Factors Influencing Tourist’s Intention to use Location Based Service on Social Network

Main Article Content

ศักดา วิจิตรคุณากร

Abstract

The purpose of this study was to develop a Structural Equation Model (SEM) of factor influencing tourist’s intention to use Location-Based Service (LBS) on social network. The research samples were 363 Chinese tourists. Concerning the research tool, a questionnaire was used. Structural Equation Model (SEM) by SPSS AMOS version 22.0 was used to analyze the data. The research revealed as follows. 1) The measurement model was consistent with empirical study was at good. ( 2 /df=2.265, GFI=0.899, AGFI=0.868, CFI=0.951, NFI=0.916, TLI=0.942, RMSEA=0.059) 2) Hedonic Motivation, Socail Influence, Performance Expectancy and Price Value have statistically signicant positive direct effects on tourist’s intention to use LBS on social network. On the other hand, Effort Expectancy and Social Influence has statistically signicant positive direct effects on Performance Expectancy. Moreover, Effort Expactancy has statistically signicant positive direct effects on Hedonic Motivation.

Downloads

Download data is not yet available.

Article Details

How to Cite
วิจิตรคุณากรศ. (2018). Factors Influencing Tourist’s Intention to use Location Based Service on Social Network. Journal of Management Science Chiangrai Rajabhat University, 10(2), 62-84. Retrieved from https://so03.tci-thaijo.org/index.php/jmscrru/article/view/127196
Section
Research Articles
Author Biography

ศักดา วิจิตรคุณากร

*นักศึกษาหลักสูตรบริหารธุรกิจมหาบัณฑิต สาขา Corporate Management, College of Management, Zhejiang University, Hangzhou, People’s Republic of China (2558)

References

Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211.

Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological bulletin, 103(3), 411.

Angst, C. M., & Agarwal, R. (2009). Adoption of electronic health records in the presence of privacy concerns: the elaboration likelihood model and individual persuasion. MIS quarterly, 33(2), 339-370.

Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models.Journal of the academy of marketing science, 16(1), 74-94.

Bansal, G., & Gefen, D. (2010). The impact of personal dispositions on information sensitivity, privacy concern and trust in disclosing health information online. Decision Support Systems, 49(2), 138-150.

Bentler, P. M., & Chou, C. P. (1987). Practical issues in structural modeling.Sociological Methods & Research, 16(1), 78-117.

Brown, S. A., & Venkatesh, V. (2005). Model of adoption of technology in households: A baseline model test and extension incorporating household life cycle. MIS quarterly, 399-426.

Bruner, G. C., & Kumar, A. (2005). Explaining consumer acceptance of handheld Internet devices. Journal of Business Research, 58(5), 553-558.

Chang, C. W., & Chen, G. M. (2014). College students’ disclosure of location-related information on Facebook. Computers in Human Behavior, 35, 33-38.

Chang, I. C., Hwang, H. G., Hung, W. F., & Li, Y. C. (2007). Physicians’ acceptance of pharmacokinetics-based clinical decision support systems.Expert Systems with Applications, 33(2), 296-303.

Chau, P. Y. (1996). An empirical assessment of a modified technology acceptance model. Journal of management information systems, 185-204.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 39-50.

Gefen, D., & Straub, D. W. (2000). The relative importance of perceived ease of use in IS adoption: a study of e-commerce adoption. Journal of the Association for Information Systems, 1(1), 8.

Gefen, D., Straub, D., & Boudreau, M. C. (2000). Structural equation modeling and regression: Guidelines for research practice. Communications of the association for information systems, 4(1), 7.

Hu, B., & Ester, M. (2013, October). Spatial topic modeling in online social media for location recommendation. In Proceedings of the 7th ACM conference on Recommender systems ACM,25-32.

Humphreys, L., & Liao, T. (2011). Mobile geotagging: Reexamining our interactions with urban space. Journal of Computer‐Mediated Communication,16(3), 407-423.

Hung, C. C., Chang, C. W., & Peng, W. C. (2009, November). Mining trajectory profiles for discovering user communities. In Proceedings of the 2009 International Workshop on Location Based Social Networks ACM,1-8.

Kang, M., Liew, B. Y. T., Lim, H., Jang, J., & Lee, S. (2015). Investigating the Determinants of Mobile Learning Acceptance in Korea Using UTAUT2. In Emerging Issues in Smart Learning. Springer Berlin Heidelberg,209-216.

Kim, K. K., Shin, H. K., & Kim, B. (2011). The role of psychological traits and social factors in using new mobile communication services. Electronic Commerce Research and Applications, 10(4), 408-417.

Li, Y. (2011). Empirical studies on online information privacy concerns: literature review and an integrative framework. Communications of the Association for Information Systems, 28(1), 453-496.

Lindqvist, J., Cranshaw, J., Wiese, J., Hong, J., & Zimmerman, J. (2011). I'm the mayor of my house: examining why people use foursquare-a social-driven location sharing application. In Proceedings of the SIGCHI conference on human factors in computing systems ,2409-2418.

Lowry, P. B., Cao, J., & Everard, A. (2011). Privacy concerns versus desire for interpersonal awareness in driving the use of self-disclosure technologies: The case of instant messaging in two cultures. Journal of Management Information Systems, 27(4), 163-200.

Malhotra, N. K., Kim, S. S., & Agarwal, J. (2004). Internet users' information privacy concerns (IUIPC): The construct, the scale, and a causal model.Information Systems Research, 15(4), 336-355.

Mathieson, K. (1991). Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior.Information Systems Research, 2(3),173-191.

Noulas, A., Scellato, S., Mascolo, C., & Pontil, M. (2011). An Empirical Study of Geographic User Activity Patterns in Foursquare. ICwSM, 11, 70-573.

Riemenschneider, C. K., Hardgrave, B. C., & Davis, F. D. (2002). Explaining software developer acceptance of methodologies: a comparison of five theoretical models. Software Engineering, IEEE Transactions on, 28(12), 1135-1145.

Sadeh, N., Hong, J., Cranor, L., Fette, I., Kelley, P., Prabaker, M., & Rao, J. (2009). Understanding and capturing people's privacy policies in a mobile social networking application. Personal and Ubiquitous Computing, 13(6), 401-412.

Shen, A. X., Cheung, C. M., Lee, M. K., & Chen, H. (2011). How social influence affects we-intention to use instant messaging: The moderating effect of usage experience. Information Systems Frontiers, 13(2), 157-169.

Slade, E., Williams, M., & Dwivdei, Y. (2013). Extending UTAUT2 To Explore Consumer Adoption Of Mobile Payments. In Proceedings of the UK Academy for Information Systems Conference, March.

Smith, H. J., Milberg, S. J., & Burke, S. J. (1996). Information privacy: measuring individuals' concerns about organizational practices. MIS quarterly, 167-196.

Steiniger, S., Neun, M., & Edwardes, A. (2011). Foundations of Location Based Services Lesson 1 CartouCHe 1-Lecture Notes on LBS, V. 1.0.

Szajna, B. (1996). Empirical evaluation of the revised technology acceptance model. Management Science, 42(1), 85-92.

Tsai, J. Y., Kelley, P. G., Cranor, L. F., & Sadeh, N. (2010). Location-sharing technologies: Privacy risks and controls. ISJLP, 6, 119.

Van der Heijden, H. (2004). User acceptance of hedonic information systems.MIS quarterly, 695-704.

Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478.

Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS quarterly, 36(1), 157-178.

Vongjaturapat, S., & Chaveesuk, S. (2013, June). Mobile technology acceptance for library information service: A theoretical model. In Information Society (i-Society), 2013 International Conference IEEE,290-292.

Zeithaml, V. A. (1988). Consumer perceptions of price, quality, and value: a means-end model and synthesis of evidence. The Journal of marketing, 2-22.

Zheng, Y., Zhang, L., Xie, X., & Ma, W. Y. (2009, April). Mining interesting locations and travel sequences from GPS trajectories. In Proceedings of the 18th international conference on World wide web,791-800.

Zhou, T., Lu, Y., & Wang, B. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in Human Behavior, 26(4), 760-767.