Skip to main content
SpringerLink
Log in

Application of PLS in Marketing: Content Strategies on the Internet

  • Chapter
  • First Online:
Handbook of Partial Least Squares

Part of the book series: Springer Handbooks of Computational Statistics ((SHCS))

Abstract

In an empirical study the strategies are investigated that content providers follow in their compensation policy with respect to their customers. The choice of the policy can be explained by the resource based view and may serve as recommendations. We illustrate how a strategy study in marketing can be analyzed with the help of PLS thereby providing more detailed and actionable results. First, complex measures have to be operationalized by more specific indicators, marketing instruments in our case, which proved to be formative in most cases. Only by using PLS it was possible to extract the influence of every single formative indicator on the final constructs, i.e., the monetary form of the partnerships. Second, PLS allows for more degrees of freedom so that a complex model could be estimated with a number of cases that would not be sufficient for ML-LISREL. Third, PLS does not work with distributional assumptions while significance tests can still be carried out with the help of bootstrapping. We recommend the use of PLS for future strategy studies in marketing because it is possible to extract the drivers at the indicator level so that detailed recommendations can be given for managing marketing instruments.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 389.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 499.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 499.99
Price excludes VAT (USA)
  • Durable hardcover 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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Albers, S. (2010). PLS and success factor studies in marketing. In V. E. Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of Partial Least Squares. Heidelberg: Springer.

    Google Scholar 

  • Anderson, J., & Gerbing, D. W. (1988). Structural equation modelling in practice: A review and recommended two-step approach. Psychological Review, 103 (3), 411–423.

    Google Scholar 

  • Bamberger, I., & Wrona, T. (1996). The resource based view of a firm and its importance for strategic management (in German). Schmalenbachs Zeitschrift für betriebswirtschaftliche Forschung, 48(2), 130–153.

    Google Scholar 

  • Barclay, D., Higgins, S., & Thompson, R. (1995). The partial least squares approach to causal modelling personal computer adoption and use as an illustration. Technology Studies, 2(2), 285–374.

    Google Scholar 

  • Chin, W. W. (1998). The partial least squares approach to structural equation modelling. In: G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295–336). Mahwah, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Chin, W. W. (2010). Bootstrap cross-validation indices for PLS path model assessment. In V. E. Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of Partial Least Squares. Heidelberg: Springer.

    Google Scholar 

  • Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–324.

    Article  Google Scholar 

  • Diamantopoulos, A. (2005). The C-OAR-SE procedure for scale development in marketing: A comment. International Journal of Research in Marketing, 22(1), 1–9.

    Article  Google Scholar 

  • Diamantopoulos, A., & Winklhofer, H. M. (2001). Index construction with formative indicators: An alternative to scale development. Journal of Marketing Research, 38, 269–277.

    Article  Google Scholar 

  • Dupacavá, J., & Wold, H. (1982). On some identification problems in ML modelling of systems with indirect observations. In K. G. Jöreskog & H. Wold (Eds.), Systems under indirect observations: Causality, structure, prediction, part 2 (pp. 293–315). New York: Amsterdam.

    Google Scholar 

  • Efron, B., & Gong, G. (1983). A leisurely look at the bootstrap, the jackknife, and cross validation. The American Statistican, 37(1), 36–48.

    Article  MathSciNet  Google Scholar 

  • Efron, B., & Tibshirani, R. J. (1993). An introduction to the bootstrap – monographs on statistics and applied probability. New York: Chapman and Hall.

    Google Scholar 

  • Finn, A., & Kayande, U. (2005). How fine is C-OAR-SE? A generalizability theory perspective on rossiter’s procedure. International Journal of Research in Marketing, 22(1), 11–21.

    Article  Google Scholar 

  • Fornell, C., & Bookstein, F. L. (1982). Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. Journal of Marketing Research, 19, 440–452.

    Article  Google Scholar 

  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39–50.

    Article  Google Scholar 

  • Hair, J. F., Jr., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis (5th ed.). Upper Saddle River, NJ: Prentice Hall.

    Google Scholar 

  • Hansen, G. (1987). Forecasting error and multicollinearty (in German). Jahrbuch für Nationalökonomie und Statistik, 203(5–6), 517–531.

    Google Scholar 

  • Hinkley, D. V. (1988). bootstrapping methods. Journal of the Royal Statistical Society, Series B, 50(3), 321–337.

    MATH  MathSciNet  Google Scholar 

  • Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal, 20, 195–204.

    Article  Google Scholar 

  • Jarvis, C. B., MacKenzie, S. B., & Podsakoff, P. M. (2003). A critical review of construct indicators and measurement model misspecifications in marketing and consumer research. Journal of Consumer Research, 30, 199–218.

    Article  Google Scholar 

  • Penrose, E. T. (1959). The theory of the growth of the firm. Oxford: Oxford University Press.

    Google Scholar 

  • Prahalad, C. K., & Hamel, G. (1990). The core competence of the corporation. Harvard Business Review, 68, 79–91.

    Google Scholar 

  • Rossiter, J. (2002). The C-OAR-SE procedure for scale development in marketing. International Journal of Research in Marketing, 19, 305–335.

    Article  Google Scholar 

  • Rossiter, J. (2005). Reminder: A horse is a horse. International Journal of Research in Marketing, 22(1), 23–25.

    Article  Google Scholar 

  • Seltin, N., & Keeves, J. P. (1994). Path analysis with latent variables. In T. Husen & Postlethwaite, T. N. (Ed.), International encyclopedia of education. (2nd ed., pp. 4352–4359). Oxford: Pergamon.

    Google Scholar 

  • Steckel, J. H., & Vanhonacker, W. R. (1993). Cross-validation regression models in marketing research. Marketing Science, 12(4), 415–427.

    Article  Google Scholar 

  • Thies, S. (2005). Content-Interaktionsbeziehungen in Internet: Ausgestaltung und Erfolg (engl.: Content-cooperations on the Internet – forms and performance). DUV: Wiesbaden.

    Google Scholar 

  • Werbach, K. (2000). Syndication – the emerging model for business in the internet Era. Harvard Business Review, 78, 85–93.

    Google Scholar 

  • Wernerfelt, B. (1984). A resource-based view of the firm. Strategic Management Journal, 5(2), 171–180.

    Article  Google Scholar 

  • Werts, C. E., Linn, R. L., & Jöreskog, K. G. (1974). Interclass reliability estimates: Testing structural assumptions. Educational and Psychological Measurement, 34, 24–33.

    Article  Google Scholar 

  • Wold, H. (1985). Partial least squares.In S. Kotz & N. L. Johnson (Eds.), Encyclopedia of statistical sciences (pp. 581–591). New York: Wiley.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Capgemini Telecom Media and Networks Deutschland GmbH, Neues Kanzler Eck 21, 10719, Berlin, Germany

    Silvia Boßow-Thies

  2. Institute of Innovation Research, Christian-Albrechts-University at Kiel, Westring 425, 24098, Kiel, Germany

    Sönke Albers

Authors
  1. Silvia Boßow-Thies
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sönke Albers
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Silvia Boßow-Thies .

Editor information

Editors and Affiliations

  1. Dept. Inf. Systems & Decision Sciences, ESSEC Business School of Paris, av. Bernard Hirsch, Cergy Pontoise CX, 95021, France

    Vincenzo Esposito Vinzi

  2. Bauer College of Business, University of Houston, Houston, 77204-6282, U.S.A.

    Wynne W. Chin

  3. Nijmegen School of Management, Radboud University, Nijmegen, Netherlands

    Jörg Henseler

  4. School of Economics & Management, BeiHang University, XueYuan Road 37, Beijing, 100083, China, People's Republic

    Huiwen Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Boßow-Thies, S., Albers, S. (2010). Application of PLS in Marketing: Content Strategies on the Internet. In: Esposito Vinzi, V., Chin, W., Henseler, J., Wang, H. (eds) Handbook of Partial Least Squares. Springer Handbooks of Computational Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32827-8_26

Download citation

Publish with us

Policies and ethics

Search

Navigation