AI transformation and AI adoption intention in B2B environment
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This study reconceptualizes the Technology Acceptance Model (TAM) from an organizational perspective to examine the factors influencing the intention to adopt artificial intelligence (AI). The proposed model incorporates three components of AI transformation – AI data-driven culture, organizational agility, and AI readiness – as independent variables, and investigates their effects on perceived usefulness and perceived ease of use. Survey data were collected from 209 employees working in purchasing, design, and sales departments of heavy manufacturing firms located in South Korea. Partial least squares structural equation modeling (PLS-SEM) was employed for analysis. The results reveal that AI data-driven culture positively affects both perceived usefulness and perceived ease of use, while organizational agility and AI readiness positively influence only perceived ease of use but not perceived usefulness. Perceived ease of use strengthens perceived usefulness, and together these factors significantly contribute to AI adoption intention. These findings underscore that in B2B environments, particularly within the heavy manufacturing industries, establishing a datadriven culture, enhancing organizational agility, and improving AI readiness are critical strategies to foster AI adoption intention.
Abstract
This study reconceptualizes the Technology Acceptance Model (TAM) from an organizational perspective to examine the factors influencing the intention to adopt artificial intelligence (AI). The proposed model incorporates three components of AI transformation – AI data-driven culture, organizational agility, and AI readiness – as independent variables, and investigates their effects on perceived usefulness and perceived ease of use. Survey data were collected from 209 employees working in purchasing, design, and sales departments of heavy manufacturing firms located in South Korea. Partial least squares structural equation modeling (PLS-SEM) was employed for analysis. The results reveal that AI data-driven culture positively affects both perceived usefulness and perceived ease of use, while organizational agility and AI readiness positively influence only perceived ease of use but not perceived usefulness. Perceived ease of use strengthens perceived usefulness, and together these factors significantly contribute to AI adoption intention. These findings underscore that in B2B environments, particularly within the heavy manufacturing industries, establishing a datadriven culture, enhancing organizational agility, and improving AI readiness are critical strategies to foster AI adoption intention.