Skip to main content

Multi-Level Latent Class Analysis of Internet Use Pattern in Taiwan

  • Conference paper

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 171))

Abstract

This study reports the Taiwan Government survey results of the Internet use pattern by Multi-level Latent Class Analysis (MLCA). Based on the stratified random sampling with total sample size of 16,133, this study discovers the use pattern of Internet consists of seven segments: knowledgeable segment, traditional use segment, amusement segment, entertainment & online shopping segment, leisure & aloof segment, business segment, Interactive segment. At a higher level, the entire Taiwan is divided into three segments: Southern Taiwan, Northern Taiwan, and metropolitan. Besides, the Internet use behaviors of instant messenger, email, or IP-phone are uncorrelated. This study enriches findings of this research to provide researchers and practitioners a good guideline for its economic development policy setting.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ahuja, M., Gupta, B., Raman, P.: An Empirical Investigation of Online Consumer Purchasing Behavior. CACM 46, 145–151 (2003)

    Article  Google Scholar 

  2. Kim, D.J., Ferrin, D.L., Rao, H.R.: Trust and Satisfaction, Two Stepping Stones for Successful E-commerce Relationships: A Longitudinal Exploration. Inf. Syst. Res. 20, 237–257 (2009)

    Article  Google Scholar 

  3. FuLler, J., MuHlbacher, H., Matzler, K., Jawecki, G.: Consumer Empowerment Through Internet-based Co-creation. JMIS 26, 71–102 (2009)

    Google Scholar 

  4. Citrin, A.V., Sprott, D.E., Silverman, S.N., Stem Jr., D.E.: Adoption of Internet Shopping: the Role of Consumer Innovativeness. Ind. Manage. Data Syst. 100, 294–300 (2000)

    Article  Google Scholar 

  5. Chang, M.K., Cheung, W., Lai, V.S.: Literature Derived Reference Models for the Adoption of Online Shopping. Inf. Manage. 42, 543–559 (2005)

    Article  Google Scholar 

  6. Zviran, M., Glezer, C., Avni, I.: User Satisfaction from Commercial Web Sites: the Effect of Design and Use. Inf. Manage. 43, 157–178 (2006)

    Article  Google Scholar 

  7. Chang, H.H., Chen, S.W.: The Impact of Online Store Environment Cues on Purchase Intention: Trust and Perceived Risk as a Mediator. Online Information Review 32, 818–841 (2008)

    Article  Google Scholar 

  8. Anderson, B., Tracey, K.: Digital Living: The Impact (or Otherwise) of the Internet on Everyday Life. Am. Behav. Sci. 45, 456–475 (2001)

    Article  Google Scholar 

  9. Yang, C., Chen, L.C.: Can Organizational Knowledge Capabilities Affect Knowledge Sharing Behavior? J. Inf. Sci. 33, 95 (2007)

    Article  Google Scholar 

  10. Huang, J.H., Yang, C., Jin, B.H., Chiu, H.: Measuring Satisfaction with Business-to-Employee Systems. Comput. Hum. Behav. 20, 17–35 (2004)

    Article  Google Scholar 

  11. Hsieh, N.C., Chu, K.C.: Enhancing Consumer Behavior Analysis by Data Mining Techniques. Int. J. Inf. Manag. Sci. 20, 39–53 (2009)

    MATH  Google Scholar 

  12. Yang, C., Wu, C.C.: Gender and Internet Consumers’ Decision-making. CyberPsychology & Behavior 10, 86–91 (2007)

    Article  Google Scholar 

  13. Tsou, C.S., Fang, H., Lo, H.C., Huang, C.H.: A Study of Cooperative Advertising in a Manufacturer-Retailer Supply Chain. Int. J. Inf. Manag. Sci. 20, 15–26 (2009)

    MathSciNet  MATH  Google Scholar 

  14. Goodman, L.: Statistical Magic and/or Statistical Serendipity: An Age of Progress in the Analysis of Categorical Data. Sociology 33, 1 (2007)

    Article  Google Scholar 

  15. Masters, G.: A Comparison of Latent Trait and Latent Class Analyses of Likert-type Data. Psychometrika 50, 69–82 (1985)

    Article  Google Scholar 

  16. Henry, K., Muthen, B.: Multilevel Latent Class Analysis: An Application of Adolescent Smoking Typologies with Individual and Contextual Predictors. Struct. Equ. Modeling 17(2), 193–215 (2010)

    Article  MathSciNet  Google Scholar 

  17. Bartholomew, D.: The Analysis and Interpretation of Multivariate Data for Social Scientists. CRC Press, Boca Raton (2002)

    MATH  Google Scholar 

  18. Moustaki, I., Knott, M.: Generalized Latent Trait Models. Psychometrika 65, 391–411 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  19. Vermunt, J.: Multilevel latent class models. Sociological Methodology 33, 213–239 (2003)

    Article  Google Scholar 

  20. Bijmolt, T., Paas, L., Vermunt, J.: Country and Consumer Segmentation: Multi-level Latent Class Analysis of Financial Product Ownership. IJRM 21, 323–340 (2004)

    Google Scholar 

  21. Van Horn, M., Fagan, A., Jaki, T., Brown, E., Hawkins, J., Arthur, M., Abbott, R., Catalano, R.: Using Multilevel Mixtures to Evaluate Intervention Effects in Group Randomized Trials. Multivariate Behav. Res. 43, 289–326 (2008)

    Article  Google Scholar 

  22. Vermunt, J., Magidson, J.: Latent Gold 4.0. User’s Guide (2005)

    Google Scholar 

  23. Van Horn, M.L., Fagan, A.A., Jaki, T., Brown, E.C., Hawkins, J.D., Arthur, M.W., Abbott, R.D., Catalano, R.F.: Using Multilevel Mixtures to Evaluate Intervention Effects in Group Randomized Trials. Multivariate Behav. Res. 43, 289–326 (2008)

    Article  Google Scholar 

  24. Buse, A.: The Likelihood Ratio, Wald, and Lagrange Multiplier Tests: An Expository Note. Amer. Statistician 36, 153–157 (1982)

    Google Scholar 

  25. Hox, J.: Multilevel Modeling: When and Why. Classification, Data Analysis, and Data Highways 2000, 54–85 (1998)

    Google Scholar 

  26. Phillips, P., Park, J.: On the Formulation of Wald Tests of Nonlinear Restrictions. Econometrica 56, 1065–1083 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  27. Wald, A.: Tests of Statistical Hypotheses Concerning Several Parameters When the Number of Observations is Large. Trans. Am. Math. Soc. 54, 426–482 (1943)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Hsieh, TC., Yang, C. (2011). Multi-Level Latent Class Analysis of Internet Use Pattern in Taiwan. In: Yonazi, J.J., Sedoyeka, E., Ariwa, E., El-Qawasmeh, E. (eds) e-Technologies and Networks for Development. ICeND 2011. Communications in Computer and Information Science, vol 171. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22729-5_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-22729-5_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-22728-8

  • Online ISBN: 978-3-642-22729-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics