My research program examines how people seek, interpret, and use information when making decisions with digital and computational systems. It integrates behavioral economics (BE) and design science research (DSR) to study human information behavior and decision-making, with particular attention to the cognitive and social processes underlying technology-mediated behavior, including interaction with AI-enabled systems. This integrated approach, conceptualized as Behavioral Information Research (BIR), supports the development of theory-driven design artifacts and experimental systems for investigating these processes.

This current research agenda is centered on BIR and builds on two earlier streams of work in online learning and ontological knowledge engineering. In online learning, I obtained six external grants, including support from the National Science Council, the Ministry of Education, and industry partners. This work examined technology-enhanced learning environments and the evaluation of instructional interventions. Research in ontological knowledge engineering focused on formal knowledge representation, domain and task modeling, and rule-based reasoning to support decision-making in management, learning, and healthcare. Between 2012 and 2018, I published 13 peer-reviewed journal and conference articles in this area, including work arising from two National Science Council-funded projects.

These earlier streams converged in my work in information science. After beginning my second doctorate at Florida State University in 2016, I developed BIR by bringing behavioral economics and behavioral decision research together with design science. Behavioral economics and behavioral decision research provide theories and experimental methods for understanding systematic patterns in judgment and decision-making, while DSR provides a rigorous framework for building and evaluating artifacts that instantiate theoretical constructs. Together, they support a research cycle in which theory guides system design, systems create controlled environments for behavioral investigation, and empirical findings refine both design and theory.

This integration addresses an important gap. Behavioral decision research has been widely used in fields such as management, health, and accounting, but remains less developed in information science and computing. My systematic review of cognitive biases in online health information seeking found a relatively small and fragmented empirical literature, with limited use of controlled experimental designs. Similar concerns have been identified in information systems and software engineering. BIR provides a way to study these behavioral mechanisms directly within the digital environments in which they occur.

A primary application domain for this research is belief updating in online health information seeking (OHIS), where people routinely evaluate uncertain, incomplete, and sometimes conflicting information. This setting provides a useful context for examining how cognitive biases, social cues, prior beliefs, and interface design influence information evaluation and decision-making. Rather than treating search simply as information retrieval, I study it as a behavioral process in which users continually interpret evidence, update beliefs, and make judgments.

This work builds on research on cognitive bias, debiasing, and health-belief dynamics in web search. It has included peer-reviewed work in HCI International and the Journal of Documentation, as well as experimental studies using custom-built search systems. My recent work uses controlled search-interface experiments to examine how users update beliefs as they encounter information and how interface and social cues shape that process. This approach allows behavioral mechanisms to be studied while preserving important features of realistic online information seeking.

A central contribution of this research is methodological as well as theoretical. By embedding behavioral constructs in working interactive systems, I can capture not only final judgments but also the sequence of searches, information exposure, evaluations, and decisions that produce them. This creates opportunities to investigate when biases emerge, how they evolve during interaction, and whether interface design can reduce undesirable effects. The same framework can be extended beyond health search to recommendation systems, decision-support tools, educational technologies, and human-AI interaction.

Looking ahead, I plan to expand BIR into AI-supported information seeking and decision-making, digital health, and educational technologies. I am particularly interested in how people evaluate AI-generated information, calibrate trust, revise beliefs, and respond to social or algorithmic cues. As generative AI increasingly mediates access to information, many questions traditionally studied in information search are becoming questions about human-AI interaction: what information users accept, what they discount, how they judge credibility, and how system design affects those judgments.

Because BIR is inherently interdisciplinary, it creates opportunities for collaboration among information scientists, computer scientists, psychologists, statisticians, designers, and domain experts. It also provides a coherent platform for externally funded research across health, education, information systems, and AI-enabled decision environments.