Paper Number
ECIS2025-1789
Paper Type
SP
Abstract
Generative artificial intelligence has accelerated the development of organizational innovative productivity, which introduces challenges in the adoption of new technologies by organizations. Publicly listed enterprises exhibit hesitation in adopting generative AI due to a lack of organizational readiness, which reflects their capacity to effectively embrace new technologies. This paper conducts discourse analysis on the Q&A data from earnings conference calls of publicly 461 listed companies, leveraging a large language model’s capability in decision-making and theoretical framework classification concerning generative AI content. Through this approach, we develop a theoretical framework for organizational readiness for the adoption of generative AI. This framework includes 26 measurement indicators, categorized under eight constructs: Resource readiness, IT readiness, Cognitive readiness, Partnership readiness, Innovation valance, Strategic readiness, Cultural readiness, and IT governance readiness. Our study advances theoretical research on GenAI adoption by developing a framework of organizational readiness, enriched by the integration of large language models and discourse analysis, offering a scalable and comprehensive approach to understanding and constructing theory.
Recommended Citation
Zuo, Renxian; Xu, Dongming; and wu, jiang, "ORGANIZATIONAL READINESS FOR GENERATIVE AI ADOPTION" (2025). ECIS 2025 Proceedings. 12.
https://aisel.aisnet.org/ecis2025/ai_org/ai_org/12
ORGANIZATIONAL READINESS FOR GENERATIVE AI ADOPTION
Generative artificial intelligence has accelerated the development of organizational innovative productivity, which introduces challenges in the adoption of new technologies by organizations. Publicly listed enterprises exhibit hesitation in adopting generative AI due to a lack of organizational readiness, which reflects their capacity to effectively embrace new technologies. This paper conducts discourse analysis on the Q&A data from earnings conference calls of publicly 461 listed companies, leveraging a large language model’s capability in decision-making and theoretical framework classification concerning generative AI content. Through this approach, we develop a theoretical framework for organizational readiness for the adoption of generative AI. This framework includes 26 measurement indicators, categorized under eight constructs: Resource readiness, IT readiness, Cognitive readiness, Partnership readiness, Innovation valance, Strategic readiness, Cultural readiness, and IT governance readiness. Our study advances theoretical research on GenAI adoption by developing a framework of organizational readiness, enriched by the integration of large language models and discourse analysis, offering a scalable and comprehensive approach to understanding and constructing theory.
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