Let us introduce Hyunjin of INSEAD Business School, which is located near to Paris, France with another campus in Singapore. Hyunjin, you recently won were runner up for an award at the 2021 Annual Meeting - congratulations! So…
What are your research interests right now?
I am deeply interested in understanding how data and algorithms are changing how firms make strategic decisions, and their implications for how firms compete and build competitive advantage. I am also interested in using field experiments in close collaboration with companies to explore these questions.
What do you think is your most exciting contribution to academia?
That as market information becomes increasingly available, firms may become more similar in their strategies, and managerial attention may increasingly be a source of competitive advantage. I explore this in a paper on The Value of Competitor Information -- I design and run a field experiment across over 3,000 local businesses in the US, and find that a majority are not able to state their competitors' price positions, and providing competitor information leads them to align more closely with their competitors rather than differentiate from them.
I’d also like to envision that my work inspires and informs more field experiment-based work in strategy that allows us to push forward our insights in the academic literature while also impacting real practice in companies.
At the 2021 Conference you were runner up to an award from TIM. Tell us about the paper and why you think its findings are important.
My dissertation looks at the question, as firms compete in an increasingly data-driven landscape, which firms are able to realize potential gains from data, and what enables (or hinders) them to do so? I explore different ways in which firms use information to inform their decisions, and point to three barriers they may face: managerial inattention that impedes awareness of even easily accessible competitor data, simple rules based on intuition that lead managers to use their discretion to dissipate gains from algorithms rather than improve them, and multiple goals that hamper how employees process and learn from information. Each paper is based on a field experiment, the first of which is the paper described above.
To describe one of the papers (for brevity), my second paper examines the extent to which the use of algorithms ultimately translates into improvements in managerial decisions. The key finding is that while algorithms can provide substantial improvements in managerial decisions, the returns to algorithmic sophistication may be limited in some contexts, and potential gains may be dissipated by managers who are intended to oversee and improve algorithmic recommendations. This has large implications for firms as they increasingly invest in data and algorithmic sophistication, generally with managers having ultimate decision authority. Our findings suggest that organizations may need to think carefully about the returns to algorithmic sophistication in each context, and how they can redesign decision-making processes to make use of managers' knowledge when using algorithms as decision aids.
Tell us something personal about yourself.
I love running, sailing, surfing, and generally anything that involves being near water. Although I love being outdoors, I grew up in some of the most magnificent cities in the world — including Seoul, New York, Montreal, London, and Boston!
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