Exploring user acceptance of technology using social networks

Journal of High Technology Management Research 20 (2009) 1–18

Contents lists available at ScienceDirect

Journal of High Technology Management Research

Exploring user acceptance of technology using social networks

Liaquat Hossain a,⁎, Anjali de Silva b

a Project Management Graduate Programme, The University of Sydney, NSW 2006, Australiab Accenture, Sydney, Australia

a r t i c l e i n f o

⁎ Corresponding author. Tel.: +61 2 9306 9110; faxE-mail address: [email protected] (L. Hossain

1047-8310/$ – see front matter © 2009 Elsevier Inc.doi:10.1016/j.hitech.2009.02.005

a b s t r a c t

Available online 27 March 2009
The technology acceptance model (TAM) has been widely used to study user acceptance of newcomputer technologies. However, it does not incorporate social structure and influence as asignificant factor. In this study, we ask the following questions: (i) What are the limitations of theexisting TAM for studying virtual community? (ii) What is effect of social networks on useracceptance of technology for virtual community? and (iii) How can the influence of different typesof social ties serve as abasis for exploring theuseracceptanceof technology inavirtual community?Here, we explore the possibility for extending TAM to incorporate the influence of the differenttypes of social ties as a new theoretical construct. Preliminary analysis of data from a virtualcommunity results show that weak and strong ties influence technology acceptance. The findingsenable HCI researchers to account for influence of social ties in future investigations using TAM.
© 2009 Elsevier Inc. All rights reserved.

Keywords:Technology acceptanceUser behaviourStrong tiesWeak tiesSocial networksVirtual community

1. Introduction

Previous research on technology acceptance highlights that the lack of user acceptance of technology can lead to loss of moneyand resources (Lee, Cho, Gay, Davison, & Ingraffea, 2003). System acceptance and usage is increasingly viewed as an importantelement for the measurement of information systems success (DeLone & McLean, 1992). In the past, many different theoreticalapproaches have been applied and designed to explain, predict and increase user acceptance of information systems. These studieson information systems acceptance continuously report that user attitudes are important factors affecting the success of thesystem (Davis, 1989). In the context of information systems, there have been two distinctive approaches to the study of attitudetowards a new technology and its acceptance. These are, the technology acceptance model (TAM) and the social informationprocessingmodel (SIPM) (Lee et al., 2003). The TAM suggests that users formulate a positive attitude toward the technology whenthey perceive it to be useful and easy to use. Furthermore, user adoption and usage of a new information system can also bedetermined by the beliefs and attitudes toward the information systems (Davis, 1989). The SIPM assumes that attitudes towardtechnology are influenced by opinions, information, and behaviours of salient others (Salancik & Pfeffer, 1978). However,combining these two models into one model where the acceptance of a technology could possibly be better explained bycombining the theoretical constructs of both the models is lacking.

TAM's referent theory, the theory of reasoned action (TRA) (Fishbein & Ajzen, 1975) includes social influence via a constructcalled subjective norm. However, the social influence construct has received little attention in the context of TAM research (Lee etal., 2003). Davis (1989) acknowledged the following areas needing further research: (i) the omission of subjective norm from TAM(ii) addressing how other variables could affect TAM. Previous studies on SIPM suggest that socially communicated perceptionsand beliefs have an influence on usage behaviour. In this study, we provide a conceptual base for extending TAM in the context of avirtual community that is based in a rural culture (see Fig. 1). A new theoretical construct for the TAM, ‘the influence of differenttypes of social ties’ is proposed in this study to enhance the understanding of technology acceptance. As a result, this researchattempts to develop an understanding of the role of ‘the influence of different types of social ties’ for the acceptance and usage of a

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Fig. 1. The research model.

2 L. Hossain, A. de Silva / Journal of High Technology Management Research 20 (2009) 1–18

virtual community. This study uses social ties (i.e., strong or weak ties) as one of the major factors in users' adoption and use of avirtual community, drawing upon the ‘strength of weak ties’ theory of Granovetter (1973). Weak ties are characterised by absent orinfrequent contact, lack of emotional closeness and reciprocal services; strong ties are those in which demonstrate stronginvestment of time and reciprocity. Pairs of actors who maintain strong ties are more likely to trust each other in knowledgesharing (Wellman, 1997) and in the decision-making process (Wellman &Wortley, 1990). Therefore, the influence of stronger tiescould be different to the influence of weak ties in technology acceptance.

The limited number of studies conducted so far on social influence and technology acceptance has not addressed the differencebetween these two different types of ties and their individual influences on technology acceptance. This study addresses thislimitation seen in the technology acceptance literature so far by extending TAM to incorporate the influence of the different typesof social ties to technology acceptance. This extended technology acceptance model is applied in this study to a new informationsystems area of a virtual community, which is based in a rural culture and promotesmandatory usage of the service. The case of theCommunity Capacity Building Network (CCBN) of the New South Wales (NSW) government sets the context for this study. TheCCBN serves as a practical resource and communication channel to people who are working together to strengthen the capacity oftheir communities (CBNSW, 2004). The research questions and exploratory model for this study is presented below. The followingquestions guide this study: What are the limitations of the existing TAM for studying virtual community? What is effect of socialnetworks on user acceptance of technology for virtual community? How can the influence of different types of social ties (i.e.strong and weak ties) serve as a basis for exploring the user acceptance of technology in a virtual community?

2. Background of the study

Community can be formally defined as the structuring of elements and dimensions to solve problems within a local area(Nelson, Ramsey & Vemer, 1960). Pickering and King (1995) further suggest that community refers to a self-organizing group ofindividuals whose organizing principle is a shared interest or a set of interests. As such a community does not have to begeographically bound but bounded by interest of the members of the community. Virtual communities enable relationships todevelop on the basis of shared interests and it is not affected by differences in social status. This is the technologically supportedcontinuation of a long-term shift to communities organised by shared interests rather than shared neighbours or kinship groups(Wellman et al., 1996). These social relationships are no less valid than those that occur in ‘real life,’ since ties to the community arealso intermittent, specialised and varying in strength (Wellman & Gulia, 1999).

The focus our study on the Community Capacity Building Network (CCBN) implemented by the New South Wales (NSW)Government with an intention to support the capacity development of rural NSW communities. The term ‘community capacitybuilding’ means developing the capacity and skills of the members of a community in such a way that they are better able toidentify and help meet their needs and to participate more fully in society. The aim is to encourage people in a community to jointogether with others and through collective effort providewhat the community needs, but in such away that those taking part alsodevelop their own potential as members of society (CBNSW, 2004). To achieve the aim of community capacity building, a virtualcommunity website (communitybuilders.nsw) was developed for community members to access information about what otherswere doing and what works to make their community safer, more vibrant and enterprising. This website is an interactiveclearinghouse where the users from diverse non-profit, government and business, urban and rural communities contribute to itscontent and ongoing development by publishing their stories and tips to the site. This process contributes towards better social,economic and environmental outcomes for these rural communities (CBNSW, 2004).

Several criteria for the measurement of success of this new web-based initiative have been put forward by the com-munitybuilders.nsw. These measurements of success include: the number of people wanting to submit their material or hyper-links to their own site, the number of visitors to the site, the feedback on the type of articles people wish to see, the use ofthe discussion forum, and assessment of other sites linking to it and periodically used user surveys (CBNSW, 2004). These

3L. Hossain, A. de Silva / Journal of High Technology Management Research 20 (2009) 1–18

success measures are used here to evaluate the effectiveness and acceptance of this new service implemented in the NSW ruralcommunity.

3. Prior research on user acceptance of technology

System acceptance and usage is an important element for the measurement of information systems success (DeLone &McLean,1992). The technology acceptance model (TAM) (Fig. 1) developed by Davis (1989) is widely used to explain the technologyacceptance process in many different contexts, cultures and usage dimensions. However, research on the TAM's application to thevirtual world in lacking so far and as such this needs to be further studied. Davis (1989) proposed that future technologyacceptance research need to address other variables that could affect the TAM. Research on the effect of social ties on technologyacceptance is lacking to date. Therefore, it is useful to extend TAM to explore how the influence of social ties shape users' attitudesin the adoption and diffusion of new technologies.

HCI researchers have investigated and replicated the two constructs of perceived use (PU) and perceived ease of use (PEoU) andagreed that they are valid in predicting the individual's acceptance of various information technologies (Mathieson, 1991).However, depending on the specific technology context, additional explanatory variables may be needed beyond the ease of useand usefulness constructs (Moon & Kim, 2000). User acceptance is defined as “the demonstrablewillingness within a user group toemploy information technology for the tasks it is designed to support” (Dillon & Morris, 1997). Although this definition focuses onplanned and intended uses of technology, studies report that individual perceptions of information technologies are likely to beinfluenced by the characteristics of the technology, as well as the interaction with other users (Lee et al., 2003). As such, one'sperception of the system is influenced by the way people around that person evaluates and use the system (Rogers, 1986).Therefore, social ties in which links that connect individuals with others through the frequency and types of communicationsamong them can be considered essential in exploring user acceptance of technology (Song & Song, 2002). According to the SIPM(Salancik & Pfeffer,1978), individuals' perceptions of technologies are also influenced by the opinions, information, and behavioursof people they communicate with. In a study using the SIPM, Fulk, Stienfiedf, Schmitz, and Power (1987) reported that technologyrelated attitudes are often influenced by social interactions and psychological processes rather than directly by objective andindependent assessments of technical characteristics (Lee et al., 2003).

Studies on social influence have for the most part not focused their attention on the process through which social influenceunfolds to impact individual IT use. Research has not looked into how individual users actively use social information into theirdecisions regarding IT adoption and use (Jasperson, Sambamurthy, & Zmud, 2000). Also, although social ties have been examinedunder a wide range of social science disciplines (Lee et al., 2003), relatively little attempt has been made to integrate both factorsinto user's adoption of information technologies in the virtual environment. Furthermore, no attempt has beenmade to distinguishbetween the influence brought about by strong and weak ties separately in accepting a technology. As such it is necessary topropose a framework that attempts to incorporate these social ties as major factors of user acceptance of information technology inthe context of a virtual community.

The strength of a tie affects the level of resource exchanged. Where a stronger tie relation takes place, resources can be freelyexchanged and shared than where a weaker tie relation takes place (Garton, Haythornthwaite, & Wellman, 1997). Although weakties have the ability to provide peoplewith information and resources that are usually unavailable to themwithin their social circle,stronger ties can provide better assistance overall and are this service is freely available (Granovetter, 1982). Strongly tiedindividuals are motivated to share what information or resources they have and thus provide a ready access to informationcirculating their network, and provide help whenever needed to any of their stronger ties. However, due to the close associationwithin strong tie networks, they are often left with access only to the same resources as others with whom they are closely tied(Haythornthwaite, 2002).

Individuals adopt innovations with mainly private personal, individual consequences. Such innovations depend on interactionsthrough strong ties, such as the community ties and face-to-face interactions (Wejnert, 2002). Furthermore, whether an individualconsiders an innovation for adoption is strongly determined by compatibility between the characteristics of an innovation and theneeds of the individual (Valente & Rogers, 1995). Strong ties impose greater demand for conformity on the individuals and they areexpected to heed the advice of their stronger ties. The affective content of these relationships strengthens the role of their influence(Ruef, 2002). As such if one individual in such a network adopts something new, it is likely that the others will conform. However, itcould be unlikely that an individual in such a network will adopt anything new since they have pressure to conform.

Individuals who directly interact with each other and have a strong social tie may tend tomimic each other's behaviour becauseof direct communication and influence. These individuals are said to be structurally equivalent. Structurally equivalent individualsare subject to similar normative pressures and standards and are therefore more likely to adopt the same innovations. Theseindividuals model their behaviour after those with whom they have direct relations and those to whom they have similar roles, insimple their strong social ties (Burt, 1987). According to Wellman (1997) pairs who maintain strong ties are more likely to sharetheir knowledge and when such knowledge is from a strong social tie it is likely that this knowledge is accepted as true. Strong tiesalso provide broader support in one's decision making (Wellman & Wortley, 1990). These provide evidence that the influence ofstronger ties have a positive effect on one's technology acceptance process. However, the influence of weak ties cannot beundermined since many researchers believe that weak ties provide a similar influence to that of strong ties.

The networks inwhich people belong affects knowledge and resource exchange. In such a network, weakly tied persons are lesslikely to share resources, however they have the ability to provide access to more diverse types of resources because each of thesepeople operates in distinct social networks and therefore has access to different knowledge and resources (Garton et al., 1997). It is


expected that one's weak ties do not have relations with one another, but one's strong ties are more likely to be a densely knitgroup. However, each of theseweak ties has a densely knit cluster of their own, whichmeans that the tie forms an important bridgebetween the two densely knit clusters. Therefore these two clusters would not be connected to each other if it were not for theexistence of theweak tie (Granovetter, 1982). This connection provided byweak ties helps in diffusion of knowledge and resourcesbetween the connecting social networks.

According to the “strength of weak ties” theory of Granovetter (1973), an innovation is diffused to a larger number ofindividuals and traverses a greater social distance when passed through weak ties rather than strong ones. As such individualswith more weak ties have greater mobility and can live up to the expectations of different people in different places and atdifferent times, which makes it possible to withhold inner attitudes while conforming to various expectations. However, peoplewith strong ties share norms so strongly that little effort is needed to determine the intentions of others, since they are all alike. Assuch weak ties allow for the adoption of innovation, which is made difficult by the conformity present within strong ties(Borgatti, 2000). Weak ties allow for more experimentation in selectively combining ideas from one source with another andimpose fewer concerns regarding social conformity (Ruef, 2002). As such, individuals relying onweak ties as sources of idea aremore likely to be innovative.

Most of the research conducted so far on the influence of ties has focused on offline relationships. However, it is also necessary toconsider online ties since there is an increase in communities built online, and as such the influence of ties online becomes asimportant as the influence of ties offline. According to Wellman et al. (1996) computer supported social networks (CSSNs) shouldnot support much reciprocity, because many online ties are between people who have never met face-to-face and are thereforeweakly tied, theyare socially andphysically distant, andnot bound into densely knitwork or community structure. As suchWellmanet al. (1996) assumes that it is unlikely that there are strong ties found online and the majority of the ties online are weak ties.However, many other researchers such as Haythronthwaite (2002) assume that the characteristics held in offline environments arethe same as those in the online environment. Thus, online ties, like offline ties, are expected to be stronger to the extent that theydemonstrate greater varieties of interaction, exchange, and emotional support. Further study is required into exploring online ties,but it can be assumed that online exchanges are as real in terms of their impact on the tie as are offline exchanges, where socialsupport given online or offline is an exchange that adds to maintaining a tie. Therefore it is said that the number and types ofexchanges determine the strength of a tie, rather than whether it is maintained online, offline or both (Haythornthwaite, 2002).

3.1. Proposed extensions to the TAM

Previous research has continuously reported that the TAM is useful in predicting and explaining technology use in varioussituations (Dillon & Morris, 1997). Davis (1989) proposed that future research on TAM should explore other variables that couldaffect PU, PEoU, and use. Researchers have noted that TAM has a weakness in its lack of explicit inclusion of external variables. As aresult, many have proposed various extensions to the TAM (Lee et al., 2003). External variables are seen to determine EOU and PUto some extent. Therefore, most extensions to TAM are based on the antecedents of EOU and PU.

As an extension to the TAM, Dishaw and Strong (1990) suggested a model including the relation between task-technology fitand PU/PEoU. They found that the extended TAM explained the variance of the dependent variable better than the original TAM(Lee et al., 2003). After reviewing that most prior TAM research had focused only on extrinsic motivation and not on intrinsicmotivation, Moon and Kim (2002) suggested a model where perceived playfulness was described as one of the antecedents ofattitude toward the use of theWorldWideWeb. Davis et al. (1989) found that another intrinsic motivation factor that of perceivedenjoyment was significantly related to PEoU and extended TAM to include this construct (Lee et al., 2003). Bandura (1982)distinguished self-efficacy judgments from outcome judgments in his social cognitive theory. Outcome judgments indicate theextent to which successful behaviour is linked to valued outcomes. Applying Bandura's arguments to the TAM, Compeau et al.(1999) proposed an extended model where performance outcome expectations and personal outcome expectations were relatedto technology use (Lee et al., 2003).

Venkatesh (2000) made a comprehensive study of the determinants of EOU. The extended model of Venkatesh (2000)extendedmodel proposes control (internally and externally-defined as computer self-efficacy and facilitating conditions), intrinsicmotivation (defined as computer playfulness), and emotion (defined as computer anxiety) as anchors that influence users' earlyperceptions about the EOU of a new system. His results strongly supported this proposed model (Han, 2003). Venkatesh and Davis(2000) published their TAM2 model, which extends the original TAM by explaining PU and usage intentions in terms of socialinfluence and cognitive instrumental processes. They defined social influence processes (subjective norm, voluntariness, andimage) and cognitive instrumental processes (job relevance, output quality, result demonstrability, and EOU) as determinants ofthe user's formulation of PU (Han, 2003).

Beside these important external variables, innovation characteristics, individual differences and situational factors have alsobeen proven in previous research to be antecedents of PU or EOU. Individual differences include the role with regard to technology,level of education, prior similar experiences, participation in training, individual traits such as personal innovativeness, cognitiveabsorption and the relationship of these traits to computer self-efficacy and computer anxiety. Situational factors include thepositive, neutral and negative mood when the individual participates in a training program (Han, 2003). Such extension fulfils thekey purpose of TAM, which was to trace the impact of external factors on internal beliefs, attitudes, and intentions. However, abetter understanding of the antecedents of EOU and PU could help practitioners to diagnose the reasons for resistance totechnology and take these into considerationwhen building new systems. It would also help them to take proper efficient externalmeasures to improve user acceptance of technology (Han, 2003).

Table 1Current areas of application for TAM in IS research.

Physical environments Virtual environments

IS area Specific studies IS area Specific studies

Office applications Lotus, Word, Excel B2C e-commerce Online bookstoreCommunication technologies Email, voice mail, dialup, fax Virtual workplace systems Online service firmWorkstations Online servicesMicrocomputers Digital librariesTelemedicine technologiesDatabase systems


One such important variable is that of the influence of different types of social ties for technology acceptance. This construct hasreceived little attention in the context of TAM research. Studies have found that the socially communicated perceptions and beliefsmay influence usage behaviour and a person's perception of a system is influenced by theway people around that person evaluatesand use that system (Rogers, 1986). So, it is important to include this variable as a theoretical construct in the TAM. Studiescontinuously report that people are not always rational in selecting and using technologies, and the attitudes towards the use oftechnology are influenced by salient others (Lee et al., 2003). As such, TAM should be extended to incorporate the influence ofdifferent types of social ties in shaping users' attitudes, in the adoption and diffusion of new technologies.

3.2. Application of the technology acceptance model

The original technology acceptance model derived by Davis (1989) has been applied and studied in many different contexts,cultures and usage dimensions. Most of these studies have validated this model with only a few studies showing otherwise. Thissection will explore the studies that have already applied this model to different contexts, culture and usage dimensions. This willincorporate studies that have validates the TAM in such situations as well as the studies that failed to validate thismodel. It alsowillprovide an overview of the areas in which the application of the TAM is lacking that future research should focus on.

Table 1 shows that there are more studies that applied the TAM to physical environments than virtual environments, thereseems to be a large difference in the number of studies conducted in these two areas. Most of the studies have continued to applythis model to the physical environment and only a few have applied it to virtual environments. This imbalance created in theresearch needs to be fixed since the virtual environments are seen to be a major part of the current and emerging informationsystems field. As a result, future research needs to focus on the virtual world and apply, validate and extend TAM to suchenvironments.

Fig. 2 shows the information systems areas inwhich TAM has been applied from 1989 to 2002. It is seen that the research areasthat have been studied follows a trend towards studying virtual environments in the future. If this trend is to be followed, the gapin the knowledge and understanding of virtual environments in term of TAM will be narrowed as a result of these future

Fig. 2. The trend in research areas from 1989 to 2002.


researches. TAM aims to be theoretically justified in predicting and explaining user behaviour across various information systemscontexts. In the very first studies, Davis et al. (1989) tested TAM to study user behaviour about WriteOne, a word processingprogram, PROFS email, XEDIT file editor and IBM PC-based graphic systems, in the context of universities and organizations, usingboth students and knowledgeworkers as subjects (Han, 2003). TAMhas been tested and adopted across awide range of informationsystems applications. These include, key office IS applications, such as Lotus 1-2-3, Word, Excel (Mathieson, 1991) (Taylor & Todd,1995) (Venkatesh & Davis, 1996); communication technologies, such as Email, voicemail, Customer dial-up system and fax (Straubet al., 1995) (Szajna, 1996) (Venkatesh & Davis, 1996); Database systems (Venkatesh et al., 2002); microcomputers (Igbaria et al.,1996); workstations (Moore & Benbasat, 1991) (Lucass & Spitler, 1999); telemedicine technology for physicians (Chau, 1996) (Huet al., 1999); and other specific systems for specific organizations (Venkatesh & Davis, 2000). TAM-based studies have also beenconducted in many organizations, such as a large financial institution in America (Straub et al., 1995), a large Canadian integratedsteel company (Montazemi, 1991), accounting firms, public tertiary hospitals in Hong Kong (Hu et al., 1999), investment bank(Lucas & Spitler,1999), andUniversities. Knowledgeworkers and students in different organizations and universities are usually thesample in TAM-based studies since these users constitute large numbers and a great diversity of users base (Han, 2003).

With boom of the Internet, some researchers studied TAM to explain user behaviour about Internet-related IS applications.These include, WWW information services, Online services (Bhattacherjee and Gerlach, 1998), Virtual workplace systems(Venkatesh et al., 2002), digital libraries (Chau & Hu, 2001). As consumers increase their purchases through Internet, TAM hasbeen used to study consumer behaviour about B2C e-commerce applications, such as in web-based bookstores. When B2C e-commerce became an important research issue, online services firm (Bhattacherjee and Gerlach, 1998) and online bookstores andother such virtual organizations emerged as research contexts. As such, online e-commerce consumers have become a new usergroup in TAM-based studies (Han, 2003).

Although these studies have adopted TAM across different IS applications, research contexts and various user populations andshown it to be both parsimonious and theoretically justified, many of them do not apply the original TAM itself into their researchdesign. They extend the model by developing what they believe to be other important constructs in predicting acceptance (Han,2003). As such, it is necessary to distinguish between the TAM that can be applied to the physical and the virtual environments,through the differences the two environments pose in the use of information systems.

Experience is an individual difference factor that can influence a user's beliefs about using a system. Experience gained throughdirect use or past usage affects the user's perception of relevant beliefs concerning the target system mostly in a positive manner.Experience is seen as an important source of information regarding the system (Han, 2003). Previous research has looked into theeffects of experience in terms of individual differences. Taylor and Todd (1995) found that users' assessment of system usage wasinfluenced by their prior experience with the system. The results showed that for inexperienced users, PU and EOU were strongerpredictors of BI. However, their assessment of attitude to behavioural intention did not differ in experienced nor inexperiencedusers. Szajna (1996) studied the relationship between beliefs, intention and acceptance at pre and post-implementation stages.The role of experience was seen to be a major part in understanding this relationship. When an individual becomes moreexperienced with the technology, PU directly determines not only intention to use but also usage behaviour. These show that it isimportant to address the experience dimension with TAM (Han, 2003). However, the results of the study by Montazemi et al.(1996) showed that years of computer experience did not have a significant effect on the PU and EOU of a software package.However, the experience with the software was not taken into consideration, which was a major limitation of this study.

The moderating effects of experience have been considered in some studies. Venkatesh and Davis (1996) tested the role ofexperience in determining EOU and its moderating effects in determining other factors influencing EOU. It was seen that the directexperience with the system was important for users to formulate system-specific EOU perceptions. Such perception was notcreated after seeing only a video mock-up, which shows the significance of the interaction between system and direct experiencein determining EOU. Venkatesh and Davis (2000) showed that experience is important in influencing andmoderating users' gener.TAM assumes that given sufficient time and knowledge about a particular behavioural activity, an individual's stated preference toperform the activity BI, closely resembles the way they do behave (Han, 2003). This assumption only applies, when the behaviouris under a person's voluntary control (Ajzen & Fishbein, 1980). Majority of the studies conducted so far follows this assumption.Mathieson (1991) proposed a perceived voluntariness scale to clarify statements about the freedom of choice in adoptinginnovations and Agarwal and Prasad (1997) examined its role in user acceptance of IT. The results showed that perceivedvoluntariness was significant in explaining current usage, however this did not affect the intention to continue use. In the study byVenkatesh and Davis (2000) voluntariness was presented as an important moderator, influencing the user's internal beliefs,attitudes and intentions with regard to a system. The results showed that both experience and voluntariness moderated the effectsof social norms on behavioural intentions. However, in the study of Lucas and Spitler (1999) where usage was mandatory, it wasseen that social norms would directly affect intention.

One of the main purposes of the TAM is to explain and predict initial adoption behaviour (Davis, 1989). Frequency and volumeof system usage can be adapted to measure the initial adoption behaviour. Information systems diffuse due to the increasingdecision of individuals to adopt them (Han, 2003). Studies such as Szajna (1996) and Agarwal and Prasad (1997) indicated thatusers maybe persuaded to use a new system early in the implementation process but the benefits offeredmay never be achieved inthe absence of continued sustained usage where some discontinuance behaviour may occur. Two types of discontinuancebehaviour may occur: replacement where users use an alternative system instead of the original one that they use initially anddisenchantment where users become dissatisfied with the systems or services and thus not use them any more (Han, 2003). Thetemporal dimension of systemusagemaygive rise to different behaviour intentions, attitudes and beliefs towards the systembeingformed. These can be used in turn, to predict the probability of usage (Han, 2003).


3.3. Limitations in past research on TAM

As mentioned before most of the studies conducted so far have applied TAM in physical environments and have not done soequally in virtual environments. However it was seen that future research is following a trend towards applying TAM to virtualenvironments, which are becoming a major part of the new technology revolution. Future studies need to apply TAM to virtualenvironments to predict if the TAM can be generalized to explain technology acceptance in such environments as well. Somestudies questioned the applicability of the TAM in their studies. Lucas and Spiter (1999) found that in broker workstations, theindividual perception variables PU and EOU of the TAM did not approach significance in predicting use. They argued that, in thiscircumstance, combining these two into a single variablewouldmake it a significant predictor of use. This provides limited supportfor the original TAM since PU and EOU are postulated as two distinct constructs. They explain that TAM's weak support is due to thenature of the system, not enough voluntary use of the system and the field environment inwhich their study was conducted. Theyconcluded that possibly TAM cannot work well for a modern, complex technology, where usage is mandatory in nature (Han,2003).

The studies by Hu et al. (1999) and Chau and Hu (2001) found that EOU had no significant influence on PU and attitude. Theysuggested that this might reflect limitations in the TAM's applicability to technologies, user populations or both. They concludedthat their study subjects, who were physicians, might differ from the subjects of prior studies. Since, physicians are professionalsthey might have exhibited considerable differences in general competence, adaptability to new technologies, intellectual capacityand the different nature of their work. They concluded that the explanatory power of the TAM, particularly the EOU factor, mightweaken as the competency of the users increases. Future studies need to look into these factors and extend the model toaccommodate these factors, which could include proposing different models for different environments and different user groups.

Different empirical designs usually have different indicators to measure system usage, one of which is self-reports. However, itis not known how accurately self-reports reflect actual behaviour. Szajna (1996) proposed that the intention–usage link isdependent on the method for measuring usage. It was seen that intentions predict self-reported usage but do not predict actualusage, so it is necessary to validate self-reported usage as a construct. Some researches used computer-recorded system usage tomeasure actual usage (e.g., Straub et al; Szajna 1996). These two constructs did not appear to be strongly related to each other. Assuch, it could be said that research that have relied on subjective measures for dependent variables, such as system usage, may notbe discovering the true, significant effects (Straub et al., 1995). Agarwal and Prasad (1997) proved that current usage was not asignificant predictor of future use intentions. This suggested that factors generated by initial use couldn't be relied on to explainand predict continued, sustained use of the target innovation. Venkatesh et al. (2002) reported that short-term use is the solepredictor of continued usage where all other variables measured at the time of initial adoption were non-significant predictors ofcontinued use. Therefore, the temporal dimension of system usage draws attention when designing empirical studies to exploresystem usage behaviour. The momentum generated by initial use should be reconsidered or modified when taking the temporaldimension into consideration (Han, 2003).

The technology acceptance model has been criticized by researchers for its exclusion of many theoretical constructs that haveshown to be important in technology acceptance. As such, much research has been carried out to include many differenttheoretical constructs that were not originally included in the TAM by Davis (1989). These researches has allowed the TAM to beextended so that constructs deemed important for technology acceptance can be addressed in the model, which provides for abetter understanding of the acceptance process of new technologies. A theoretical construct that has received very little attentionin TAM research is that of the influence brought about by one's social ties when accepting a new technology. Davis (1989)highlighted that the role of social influences in information technology acceptance and usage represented an important area forbetter understanding the applications of TAM. As such, some research has addressed social influence to be an important theoreticalconstruct in TAM. Venkatesh and Morris (2000) looked at the social influence in technology acceptance by studying the effects ofgender on this process. Malhotra and Galletta (1999) extended TAM to include the social influence brought about by psychologicalattachment. Lee et al. (2003) proposed social expectation as an extension to TAM and looked at the social networking process indistance learning. Song and Song (2002) extended the technology acceptance model to include the influence of social ties.

Social ties refer to the links that connect individuals with others through the frequency and types of communications amongthem (Pickering & King, 1995). Previous studies have examined the relationship between collaborative electronic media and socialties: exploitation of inter-organizational computer-mediated communication infrastructure (Pickering & King,1995); usefulness ofelectronic ties through broadcast messages (Constant et al., 1996); and electronic media usage for information exchange(Haythornthwaite &Wellman,1998). However, most of these researches only took into consideration the influence of social ties forperceived usefulness but did not relate it to the other constructs of the original TAM. Also, all the studies that looked into social tiesas a new theoretical construct for the TAM, did not distinguish between the different types of strength of the ties such as theinfluence of strong ties versus the influence of weak ties in accepting a new technology.

3.4. Extended technology acceptance model

The extended technology acceptance model (Fig. 3) proposed in this study incorporates a new theoretical construct call the‘influence of different types of social ties.’ A social tie is a connection between individuals through one or more relations. Ties oftenvary in strength, content and direction and are primarily referred to as either strong ties or weak ties. Weak ties are, on the mostpart, rarely maintained and non-intimate relations that may exist between co-workers who do not have any joint roles and haveonly occasional interactions. Strong ties, on the other hand, include a combination of familiarity, a give and take relationship,

Fig. 3. Path analysis results for the original technology acceptance model.


regular communication and common characteristics, such as with close friends (Garton et al., 1997). Different strengths of ties playan important role in access to resources and influences behaviour of the individual. Also, these strengths of ties are present in thevirtual environment as much as it is in the physical environment.

IS researchers have investigated and replicated the two constructs of PU and PEoU and agreed that they are valid in predictingthe individual's acceptance of various information technologies (Mathieson,1991). However, depending on the specific technologycontext, additional explanatory variables may be needed beyond the ease of use and usefulness constructs (Moon & Kim, 2000).Davis (1989) claimed that future technology acceptance research needs to address how other variables affect usefulness, ease ofuse, and user. acceptance.

User acceptance is defined as “the demonstrable willingness within a user group to employ information technology for thetasks it is designed to support” (Dillon & Morris, 1997). Although this definition focuses on planned and intended uses oftechnology, studies report that individual perceptions of information technologies are likely to be influenced by the characteristicsof the technology, as well as the interactionwith other users (Lee et al., 2003). As such, one's perception of the system is influencedby the way people around that person evaluates and use the system (Rogers, 1986).

Davis (1989) had observed that the omission of subjective norm from TAM represented an important area needing furtherresearch. Davis (1989) observed that: “the subject may want to do what Referent X thinks he/she should do, not because of X'sinfluence, but because the act is consistent with the subject's own [attitude]” and underscored that the role of social influences ininformation technology acceptance and usage represented an important area for better understanding of ‘real world’ applicationsof TAM. Davis (1989) highlighted the importance of developing knowledge in this area (Malhotra & Galletta,1999). Social ties referto the links that connect individuals with others through the frequency and types of communications among them (Song & Song,2002). Previous studies have looked at the relationship between social ties and technology in areas such as: exploitation of inter-organizational computer-mediated communication infrastructure (Pickering & King 1995); usefulness of electronic ties throughbroadcast messages (Constant et al., 1996); and electronic media usage for information exchange (Haythornthwaite & Wellman1998).

A consistent finding from prior research on technology use is that user attitude toward new technology is the key factor forsuccessful deployment. Findings suggest that attitude formation is influenced by the objective characteristics of the system, theextent of use, and individual user differences (Lee et al., 2003). However, studies also continuously report that people are notalways rational in selecting and using technologies, and attitudes toward and use of technology are influenced by culture, norms,social contexts, or salient others (Lee et al., 2003).

As an explanation of these results, Salancik and Pfeffer (1978) developed the SIPM. According to the SIPM, individuals'perceptions of technologies are also influenced by the opinions, information, and behaviours of people they communicate with. Ina study using the SIPM, Fulk et al. (1987) reported that technology related attitudes are often influenced by social interactions andpsychological processes rather than directly by objective and independent assessments of technical characteristics (Lee et al.,2003).

Prior studies of social influence have for themost part not focused their attention on the process throughwhich social influenceunfolds to impact individual IT use. Research has not looked into how individual users actively use social information into theirdecisions regarding IT adoption and use (Jasperson et al., 2000). Also, although social ties have been examined under a wide rangeof social science disciplines (Lee et al., 2003), relatively little attempt has been made to integrate both factors into user's adoptionof information technologies in the virtual environment. Also, no attempt has been made to distinguish between the influencebrought about by strong and weak ties separately in accepting a technology. As such it is necessary to propose a framework thatattempts to incorporate these social ties as major factors of user acceptance of information technology in the context of a virtualcommunity.


3.4.1. The influence of strong social ties on technology acceptanceThe strength of a tie affects the level of resource exchanged. Where a stronger tie relation takes place, resources can be freely

exchanged and shared than where a weaker tie relation takes place (Garton et al., 1997). Although weak ties have the ability toprovide people with information and resources that are usually unavailable to them within their social circle, stronger ties canprovide better assistance overall and are this service is freely available (Granovetter, 1982). Strongly tied individuals are motivatedto share what information or resources they have and thus provide a ready access to information circulating their network, andprovide help whenever needed to any of their stronger ties. However, due to the close associationwithin strong tie networks, theyare often left with access only to the same resources as others with whom they are closely tied (Haythornthwaite, 2002).

Individuals adopt innovations with mainly private personal, individual consequences. Such innovations depend on interactionsthrough strong ties, such as the community ties and face-to-face interactions (Wejnert, 2002). Furthermore, whether an individualconsiders an innovation for adoption is strongly determined by compatibility between the characteristics of an innovation and theneeds of the individual (Valente & Rogers 1995). Strong ties impose greater demand for conformity on the individuals and they areexpected to heed the advice of their stronger ties. The affective content of these relationships strengthens the role of their influence(Ruef, 2002). As such if one individual in such a network adopts something new, it is likely that the others will conform. However, itcould be unlikely that an individual in such a network will adopt anything new since they have pressure to conform.

Individuals who directly interact with each other and have a strong social tie may tend tomimic each other's behaviour becauseof direct communication and influence. These individuals are said to be structurally equivalent. Structurally equivalent individualsare subject to similar normative pressures and standards and are therefore more likely to adopt the same innovations. Theseindividuals model their behaviour after those with whom they have direct relations and those to whom they have similar roles, insimple their strong social ties (Burt, 1987).

According to Wellman et al. (1997) pairs who maintain strong ties are more likely to share their knowledge and when suchknowledge is from a strong social tie it is likely that this knowledge is accepted as true. Strong ties also provide broader support inone's decision making (Wellman &Wortley, 1990). These provide evidence that the influence of stronger ties have a positive effecton one's technology acceptance process. However, the influence of weak ties cannot be ignored sincemany researchers believe thatweak ties provide a similar influence to that of strong ties.

3.4.2. The influence of weak social ties on technology acceptanceThe networks inwhich people belong affects knowledge and resource exchange. In such a network, weakly tied persons are less

likely to share resources, however they have the ability to provide access to more diverse types of resources because each of thesepeople operates in distinct social networks and therefore has access to different knowledge and resources (Garton et al., 1997).

It is expected that one's weak ties do not have relations with one another, but one's strong ties are more likely to be a denselyknit group. However, each of these weak ties has a densely knit cluster of their own, which means that the tie forms an importantbridge between the two densely knit clusters. Therefore these two clusters would not be connected to each other if it were not forthe existence of the weak tie (Granovetter, 1982). This connection provided by weak ties helps in diffusion of knowledge andresources between the connecting social networks.

According to the “strength of weak ties” theory of Granovetter (1973), an innovation is diffused to a larger number of individualsand traverses a greater social distancewhenpassed throughweak ties rather than strong ones. As such individuals withmoreweakties have greater mobility and can live up to the expectations of different people in different places and at different times, whichmakes it possible to withhold inner attitudes while conforming to various expectations. However, people with strong ties sharenorms so strongly that little effort is needed to determine the intentions of others, since they are all alike. As suchweak ties allow forthe adoption of innovation,which ismadedifficult by the conformity presentwithin strong ties (Borgatti, 2000).Weak ties allow formore experimentation in selectively combining ideas from one source with another and impose fewer concerns regarding socialconformity (Ruef, 2002). As such, individuals relying on weak ties as sources of idea are more likely to be innovative.

Weak ties are actually crucial to diffusion of innovations because they bring in new information to the clique where peoplepretty much know the same material. Close interpersonal networks of strong ties rarely see someone within exerting stronginfluence when new information is encountered because all members have the same information (Rogers, 1986). According toGranovetter (1973), people don't trust intimates regarding credibility of new information. People model their behaviour on keyinfluencers in their key circles who seem to share their values. When the key influencers transmit new information, the peoplewatching them as models consider it may be true and behave accordingly. There is a weak social tie between the key influencersand other people. Thus, new information travels along the route of a weak human tie rather than a strong one (Granovetter, 1973).

3.4.3. The influence of social ties in physical and virtual contextsMost of the research conducted so far on the influence of ties has focused on offline relationships. However, it is also necessary

to consider online ties since there is an increase in communities built online, and as such the influence of ties online becomes asimportant as the influence of ties offline.

According toWellman et al. (1996) computer supported social networks (CSSNs) should not support much reciprocity, becausemany online ties are between people who have never met face-to-face and are therefore weakly tied, they are socially andphysically distant, and not bound into densely knit work or community structure. As such Wellman et al. (1996) assumes that it isunlikely that there are strong ties found online and the majority of the ties online are weak ties. However, many other researcherssuch as Haythornthwaite (2002) assume that the characteristics held in offline environments are the same as those in the onlineenvironment. Thus, online ties, like offline ties, are expected to be stronger to the extent that they demonstrate greater varieties of


interaction, exchange, and emotional support. Further study is required into exploring online ties, but it can be assumed that onlineexchanges are as real in terms of their impact on the tie as are offline exchanges, where social support given online or offline is anexchange that adds tomaintaining a tie. Therefore it is said that the number and types of exchanges determine the strength of a tie,rather than whether it is maintained online, offline or both (Haythornthwaite, 2002).

There are numerous online ties, whichmeet the majority of the criteria for strong ties. That is, these ties facilitate reciprocation,companionship, and provide supportive contact. The anonymity of CSSN interactions facilitates long-term contact without the lossof relationships that occur through residential mobility. Numerous online ties are reasonably strong “intimate secondaryrelationships,”which occur in only one specialised domain via computer-mediated communication to provide support on a regularbasis. These relationships may become more personal and intimate over time. However, the limited social presence slows downthe development of intimacy, but online interactions eventually do develop to become as sociable and intimate as offlineinteractions. For these reason the concerns about whether online ties can become as strong as face-to-face ties are wronglyspecified (Wellman et al., 1996).

CSSN transcends time and space, and as such it is likely that most ties are neither totally online or offline, much online contact isbetween people who actually see each other in person and live locally and this contributes to the strength of the ties being strongand weak in both the domains of offline and online (Wellman et al., 1996). As such the influence that these two types of ties haveon one's technology acceptance process offline, also could be prominent in the different influences these two types of ties have insuch situations online.

4. Methodology: social network analysis

A social network is a set of actors and the relations that hold these actors together. Actors can be individual people or aggregateunits such as departments, organizations, or families. Actors exchange one or many resources with each other, which then allowthem to be connected in a social network. Such resources that could be exchanged between these actors include data, information,goods and services, social support or financial support. These kinds of resource exchanges are considered a social network relation,where individuals who maintain the relation are said to maintain a tie (Haythornthwaite & Wellman, 1998). The strength of theirtie may range from weak to strong, which depends on the number and types of resources they exchange, the frequency ofexchanges and the intimacy of the exchanges (Marsden & Campbell, 1984). Social network analysis (SNA) provides both a visualand a mathematical analysis of human relationships. SNA is used to understand networks and their participants by evaluating theposition, structure and ties of actors in the network. These measures help determine the importance, or prominence, of specificnodes in the network. The greatest benefit of network research is that it considers how the communication network structure of agroup shapes participant behaviour and cognition (Cho, Stefanone, & Gay, 2002).

A survey was designed to collect data firstly to explore each of the constructs in the original Technology acceptance modelwhen applied to a virtual community. This data was collected using 7-point Likert type scales for each question. Secondly, thesurvey collected data for the influence of different types of social ties on an individual's technology acceptance process asmentioned in the extended technology acceptance model derived for this study. This data was collected by asking participants toname their ties and how much the communication with these ties influenced their acceptance of this virtual community. Thediscussion forum of the communitybuilders.nsw website was used to implement the survey instrument. As such, the survey wasconducted online and was aimed at the users of the communitybuilders.nsw website. Web-based surveys have a number ofbenefits over conventional paper-based surveys. These include the following: they are more inclusive allowing a further reach,cheap to carry out, can recruit large number of participants, data is captures in electronic format making analysis faster and allowsrapid updating of questionnaire content (Wyatt, 2000). In this study, to collect data, a posting was made in the discussion forumasking its users to participate in this study by completing the survey instrument. 30 members of communitybuilders.nswparticipated in the study, of whom 18 were female and 12 were male. All of these participants have been active members of thecommunitybuilders.nsw community for more than 6 months. As such it was assumed that these members have accepted thistechnology as something that is helpful in their work. It was this acceptance process that was looked into in this study.

The questionnaire contained multiple measurement items related to each of the constructs in the research model (Fig. 1). Asobserved in prior literature that looked into the validity of the Technology acceptance model, multi-item self-report Likert typescales ranging from 1 to 7, were used to measure the validity of all the constructs. The questionnaire was partitioned into sixsections, each section collecting data on a specific theoretical construct in the research model (Fig. 1). These sections included:

Perceived usefulness (section.1): this section consisted of five items from Davis (1989), measuring the extent to which a personbelieved that the technology was capable of being used advantageously and provided positive expected outcomes. Thereliability coefficient (Chronbach's alpha) for this section was .92.Perceived ease of use: this is aimed atmeasuring the degree towhich a person believed that using a particular technology systemwould be free of cognitive effort. The scale consisted of five items, developed and validated by Davis (1989) (Chronbach'salpha=.90).Attitude toward use: Taylor and Todd (1995) devised and validated the 4-item attitude scale, which measured whetherindividuals like/dislike using the technology and how they felt using the technology (Chronbach's alpha=.90).Behavioural intention: this section consisted of five items from Davis (1989), measuring the extent to which a person believedthat the technology was capable of being used advantageously and provided positive expected outcomes (Chronbach'salpha=.91).


Actual use: use of collaborative tools was measured by a scale consisting of four items adapted from Cheung, Chang, and Lai(2000). The scale measured the frequency and intensity of technology use and the extent to which students used thetechnology for various purposes (Chronbach's alpha=.81).Influence social tie: this section consisted of two matrices. One matrix for the strong ties and the other for the weak ties. Therows of the matrix were the names of the relevant ties and the columns were the influence these ties have on the fivetheoretical constructs of PEoU, PU, A, BI and AU. The influence was rated using a 7-point scale. This measured the extent towhich a person's different types of social ties influenced these five theoretical constructs (Chronbach's alpha=.89).

Data analysis was performed in order to explore firstly, the original TAM derived by Davis (1989) in a virtual community andsecondly, to provide preliminary justifications about the applicability of extended TAM derived in this study (Fig. 1). The first partof the analysis provided a conformity analysis of the original TAM in a virtual community context to see if the acceptance of thevirtual community followed this model. The second part of the analysis provided the validity of the extended TAM, whichincorporated the influence of social ties when accepting a new technology to see if one's social ties influenced their acceptance of anew technology, which in this case was a virtual community.

For this study, the data to be analysed using social network analysis include two n×m matrices of two-mode data. Two-modedata also known as affiliation networks are “non-dyadic because the affiliation relation relates each actor to a subset of events, andrelates each event to a subset of actors” (Faust, 1997). The two-mode matrices for this study consist of one set of actors and one setof events. In these n×m matrices, the rows represented the actors influencing the technology acceptance while the columnsrepresented the events, which were the five constructs for technology acceptance according to TAM, which these actors mayinfluence. There were two such matrices, one for strong ties and one for weak ties. The data collected for these matrices were‘higher level’ data, which were transformed into binary scores before analysis to conduct binary measures of relations. This wasdone by selecting a ‘cut point’ and re-scoring cases below the cut point as zero and above the cut point as one, to show relationsbeing absent and present (Hanneman, 2001). Ucinet (Borgatti, Everett, & Freeman, 1999) was used to create the matrices andanalyse social network data.

The visual representation of 2-mode data was conducted using Pajek to create an actor by event network visualization. Pajek iscapable of visualizing two-mode networks in their duality. The 2-D Fruchterman Reingold drawing algorithmwas used to visuallyrepresent the data and identify cluster patterns. Certain patterns are apparent from this visualization such as the clustering intogroups of the actors and the events. In the visualization, the vertices represent the events and actors, and are assigned differentshapes. Using different shapes to visually represent the different modes helps distinguish between the two modes easier and assuch understand the relationship better.

5. Results and analysis

This chapter presents the analysis of the results gathered from the data collection procedures described in the previous chapter.It analyses the results of the two research questions derived in this study and presents them as two groups of hypotheses. Firstly itlooks at validating the original Technology acceptancemodel derived by Davis (1989), in a virtual community. Secondly, it will lookat the extended technology acceptance model derived in this study that incorporates the influence of different types of social tiesas a new theoretical construct and validate this model in a virtual community. These validation procedures will incorporate theanalysis of the gathered data by using descriptive statistics, path analysis and social network analysis.

5.1. Validation of the original technology acceptance model in a virtual community

The first research question and hypotheses one to six for this study looks into validating the original technology acceptancemodel derived by Davis (1989) in a virtual community. As it was pointed out in the literature review, this original TAM althoughhas been validated in many contexts, has not yet been validated in a virtual community such as that of the community ofcommunitybuilders.nsw.

This section will present the analysis of the data collected from the virtual community of the communitybuilders.nsw withregards to validating the original TAM in a virtual community. The analysis procedures incorporate descriptive statistics, pathanalysis and social network analysis in order to validate this model in such a context.

5.1.1. Descriptive statisticsTable 2 summarizes the descriptive statistics for the constructs proposed in the original technology acceptance model by Davis

(1989). Each scale is based on a seven-on a Likert scale. The mean value for the attitude construct has the highest value followed bybehavioural intention to use. Perceived ease of use and perceived usefulness constructs have mean values that are very much similar.However, perceived ease of use has a lowermeanvalue than that of perceived usefulness. Themeanvalues for these constructs followsthe same pattern as what was found in the study by Lee et al. (2003). Actual use has the lowest mean value from all of the constructsstudied. The statistical validity of these constructs and their relationships are further analysed using a path analysis below.

5.1.2. Path analysisFig. 3 illustrates the results of the path analysis for the original technology acceptance model derived by Davis (1989). Each

arrow in the diagram represents a statistically significant relationship (pb .05) between the constructs. It is seen here that all the

Table 2Descriptive statistics for the main constructs in the original TAM.

Original TAM constructs Mean Std. dev.

Perceived ease of use 3.96 1.12Perceived usefulness 4.17 1.06Attitude towards use 5.18 1.01Behavioural intention to use 4.61 1.04Actual use 3.71 1.18


relationships proposed by Davis (1989) are statistically significant and valid when tested in a virtual community. The output pathdiagram shows a strong direct influence of perceived usefulness on attitude. The other relationships are very much similar in theircorrelations. All relationships to attitude and behaviour intention seem to have a higher correlation than the other relationships.The hypotheses derived in this study regarding the validation of the original TAM, H1, H2, H3, H4, H5 and H6 were supported. So, itcould be said that in this case the original technology acceptance model holds true for the virtual community studied and it couldbe possible to generalize this result to any virtual community.

5.1.3. Social network analysisIn this visualization (see Fig. 4) using the 2-D Fruchterman Reingold Algorithm, the nodes (i.e. the participants) that are alike

are clustered together. Certain patterns are apparent from this initial visualization showing to what extent the participants'acceptance process of the virtual community is predicted by the theoretical constructs of the original TAM. There are three majorclusters of participants seen in the visualization. One cluster's acceptance of the virtual community is predicted by the BI, A, PEoUand PU with five participants belonging to this cluster. The second cluster's acceptance is predicted by BI, A, PU and AU with fourparticipants belonging to this cluster. The third cluster's acceptance is predicted by BI, A and AU with three participants belongingto this cluster. Five participants are seen to be outliers in this network. This is a very low number, predicting that other participantsfollow somewhat similar patterns when predicting their acceptance process. The TAM constructs are seen to be quite central in thisvisualization and are clustered together, which suggests that they served as ‘bridge’ events in that most of the participants'acceptance process can be predicted by majority of these constructs. However, actual use construct located furthest away from the

Fig. 4. Social network visualization of the acceptance of the virtual community using 2-D Fruchterman Reingold Algorithm.

Table 3Descriptive statistics for the new constructs in the extended TAM.

Proposed TAM constructs Mean Std. dev.

Influence of strong ties 4.92 1.06Influence of weak ties 3.76 1.12


other constructs implies its dissimilarity to other constructs. This is also prominent when looking at the descriptive statistics forthe actual use construct, which shows the lowest mean value.

5.2. Validation of the extended technology acceptance model

The second research question and hypotheses seven to eleven proposed in this study looks at validating the extendedtechnology acceptance model derived in this study. This extended TAM incorporates a new theoretical construct that of theinfluence of different types of social ties in accepting a new technology, in this case a virtual community. This section presents theanalysis of the data collected from the communitybuilders.nsw with regards to validating the extended TAM in a virtualcommunity. The analysis procedures incorporate descriptive statistics, path analysis and social network analysis in order tovalidate this model in such a context.

5.2.1. Descriptive statisticsTable 3 summarizes descriptive statistics for the new constructs proposed in this study to be incorporated into the extended

technology acceptance model. Each scale is based on a seven-on a Likert scale. The mean values for the influence of the twodifferent types of ties indicate that strong ties have a stronger influence than that of the weak ties. There is a significant differencebetween the influence of strong ties and the influence of weak ties where its p-value is pb .05. This significant difference is furtheranalysed below using t-tests. This shows if there is a significant difference between these two constructs in its influence on theoriginal constructs of the TAM. A p-value of less than.05 implies there is a significant difference.

Table 4 shows the results of the t-tests. A p-value of less than.05 implies that there is a significant difference between theinfluence of strong and weak ties in its relationship to the specific constructs of the TAM. There seems to be no significantdifference in the influence the two types of ties have on the attitude towards use and perceived usefulness. This implies that strongand weak ties influences the attitude and perceives usefulness to the same extent. All other constructs showed a significantdifference implying that strong and weak ties are seen to influence these constructs in different ways, where one type of tie has asignificantly stronger influence than the other. The statistical validity of these constructs and their relationship to the original TAMconstructs are analysed in the path analysis below.

5.2.2. Path analysis

5.2.2.1. The influence of strong ties. Fig. 5 illustrates the result of the path analysis for the extended technology acceptancemodel,which includes the new theoretical construct of the influence of strong ties. Each arrow, except the dotted arrows, in the diagramrepresents a statistically significant relationship (pb .05). All hypotheses of the original TAM were supported when this newconstruct was added to extend the model. However, of the five hypotheses developed for the influence of strong ties in theextended TAM, Only H7, H9, H10 and H11 were supported. There is a strong direct relationship between perceived usefulness andattitude. The influence of strong ties affected perceived usefulness, which then affected the attitude. However, the influence ofstrong ties and perceived ease of use did not show a significant relationship.

5.2.2.2. The influence of weak ties. Fig. 6 illustrates the result of the path analysis for the extended technology acceptance model,which includes the new theoretical construct of the influence of weak ties. Each arrow, except the dotted arrows, in the diagramrepresents a statistically significant relationship (pb .05). All hypotheses of the original TAM were supported when this newconstruct was added to extend themodel. However, of the five hypotheses developed for the influence of weak ties in the extendedTAM, Only H7 and H9 were supported. The influence of weak ties affected perceived usefulness, which then affected the attitude.Attitude was also directly influenced through strong ties. However, the influence of strong ties did not show a significantrelationship to perceived ease of use, behavioural intention to use and actual use.

Table 4Significance of the new constructs in the extended TAM.

Original TAM constructs p-value

Perceived ease of use 0.041Perceived usefulness 0.052Attitude towards use 0.064Behavioural intention to use 0.036Actual use 0.032

Fig. 5. Path analysis results for the influence of strong ties in extended TAM.


5.2.2.3. Social network analysis. Certain patterns are apparent from this initial visualization showing how the strong tiesinfluenced the acceptance process of the virtual community in terms of the original theoretical constructs of the original TAM.There are four major clusters of participants in the visualization. In one cluster the strong ties influence the BI, A, PU and AU withnine participants belonging to this cluster. In the second cluster the strong ties influence A, PU and AU with five participantsbelonging to this cluster. The third influences PU and Awith four participants belonging to this cluster. The forth influences PEoU,BI, A, PU and AU with three participants belonging to this cluster. PU, A, BI and AU are quite central in this visualization and areclustered together, which suggests that they serve as “bridge” events in that most of strong ties have an influence on most of theseconstructs. The PEoU construct is an outlier event implying its dissimilarity to the other constructs and is primarily associated withonly three participants. This is consistent with the findings from the path analysis (see Fig. 7) where perceived ease of use did notyield a significant relationship to any of the other constructs.

Certain patterns are apparent from this initial visualization showing how theweak ties influenced the acceptance process of thevirtual community in terms of the original theoretical constructs of the original TAM. There are three major clusters of participantsin this visualization. In one group the weak ties influence the A, PU and AU with four participants belonging to this cluster. In thesecond group theweak ties influence PU and AUwith three participants belonging to this cluster. The third group has influences on

Fig. 6. Path analysis results for the influence of weak ties in extended TAM.

Fig. 7. Social network visualization of the influence of strong social ties using 2-D Fruchterman Reingold Algorithm.


PU and BI with three participants belonging to this cluster. The fourth group has influences on PU and A with six participantsbelonging to this cluster. PU, A and AU seem to be quite central in this visualization and are clustered together, which suggests thatthey serve as ‘bridge’ events in that the influence of theweak ties affect most of these constructs. PEoU and BI are the outlier eventsthat do not seem to be influenced by weaker ties to a great extent. This is consistent with the findings from the path analysis (seeFig. 8) where these two constructs did not yield a significant relationship to any of the other constructs.

We have provided a detailed analysis of the data gathered from the virtual community of the communitybuilders.nsw initiative.This analysis validates the original technology acceptance model derived by Davis (1989) and the extended technology acceptancemodel derived in this study. After conducting analysis the final validated and statistically significant extended technologyacceptance model is illustrated in Fig. 9.

Fig. 8. Social network visualization of the influence of weak social ties using 2-D Fruchterman Reingold Algorithm.

Fig. 9. The extended technology acceptance model.


From the eleven hypotheses derived for this study only ten proved to be statistically significant. It was seen that both the twotypes of social ties do not influence the perceived ease of use in a virtual community. However, both these types of ties have aninfluence on the perceived usefulness and attitude towards use. Only the strong ties have an influence on the behavioural intentionto use and the actual use of the virtual community. It is seen that stronger ties have a greater influence towards a person'sperceived usefulness, attitude, behavioural intention, and actual use of a virtual community. However, the attitude constructshowed somewhat similar values for the influence of strong and weak ties, indicating that both these types of types to a similarextent could influence one's attitude. However, with the other constructs, the different types of ties have different effects on one'sacceptance process. These results and their implications will be discussed further in the net chapter.

6. Discussions and conclusions

Davis (1989) claimed that future technology acceptance research should explore other additional explanatory variables, whichmay affect the originally proposed constructs of the TAM. The proposed model here integrated original constructs from TAM withtheories related to sociology and social network research. It is based on the proposition of Roger (1986) that an individual'sperception of a system is influenced by the way people around that person evaluates and uses that system. As such, the extendedTAM incorporates a new theoretical construct that looks into the influence of social ties in an individual's acceptance process of anew technology. It was proposed that this new theoretical construct could have an effect on all the constructs in the originaltechnology acceptance model.

The findings of this study suggest that influence of social ties play an important role in determining the acceptance and usagebehaviour of new adopters of new information technologies, specifically a virtual community. Hence, the consideration of theinfluence of one's social ties and how they affect the commitment of the user toward use of the information system seemsimportant for understanding, explaining and predicting system usage and acceptance behaviour. These results on social influenceas predictor of attitude are consistent with findings from other studies (Malhotra & Galletta, 1999; Dennis, Venkatesh, & Ramesh,2003). Lee et al. (2003) studied the influence of peers in accepting a distributed learning environment and clearly illustrated thatalthough students initially formed attitudes based on their PU of the system, influences from their peers significantly affected theirattitude change. The study revealed that the degree of attitude change was determined by the amount of social influence withgroup members.

Similar to findings from the above-mentioned studies, this study found both weak and strong ties having a strong correlationwith the attitude construct to a great extent. Furthermore, the attitude construct was centralised in the social networkvisualizations showing that it is influenced by the social ties. However, although it was proposed that strong ties would have astronger influence on this construct than the weak ties, results did not show any significant different between the influence ofthese two types of ties. A consistent finding fromprior research is that users' attitude towards a new technology is the key factor forsuccessful deployment and this attitude is influenced by culture, norms, social contexts or salient others (Lee et al., 2003). As Fulket al. (1987) proposed, technology related attitudes are often influenced by social interactions and psychological processes ratherthan directly by objective and independent assessment of technical characteristics.

In summary, the results suggest that there is a strong connection between the constructs that were deemed important by Davis(1989) to predict the technology acceptance process. However, since correlation does not imply causation, it cannot be said that


the causal relationships were validated in this study, but the correlations does provide some evidence towards the importantconstructs in the TAM when accepting a virtual community. There was evidence from correlation results and social networkvisualizations for the extended TAM's contention that the influences of different types of social ties have an effect on an individual'sintention to use an information system. However, this influence of social ties does not have an effect on all the constructs of theoriginal TAM and the different types of ties have different effect on the original constructs. The new construct is related toperceived ease of use, perceived usefulness, attitude and actual use though not to behavioural intentions. Social ties may have asmall effect on BI, but this is uncertain. For a successful acceptance of technology, researchers and practitioners should activelypursue various ways to facilitate and encourage people to share with others their beliefs regarding that particular system. Whenexamining the extended model and the findings, there were some findings that were perhaps unique to virtual communities ingeneral and some that were perhaps unique to the specific virtual community under study. These provide some importantimplications for further research and practice in the HCI domain.

The findings of this study suggests that the TAM could offers a theoretically sound and parsimonious method for evaluatingvirtual communities in existence, however causality of the model needs to be studied to determined if the model is valid inexplaining the technology acceptance in such a context. By gathering user perceptions of a system's usefulness and ease of use,developers can more accurately assess whether the users will ultimately accept the system. While this study examined a system,which was already available for use, there is no reason why developers could not gather user perceptions of a system's usefulnessor ease of use based on prototypes or storyboards earlier in the development lifecycle (Morris & Dillon, 1997). In fact, given TAM'slow cost and ease of application, developers could easily collect data at various points during system development and monitorshifting user attitudes about the system as it moves from conceptual design stages to actual implementation.

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Exploring user acceptance of technology using social networks

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