Modeling the relationship between artificial intelligence, service innovation, and financial performance: A dual SEM–ANN perspective

Understanding the importance of factors that influence financial performance is a critical challenge for organizations in the modern economic system. The current research aimed to investigate the impact of Artificial Intelligence (AI) on corporate financial performance with the mediating role of service innovation capability. The study responds to a gap in the international literature: many studies show that AI may improve performance, but fewer studies explain how this effect is transferred through an organizational capability. Therefore, this paper not only tests whether AI is related to financial performance; it tests whether service innovation capability is a pathway through which AI is turned into financial value. The participants included ۱۹۶ managers from financial, IT, R&D, and operations departments of companies that utilize AI to enhance their processes and services. A questionnaire was used for data collection. An integrated Structural Equation Modeling and Artificial Neural Network (SEM-ANN) method was applied for data analysis. The results showed that the impact of AI on service innovation capability and on corporate financial performance is positive and significant. The effect of service innovation capability on corporate financial performance is also positive and significant. Furthermore, the mediating role of service innovation capability in the relationship between AI and corporate financial performance is positive and significant. The ANN results add a predictive view by showing that service innovation capability is the most important predictor of financial performance among the model inputs. The findings suggest that AI can support financial performance more effectively when it is linked to service innovation capability, rather than being treated only as a technology adoption decision.

کلیدواژه ها:

Artificial intelligence ، Service Innovation Capability ، financial performance ، structural equation modeling ، Artificial Neural Network