Table 3 Measurement model statistics...

Table 3

Measurement model statistics

Construct and item	Mean	SD	VIF	Loading	AVE	α	ρ	Source
Fairness					0.671	0.901	0.924	Morse et al. (2022), Rana et al. (2024), Shin (2021), Shin and Park (2019)
GenAI does not perform any favoritism and discrimination against any individuals in our organization	4.01	1.023	2.74	0.876
GenAI outputs are benchmarked and identical for all individuals for specific matters	3.72	0.997	1.54	0.670
GenAI follows the defined process with impartiality and no bias	3.90	1.093	2.78	0.860
GenAI ensures fairness and treats everyone equally in our organization	4.09	1.005	2.89	0.871
GenAI provides consistent and standardized outputs for specific matters	4.03	0.916	1.94	0.786
GenAI adheres to a defined, unbiased process	3.93	0.999	2.39	0.834
Accountability					0.578	0.633	0.803	Rana et al. (2024), Shin (2021), Shin and Park (2019)
Someone should monitor GenAI and be accountable for its adverse societal and individual effects	3.87	1.073	1.35	0.806
GenAI algorithms should be designed to enable third parties to scrutinize and assess their responses	3.70	1.109	1.36	0.809
AI algorithms should allow modifications to a GenAI system's complete configuration through certain manipulations	3.43	0.989	1.13	0.655
Transparency					0.675	0.759	0.862	Kieslich et al. (2022), Rana et al. (2024), Shin and Park (2019)
The criteria for evaluating GenAI algorithms should be publicly disclosed to ensure understanding	4.26	0.904	1.58	0.833
The results/decisions made by GenAI should be explainable to the people affected by those results and decisions	4.28	0.883	1.53	0.819
The GenAI algorithm function should allow people to assess how well the internal states of machine learning models can be understood from their external outputs	4.09	0.937	1.50	0.813
Accuracy					0.755	0.837	0.902	Rana et al. (2024), Song et al. (2022)
GenAI instantly replies to all individuals in our organization	4.04	0.932	1.89	0.850
GenAI interacts accurately with all individuals in our organization	3.96	0.905	1.89	0.849
GenAI completely replies to all individuals in our organization	4.04	1.014	2.43	0.906
Autonomy					0.630	0.900	0.920	Mökander and Floridi (2022), Rana et al. (2024)
GenAI can autonomously provide choices for further courses of action	3.98	0.856	1.84	0.739
GenAI can independently offer recommendations for action plans for cited problems and matters	4.08	0.827	2.27	0.804
GenAI can autonomously suggest steps for executing a plan for assigned matters/issues	4.00	0.868	2.13	0.793
GenAI can autonomously recommend what actions should be taken	4.01	0.856	2.27	0.817
GenAI can independently suggest options for our next steps	3.91	0.946	2.22	0.784
GenAI can independently recommend action plans for the given problems and matters	3.95	0.913	2.63	0.799
GenAI can independently suggest steps to execute plans for assigned matters or issues	3.98	0.873	2.30	0.818
GenAI usage					0.760	0.850	0.900	Lin et al. (2018), Pillai et al. (2022), Rana et al. (2024)
Our organization proposes to use GenAI in the near future	4.00	1.013	1.58	0.848
Our organization is inclined to increase the use of GenAI	3.83	1.084	3.12	0.892
Our organization has the capacity to use GenAI	3.83	1.064	2.98	0.886
Ethical leadership					0.600	0.820	0.870	Al Halbusi et al. (2023), Kumar et al. (2025)
My supervisor listens to what employees have to say	4.10	0.946	1.65	0.730
My supervisor disciplines employees who violate ethical standards	4.02	1.026	1.25	0.617
My supervisor conducts work in an ethical manner	4.23	0.878	2.38	0.828
My supervisor has the best interests of employees in mind	3.99	1.010	2.64	0.837
My supervisor discusses business ethics or values with employees	4.01	0.964	1.94	0.821
Organizational performance					0.780	0.860	0.910	Kumar et al. (2025)
Using GenAI helps the firm to earn better business profit	3.88	0.963	2.09	0.878
Using GenAI helps the firm become more competitive	3.99	0.934	2.60	0.910
Usage of GenAI in co-creation activities makes the firm more innovative	4.04	0.861	2.20	0.873

Construct and item	Mean	SD	VIF	Loading	AVE	α	ρ	Source
Fairness					0.671	0.901	0.924	Morse et al. (2022), Rana et al. (2024), Shin (2021), Shin and Park (2019)
GenAI does not perform any favoritism and discrimination against any individuals in our organization	4.01	1.023	2.74	0.876
GenAI outputs are benchmarked and identical for all individuals for specific matters	3.72	0.997	1.54	0.670
GenAI follows the defined process with impartiality and no bias	3.90	1.093	2.78	0.860
GenAI ensures fairness and treats everyone equally in our organization	4.09	1.005	2.89	0.871
GenAI provides consistent and standardized outputs for specific matters	4.03	0.916	1.94	0.786
GenAI adheres to a defined, unbiased process	3.93	0.999	2.39	0.834
Accountability					0.578	0.633	0.803	Rana et al. (2024), Shin (2021), Shin and Park (2019)
Someone should monitor GenAI and be accountable for its adverse societal and individual effects	3.87	1.073	1.35	0.806
GenAI algorithms should be designed to enable third parties to scrutinize and assess their responses	3.70	1.109	1.36	0.809
AI algorithms should allow modifications to a GenAI system's complete configuration through certain manipulations	3.43	0.989	1.13	0.655
Transparency					0.675	0.759	0.862	Kieslich et al. (2022), Rana et al. (2024), Shin and Park (2019)
The criteria for evaluating GenAI algorithms should be publicly disclosed to ensure understanding	4.26	0.904	1.58	0.833
The results/decisions made by GenAI should be explainable to the people affected by those results and decisions	4.28	0.883	1.53	0.819
The GenAI algorithm function should allow people to assess how well the internal states of machine learning models can be understood from their external outputs	4.09	0.937	1.50	0.813
Accuracy					0.755	0.837	0.902	Rana et al. (2024), Song et al. (2022)
GenAI instantly replies to all individuals in our organization	4.04	0.932	1.89	0.850
GenAI interacts accurately with all individuals in our organization	3.96	0.905	1.89	0.849
GenAI completely replies to all individuals in our organization	4.04	1.014	2.43	0.906
Autonomy					0.630	0.900	0.920	Mökander and Floridi (2022), Rana et al. (2024)
GenAI can autonomously provide choices for further courses of action	3.98	0.856	1.84	0.739
GenAI can independently offer recommendations for action plans for cited problems and matters	4.08	0.827	2.27	0.804
GenAI can autonomously suggest steps for executing a plan for assigned matters/issues	4.00	0.868	2.13	0.793
GenAI can autonomously recommend what actions should be taken	4.01	0.856	2.27	0.817
GenAI can independently suggest options for our next steps	3.91	0.946	2.22	0.784
GenAI can independently recommend action plans for the given problems and matters	3.95	0.913	2.63	0.799
GenAI can independently suggest steps to execute plans for assigned matters or issues	3.98	0.873	2.30	0.818
GenAI usage					0.760	0.850	0.900	Lin et al. (2018), Pillai et al. (2022), Rana et al. (2024)
Our organization proposes to use GenAI in the near future	4.00	1.013	1.58	0.848
Our organization is inclined to increase the use of GenAI	3.83	1.084	3.12	0.892
Our organization has the capacity to use GenAI	3.83	1.064	2.98	0.886
Ethical leadership					0.600	0.820	0.870	Al Halbusi et al. (2023), Kumar et al. (2025)
My supervisor listens to what employees have to say	4.10	0.946	1.65	0.730
My supervisor disciplines employees who violate ethical standards	4.02	1.026	1.25	0.617
My supervisor conducts work in an ethical manner	4.23	0.878	2.38	0.828
My supervisor has the best interests of employees in mind	3.99	1.010	2.64	0.837
My supervisor discusses business ethics or values with employees	4.01	0.964	1.94	0.821
Organizational performance					0.780	0.860	0.910	Kumar et al. (2025)
Using GenAI helps the firm to earn better business profit	3.88	0.963	2.09	0.878
Using GenAI helps the firm become more competitive	3.99	0.934	2.60	0.910
Usage of GenAI in co-creation activities makes the firm more innovative	4.04	0.861	2.20	0.873

Note(s): AVE = Average variance extracted. SD = Standard deviation. VIF = Variance inflation factor. α = Cronbach's alpha. ρ = Composite reliability

Source(s): Authors' own compilation

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