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Appendix A
Overview of Partial Least Squares (PLS) analysis
PLS enables analysis of complex nomological networks of constructs, as in the present study,
which would be impossible or dicult in the context of traditional techniques (Falk and Miller,
1992). PLS does not make assumptions about (a) data distributions to estimate model
parameters, (b) observation independence, or (c) variable metrics (Fornell and Larcker, 1981).
Because of its less restrictive assumptions, PLS is highly suitable for the early stages of theory
building and testing and/or when sample sizes are small (Falk and Miller, 1992; Wold, 1985).
This feature of PLS makes it suitable even over LISREL, another structural modeling technique,
which is suitable for con®rmatory stages of theory building and testing (Falk and Miller, 1992).
PLS simultaneously assess both the structural component, representing the relationship among
constructs, and the measurement component, representing the relationship between constructs
and their measures (Fornell and Larcker, 1981). The simultaneous analysis of structural and
measurement components facilitates measurement reliability and validity assessments within the
context of the theoretical model being tested. Thus, in accordance with contemporary philosophy
of science views (Falk and Miller, 1992), PLS acknowledges that psychometric properties of
measures derive their meaning from the nomological network of relationships in which the
measures are employed. In traditional analysis, assessment of the measurements component is
performed separately from the relationship among constructs (e.g., Cronbach's are estimated for
scales of constructs ®rst to assess their reliability and then these scales are used in regression
analysis to examine the relationship among constructs). Results generated by PLS can be
interpreted by considering them in the context of regression and principal components factor
analysis (Bookstein, 1986). PLS generates estimates of standardized regression path coecients,
computes R 2 (i.e., proportion of variance explained) for endogenous constructs, and produces
factor loadings and weights of indicators of constructs. The weights of indicators are the
regression coecients by which the standardized scores of the measures of the construct are
multiplied before being summed up to obtain factor scores for the construct.
(Adapted from Sosik, J. J., Avolio, B. J., & Kahai, S. S. (1998) Inspiring group creativity. Small
Group Research, 29(1), 3±31. Reprinted by permission of Sage Publications, Inc.)
Copyright # 2000 John Wiley & Sons, Ltd. J. Organiz. Behav. 21, 365±390 (2000)