This research reports research procedures and findings of the two empirical studies (i.e., a composite research) on developing and validating an Artificial Intelligence (AI) success theory in the organizational context. Upon a grounded theory-based literature review, the qualitative Study 1 examines research concepts, topics, methodologies, and models/paradigms of AI literature in the Information Systems (IS) discipline. The study extends the well-established D&M IS success model into the AI research context, synthesizes perspectives and findings of literature, identifies critical success factors and interrelationships of AI, and develops an AI success theory in the organizational context. Built upon the AI success theory of Study 1, the quantitative Study 2 develops a research model and conducts a firm-level survey of the Chinese and USA organizations to estimate research hypotheses of the model. The data analyses of Study 2 empirically illustrate psychometric properties of research variables and hypothesized interrelationships encompassed in the study. The paper discusses research opportunities and challenges of AI success and is concluded with implications, contributions, and limitations of the composite research.
Tales of artificial intelligence success: two empirical studies
Tao “Eric” Hu
Speakers
Day 2
University / Institution
California State University
Representing
USA