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Exploring trust to reduce communication barriers in virtual world collaborations Chandra,S., Theng, Y.L., O’Lwin, M., & Foo, S. (2011). 60th Annual International Communication Association (ICA) Conference, Boston, U.S., May 26-30.

Exploring Trust to Reduce Communication Barriers in Virtual World Collaborations

Shalini Chandra, Yin-Leng Theng, May O. Lwin and Schubert Foo Shou-Boon

Wee Kim Wee School of Communication and Information Nanyang Technological University

Singapore

Abstract

Virtual worlds (VW) have paved a new and important channel for workplace collaborations. However, analysts have noted that several organizations that made a strong entrance into using VW as a nouveau channel for communication and collaboration are stepping back due to limited user response. Motivated by this fact, we propose a trust-theoretic ‘virtual world model’ for communication and collaborations in virtual worlds. The model, grounded in literature on ‘organizational communication’ and ‘trust’, theoretically examines the role of trust in developing behavioural intentions for using this rich virtual communication medium for collaborations. Results establish the important roles of perceived structural assurance and perceived social presence for fostering user trust in VW. Further, results also indicate that user trust is a key factor in influencing organizational members to develop behavioural intention to use the VW for collaborations. Implications for research and practice are discussed.

Keywords virtual worlds, trust, behavioural intention, collaboration, organizational communication

INTRODUCTION Virtual Worlds (VWs), referred to as three dimensional (3D) simulated virtual environments, have existed largely as game-oriented environments such as World of Warcraft, Sims Online and Everquest. Though originally conceived as a platform for game playing, VWs are gradually emerging into a communication media for social and commercial interaction. The enterprise version of online VW such as Second Life, are becoming highly interactive and collaborative. Some analysts and researchers have predicted that VWs will become the dominant means to access and share information over the Internet in the future (Gartner, 2007). In virtual world Second Life, several multinational organizations such as IBM, Nissan, Toyota, Adidas, Reebok, Dell and Vodafone are conducting operations. Universities like Harvard are holding classes and Edinburgh University runs an e-learning degree in part using Second Life (see http://www.education.ed.ac.uk/e-learning/). Information technology research and advisory firms such as Gartner and Forrester research note the major benefits of VW as a collaboration tool compared to the current collaboration tools such as web/video conferencing and teleconferencing suggesting a bright future of VW for workplace collaborations (Gonsalves, 2008; Lynch, 2008).

Face-to-face communication is undoubtedly the richest medium of communication as it allows rapid mutual feedback and simultaneous communication of multiple cues (Daft et al., 1987). However, the multinational organizations are spreading their wings worldwide with distributed workforce. Employees are scattered across multiple offices located in different

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countries around the globe. This calls to use new technologies that would enable disparate employees distributed around the world to communicate, collaborate and create more efficiently and effectively (Second Life, 2010). Answering to this call, VWs are emerging as a promising communication media for workplace collaborations. ‘Virtual world collaboration’ is defined as the existence of mutual influence among [geographically dispersed virtual] members that enables open and direct communication, resulting in idea generation and conflict resolution and support for innovation and experimentation (Aram and Morgan, 1976). The high degree of involvement and engagement that one can experience in 3DVWs, coupled with the creative characteristic and richness of the environment, provide businesses with ample opportunities for collaboration and information sharing activities.

However, many companies that made a strong entrance into VW during the first wave of creating virtual presence are stepping back. Despite a high failure rate, experts sense a big future of VW for workplace collaborations and compare the current problems in VW as the problems faced by the web in 1990s. Though the analysts believe that the VW mini-dot com boom has begun, many companies are still struggling to successfully use VW (Wagner, 2007). Though the researchers and practitioners note the potential for rich and engaging collaboration in VWs, they are concerned with the user reactions for the usage of VW. Davis et al. (2009) clearly state the need to understand the user behaviours in VW. Further, Gartner comments that 90 percent businesses fail in VW as these companies give extra emphasis to technology rather than understanding the needs, behaviours and motivations of VW communities (Gonsalves, 2008).

Highlighting the need to understand the user needs, perceptions and behaviours, it is imperative to note that though VWs are interactive with multiple cues, yet, the collaboration among geographically dispersed and disparate employees involves several IT related security risks and problems of identity authentications as individuals interact via avatars, which are computer generated representations of individuals (Gartner, 2007). Consequently, users perceive several risks and uncertainties. Also, VW collaboration projects involve disparate teams, and virtual processes may differ from what the organizational employees are used to. VWs blur individual boundaries and thrust team members into unfamiliar interactions resulting in ambiguities in collaboration. This communication ambiguity or equivocality developed among geographically dispersed communicating members would lead to disagreement, lack of understanding and confusions (Daft et al., 1987).

Uncertainty and equivocality inherent in interactions through virtual environments might influence people to revert to traditional face-to-face meetings and collaborations and thus may constrain virtual interactions. This high level of uncertainty and equivocality in VWs demand understanding and emergence of common perspective (Daft et al., 1987). Organizations and individuals must develop mechanisms capable of coping with ambiguous and unstructured environment (Daft et al., 1987).

Trust, by definition, mitigates such constrains (Brown, 2004). In the VW, trust is a way to manage people whom you do not see (Handy 1995, p. 41). Hence, ‘user trust’, which reflects the user’s concerns about not revealing their true identities and the risks with technology-enabled interactions, seems to be essential for understanding interpersonal behaviour in virtual environments and motivating them to use this nouveau and rich ‘collaboration tool’ (Paul and McDaniel, 2004). Trust is particularly important in virtual collaborations to reduce communication uncertainty and equivocality and to develop shared understanding and virtual collaborative relationships (McKnight et al. 1998; Newall and Swan 2000). The virtual members must develop trust in other members and listen to them with open minds, be supportive to other members and develop the nuances of working together virtually.

Though trust is critically important in collaborations through VWs, it is difficult to create and maintain trust in such virtual environments. VW collaborations involve disparate team

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members working together on the same project. Trust in such differentiated teams can be especially difficult to create and maintain (Luhmann 1979). Motivated by the imperative need to understand the usage of VW as a collaborative tool and positing user trust manifesting in VW as the key enabler for usage intentions of VW for collaborations, in this paper, taking a trust-theoretic stance, we first theorize and then empirically test the trust building blocks in VWs. Further, we also examine the role of trust for developing behavioural intentions (BI) to use VWs for collaborations. Though user trust has been studied in several contexts (Pavlou, 2003; Teo et al., 2009), its role in the context of VW has received scant attention. An understanding from the trust perspective can provide insights for usage of this new rich medium of communication for collaboration. The two specific research questions for this study are:

1. How do the communication barriers of uncertainty and equivocality influence ‘trust-building’ in VW collaborations?

2. What is the role of ‘user trust’ in developing BI for VW collaboration? There are three primary contributions of the current study. First, we introduce a trust-

theoretic research model for BI to use VWs for communication and collaborations. To our knowledge, this is the first study that simultaneously investigates the antecedents and the consequences of ‘user trust’ on VW collaborations. Second, we extend the literature on trust by integrating the literature on trust and research on organizational communication. There is no empirical work of this kind. Third, by conceptualizing the impact of perceived structural assurance and perceived social presence on user trust, the study highlights the means to reduce communication ambiguities and uncertainties in VW interactions drawing the attention of VW designers and developers implementing such systems.

THEORY AND HYPOTHESES Drawing upon the literature on trust and research in organizational communication, this paper theoretically develops and empirically validates a research model as shown in Figure 1 that predicts usage intentions of VW for collaborations. The study investigates the facilitators of user trust and also the role of user trust in determining the BI to use VW for collaborations. User Trust

Lewis and Weigert (1985) describe trust as a cognitive process that discriminates amongst persons and institutions that are trustworthy, distrusted and unknown. Luhmann (1979) suggested “One should expect trust to be increasingly in demand as a means of enduring the complexity of the future which technology will generate” (p: 16). VW are technology-enabled complex worlds. Trust is an effective complexity-reduction tool and a key enabler of virtual collaborations (Paul and McDaniel, 2004). Yet, trust in VW is difficult to build as compared to face-to-face interactions. Hence, understanding the factors accounting for trust in VW is critically important for developing VW usage.

Zucker (1986) emphasized on institutional trust while Rousseau et al., (1998) argued that trust can be institutional or relational. Institutional trust is formed by providing assurances through various institutional structures that supports the risk-taking ability and develops trust behaviour in the user while relational trust is established by repeated interactions between trustor and trustee. Further, social scientists have identified three types of trust. System trust- is based on the reliance of a system, interpersonal trust- is the trust between the interacting parties or individuals on a personal level and applies to specific parties and contexts, or institution and dispositional trust is the general attitude of the individuals seeking trust and does not rely on any party or context (Leimeister et al., 2005). According to McKnight et al.

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(1998), there can be no factor that can have direct effect on dispositional trust. In contrast, system trust and interpersonal trust are supported by specific factors. Consequently, in our study we examine user trust being established by system trust and interpersonal trust and determine factors for establishing both system and interpersonal trust. Nonetheless, we control for the dispositional trust in the research model.

System Trust is the trust in the party established by assuring that all impersonal structures are in place for secured virtual interactions without any risk and privacy issues. Thus system trust gives assurance to the user that proper impersonal structures are in place (Pennington et al., 2003). Interpersonal Trust or the confident positive expectations regarding others’ conduct has been shown by previous IS researchers as an important factor in determining information sharing behaviours and a desire for future interactions (Naquin and Paulson, 2003). It reflects trust that has been cultivated over time (Pennington et al., 2003). System trust helps the user reduce the perceived risks and uncertainties associated with technology while interpersonal trust or the trust on the abilities and intentions of collaborating members helps reduce the ambiguities or equivocality (difference in opinions) amongst the team members during collaborations. Communication Barriers: Uncertainty and Equivocality To understand the workplace collaborations among geographically dispersed employees through VWs, it is important to delineate the two basic barriers it encounters. After discussing these barriers, we suggest the means by which they can be overcome so as to develop BI to use VWs for collaborations. Research in organizational communication suggests that there are two key problems while processing information in organizations- Uncertainty and Equivocality (Daft and Lengel, 1984; Daft and Lengel, 1986; Daft et al., 1987). In a similar vein, we suggest that workplace communication and collaborations among disparate employees in a virtual setting using VWs has two main obstacles- Uncertainty and Equivocality. The information richness concept (Daft and Lengel, 1984) provides the link between uncertainty and equivocality and the choice of media for communications. Information richness is defined as the capacity of information to provide substantial new consensual understanding (Trevino et al., 1990).

Uncertainty means the absence of information. Thus, uncertainty is the gap between the data that is needed and the available data to solve a problem or an issue. In uncertain situations, consensual understanding already exists. The organizational theory suggests that uncertainty can be reduced by filling in the information gap and by assuring proper structure for the organizations through reports, rules and procedures (Daft et al., 1987; Trevino et al., 1990). Similarly, VW have several uncertainties due to IT related security risks, problems of confidentiality and lack of verifiable identity control as users can open several accounts and easily hide their true identities (Gartner, 2007). Consequently, uncertainties in VWs are the behavioural and environmental risks which the users perceive. Behavioural uncertainties are present due to the spatial and temporal separation between the interacting users and the lack of identity control. Environmental uncertainties are a concern in VW as it is a technology-enabled interaction. Several researchers suggest developing trust as a means to reduce such uncertainties (Pavlou, 2003). Thus uncertainties in communication due to technological infrastructure in VWs can be reduced by filling the information gap and developing system trust. System trust is developed by giving structural assurance to the users. System Trust is the trust in the party established by assuring that all impersonal structures are in place for secured virtual interactions without any risk and privacy issues. Thus perceptions of structural assurance to the collaborating members that proper impersonal structures are in

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place reduce uncertainties and builds system trust amongst the users (Pennington et al., 2003).

Perceived structural assurance describes the efficacy of the institutional environment

(here VW), that all structures like guarantees, regulations, and promises are operational for safe, secure and reliable transactions (Zucker, 1986). Perceived Structural Assurance for VW can be developed by building the system trust or institution-based trust which can be achieved by developing adequate legal and technological safeguards that assure and make the virtual members feel safe depending on the other interacting members by reducing uncertainties and hence enabling trust. Tan and Thoen (2000) suggested that the perception of security and privacy control through developing structural assurance helps reduce uncertainties and build user trust for online activities. Hence we hypothesize:

Hypothesis 1: Perceived Structural Assurance is positively associated with user trust in VW.

Equivocality means ambiguity or the existence of multiple and conflicting interpretations of an issue (Trevino et al., 1990). Equivocality means disagreement, lack of understanding and confusions in interpreting a situation. In contrast to uncertainty, equivocal interpretations are subjective and open to disagreements. Equivocality is high when the frames of reference of users differ (Daft et al., 1987). Geographically dispersed collaborating members from different cultures and countries are disparate with varied frames of reference. This may result in high levels of equivocality in communicating through virtual platforms. Equivocal situations result in disagreements about situational interpretations resulting in conflicting opinions during collaboration (Daft et al., 1987). Past researchers have suggested that equivocality can be reduced by interpersonal interactions or communication among team members through a rich medium that facilitates insight and rapid understanding (Daft et al., 1987). This interpersonal interaction with the other individual or organization over extended periods of time results in building trust and more specifically interpersonal trust (Gefen and Straub, 2004). Thus, communication ambiguities and equivocality are reduced by developing interpersonal trust. Interpersonal Trust or confident positive expectations regarding others’ conduct will be higher in rich environments with high social presence as social presence is the degree to which a person is perceived to be real in a mediated environment. Gefen and Straub (2003) also confirmed social presence as a necessary condition to reduce ambiguities or equivocality and develop trust.

Perceived social presence is the extent to which a communication medium is perceived to

convey the presence of the communicating participants (Rice, 1993). Rich communication media results in higher levels of interactivity due to immediate feedback and more cues by the medium (Daft and Lengel, 1986). Richer communication reflects higher levels of social presence with language variety and cognitive clarifications (Jahng et al., 2007). Various studies have investigated that virtual environments and 3D avatars enhance consumers' feelings of telepresence (a perceptual illusion of the user to be present in a remote environment by means of a communication medium; Choi et al., 2001) and these avatars can significantly influence consumers' perceptions of presence. VWs with visual and aural cues and an ability to display emotions are rich communication media with high levels of social presence. Thus rich communication media like VWs with high social presence would help reduce ambiguities (equivocality) and build trust (Simon, 2001).

The lack of social presence reduces the multiple cues and results in varied levels of meaning leading to equivocality and ambiguity for the task at hand (here virtual

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collaborations) (Jahng et al., 2007). It is easier to hide information and engage in untrustworthy behaviour when communicating in media of lean social presence than in a high social presence environment (Hassanein and Head, 2007). As most of the communication media are broadly text based with voice alternatives such as e-mail, instant messaging and chat rooms, they constitute a fairly lean form of media due to the lack of perceptions of presence (Naquin and Paulson, 2003). The lack of verbal and non verbal cues present during communication (Rice, 1993) results in equivocality and ambiguity thereby reduced interpersonal trust. However, since human interaction, whether face-to-face or by any other means is a pre condition for trust hence rich media with high interactivity level and enhanced social presence among the collaborating members would build user trust (Gefen and Straub, 2004). Luhmann (1979) suggested that trust is increased when the trusted party shows verbal and non verbal cues in accordance with one’s expectations. Gefen and Straub (2004) argue that perceptions of social presence in a relationship would contribute to building trust and making a relationship devoid of social presence would reduce trust. Thus social presence improves the richness of the communication media accounting for reduced equivocality and enhanced interpersonal trust. Hence it follows,

Hypothesis 2: Perceived Social Presence is positively associated with user trust in VW.

Hoffman et al. (1999) argued that lack of trust would prevent users from getting involved in online activities mainly because the users are concerned about the uncertainties and ambiguities involved in the technological infrastructure. Trust has been shown to be related to positive attitudes which are more likely to influence the user’s intention to adopt the technology (Gefen, 1997). The works of Dirks and Ferrin (2001) is based on the assumptions that trust reduces uncertainties and ambiguities in social perceptions, thereby facilitating positive attitudinal and behavioural outcomes. Following Theory of Reasoned Action, which posits that beliefs lead to attitudes which in turn lead to BI, trust is a belief that creates positive attitudes and affects BI to use VW. Hence we hypothesize:

Hypothesis 3: User trust in VW is positively associated with BI to use VW for collaborations.

RESEARCH METHOD, DATA AND ANALYSES

Survey method was used to collect data for testing the research hypotheses as it enhances the generalizability of results (Dooley, 2001). A survey instrument (on a 7-point Likert scale) was first developed by identifying and adapting appropriate measures from the existing literature to the case of VWs, thereby ensuring content validity. Appendix A details the scales developed for various measures from prior studies. The designed questionnaire was pilot-tested with three research students before being used for final survey.

In addition to the focal research constructs we incorporated suitable control variables in the research model as they may explain significant variance in the dependent variable. These control variables were classified as demographic and non-demographic control variables. For the demographic control variables, we added gender, age, profession (IT or non-IT) as controls because demographic variables have been found to be significantly affecting technology adoption intentions (Venkatesh et al., 2003). Further, in order to see if the choice of VW makes a difference in the decisions made by individuals, we controlled for the

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‘preferred VW’ in the research model. Currently users have the option of choosing from several VW platforms such as Second Life, World of Warcraft, Kaneva, There, Maid Marian and Active Worlds. In our sample, Second Life emerged as the dominant ‘preferred VW’ (78.7 percent users preferred Second Life), hence we controlled for the ‘preferred VW’ by adding a dummy variable for Second Life users. We found that the neither the demographic variables nor the choice of VWs was significantly associated with the BI.

In addition to the above mentioned demographic control variables, based on previous research, we also applied non-demographic control variables for the final dependent variable of BI as well as the intermediate variable of user trust. Given that the traditional technology acceptance model (TAM) constructs of perceived usefulness (PU) and perceived ease of use (PEOU) have well established relationships with IT adoption, we controlled BI for these variables so as to understand the significant effects of user trust on BI over and above PU and PEOU (Davis, 1989; Davis et al., 1989, Venkatesh and Davis, 2000). Further, disposition to trust, was included as a control variable for user trust, as previous studies have highlighted that people who have a higher propensity to trust will in general be more trusting (e.g. McKnight et al., 2002).

The sampling frame for this study comprised of population who have used and experienced VWs for recreational tasks like gaming and socializing. For conducting the survey, the first step was to pre-screen users of VW who had prior experience with VW for recreational activities. Using these criteria we distributed paper-based survey questionnaires to more than 300 part time students (who are also working in various organizations) in two large university campuses in Singapore. Subsequently, we had responses from 226 respondents out of which we considered only 197 for data analysis. The average age of the respondents was 29.3 with a standard deviation of 5.8.

The study was conducted in Singapore as we believe it provides a good location for conducting VW related research. Many Singapore firms are eyeing VW to sell their real-life goods and services (Wai-Leng, 2007). IBM has also dedicated sales staff at its virtual IBM business centre to serve customers from Singapore. In addition, Singapore is a popular place for researchers to test out new technologies and government has set aside several million dollars for companies like Linden Labs, the creators of Second Life, to consider locating here (Siew, 2007).

We used Partial Least Squares (PLS), specifically, SmartPLS 2.0 to analyze the data in this study (Ringle et al., 2005; Vance et al., 2008). Various IS studies have employed PLS and have found it to be an effective method of analysis (Subramani, 2004; Teo et al., 2009).

Measurement Model Following the recommended two-stage analytical procedure (Anderson and Gerbing, 1988; Hair et al., 1998), the first stage of data analysis is the evaluation of the measurement properties of the instruments followed by an examination of the structural relationships. For assessing the measurement properties, three types of validity were tested namely, content validity, convergent validity, and discriminant validity. Content validity assess if the measures chosen appropriately capture the full domain of the construct (Straub et al., 2004). In this research, content validity was examined by checking for consistency between the measurement items and the existing literature followed by pilot-testing the instrument (Srivastava and Teo, 2007).

Convergent validity refers to the extent to which the items under each construct are actually measuring the same construct. To test the convergent validity, the item reliability was examined for each item, which suggested that the factor loading of each item on its

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corresponding construct must be higher than 0.55 (Falk and Miller, 1992). As shown in Table 2, all items had a loading above the suggested threshold. Convergent validity was also tested by examining the composite reliability (CR) and average variance extracted (AVE). Many studies using PLS have taken 0.5 as the threshold for CR of the measures, however, 0.7 is the suggested threshold for reliable measurement (Chin, 1998). The AVE measures the variance captured by the indicators relative to measurement error (Fornell and Larcker, 1981), and it should be greater than 0.50 to justify using a construct (Barclay et al., 1995). Table1 shows that CR values ranged from 0.94 to 0.97 and AVE ranged from 0.77 to 0.87, which are all above the acceptable values.

Discriminant validity refers to the extent to which a given construct differs from other constructs. Barclay et al. (1995) have suggested two criteria to examine discriminant validity. The first criterion requires that values of the square root of the AVE (reported on the diagonal in Table 1) are all greater than the correlations between the constructs, indicating that constructs share more variance with their own measures with other constructs. This criterion is satisfied by the current data, as demonstrated in Table 1, thereby exhibiting satisfactory discriminant validity. The second criterion requires that no item loads higher on another construct than it does on the construct it is designed to measure. As all items loaded more heavily on their corresponding constructs rather than on other constructs (Table 2), discriminant validity was satisfied.

Internal consistency was assessed using composite reliability. Cronbach’s alpha assumes equal weights of all the items of a construct and is influenced by the number of items whereas composite reliability assesses actual loadings to compute the factor scores and thus provides a better indicator for measuring internal consistency (Teo et al., 2009). As shown in Table 1, the composite reliability values for all constructs in the model were above the threshold value of 0.7 (Fornell and Larcker, 1981; Teo et al., 2009).

Common Method Bias The effect of common method variance is a major potential validity threat in social sciences research (Podsakoff et al. 2003). Variance occurring due to the measurement method rather than the constructs of interest may result in systematic measurement error and further bias the true relationship among the theoretical constructs. As the data on all the variables for this study is self reported and collected through the same questionnaire during the same period of time with cross sectional research design, there is a potential for common method bias. To address this threat of common method bias we performed two statistical analyses. First, we performed Harman’s one factor test (Podsakoff and Organ, 1986). All the variables in the study were loaded into exploratory factor analysis and we examined the factor solution to determine the number of factors essential to account for the variance in the variables (Podsakoff et al., 2003). If all indicators load on one factor that accounts for more than 50% of the variance, common method bias is of concern. The test indicated the presence of four factors accounting for a total of 84.9 % of the variance, of which the first factor accounted for merely 24.2 % of the variance. Since a single factor did not emerge and one general factor did not account for most of the variance, we concluded that common method bias is not a significant problem with the data (Podsakoff et al., 2003). Second, we adopted the technique recommended by Liang et al. (2007) using PLS to assess the magnitude of common method bias in the data. This we did by introducing a ‘common method factor’ whose indicators included all the principal constructs’ indicators and calculated each indicator’s variances substantively explained by the corresponding ‘principal construct’ and also the ‘common method factor’. As shown in Table3, the average substantively explained variance of the indicators is 0.849, whereas the average method based variance is only 0.009. The ratio of

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substantive construct variance to common method variance is about 94:1. Further, most method factor loadings are not significant, indicating that common method is not a serious concern for this research (Liang et al., 2007). These tests helped us preclude the possibility of common method bias contaminating the results from this research. Structural Model After validating the measurement model the proposed hypothesis were tested using PLS. The results of the analysis are depicted in Figure 2.

Examining the trust building antecedents, we find that the relationship of perceived structural assurance with trust in VW (path=0.43, t=5.99, p<0.01) is significant thereby supporting H1. Also, the relationship of perceived social presence with trust in VW (path=0.30, t=4.25, p<0.01) is significant rendering support to H2. The two trust-building antecedents explain 58.9% of the variance in ‘user trust’. This suggests the high explanatory power of the theorized antecedents of user trust, providing empirical validation for the proposed research model. Among the control variables, the relationship of disposition to trust (path=0.19, t=2.65, p<0.01) with user trust and perceived usefulness (path=0.40, t=5.99, p<0.01) and perceived ease of use (path=0.21, t=3.28, p<0.01) with BI are all strongly significant. Next we observe that user trust has a strong relationship with BI (path=0.32, t=5.72, p<0.01), rendering strong support to hypothesis H3. Further, after controlling for the demographic variables and the significant technology adoption variables of perceived usefulness and perceived ease of use, we observe that user trust explain 62.4% variance in BI for VW collaborations highlighting the key role of user trust in developing behavioural intentions to use VWs for collaborations. Post Hoc Analysis Antecedents of User Trust This study proposes two major determinants of user trust as perceived structural assurance and perceived social presence as per the organizational communication research to reduce uncertainty and equivocality in communications. It will be interesting to examine if both structural assurance and social presence enhance the explanatory power of user trust construct significantly in the VW context. Chin (1998) states the effect size of independent variables on a dependent variable can be determined by comparing the R2 of the dependent variable with and without the presence of each independent variable. Hence, to examine the effect size of both the antecedents on user trust, we compared the hypothesized model with the modified model (by dropping structural assurance in modified model 1 and dropping social presence in modified model 2) in terms of R2 change for the dependent variable – ‘user trust’. For R2 comparison, we used Cohen’s (1988) formula for calculating effect size f2 as:

f2 = (R2 hypothesized - R2

modified)/ (1 - R2hypothesized)

The value of f2 captures whether the impact of a particular independent construct on a dependent construct is substantive. Cohen (1988) suggests the following criteria for interpreting effect size: (1) for small effect size, 0.02 < f 2 ≤ 0.15; (2) for medium effect size, 0.15 < f 2 ≤ 0.35; and (3) for large effect size, f 2 > 0.35.

For the modified model 1 (without the perceived structural assurance construct), we observe the decrease in R2 of user trust from 0.589 to 0.468(f2=0.294). For modified model 2(without the perceived social presence construct), we observe the decrease in R2 of user trust from 0.589 to 0.530(f2=0.144). The results confirm the explanatory power of both the trust antecedents. The results also shows that though both the trust building factors are significant in explaining user trust, perceived structural assurance (medium effect) has a larger effect on

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building user trust than perceived social presence (small to medium effect). Hence we conclude that reducing both uncertainty and ambiguity by way of developing user perceptions of structural assurance and social presence explains a significant variance in user trust. Thus, in the context of VWs the suggested conceptualization of user trust being developed by both structural assurance and social presence offers a better explanation of the phenomenon.

DISCUSSION

Among the two main factors modeled as antecedents of user trust in VWs, results indicate both ‘perceived structural assurance’ and ‘perceived social presence’ as significant determinants of user trust in VWs. Results from the ‘consequences’ part of the research model indicate strong significant relationships of user trust with BI. This indicates that based on the unique features of VWs, it is important to consider user trust to understand the BI to use VWs for workplace collaborations. Past studies in the context of technology adoption have empirically demonstrated the strong relationships of user trust (e.g. Gefen, 1997; Jarvenpaa and Tractinsky, 1999; Jarvenpaa et al., 2000) with intention to use the focal technology. Our study stretches the results to the context of VWs as well and is true across all subgroups.

Through a post-hoc analysis, by comparing competing models with the hypothesized model, we demonstrate the imperative importance to consider both ‘perceived structural assurance’ and ‘perceived social presence’ as trust antecedents in VW collaborations.

LIMITATIONS Though the study makes several significant contributions, there are a few limitations. First, the data was self-reported and thus may be subjected to the respondent’s varying patterns of using the scale while answering the questions. Additionally, our study focuses on trust to study BI to use VWs. Expanding the scope of factors determining BI to use VWs is the next step towards our understanding of the factors that would attenuate or enhance the use of VWs for collaborations. Another limitation is that we have identified a few trust antecedents focussing on organizational communication and trust theories and did not explore other factors which might influence user trust, such as the reputation of VWs and the quality and sensitivity of the information being shared. These can be avenues for future research in this area.

IMPLICATIONS Implications for Research First, we present a VW collaboration model for BI to use VWs for collaborations. This validated model can be used as a point of reference for future research in VWs. Second, the study extends the research on organizational communication by proposing two key factors that can reduce the barriers of uncertainty and equivocality in virtual communication to develop trust. This study can be taken as a basis for future studies on ways to improve organizational communication and collaboration among virtual team members. Third, the study highlights the critical role of user trust in motivating the users to develop intentions to use VWs for collaborations. Hence, we need to understand the critical role of user trust and how user trust is fostered in VWs for workplace collaborations. Fourth, as past studies have examined perceived usefulness and perceived ease of use as key factors influencing user

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behaviour (Davis, 1989; Davis et al., 1989, Venkatesh and Davis, 2000), this study highlights the role of user trust over and above perceived usefulness and ease of use by controlling these two factors in developing behavioural intentions for VWs. This study guides future research to understand the significance of developing user trust over and above perceptions of usefulness and ease of use for successful information systems. Fifth, in the proposed VW collaboration model, the theorized antecedents of user trust explain a significantly high percentage of variance (62.4%) in user trust highlighting the importance of the identified facilitators in the context of VWs. On the basis of these identified facilitators future research can expand the list of facilitators of user trust. Implications for Practice First, the study clearly highlights ‘user trust’ as the key driver for forming intentions for use VWs for workplace collaborations. Past studies have been emphatic on the role of technology adoption factors like usefulness and ease of use for developing usage intentions of technologies. However, in the current study we control for these technology adoption factors to highlight the critical role of user trust over and above the perceptions of usefulness and ease of use, in developing behavioural intentions to use VWs for collaborations. Thus, practitioners and VW designers have to understand this key role of user trust while finalizing their VW designs. Second, the study provides clear guidelines to the VW designers and practitioners on the means to foster ‘user trust’ for successful workplace collaborations in VWs. Third, the study develops the theory on how the perceptions of structural assurance and social presence can reduce uncertainty and equivocality in virtual communications and build system trust and interpersonal trust. The VW practitioners can improve virtual communications by developing user trust in VWs by reducing both uncertainty and equivocality in their designs. Fourth, the study clearly highlights the importance of social presence for task-oriented communication as is needed for social communication. For enhanced social presence in the VW interfaces, the future VW design should incorporate expressive interfaces which would elicit trust in the users and would further develop positive responses from the user such as ease of use, satisfaction, and enjoying the experience of use. In future, we expect expressive VW interfaces for increased social presence so as to make the design “affective” and thus enhance the trust and usability of the system. In future designs we expect avatars to convey enhanced social presence by expressing emotive behaviours to exhibit contextually appropriate facial expressions and expressive gestures to successfully exploit this visual channel. Fourth, structural assurances to the users are vital in VW interfaces to develop user trust. The current VW systems lack structural assurances due to some problems like limited bandwidth and processing power in user’s computer, lack of web-browser based VWs and other technological issues resulting in low levels of structural assurances for the users leading to low trust levels. Analysts predict web browser based VWs in future for increased structural assurances (Ferguson, 2008). Fifth, our model ties and controls for dispositional trust to trusting beliefs suggesting that personality traits also play a key role in building trust in such systems.

CONCLUSION The research proposes and tests a VW collaboration model which can guide companies strategizing to succeed in VWs by developing trust beliefs in its employees to use VWs for workplace collaborations. In contrast to previous research which focuses on motivating users (by technology adoption factors like perceived usefulness and perceived ease of use) to use the new technologies effectively, this paper highlights the key role of user trust in fostering motivations for VWs usage. The paper provides a number of directions for future research to

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plan and implement strategies for VW usage for companies contemplating investment in VWs.

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Table 1 Descriptives and Correlations

VAR Nos.

Items Mean SD CR AVE BI DTR PEOU PSA PSP PU UTR

BI 4 4.22 1.50 0.94 0.80 0.89 DTR 5 4.45 1.49 0.94 0.77 0.41 0.88

PEOU 5 4.37 1.40 0.94 0.77 0.60 0.36 0.88 PSA 4 3.87 1.54 0.96 0.85 0.66 0.43 0.43 0.92 PSP 4 3.95 1.53 0.96 0.87 0.59 0.44 0.41 0.56 0.93 PU 5 4.15 1.43 0.97 0.87 0.71 0.30 0.61 0.53 0.56 0.93

UTR 5 3.48 1.49 0.97 0.86 0.66 0.51 0.47 0.69 0.63 0.61 0.93 Key: BI: Behavioural Intention, DTR: Disposition to Trust, PEOU: Perceived Ease of Use, PSA: Perceived Structural Assurance, PSP: Perceived Social Presence, PU: Perceived Usefulness, UTR: User Trust. Note: The numbers in bold in the shaded cells of the diagonal row are the square roots of the average variance extracted.

Table 2

Item Loadings and Cross Loadings

Variables 1 2 3 4 5 6 7 PU1 0.75 0.26 0.23 0.16 0.21 0.16 0.26 PU2 0.85 0.23 0.04 0.26 0.21 0.11 0.13 PU3 0.80 0.28 0.01 0.26 0.19 0.15 0.20 PU4 0.79 0.29 0.08 0.21 0.23 0.17 0.21 PU5 0.80 0.27 0.07 0.15 0.16 0.14 0.19

PEOU1 0.29 0.83 0.08 0.12 0.07 0.06 0.09 PEOU2 0.13 0.82 0.10 0.05 0.13 0.17 0.24 PEOU3 0.16 0.82 0.12 0.14 0.11 0.24 0.11 PEOU4 0.22 0.86 0.18 0.11 0.08 0.05 0.05 PEOU5 0.33 0.66 0.15 0.20 0.16 0.02 0.24 DTR1 0.08 0.09 0.84 0.16 0.11 0.10 0.13 DTR2 -0.06 0.09 0.81 0.17 0.20 0.18 0.09 DTR3 0.17 0.23 0.81 0.07 0.17 0.08 0.11 DTR4 0.02 0.11 0.82 0.22 0.19 0.17 0.02 DTR5 0.12 0.06 0.86 0.14 -0.01 0.05 0.05 UTR1 0.25 0.13 0.25 0.75 0.29 0.18 0.15 UTR2 0.20 0.19 0.22 0.80 0.19 0.27 0.17 UTR3 0.21 0.16 0.23 0.83 0.23 0.25 0.19 UTR4 0.24 0.17 0.25 0.76 0.23 0.27 0.21 UTR5 0.28 0.12 0.19 0.65 0.24 0.30 0.10 PSP1 0.31 0.09 0.12 0.23 0.81 0.09 0.10 PSP2 0.12 0.17 0.21 0.24 0.84 0.20 0.12 PSP3 0.22 0.18 0.20 0.22 0.81 0.20 0.15 PSP4 0.21 0.09 0.18 0.21 0.79 0.20 0.21 PSA1 0.33 0.07 0.13 0.30 0.20 0.74 0.19 PSA2 0.30 0.03 0.12 0.26 0.29 0.73 0.21 PSA3 0.08 0.23 0.24 0.25 0.12 0.83 0.14 PSA4 0.05 0.23 0.20 0.27 0.19 0.83 0.19 BI1 0.34 0.35 0.11 0.17 0.12 0.21 0.70 BI2 0.35 0.26 0.11 0.29 0.10 0.34 0.65

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BI3 0.26 0.20 0.19 0.31 0.30 0.24 0.67 BI4 0.31 0.18 0.17 0.16 0.32 0.19 0.68

Key: PU: Perceived Usefulness, PEOU: Perceived Ease of Use, DTR: Disposition to Trust, UTR: User Trust, PSP: Perceived Social Presence, PSA: Perceived Structural Assurance, BI: Behavioural Intention.

Table 3 Common Method Bias

Construct Indicator Substantive

Factor Loading (R1)

R12 Method Factor

Loading (R2)

R22

Perceived Structural Assurance

PSA1 0.833*** 0.694 0.093 0.009 PSA2 0.789*** 0.623 0.131 0.017 PSA3 1.058*** 1.119 -0.156* 0.024 PSA4 0.992*** 0.984 -0.064 0.004

Perceived Social Presence PSP1 0.972*** 0.945 -0.077 0.006PSP2 0.953*** 0.908 -0.007 0.000 PSP3 0.907*** 0.823 0.045 0.002 PSP4 0.890*** 0.792 0.037 0.001

User Trust

UTR1 0.930*** 0.865 -0.019 0.000 UTR2 1.008*** 1.016 -0.069 0.005 UTR3 1.025*** 1.051 -0.056 0.003 UTR4 0.878*** 0.771 0.073 0.005 UTR5 0.775*** 0.601 0.079 0.006

Extended Use Intention BI1 1.060*** 1.124 -0.196* 0.038 BI2 0.879*** 0.773 0.034 0.001 BI3 0.780*** 0.608 0.151* 0.023 BI4 0.857*** 0.734 0.005 0.000

Average 0.917 0.849 0.000 0.009 *p < .1; **p < .05; ***p < .01

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FIGURE 1

Research Model

FIGURE 2

Structural Model Results

0.21*** t=3.28

0.40*** t=5.99

0.32*** t=5.72

0.19*** t=2.65

R2=0.589 0.30*** t=4.25

0.43*** t=5.99

User Trust BI for VW collaboration

Disposition to trust

Control Variable

Control Variables

Perceived Usefulness

Perceived Ease of Use

Perceived Social

Presence

Reduce Equivocality

Perceived Structural Assurance

Reduce Uncertainty

*p<_0.1 **p<_0.05

***p<_0.01

R2=0.624

H3 (+)

H2 (+)

H1 (+)

User Trust BI for VW collaboration

Disposition to trust

Control Variable

Perceived Social

Presence

Reduce Equivocality

Perceived Structural Assurance

Reduce Uncertainty

Control Variables

Perceived Usefulness

Age IT Prof.

Gender

Perceived Ease of Use

Pref VW

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Appendix A. Scales and Items Perceived Structural Assurance Cronbach’s Alpha= 0.94 (McKnight et al., 2002)

1. I believe virtual world has enough safeguards to make me feel comfortable using it for collaboration and information sharing

2. I feel assured that legal and technological structures adequately protect me from problems on the virtual world.

3. I feel confident that encryption and other technological advances on the virtual world make it safe for me to collaborate and share information

4. In general, the virtual world now provides robust and safe environment to perform collaboration and information sharing

Perceived Social Presence Cronbach’s Alpha= 0.95 (Gefen and Straub, 2004)

1. I believe there is a sense of human contact in using virtual worlds for interactions 2. I believe there is a sense of personalness in using virtual worlds for interactions 3. I believe there is a sense of human warmth in using virtual worlds for interactions 4. I believe there is a sense of human sensitivity in using virtual worlds for interactions

Disposition to Trust Cronbach’s Alpha=0.92 (Gefen, 2000)

1. I generally trust other people. 2. I tend to count on other people. 3. I generally have faith in humanity. 4. I feel that people are generally reliable. 5. I generally trust other people unless they give me reason not to

User Trust Cronbach’s Alpha=0.96 (Gefen , 2000 and Jarvenpaa and Tractinsky, 1999)

1. I trust virtual worlds to be reliable. 2. I trust virtual worlds to be secure. 3. I believe virtual worlds are trustworthy. 4. I trust virtual worlds. 5. Even if the virtual worlds are not monitored, I'd trust them to do the job correctly.

Perceived Usefulness Cronbach’s Alpha=0.96 (Davis, 1989)

1. Using virtual worlds would enable me to accomplish collaborations tasks more quickly. 2. Using virtual worlds for collaborations tasks would improve my performance. 3. Using virtual worlds for collaborations tasks would enhance my effectiveness. 4. Using virtual worlds would make it easier for me to carry out collaborations tasks. 5. Overall, I find that virtual worlds are useful for collaborations tasks.

Perceived Ease of Use Cronbach’s Alpha=0.93 (Davis, 1989)

1. Learning to use virtual worlds would be easy for me. 2. It would be easy to get virtual worlds to do what I want it to do. 3. My interaction with virtual worlds would be clear and understandable. 4. It would be easy for me to become skilful at using virtual worlds. 5. Overall, I would find virtual worlds easy to use.

Behavioural Intention Cronbach’s Alpha=0.92 (Davis, 1989; Davis et al., 1989; Venkatesh and Davis, 2000)

1. Given a chance, I intend to use virtual worlds for collaborations and information sharing in the future. 2. Given a chance, I predict that I will frequently use virtual worlds for collaborations and information

sharing in the future. 3. I will strongly recommend others to use virtual worlds for collaborations. 4. Do you foresee the use of virtual worlds in organizations for collaborative purposes?