CoCo Seminar Series
Spring 2024

The Measure of Complementarities and Synergies: A Systematic Review of the Organizational Science Literature and an Interdisciplinary Exploration

Minjong Jun, PhD candidate
School of Management, Binghamton University

Wednesday April 17, 2024 12:00-1:00pm EDT
Hybrid (EB T1 and Zoom; link below)
https://binghamton.zoom.us/j/98463329955?pwd=NlhLSzl4NitKR0JmdlA0SndqUjV2dz09

Abstract:
Resource complementarity/synergy, which is defined as conditions where the return of one resource increases in the presence of the other resource(s), is frequently suggested to play a critical role in the development of sustained competitive advantage. However, given that complementarities/synergies emerge through interrelationships between multiple elements of a system and their fit with contextual factors, there are an array of complex factors involved that make them inherently challenging to measure. It is thus no surprise that researchers often face challenges in representing complementarities/synergies associated with resources in empirical studies. In general, there is a lack of understanding of approaches to empirically capture and represent complementarities and the broader notion of synergy. To make progress on this issue, this paper conducted a systematic review of the empirical studies published in ten organizational sciences that use the notion of complementaries/synergies and also included an exploratory interdisciplinary aspect by having discussions with researchers in a variety of other disciplines about how they measure or otherwise empirically capture the notion of synergy and complementarities.

Speaker bio:
Minjong Jun is a Ph.D. candidate in Leadership and Organizational Science for the School of Management at Binghamton University. He is doing research on strategic HRM (AMO bundles, system complementarity, training & turnover), strategic human capital resource (emergence process and its links to HR management), and leadership (strategic leadership, shared leadership, leader-follower congruence) through leveraging advanced statistical methodologies such as multilevel modeling, polynomial regression analysis, panel analysis, and computational modeling. He serves as a research assistant for the Bernard M. & Ruth R. Bass Center for Leadership Studies. Also, he earned an Advanced Graduate Certificate in Complex System Science and Engineering from the Systems Science and Industrial Engineering department of Binghamton University in 2021.

For more information, contact Hiroki Sayama (sayama@binghamton.edu). http://coco.binghamton.edu/

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