Integrating is success and open innovation in enterprise LLM adoption: a dual-path model of net benefits and usage growth

Firms are increasingly adopting Large Language Models (LLMs) to enhance productivity and eventually save costs and increase revenue. Continued LLM adoption requires that LLMs demonstrate tangible Return-on-Investment, making it essential to verify whether cost reductions and revenue contribution stem from the quality of the LLMs or from organisational capabilities in learning from LLMs. Based on the theories of information success, open innovation, and organisational learning, this paper examines how firms’ evaluations of LLM quality and their learning and innovation capabilities shape financial performance perceptions, which in turn drive increased utilisation of LLMs. A post-hoc experiment was conducted to reexamine the relationship between information quality and user satisfaction with LLMs. The survey sample consisted of 261 firm representatives (one key informant per firm). For the post-hoc experiment, 148 corporate employees with prior experience utilising LLM-based services were recruited. Evaluation of the measurement properties, structural model testing, and post-hoc analyses were performed using SPSS-29.0, PLS-SEM-4.1, and Lavaan package (v0.6-15). The results reveal a finding that challenges conventional assumptions about how information quality shapes user satisfaction in LLM contexts. This study aims to provide practical implications for firms seeking to strengthen the organisational foundations necessary for sustainable value creation through LLM adoption.

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Publication Details

Journal
Behaviour and Information Technology
Published
2026-08-03
DOI
https://doi.org/10.1080/0144929x.2026.2711036
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
0.00

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article

Integrating is success and open innovation in enterprise LLM adoption: a dual-path model of net benefits and usage growth

Ohbyung Kwon, Taekyung Kim, Sujin Bae, Kyung Young Lee
Behaviour and Information Technology
AI in Service Interactions
article

Integrating is success and open innovation in enterprise LLM adoption: a dual-path model of net benefits and usage growth

Ohbyung Kwon, Taekyung Kim, Sujin Bae, Kyung Young Lee
article en

Abstract

Firms are increasingly adopting Large Language Models (LLMs) to enhance productivity and eventually save costs and increase revenue. Continued LLM adoption requires that LLMs demonstrate tangible Return-on-Investment, making it essential to verify whether cost reductions and revenue contribution stem from the quality of the LLMs or from organisational capabilities in learning from LLMs. Based on the theories of information success, open innovation, and organisational learning, this paper examines how firms’ evaluations of LLM quality and their learning and innovation capabilities shape financial performance perceptions, which in turn drive increased utilisation of LLMs. A post-hoc experiment was conducted to reexamine the relationship between information quality and user satisfaction with LLMs. The survey sample consisted of 261 firm representatives (one key informant per firm). For the post-hoc experiment, 148 corporate employees with prior experience utilising LLM-based services were recruited. Evaluation of the measurement properties, structural model testing, and post-hoc analyses were performed using SPSS-29.0, PLS-SEM-4.1, and Lavaan package (v0.6-15). The results reveal a finding that challenges conventional assumptions about how information quality shapes user satisfaction in LLM contexts. This study aims to provide practical implications for firms seeking to strengthen the organisational foundations necessary for sustainable value creation through LLM adoption.

Behaviour and Information Technology
Dalhousie University (CA), Kyung Hee University (KR)
Ministry of Education, National Research Foundation of Korea
Industry, innovation and infrastructure
Openalex Percentile: Top 7%
AI in Service Interactions
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