{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/152746"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/152746","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Industry Platforms: Case Studies to Measure Platform Capabilities for US Unicorns","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. 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. 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. 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.","abstract_html":"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. 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. 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. 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.","abstract_has_math":false,"creators":["AlSadah, Yousif Fayez"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"System Design and Management Program.","school":null,"contributors":[],"advisors":["Cusumano, Michael A."],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-09","date_published":"2023-09","updated_at":"2026-07-22T22:21:44Z","subjects":[],"languages":[],"rights":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"],"rights_urls":["https://rightsstatements.org/page/InC-EDU/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/152746","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Cusumano, Michael A."]},{"key":"dc:contributor.department","label":"Department","values":["System Design and Management Program."]},{"key":"dc:creator","label":"Author","values":["AlSadah, Yousif Fayez"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2023-11-02T20:12:50Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2023-11-02T20:12:50Z"]},{"key":"dc:date.issued","label":"Date","values":["2023-09"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master","Master of Science in Engineering and Management"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"]},{"key":"dc:rights.uri","label":"Rights URI","values":["https://rightsstatements.org/page/InC-EDU/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1721.1/152746"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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. 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. 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. 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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M."]},{"key":"dc:title","label":"Title","values":["Industry Platforms: Case Studies to Measure Platform Capabilities for US Unicorns"]}]}],"canonical_facts":{"dc:contributor.advisor":["Cusumano, Michael A."],"dc:contributor.department":["System Design and Management Program."],"dc:creator":["AlSadah, Yousif Fayez"],"dc:date.accessioned":["2023-11-02T20:12:50Z"],"dc:date.available":["2023-11-02T20:12:50Z"],"dc:date.issued":["2023-09"],"dc:description.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. 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. 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. 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."],"dc:description.degree":["S.M."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/152746"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"],"dc:rights.uri":["https://rightsstatements.org/page/InC-EDU/1.0/"],"dc:title":["Industry Platforms: Case Studies to Measure Platform Capabilities for US Unicorns"],"dc:type":["Thesis"],"thesis:degree_name":["Master","Master of Science in Engineering and Management"]},"updated_at":"2026-07-22T22:21:44Z"}