This research investigates how Hofstede's cultural dimensions impact the application of AI technology in fulfilling Environmental, Social, and Governance (ESG) objectives by concentrating on organizational attitude and policy as moderating factors. The framework formulated using cultural theory attempts to explain the influence that power distance, uncertainty avoidance, and individualism have on the application of AI to various sociocultural settings. This study used a sequential exploratory mixed-methods design involving qualitative grounded theory and in-depth interviews with stakeholders and key informants from different domains to identify culturally relevant constructs pertaining to AI use for ESG objectives in the exploratory phase. In the confirmatory phase, the relationships formulated in the previous step were tested using PLS-SEM with survey data from 448 respondents in India, processed with SMART PLS 4.0, ensuring the results met the various PLS-SEM validity criteria of reliability, validity, and theoretical rigor. It is noted that positive organizational attitudes increase the effects of AI on ESG outcomes, while formal policies, save for a few, do not exert any meaningful moderating influence because they are too rigid and lack context. This research contributes to culture theory by illustrating the intersections of AI adoption and ESG performance, which are relatively under-discussed in modern academic research. Addressing this gap highlights the inadequacy of culturally sensitive attitude-centered frameworks tailored for responsible organizational leadership shaped by sustainable ethical guiding global principles.

Appolloni, A., Chakraborty, D., Jain, R. (2025). Culturally Responsive AI: Integrating Hofstede's Cultural Model in AI-Driven ESG Strategies. BUSINESS STRATEGY AND THE ENVIRONMENT [10.1002/bse.70248].

Culturally Responsive AI: Integrating Hofstede's Cultural Model in AI-Driven ESG Strategies

Appolloni A.;
2025-01-01

Abstract

This research investigates how Hofstede's cultural dimensions impact the application of AI technology in fulfilling Environmental, Social, and Governance (ESG) objectives by concentrating on organizational attitude and policy as moderating factors. The framework formulated using cultural theory attempts to explain the influence that power distance, uncertainty avoidance, and individualism have on the application of AI to various sociocultural settings. This study used a sequential exploratory mixed-methods design involving qualitative grounded theory and in-depth interviews with stakeholders and key informants from different domains to identify culturally relevant constructs pertaining to AI use for ESG objectives in the exploratory phase. In the confirmatory phase, the relationships formulated in the previous step were tested using PLS-SEM with survey data from 448 respondents in India, processed with SMART PLS 4.0, ensuring the results met the various PLS-SEM validity criteria of reliability, validity, and theoretical rigor. It is noted that positive organizational attitudes increase the effects of AI on ESG outcomes, while formal policies, save for a few, do not exert any meaningful moderating influence because they are too rigid and lack context. This research contributes to culture theory by illustrating the intersections of AI adoption and ESG performance, which are relatively under-discussed in modern academic research. Addressing this gap highlights the inadequacy of culturally sensitive attitude-centered frameworks tailored for responsible organizational leadership shaped by sustainable ethical guiding global principles.
2025
Pubblicato
Rilevanza internazionale
Articolo
Esperti anonimi
Settore ECON-07/A - Economia e gestione delle imprese
English
AI adoption
attitude
ESG goals
Hofstede's cultural factors
policies
Appolloni, A., Chakraborty, D., Jain, R. (2025). Culturally Responsive AI: Integrating Hofstede's Cultural Model in AI-Driven ESG Strategies. BUSINESS STRATEGY AND THE ENVIRONMENT [10.1002/bse.70248].
Appolloni, A; Chakraborty, D; Jain, R
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/435248
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