Abstract
Whereas Chap. 5 mainly described the preliminary steps of the survey, this chapter comprehensively explains the several steps analyzing the collected data. This chapter is divided into three subsections: Sect. 6.1 reports the evaluation criteria and results of the non-user sample. Section 6.2 describes the same for the user sample. In Sect. 6.3 the results of an exploratory comparison of both samples are shown. In this study SmartPLS Version 2.0 (M3) (Ringle et al. http://www.smartpls.de, 2005) was used for analyzing the data. PLS models are typically analyzed in two stages: The first stage involves “the assessment of the reliability and the validity of the measurement model,” and the second stage involves “the assessment of the structural model” (Hulland. Strategic Management Journal 20:198, 1999). With respect to the evaluation and reporting criteria, this study follows the recommendations by Gefen et al. (MIS Quarterly 35:iii–xiv, 2011) who recently published an updated guideline on the reporting standards for structural equation modeling.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423.
Bagozzi, R. P., & Phillips, L. W. (1982). Representing and testing organizational theories: A holistic construal. Administrative Science Quarterly, 27(3), 459–489.
Bhattacherjee, A., & Premkumar, G. (2004). Understanding changes in belief and attitude toward information technology usage: A theoretical model and longitudinal test. MIS Quarterly, 28(2), 229–254.
Boudreau, M. C., Gefen, D., & Straub, D. W. (2001). Validation in information systems research: A state-of-the-art assessment. MIS Quarterly, 25(1), 1–16.
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, 29(3), 399–426.
Brown, S., Venkatesh, V., & Hoehle, H. (2012). Understanding technology adoption in the household context: A comparison of seven theoretical models.
Chin, W. W. (1998a). Commentary: Issues and opinion on structural equation modeling. MIS Quarterly, 22(1), vii–xvi.
Chin, W. W. (1998b). The partial least squares approach for structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295–336). Mahwah: Lawrence Erlbaum.
Chin, W. W. (2001). PLS-graph user’s guide. Houston: CT Bauer College of Business.
Chin, W. W. (2004). Multi-group analysis with PLS. Frequently asked questions-partial least squares & PLS-graph.
Chin, W. W. (2010). How to write up and report PLS analyses. In V. E. Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares (pp. 655–690). Berlin/Heidelberg: Springer.
Chin, W. W., Marcolin, B. L., & Newsted, P. R. (2003). A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Information Systems Research, 14(2), 189–217.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale: Lawrence Erlbaum.
Cronbach, L. J., & Thorndike, R. L. (1971). Test validation. In Educational measurement (pp. 443–507). Washington: American Council on Education.
Fornell, C., & Bookstein, F. L. (1982). Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. Journal of Marketing Research, 19(4), 440–452.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.
Gefen, D., & Straub, D. W. (2005). A practical guide to factorial validity using PLS-graph: Tutorial and annotated example. Communications of the Association for Information Systems, 16(1), 91–109.
Gefen, D., Straub, D. W., & Rigdon, E. E. (2011). An update and extension to SEM guidelines for administrative and social science research. MIS Quarterly, 35(2), iii–xiv.
Geisser, S. (1975). The predictive sample reuse method with applications. Journal of the American Statistical Association, 70(350), 320–328.
Götz, O., Liehr-Gobbers, K., & Krafft, M. (2010). Evaluation of structural equation models using the partial least squares (PLS) approach. In V. E. Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares (pp. 691–711). Berlin/Heidelberg: Springer.
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. The Journal of Marketing Theory and Practice, 19(2), 139–152.
Herrmann, A., Huber, F., & Kressmann, F. (2006). Varianz-und kovarianzbasierte Strukturgleichungsmodelle–ein Leitfaden zu deren Spezifikation, Schätzung und Beurteilung. zfbf, 58(2), 34–66.
Homburg, C., & Baumgartner, H. (1995). Beurteilung von Kausalmodellen: Bestandsaufnahme und Anwendungsempfehlungen. Marketing ZfP, 17(3), 162–176.
Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal, 20(2), 195–204.
Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879.
Ringle, C. M., Wende, S., & Will, A. (2005). SmartPLS 2.0 (M3) Beta. Hamburg http://www.smartpls.de.
Rogers, T. B. (1995b). The psychological testing enterprise: An introduction. Pacific Grove: Brooks/Cole.
Rönkkö, M., & Ylitalo, J. (2011). Pls marker variable approach to diagnosing and controlling for method variance. ICIS 2011 proceedings.
Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society. Series B (Methodological), 36(2), 111–147.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Wunderlich, P. (2013). Analysis. In: Green Information Systems in the Residential Sector. Progress in IS. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36769-4_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-36769-4_6
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36768-7
Online ISBN: 978-3-642-36769-4
eBook Packages: Business and EconomicsBusiness and Management (R0)