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. 2023 Jul 7;9(7):e17847. doi: 10.1016/j.heliyon.2023.e17847

Table 3.

Analysis of discriminant validity.

Panel A: Discriminant validity through Fornell-Larcker criterion
Variables FD CL ESG SIZE LEV AGE ROA COV SEC
FD 1.000
CL −0.488*** 1.000
ESG −0.034 0.019 1.000
SIZE 0.140*** −0.128*** 0.101*** 1.000
LEV 0.480*** −0.326*** 0.083*** 0.176 1.000
AGE 0.032 −0.038* 0.013 0.019 0.112*** 1.000
ROA −0.462*** 0.351*** 0.013 −0.106 −0.373*** −0.029 1.000
COV 0.070*** −0.048** 0.000 0.034 0.031 0.000 −0.047** 1.000
SEC −0.222*** 0.201*** −0.001 −0.074** −0.092*** 0.023 0.120*** 0.000 1.000
Panel B: Multicollinearity test
Criterion ESG CL SIZE LEV AGE ROA COV SEC
VIF 1.22 2.19 1.74 1.76 1.12 2.27 1.01 1.11
Tolerance 0.818 0.456 0.576 0.569 0.889 0.440 0.990 0.904

Note: *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively. This table reports the discriminant validity of the variables used in this study through the Fornell-Larcker criterion. The variables include financial distress (FD), environmental, social, and governance (ESG), cost leadership (CL), firm size (SIZE), firm leverage (LEV), firm age (AGE), return on assets (ROA), coronavirus crisis (COV), and sector classification (SEC). The discriminant validity ratios are below the threshold of 0.85. Therefore, we can conclude that the measurement model is satisfactory. Furthermore, the results showed no multicollinearity problems when calculating the variance inflation factor (VIF), as the highest value was 2.27. Similarly, the tolerance values of the variables ranged from 0.440 to 0.990. Thus, there was no multicollinearity concern among the latent variables.