My three interwoven research streams are: learning, ontological knowledge engineering, and Behavioral Information Research (BIR). In the learning stream, I obtained six external grants, including one from the National Science Council, one from the Ministry of Education, and four from the industry. The ontological knowledge engineering stream involves formal knowledge representation via knowledge and task domain modeling and rule-based reasoning to achieve decision support; with applications in management, learning, and healthcare. From 2012 to 2018, 13 peer-reviewed journal and conference articles were published in this stream, with three as the outcome of two projects funded by the National Science Council. In 2016, I came to Florida State University to pursue my second doctorate in Information Science and began my development of BIR by coupling Behavioral Economics (BE) (of, e.g., John Payne, Daniel Kahneman, & Richard Thaler) and design science research (DSR) (e.g., Alan Hevner & Ken Peffers) to study human information behavior and decision-making with the intention to contribute to research in human-computer interaction (HCI).

BE is intriguing in its addressing of human’s systematic deviation from optimal decision-making. Historically, BE is known for borrowing insights, e.g., heuristics and biases, from psychology to investigate economic behavior. When awarded the Nobel Prize in Economic Sciences, Daniel Kahneman’s contribution was stated as “[i]ntegrate economic analysis with fundamental insights from cognitive psychology.” Since cognitive psychology is predominantly experimental, Hevner’s DSR, as a research framework proposed to promote research rigor in information systems research with an emphasis in the building of design artifacts to embed solutions to research problems could well complement with the BE research approach.

While BE, along with the broader behavior decision research, has been well received by various disciplines such as management, health, and accounting research, they seem to have attracted less attention from areas in information and computational sciences, albeit historically there is no lack of proposals urging the embracing of cognitive psychology in those fields. Recent reviews have shown this lack of enthusiastic adoption. For example, a systematic review (Chen, 2021) on cognitive biases in online health information seeking (OHIS) found a small number of identified studies, low percentage of experimental studies, scattered publication venues, and limited areas of investigations. Similar observations have also been made by Fleischmann et al. (2014) and Mohanani et al., 2020) in information systems and software engineering.

Referencing BE’s theoretical insights and DSR’s research rigor*, a possible disciplinary goal of BIR is to bridge the gap between social/cognitive psychology and the information and computational sciences. Such goal has implications in both applied and basic research. For applied research, information and computational scientists will find themselves tapping into the vast world of behavioral and decision insights, which will allow re-examination of their research topics to say the least. For example, researchers may take advantage of BIR to empirically validate their existing conceptual models through new design artifacts for behavioral data collection. For basic research, while BIR inherits problems from the reference disciplines (e.g., the incoherence of cognitive bias mechanisms), DSR’s rigor of research and theoretical feedback to domain could encourage a comprehensive and systematic approach for the examination of behavioral and cognitive traits, a trajectory BIR could take to further the theoretical foundations of BE.

In the era of ubiquitous internet connectivity with constant exposure to devices and information, BIR offers unique research opportunities to explore human behavior and decision-making through, e.g., devising measures for the detection, analysis, interpretation, and intervention of suboptimal human decision-making in terms of information objects, interface, design, technology, and their interaction. For example, in OHIS, exemplar BIR studies include Lau & Coiera (2007, 2009), investigating cognitive biases and debiasing in OHIS; and White (2014), examining health belief dynamics in Web search. My current BIR pipeline focuses on the area of HCI in OHIS, beginning with replicating Lau & Coiera (2007, 2009) and extending to social biases in OHIS. So far, I have published a peer-reviewed conference paper in HCI International 2017, a conference presentation in 2020, and a journal article with the Journal of Documentation (Chen, 2021), with two forthcoming experimental investigations on cognitive biases and social biases in a custom-built OHIS system taking participants from MTurk. The social bias study especially addresses ethnicity and gender stereotypes to replicate earlier large-scale field studies in the OHIS context. These studies are examples of how BIR helps gain better understanding in the behavioral decision and HCI by using design artifacts to collect user behavioral data to infer decision-making.

As an emerging research approach, BIR allows researchers to tackle the complexity of human behavior for contributions in the information and computational sciences. BIR may be applied in any research areas as long as human behavior and decision making are of interest, which means BIR is by nature interdisciplinary and could be used to complement virtually any other areas for collaboration. The adoption of BIR would therefore result in researchers building interdisciplinary teams to include information scientists, designers, psychologists, statisticians, computer and data scientists, and domain experts to exploit the possibilities BIR has to offer, which should be able to attract attention from granting agencies such as NSF, NIH, IMLS, and DOE.

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