Toward developing a social network site-based model for knowledge sharing in the travel industry.
Social network sites (SNSs), an increasingly important media for internet marketing and tourism promotion in the travel industry (Litvin et al., 2008), enable people to participate in virtual commonality of interests and have changed the nature of communication among travelers. Recently, Hargittai (2008) clarifies SNSs as web-based services that allow individuals to (1) construct a public or semi-public profile within a bounded system, (2) articulate a list of other users with whom they share a connection, and (3) view and traverse their list of connections and those made by others within the system. Bernoff and Li (2008) note that "people are connecting with one another in increasing numbers, thanks to blogs, social networking sites like MySpace and countless communities across the Web" (p. 36). In essence, SNSs offer a powerful collaborative communication channel for developing content-specific online documents. Travelers and tourism managers, as well as government agents responsible for checking the tourism facility standards, would all find SNSs useful to some extent in expanding community involvement in their subjects and interests.
A number of popular SNSs are available using a simple Google key word search. For example, internet users can share their travel experience with others via tripadvisor.com. This site is touted by the company as "the largest travel community in the world, ... featuring real advice from real travelers, ... with more than 25 million monthly visitors, six million registered members and 15 million reviews and opinions" (Tripadvisor.com website. Retrieved on July 28, 2008). However, despite great social influences and likely monetary returns (Bernoff & Li, 2008), it takes substantial effort to start and maintain an "active" social network site, which needs interested online surfers to update the content on a frequent basis.
Surprisingly, to the best knowledge of the authors, little empirical research has gone into examining the facilitating factors associated with people's intention to be involved in the SNSs. Thus, to address this research gap, the technology acceptance model (TAM), one of the most widely used behavior models in explaining the adoption of a new technology, is employed to explore the main determinants of SNSs acceptance. TAM was originally developed by Davis (1986) to illustrate computer-usage behavior and it posits that perceived usefulness (PU) and users' attitudes (ATTI) have a direct effect on behavioral intentions (BI), while perceived ease of use (PEOU) has both a direct effect and an indirect effect on behavioral intentions through perceived usefulness (Davis, 1989). Notably, TAM is grounded in social psychology theory in general and the theory of reasoned action (TRA) in particular (Fishbein & Azjen, 1975). A key purpose of TAM is to provide a basis for tracing the impact of external variables on internal beliefs, attitudes and intentions.
An extensive body of TAM literature has accumulated in the past two decades, and the basic TAM framework has been validated as a powerful and parsimonious model to explain users' adoption of information technology (Venkatesh et al., 2000). Though PU and PEOU are two key factors explaining end-users' adoption behavior toward a technological innovation, existing empirical findings on TAM point out some inconsistency related to these two factors' level of importance in a TAM framework. It has been suggested that PEOU would play a more important role in new and complex technologies (Schepers & Wetzels, 2007). Castaneda et al. (2007) review 66 studies examining Internet user acceptance and find that 18% centered on the acceptance of the Internet as medium, 45% on the acceptance of e-commerce and e-commerce sites, 12% on e-mail, 12% on e-learning, and 8% on other Internet-mediated services. Less than 5% of the examined studies centered on free-content websites. Interestingly, Castaneda et al. (2007) maintain that, for e-commerce websites, PU would be a more significant factor than PEOU. Given SNSs' hybrid nature (i.e., a relatively new e-commerce communication channel), the current study attempts to reconcile the difference by extending the TAM framework to an innovative user-controlled online environment.
SNSs have two distinctive functionalities which make them stand out from other online services: (1) advanced tools for sharing digital objects (texts, pictures, music, videos, tags, bookmarks, etc.), and (2) advanced tools for communication and socialization between members (Cachia et al., 2007). The purpose of this study is to examine how the TAM framework could effectively interpret end users' intentions to share travel-related information online. Findings were expected to provide insights for developing strategies to understand and promote SNSs-based travel knowledge exchange in today's techno-driven, customer ruled marketplace.
THEORETICAL FRAMEWORK AND HYPOTHESES
Figure 1 depicts the proposed SNSs-based travel knowledge sharing model, in which three external variables along with four commonly studied TAM variables are simultaneously examined. specifically, this extended TAM framework focuses on testing the relationships between individual difference variables (i.e., gender, level of education, and age) and the typical technology acceptance constructs (i.e., PU, PEOU, ATTI, and BI to continue SNSs-based travel knowledge sharing). Below we define each of these constructs and offer theoretical rationale for the proposed relationships depicted in the model.
A. Technology Acceptance Constructs
[FIGURE 1 OMITTED]
As mentioned above, key constructs in a typical TAM framework include PU, PEOU, attitude and behavioral intention to use (Davis et al., 1989). PU and PEOU form end users' opinion of a specific technology and, therefore, predict their attitude toward the new technology, which in turn predicts whether the technology is likely to be accepted (Ma & Liu, 2004). In the present study, PU is defined as the degree to which a person believes that using a social network site enhances his or her performance in travel knowledge exchange. On the other hand, PEOU refers to the degree to which a person believes that using a social network site is not difficult. Further, ATTI reflects user preferences when exchanging travel information in a social network site. Finally, BI refers to the extent to which an individual would like to continue to be actively involved in SNSs-based travel knowledge sharing in the future. The usefulness of any social networking site is (and perhaps should be) measured in how useful they are in solving some real life's problems. SNSs-based travel knowledge sharing allows the online community to share critical knowledge about travel related issues. As the adoption of innovative technologies is highly dependent on perceived usefulness and perceived ease of use in a variety of contexts (Davis, 1986; Celik, 2008; Hsu et al., 2009), it is the authors' contention that both PU and PEOU will positively affect the users' attitude and behavioral intentions toward SNSs-based travel information sharing. The authors also found through a literature review that PEOU would be an antecedent of PU, while both factors directly influence attitude toward system usage (Schepers & Wetzels, 2007; Castaneda et al., 2007). Accordingly, we hypothesized:
H1: Perceived usefulness will positively affect user attitude toward SNSs-based travel knowledge sharing.
H2: Perceived ease of use will positively affect user attitude toward SNSs-based travel knowledge sharing.
H3: Attitude will positively affect user intentions to continue SNSs-based travel knowledge sharing.
H4: Perceived usefulness will positively affect user intentions to continue SNss-based travel knowledge sharing.
H5: Perceived ease of use will positively affect perceived usefulness of SNSs-based travel knowledge sharing.
B. The Role of External Variables
TAM postulates that external variables indirectly influence the adoption of a new technology via PU and PEOU. Agarwal and Prasad (1999) argue that individual differences imply differences of user learning. Theory of reasoned action, behavioral psychology (Skinner, 1969), and social learning theory (Bandura, 1977) all support the view that differences in learning lead users to form varying opinions about the consequences of using IT (Thompson et al., 1991).
Previous studies have recognized gender to be associated with different usage of social network sites. It is suggested that women are more likely to engage in person-to-person communication online than men (Hargittai, 2008). Therefore in our study it follows that:
H6: Significant differences exist between young men and women in terms of their levels of perceived ease of use and perceived usefulness upon SNSs-based travel knowledge sharing.
Also the relationship between users' age and IT has been well documented in the literature. Older workers tend to resist change and are, therefore, expected to perceive new IT as less useful, finding it more difficult to learn and use unfamiliar technology (Gomez, 1986). Older individuals tend to perceive a reduction in their own cognitive capabilities to learn (Hertzog and Hultsch, 2000) and have lower perceptions of self-efficacy with regard to cognitive functioning (Bandura, 1997).
H7: significant differences exist between young adults of varying ages in terms of their levels of perceived ease of use and perceived usefulness upon SNSs-based travel knowledge sharing.
Zmud (1979) suggests that a user's level of education influences their success in using IT. Empirical studies also support the benefit of education (Davis, 1990). Education can have a positive impact on PEOU by lowering users' possible anxiety over using a new technology (Igbaria, 1989; Lucas Jr., 1978), and by providing a store of knowledge that enables more effective and adaptive learning (Ashcraft, 2002). Given the decision to adopt a new technology is related to the knowledge one has regarding how to use that technology appropriately, early adopters of new technologies tend to have higher educational levels, perhaps reflecting their ability to understand "how-to" knowledge more quickly than those with less education (Rogers, 1995). Empirical studies show that education are associated differentially with perceived ease of use (Agarwal and Prasad, 1999) and perceived usefulness (Teo et al., 1999). Therefore we hypothesized:
H8: significant differences exist between young adults with different educational levels in terms of their levels of perceived ease of use and perceived usefulness upon SNSs-based travel knowledge sharing.
In the next section of this paper we discuss the methodology for our study including the profile of the respondents for our study.
The survey questionnaire consists of two parts. The first section records the survey respondent's perception toward the indicators associated with the scales of PU, PEOU, ATTI, and BI to use a new technology adapted from previous studies on TAM (Legris et al., 2003; Wu & Wang, 2005; Castaneda et al., 2007; Lu & Hsiao, 2007). Each item was measured on a seven-point Likert-type scale, ranging from "strongly disagree (1)" to "strongly agree (7)". The second section records the respondent's demographic information, including gender, age and level of education, etc.
Once the draft questionnaire was generated, a pilot study including personal interviews with business faculty and college/graduate students were conducted to refine the instruments. Specifically, these interviews enabled the researchers to gauge the clarity of the questions, assess whether the instrument was capturing the desired data, and verify that important aspects had not been omitted. The final version of the questionnaire consisted of 16 items measuring four latent constructs (PU, PEOU, ATTI and BI; see Appendix for details) as well as questions related to the respondent's demographic background.
B. Data Collection
Given the prosperous development of youth tourism in the U.S. and the high connectivity level of young adults, college students in the U.S. constitute an ideal population to study differences in particular types of SNS-based travel knowledge sharing. It is reported that youth travel is one of the fastest growing sectors in the tourism industry, representing approximately 20% of all international arrivals in 2007. In particular, today's young travelers stay longer and spend more than ever. Since 2002, the average spent per trip has increased 40% to 1,915 Euro in 2007 (World Youth & Student Educational Travel Confederation and World Tourism organization, 2008).
An online survey was posted on a public state university's internet survey portal in spring 2008 and summer 2008 semesters. The university, located in the Midwest region of the U.S., has roughly 11,000 undergraduate and graduate students. A database was designed on the back-end of the web survey to receive and store the online responses automatically. Web surveys have been found useful in a number of studies concerning user motivation (Wang & Fesenmaier, 2003; Stoeckl et al., 2007). In order to generate interest to the online questionnaire among the target audience (i.e., college students), several business professors from three universities in the Midwest and Southwest regions of the U.S. were contacted directly and asked to encourage their students to answer the online survey. Extra credit was given to student respondents by professors in their classes as an incentive. Among the 272 online responses, 74 were dropped from the database because the respondents either reported no experience/interest in sharing travel knowledge with others via SNSs or gave incomplete answers. As a result, 198 valid responses were used for statistical analysis in this study. Table 1 provides a profile of the student respondents.
Data Analysis and Results
To perform the analysis, we used Partial Least squares method (PLS), a structural modeling technique that is well suited for simple or highly complex predictive models (Wold & Joreskog, 1982). One notable advantage of PLS is its capability to handle a relatively small sample. specifically, its sample size requirement is 10 times the greater of (1) the number of items comprising the most formative construct and (2) the number of independent constructs influencing a single dependent construct (Chin, 1998).
Raw data were analyzed in two separate but sequentially related stages of analysis using the Visual PLS 1.04 program. First, the measurement model with no structural relationship was examined by performing a reliability and validity analysis on each of the constructs shown in the research model. second, the structural model with hypothesized paths was tested by examining the statistical significance of the coefficients as well as the overall performance of the model.
A. Instrument Validation
To assess the psychometric properties of the measurement instruments, a measurement model with no structural relationships was specified. That is, all constructs were correlated to each other at this stage. We evaluated construct reliability by means of composite reliability (CR) and average variance extracted (AVE) (see Table 2). For all measures, the CR was well above the recommended cut-off value of .70, and the AVE also exceeded the suggested .50 benchmark (Fornell & Larcker, 1981).
We next assessed the discriminant validity of the measures. In principle, a construct should share more variance with its direct indicators than it shares with other constructs (Howell & Aviolo, 1993). Technically, discriminant validity is confirmed when the AVE exceeds the intercorrelation square of the construct with the other constructs in the measurement model (Fornell & Larcker, 1981). In our study, none of the intercorrelation squares of the constructs exceeded the AVE of the constructs, which provided supportive evidence to the constructs' discriminant validity.
B: Hypotheses Testing
PLS path modeling was used to assess the explanatory power of our hypothesized research model (see Figure 2). Each path's level of statistical significance was estimated by a bootstrapping procedure (Ravichandran & Rai, 2000) with 100 resamples, which provided needed standard error estimates.
Hypotheses 1 through 5 address the relationships among PU, PEOU, ATTI, and BI. Four of the five hypotheses were supported. PEOU was found to indirectly influence on ATTI and BI through PU, supporting hypotheses 1 and 5. The effect size of attitude and PU explained approximately 65% of the variance in the endogenous variable (BI), supporting hypotheses 3 and 4. The only exception is hypothesis 2 in that there was no significant effect running from PEOU to ATTI. Moreover, hypotheses 6 through 8 were partially supported, with gender, age and educational attainments causing differing opinions in terms of level of PEOU and PU upon SNSs-Based travel knowledge sharing.
DISCUSSION AND CONCLUSION
This study presented a SNSs-based travel knowledge sharing model, with an attempt to understand the influential factors related to the usage of social network sites. A number of important implications are summarized below.
First, PU appeared to be the most important variable in the context of travel knowledge sharing SNSs, which is consistent with the notion that PU would be a more significant factor than PEOU is in the context of e-commerce oriented websites (Castaneda et al., 2007). It is found that PU directly influences end users' attitudes, and their intention to continue SNSs-based travel knowledge exchange. This underscored the value of travel information exchanged on the social network sites.
[FIGURE 2 OMITTED]
Second, PEOU was also an essential factor in the model. Though it does not directly influence attitude or behavioral intentions to continue SNSs-based travel knowledge exchange, it indirectly affects attitude and intention through PU, which is consistent with the work of Wu and Wang (2005). That is, an easy-to-use interface could positively influence users' preferences while a difficult interface may cause resistance. This reinforces the general beliefs that social network sites should continue to develop tools that require minimum effort to learn and use. This is because too much information on a travel-related SNS may cause clogs that intervene with end users' online information searches, which eventually "turn off the users. In order to enhance end users' PEOU, how to access content on the SNSs, navigate the site, and edit the material needs to be specified in a clear and concise manner.
Third, the individual difference variables, including gender, age and education, partially influenced PU and PEOU toward SNSs-based travel knowledge sharing. While differences have been found in previous research regarding online behavior between men and women in general, findings from the current study suggest that female users encounter more difficulty in using SNSs to find travel information. In contrast to their male counterparts, women are relatively inhibited to voice their opinions in the online information sharing context. In addition, the results showed that graduate students feel more at ease sharing travel knowledge in a social network site. This is in line with the contention of Chen (2006), arguing that higher education implies greater knowledge, which can make a person more confident and resourceful. The empirical findings also indicate that young adults of different age groups generally have different perceptions of the usefulness of travel knowledge on social network sites. among the young respondents, the relatively more matured young users (in terms of age) tend to regard the SNSs-based travel information more helpful. Thus, tourism marketers and developers of social network sites should consider segmenting the end users based on their demographics and tailor their services and products to meet the needs of different users.
Although this study enhances the current understanding of travel knowledge sharing in social network sites, it has a number of limitations. First, the sample was selected from a group of highly educated young adults among three U.S. universities, thus the findings may not be generizable to other population (e.g., less educated young adults or baby boomers). Second, other external variables, such as SNSs experience and travel experience, should be considered in the future. Third, other than the key TAM factors (e.g., PU and PEOU), this study does not investigate factors (e.g., a sense of community belonging, information content) that may also influence the adoption of SNSs.
Though customers are gaining more control by voicing their opinions via blogs and social network sites, scholars still have a limited understanding of who is and who is not using SNSs, why and for what purpose (e.g., recommending a rental apartment). Notably, as research shows customers tend to report unsatisfactory experiences more often than they report satisfactory experiences, future research may explore to what extent that SNSs may not be viewed as a reliable news network for users to obtain a trustful and unbiased perspective on rapidly evolving issues. As mobile applications may allow social networking to develop and add new value for business and personal users (DeJean, 2008), it is hoped that this exploratory study will stimulate further scholarly discussion on the impact of technological innovations toward e-tourism and social networking.
APPENDIX: LIST OF ITEMS BY CONSTRUCT
Perceived usefulness [Wu & Wang (2005); Legris, Ingham & Collerette (2003)]
In a social network site, ...
PU1.... exchanging travel information helps its members make travel decisions.
PU2.... exchanging travel information enables its members to make travel decision more quickly.
PU3.... exchanging travel information enhances the effectiveness of its members' travel decision-making process.
PU4.... exchanging travel information makes it easier for its members to make travel decisions.
PU5.... I find useful information when planning trips.
Perceived ease of use [Wu & Wang (2005); Legris, Ingham & Collerette (2003)]
In a social network site, ...
PEOU1.... Learning to exchange travel information would be easy for me.
PEOU2.... I would find it easy to exchange travel information.
PEOU3.... it would be easy for me to become skillful at exchanging travel information.
PEOU4.... I would find it easy to use for exchanging travel information.
Attitude towards SNSs-based Travel Knowledge sharing [Castaneda, Munoz-Leiva & Luque (2007)]
When it comes to using a social network site, ...
ATTI1.... I like exchanging travel information.
ATTI2.... I consider it a good way to exchange travel information.
ATTI3.... I think it is a nice channel to exchange travel information.
Intention to continue SNSs-based Travel Knowledge sharing [Lu & Hsiao (2007)]
In the future, ...
BI1.... I will post new travel-related information in a social network site.
BI2.... I will share my travel experience with other social network site members.
BI3.... I will consult other social network site members when I am planning trips.
BI4.... I will seek travel-related information in a social network site.
Agarwal, R. & Prasad, J. (1999). Are individual differences germane to the acceptance of new information technologies? Decision Sciences, 30(2), 361-391.
Ashcraft, M. H. (2002). Cognition, 3rd ed., Upper Saddle River, NJ: Prentice Hall
Bandura, A. (1977). Social learning theory. Englewood Cliffs, NJ: Prentice Hall.
Bandura, A. (1997). Self-efficacy: the exercise of control, New York: W.H. Freeman.
Bernoff, J. & Li, C. (2008). Harnessing the power of the oh-so-social web. MIT Sloan Management Review, 49(3), 36-42.
Cachia, R., Compano, R. & Costa, O. D. (2007). Grasping the potential of online social networks for foresight. Technological Forecasting & Social Change, 74(8), 1179-1203.
Castaneda, J. A., Munoz-Leiva, F. & Luque, T. (2007). Web acceptance model (WAM): Moderating effects of user experience. Information & Management, 44(4), 384-396.
Celik, H. (2008). What determines Turkish customers' acceptance of internet banking? International Journal of Bank Marketing, 26(5), 353-370.
Chen, C. (2006). Identifying significant factors influencing consumer trust in an online travel site. Information Technology & Tourism, 8, 197-214.
Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In G. A. Marcoulides (Ed.), Modern Methods for Business Research (pp.295-336). Mahwah, NJ: Lawrence Erlbaum Associates.
Davis, F. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results. Doctoral Dissertation, Sloan School of Management, MIT.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technologies. MIS Quarterly, 13(3), 319-340.
Davis, F. D. (1993). User acceptance of information technology: system characteristics, user perceptions and behavioral impacts. International Journal of Man-Machine Studies, 38(3), 475-487.
Davis, L. D. & Davis, F. D. (1990). The effect of training techniques and personal characteristics on training end users of information systems. Journal of Management Information Systems, 7(2), 93-110.
DeJean, D. (2008, April 7). Social networking gets moving. Computerworld, 42(15), 30-31.
Fishbein, M. & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.
Fornell, C. & Larcker, D. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
Gefen, D. & Straub, D. W. (1997). Gender differences in the perception and use of e-mail: an extension to the Technology Acceptance Model. MIS Quarterly, 21(4), 389-400.
Gomez, L. M., Egan, D. E. & Bowers, C. (1986). Learning to use a text editor: some learner characteristics that predict success. Human Computer Interaction, 2(1), 1-23.
Hargittai, E. (2008). Whose space? Differences among users and non-users of social network sites. Journal of Computer-Mediated Communication, 13(1), 276-297.
Hertzog, C. & Hultsh, D. F. (2000). Metacognition in adulthood and old age. In F. Craik and T. Salthouse (Ed.), The Handbook of aging and cognition (2nd edition) (pp. 417-466), Mahwah, NJ: Lawrence Erlbaum Associates.
Howell, J. M. & Aviolo, B. J. (1993). Transformational leadership, transactional leadership, locus of control, and support for innovation: key predictors of consolidated-business-unit-performance. Journal of Applied Psychology, 78(6), 891-902.
Hsu, M. K., Wang, S. & Chiu, K. K. (2009). Influence of attitude, anxiety and self-efficacy toward statistics and technology on statistical package software usage behavior. Computers in Human Behavior, 25, 412-420. http://www.tripadvisor.com/ pages/about_us.html, accessed on June 12, 2008.
Igbaria, M. & Parsuraman, S. (1989). A path analytic study of individual characteristics, computer anxiety, and attitudes towards microcomputers. Journal of Management, 15(3), 373-388.
Lai, V. S. & Li, H. (2005). Technology acceptance model for internet banking: an invariance analysis. Information & Management, 42(2), 373-386.
Legris, P., Ingham, J. & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Management, 40(3), 191-204.
Litvin, S. W., Goldsmith, R. E. & Pan, B. (2008). Electronic word-of-mouth in hospitality and tourism management. Tourism Management, 29(3), 458-468.
Lu, H.-P. & Hsiao, K.-L. (2007). Understanding intention to continuously share information on weblogs. Internet Research, 17(4), 345-361.
Lucas Jr., H. C. (1978). Empirical evidence for a descriptive model of implementation. MIS Quarterly, 2(1), 27-41.
Ma, Q. X. & Liu, L. P. (2004). The technology acceptance model: a meta analysis of empirical findings. Journal of Organizational and End User Computing, 16(1), 59-72.
Ravichandran, T. & Rai, A. (2000). Quality management in systems development: an organizational system perspective. MIS Quarterly, 24(3), 381-415.
Schepers, J. & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & Management, 44(1), 90-103.
Shih, H. P. (2004). Extended technology acceptance model of internet utilization behavior. Information & Management, 41(6), 719-729.
Skinner, B. F. (1969). Contingencies of reinforcement: A theoretical analysis, Englewood Cliffs, NJ: Prentice Hall.
Stoeckl, R., Rohrmeier, P. & Hess, T. (2007). Motivations to produce user generated content: differences between webloggers and videobloggers. In proceedings of 20th Bled eConference, June 4-6, Bled, Slovenia.
Teo, T., Lim, V., Lai R. (1999) Intrinsic and extrinsic motivation in Internet usage. Omega, 27(1), 25-37.
Thompson, R. L., Higgins, C. A. & Howell, J. M. (1991). Towards a conceptual model of utilization. MIS Quarterly, 15(1), 125-143.
Venkatesh, V. & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204.
Wang, Y. & Fesenmaier, D. R. (2003). Assessing motivation of contribution in online communities: An empirical investigation of an online travel community. Electronic Markets, 13(1), 33-45.
Wold, H. & Joreskog, H. (1982). Systems under Indirect Observation: Causality, Structure, Prediction (2). Amsterdam: North-Holland.
World Youth & Student Educational Travel Confederation and World Tourism Organization. (2008). Youth travel matters: Understanding the global phenomenon of youth travel. Retrieved from http://www.aboutwysetc.org/ Communications.aspxtfPublications.
Wu, J. H. & Wang, S. C. (2005). What drives mobile commerce? An empirical evaluation of the revised technology acceptance model. Information & Management, 42(5), 719-729.
Zmud, R. W. (1979). Individual differences and MIS success: a review of the empirical literature. Management Science, 25(10), 966-979.
Xiamen University, China
Maxwell K. Hsu
University of Wisconsin-Whitewater, USA
Xiamen University, China
TABLE 1: PROFILE OF RESPONDENTS Characteristic Frequency Percentage Gender Female 87 43.3% Male 111 55.2% Age 18-21 40 20.2% 22-25 64 32.3% 26-30 58 29.3% Above 30 36 18.2% Education Undergraduate 82 41.4% Graduate 116 58.6% TABLE 2: INTERCORRELATIONS OF LATENT VARIABLES C.R. PU PEOU ATTI BI PU 0.95 0.79 PEOU 0.97 0.61 0.88 ATTI 0.95 0.61 0.43 0.85 BI 0.95 0.60 0.38 0.79 0.82 Note: PU = Perceived usefulness; PEOU = Perceived ease of use, ATTI = Attitude towards SNSs-based Travel Knowledge sharing; BI = Behavioral intentions to continue SNSs-based Travel Knowledge Sharing; BI = Behavioral intentions to continue SNSs-based Travel Knowledge sharing. Diagonal elements (in italics) are the average Variance extracted (AVE).
|Printer friendly Cite/link Email Feedback|
|Author:||Huang, Yinghua; Hsu, Maxwell K.; Basu, Choton; Huang, Fucai|
|Publication:||Issues in Innovation|
|Date:||Mar 22, 2009|
|Previous Article:||On a logical structure for the authoritative accounting literature: a discussion of the FASB's codification structure.|
|Next Article:||Manufacturing strategy for high tech start-ups: reconsideration.|