<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-09-20T07:03:54Z</responseDate><request verb="GetRecord" identifier="oai:dspace.mit.edu:1721.1/152746" metadataPrefix="dim">https://dspace.mit.edu/server/oai/request</request><GetRecord><record><header><identifier>oai:dspace.mit.edu:1721.1/152746</identifier><datestamp>2023-11-03T03:32:35Z</datestamp><setSpec>com_1721.1_7582</setSpec><setSpec>com_1721.1_7581</setSpec><setSpec>col_1721.1_131023</setSpec></header><metadata><dim:dim xmlns:dim="http://www.dspace.org/xmlns/dspace/dim" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:doc="http://www.lyncode.com/xoai" xsi:schemaLocation="http://www.dspace.org/xmlns/dspace/dim http://www.dspace.org/schema/dim.xsd">
   <dim:field mdschema="dc" element="contributor" qualifier="advisor">Cusumano, Michael A.</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="author">AlSadah, Yousif Fayez</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="department">System Design and Management Program.</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="accessioned">2023-11-02T20:12:50Z</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="available">2023-11-02T20:12:50Z</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="issued">2023-09</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="submitted">2023-10-10T21:04:30.426Z</dim:field>
   <dim:field mdschema="dc" element="identifier" qualifier="uri">https://hdl.handle.net/1721.1/152746</dim:field>
   <dim:field mdschema="dc" element="identifier" qualifier="orcid">http://orcid.org/0009-0008-0615-2482</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="abstract">Large-sample empirical research by Cusumano et. al. found that US privately-held unicorns with platform capabilities command on average 123% premium over non-platforms. However, measuring the extent to which a company is platform or non-platform based is a difficult problem given the complexities of business organizations and how these activities interact with each other in non-linear ways.  &#xd;
&#xd;
This thesis attempts to address this by proposing a systems thinking, case-based approach to evaluate the key business activities of a firm with potential platform capabilities using the author’s proposed Platform Classification Matrix on five of the largest US privately-held firms: Epic Games, Databricks, Plaid Technologies, Stripe, and Instacart. Each business activity for a firm is classified as platform or nonplatform, and if it is a platform then it is assessed based on its revenue contributions to the firm and three strength metrics: Network effects, strength against multihoming, and new entrant deterrence. This matrix generates a ‘platform strength’ metric and allows identification of the platform activity with the most potential towards a winner take all or most case.&#xd;
&#xd;
The author further proposes combining this matrix with a system dynamic approach to identify how differing business activities can boost or hinder the leading platform service which allows decision makers to assess whether retaining or subsidizing seemingly low-performing business lines is strategic for their leading platform.&#xd;
&#xd;
The thesis concludes by advocating for using both methods as well as the generated metrics to perform a holistic analysis when evaluating firms with platform capabilities potential.</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="degree">S.M.</dim:field>
   <dim:field mdschema="dc" element="publisher">Massachusetts Institute of Technology</dim:field>
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   <dim:field mdschema="dc" element="title">Industry Platforms: Case Studies to Measure Platform Capabilities for US Unicorns</dim:field>
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   	&lt;Title>Industry Platforms: Case Studies to Measure Platform Capabilities for US Unicorns&lt;/Title>
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   	&lt;PublicationDate>2023-09&lt;/PublicationDate>
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        	&lt;DisplayName>AlSadah, Yousif Fayez&lt;/DisplayName>
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   	&lt;Abstract>Large-sample empirical research by Cusumano et. al. found that US privately-held unicorns with platform capabilities command on average 123% premium over non-platforms. However, measuring the extent to which a company is platform or non-platform based is a difficult problem given the complexities of business organizations and how these activities interact with each other in non-linear ways.  &#xd;
&#xd;
This thesis attempts to address this by proposing a systems thinking, case-based approach to evaluate the key business activities of a firm with potential platform capabilities using the author’s proposed Platform Classification Matrix on five of the largest US privately-held firms: Epic Games, Databricks, Plaid Technologies, Stripe, and Instacart. Each business activity for a firm is classified as platform or nonplatform, and if it is a platform then it is assessed based on its revenue contributions to the firm and three strength metrics: Network effects, strength against multihoming, and new entrant deterrence. This matrix generates a ‘platform strength’ metric and allows identification of the platform activity with the most potential towards a winner take all or most case.&#xd;
&#xd;
The author further proposes combining this matrix with a system dynamic approach to identify how differing business activities can boost or hinder the leading platform service which allows decision makers to assess whether retaining or subsidizing seemingly low-performing business lines is strategic for their leading platform.&#xd;
&#xd;
The thesis concludes by advocating for using both methods as well as the generated metrics to perform a holistic analysis when evaluating firms with platform capabilities potential.&lt;/Abstract>
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