<p>The advent and spread of counterfeit goods in medical supply chains is an opportunistic activity by actors who take advantage of information asymmetry between themselves and the rest of the supply chain. Counterfeiters, fraudsters, and companies who intend to cut corners have asymmetric knowledge of the quality (substandard) of the products they possess and are not obligated, but have a disincentive, to share that asymmetric knowledge. This has led to a medical supply chain that is riddled with asymmetric information from consumers, all the way upstream to manufacturers. The asymmetric information present in the supply chain allows agents to take advantage of the demand and chaos in the system to act contrary to the principal’s, in this case the supply chain, best interest as described in the agent-principal theory. The problem related to information asymmetry in principal-agent relationships, including those encapsulated in supply chains, is well documented in prior literature. The missing piece of research deals with quantification of information asymmetry metrics and assessing supply chain of goods.</p>
<p>This research explored current and proposed information asymmetry mitigating activities including the potential applications of technology-based methods of reducing information asymmetry within the medical supply chains including distributed ledger technology. Five data aggregation services were searched for relevant literature generating a final sample for analysis of 90 documents (ndocuments = 90). A qualitative meta-analysis methodology was conducted using Nvivo as exploratory research to analyze content in the corpus of documents and extract key themes relevant to each research question then synthesize frequencies of key themes such that information asymmetry in medical supply chains can be decomposed into agents, conditions, and contributing factors.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/22584394 |
| Date | 12 April 2023 |
| Creators | Alexander Thomas Miller (15204598) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Dissertation_Miller_Alexander_DTECH_5APR2023_pdf/22584394 |
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