Kenneth Younge

kenneth.younge@epfl.ch +41 21 693 00 09 http://tis.epfl.ch

EPFL CDM MTEI TIS
ODY 2 01.2 (Odyssea)
Station 5
CH-1015 Lausanne

+41 21 693 00 09
+41 21 693 00 77
Office: ODY 2 02
EPFL > CDM > MTEI > TIS

Web site: Web site: https://www.epfl.ch/labs/tis/

Fields of expertise

- Technology and Innovation Strategy
- Data Science
- Computational Law
- Intellectual Property
- Patenting
- Knowledge Spillovers
- Employee Mobility

Biography

Kenneth is Associate Professor in Corporate Entrepreneurship and Chair of Technology and Innovation Strategy at EPFL. He is an applied economist and data scientist, using experimental economics and machine learning methods to examine the strategic importance of patent portfolios, knowledge spillovers, financial risk disclosure, computational law, and employee mobility. His work has been published in the top journals in economics and strategy, and he is the past winner of the Strategic Management Society’s Best Conference Paper Award, the Academy of Management’s BPS Outstanding Dissertation Award, and numerous teaching awards.

Prior to returning to academia, Professor Younge worked for 14 years in industry as a Chief Technology Officer, President, and Director of Development. He has co-founded four firms over the course of his career and his research aims to combine theoretical economics with real-world impact.

Publications

Other publications

Top Publications

RESTAT
Strategic Citation: A Reassessment (Accepted for Publication)

RESTAT
Motivating Innovation: The Effect of Loss Aversion on the Willingness to Persist.

RAND
Patent Citations Reexamined.

IEEE-ICLMA
Text Similarity in Vector Space Models: A Comparative Study.

JEBO
Competitive Pressure on the Rate and Scope of Innovation.

ACC Docket
Duty of Disclosure for Patent Applicants: Trends, Technologies, and Best Practices.

JEMS
The Value of Employee Retention: Evidence from a Natural Experiment.

NBER
Innovation and Entrepreneurship in Renewable Energy.

SMJ
How Anticipated Employee Mobility Affects Acquisition Likelihood: Evidence from a Natural Experiment.

SSRN
Patent-to-Patent Similarity: A Vector Space Model.

NREL
Clean Energy Innovation: Sources of Technical and Commercial Breakthroughs.

DISSERTATION
Employee Mobility and the Appropriation of Value from Knowledge: Evidence from Three Essays.
Winner of the Wiley Blackwell Outstanding Dissertation Award

Teaching & PhD

Teaching

Management of Technology

PhD Programs

Doctoral Program in Technology Management

PhD Students

Hofer Maximilian Wieland,

Courses

Technology & innovation strategy

This course focuses on the economic and organizational conditions that shape technological innovation by firms. The intent is for students to learn core concepts that can make innovation initiatives within a firm more successful, and to then apply those concepts to real business problems and cases.

Data science for business

Students will learn the basic concepts of Data Science so that they can make better business decisions. Students will also learn how to apply these concepts to real programming problems.

Computational research methods for social sciences

The objective of this course is to introduce doctoral students to computational methods for data-driven research in the social sciences.

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