Abstract
Since the 1970s, structural equation modeling (SEM) has been applied for the analysis of causal relationships in economic and social sciences (Götz and Liehr-Gobbers 2004: 714; Henseler 2005: 70; Herrmann et al. 2006: 35). SEM is a multivariate statistical technique that – by integrating different regression- and factor-analytic methods – allows the testing and estimation of theoretically derived, casual relationships between variables (Bortz 1993: 436; Rigdon 1998: 251). Its high popularity in economic and social sciences is mainly attributed to two factors. First, it is the only multivariate statistical method that allows one to simultaneously assess the quality of construct measurement in terms of reliability and validity on the one hand, while on the other hand estimating the strength of a relationship between constructs (Backhaus et al. 2008: 511; Henseler 2005: 70). Second, SEM enables scientists to measure not only observable (manifest) variables, but also unobservable (latent) variables (Chin 1998b: 296; Chin and Newsted 1999: 307; Herrmann et al. 2006: 35; Rigdon 1998: 251). As latent variables are very common in economic and social sciences, the ability to model latent variables is of particular importance (Chin 1998b: 296). Due to the fact that latent variables cannot be observed directly, they are assigned manifest variables – which can be derived empirically and measured on the basis of metric scales – in a measurement model (or outer model) (Backhaus et al. 2008: 513; Henseler 2005: 70), as illustrated in Fig. 3.1
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Notes
- 1.
The independent variables are termed exogenous, as their causes lie outside the structural model, while the endogenous variables are determined by variables within the structural model (Bollen 1989: 12). The characteristics of structural models and measurement models, as well as the applied nomenclature in Fig. 3.1, will be explained in more detail in Sects. 3.2.1 and 3.2.2.
- 2.
As SEM is regarded as an extension of first-generation approaches, any constraints or assumptions that limit its application would set it back to a first-generation procedure (Chin and Newsted 1999: 308).
- 3.
While using highly correlated measures as independent variables in regression analyses leads to multicollinearity (Homburg 1992: 499), in SEM – as correlated items are modeled as measures of one common variable, and as only this one variable enters the causal relationship – high correlations may even improve the robustness of the applied measurement and/or structural model (Rigdon 1998: 254–255).
- 4.
The distinctive characteristics of reflective and formative measurement models will be presented in Sect. 3.2.2.
- 5.
The estimates of imperfections in the parameters of the measurement model give important information about the reliability and validity of the measurement model, which is an important pre-condition to derive reliable and valid insights from the theoretical substance of the relationship between the latent variables (Hildebrandt 2004: 545; Rigdon 1998: 254).
- 6.
- 7.
The growing popularity of PLS path modeling is reflected by its increased utilization in recent journal publications (Henseler et al. 2009: 282).
- 8.
The term “covariance” is used here in a broader sense, including both variance and correlation.
- 9.
PLS is described as a variance analytic approach (Backhaus et al. 2008: 515).
- 10.
- 11.
The limited consistency of the parameter estimates in PLS is due to the fact that a consistent approximation of the correct parameter value requires a large sample and numerous indicators for each latent construct (consistency at large) (Albers and Hildebrandt 2006: 15; Bliemel et al. 2005: 11; Chin 1998b: 329–330). Therefore, while PLS is coherent in a predictive sense (Chin 1998b: 303), its parameter estimation process – in contrast to LISREL – is not consistent (Scholderer and Balderjahn 2005: 92).
- 12.
Cf. Sect. 2.4.1.
- 13.
The distinctive characteristics of reflective and formative measurement models will be presented in Sect. 3.2.2.
- 14.
- 15.
Cf. Sect. 4.1.1.
- 16.
- 17.
- 18.
Despite not being relevant for this work, it should be noted that – apart from moderating variables – mediating variables may also influence this relationship (Shrout and Bolger 2002: 422). In the case of such mediation variables, the impact of an exogenous variable on a dependent, endogenous variable is partly or completely mediated by a so-called mediator (Eggert et al. 2005: 103). The existence of mediation effects thus leads to a partial or complete disconnection of the relationship between the exogenous constructs and the endogenous variables (Eggert et al. 2005: 103–104). Instead of the disconnection of this relationship, only effects on its strength are assumed in this work, which are better conceptualized by moderating variables (Baron and Kenny 1986: 1174; Eggert et al. 2005: 103–104). In Sect. 4.1.3, we will explain the moderating effects on the strength of this relationship in more detail.
- 19.
Latent interaction variables do not conform to this assumption, as their indicators result from multiplication with an exogenous variable, and thus inevitably share a part of their variance with the indicators of the exogenous construct (Eggert et al. 2005).
- 20.
In Sect. 3.2.2., the distinctive characteristics of reflective and formative measurement models will be presented.
- 21.
Even though formative indicators are usually slightly positively or negatively correlated, it is possible that formative indicators are highly correlated with each other, which leads to the problem of multicollinearity (Krafft et al. 2005: 78). Appropriate quality criteria to control for this problem will be presented in Sect. 3.2.3.
- 22.
The academic journals of their study included the Journal of Consumer Research, the Journal of Marketing, the Journal of Marketing Research, and Marketing Science (Jarvis et al. 2003: 206).
- 23.
The nomological aspects of indicators may differ in the case of formative measurement models (i.e., they are not required to have the same antecedents and consequences) (Jarvis et al. 2003: 203). Therefore, in contrast to reflective measurement models (i.e., where indicators have the same antecedents and consequences), the elimination of an indicator in a formative measurement model requires a check, if the new model version functions in predictable ways (Diamantopoulos and Winklhofer 2001: 273; Jarvis et al. 2003: 203).
- 24.
His analysis covered the following German academic publications: Zeitschrift für Betriebswirtschaft, Die Betriebswirtschaft, and Zeitschrift für betriebswirtschaftliche Forschung (Fassott 2006: 74).
- 25.
Cf. Sect. 4.1.
- 26.
The terms reliability and validity will be explained in more detail in the next chapter.
- 27.
First-generation quality criteria (e.g., explorative factor analysis, Cronbach’s alpha) were originally developed in the 1950s in the area of psychology/psychometrics and have been promoted mainly by the work of Churchill (1979) in marketing research (Homburg and Giering 1996: 8). With the introduction and spread of the confirmatory factor analysis in marketing research, increasingly new and more powerful quality criteria (e.g., indicator reliability, construct reliability) were developed – which were termed second-generation quality criteria (Homburg and Giering 1996: 8, 13).
- 28.
For a detailed description of explorative factor analysis cf. Hüttner and Schwarting (1999).
- 29.
The scales of the questionnaire applied in our survey have been developed for the purpose of this work and have not been utilized before (cf. Sect. 5.2.2).
- 30.
- 31.
The reasoning for avoiding the elimination of indicators in formative measurement models has been explained in Sect. 3.2.2.2.
- 32.
Cf. Sect. 3.2.3.1.
- 33.
The correlation coefficient is calculated by dividing the covariance of two metric formative indicators by the product of their standard deviations (Schulze 1998: 128).
- 34.
The external or nomological validity has been introduced to evaluate formative measurement models, as individual item reliability and convergent validity of reflective measurement models are irrelevant here, given that the latent variable is modeled as an effect instead of a cause of the manifest indicator variables (Götz and Liehr-Gobbers 2004: 729; Hulland 1999: 201).
- 35.
The MIMIC model allows a single latent variable to be measured by both formative and reflective indicators (Krafft et al. 2005: 80). As the MIMIC model is currently not supported by all PLS techniques (e.g., SmartPLS), a two-construct model is often applied to assess the measurement errors (Diamantopoulos and Winklhofer 2001: 272–273; Götz and Liehr-Gobbers 2004: 720). Here, for the external validation of the formative measurement model, an additional reflective measurement model (phantom model) is designed to specify a phantom variable (Götz and Liehr-Gobbers 2004: 720; Rindskopf 1984: 38). External validity is confirmed if a strong and significant relationship exists between the formatively operationalized latent construct and the reflectively measured, latent phantom variable (Krafft et al. 2005: 82; Rindskopf 1984: 38). If, however, no reflective indicators are available to specify the latent phantom variable, nomological validity may be assessed alternatively (Diamantopoulos and Winklhofer 2001: 273; Eggert and Fassott 2005: 41; Götz and Liehr-Gobbers 2004: 730). No reflective indicator variables were added in the survey of this work – that would correspond to the formatively measured, latent constructs – to achieve a sufficient response rate based on a minimum amount of questions (cf. Sect. 5.2.2).
- 36.
The less restrictive assumptions of PLS versus the covariance-based approach LISREL have been explained in Sect. 3.1. It should be noted here that even though a parametric approach – which recognizes the classical assumptions, particularly regarding the distribution of residuals – would allow an estimation of the standard errors of the parameters, this does not correspond to the PLS philosophy of soft modeling (Balderjahn 1986: 147; Chin 1998b: 315; Chin and Newsted 1999: 324; Lohmöller 1989: 64), and thus will not be considered in this work.
- 37.
In Sect. 4.1, the hypotheses of this work will be presented and explained in detail.
- 38.
Cf. Sect. 3.2.3.1.
- 39.
In the literature, this measure of the effect size of the interaction term is usually termed solely “effect size” (Chin et al. 2003: 195–196; Eggert et al. 2005: 109). To distinguish this measure of the effect size of interaction variables from the effect size of the exogenous variables in the basic structural model, we utilize the term “interaction effect size”.
- 40.
- 41.
A reflective relationship between the dimensions and the second-order construct would imply that the dimensions are interchangeable measures of the second-order construct, and thus are not inseparable – calling into question the necessity of a second-order construct (Albers and Götz 2006: 672–673; Maloney 2007: 258–259).
References
Albers, S., & Götz, O. (2006). Messmodelle mit Konstrukten zweiter Ordnung in der betriebswirtschaftlichen Forschung. Die Betriebswirtschaft, 66(6), 669–677.
Albers, S., & Hildebrandt, L. (2006). Methodische Probleme bei der Erfolgsfaktoren-forschung – Messfehler, formative versus reflektive Indikatoren und die Wahl des Strukturgleichungs-Modells. Zeitschrift für betriebswirtschaftliche Forschung, 58(2), 2–33.
Anderson, J. C., & Gerbing, D. W. (1991). Predicting the performance of measures in a confirmatory factor analysis with a pretest assessment of their substantive validities. Journal of Applied Psychology, 76(5), 732–740.
Backhaus, K., Blechschmidt, B., & Eisenbeiß, M. (2006). Der Stichprobeneinfluss bei Kausalanalysen. Die Betriebswirtschaft, 66(6), 711–726.
Backhaus, K., & Ebers, M. (2006). Die Anwendung der Kausalanalyse in der betriebswirtschaftlichen Forschung. Die Betriebswirtschaft, 66(6), 607–608.
Backhaus, K., Erichson, B., Plinke, W., & Weiber, R. (2008). Multivariate Analyse-methoden: Eine anwendungsorientierte Einführung. Berlin/Heidelberg: Springer.
Bagozzi, R. P. (1977). Structural equation models in experimental research. Journal of Marketing Research, 14(2), 209–226.
Bagozzi, R. P. (1998). A prospectus for theory construction in marketing: Revisited and revised. In L. Hildebrandt & C. Homburg (Eds.), Die Kausalanalyse: Ein Instrument der empirischen betriebswirtschaftlichen Forschung (pp. 45–81). Stuttgart: Schäffer-Poeschel Verlag.
Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94.
Balderjahn, I. (1986). Das umweltbewusste Konsumentenverhalten: Eine empirische Studie. Berlin: Duncker & Humblot.
Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182.
Belsey, D. A., Kuh, E., & Welsch, R. E. (1980). Regression diagnostics: Identifying influential data and sources of collinearity. New York, NY: John Wiley and Sons.
Belsey, D. A., Kuh, E., & Welsch, R. E. (2004). Regression diagnostics: Identifying influential data and sources of collinearity. New York, NY: John Wiley and Sons.
Betzin, J., & Henseler, J. (2005). Einführung in die Funktionsweise des PLS-Algorithmus. In F. Bliemel, A. Eggert, G. Fassott, & J. Henseler (Eds.), Handbuch Pfadmodellierung: Methode – Anwendung – Praxisbeispiele (pp. 49–69). Stuttgart: Schäffer-Poeschel Verlag.
Bliemel, F., Eggert, A., Fassott, G., & Henseler, J. (2005). Die PLS-Pfadmodellierung: Mehr als eine Alternative zur Kovarianzstrukturanalyse. In F. Bliemel, A. Eggert, G. Fassott, & J. Henseler (Eds.), Handbuch Pfadmodellierung: Methode – Anwendung – Praxisbeispiele (pp. 9–16). Stuttgart: Schäffer-Poeschel Verlag.
Bohrnstedt, G. W. (1970). Reliability and validity assessment in attitude measurement. In G. F. Summers (Ed.), Attitude measurement (pp. 80–99). London: Wiley.
Bollen, K. A. (1989). Structural equations with latent variables. New York, NY: John Wiley and Sons.
Bollen, K. A., & Lennox, R. (1991). Conventional wisdom on measurement: A structural equation perspective. Psychological Bulletin, 110(2), 305–314.
Bollen, K. A., & Ting, K. (2000). A tetrad test for causal indicators. Psychological Methods, 5(1), 3–22.
Bortz, J. (1993). Statistik für Sozialwissenschaftler. Berlin/Heidelberg: Springer.
Carmines, E. G., & Zeller, R. A. (1979). Reliability and validity assessment. Beverly Hills, CA: Sage.
Carnap, R. (1966). Philosophical foundations of physics: An introduction to the philosophy of science. New York, NY: Basic Books.
Cassel, C., Hackl, P., & Westlund, A. H. (1999). Robustness of partial least-squares method for estimating latent variable quality structures. Journal of Applied Statistics, 26(4), 435–446.
Chin, W. W. (1998a). Issues and opinion on structural equation modeling. Management Information Systems Quarterly, 22(1), 7–16.
Chin, W. W. (1998b). 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.
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.
Chin, W. W., & Newsted, P. R. (1999). Structural equation modeling analysis with small samples using partial least squares. In R. H. Hoyle (Ed.), Statistical strategies for small sample research (pp. 307–342). Thousand Oaks, CA: Sage.
Churchill, G. A. (1979). A paradigm for developing better measures of marketing constructs. Journal of Marketing Research, 16(1), 64–73.
Churchill, G. A. (1987). Marketing research: Methodological foundations. Fort Worth, TX/Chicago, IL: Dryden Press.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Hillsdale: Lawrence Erlbaum Associates.
Costner, H. L. (1969). Theory, deduction, and rules of correspondance. American Journal of Sociology, 75(2), 245–263.
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334.
Diamantopoulos, A., & Papadopoulos, N. (2010). Assessing the cross-national invariance of formative measures: Guidelines for international business researchers. Journal of International Business Studies, 41(2), 360–370.
Diamantopoulos, A., & Winklhofer, H. M. (2001). Index construction with formative indicators: An alternative to scale development. Journal of Marketing Research, 38(2), 269–277.
Diller, H. (2006). Probleme der Handhabung von Strukturgleichungsmodellen in der betriebswirtschaftlichen Forschung. Die Betriebswirtschaft, 66(6), 611–617.
Eberl, M. (2006). Formative und reflektive Konstrukte und die Wahl des Strukturgleichungsverfahrens. Die Betriebswirtschaft, 66(6), 651–668.
Efron, B., & Gong, G. (1983). A leisurely look at bootstrap, the jacknife and cross-validation. The American Statistican, 37(1), 36–48.
Efron, B., & Tibshirani, R. J. (1993). An introduction to the bootstrap. New York, NY: Chapman & Hall.
Eggert, A., & Fassott, G. (2005). Zur Verwendung formativer und reflektiver Indikatoren in Strukturgleichungsmodellen: Bestandsaufnahme und Anwendungsempfehlungen. In F. Bliemel, A. Eggert, G. Fassott, & J. Henseler (Eds.), Handbuch Pfadmodellierung: Methode – Anwendung – Praxisbeispiele (pp. 31–47). Stuttgart: Schäffer-Poeschel Verlag.
Eggert, A., Fassott, G., & Helm, S. (2005). Identifizierung und Quantifizierung mediierender und moderierender Effekte in komplexen Kausalstrukturen. In F. Bliemel, A. Eggert, G. Fassott, & J. Henseler (Eds.), Handbuch Pfadmodellierung: Methode – Anwendung – Praxisbeispiele (pp. 101–116). Stuttgart: Schäffer-Poeschel Verlag.
Fassott, G. (2005). Die PLS-Pfadmodellierung: Entwicklungsrichtungen, Möglichkeiten, Grenzen. In F. Bliemel, A. Eggert, G. Fassott, & J. Henseler (Eds.), Handbuch Pfadmodellierung: Methode – Anwendung – Praxisbeispiele (pp. 19–29). Stuttgart: Schäffer-Poeschel Verlag.
Fassott, G. (2006). Operationalisierung latenter Varialben in Strukturgleichungsmodellen: Eine Standortbestimmung. Zeitschrift für betriebswirtschaftliche Forschung, 58(2), 67–88.
Fincham, R., & Clark, T. (2009). Introduction: Can we bridge the rigour-relevance gap? Journal of Management Studies, 46(3), 510–515.
Fornell, C. (1987). A second generation of multivariate analysis: Classification of methods and implications for marketing research. In M. J. Houston (Ed.), Review of marketing (pp. 407–450). Chicago, IL: American Marketing Association.
Fornell, C. (1989). The blending of theoretical and empirical knowledge in structural equations with unobservables. In H. Wold (Ed.), Theoretical empiricism: A general rationale for scientific model-building (pp. 153–173). New York, NY: Paragon House.
Fornell, C., & Cha, J. (1994). Partial least squares. In R. P. Bagozzi (Ed.), Advanced methods of marketing research (pp. 52–78). Cambridge, MA: Blackwell.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.
Geisser, S. (1975). The predictive sample reuse method with applications. Journal of the American Statistical Association, 70(350), 320–328.
Giere, J., Wirtz, B. W., & Schilke, O. (2006). Mehrdimensionale Konstrukte. Die Betriebswirtschaft, 66(6), 678–695.
Giering, A. (2000). Der Zusammenhang zwischen Kundenzufriedenheit und Kundenloyalität: Eine Untersuchung moderierender Effekte. Wiesbaden: Gabler.
Götz, O., & Liehr-Gobbers, K. (2004). Analyse von Strukturgleichungsmodellen mit Hilfe der Partial-Least-Squares(PLS)-Methode. Die Betriebswirtschaft, 64(6), 714–738.
Grace, J. B. (2006). Structural equation modeling and natural systems. New York, NY: Cambridge University Press.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis. Upper Saddle River, NJ: Prentice-Hall.
Hartmann, F. G. H., & Moers, F. (1999). Testing contingency hypotheses in budgetary research: An evaluation of the use of moderated regression analysis. Accounting, Organizations and Society, 24(4), 291–315.
Harzing, A.-W. (1997). Response rates in international mail surveys: Results of a 22-country study. International Business Review, 6(6), 641–665.
Harzing, A.-W. (2000). Cross-national industrial mail surveys: Why do response rates differ between countries? Industrial Marketing Management, 29(3), 243–254.
Hauser, R. M., & Goldberger, A. S. (1971). The treatment of unobservable variables in path analysis. In H. L. Costner (Ed.), Sociological methodology (pp. 81–117). San Francisco, CA: Jossey-Bass.
Helm, S. (2005). Entwicklung eines formativen Messmodells für das Konstrukt Unternehmensreputation. In F. Bliemel, A. Eggert, G. Fassott, & J. Henseler (Eds.), Handbuch Pfadmodellierung: Methode – Anwendung – Praxisbeispiele (pp. 241–254). Stuttgart: Schäffer-Poeschel Verlag.
Hempel, C. G. (1951). The concept of cognitive significance: A reconsideration. Proceedings of the American Academy of Arts and Sciences, 80(1), 61–77.
Henseler, J. (2005). Einführung in die PLS-Pfadmodellierung. Wirtschaftswissen-schaftliches Studium, 34(2), 70–75.
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In R. R. Sinkovics & P. N. Ghauri (Eds.), New challenges to international marketing (Advances in international marketing, Vol. 20, pp. 277–319). Bingley: Emerald Group Publishing Limited.
Herrmann, A., Huber, F., & Kressmann, F. (2006). Varianz- und kovarianzbasierte Strukturgleichungsmodelle – Ein Leitfaden zu deren Spezifikation, Schätzung und Beurteilung. Zeitschrift für betriebswirtschaftliche Forschung, 58(2), 34–66.
Hildebrandt, L. (2004). Strukturgleichungsmodelle für die Konsumentenverhaltensforschung – Methodische Trends und Softwareentwicklungen. In A. Gröppel-Klein (Ed.), Konsumentenverhaltensforschung im 21. Jahrhundert (pp. 541–564). Wiesbaden: Gabler.
Hildebrandt, L., & Temme, D. (2006). Probleme der Validierung mit Strukturgleichungs-modellen. Die Betriebswirtschaft, 66(6), 618–639.
Hodgkinson, G. P., & Rousseau, D. M. (2009). Bridging the rigour-relevance gap in management research: It’s already happening! Journal of Management Studies, 46(3), 534–546.
Homburg, C. (1992). Die Kausalanalyse: Eine Einführung. Wirtschaftswissenschaftliches Studium, 21(10), 499–508. 541–544.
Homburg, C., & Baumgartner, H. (1998). Beurteilung von Kausalmodellen: Bestandsaufnahme und Anwendungsempfehlungen. In L. Hildebrandt & C. Homburg (Eds.), Die Kausalanalyse: Ein Instrument der empirischen betriebswirtschaftlichen Forschung (pp. 343–369). Stuttgart: Schäffer-Poeschel Verlag.
Homburg, C., & Dobratz, A. (1998). Iterative Modellselektion in der Kausalanalyse. In L. Hildebrandt & C. Homburg (Eds.), Die Kausalanalyse: Ein Instrument der empirischen betriebswirtschaftlichen Forschung (pp. 447–474). Stuttgart: Schäffer-Poeschel Verlag.
Homburg, C., & Giering, A. (1996). Konzeptualisierung und Operationalisierung komplexer Konstrukte: Ein Leitfaden für die Marketingforschung. Marketing – Zeitschrift für Forschung und Praxis, 18(1), 5–24.
Homburg, C., & Giering, A. (2001). Personal characteristics as moderators of the relationship between customer satisfaction and loyalty – An empirical analysis. Psychology & Marketing, 18(1), 43–66.
Homburg, C., & Hildebrandt, L. (1998). Die Kausalanalyse: Bestandsaufnahme, Entwicklungsrichtungen, Problemfelder. In L. Hildebrandt & C. Homburg (Eds.), Die Kausalanalyse: Ein Instrument der empirischen betriebswirtschaftlichen Forschung (pp. 15–43). Stuttgart: Schäffer-Poeschel Verlag.
Homburg, C., & Klarmann, M. (2006). Die Kausalanalyse in der empirischen betriebswirtschaftlichen Forschung – Problemfelder und Anwendungsempfehlungen. Die Betriebswirtschaft, 66(6), 727–748.
Huber, F., Heitmann, M., & Herrmann, A. (2006). Ansätze zur Kausalmodellierung mit Interaktionseffekten. Die Betriebswirtschaft, 66(6), 696–710.
Huber, F., Herrmann, A., Meyer, F., Vogel, J., & Vollhardt, K. (2007). Kausalmodellierung mit Partial Least Squares: Eine anwendungsorientierte Einführung. Wiesbaden: Gabler.
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.
Hüttner, M., & Schwarting, U. (1999). Explorative Faktorenanalyse. In C. Homburg & A. Herrmann (Eds.), Marktforschung: Methoden – Anwendungen – Praxisbeispiele (pp. 381–412). Wiesbaden: Gabler.
Jarvis, C. B., MacKenzie, S. B., & Podsakoff, P. M. (2003). A critical review of construct indicators and measurement model misspecification in marketing and consumer research. Journal of Consumer Research, 30(2), 199–218.
Jöreskog, K. G. (1971). Statistical analysis of sets of congeneric tests. Psychometrika, 36(2), 109–133.
Jöreskog, K. G. (1973). A general method for estimating a linear structural equation system. In A. S. Goldberger & O. D. Duncan (Eds.), Structural equation models in the social sciences (pp. 85–112). New York, NY: Academic Press.
Jöreskog, K. G., & Sörbom, D. (1988). LISREL 7: A guide to the program and applications. Chicago, IL: SPSS, Inc.
Jöreskog, K. G., & Wold, H. (1982). The ML and PLS technique for modeling with latent variables: Historical and comparative aspects. In K. G. Jöreskog & H. Wold (Eds.), Systems under indirect observation: Causality, structure, prediction (pp. 263–270). Amsterdam: North-Holland.
Kieser, A., & Leiner, L. (2009). Why the rigour-relevance gap in management research is unbridgeable. Journal of Management Studies, 46(3), 516–533.
Kieser, A., & Nicolai, A. T. (2003). Mit der Theorie die wilde Praxis reiten, valleri, vallera, valleri? Die Betriebswirtschaft, 63(5), 589–594.
Kieser, A., & Nicolai, A. T. (2005). Success factor research: Overcoming the trade-off between rigor and relevance? Journal of Management Inquiry, 14(3), 275–279.
Kirstein, S. (2009). Unternehmensreputation: Corporate social responsibility als strategische Option für deutsche Automobilhersteller. Wiesbaden: Gabler.
Kleinbaum, D. G., Kupper, L., & Muller, K. E. (1988). Applied regression analysis and other multivariate methods. Boston: PWS-Kent Publishing Company.
Krafft, M. (1999). An empirical investigation of the antecedents of sales force control systems. Journal of Marketing, 63(3), 120–134.
Krafft, M., Götz, O., & Liehr-Gobbers, K. (2005). Die Validierung von Strukturgleichungs-modellen mit Hilfe des Partial-Least-Squares (PLS)-Ansatzes. In F. Bliemel, A. Eggert, G. Fassott, & J. Henseler (Eds.), Handbuch Pfadmodellierung: Methode – Anwendung – Praxisbeispiele (pp. 71–86). Stuttgart: Schäffer-Poeschel Verlag.
Lee, D. Y. (2000). Retail bargaining behaviour of American and Chinese customers. European Journal of Marketing, 34(1/2), 190–206.
Lohmöller, J.-B. (1984). LVPLS, program manual. Latent variables path analysis with partial least-squares estimation. Köln: Zentralarchiv für empirische Sozialforschung.
Lohmöller, J.-B. (1989). Latent variable path modeling with partial least squares. Heidelberg: Physica-Verlag.
Malhotra, N. K. (1993). Marketing research: An applied orientation. Englewood Cliffs, NJ: Prentice-Hall.
Maloney, P. (2007). Absatzmittlergerichtetes, identitätsbasiertes Markenmanagement: Eine Erweiterung des innengerichteten, identitätsbasierten Marken-managements unter besonderer Berücksichtigung von Premiummarken. Wiesbaden: Gabler.
McNaughton, R. B. (2002). The use of multiple export channels by small knowledge-intensive firms. International Marketing Review, 19(2/3), 190–203.
Nacif, R. C. (2003). Online customer loyalty. Wiesbaden: DUV.
Nunally, J. C. (1978). Psychometric theory. New York, NY: McGraw Hill.
Peter, S. I. (1997). Kundenbindung als Marketingziel: Identifikation und Analyse zentraler Determinanten. Wiesbaden: Gabler.
Reinartz, W., Krafft, M., & Hoyer, W. D. (2004). The customer relationship management process: Its measurement and impact on performance. Journal of Marketing Research, 41(3), 293–305.
Reinecke, J. (2005). Strukturgleichungsmodelle in den Sozialwissenschaften. München/Wien: Oldenbourg.
Rigdon, E. E. (1998). Structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 251–294). Mahwah, NJ: Lawrence Erlbaum Associates.
Rindskopf, D. (1984). Using phantom and imaginary latent variables to parameterize constraints in linear structural models. Psychometrika, 49(1), 37–47.
Ringle, C. M. 2004. Gütemaße für den Partial Least Squares-Ansatz zur Bestimmung von Kausalmodellen, Working paper no. 16. Hamburg: Institut für Industriebetriebslehre und Organisation, Universität Hamburg.
Ringle, C. M., Boysen, N., Wende, S., & Will, A. (2006). Messung von Kausalmodellen mit dem Partial-Least-Squares-Verfahren. Das Wirtschaftsstudium, 35(1), 81–87.
Ringle, C. M., Wende, S., & Will, A. 2005. SmartPLS: Release 2.0 M3 (beta). http://www.smartpls.de. University of Hamburg, Germany.
Rossiter, J. R. (2002). The C-OAR-SE procedure for scale development in marketing. International Journal of Research in Marketing, 19(4), 305–335.
Sarkar, S., & Pfeifer, J. (2005). The philosophy of science: An encyclopedia. London: Routledge.
Scholderer, J., & Balderjahn, I. (2005). PLS versus LISREL: Ein Methodenvergleich. In F. Bliemel, A. Eggert, G. Fassott, & J. Henseler (Eds.), Handbuch Pfadmodellierung: Methode – Anwendung – Praxisbeispiele (pp. 87–98). Stuttgart: Schäffer-Poeschel Verlag.
Scholderer, J., Balderjahn, I., & Paulssen, M. (2006). Kausalität, Linearität, Reliabilität: Drei Dinge, die Sie nie über Strukturgleichungsmodelle wissen wollten. Die Betriebswirtschaft, 66(6), 640–650.
Schulze, P. M. (1998). Beschreibende Statistik. München/Wien: Oldenbourg.
Seltin, N., & Keeves, J. P. (1994). Path analysis with latent variables. In T. Husen & T. Postlethwaite (Eds.), The international encyclopedia of education (pp. 4352–4359). Oxford: Oxford University Press.
Shrout, P. E., & Bolger, N. (2002). Mediation in experimental and nonexperimental studies: New procedures and recommendations. Psychological Methods, 7(4), 422–445.
Starkey, K., Hatchuel, A., & Tempest, S. (2009). Management research and the new logics of discovery and engagement. Journal of Management Studies, 46(3), 547–558.
Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society (Series B), 36(2), 111–147.
Tenenhaus, M., Vinzi, V. E., Chatelin, Y.-M., & Lauro, C. (2005). PLS path modeling. Computational Statistics & Data Analysis, 48(1), 159–205.
Venaik, S., Midgley, D. F., & Devinney, T. M. (2005). Dual paths to performance: The impact of global pressures on MNC subsidiary conduct and performance. Journal of International Business Studies, 36(6), 655–675.
Venkatraman, N. (1989a). Strategic orientation of business enterprises: The construct, dimensionality, and measurement. Management Science, 35(8), 942–962.
Weiber, R., & Adler, J. (2002). Hemnisfaktoren im Electronic Business. Ansatzpunkte einer theoretischen Systematisierung und empirische Evidenzen. Marketing Zeitschrift für Forschung und Praxis, 24(Special Issue “E-Marketing”), 5–12.
Werts, C. E., Linn, R. L., & Jöreskog, K. G. (1974). Intraclass reliability estimates: Testing structural assumptions. Educational and Psychological Measurement, 34(1), 25–33.
Wold, H. (1966). Estimation of principal components and related models by iterative least squares. In P. R. Krishnaiah (Ed.), Multivariate analysis (pp. 391–420). New York, NY: Academic Press.
Zinnbauer, M., & Eberl, M. (2005). Überprüfung der Spezifikation und Güte von Strukturgleichungsmodellen. Wirtschaftswissenschaftliches Studium, 34(10), 566–572.
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Heinecke, P. (2011). Structural Equation Methodology. In: Success Factors of Regional Strategies for Multinational Corporations. Contributions to Management Science. Physica-Verlag HD. https://doi.org/10.1007/978-3-7908-2640-1_3
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