## Abstract

Multivariate research techniques are powerful and well-suited tools to analyze strategic management or organizational behavior constructs such as entrepreneurial orientation. This chapter will firstly present multivariate techniques of the so-called “first generation”, and discuss their strengths and weaknesses. Secondly, it will introduce and discuss further advanced “second-generation” multivariate research techniques, in particular variance-based and covariance-based structural equation modeling. A comparison of the techniques across generations will lead to the assessment that second-generation multivariate research techniques are more suitable to contribute as a methodology to the problem of influence of entrepreneurial orientation on technology transfer performance.

## Keywords

Structural Equation Modeling Entrepreneurial Orientation Manifest Variable Formative Indicator Interval Scaling## Preview

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## References

- 349.See Shook, Ketchen, Hult and Kacmar (2004) as to the development of multivariate research tech-niques and structural equation modeling in strategic management literature. The authors present an analysis of 92 strategic management studies published from 1994 to 2002. Despite the relatively wide-spread usage of multivariate research techniques, the authors critically comment that this usage has often been less than ideal, though, and that researchers might draw erroneous conclusions about rela-tionships among variables.Google Scholar
- 350.See Hoyle (1995b) for a general introduction into the concept of structural equation modeling.Google Scholar
- 351.Anderson and Gerbing (1988), pp. 411–412, argue that “although it is convenient to distinguish be-tween exploratory and confirmatory research, in practice this distinction is not as clear-cut. … Rather then strict dichotomy, then, the distinction in practice between exploratory and confirmatory analysis can be thought of that as an ordered progression.”Google Scholar
- 352.Fornell (1982), pp. 1–2.Google Scholar
- 353.See Fornell (1982), p. 2. In addition, Sheth (1971), pp. 13–17, and Kinnear and Taylor (1971), pp. 56–59, provide classifications of multivariate methods.Google Scholar
- 354.See McDonald (1996), p. 239. McDonald states that “a random variable is observable if and only if its values can be obtained by means of a real-world sampling experiment.”Google Scholar
- 355.See Bagozzi, Yi and Phillips (1991), p. 459, and Fornell (1982), p. 3. Fornell argues that “very few, if any, measures in the social (or natural) sciences are free from error. Variables such as earnings or sales, population growth, and age are subject to error because of inaccurate statistics, faulty record keeping, or imperfect coding. In ignoring these errors, the analyst runs the risk of obtaining biased pa-rameter estimates.”Google Scholar
- 356.See Fornell (1982), pp. 2–3, and Anderson and Gerbing (1988), pp. 411–412.Google Scholar
- 357.See Autio, Keeley, Klofsten and Ulfstedt (1997), who examined the entrepreneurial intent construct using SEM.Google Scholar
- 358.Chin (1998), p. 296.Google Scholar
- 359.It is beyond the scope of this dissertation to provide in-depth detail about the alternative multivariate techniques. Much more, this introduction serves to present a very brief general framework. The inter-ested reader is referred to Hänlein and Kaplan (forthcoming), p. 2, and Fornell (1982), pp. 3–4, for a more comprehensive introduction to these methods. In the following, only the models of LISREL and PLS will be examined to a greater extend.Google Scholar
- 360.See Lumpkin and Dess (1996), p. 152.Google Scholar
- 361.See Fornell (1982), pp. 3–4.Google Scholar
- 362.See Hänlein and Kaplan (forthcoming), p. 5, and Fornell (1982), p. 10.Google Scholar
- 363.Fornell (1982), p. 5.Google Scholar
- 364.See Fornell (1982). p. 5.Google Scholar
- 365.See Fornell (1982), pp. 6–7.Google Scholar
- 366.See Fornell (1982), p. 7.Google Scholar
- 367.See Knapp (1978), pp. 410–416, for an introduction into canonical correlation analysis, van den Wollenberg (1977), pp. 207–219 for an introduction into redundancy analysis, and Fornell (1979), pp. 323–338, for an introduction into ECCSA.Google Scholar
- 368.See Fornell and Larcker (1981), p. 39, who refer to Jöreskog (1967) and Jöreskog (1970) for the foundations of structural equation modeling.Google Scholar
- 369.See Hänlein and Kaplan (forthcoming), p. 4.Google Scholar
- 370.Hoyle (1995a), p. 1.Google Scholar
- 371.Shook, Ketchen, Hult and Kacmar (2004), p. 397.Google Scholar
- 372.Examples for covariance-based methods are LISREL, EQS, AMOS, SEPATH, and RAMONA. See Chin (1998), p. 295.Google Scholar
- 373.See Jöreskog (1970), Keesling (1972), and Wiley (1973) for the foundations of this approach.Google Scholar
- 374.Chin (1998), p. 297.Google Scholar
- 375.See Chin and Newsted (1999), p. 307. See also Diamantopoulos (1994) for an introduction into LISREL.Google Scholar
- 376.See Meier (2006), p. 73.Google Scholar
- 377.See Chin (1998), p. 314.Google Scholar
- 378.See West, Finch and Curran (1995), pp. 56–75, for issues regarding nonnormal variables.Google Scholar
- 379.See Jöreskog (1967), p. 443.Google Scholar
- 380.See Hu and Bentler (1995), pp. 76–99, for an introduction into model fit.Google Scholar
- 381.See Jöreskog and Sorbom (1982), p. 408.Google Scholar
- 382.See Kaplan (1995), p. 100.Google Scholar
- 383.See Bielby and Hauser (1977), p. 153. Not rejecting a false hypothesis is a type II error.Google Scholar
- 384.See Bagozzi (1981a), p. 380, and Fornell and Larcker (1981), p. 39.Google Scholar
- 385.See Bollen (1987), p. 375.Google Scholar
- 386.See Boomsma (1985), p. 345.Google Scholar
- 387.See Marsh, Hau, Balla and Grayson (1998), p. 187.Google Scholar
- 388.See MacCallum and Browne (1993), p. 540.Google Scholar
- 389.McDonald (1996), p. 240, states that “Partial Least Squares appears to be, currently, the most fully developed general system for path analysis with composites.”Google Scholar
- 390.See Bollen and Long (1992), p. 128.Google Scholar
- 391.See Wold (1989), p. vii.Google Scholar
- 392.See Chin (1998), p. 311.Google Scholar
- 393.Chin (1998), p. 303.Google Scholar
- 394.See Anderson and Gerbing (1988), p. 412.Google Scholar
- 395.Fornell and Cha (1994), p. 66.Google Scholar
- 396.See Chin, Marcolin and Newsted (1996), p. 31.Google Scholar
- 397.Chin (1998), p. 332.Google Scholar
- 398.See Chin (1998), p. 304.Google Scholar
- 399.Fornell and Cha (1994), p. 74.Google Scholar