TY Chen
Research Statement
I have three interwoven research streams: e-learning, ontological problem-solving, and behavioral information research. In the e-learning stream, I obtained six external grants, including two from the National Science Council and Ministry of Education and four from the industry. The ontological problem-solving 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 health. 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 pondering on the idea of 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 make a research program as Behavioral Information Research (BIR), which has great potential for practical and theoretical contributions and I expect to work with my colleagues in acquiring funding from agencies such as NIH and NSF by applying BIR in fields of learning, information science, and health.
BE is intriguing in its addressing human anomalies and with behavioral decision research as a broader framework. 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, Kahneman’s contribution was stated as “[i]ntegrate economic analysis with fundamental insights from cognitive psychology.” In 2017, Thaler received a Nobel Prize for his contributions to BE, but Thaler was already famous for nudge and choice architecture before the award. Nudge has been widely applied in fields such health and finance and has substantial influence as a public policy tool. Although cognitive psychology is predominantly experimental, disciplinary influence is seen in fields drawing from psychology. DSR, as a research framework proposed to promote research rigor in information systems research, emphasizing the building of design artifacts to embed solution to research problem and therefore would complement with the BE approach. Hevner’s DSR has also paid special attention to the issue of relevance and the theoretical feedback to the domain as a result of research.
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 such less-than-desired adoption. For example, a recent systematic review (Chen, 2020) on cognitive biases in online health information seeking (OHIS) found only 40 BE-related studies from 1995 to 2019. A review by Fleischmann et al. (2014) on cognitive biases in information systems found 84 articles (1992-2012), yet only 44% of them are experimental, and 58% addressing the topic of system usage. Another review by Mohanani et al. (2020) on cognitive biases in software engineering found 65 studies (1990-2016), with only 46% experimental, and the articles dispersed around 45 journals and proceedings. Given this emerging phenomenon of BE adoption, now might be time for researchers in information and computational science to embrace the BIR approach to take advantage of the strengths of BE’s theoretical perspective of behavioral decision-making and DSR’s methodological rigor of the problem-artifact-theory cycle from.
As reviewed by Chen (2020), a few example BIR studies exist in information science; e.g., Lau & Coiera (2007, 2009) examined cognitive biases and debiasing in OHIS and White (2014) examined health belief dynamics in web search. My current BIR pipeline project applies BIR in the area of HCI in OHIS context, beginning with replicating Lau & Coiera (2007, 2009). So far, I have published a conference paper in HCI International 2017, a conference presentation in 2020, and a journal article with the Journal of Documentation (2020). In this pipeline, two experimental investigations on cognitive biases and social biases in a custom-built OHIS system taking participants from MTurk are forthcoming. This project follows the BIR approach and will offer insights in the suboptimal health decision-making behavior of the information seekers. The social bias study especially addresses ethnicity and gender stereotypes, replicating earlier field studies, in OHIS context. These studies are examples of how BIR helps gain better understanding in the behavioral decision aspects of user interacting with the interfaces and information objects in the contemporary digital ecosystems.
Referencing BE’s theoretical insights and DSR’s research rigor , the disciplinary goal of BIR is to bridge the gap between social-cognitive psychology and the information and computational sciences. Such goal has implications in applied and basic research: For applied research, information and computational science researchers will find themselves tapping into the vast world of behavioral and decision insights to re-examine and become creative in their research areas and topics. For example, researchers may take advantage of BIR by beginning with the empirical validation of existing conceptual models in their areas through developing design artifacts for behavioral data collection and examine the models using behavioral perspectives. For basic research, while BIR also inherits problems from the reference disciplines (e.g., the incoherence of cognitive bias mechanisms), nudge theory has shown us that a domain can evolve from borrowing insights into basic research as domain development, a trajectory BIR could take.
In the era of ubiquitous 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 even intervention of human behavior in terms of information objects, interface, technology, and their interaction. As an emerging research approach, BIR allows researchers to tackle the complexity of human behavior for practical and theoretical contributions in the information and computational sciences. Further, BIR may be applied in any areas as long as human behavior and decision making are of interest, which means BIR is by nature interdisciplinary and information and computational scientists could use BIR 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.
Note: As an over-generalization, BIR differs from the newly burgeoning field of Behavioral Informatics by referencing more to behavioral decision research than computational modeling.