An empirical investigation of transaction dynamics in online surplus networks: A complex adaptive system perspective

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Online material and waste exchanges (OMWEs) provide an online platform connecting suppliers of surplus material with potential buyers. These exchanges aim to improve the environment by helping industrial organizations repurpose surplus material and avoid landfill disposal. OMWEs represent an emerging surplus-driven supply network, which exhibits different internal complexities and dynamics from traditional supply networks, with similarly distinct transactional patterns and outcomes. Drawing on complex adaptive system (CAS) theory, this study investigates how the complex network adapts to alter the likelihood of transactions between buyers and suppliers. Using data from, an OMWE operating in the US state of Minnesota, we observe that, at the node level, buyers adapt their searches for products over time to increase transaction success; at the dyadic level, buyer-supplier pairing patterns adapt over time through homophilous relations to facilitate negotiations and increase transaction success; and at the network level, the entire exchange system structurally selforganizes to increase transaction rates. Throughout, buyers’ competition influences transaction success, contingent on whether the product has been on the market for a long time, and buyers with more experience more quickly identify favorable conditions (among products and suppliers) leading to higher transaction success than less experienced competitors. Overall, the results demonstrate multilevel network emergence, in which complex, adaptive, and longitudinal buyer-supplier interactions resolve uncertainty and increase transactions. These findings disentangle OMWEs’ operation to allow managers and policymakers to increase the overall buyer-supplier transaction rates with an eye toward improved environmental outcomes.

Original languageEnglish (US)
Pages (from-to)160-189
Number of pages30
JournalJournal of Operations Management
Issue number2
StatePublished - Mar 2019

All Science Journal Classification (ASJC) codes

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering


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