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As technological revolutions continue to advance, ai increasingly emerges as a focal driver for enhancing innovation quality This study aims to identify the key factors and causal configurations that influence designers’ intentions to adopt gen ai in cpd Topic information dear colleagues, due to the rapid development of algorithms, frameworks, hardware, networks, and the increased volume of data, artificial intelligence (ai) algorithms (e.g., deep learning, neural computing, biological computing, etc.) have been widely employed to solve problems in many complex systems
While prior studies have emphasized strategic benefits and readiness models, there remains a lack of operational guidance tailored to sme realities—particularly regarding implementation barriers, resource. While generative artificial intelligence (gen ai) is accelerating digital transformation and innovation in corporate product design (cpd), limited research has explored how designers adopt this technology The aim of this work is to introduce a computer science solution to manage emotions and affections and connect them to the causes as in humans
The scientific foundation of this work lies in the ability to model the affective and emotional states of an individual or artificial intelligence (ai)
Then, in this study, we go a step further by exploring how to extend this capability by linking it. In recent years, artificial intelligence (ai) has emerged as a transformative force across various sectors of modern society, reshaping economic landscapes, social interactions, and ethical considerations By synthesizing recent research and case studies, we. The emergence of artificial intelligence (ai) has impacted the world of higher education, and institutions are faced with challenges in integrating ai into curricula
Third, it is hypothesized that the benefits of ai integration are moderated by factors such as ai complexity, areas of ai utilization, and employee characteristics (hypothesis 3) This is supported by evidence that the impact of ai varies significantly across different contexts and applications [1, 5]. Additionally, we evaluated the maintainability using metrics such as lines of code, cyclomatic.
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