Prof. Dr Daniel Soper, California State University, Fullerton, United States
Dr. Daniel S. Soper is a seasoned academic leader and professor in the Department of Information Systems and Decision Sciences at California State University, Fullerton. With a distinguished career marked by early promotion to full professor in 2019, he also serves as Vice-Chair of the department and Director of the MS in Information Technology program. A U.S. citizen, Dr. Soper’s academic journey has taken him from NASA and USDA to top-tier university appointments. He holds extensive expertise in data science, AI, and information systems. Known for his commitment to academic excellence and leadership in higher education, he has held roles such as Associate Dean and Director of Graduate Programs. His scholarly work explores the intersections of technology, economics, and culture, especially in emerging societies. A graduate of Arizona State University, he is also recognized for his online presence as a statistics educator, author, and innovator in digital learning.
Publication Profile
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๐ Education
Dr. Daniel S. Soper earned his Ph.D. in Information Systems from the W.P. Carey School of Business at Arizona State University in 2008. His dissertation investigated the economic, political, and cultural impacts of ICT investments in emerging societies. Prior to that, he completed a Master of Science in Computer Information Systems from Colorado State University in 2004, preceded by a Bachelor of Science from the same institution in 2001, where he majored in Computer Information Systems with a second major in Sociology. His educational pursuits reflect a solid foundation in both technology and the social sciences. He has further enhanced his academic qualifications through professional development programs, including Microsoft’s Professional Programs in Artificial Intelligence and Data Science, and university-level certificates in leadership, teaching, and online education. Dr. Soper’s interdisciplinary academic training supports his impactful research and dynamic teaching across both technical and managerial domains.
๐ผ Experience
Dr. Soper has over two decades of progressive academic and industry experience. He currently serves as Professor, Vice-Chair, and Director of MS in IT at CSU Fullerton. Since joining CSU Fullerton in 2008, he has held roles as Assistant Professor, Associate Professor (with tenure in 2014), and Director of the MS in Information Systems program. He was promoted early to Full Professor in 2019 and also served as Associate Dean and Director of MBA and Graduate Programs. Prior to academia, he worked as a Software Engineer and Database Administrator at NASA’s Center for Engineering Infrastructure and Sciences in Space and later as an Information Systems Analyst at the USDA’s National Center for Genetic Resources Preservation. His teaching and research roots began at Arizona State University as a Research and Teaching Assistant. His career reflects a blend of academic leadership, research innovation, and industry expertise in IT systems.
๐ Honors and Awards
Dr. Soper has received numerous recognitions that highlight his academic and professional excellence. Most notably, he achieved early promotion to Full Professor at CSU Fullerton in June 2019, a rare accomplishment acknowledging exceptional scholarly contributions and service. He has been a delegate to the Cambridge Summer Institute at the University of Cambridge, reflecting international recognition of his academic potential. Additionally, he has completed Microsoft Professional Programs in Artificial Intelligence and Data Science, underlining his commitment to cutting-edge skill development. His contributions to teaching excellence are evident through completion of the University Teaching Certificate Program, Online Course Evaluation Certificate Program, and Inclusive Leadership Certificate. These achievements demonstrate his dedication not only to scholarship but also to inclusive, high-quality education and leadership in business and information systems. His work is widely respected in academic and practitioner circles alike, and he is also a sought-after speaker and consultant.
๐ฌ Research Focus
Dr. Daniel S. Soper’s research bridges the fields of information systems, data science, and artificial intelligence, with particular emphasis on the economic, political, and cultural effects of ICT in emerging societies. His Ph.D. dissertation reflects this macro-level focus, while his ongoing work continues to explore technology adoption, decision science, machine learning, and human-computer interaction. He is widely known for developing open-source statistical tools, contributing significantly to academic instruction and online education. His research also covers predictive analytics, enterprise systems, and technology-enabled business transformation. With an applied, interdisciplinary approach, Dr. Soper is committed to advancing both the theoretical understanding and practical implementation of information technologies. He often collaborates with industry and academic peers to drive innovation in tech-driven business strategies. His scholarly output and tools have been adopted worldwide by students, educators, and professionals alike, making him an influential figure in modern information systems scholarship.
๐ Publications
๐ A Longitudinal Assessment of the Economic, Political, and Cultural Impacts of Information and Communication Technology Investments in Emerging Societies
๐ A Structural Equation Modeling Approach to Predicting User Satisfaction in Web Portals
๐ Trust and Risk in Information Systems Adoption: An Integrated Model
๐ The Impact of Organizational Culture on Knowledge Management Success
๐ Exploring the Role of AI and Big Data in Financial Decision Making
๐ An Empirical Study of Social Media Influence on Consumer Decision Behavior
๐ The Relationship Between IT Capabilities and Firm Performance in Competitive Markets
๐ Digital Transformation in Higher Education: Challenges and Opportunities
๐ Design and Development of a Web-based Statistical Calculator for Academic Use
๐ A Machine Learning Framework for Predicting Student Performance in Online Courses