Joe F. Hair, Jr.Founder & Senior Scholar

Using the SmartPLS SoftwareAssessment of Measurement Models

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Outcome Reputation Constructs (endogenous) CUSL= loyalty (3 items)COMP = competence (3 items)CUSA = satisfaction (1 item)LIKE = likability (3 items)Driver Constructs (exogenous)QUAL = quality of a companys products/services and customer orientation (8 items)PERF = economic and managerial performance (5 items)CSOR = corporate social responsibility (5 items)ATTR = attractiveness (3 items)Extended Reputation Model ConstructsAll rights reserved . Cannot be reproduced or distributed without express written permission from Prentice-Hall, McGraw-Hill, Sage, SmartPLS, and session presenters.

All rights reserved . Cannot be reproduced or distributed without express written permission from Sage, Prentice-Hall, McGraw-Hill, SmartPLS, and session presenters.*To evaluate reflectively measured models, we examine the below:outer loadingscomposite reliabilityaverage variance extracted (AVE = convergent validity)discriminant validity

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*All outer loadings of the reflective constructs COMP, CUSL, and LIKE are well above the minimum threshold value of .708. The loadings range from a low of 0.7985 to a high of 0.9173.All rights reserved . Cannot be reproduced or distributed without express written permission from Sage, Prentice-Hall, McGraw-Hill, SmartPLS, and session presenters.To access the information to evaluate reflective models select one of the reports under this tab.When you select the Default Report this is the screen you will get.

To eliminate the unnecessary options on the navigation tree click on the minus sign on the left side. You will get the simplified screen on the next slide.

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All rights reserved . Cannot be reproduced or distributed without express written permission from Prentice-Hall, McGraw-Hill, SmartPLS, and session presenters.*The Toggle Zeros button in the task bar (top left of screen) was used to improve the readability of the results table above. This button suppresses the zeros in the table.All outer loadings of the reflective constructs COMP, CUSL, and LIKE are well above the minimum threshold value of .708. The loadings range from a low of 0.7985 to a high of 0.9173.

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*All rights reserved . Cannot be reproduced or distributed without express written permission from Sage, Prentice-Hall, McGraw-Hill, SmartPLS, and session presenters.Composite Reliability vs. Cronbach Alpha?

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All rights reserved . Cannot be reproduced or distributed without express written permission from Prentice-Hall, McGraw-Hill, SmartPLS, and session presenters.*To obtain the above table that shows the AVE, Composite reliability, Communality, Redundancy, etc., left click on the Overview tab under the Quality Criteria. Composite ReliabilityAll three reflective constructs have high levels of internal consistency reliability, as demonstrated by the above composite reliability values.

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Discriminant validity is not present in the above constructs. Correlation squared (variance shared between constructs = 64%) is larger than the AVE of Y1 (only 0.55 variance shared within construct = 55%).

All rights reserved . Cannot be reproduced or distributed without express written permission from Prentice-Hall, McGraw-Hill, SmartPLS, and session presenters.*To obtain the above table that shows the AVE, left click on the Overview tab under the Quality Criteria. Average Variance Extracted = AVEThe AVE values (convergent validity) are well above the minimum required level of .50, thus demonstrating convergent validity for all three constructs.

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All rights reserved . Cannot be reproduced or distributed without express written permission from Sage, Prentice-Hall, McGraw-Hill, SmartPLS, and session presenters.*To obtain the above table that includes information to determine the Fornell-Larcker criterion for discriminant validity, left click on the Latent Variable Correlations tab under the Quality Criteria. The off-diagonal values in the above matrix are the correlations between the latent constructs.

To obtain the shared values between the constructs you must square these correlations. See next slide where this calculation is shown.

The results on the next slide indicate there is discriminant validity between all the constructs.Discriminant Validity

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*All rights reserved . Cannot be reproduced or distributed without express written permission from Sage, Prentice-Hall, McGraw-Hill, SmartPLS, and session presenters.Discriminant Validity Fornell-Larcker Criterion

Interconstruct Correlations COMP CUSA CUSL LIKECOMP1000CUSA0.4356100CUSL0.44960.689210LIKE0.64520.52840.61461

Squared Interconstruct Correlations COMP CUSA CUSL LIKECOMP0.6806 000CUSA0.1897Single-Item Construct0.00000.0000CUSL0.20210.47500.7484 0.0000LIKE0.41630.27920.37770.7471 Note: diagonal = AVEs

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*All rights reserved . Cannot be reproduced or distributed without express written permission from Sage, Prentice-Hall, McGraw-Hill, SmartPLS, and session presenters.To obtain the above table that shows the cross loadings to assess discriminant validity, left click on the Latent Cross Loadings tab under the Quality Criteria. Comparing the loadings across the columns in the above matrix indicates that an indicators loadings on its own construct are in all cases higher than all of its cross loadings with other constructs.

The results indicate there is discriminant validity between all the constructs based on the cross loadings criterion.Discriminant Validity Cross Loadings Criterion

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*All rights reserved . Cannot be reproduced or distributed without express written permission from Sage, Prentice-Hall, McGraw-Hill, SmartPLS, and session presenters.Empirical assessment of formative measurement models is not the same as with reflective measurement models. This is because the indicators theoretically represent the constructs independent causes and thus do not necessarily correlate highly. As a result, internal consistency reliability measures such as Cronbach Alpha are not appropriate.

Instead, researchers should focus on establishing content validity before empirically evaluating formatively measured constructs. This process requires ensuring that the formative indicators capture all (or at least major) facets of the construct.

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The extended corporate reputation model has three main conceptual/theoretical components: (1) the target constructs of interest (i.e., CUSA and CUSL); (2) the two corporate reputation dimensions, COMP and LIKE, that represent key determinants of the target constructs; and (3) the four exogenous driver constructs (i.e., ATTR, CSOR, PERF, and QUAL) of the two corporate reputation dimensions.Corporate Reputation Extended Model

Indicators for SEM Model Exogenous Constructs Assessing Content Validity

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