Puerto Rico , October 2-5 , 2013 3 DEBACLES IN THE DECISION MAKING PROCESS AROUND TECHNOLOGY IMPLEMENTATION : A CASE STUDY OF COMPANY ACME DURING A DATA ANALYSIS AND WAREHOUSING PROJECT

Information systems researchers are increasingly using structural equation modeling (SEM) software, such as EQS, LISREL, AMOS, and PLS, to address a wide range of research questions. These SEM modeling approaches have advantages over first-generation statistical modeling approaches, including multiple regression and principal components analysis, due to their ability to simultaneously model relationships among multiple independent and dependent variables, as well as their ability to construct unobservable latent variables and test a priori measurement assumptions against empirical data (Gerbing and Anderson 1988; Chin 1998; Gefen et al. 2000). Among the SEM approaches, there are several well-recognized differences, especially between the covariance-based approach (utilizing software such as EQS, AMOS, and LISREL) and the partial-least-squares or component-based technique of PLS. For example, the objective of the covariance-based SEM approach is overall model fit (i.e., whether the data support the theory/model being tested), and the objective of the component-based approach is variance explanation (i.e., maximizing R 2 and the significance of individual paths) (Gefen et al. 2000). In addition, covariance-based techniques use factor analysis to determine factor loadings whereas PLS uses a principle components approach to determine factor loadings (Chin 1995). Finally, covariance-based SEM supports confirmatory research, requires a multivariate normal distribution (if using Maximum Likelihood (ML)), and requires a minimum sample size of at least 100 cases (Gefen et al. 2000). On the other hand, the component-based SEM technique supports confirmatory and exploratory research, is robust to departures from a multivariate normal distribution, and supports smaller sample sizes (i.e., at least 10 times the number of items in the complex construct) (Gefen et al. 2000; Falk and Miller 1992; Hair et al. 1998). The reason to explore these differences is to ensure that researchers are aware of them when interpreting results. To compare the EQS and PLS SEM techniques, data is used from a study that examines the factors affecting the job turnover of information systems personnel (McKnight et al, 2009). The model proposes that two contextual factors (structural assurance and job security) and two job-related factors (autonomy and workload) directly and indirectly impact turnover intention through work outcomes (job satisfaction and work exhaustion). While the objective of PLS is to maximize variance explained, the variance explained (R 2 ) in the dependent variables using EQS is similar to or better than PLS. Using PLS, job satisfaction has a R 2 of 34%, work exhaustion has a R 2 of 23%, and turnover intention has a R 2 of 33%. Using EQS, job satisfaction has a R 2 of 42%, work exhaustion has a R 2 of 23%, and turnover intention has a R 2 of 40%. Regardless of the approach that is used, when examining variance explained (i.e., R 2 ), one can conclude that the model produces results that suggest the data fit the model. However, in terms of overall model fit using EQS, the fit indices (chi-square divided by degrees of freedom 2.25, CFI 0.86, SRMR 0.09, RMSEA 0.09) results suggest the data does not fit the structural model very well. Thus, the results of this study introduce the possibility that the use of different modeling techniques can lead to different conclusions about one’s research model. Many researchers using PLS emphasize variance explained without regard for overall model fit. PLS, thus, does not necessarily reveal overall fit problems easily whereas using a covariance-based SEM approach readily exposes potential flaws in the data for overall model fit.