Predicting user acceptance of collaborative ... - Semantic Scholar

Computers & Education 63 (2013) 160–175

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Computers & Education journal homepage: www.elsevier.com/locate/compedu

Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning Ronnie Cheung a, *, Doug Vogel b a b

University of South Australia, Australia City University of Hong Kong, Hong Kong, People’s Republic of China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 14 March 2011 Received in revised form 26 November 2012 Accepted 3 December 2012

Collaborative technologies support group work in project-based environments. In this study, we enhance the technology acceptance model to explain the factors that influence the acceptance of Google Applications for collaborative learning. The enhanced model was empirically evaluated using survey data collected from 136 students enrolled in a full-time degree program that used Google Applications to support project work. According to the research results, determinants of the technology acceptance model are the major factors influencing the adoption of the technology. In addition, the subjective norm represented by peers is found to significantly moderate the relationship between attitude and intention toward the technology. However, our results do not show a significant effect of subjective norms represented by instructors and mass media on students’ intentions to use the technology. The ability to share information in the collaborative learning environment is found to influence intention and behavior toward the Google Applications platform. Ó 2012 Elsevier Ltd. All rights reserved.

Keywords: Technology acceptance model Collaborative technologies

1. Introduction Despite the growing interest in Web 2.0 applications and Internet-based collaborative learning technologies, there is a lack of studies investigating the adoption behaviors of these technologies. Web 2.0 (McAfee, 2006) can be described as an “architecture of participation,” which facilitates ease of usage, gives immediate feedback on the user interface and structural levels, and values each user’s contribution. Recently, we have experienced a significant increase in the number of users who voluntarily engage in Web 2.0 activities. Examples of activities relating to Web 2.0 applications include blogs, wikis, tagging, RSS feeds, file and media sharing, social networking, and online messaging. These technologies are often associated with social communication as well as rich user experiences and opportunities for playfulness. The widespread proliferations of online collaboration tools allow communities of common interest to share content and commentary via online participation with wikis, discussion forums, and through various file formats that can be shared or edited online. As of 2010, the collaboration tools of Facebook have attracted 450 million users. Social networking sites such as Facebook have increasing influence over university students with usage rate of over 90% per year at most campuses (Lampe, Ellison, & Steinfield, 2006). Educational institutes are starting to prepare students to collaborate in a world in which various tasks can be accomplished with an abundance of available collaborative tools through the Internet. Researchers in the areas of e-learning in Web 2.0 environments (Chatti, Dahl, Jarke, & Vossen, 2008) emphasize the importance of learner-centered approaches while considering the use of self-publishing, peer-driven online learning, and social networking (Boyd & Ellison, 2007). This suggests a greater focus on student-generated content, collaborative use of online tools such as Web 2.0 applications, and modular tutoring. These innovative disruptions (Christensen, Anthony, & Roth, 2004; Christensen, Horn, & Johnson, 2008) are prompted by the development of Web 2.0 technologies that force us to think in new ways in preparation for the new challenges that are brought about by these changes.

* Corresponding author. 1215, 12/F, Hong Kong Community College West Kowloon Campus, Hoi Ting Road, Yau Ma Tei, Kowloon, Hong Kong. Tel.: þ852 9630 5750; fax: þ852 2363 0540. E-mail addresses: [email protected], [email protected] (R. Cheung), [email protected] (D. Vogel). 0360-1315/$ – see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.compedu.2012.12.003

R. Cheung, D. Vogel / Computers & Education 63 (2013) 160–175

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Collaborative learning technologies refer to a set of tools for task-specific collaborations, and are associated with goal and work-oriented activities. Collaborative technologies such as Google Applications – the so-called “Applications of the Web” (AoW) – have triggered a new wave of free online wikis, word processing, spreadsheets, presentations and discussion forum software since they were introduced in 2005 (Rienzo & Han, 2009). They bring a level of functionality originally associated with desktop applications to a Web browser, introducing ubiquitous possibilities for content creation, editing, and sharing. Consequently, the integration of Google Applications as a kind of AoW for project-based collaborative learning has become an important topic for academic institutes. However, there are few studies that focus on predicting user acceptance of e-learning technologies in Web 2.0 environments for teaching and learning. The development of Web 2.0 technologies excites a creative explosion of new ideas for collaborative learning, but they are rarely designed with formal teaching and learning in mind. In particular, the Google Applications technology provides exciting opportunities for supporting collaborative learning online, and education has not been the main source of user requirements analysis for exploring these new technologies. As these technologies and tools are developed and become easier to integrate, understanding the adoption behaviors of collaborative technologies is important because acceptance is a prerequisite for participation. In other words, it is necessary to develop a learning platform using Google Applications technology and investigate the factors that influence students’ decision to adopt the technology for learning. In order to understand students’ adoption behaviors, a research model was developed by extending the technology acceptance model (TAM) (Davis, 1989) and the theory of planned behavior (TPB) (Ajzen, 1991; Mathieson, 1991) to explain the factors affecting user perceptions and acceptance of collaborative technologies. Prior studies have developed theoretical models to understand the behaviors associated with adoption of learning technologies among students (Gong, Xu, & Yu, 2004; Sanchez-Franco, 2010). However, most of the research models on technology adoption have been developed for individual use of a technology. There are a number of researches that investigate the adoption behaviors of e-collaboration technologies (Dasgupta, Granger, & McGarry, 2002; Jarvenpaa & Staples, 2000), but they are not developed for technology adoption in a Web 2.0 environment. By developing extensions to the technology acceptance models for Web 2.0 based collaborative technologies, this study also addresses the need for developing research models that explain the adoption behaviors of collaborative technologies for project information sharing. The main contributions of this paper are the examinations of key determinants of a combined model and the decomposed constructs that explain the adoption behaviors of collaborative technologies. The rest of the paper is organized as follows. Section 2 presents the background of the study and the learning environment. Section 3 describes the details of a computer-supported collaborative learning environment employing the use of Google Applications technology. Section 4 provides a summary of related literature. The proposed research model and hypotheses are presented in Section 5. Section 6 includes a description of the research methodology. The research results are presented in Section 7. Discussion on the research results is presented in Section 8 and conclusions are provided in Section 9. 2. Background of the study Collaborative environments in education involve small groups of students working together to solve problems for the purpose of learning. Google Applications (Rienzo & Han, 2009) consist of a set of tools to facilitate collaborative work. They incorporate features found in traditional office applications, as well as providing a common space for sharing. There are three main applications provided by Google Applications that facilitate collaboration: Google Docs, Google Forms, and Google Sites. Google Docs technology is a common platform for sharing documents in Google accounts. Google Forms provide spreadsheet facilities that can be used for developing online forms and surveys. Google Sites facilitate the development of Web sites for collaborative work, handling documents, managing updates and wikis, and hosting forums for discussions. The Google Applications platform was introduced at the Hong Kong Polytechnic University to be used for courses involving collaborative projects in an undergraduate course. To enhance students’ effectiveness in using collaborative technology, the current study introduces the use of Google Applications technology for education involving collaborative projects. In this study, the research problem focuses on the application of project-based learning for the BA in Marketing and Public Relations program at the Hong Kong Polytechnic University. The main pedagogical principle with the design of the program is shaped around projectbased learning (Blumenfeld et al., 1991; Hardless, Lindgren, & Schultze, 2007; Helic, Krottmaier, Maurer, & Scerbakov, 2005). Each subject in the course also includes a significant group project as part of the coursework assessment. In this study, we introduce computer-supported collaborative learning (Stahl, Koschmann, & Suthers, 2006) through a pedagogical design that explores learning activities with a projectbased course implementation. The research is based on the Web-based environment for supporting the subject “Advanced Marketing Research” (AMR). Through a subject that is assessed solely on project-based coursework, students develop enquiries into consumer behaviors as their entire learning process. Furthermore, they need to form groups with four to six students to understand and develop a solution to the problem. Through the project-based learning process, students go through different stages of systematic investigation, problem formulation, theoretical and methodological considerations, investigations, analysis and reflection (Ayas & Zeniuk, 2001). For example, a project group may work on a research topic relating to an online Web site Taobao.com in China (Ng & Cheung, 2011), and work as a group to conduct empirical marketing research projects. They were required to collaborate with their team members to conduct real world research, generate a market research report and provide presentations to demonstrate their understandings of the research issues involved in the marketing process. Through the project-based learning process, students went through different stages of systematic investigation, problem formulation, theoretical and methodological investigations, analysis and reflection. Self-reflection and communications on the project-based activities constituted the major assessment of the course. Previous experience with mini-projects for marketing research revealed that group-based project work was difficult to implement and monitor. Most students finished their project reports during the last week of the course. Students were required to provide a summary of the division of labor for their contributions, but it was difficult or impossible to trace the collaborative activities and interactions during the last week of the course. The new set of collaborative tools that is facilitated by the “participatory media” (Jenkins, Clinton, Purushotmas, Robinson, & Wiegel, 2006) offers a more authentic learning experience (Herrington, Reeves, & Oliver, 2010) based on experimentation and action. With the help of the Internet and collaborative tools, students are able to co-construct knowledge and make connections with other learners and professional researchers. With access to online research information and research communities, learners are able to gain a deeper sense of a discipline and develop a “research culture”. Furthermore, the learning activities, communications, interactions, and

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collaborations are more visible through a computer-supported collaborative platform (Dillenbourg, 2005). Students are able to exercise judgment and exploration through social communications with the peers and communities in their learning process, just as it is in an actual workplace. 3. The Google Applications project environment The type of collaborative learning (Dillenbourg, 1999) undertaken in this project is commonly known as computer-supported collaborative learning (Dillenbourg, 1999; Dillenbourg, Järvelä, & Fischer, 2009). The Web-based platform for supporting collaborative learning integrates the five components of Google Applications. They are implemented using Web 2.0 facilities, including Google Docs, Google Forms, and Google Sites, Google Group Forums and Google Drive for sharing. These facilities promote interactions and collaborative learning among the participants. Students not only learn by participation, they also see how other students work in the Web 2.0 environment. The Web 2.0 environment promotes a “meaningful discourse” to the learning activities, where knowledge is constructed through collaborative facilities provided by Google Applications. Fig. 1 shows the details of a project Web site for supporting an advanced marketing research project. The use of Google Technology also enables the Web site contents to be accessible through a standard Web browser. The various features of the project Web site can be accessed through the menu bar on the left side of the screen. The features of the Web-based platform to support learning include: a wiki page for supporting project definition, project discussions, file updates, benchmark dates, survey form, and time trackers. The front page of the project Web sites also shows various levels of activities in a project group, highlighting the recent updates, shared resources, comments and activities within a project. The development of the computer-mediated environment was based on the pedagogical approach that takes advantage of Web 2.0 affordances for knowledge sharing. In this example, students collaborated on a marketing research project relating to the tourism industry. By employing a collaborative Web-based platform, individual and group perspectives are involved in the development and deployment of collaborative learning environments. Through a social learning approach, learning is located in contexts and relationships rather than merely in the minds of individuals, and that learning drives from participation in joint activities and social practices that are mediated by artifacts and collaborative tools. This research integrates Web 2.0 research and collaborative learning. As shown in Fig. 1, the

Fig. 1. A student project Web site.


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collaborative environment integrates Google Sites, Discussion Forums, Google Docs, Google Forms and shared file space for group-based projects. A collaborative platform provided opportunities for constructivist learning through provision of resource-based, student-centered settings, and by enabling learning to relate to the practical aspects of marketing research. Sharing and collaborations were achieved through the various tools provided by the collaborative platform. An important element in the design of the collaborative platform was the use of wiki in the project Web site pages. Each page in the project-based platform was developed using wiki tools for collaborative editing (as shown in Fig. 1). The benefits provided by wiki tools were that these wiki pages were easy to create and modify. As depicted in Fig. 2, all project group members can construct the problem definition collaboratively by editing on different sections in the wiki page relating objectives, research questions and hypotheses. An advantage offered by the wiki tools was the positive interdependence offered in the design so that the success of individual work could be linked to the group collaborations for the project. Furthermore, the activity log also showed the contribution of all group members, allowing the instructor to assess the quality and quantity of contribution from different project team members. By employing Web 2.0 tools that were facilitated through the Web-based learning platform, students become producers as well as consumers of information. Such kinds of student-generated contents may also include peer evaluations of the work of other group members. The students-generated contents were also recorded as activities that appeared in the log file in Google site. The various types of information can be represented as e-Portfolios (Love, Mckean, & Gathercoal, 2004) that incorporated evidence of the process of learning through reflections on students’ work. The collaborative reflections (Prilla, Knipfer, Degeling, Cress, & Herrmann, 2011) were supported by individual reflection through sharing of the understandings on the marketing research problems. The various sharing facilities were integrated to the project platform using Google sites, which was more complicated compared with conventional learning management systems. Furthermore, students’ use of the collaborative platform was characterized by collaborative aspects involving the use of Web 2.0 technologies, which were different from the individual use of a technology. Without effective use of group-based interactions through a collaborative platform, the advantages offered by group-based learning in marketing research were lost (Soller, 2001). Therefore, understandings on students’ attitude toward the collaborative platform are important. 4. Related literature To understand the factors that influence the use of the Google Applications technological platform, research in behavioral aspects of information systems offers the theoretical models for understanding technology acceptance. There are several competing theoretical models that can be used to investigate the determinants that affect the acceptance of information technologies (Venkatesh, Morris, Davis, & Davis, 2003). The theoretical models employed to study user acceptance and behavior include the theory of reasoned action (TRA) (Ajzen & Fishbein, 1980; Fishbein & Ajzen, 1975) and the theory of planned behavior (TPB) (Ajzen, 1991; Mathieson, 1991), as well as the technology acceptance model (TAM) (Davis, 1989; Davis, Bagozzi, & Warshaw, 1989). According to the TRA (Ajzen & Fishbein, 1980), the immediate determinant of behavior is the individual’s intention to perform or not to perform a behavior. Intentions, in turn, are influenced by two factors: attitudes and subjective norms. Often, the performance of a behavior is constrained by the lack of appropriate opportunities, skills, and resources. Even if a person is highly motivated by positive attitudes and norms, he or she may not actually perform a certain behavior because of feeling of a lack of control over his or her own activities. For this reason, the TPB (Ajzen, 1991; Ajzen & Madden, 1986) is developed in order to extend the TRA model to include an additional variable – perceived behavioral control. Perceived behavioral control refers to an individual’s perception of his or her ability to perform a behavior

Fig. 2. A problem definition wiki page.

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(Ajzen, 1991). The three components of the TPB model (attitudes, subjective norms, and perceived behavioral control) collectively explain behavioral intentions, and the TPB model has been widely adopted to investigate behaviors related to e-learning. To understand the adoption of collaborative technologies, the technology acceptance model (TAM) is used to describe individual users’ acceptance of information systems (Lee, Kozar, & Larsen, 2003). The model assumes that an individual’s acceptance of a system is determined by two major factors: perceived usefulness and perceived ease of use (Davis et al., 1989). A number of studies have used both the original and extended version of TAM to explore students’ acceptance of virtual learning environments (Arteaga Sánchez & Duarte Hueros, 2010; Limayem & Cheung, 2011; Ngai, Poon, & Chan, 2007; Ong, Lay, & Wang, 2004; Saade & Bahli, 2005; Selim, 2003, 2007; Venkatesh et al., 2003). The extension of TAM could indicate that, within the realm of virtual learning environment research, the original model is insufficient for explaining all aspects of user acceptance. Ngai et al. (2007) propose that perceived access to technical support also positively influences perceived ease of use and usefulness. Karahanna, Agarwal, and Angst (2006) extend TAM with the construct of compatibility that originally developed as part of innovation diffusion theory (Rogers, 1995). In the context of e-learning research, TAM has also been extended to address constructs of computer self-efficacy (Gong et al., 2004; Ong et al., 2004). The research from Gong et al. (2004) shows that computer selfefficacy directly influences the ease of use and the intention to use technologies to support learning. Manuel and Sanchez-Franco (2009) enhance the TAM model with the effect of perceived affective quality. They posit that perceived affective quality exhibit significant moderating effect on extending the technology acceptance model. The TAM was conceived at the individual level when it was originally developed, and research into the influence of subjective norms is considered to be one the major directions for enhancement of TAM. Davis et al. (1989) purport that a more multi-personal application, such as group decision support systems, will increase social influence on individuals’ behavior. Recently, researchers have examined the effects of social ties on the adoption of information technologies in the virtual environment. Hossain and de Silva (2009) explore user acceptance of technology by considering social ties in social networking systems. They investigate the effect of strong ties and weak ties on the user acceptance of a community capacity building network. TAM is enhanced by investigating the influence of different types of social ties on the user acceptance of a social networking system. Their research shows that peer influence in the form of strong ties has significant influence on behavioral intention, and that the influence of weak ties on behavioral intention is not significant. Therefore, different components of subjective norms can be used to investigate the influence on the adoption of computer-supported collaborative learning environments. To extend TAM for collaborative learning, researchers have considered the various constructs that characterize the collaborative aspects of technology adoption. Jarvenpaa and Staples (2000) investigate the factors that influence the use of collaborative electronic media. Information ownership and propensity to share are found to have a direct influence on the usage of collaborative technologies. Dasgupta et al. (2002) enhance the technology acceptance model with the measures of student performance from course-related assessments. Their research shows that the usage of an e-collaboration system has a positive effect on individual performance on course-related assessments, showing the importance of adoption and use of e-collaboration technologies. By incorporating technology specific variables into TAM, researchers have derived better models that address the collaborative use of information technology. Collaborative learning environments are developed to support collaborations and sharing among users, rather than being designed for individual use. To extend research models beyond individual attitudes toward a system, researchers have shown that the use of a system is influenced by collaborative activities among peers in a knowledge management environment. Liaw, Chen, and Huang (2008) develop a Web-based collaborative learning system for knowledge construction and sharing, and investigate the learners’ attitude toward the system. Based on TPB, the research model extends technology acceptance with constructs relating collaborative activities and learner characteristics. The acceptance model for Web-based collaborative learning also concludes that collaborative activities and learner characteristics have a direct effect on acceptance of the collaborative learning system. 5. Research model and hypotheses Having identified the need for a comprehensive TAM that incorporates the theory of planned behavior, this study aims at identifying the factors that influence the acceptance of Google Applications as an e-Learning platform for project sharing. This research significantly contributes to the development of attitude-behavior theories that explain students’ acceptance of collaborative learning technologies. It is based on the determinants that emerge from the integration of TAM, TRA and TPB. 5.1. The traditional TAM hypotheses TAM has been widely used to test the acceptance of e-learning technologies. Studies that use TAM (Davis, 1989) have addressed perceived usefulness, ease of use, attitude, behavioral intention, and system usage as the major determinants that predict the acceptance of a new technology (as shown in Fig. 3). In this study, perceived ease of use is defined as the degree to which the user believes that using Google Applications would be free of effort, and perceived usefulness is defined as the degree to which the user believes that using Google Applications would enhance his/her project performance (Davis, 1989). According to Davis (1989), perceived usefulness and perceived ease of use are important factors that influence the attitude of individuals toward a particular technology. The TAM posits that perceived ease of use and perceived usefulness have a direct effect on the attitude toward the use of a technology, and perceived ease of use has a positive effect on perceived usefulness. Attitude is defined as the degree to which a user is interested in using the system, and attitude toward the system determines behavioral intentions, which, in turn, lead to actual system usage. A number of researchers have applied TAM to explain user acceptance of collaborative systems. TAM has been applied to provide an empirical research model to explain user acceptance toward groupware (Lou, Luo, & Strong, 2000), and according to Padilla-Meléndez, Garrido-Moreno, and Aguila-Obra (2008), the results of research into the factors affecting e-collaboration technology in education also indicate a good fit for a modified version of TAM. Therefore, the following hypotheses based on TAM are proposed: H1 Perceived usefulness will positively influence attitudes toward the Google Applications platform. H2 Perceived ease of use will positively influence attitudes toward the Google Applications platform. H3 Perceived ease of use will positively influence perceived usefulness.


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Perceived Usefulness

H1 H3

H4 Attitude

Behavioral Intention

H5

System Usage

H2 Perceived Ease of Use

Fig. 3. Determinants from the technology acceptance model.

H4 Attitudes toward the Google Applications platform will positively influence intention to use. H5 Intention to use the Google Applications platform will positively influence system usage. According to Davis (1989), other external variables should be included in TAM for measuring the specific technology since they may influence the perceived ease of use and perceived usefulness of that technology. A number of researchers have investigated compatibility as an external variable that influence adoption of a new technology (Chau & Hu, 2001; Chen, 2011; Corrocher, 2011; Liao & Lua, 2008; Moore & Benbasat, 1991). According to the literature on innovation and diffusion theory, compatibility is defined as the degree to which an innovation is perceived as being consistent with existing values, needs, and experiences of potential adopters (Moore & Benbasat, 1991). In a previous study by Chau and Hu (2001), compatibility is found to have a significant influence on the ease of use associated with a new technology. In this study, compatibility is defined as the degree to which using Google Applications for project work is perceived as consistent with students’ experiences and needs (Corrocher, 2011). Since the collaborative learning platform provides an environment for handling project documents, it is important to consider the compatibility of the Google Applications platform with existing office tools for documentation and sharing. Significant incompatibility with existing office tools necessitates major work-related changes that often require considerable learning for the students, who as a result, will likely not perceive the technology to be easy to use. When it comes to Web 2.0 collaborative technologies, compatibility of Web-based tools with students’ needs is expected to influence behavioral intention through attitude (Hartshorne & Ajjan, 2009; Taylor & Todd, 1995). Since the Google Applications framework is considered a new technology for Web-based collaborations, we propose following hypotheses: H6 Compatibility will positively influence attitudes toward the Google Applications platform. H7 Compatibility will positively influence perceived ease of use. To extend the TAM, researchers have identified the perception of resources and support as a major external factor that affects the adoption of information technologies (Lee, 2008; Mathieson, 1991; Ngai et al., 2007). Perceived resources can be defined as the personal and organizational resources needed to use an information system (Mathieson, 1991). Ngai et al. (2007) investigate the supporting aspects associated with the use of an e-learning system, and posit that the perceived technical support is an important determinant that affects ease of use and usefulness of an e-learning system. They define technical support as “knowledge people assisting the users of computer hardware and software projects.” The use of Google Applications as a collaborative environment requires frequent interactions through the Internet. The perceived resource required to support the use of Google Applications includes the provision of Internet facilities and technical support. They have a significant effect on the efforts required for learning to use the collaborative tools. We thus derive the following hypothesis: H8 Perceived resource will positively influence ease of use. 5.2. The influence of subjective norms TAM was derived from the TRA proposed by Fishbein and Ajzen (1975), asserting that both attitudes and subjective norms impact behavioral intention, which, in turn, affects an individual’s actual behavior when using a certain technology. Subjective norms are generated by individuals’ perceptions that normative beliefs will influence their use of a technology. Arteaga Sánchez and Duarte Hueros (2010) incorporate subjective norms into TAM, showing that it has a significant impact on behavioral intention. Several researchers working in the area of IT usage (Ajzen, 1991; Davis et al., 1989; Fishbein & Ajzen, 1975; Mathieson, 1991; Taylor & Todd, 1995) suggest that normative beliefs should be multidimensional. In an e-learning environment in which Google Applications are used, the subjective norms that influence the behavioral intention to use a technology include instructor, peer, and external influences. Since Google Applications facilitate a social networking environment, this decomposition is consistent with the classification of strong and weak social ties (Hossain & de Silva, 2009). Therefore, the following hypotheses are presented in order to test the relationship between the factors related to distinct subjective norms and behavioral intention: H9a Peer groups will positively influence intentions to use the Google Applications platform. H9b External media will positively influence intentions to use the Google Applications platform. H9c The lecturer will positively influence intentions to use the Google Applications platform.

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5.3. The theory of planned behavior The proposed hypotheses seek to define the relationship between TAM and TRA, which was explicitly designed to explain volitional behavior (Ajzen, 1988). In order to use an Internet-based collaborative platform in an e-learning environment, it is necessary for students to make judgments regarding the skills and resources that they possess. To address the effect of perceived skills on collaborative e-learning, TAM needs to be enhanced to include determinants of behavioral control from TPB. Perceived behavioral control refers to an individual’s perception and assessment of his or her own ability to perform a behavior (Ajzen, 1991). In our research, we aim to enhance the TAM with additional behavior-related determinants derived from TPB, specifically those pertaining to sharing, collaboration, support, and control. Bandura (1982) has defined self-efficacy as “.people’s judgment of their capabilities to organize and execute courses of action required to attain designated types of performance. It is concerned not with the skill one has, but with the judgment of what one can do with whatever skills one possesses.” According to the TPB model, in a Web 2.0 e-learning environment, self-efficacy can be considered a component of behavioral control. Recent models for IT usage have incorporated self-efficacy as an antecedent of intention (Henry & Stone, 1995; Venkatesh & Davis, 2000; Yi & Hwang, 2003). In a Google Applications learning environment implementation using Web 2.0 technologies, self-efficacy is an important component that comes from students’ beliefs regarding their ability to perform in an interactive online environment. Therefore, we propose the following hypothesis: H10 Self-efficacy will positively influence intention to use the Google Applications platform. According to Wasko and Faraj (2005), sharing is defined as “the perception that participation enhances one’s professional reputation and individual’s experience in the practice is an important predictor of an individual’s contribution.” In a collaborative project-based environment, sharing is an important aspect. Students use collaborative platforms such as Google Applications in order to share information and documents, participate in online discussions, and manage resources and project Web sites. Therefore, sharing will affect an individual’s attitude and perception of usefulness. We propose the following hypotheses: H11a Sharing will positively influence perceived usefulness. H11b Sharing will positively influence attitudes toward the Google Application platform. Chen, Chen, and Kinshuk (2009) investigate knowledge sharing in virtual learning communities. They extend the perception of sharing in terms of knowledge creation self-efficacy. Knowledge sharing is defined as “a learner’s belief about his or her capabilities to articulate the ideas and experiences, synthesize knowledge from different sources, and learn from others.” The research results show that self-efficacy directly influences behavioral intention and system usage. In the current study, Google Applications are used as a collaborative learning technology for project sharing. According to Ajzen’s (1991) TPB, the perception of sharing can be considered as an assessment of one’s own ability and resources to perform a behavior (Dillon & Morris, 1996). As a kind of behavioral control, sharing from collaborative activities has a direct effect on behavioral intention (Liaw et al., 2008). It also has a direct effect on system usage (Jarvenpaa & Staples, 2000). We thus derive the following hypotheses: H11c Sharing will positively influence behavioral intentions. H11d Sharing will positively influence system usage. Many extended studies of TAM have investigated the effects of subjective norms on behavioral intentions to use a technology (Arteaga Sánchez & Duarte Hueros, 2010). Some have already found subjective norms to have a significant effect on dependent variables. In the studies concerning peer effects conducted by Lou et al. (2000), critical mass is considered a significant factor influencing collaborative technology which requires sharing and interaction among members. Therefore, once a sufficient number of peer members are participating in the collaborative platform, the use of a technology requires less explicit attention and often occurs unconsciously. We thus argue that subjective norms reduce the power of attitude to predict behavioral intention. At the introductory stage of a collaborative learning platform, an attitude toward a technology is an important factor that influences the intention to use it. As more peer members join a platform, intention becomes unconsciously directed by peers: the stronger the effect of peer influence, the weaker the relationship between attitude and behavioral intention. Therefore, we propose that peer influence has a negative moderating effect on the relationship between attitude and intention: H12 Subjective norms will have a negative moderating effect on the relationship between attitude and intention to use Google Applications. Fig. 4 presents the research framework and hypotheses for this study. 6. Research methodology 6.1. Questionnaire development In this research, we used a structured questionnaire consisting of two parts to test our theoretical model. The first part of the questionnaire measures the constructs included in the research model, while the second part collects demographic information about the participants. The items of the constructs were measured using a seven-point Likert scale, with answer choices ranging from “strongly disagree” (1) to “strongly agree” (7). All constructs derived from the literature, primarily from previously tested survey instruments, were meant to take advantage of well-tested psychometric measures (Straub, 1989). Most of the constructs were operationalized by modifying


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Sharing

H11a H11b

H11c

H11d


H1 H4

Attitude

H5

Behavioral Intention

System Usage

H3 H2

Perceived Ease of Use

H6 H12 H9a

H8

Perceived Resource

H9b

H9c

H10

H7

Compatibility

Subj Norm - Peer

Subj Norm - Media

Subj Norm - Lecturer

SelfEfficacy

Direct Effect Moderating Effect Fig. 4. The research framework.

previously validated scales. The scale items for perceived usefulness, attitude, and perceived ease of use were adapted from Davis (1989); the scale items for compatibility were adapted from Moore and Benbasat (1991); the scale items for subjective norms and self-efficacy were adapted from Taylor and Todd (1995); the scale items for facilitating resources were adapted from Ngai et al. (2007) and Mathieson, Peacock, and Chin (2001); and the scale items for sharing were adapted from Chen et al. (2009), together with items from Wasko and Faraj (2005). Behavioral intentions were assessed using adapted measures from Bhattacherjee (2001). Since the collaborative platform did not provide complete reports on the actual usage of the system, self-reported measures adapted from Fishbein and Ajzen (1975) were used. Each construct was measured using multiple indicators in order to capture the underlying theoretical dimensions effectively (Premkumar & Ramamurthy, 1995). The questionnaire consisted of 39 questions that addressed the factors included in the research model. The details of the questionnaire items and the source of literature for each construct are presented in Appendix A. 6.2. Data collection This study aims to investigate user perceptions of collaborative learning technologies. In order to avoid over generalizations, it focuses on the factors affecting student acceptance in a specific course delivery context. We investigated the attitudes of students toward the use of the technology in an “Advanced Marketing Research” course conducted at the Hong Kong Polytechnic University in December, 2010. During the course, students were required to work collaboratively in groups to complete a marketing research project for selected companies in Hong Kong. During the delivery of the subject by the researcher, different tools in Google Applications were used to facilitate collaborations, including Google’s share spaces, Forms, Google Docs, discussion forums, and Sites. The structured questionnaire was distributed to a sample of 150 students enrolled in this course, and a total of 136 questionnaires were collected and used for data analysis. The overall response rate was 91%. The data collected from the survey was used to investigate the critical factors affecting students’ acceptance of the technology. 7. Data analysis The partial least squares (PLS) technique was used for data analysis. This is a type of structural equation modeling technique used to statistically analyze and measure latent, unobserved concepts with multiple observed indicators (Chin, 1998b; Jöreskog & Wold, 1982). It can

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also be used to confirm the validity of the constructs of a particular instrument and assess the structural relationships among constructs (Chin, 1998a; Gefen, Straub, & Boudreau, 2000). PLS is most effective when used for theory development, whereas linear Structural Relationships (LISREL) is recommended for confirmatory analysis, as it requires a more stringent adherence to distributional assumptions (Jöreskog, 1989). We used PLS because its premises are less limiting than LISREL, and our sample size was relatively small. The statistical software SmartPLS 2.0 was used to assess the measurement and structural models of our research. 7.1. The measurement model The PLS measurement model for the reflective constructs was evaluated by examining the convergent and discriminant validity of individual indicators and the composite reliability of a block of indicators. Convergent validity, which tests the relationships among indicators in the same construct, was assessed by examining the correlation (loading) between the indicators, and construct scores were computed using PLS. We evaluated the measurement scales using the following three criteria suggested by Fornell and Larcker (1981) and Chin (1998b): (1) All indicator factor loadings should be significant and exceed 0.5; (2) Composite reliability should exceed 0.7; (3) Average variance extracted (AVE) from each construct should exceed 0.5. Convergent validity also shows the degree to which the items of a certain instrument are related. Table 1 shows the loadings of our model. All the reflective measures met the recommended levels for composite reliability and AVE. The Cronbach’s alpha scores ranged from 0.81 to 0.95, as shown in Table 1, indicating that the constructs have Cronbach’s alpha scores greater than the recommended minimum level of 0.7, and that they exhibit strong internal reliability. In addition, confirmatory factor analysis of the measurement model showed that all the standard factor loading values exceeded 0.5 and were significant at the 0.001 level. The composite reliabilities of constructs ranged from 0.87 to 0.97, again with all values above the recommended level of 0.7. The AVE values, ranging from 0.57 to 0.94, were greater than the variance due to measurement error. Therefore, all three conditions for convergent validity were met. In addition, a rule for assessing the discriminant validity requires that the square root of the AVE be larger than the correlations between the construct and any other construct in the model (Chin, 1998b). In Table 2, the diagonal entries represent the square root of the AVE for each construct. All other entries represent the corresponding correlation coefficients among the constructs. As shown in Table 2, all diagonal values exceeded the inter-construct correlations. Thus, all constructs in the model display adequate discriminant validity.

Table 1 The measurement model. Constructs

Indicators

Factor loadings

t-Value

Average Variance Extracted (AVE)

Composite Reliability (CR)

Cronbach’s alpha (a)

Attitude (ATT)

ATT1 ATT2 ATT3 BI1 BI2 BI3 CMPA1 CMPA2 CMPA3 RES1 RES2 RES3 RES4 RES5 PEOU1 PEOU2 PEOU3 PU1 PU2 PU3 SHA1 SHA2 SHA3 SHA4 SE1 SE2 SE3 SN1 SN2 SN3 SN4 SN5 SN6 SN7 SN8 B1 B2 B3 B4

0.91 0.89 0.91 0.95 0.96 0.95 0.78 0.87 0.90 0.79 0.76 0.73 0.82 0.67 0.92 0.95 0.87 0.84 0.85 0.91 0.78 0.88 0.85 0.79 0.95 0.95 0.91 0.89 0.83 0.85 0.97 0.97 0.89 0.91 0.86 0.84 0.88 0.79 0.82

46.86 34.87 42.33 82.30 93.76 72.11 16.84 26.58 59.53 11.99 14.72 10.83 16.38 7.97 64.85 93.48 53.25 29.38 21.44 68.55 12.24 31.01 26.14 20.30 92.94 89.37 55.00 34.98 11.90 21.34 106.23 148.71 5.22 5.76 6.30 22.55 37.50 15.77 23.01

0.82

0.93

0.89

0.91

0.97

0.95

0.73

0.89

0.81

0.57

0.87

0.81

0.83

0.94

0.90

0.76

0.90

0.84

0.68

0.90

0.84

0.88

0.96

0.93

0.73

0.89

0.82

0.94

0.97

0.93

0.79

0.92

0.87

0.70

0.90

0.84

Behavioral Intention (BI)

Compatibility (COMPA)

Perceived Resource (RES)

Perceived Ease of Use (PEOU)

Perceived Usefulness (PU)

Sharing (SHA)

Self-efficacy (SELF-EF)

Subjective Norm-Media (SN-MEDIA)

Subjective Norm-Peer (SN-PEER) Subjective Norm-Lecturer (SN-LEC)

System Usage (USAGE)


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Table 2 Correlation matrix and discriminant validity. Construct

ATT

BI

COMPA

PEOU

RES

SELF-EFF

SHA

SN-MEDIA

SN-PEER

SN-LEC

USAGE

PU

ATT BI COMPA PEOU RES SELF-EFF SHA SN-MEDIA SN-PEER SN-LEC USAGE PU

0.91 0.68 0.65 0.72 0.58 0.69 0.65 0.51 0.40 0.16 0.51 0.69

0.95 0.59 0.54 0.46 0.64 0.67 0.41 0.51 0.21 0.59 0.55

0.85 0.60 0.53 0.48 0.54 0.44 0.45 0.05 0.46 0.52

0.91 0.45 0.67 0.48 0.44 0.31 0.15 0.40 0.63

0.75 0.55 0.54 0.45 0.40 0.19 0.46 0.44

0.94 0.60 0.49 0.39 0.23 0.56 0.61

0.82 0.53 0.52 0.22 0.55 0.49

0.85 0.53 0.05 0.48 0.38

0.97 0.04 0.44 0.17

0.89 0.25 0.21

0.84 0.55

0.87

7.2. The structural model The structural model (see Fig. 5) was evaluated by examining the structural paths, t-statistics, and variance explained (the R-squared value). Path significances were determined by running the model through a bootstrap re-sampling routine (Efron & Tibshirani, 1993). Bootstrapping is a nonparametric method to assess the significance level of partial least square estimates (Chin, 1998b). It generates a certain number of subsamples by randomly choosing a case from the original data set. In this study, the number of cases used for bootstrapping is equal to the sample size, which is equal to 136 cases. The number of re-samples used for this study is equal to 1000. The results of data analysis are presented in Table 3. The seventeen hypotheses presented above were tested using the PLS approach. The path significance of each hypothesized association included in the research model and the variance explained (R2) by each path were examined. In this study, a one-tailed t-test was used since all hypotheses in this study are directional (Lee & Chen, 2010). According to the one-tailed t-test (df ¼ 135), the 0.05 significance level, or p < 0.05, requires a t-value >1.657, and the 0.01 significance level, or p < 0.01, requires a t-value >2.354. The 0.001 significance level, or p < 0.001, requires the corresponding t-value >3.152. Table 3 shows the path coefficients and their significance. Fifteen of the proposed hypotheses were supported, and all of the hypotheses derived from TAMdH1, H2, H3, H4, and H5dwere supported. As proposed, self-efficacy was found to have a positive influence on behavioral

Sharing

0.24**


0.25**

0.28***

0.29**

0.25***

R2 = 0.44 Attitude R2 = 0.70

0.52***

0.58**

0.33***

Perceived Ease of Use R2 = 0.39

0.16**

Behavioral Intention R2 = 0.62

0.39***

System Usage R2 = 0.39

-0.54* 0.58*

0.20* -0.08

0.18*

0.06

0.51***

Perceived Resource

Compatibility

Subj Norm - Peer

Subj Norm - Media

Fig. 5. Hypotheses testing results.

Subj Norm - Lecturer

Self Efficacy

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Table 3 Path coefficients. Path

b

t-value

Sig.

R2

Hyp/supported

Compatibility / perceived ease of use Perceived resource / perceived ease of use Sharing / perceived usefulness Perceived ease of use / perceived usefulness Compatibility / attitude Perceived ease of use / attitude Perceived usefulness / attitude Sharing / attitude Subjective Norm-Media / behavioral intention Subjective Norm-Lecturer / behavioral intention Subjective Norm-Peer / behavioral intention Sharing / behavioral intention Self-efficacy / behavioral intention Attitude / behavioral intention Attitude*Subjective Norm-Peer / behavioral intention Sharing / system usage Behavioral intention / system usage

0.51 0.18 0.24 0.52 0.16 0.33 0.25 0.28 0.08 0.06 0.58 0.25 0.20 0.58 0.54 0.29 0.39

6.64 2.17 3.13 6.13 2.42 3.59 3.34 4.30 0.92 1.07 2.28 2.83 2.20 3.13 1.71 2.79 3.79

*** * ** *** ** *** *** ***

0.39

H7/Yes H8/Yes H11a/Yes H3/Yes H6/Yes H2/Yes H1/Yes H11b/Yes H9b/No H9c/No H9a/Yes H11c/Yes H10/Yes H4/Yes H12/Yes H11d/Yes H5/Yes

0.44 0.70

0.62 * ** * ** * ** ***

0.39

Note: ***p < 0.001, **p < 0.01, *p < 0.05.

intention, supporting H10. Perceived resource was found to have a positive influence on ease of use, supporting H8. Compatibility was found to have a positive influence on perceived ease of use and attitude, supporting both H7 and H6. In addition, sharing was found to have a positive influence on perceived usefulness, attitude, behavioral intention, and system usage, supporting H11a, H11b, H11c, and H11d. However, our experimental results did not show that the instructor and mass media had a significant effect on intentions to use a technology, and thus H9b and H9c were rejected. As a component of subjective norms, peer influence was found to have a positive influence on behavioral intention, and H9a was supported. Finally, peer influence was also found to have a significant negative moderating effect on the relationship between attitude and behavioral intentions, thus supporting H12. The behavioral intention construct was predicted by subjective norm-peer, sharing, attitude, and self-efficacy, and these variables together explained 62% (R2 ¼ 0.62) of the variance in behavioral intention, indicating a high overall R-squared value. Attitude was predicted by perceived ease of use, perceived usefulness, compatibility, and sharing; together, these variables explained 70% (R2 ¼ 0.70) of the variance in attitude. Perceived ease of use was predicted by compatibility and perceived resource, and these variables explained 39% (R2 ¼ 0.39) of the variance in perceived ease of use. Perceived usefulness was predicted by perceived ease of use and sharing, and these variables explained 44% (R2 ¼ 0.44) of the variance in perceived usefulness. System usage, in turn, was predicted by behavioral intention and sharing, and these variables explained 39% (R2 ¼ 0.39) of the variance in system usage. 7.3. The moderating effect of peer influence In the path analysis, the subjective norms construct for peers had a negative moderating effect on the link between attitude and behavioral intention, with a path coefficient of 0.54, and a t-value of 1.71. The negative path coefficient of 0.54 for the moderating link implied that the effect of attitude on behavioral intention would decrease with any increase in peer influence. To study interactive effects, Chin, Marcolin, and Newsted (2003) recommend a hierarchical process for comparing R-squared values for the interaction and the main effect model. Thus, in testing the interaction effects of peer influence on the relationship between attitude and behavior, we compared the Rsquared value of the interaction effect model (as shown in Fig. 3) with the main effect model (which was found by deleting the interaction construct corresponding to the model’s moderating link). The difference in R-squared values was used to assess the size of the overall effect (f2) in order to determine the moderating effect of peer influence. The interaction effect model (with the moderating effect included) resulted in an R-squared value of 0.622. With the interaction effect excluded, the main effect model resulted in an R-squared value of 0.608. As shown in Table 4, the proposed link for moderating the relationship possessed significantly higher explanatory power than the main effect model. According to Cohen (1988), the values 0.02, 0.15, and 0.35 can be used to gauge whether a latent predictor variable has a small, medium, or large effect at the structural level. The effect is calculated using the formula f2 ¼ [R2 (interaction effect model) R2 (main effect model)]/ [1 R2 (main effect model)]. Applying this hierarchical test showed that the value of the size effect (f2) for the moderating link was 0.04, which represented a small effect. Given the significant negative path coefficient for the moderating link, it should be noted that a small size effect does not imply an unimportant effect (Limayem & Cheung, 2011). Actually, the inclusion of the moderating effect indicated a notably strong beta value of 0.54, increasing the R-squared value for intention to 0.622. 8. Discussion The use of technology to support project-based collaborations has changed over the past few years. Collaborative technologies such as Google Applications have significant contributions for enhancing project-based collaborations. They bring a level of functionality originally Table 4 Hierarchical test. R2 Main effect model Interaction effect model f2

0.608 0.622 0.04


171

associated with desktop applications to a Web browser, introducing ubiquitous possibilities for content creation, editing, and sharing. To better understand the factors leading to the adoption and use of Google Applications by students, this study aims at identifying the key factors that influence students’ intentions to use Google Applications. In order to understand the specific factors that may have a significant influence on the adoption of Web 2.0 collaborative technologies, we use TAM and TPB as the theoretical framework and investigate the decomposed constructs that influence the adoption of the technology. A detailed investigation of these factors has significant implications for those who might want to introduce collaborative technologies in a teaching and learning environment. In this study, we enhance the TAM to predict the acceptance of collaborative learning technologies. All hypotheses related to the original TAM are supported. In addition, our results are supported by the results of previous studies on the application of the TAM model to e-learning (Ngai et al., 2007; Roca, Chiu, & José, 2006; Van Raaij & Schepers, 2008). According to the results, determinants consistent with the technology acceptance model are major factors influencing the acceptance of collaborative technologies. Perceived ease of use and perceived usefulness are found to influence the attitude of students toward the technology. Perceived ease of use predicts usefulness and is found to be a stronger predictor of attitude than perceived usefulness. This is consistent with previous research on social networking systems, which support the fact that perceived ease of use more important than perceived usefulness for technology adoption in a Web 2.0 environment (Kwon & Wen, 2010). Consistent with the research by Davis (1989), attitude is the determinant of behavioral intention, which, in turn, predicts system usage. In addition to the original TAM constructs, we include a number of decomposed constructs from TPB in our enhanced model. The decomposed constructs that proved to be important in the collaborative learning context include: compatibility, perceived resource, selfefficacy, and sharing and peer influence, which are also found to be important determinants affecting the adoption of collaborative technologies. Compatibility is found to have a significant effect on attitude. This is supported by previous research on students’ adoption of Web 2.0 technologies (Hartshorne & Ajjan, 2009). Perceived resource is found to have a direct influence on perceived ease of use. This is supported by previous research e-learning adoption by Ngai et al. (2007). In a Web 2.0 collaborative environment, self-efficacy is considered as an important component of behavioral control. Consistent with other research (Hartshorne & Ajjan, 2009; Yi & Hwang, 2003), self-efficacy is found to have a direct effect on behavioral intention. Sharing is also found to be an important factor in a collaborative environment affecting usefulness, attitude, behavioral intention and system usage. This result is supported by the research results from Jarvenpaa and Staples (2000), which show that sharing has a direct effect on the adoption of collaborative technologies. In our study, the subjective norms component that is represented by student peer groups shows a significant effect on behavioral intention. This is consistent with previous studies on the direct effect of subjective norms on behavioral intention to use a collaborative technology (Lou et al., 2000). Although peer influence is less significant compared with attitude for predicting intention, its path coefficient (b ¼ 0.58) is the same as that for attitude in the path model, showing the large influence from peers in a collaborative context. As part of a group, potential users may feel obligated to use the technology because they feel that they belong to the group. They may realize that their participation is important for other group members to make use of Google Applications for project-based collaborations. However, our experimental results do not support the hypotheses on the direct influence of the instructor and mass media on the intention to use Google Applications. Even with positive communications about the collaborative platform, their influence on behavioral intention is not significant. The result is consistent with previous studies involving weak social ties in a social networking environment (Hossain & de Silva, 2009). As a critical mass effect (Lou et al., 2000), peer influence also moderates the relationship between attitude and behavioral intention. At the introductory stage of the Google Applications platform, attitude is found to be a good predictor of behavioral intention. As the influence of peers on behavioral intention increases and becomes dominant, the predictive power of attitude on intention decreases. 8.1. Limitations and future directions This study has some limitations that create opportunities for future research. First, any generalization of these research results to other Web 2.0 e-learning systems should be done cautiously, as the various Web 2.0 systems facilitate collaborative learning differently. Our study was limited to the following features of Google Applications: Google Sites, Forms, Docs, and discussion forums. Second, prior research has demonstrated the differences between self-reported usage measures and computer-recorded measures. As this study employed a studentreporting system for measuring usage behavior, it is possible that students over-reported their usage (Straub, Limayem, & KarahannaEvaristo, 1995). Finally, the use of Google Applications involves Web 2.0 facilitiesdsuch as user profiling, photograph and media sharing, and social networkingdthat might also influence students’ behavior (Cheung & Vogel, 2011). These factors were not considered in the current study. Future research could focus on other Web 2.0 features, identifying additional decomposed constructs that could further explain students’ motivational perceptions in Web 2.0 environments and provide greater insight into the determinants of technology acceptance. 8.2. Implications In recent years, numerous researchers have focused on how to employ Web 2.0 technologies to create new learning experiences in blended environments. Instructional and e-learning designers should make full use of Web 2.0 and collaborative learning technologies for project-based collaborations. This research addresses the need for a shift away from the use of traditional e-learning systems (such as WebCT) to collaborative learning technologies. Our study has shown that the adoption behavior of collaborative learning technologies is significantly different from that of other standard e-learning platforms. Therefore, the research has practical significance in that it addresses key factors relevant to practitioners and Web 2.0 e-learning designers. The Google Applications framework is implemented in a Web 2.0 environment. The communications and activities of project groups in Google Applications can be interpreted as collaborative tasks within a social networking environment. There were 26 groups in the Google Applications project-based environment. They formed 26 peer groups with strong social ties within a group, and these strong social ties were associated with strong investment of time, reciprocity and sharing (Wellman, 1997). According to the “strength of weak ties theory” of Granovetter (1973), weak ties are characterized by absent or infrequent contact, lack of emotional closeness and reciprocal services. We have

172


examined the amount of online interaction of the instructor within each project group. According to the approach used by Hossain and de Silva (2009), the instructor can be classified as a “weak social tie” in the e-learning system. Therefore, being considered as a “weak social tie” component of subjective norms, the instructor’s influence on behavioral intention is insignificant. Instructors involved in Web 2.0 collaborative learning systems should make sure that they show a significant amount of reciprocity and sharing to cope with the shift toward peer-learning environments. A significant implication resulting from this study is that while an emphasis on what the lecturer does to improve teaching is important, it is of greater importance to consider “what the students do” (Biggs, 2003) to cope with the changes toward student-centered learning environments. 9. Conclusions This study attempts to integrate the research models associated with the behavioral aspects of e-learning by employing TAM and TPB. Previous researchers have extended TAM with additional constructs for e-learning. However, few researches have sought to understand the determinants of collaborative technology adoption by extending TAM with a research model for collaborative learning environments. Thus, the significance of the current study is the identification of underlying factors that influence students’ intentions to use new learning technologies involving collaborative features. This study fills the gaps in the literature by incorporating students’ perceptions regarding Web 2.0 collaborative technologies into the empirical research model. Our study shows that the technology acceptance model can be applied to collaborative technologies for project-based learning. Furthermore, we extend the technology acceptance model by incorporating additional variables that are specific to the technology under study. The new constructs include compatibility, perceived resource, self-efficacy, sharing and peer influence. By incorporating these constructs, the enhanced model helps us better understand the factors affecting user acceptance decisions on collaborative technologies. A significant implication of the enhanced model is that while attitude is the most significant determinant of behavioral intentions (Davis, 1989), peer influence is also important in a collaborative context. Compared with other elearning applications, the adoption of collaborative technology is strongly influenced by peers. Furthermore, because of the highly interactive nature of the collaborative platform, the perceived ease of use of the system is important and is strongly predicted by compatibility with existing tools and practices. As Google Applications is only one of the widely used tools for collaborations, more research is needed before we can generalize our findings to other technologies for Web-based collaboration. Appendix A. Questionnaire items

Table 5 Definition and source of literature for the constructs. Constructs Perceived usefulness

PU1 PU2 PU3

Perceived ease of use

PEOU1 PEOU2 PEOU3

Compatibility

CMPA1 CMPA2

CMPA3 Subjective norms

SN1 SN2 SN3 SN4 SN5

SN6 SN7

Operational definition

Source of literature

Google Applications are of benefit to me. The advantages of Google Applications outweigh its disadvantages. Overall, Google Applications are advantageous. Learning to operate Google Applications is easy for me. It is easy for me to become skillful at using Google Applications. Overall, Google Applications are easy to use. Google Applications are compatible with my office tools. Google Applications fit well with the way I like to collaborate with other team members online. Using Google Applications fit into my work style for collaborative projects. I read/saw news report that using Google Applications was a good way of learning. The popular press depicted a positive sentiment for using Google Applications. Mass media reports influenced me to try out Google Applications. My team members expect me to use Google Applications for my project. My team members want me to use Google Applications for project collaboration frequently. My lecturer expects me to use Google Applications for project collaboration. My lecturer wants me to use Google Applications for project collaboration frequently.

Davis (1989)

Davis (1989)

Moore and Benbasat (1991)

Taylor and Todd (1995)


173

Table 5 (continued ) Constructs

Operational definition SN8

Sharing

SHA1

SHA2 SHA3

SHA4

Behavioral intention

BI1 BI2

BI3 Self-efficacy

SE1 SE2 SE3

Perceived resource

RES1

RES2 RES3 RES4 RES5

Attitude

ATT1 ATT2 ATT3

System usage

B1 B2 B3 B4

My lecturer is very supportive in the use of Google Applications for my project. I feel confident responding to others’ messages and shared project work through the Google Applications platform. I can share my project work through the Google Applications platform. I feel confident to be able to share information to help others to solve their problems. I am able to share information through the Google Applications collaboration tools and project Web sites. All things considered, I expect to continue using Google Applications for the project. All things considered, it is likely that I will continue to use Google Applications for the project. If I could, I would like to continue my use of Google Applications for the project. I would feel comfortable using the Google Applications on my own. If I want to, I can use Google Applications on my own easily. I would be able to use Google Applications even if there is no one around to show me how to use it. Guidance is available online or face-to-face during training sessions when I need help relating to Google Applications. The guidelines and demonstrations on using Google Applications are easily accessible. I can get support or help on using Google Applications from other fellow students. The computer I use to access Google Applications meets my needs. The speed of the Internet connection I use to access Google Applications meets my requirements. Using Google Applications is a good idea. I like using Google Applications. It is desirable to use Google Applications for learning that involves collaborative projects. I frequently use Google Applications for communicating project matters. I frequently edit Web pages in Google Sites. I frequently browse Web pages and information in Google Sites. I frequently post and read messages in Google forum in the AMR subject Web site.

Source of literature

Wasko and Faraj (2005); Fishbein and Ajzen (1975); Chen et al. (2009)

Bhattacherjee (2001)

Taylor and Todd (1995)

Mathieson et al. (1991); Ngai et al. (2007)

Davis (1989)

Fishbein and Ajzen (1975)

References Ajzen, I. (1988). Attitudes, personality, and behavior. Chicago: Dorsey Press. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Cliffs, NJ: Prentice-Hall. Ajzen, I., & Madden, T. J. (1986). The prediction of goal-directed behavior, attitudes, intentions, and perceived behavioral control. Journal of Experimental Social Psychology, 22, 453–474. Arteaga Sánchez, R., & Duarte Hueros, A. (2010). Motivational factors that influence the acceptance of Moodle using TAM. Computers in Human Behavior, 26, 1632–1640. Ayas, K., & Zeniuk, N. (2001). Project-based learning: building communities of reflective practitioners. Management Learning, 32(1), 61–76. Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37(2), 122–147. Bhattacherjee, A. (2001). Understanding information systems continuance: an expectation-confirmation model. MIS Quarterly, 25(3), 351–370. Biggs, J. (2003). Teaching for quality learning at university. Maidenhead, Berkshire: Open University Press. Blumenfeld, P. C., Soloway, E., Marx, R. W., Krajcik, J. S., Guzdial, M., & Palincsar, A. (1991). Motivating project-based learning: sustaining the doing, supporting the learning. Educational Psychologist, 26(3–4), 369–398. Boyd, D. M., & Ellison, N. B. (2007). Social network sites: definition, scholarship and history. Journal of Computer-Mediated Communication, 13(1), 210–230. Chatti, M. A., Dahl, D., Jarke, M., & Vossen, G. (2008). Towards Web 2.0 driven learning environments. In Proceedings of the fourth international conference on web information systems and technologies (pp. 370–375).

174


Chau, P. Y. K., & Hu, P. J.-H. (2001). Information technology acceptance by individual professionals: a model comparison approach. Decision Sciences, 32(4), 699–719. Chen, J. L. (2011). The effects of education compatibility and technological expectancy on e-learning acceptance. Computers & Education, 57(2), 1501–1511. Chen, I. Y. L., Chen, N. S., & Kinshuk. (2009). Examining the factors influencing participants’ knowledge sharing behavior in virtual learning communities. Educational Technology & Society, 12(1), 134–148. Cheung, R., & Vogel, D. (2011). Can Facebook enhance the communications between teachers and students? The International Journal of Learning, 17(11), 385–397. Chin, W. W. (1998a). Issues and opinion on structural equation modeling. MIS Quarterly, 22(1), 7–16. Chin, W. W. (1998b). The partial least squares approach to structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295–336). London: Lawrence Erlbaum Associates. Chin, W. W., Marcolin, B. L., & Newsted, P. R. (2003). A partial least squares latent variable modeling approach for measuring interaction effects: results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Information Systems Research, 14(2), 189–217. Christensen, C. M., Anthony, S., & Roth, E. (2004). Seeing what’s next: Using the theories of innovation to predict industry change. Boston, MA: Harvard Business School Press. Christensen, C. M., Horn, M. B., & Johnson, C. W. (2008). Disrupting class: How disruptive innovation will change the way the world learns. New York: McGraw-Hill. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale: NJ: Lawrence Erlbaum Associates. Corrocher, N. (2011). The adoption of Web 2.0 services: an empirical investigation. Technological Forecasting & Social Change, 78(4), 547–558. Dasgupta, S., Granger, M., & McGarry, N. (2002). User acceptance of e-collaboration technology: an extension of the technology acceptance model. Group Decision and Negotiation, 11(2), 87–100. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13, 319–340. Davis, F. D., Bagozzi, R., & Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management Science, 35(8), 982–1003. Dillenbourg, P. (1999). What do you mean by collaborative learning? Collaborative learning: Cognitive and computational approaches. Amsterdam, NL: Pergamon Elsevier Science. Dillenbourg, P. (2005). Designing biases that augment socio-cognitive interactions. In R. R. Bromme, F. F. Hesse, & H. H. Spada (Eds.), Barriers and biases in computer-mediated knowledge communications – How they may be overcome (pp. 243–264). Dordrecht, Netherlands: Kluwer Academic Publisher. Dillenbourg, P., Järvelä, S., & Fischer, F. (2009). The evolution of research on computer-supported collaborative learning. In N. Balacheff, S. Ludvigsen, T. Jong, A. Lazonder, & S. Barnes (Eds.), Technology-enhanced learning (pp. 3–19). Netherlands: Springer. Dillon, A., & Morris, M. G. (1996). User acceptance of information technology: theories and models. Annual Review of Information Science and Technology, 31, 3–32. Efron, B., & Tibshirani, R. J. (1993). An introduction to bootstrapIn Monongraphs on statistics and applied probability, Vol. 57. New York, NY: Chapman and Hall. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intentions and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–47. Gefen, D., Straub, D. W., & Boudreau, M. C. (2000). Structural equation modeling and regression: guidelines for research practice. Communications of the Association for Information Systems, 4(7), 1–79. Gong, M., Xu, Y., & Yu, Y. (2004). An enhanced technology acceptance model for web-based learning. Journal of Information Systems Education, 15(4), 365–374. Granovetter, M. (1973). The strength of weak ties. American Journal of Sociology, 78(6), 1360–1380. Hardless, C., Lindgren, R., & Schultze, U. (2007). Technology-mediated learning systems for project work. Scandinavian Journal of Information Systems, 19(2), 3. Hartshorne, R., & Ajjan, H. (2009). Examining student decisions to adopt Web 2.0 technologies: theory and empirical tests. Journal of Computing in Higher Education, 21(3), 183–198. Helic, D., Krottmaier, H., Maurer, H., & Scerbakov, N. (2005). Enabling project-based learning. International Journal on E-Learning, 4(4), 445–461. Henry, J. W., & Stone, R. W. (1995). A structural equation model of job performance using a computer-based order entry system. Behavior and Information Technology, 14(3), 163–173. Herrington, J., Reeves, T. C., & Oliver, R. (2010). A guide to authentic e-learning. New York: Routledge. Hossain, L., & de Silva, S. (2009). Exploring user acceptance of technology using social networks. Journal of High Technology Management Research, 20(1), 1–19. Jarvenpaa, S. L., & Staples, D. S. (2000). The use of collaborative electronic media for information sharing: an exploratory study of determinants. The Journal of Strategic Information Systems, 9(2), 129–154. Jenkins, H., Clinton, K., Purushotmas, R., Robinson, A., & Wiegel, M. (2006). Confronting the challenges of participatory culture: Media education for the 21st century. Chicago: MacArthur Foundation. Jöreskog, K. G. (1989). LISREL 7: A guide to the program and applications. Chicago: SPSS. Jöreskog, K. G., & Wold, H. O. A. (1982). Systems under indirect observation: Causality, structure, prediction. Amsterdam: North Holland. Karahanna, E., Agarwal, R., & Angst, C. M. (2006). Reconceptualizing compatibility beliefs in technology acceptance research. MIS Quarterly, 30(4), 781–804. Kwon, O., & Wen, Y. (2010). An empirical study of the factors affecting social networking service use. Computers in Human Behavior, 26(2), 254–263. Lampe, C., Ellison, N., & Steinfield, C. (2006). A Face(book) in the crowd: social searching vs. social browsing. In Proceedings of the 20th anniversary conference on computer supported cooperative work. Banff, Alberta, Canada: ACM. Lee, Y. C. (2008). The role of perceived resources in online learning adoption. Computers & Education, 50(4), 1423–1438. Lee, S. M., & Chen, L. (2010). The impact of flow on online consumer behavior. Journal of Computer Information System, 50(4), 1–10. Lee, Y., Kozar, K. A., & Larsen, K. R. T. (2003). The technology acceptance model: past, present, and future. Communications of the Association for Information Systems, 12, 780. Liao, H. L., & Lua, H. P. (2008). The role of experience and innovation characteristics in the adoption and continued use of e-learning websites. Computers & Education, 51(4), 1405–1416. Liaw, S.-S., Chen, G.-D., & Huang, H.-M. (2008). Users’ attitudes toward web-based collaborative learning systems for knowledge management. Computers & Education, 50(3), 950–961. Limayem, M., & Cheung, C. M. K. (2011). Predicting the continued use of Internet-based learning technologies: the role of habit. Behavior and Information Technology, 31(1), 91–99. Lou, H., Luo, W., & Strong, D. (2000). Perceived critical mass effect on groupware acceptance. European Journal of Information Systems, 9, 91–102. Love, D., Mckean, G., & Gathercoal, P. (2004). Portfolios to webfolios and beyond: levels of maturation. EDUCASE Quarterly, 25(2), 29–37. Manuel, J., & Sanchez-Franco. (2009). WebCT – the quasimoderating effect of perceived affective quality on an extending Technology Acceptance Model. Computers & Education, 54, 37–46. Mathieson, K. (1991). Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2(3), 173–191. Mathieson, K., Peacock, E., & Chin, W. W. (2001). Extending the technology acceptance model: the influence of perceived user resources. The Database for Advances in Information Systems, 32(3), 86–112. McAfee, A. P. (2006). Enterprise 2.0: the dawn of emergent collaboration. MIT Sloan Management Review, 47(4), 21–28. Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192–222. Ng, P., & Cheung, R. (2011). Predicting online purchase intentions for Taobao.com. In P. Kommers, J. Zhang, T. Issa, & P. Isaias (Eds.), Proceedings of the IADIS international conference on Internet technologies & society, China (pp. 246–250). Shanghai: IADIS Press. Ngai, E., Poon, J., & Chan, Y. (2007). Empirical examination of the adoption of WebCT using TAM. Computers & Education, 48(2), 250–267. Ong, C. S., Lay, J. Y., & Wang, Y. S. (2004). Factors affecting engineers’ acceptance of asynchronous e-learning systems in high-tech companies. Information & Management, 41(6), 795–804. Padilla-Meléndez, A., Garrido-Moreno, A., & Aguila-Obra, A.,R.,D. (2008). Factors affecting e-collaboration technology use among management students. Computers & Education, 5(1), 609–623. Premkumar, G., & Ramamurthy, K. (1995). The role of interorganizational and organizational factors on the decision mode for adoption of interorganizational systems. Decision Sciences, 26(3), 303–336. Prilla, M., Knipfer, K., Degeling, M., Cress, U., & Herrmann, T. (2011). Computer support for collaborative reflection on captured teamwork data. In W. Reinhardt, T. D. Ullmann, P. Scott, V. Pammer, O. Conlan, & A. J. Berlanga (Eds.), First European workshop on awareness and reflection in learning networks (pp. 56–61). Rienzo, T., & Han, B. (2009). Microsoft or Google Web 2.0 tools for course management. Journal of Information Systems, 20(2), 123–127. Roca, J. C., Chiu, C. M., & José, F. (2006). Understanding e-learning continuance intention: an extension of the technology acceptance model. International Journal of Human-Computer Studies, 64(8), 683–696. Rogers, E. M. (1995). Diffusion of innovations (4th ed.). New York: Free Press. Saade, R., & Bahli, B. (2005). The impact of cognitive absorption on perceived usefulness and perceived ease of use in on-line learning: an extension of the technology acceptance model. Information & Management, 42(2), 317–327.


175

Sanchez-Franco, M. J. (2010). WebCT – the quasimoderating effect of perceived affective quality on an extending Technology Acceptance Model. Computers & Education, 54(1), 37–46. Selim, H. M. (2003). An empirical investigation of student acceptance of course websites. Computers & Education, 40(4), 343–360. Selim, H. M. (2007). Critical success factors for e-learning acceptance: confirmatory factor models. Computers & Education, 42(9), 396–413. Soller, A. (2001). Supporting social interaction in an intelligent collaborative learning system. International Journal of Artificial Intelligence in Education, 12, 40–62. Stahl, G., Koschmann, T., & Suthers, D. (2006). Computer-supported collaborative learning: an historical perspective. In R. K. Sawyer (Ed.), Cambridge handbook of the learning sciences (pp. 409–426). Cambridge: Cambridge University Press. Straub, D. W. (1989). Validating instruments in MIS research. MIS Quarterly, 13(2), 147–166. Straub, D., Limayem, M., & Karahanna-Evaristo, E. (1995). Measuring system usage: implications for IS theory testing. Management Science, 41(8), 1328–1342. Taylor, S., & Todd, P. A. (1995). Understanding IT usage: a test of competing models. Information Systems Research, 6(2), 144–176. Van Raaij, E. M., & Schepers, J. J. L. (2008). The acceptance and use of a virtual learning environment in China. Computers & Education, 50(3), 838–852. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: four longitudinal field studies. Management Science, 46(2), 186–204. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: toward a unified view. MIS Quarterly, 27(3), 425–478. Wasko, M. M., & Faraj, S. (2005). Why should I share? Examining social capital and knowledge contribution in electronic networks of practice. MIS Quarterly, 29(1), 35–57. Wellman, B. (1997). An electronic group is virtually a social network. In S. Kiesler (Ed.), Culture of the Internet (pp. 179–205). Hillsdale, New Jersey: Lawrence Erlbaum. Yi, M. Y., & Hwang, Y. (2003). Predicting the use of web-based information systems: self-efficacy, enjoyment, learning goal orientation, and the Technology Acceptance Model. International Journal of Human-Computer Studies, 59(4), 431–449.