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As formulated by John Thibaut and Harold Kelley, Social Exchange Theory explains human relationships as economic-like exchanges that maximize rewards and minimize costs. This theory suggests that individuals engage in relationships to gain benefits and reduce burdens, similar to economic transactions. It has been widely applied to various types of relationships, including romantic, professional, and social interactions.Rewards and Costs in RelationshipsRelationship rewards include emotional...
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Scientific Collaborations: How Do We Measure the Return on Relationships?

Jeanne M Fair1, Martha Mangum Stokes1, Deana Pennington2

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|February 26, 2016
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Summary

Transdisciplinary scientific collaboration is key to addressing emerging infectious diseases (EIDs). A new "return on relationships" (ROR) metric, applied to biosurveillance networks, can measure the value of these crucial partnerships.

Keywords:
HantavirusMERSreturn on relationshipsscientific collaborationsystems dynamics

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Area of Science:

  • Epidemiology
  • Public Health
  • Biosurveillance

Background:

  • Emerging infectious diseases (EIDs), primarily zoonotic, pose significant global public health and biosurveillance challenges.
  • Effective response requires transdisciplinary research and collaboration, often catalyzed by outbreaks.
  • Existing value assessment methods for international cooperation overlook the importance of relationships.

Purpose of the Study:

  • To introduce and propose a method for measuring the "return on relationships" (ROR) in transdisciplinary scientific collaborations.
  • To demonstrate how ROR can be measured using system dynamics modeling.
  • To highlight the value of pre-established scientific networks for outbreak response and prevention.

Main Methods:

  • Applied system dynamics modeling, a framework common in epidemiology, to measure ROR.
  • Tracked and visualized scientific collaborations emerging from a 2014 bat biosurveillance workshop in Singapore.
  • Analyzed the relationship networks and outcomes generated by these collaborations.

Main Results:

  • Demonstrated a methodology for visualizing and quantifying the ROR within scientific networks.
  • Illustrated the long-term benefits of collaborative networks through the example of the 1993 Hantavirus outbreak response in New Mexico.
  • Showcased how pre-organized transdisciplinary collectives can enhance outbreak response.

Conclusions:

  • Return on Relationships (ROR) offers a novel framework for assessing the value of scientific collaborations.
  • System dynamics modeling provides a viable method for measuring ROR in biosurveillance networks.
  • Proactive, transdisciplinary scientific networking is essential for transforming global outbreak response and prevention strategies.