons) to very different cues more than just text content with the same click-and-post ease (Du and Wagner 2006). Consequently, the media richness of blogs can be measured by the set of media richness criteria provided by Daft and Lengel, and blogs fall somewhere on the media richness scale.

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Numerous studies have been conducted using the MRT to investigate media selection and usage behavior (e.g. Dennis and Kinney 1998, Carlson and Zmud 1999, Trevino et al. 2000, Lim and Benbasat 2000, Cameron & Webster, 2005). Trevino (2000), for example, found that general attitudes toward different media (including e-mail, fax, letters, and face-to-face meeting) were influenced by perceived media richness. Moreover, new media attitudes were also influenced by person/technology interaction factors. Dennis and Kinney (1997) tested the media richness in computer-mediated and video communication to exam the effect of cues, feedback, and task equivocality. Therefore, media richness was proposed as the factor that reflected the acceptance behaviors of blog. In this study, we used the four criteria (immediate feedback, multiple information cues, personalization and language verity) defined by Daft and Lengel to measure the media richness of blogs.

2.2.3.2. Critical mass

Critical mass refers to “the idea that some threshold of participants or actions has to be crossed before a social movement explodes into being” (Oliver et al. 1985). This definition suggests that critical mass is the basis for producing collective actions. Blog acceptance requires the participation and collective actions from all individuals whose activities are affected by the technology. Markus (1987) indicated that “even individuals who would prefer to use interactive media may not really perceive these media to be viable options in the absence of universal access”. Moreover, Markus and Connolly (1990) showed that interactive media might fail without securing a critical mass of users for the technology. Hence, the success of blog acceptance is not only dependent on an individual’s use of blogs, but on other responses to this use. If few people are willing to contribute to the blog, it will not be effectively used. Furthermore, from the network externality perspective, critical mass refers to the effect that the value of technology to a user increases with the number of people who

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adopt it (Nault and Dexter 1994, Wang and Seidmann 1995).For example, the more people who use e-mail, the more valuable e-mail is to each user. Online social networks work in the same way, with sites like Twitter and Facebook being more useful as more users join. Applying network externality perspective, Luo and Strong (2000) pointed out that the users may develop a perceived critical mass through interaction with others. The perception of critical mass is rapidly strengthened as more people participate in network activities. Consequently, achieving a “critical mass” of users has been recognized as the key for successful media acceptance (Markus and Connolly 1990, Grudin 1994, Lim et al. 2003, Cameron & Webster, 2005; Slyke et al. 2007). Therefore, critical mass was proposed as the factor that reflected blog acceptance behaviors.

2.2.3.3. Media experience

Media experience, representing individual use, skills, and comfort with the media (King and Xia 1997), plays an important role in influencing user attitudes and can facilitate or constrain choices and general use (Sitkin et al. 1992, King and Xia 1997). Some individuals may have little or no experience or skill with media and as a result have negative attitudes toward it and/or may avoid using it (Webster 1998). Schmitz and Fulk (1991) indicated that expertise in using new media facilitates choice and use. Moreover, human behavior is more about self-interest, and efficiency oriented than rationality motivated (Williams 1985). But human behavior is also experience based. If individuals are uncomfortable or unfamiliar using a new medium, and view learning to use a new medium as more time consuming and inefficient than traditional media, they will choose a familiar medium rather than a rationally efficient medium (King and Xia 1997).

Carlson and Zmud (1994) indicated that media choice is determined by the fit of the perceived media richness and the perceived information richness. These perceptions are built

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upon previous experience with the media in addition to the objective view of media characteristics. Experience enables the development of familiarity, expertise, and comfort with the media, which in turn enables users to improve his attitude toward using the media, and to facilitate appropriate media choice. For example, because individuals have high levels of expertise and familiarity with face-to-face communication, they would naturally and instinctively prefer this medium over other unfamiliar ones. This argument is consistent with the study by Schmitz and Fulk (1991), which found face-to-face communication to be the richest medium. Empirical studies (Schmitz and Fulk 1991, Webster 1998) provided confirmation of positive relationships between media attitudes and media experience. Accordingly, media experience was proposed as the factor that best reflected blog acceptance behaviors.

2.2.3.4. Social influence

Fulk et al. (1991) presented the social influence model of technology usage to explain media choices. They suggested that social influences such as work group norms, and coworker and supervisor attitudes and behaviors can influence individual/organizational media attitudes, use and choice. According to social influence theory, it is proposed that media perceptions vary and are, at least in part, socially constructed. In addition, based on the theory of reasoned action, an individual’s behavioral intentions are influenced by subjective norms as well as attitude. Innovation diffusion research also suggests that user adoption decisions are influenced by a social system that goes beyond an individual’s decision style and the characteristics of the IT (Valente 1996).

The effect of social influences on media choices has been empirically supported in a number of studies (Schmitz and Fulk 1991, Fulk 1993, Kraut et al. 1998, Gu and Higa 2009). Fulk et al. (1991), for example, found that attitudes and e-mail usage were affected by social influences from coworkers, supervisors, and networks. Kraut et al. (1998) found that people

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used a particular system (e.g. video telephone) more when more people in general were using it. Gu and Higa (2009) indicated that social influence was identified as the most important factor for the primary medium selection in multiple media usage settings, followed by a rational factor and environment factor. As empirical examples, Facebook and Twitter are famous for their social network service. They successfully used social influence to quickly improve their customer base. Accordingly, social influence was proposed as the factor that reflected blog acceptance behaviors.

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2.3. Research model and hypotheses

2.3.1. Research model

Figure 2.1 illustrates our proposed model, which incorporates TAM with media choice factors in the blog context. The proposed model has as its core the TAM constructs and the four media choice factors: media richness, critical mass, social influence, and media experience. The specific elements of the model and related hypotheses are presented in further detail below.

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2.3.2. Hypotheses

2.3.2.1. Perceived media richness

Following Daft and Lengel (1986), perceived media richness (PMR) is the degree to which an individual perceives the capacity of blog to evolve shared meaning, overcome different frames of reference, and clarify ambiguous issues to change understanding in a timely manner. Blogs offer various communication functions to provide a more efficient channel to interchange information than other forms of Web sides. Trevino et al. (1987) argued that rich media can better handle a greater variety of tasks because they more effectively can convey equivocal messages. If individuals perceive a medium as rich, it is likely to be perceived as more useful because it may be used successfully for more and different tasks. Fulk (1993) and Lim and Benbasat (2000) indicated that media richness has a positive effect on perceived usefulness. Furthermore, if resources are not constrained, individuals will tend to use a rich media than a lean one. For example, individuals can have a very positive attitude toward a face-to-face communication, because it is the richest medium providing the largest capacity for communication. Researchers provided empirical evidence that perceived media richness has been associated with positive media attitudes (Trevino et al. 2000). Therefore, PMR could affect both PU and attitude toward using blog (ATT). Consequently, the following hypotheses were proposed:

H1-1a: Perceived media richness (PMR) will have a positive effect on perceived

usefulness (PU) of blog.

H1-1b: Perceived media richness (PMR) will have a positive effect on attitude toward

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2.3.2.2. Perceived critical mass

We defined perceived critical mass (PCM) as the degree to which a person believes that most of his or her peers are using blogs. Critical mass refers to the fact that the value of technology to a user increases with the number of its adopters (Markus 1987). One reason is that using technology that has reached critical mass allows users to communicate to the largest number of people with the least effort. As a blog increases in popularity it becomes increasingly useful, attracting more users to adopt the media technology. Lou and Strong’s study (2000) provided empirical evidence of the positive impact of perceived critical mass on perceived usefulness. Besides, from social psychological and economic perspectives, perceived critical mass is a type of social influence. Rice and Aydin (1991) indicated that individuals’ attitudes toward a new medium are affected by social influence. That is, critical mass affects individuals’ attitudes toward a new medium. Hsu and Lu (2004) and Slyke et al. (2007) found that critical mass positively affects individual attitudes toward using IT. Therefore, PCM could affect both PU and ATT. Consequently, the following hypotheses were proposed:

H1-2a: Perceived critical mass (PCM) will have a positive effect on perceived

usefulness (PU).

H1-2b: Perceived critical mass (PCM) will have a positive effect on attitude toward

using blog (ATT).

2.3.2.3. Media experience

Following King and Xia (1997), media experience (ME) is the degree to which an individual perceives his use, skills, and comfort with the blog. Experience enables the development of familiarity, expertise, and comfort with the media, which in turn enables users

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to improve their attitude toward using the media. Empirical studies (Fulk et al. 1995, Webster 1998) provided confirmation of positive relationships between media attitudes and media experience. Consequently, the following hypothesis was proposed:

H1-3: Media Experience (ME) will have a positive effect on attitude toward using blog

(ATT).

2.3.2.4. Social influence

In our study, social influence (SI) was defined as the degree to which an individual perceived that others approved of their participation in blogs. Social psychological theory has suggested that group members tend to comply with the social norm, and moreover, that these in turn influence the perceptions and behaviors of members (Lascu and Zinkhan 1999). According to this theory, perceptions of media are proposed to vary and be, at least in part, socially constructed. TRA identified that attitude and social influence (social norm) as two sole determinants of behavioral intention (Fishbein & Ajzen, 1975). When blog users interact with each other, they tend to comply with the social norm, and influence each others’ behaviors. Werster and Trevino (1995) suggested that social influence will more positively affect choices of new media. Furthermore, Schmitz and Fluk (1991) discovered that co-worker use of e-mail and supervisors’ perceptions of usefulness of the medium, namely social influence, had a significant effect on perceived usefulness of e-mail. Clearly, SI could affect both PU and behavioral intentions to use blog (BI). Consequently, the following hypotheses were proposed:

H1-4a: Social influence (SI) will have a positive effect on perceived usefulness (PU). H1-4b: Social influence (SI) will have a positive effect on behavioral intentions to use

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2.3.2.5. TAM

This research model adopted the TAM’s belief–attitude–intention–behavior relationship. The present study revalidates those relationships in the context of the blog with the following hypotheses:

H1-5: Perceived ease-of-use (PEOU) will have a positive effect on perceived usefulness

(PU).

H1-6: Perceived ease-of-use (PEOU) will have a positive effect on attitude toward using

blog (ATT).

H1-7: Perceived usefulness (PU) will have a positive effect on attitude toward using blog

(ATT).

H1-8: Perceived usefulness (PU) will have a positive effect on behavioral intentions to

use blog (BI)

H1-9: Attitude toward using blog (ATT) will have a positive effect on behavioral

intentions to use blog (BI).

2.3.2.6. Control variables

To lower the impact of individual specific characteristics on bias levels, control variables were introduced, including blog experiences and demographics which may influence attitudes and behavioral intentions to use the blog. Schmitz and Fulk (1991) suggested that e-mail experience positively influenced e-mail use. Fulk (1993) used the response’s age and education as control variables to study social construction of communication technology. Therefore, the responder’s blog experience, age, and education were introduced as control variables. Without loss of generality, these three variables may act as antecedents of all dependent and mediating variables in the proposed model, and thus are controlled.

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2.4. Methodology

2.4.1. Measurement development

The questionnaires were developed from the literature. Items used to measure the constructs were adopted from prior research to ensure content validity. PEOU and PU were developed from the study of Davis (1989), and were slightly modified to fit the blog context. ATT was measured using five standard 7-point semantic differential rating scales as suggested by Ajzen and Fishbein (1980) for operationalizing attitudes toward behaviors. The scale items to measure BI were adopted from Agarwal and Karahanna (2000). Additionally, the scale items to measure PCM were developed from Luo and Strong (2000). SI was measured by items taken from Venkatesh and Morris (2000). ME was measured by items modified from King and Xia (1997). Finally, four items for each set of criteria to measure PMR were adapted from Carlson and Zmud (1999). Seven point Likert scales with anchors ranging from “strongly disagree” (1) to “strongly agree” (7) were used for all items except for the items of ATT. The list of items is presented in Appendix A.

Both a pre-test and pilot test of the measures were conducted by the selected users, as well as experts in the field of web design. In the process of pre-test, five blog experts were asked to comment the design of questionnaires. Based on the expert feedback, a slight modification was made in the questionnaires and in the wording of some of the items to reflect the practices in the blogosphere. The second stage of pilot test involved 50 blog users answering the questionnaire so that the length, tone, and content were improved.

2.4.2. Data collection and analysis

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placed on Hwww.my3q.comH, which is a specific online questionnaire website that allows

people to respond voluntarily. To increase the response rate of participants, we placed several survey messages on the top ten heavily trafficked online message boards in Taiwan (MIC 2009), such as the Wretch blog(Hhttp://www.wretch.cc/blogH), the Yahoo! Kimo blog

(HHttp://tw/blog/yahoo.com/H), the Sina blog (Hhttp://blog.sina.com.tw/H), the Xuite blog

(Hhttp://blog.xuite.net/H), and the Pixnet blog (Hhttp://www.pixnet.net/blogH), during the

two-month period of data collection. The message stated the study purpose, and provided a hyperlink to our online questionnaire on Hwww.my3q.comH. The participants could respond to

the online questionnaire by entering the URL provided on the message.

Incomplete responses to questionnaires were considered invalid samples. Those without blogging experience were not taken into account when testing the hypotheses. As a result, the number of valid samples was 521. The characteristics of the samples are shown in Table 2.1. The data indicates that respondents have a gender ratio of F : M= 53.8% : 46.2%, 88% are ages between 16 and 25, 83.7% have college degrees, 80% use blogs at home, 50.6% have 1 to 3 years of blogging experiences, and 84.9% currently maintain their own blogs. Their demographic findings from respondents were roughly consistent with the Market Intelligence and Consulting Institute (MIC) 2008 report (MIC 2009).

When participants fail to return a survey or fill it out completely, the results can affect the characteristics of the sample. Non-response bias occurs when some subjects choose not to respond to particular questions and when the non-responders are different in some way from those who do respond (Armstrong and Overton, 1976). Palmquist and Stueve (1996) suggested that younger and more affluent males are the individuals most likely to respond to surveys on the web. To measure non-response bias, we divided respondents into two groups (Armstrong and Overton 1976, Straub 1989): early respondents (71%) and latter respondents (29%) (the latter were surveyed one month later than the former). These two groups were

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compared on the basis of their sample characteristics of age, education, blog and computer experiences. The MANOVA’s results showed no significant difference (Wilks’ Lambda= 0.92, F=0.80, p=0.53). The result indicated that non-response to the internal validity of this study’s results were limited, suggesting that the respondent sample was a random subset of the total population.

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Table 2.1. Demographic profile

Measure items Frequency Percent (%) Cumulative (%) Gender Male 242 46.2 46.2 Female 279 53.8 100.0 Age Less than 15 3 0.6 0.6 16-20 241 46.3 46.9 21-25 219 42.2 89.0 26-30 34 6.4 95.5 31-35 14 2.6 98.1 36-40 4 0.8 98.9 Over 41 6 1.1 100.0 Education

Junior high school or less 3 1.1 1.1

High school 61 11.6 12.7 Bachelor' degree 438 83.7 96.4 Graduate degree 17 3.2 99.6 doctor's degree 2 0.4 100.0 Place of blogging Home 415 80.0 80.0 Campus 65 12.3 92.3 Office 32 6.0 98.3 Other 9 1.7 100.0 Experience in blogging under 6 months 76 14.4 14.4 6 months - 1 year 86 16.3 30.6 1 year - 2 years 144 27.2 58.0 2 year - 3 years 117 23.4 81.5 3 year - 4 years 50 9.5 90.9 4 year - 5 years 30 5.7 96.6 over 5 years 18 3.4 100.0

Currently maintain their own blog

Yes 441 84.9 84.9

No 80 15.1 100.0

Following data collection, structural equation modeling (SEM) was used for analysis. Data analysis was performed in accordance with a two-stage methodology developed by Anderson

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and Gerbing (1988). In the first stage, the measurement model was assessed in terms of item loading, internal consistency, convergent and discriminant validity of the constructs with the confirmatory factor analysis (CFA). Next, the structural model was used to evaluate the proposed research model by examining the path coefficients. The SAS and AMOS were adopted as the tools to compute the results.

2.5. Results

2.5.1. Measurement model

The measurement model in CFA was revised by dropping items. The modification index (MI) is the index used to select indicator variables (Joreskog and Sorbom 1984). Through repeated filtering, three indicator variables were deleted (see the items with the * symbol in Appendix A). After dropping items, the CFA showed an acceptable model fit (Hatcher 1994). The chi-square to degrees of freedom ratio (chi-square/d.f.) was less than 2.0. The CFI, GFI, NFI, and NNFI were all greater than 0.9; the RMSEA is less 0.05. The overall goodness-of-fit indices are shown in Table 2.2.

Table 2.2. Goodness-of-fit indices for measurement model

Indices 2 df p-value NFI NNFI CFI GFI AGFI RMSEA

Measurement

model 516.87 312 <0.001 0.96 0.98 0.98 0.94 0.92 0.04

Descriptive statistics for the research constructs are presented in Table 2.3. Fornell and Larcker (1981) recommended that item loading (standardized factor loading) and internal consistencies greater than 0.7 are considered acceptable. Overall item loadings exhibited high

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loadings (>0.7) on their respective constructs. In CFA, composite reliability can reflect the internal consistency of the indicators with their respective constructs. In Table 2.4, all composite reliabilities greater than 0.7, Cronbach’s alphas greater than 0.7, and average variance extracted (AVE) greater than 0.5, exhibit good internal consistency with the measurement model (Hatcher 1994).

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Table 2.3. Descriptive statistics and reliability

Variables Mean Standard deviation Standardized factor loading t-value Perceived media richness

PMR1 4.98 1.35 0.83*** 21.8

PMR2 4.88 1.33 0.83*** 21.7

PMR3 5.09 1.33 0.83*** 21.7

PMR4 5.21 1.35 0.84*** 21.9

Perceived critical mass

PCM1 5.20 1.44 0.94*** 28.3 PCM2 5.07 1.48 0.90*** 26.6 PCM3 5.16 1.50 0.92*** 27.7 Media experience ME1 3.32 1.40 0.69*** 16.6 ME2 3.74 1.23 0.94*** 13.8 Social Influence SI1 4.57 1.42 0.88*** 24.9 SI2 4.69 1.40 0.90*** 25.2 Perceived usefulness PU1 5.04 1.37 0.84*** 23.2 PU2 5.01 1.29 0.83*** 22.4 PU3 4.84 1.36 0.80*** 21.6 PU4 4.80 1.40 0.73*** 19.0 PU5 5.07 1.33 0.87*** 24.6 Perceived ease-of-use PEOU1 5.22 1.30 0.85*** 23.9 PEOU2 5.04 1.33 0.84*** 22.9 PEOU3 5.07 1.32 0.87*** 24.5 PEOU5 5.10 1.33 0.87*** 24.7 PEOU6 5.12 1.36 0.90*** 26.2

Attitude toward using blog

AT1 5.12 1.30 0.88*** 25.2

AT2 5.06 1.35 0.93*** 27.7

AT3 5.09 1.35 0.89*** 25.6

AT4 4.96 1.28 0.86*** 24.3

Behavioral intention to use blog

BI1 4.91 1.34 0.87*** 24.0

BI2 4.86 1.41 0.84*** 22.3

BI3 4.97 1.40 0.86*** 23.5

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Table 2.4. Discriminant validity and composite reliability Composite Reliability (CR) Cronbach’s alphas AVE 1 2 3 4 5 6 7 8

1. Perceived media richness

(PMR) 0.90 0.92 0.69 0.83

2. Perceived critical mass

(PCM) 0.94 0.94 0.85 0.59 0.92 3. Media experience (ME) 0.81 0.79 0.71 0.26 0.35 0.84 4. Social Influence (SI) 0.88 0.88 0.82 0.58 0.74 0.38 0.90 5. Perceived usefulness (PU) 0.91 0.91 0.66 0.63 0.47 0.29 0.50 0.81 6. Perceived ease-of-use (PEOU) 0.94 0.95 0.75 0.66 0.56 0.33 0.51 0.67 0.87

7. Attitude toward using blog

(ATT) 0.94 0.93 0.79 0.66 0.57 0.34 0.65 0.72 0.69 0.89 8. Behavioral intention to use

blog (BI) 0.89 0.92 0.74 0.59 0.60 0.45 0.70 0.64 0.66 0.78 0.86 Note: Diagonals elements are the square root of average variance extracted (AVE). Off-diagonals elements are the correlations between the different constructs.

To assess convergent validity, the t test for the factor loading in the same construct should be statistically significant (Hatcher 1994). Table 2.3 indicates that all indicators were satisfactory. To assess discriminant validity, the square root of AVE should exceed the inter-construct correlation (Hatcher 1994, Hair et al. 2006). As shown in Table 2.4, the inter-construct correlation among indicators was smaller than the square root of AVE by the constructs. Therefore, these results indicate that convergent and discriminant validities were all achieved.

2.5.2. Structure model

Figure 2.2 shows the standardized path coefficients and explains the variances. This study used maximum likelihood estimates for each parameter. The analytical results for the proposed research model are presented in Table 2.5. Most of the hypotheses are supported except for Hypothesis 2a. PMR had significant positive effects on PU (1a 0.28, p0.01)

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and ATT (1b 0.20, p0.01), rendering support for Hypotheses 1-1a and 1-1b. PCM had significant effect on ATT (2b 0.15, p0.01), providing support for Hypothesis 1-2b. Unexpectedly, PCM had no significant effect on PU (2a 0.06, p0.05), and did not support Hypothesis 1-2a. PMR had significant positive effects on ATT (3 0.07, p0.05), validating Hypotheses 1-3. SI had significant positive effects on PU (4a 0.16, p0.05) and BI (4b 0.35, p0.01), validating Hypotheses 1-4a and 1-4b. TAM hypotheses were all significant ( 5 0.43, p0.01 ; 6 0.22, p0.01 ; 7 0.36, p0.01 ; 01 . 0 , 13 . 0 8  p

 ; 019 0.48, p0. ), providing support for Hypotheses 1-5, 1-6, 1-7, 1-8, and 1-9.

ATT is directly and significantly affected by PMR, PCM, ME, PU, and PEOU. Together, they accounted for 65 percent of the variance in ATT. Moreover, PU is directly and significantly affected by PMR, SI, and PEOU. Together, they accounted for 53 percent of the variance in PU. Finally, BI is significantly affected by ATT, SI, and PU. Together, they accounted for 69 percent of the variance in BI.

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) 53 . 0 ( 2 R ) 65 . 0 ( 2 R (R20.69)

Figure 2.3. Results of structural modeling analysis

Table 2.5. Parameter estimates for hypothesized paths in structure equation model Research

hypothesis Path description Path coefficient Result

H1-1a PMR  PU 0.28** Supported

H1-1b PMR  ATT 0.20** Supported

H1-2a PCM  PU -0.06 Not supported

H1-2b PCM  ATT 0.15** Supported

H1-3 MEATT 0.07* Supported

H1-4a SI  PU 0.16* Supported

H1-4b SI  BI 0.35** Supported

H1-5 PEOU  PU 0.44** Supported

H1-6 PEOU ATT 0.22** Supported

H1-7 PU ATT 0.36** Supported

H1-8 PU BI 0.13** Supported

H1-9 ATT BI 0.48** Supported

Note: PMR= Perceived media richness; PCM= Perceived critical mass; ME= Media

experience; SI= Social Influence; PU= Perceived usefulness; PEOU= Perceived ease-of-use; ATT= Attitude toward using blog; BI= Behavioral intention to use blog (BI).

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2.6. Discussions

Numerous issues affect an individual’s decision to select media. This study examined two theoretical aspects of this decision, technology acceptance and media choice factors, and showed how these aspects relate to the acceptance of a blog. The results of this proposed model show that the TAM hypotheses were all supported, and the media choice factors, significantly and directly, affect attitude toward using blog and behavioral intention to use blog in different ways. Incorporating technology acceptance with the media choice factors perspectives predicts attitude toward using blog ( 2 0.65

R ) and behavioral intention to use

blog ( 2 0.69

R ).

By examining the relative importance of the four media choice antecedents identified in this study, we found that perceived media richness, perceived critical mass, and media experience beliefs of structural assurances have a direct effect on attitude toward using blog. perceived media richness has by far the largest effect on attitude toward using blog of the four media choice factors. The standardized path coefficient of media richness was 0.28, whereas the other path coefficients of perceived critical mass and media experience were 0.15 and 0.07, respectively. A possible explanation of this finding may be as follows: Since the blog was in its infant stage, users were not very popular, and most users had limited experience. However, the blog offered various communication functions to support users to effectively interact with each other. Therefore, users consider perceived media richness as more important than other media choice factors. Moreover, perceived media richness and social influence have an indirect effect on attitude toward using blog through perceived usefulness. Perceived usefulness was an effective mediator between the media choice factors and attitudes toward use. Therefore, media choice factors not only directly influence attitude toward using blog, but also indirectly and partially influence attitude toward using blog through perceived usefulness.

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In addition, the TAM construct perceived usefulness remains an important predictor of acceptance behaviors, as in many previous studies (Venkatech and Morris 2000, Gefen et al. 2000 2003). Of the significant standardized  coefficients, perceived usefulness is larger (=0.36) than perceived ease-of-use and media choice factors. This suggests that while all factors are important, perceived usefulness is a stronger direct predictor than other TAM and media choice factors.

2.7. Limitations

Although our findings have meaningful implications for enhancing the understanding of an individual’s blog acceptance behavior, this study has some limitations. First, because the subjects of this study were internet users and were not randomly selected from a specific population, the potential for self selected samples exists. Even though the no-response bias was not significant, the issue of sample bias may still exist. In addition, given that measurements of all structures were taken at the same time and using the same instrument, causality can only be inferred with the potential for common method variance. Finally, because of the blog features and the restrictions on research methods, some media choice factors were not taken into account.

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Chapter 3.

2B

A blog application: Assessing the effects of

interactive blogging on student attitudes towards peer

interaction, learning motivation, and academic achievements

3.1. Research background

Blog use in educational sectors has grown extensively in the last decade and considerable research has focused on the educational use of blogs (Oravec 2002, Williams & Jacobs 2004, Richardson 2006; Dailey 2006, Churchill 2009, Kerawalla et al. 2009, Yang 2009). Although blogs did not originate in education sectors, they have become useful in various educational levels and settings, and as an authoring tool. For example, blogs often serve as a digital portfolio of student assignments and achievements (Liu & Chang 2010). Most blog platforms provide a personal writing space, which is easy to publish, sharable, and automatically archived, empowering users to form learning communities through inter-linkages. Therefore, blogs can combine solitary reflection and peer interaction in learning processes (Richardson 2006, Yang 2009). Williams and Jacobs (2004) suggest that students learn as much from each other as they do from an instructor or a textbook - what matters is finding an appropriate vehicle for facilitating this learning. They considered blogging as potential transformational technology for teaching and learning. Oravec (2002) observed the many blog dimensions suited to the individual voices of students, empowering them, and encouraging them to become more critically analytical in their thinking. The typical scenario when a teacher poses a question in a classroom is that a few dominate the discussion. Students also express their opinions without solid support from content they are studying, perhaps because there is not enough time to prepare or reflect. Disengaged students frequently remain silent and others

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who might have something truly relevant to contribute, are too shy to do so. Dailey (2006) considered time as the biggest advantage of blogs. Blogging gives students the time to think. This motivates us to investigate ways of using blogs to engage students in learning when they are not in class.

Critical reflection is the process of thinking back on prior learning to determine whether what one has learned is justified under present circumstances (Mezirow & Associate 1990). The process of creating comments involves reflecting on prior readings of original posts authored by a peer and on other prior knowledge acquired to date. Hall and Davison (2007) investigated hundreds of blog comments in an interactive learning environment and observed a significant number of reflective comments, posting messages, and noteworthy comments leading to an inquiry that accentuated critical reflection. In particular, composing blog comments involves stepping back, reflecting, and analyzing, which enables individuals to become more thoughtful and mindful of their work. Yu et al. (2010) indicated that sharing culture is strongly linked to member knowledge sharing behavior via weblogs. Participants appreciated the opportunity to use technology to communicate with peers.

Classroom discussion is often teacher-student centered, rather than a student-student dialog. The blog is a vehicle to ensure that everyone has a voice and is a valued member of the learning community. The instructor has previously been the only person to access student work. Students turn in their work to instructors and have no way to learn how their peers scored on the same assignments, thereby losing the potential of appreciating student work. In contrast, peer learning is a form of cooperative learning that enhances the value of student-student interaction. Students using interactive blogs can read peer postings and leave comments, which enable them to learn from peers by creating dialogues with each other (Kay 2006). Students enhance their analytical thinking while engaged in such meaningful dialogues. In this sense, blogs may have the potential to transform personal learning to

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collaborative learning. This motivates us to compare and contrast the individual use of student blogs for personal e-portfolios/learning diaries with their collaborative use in the context of a wider social network.

A general awareness of blogs exists in higher education, and some students and faculty may even have maintained personal blogs for years in non-educational settings (Makri & Kynigos 2007). However, blogs in educational environments have not generated a comparable level of momentum as those in non-educational use. Some researchers (Hall and Davison, 2007) argue that our understanding of how to use social software such as discussion forums and blogs in an effective and meaningful way is minimal at best. The problem partially rests in the absence of more cohesive information to assess quality and effectiveness. Enthusiastic claims for blogging in educational settings need to focus on a comprehensive research base broadly supported by vigorous empirical studies conducted in the framework of educational theory.

Research methods such as the user survey (Williams & Jacobs 2004) and ethnography (Dailey 2006, Hall & Davison 2007, Makri & Kynigos 2007, Yang 2009) have been used to assess blogging quality and effectiveness, but the perspective differences in surveys and methodologies make it difficult to combine the results into a cohesive knowledge base that can guide practice and education (Hall & Davison 2007). This study will provide substantial insights beyond enthusiasm for educators who consider the use of blogs to improve the quality of student learning experiences. This study investigates blog technology for encouraging interactions between students, as well as its consequences in terms of attitude changes towards learning motivation, peer learning, and academic achievement.

We proposed a hypothesis testing approach to assess the effects of blogs on student attitudes towards peer learning, academic achievements, and community interaction. The hypotheses to contrast solitary and interactive use of blogs were tested through a quasi-experiment employing a repeated measures design (Rosenthal & Rosnow 1991) where

數據

Figure 2.1. The technology acceptance model

Figure 2.1.

The technology acceptance model p.21
Figure 2.1 illustrates our proposed model, which incorporates TAM with media choice  factors in the blog context

Figure 2.1

illustrates our proposed model, which incorporates TAM with media choice factors in the blog context p.29
Table 2.1. Demographic profile

Table 2.1.

Demographic profile p.37
Table 2.2. Goodness-of-fit indices for measurement model

Table 2.2.

Goodness-of-fit indices for measurement model p.38
Table 2.3. Descriptive statistics and reliability

Table 2.3.

Descriptive statistics and reliability p.40
Table 2.4. Discriminant validity and composite reliability  Composite Reliability (CR)  Cronbach’s alphas  AVE 1 2 3 4 5 6 7 8  1

Table 2.4.

Discriminant validity and composite reliability Composite Reliability (CR) Cronbach’s alphas AVE 1 2 3 4 5 6 7 8 1 p.41
Table 2.5. Parameter estimates for hypothesized paths in structure equation model  Research

Table 2.5.

Parameter estimates for hypothesized paths in structure equation model Research p.43
Figure 2.3. Results of structural modeling analysis

Figure 2.3.

Results of structural modeling analysis p.43
Table 3.1. Coding scheme for reflection  Dimension Code Interpretation  Evidence

Table 3.1.

Coding scheme for reflection Dimension Code Interpretation Evidence p.57
Table 3.2. t-test results of I-Group and S-Group in two consecutive semesters  Fall 2006  I-Group  (n=23)  S-Group (n=48)  t-value  Cronbach’s

Table 3.2.

t-test results of I-Group and S-Group in two consecutive semesters Fall 2006 I-Group (n=23) S-Group (n=48) t-value Cronbach’s p.58
Table 3.4. Level of academic achievements versus perceived attitudes towards educational  blogs

Table 3.4.

Level of academic achievements versus perceived attitudes towards educational blogs p.60

參考文獻

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