My research program integrates behavioral economics (BE) and design science research (DSR) to study human information behavior and decision-making within information and computational systems. This integrated approach, conceptualized as Behavioral Information Research (BIR), supports the development of theory-driven design artifacts and experimental systems to investigate the cognitive and social processes underlying technology-mediated behavior, including behavior in AI-enabled systems.

My research agenda comprises three interwoven streams: online learning, ontological knowledge engineering, and Behavioral Information Research (BIR). In the online learning stream, I have obtained six external grants, including one from the National Science Council, one from the Ministry of Education, and four from industry partners. This work has focused on technology-enhanced learning environments and the evaluation of instructional interventions.

My ontological knowledge engineering stream centers on formal knowledge representation through domain and task modeling and rule-based reasoning to support decision-making, with applications in management, learning, and healthcare. Between 2012 and 2018, I published 13 peer-reviewed journal and conference articles in this area, including three arising from two National Science Council–funded projects.

In 2016, I joined Florida State University to pursue my second doctorate in Information Science, where I began developing BIR by formally coupling BE (e.g., Kahneman, Thaler, Payne) with DSR (e.g., Hevner, Peffers). This work situates my research at the intersection of information science, human-computer interaction (HCI), and behavioral decision research.

Behavioral economics provides a robust theoretical foundation for understanding systematic deviations from optimal decision-making, drawing heavily from cognitive psychology’s experimental traditions. DSR complements BE by offering a rigorous framework for building and evaluating artifacts that embody theoretical constructs and address practical research problems. Together, BE and DSR enable a research paradigm in which theory informs artifact design, and artifact evaluation feeds back into theory development.

Although BE and behavioral decision research have been widely adopted in disciplines such as management, health, and accounting, they remain underutilized in information and computational sciences. A systematic review of cognitive biases in online health information seeking (Chen, 2021) revealed a small body of empirical work, limited experimental designs, and fragmented publication venues. Similar gaps have been identified in information systems and software engineering research (Fleischmann et al., 2014; Mohanani et al., 2020).

BIR addresses this gap by bridging psychology and information/computational sciences. In applied research, BIR enables researchers to embed behavioral constructs into interactive systems for empirical validation. In basic research, BIR leverages DSR’s emphasis on rigor and theoretical contribution to support more systematic examination of behavioral and cognitive mechanisms, thereby strengthening theoretical foundations in BE and related fields.

My primary application domain for BIR is online health information seeking (OHIS) with an emphasis on HCI implications, a context in which individuals routinely make complex, high-stakes decisions under uncertainty. BIR offers a framework for detecting, analyzing, and intervening in suboptimal decision-making through interface design, information presentation, and system features.

My work in this area builds on and extends foundational studies by Lau and Coiera (2007, 2009) on cognitive biases and debiasing in OHIS and White (2014) on health belief dynamics in web search. To date, I have published a peer-reviewed paper in HCI International (2017), presented conference work in 2020, and published a journal article in the Journal of Documentation (2021). I am currently conducting two experimental studies using a custom-built OHIS system with MTurk participants to examine cognitive and social biases in the evaluation of health information. The social bias study specifically investigates ethnicity and gender stereotypes, replicating and extending large-scale field findings within a controlled experimental environment.

These projects demonstrate how BIR advances understanding of behavioral decision-making and HCI through theory-driven artifact design and behavioral data collection, and establish a foundation for studying user behaviors in various information search, recommendation, decision-support, and AI-enabled systems.

Looking ahead, I plan to expand BIR into additional domains such as digital mental health, educational technologies, and AI-supported decision systems. I am particularly interested in developing adaptive interfaces that incorporate behavioral insights to support more accurate, equitable, and transparent decision-making.

Because BIR is inherently interdisciplinary, it lends itself to collaboration among information scientists, computer scientists, psychologists, statisticians, designers, and domain experts. This positioning aligns well with funding priorities of agencies such as NSF, NIH, IMLS, and DOE, and provides a strong foundation for sustained external funding.

*Note: BIR references more to behavioral decision research than computational modeling as in contrast to the burgeoning field of behavioral informatics.