Table 3. Correlation matrix of latent...

Table 3.

Correlation matrix of latent constructs – discriminant validity assessment

Variables	AI adoption	Collaborative partnership	Cultural context	Employee empowerment	Motivation to protection	Regulatory framework	Sustainability development	Cultural context x AI adoption
AI adoption
Collaborative partnership	0.565
Cultural context	0.200	0.275
Employee empowerment	0.415	0.647	0.451
Motivation to protection	0.154	0.282	0.625	0.394
Regulatory framework	0.155	0.151	0.229	0.062	0.067
Sustainability development	0.374	0.566	0.126	0.702	0.480	0.181
Cultural context x AI adoption	0.042	0.036	0.052	0.042	0.017	0.073	0.015
Regulatory frameworkx AI adoption	0.098	0.094	0.083	0.113	0.025	0.034	0.194	0.013

Variables	AI adoption	Collaborative partnership	Cultural context	Employee empowerment	Motivation to protection	Regulatory framework	Sustainability development	Cultural context x AI adoption
AI adoption
Collaborative partnership	0.565
Cultural context	0.200	0.275
Employee empowerment	0.415	0.647	0.451
Motivation to protection	0.154	0.282	0.625	0.394
Regulatory framework	0.155	0.151	0.229	0.062	0.067
Sustainability development	0.374	0.566	0.126	0.702	0.480	0.181
Cultural context x AI adoption	0.042	0.036	0.052	0.042	0.017	0.073	0.015
Regulatory frameworkx AI adoption	0.098	0.094	0.083	0.113	0.025	0.034	0.194	0.013

Note(s):

Table 3 presents the correlation matrix of latent constructs used in the study. The table is utilized to assess discriminant validity, ensuring that each construct is empirically distinct from the others. Discriminant validity is confirmed when the correlations between constructs remain below the threshold of 0.85, indicating that no significant multicollinearity exists among the variables (Fornell and Larcker, 1981; Kline, 2011). All interconstruct correlations in this matrix are within acceptable limits, supporting the discriminant validity of the model’s constructs

Source(s): Authors’ own creation

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