Lee Fleming - Innovative search as exploration vs. exploitation

Seminar | February 13 | 3:30-5:30 p.m. | 3108 Etcheverry Hall

 Lee Fleming, University of California Berkeley

 Industrial Engineering & Operations Research

For this seminar, Dr. Fleming (Berkeley IEOR) and his collaborators Dr. Gustavo Manso (Berkeley-Haas) and Tristan Fitzgerald (Berkeley-Haas) will present three papers with similar themes and datasets, all around the type and direction of innovative search. They will be targeting the strategy literature with the first paper and spend most of the time looking at the data. The second is more theory-based, and the third is an asset pricing paper.

Paper 1: Escaping competition and competency traps: identifying how innovative search strategy enables market entry

Abstract: Innovation is usually assumed to be a crucial component of firm performance, yet the optimal strategy and potential paths from invention to performance remain unclear and poorly identified empirically. Likewise the idea of a fundamental tradeoff between exploration and exploitation has been extremely influential, yet the stages and causal linkages between search strategy and performance have not been established. We first demonstrate that a variety of simple patent based measures clearly load onto exploration and exploitation principal components and illustrate the temporal relationship between exploration and new market entry. To identify the effect of innovative strategy on entry and successful entry, we rely on exogenous shocks that precede and increase (non-compete enforcement switch) and decrease (anti-takeover regulatory reform) in exploration. Using these exogenous shocks with different and opposite mechanisms but consistent effects on market entry, we isolate one pathway from invention to performance and demonstrate how exploration enables market entry and increased sales in new markets. Exploration strategies appear less effective when the firm operates in a crowded technological space and unaffected by competition in similar markets.

Paper 2: Heterogeneous Innovation Over the Business Cycle

Previous research has argued that innovative activities should be concentrated in recessions. However, innovation, as measured by R&D expenditure, seems to concentrate in booms. We argue that R&D expenditures do not capture the different dimensions of firms’ innovative search strategies. We introduce a model of innovative exploration and exploitation over the business cycle and present supporting evidence from a battery of patent-based measures. Exploitation strategies are more prevalent in booms while exploration strategies are more prevalent in recessions.

Paper 3: Innovation Search Strategy and Predictable Returns: A Bias for Novelty

Abstract: Because of the intangible and highly uncertain nature of innovation, investors may have difficulty processing information associated with a firm’s innovation and innovation search strategy. Due to differences in salience, investors are likely to pay more attention to novel and explorative patents rather than incremental and exploitative patents. We find that firms focusing on exploitation rather than exploration tend to generate superior subsequent operating performance. Analysts do not seem to detect this, as firms currently focused on exploitation tend to outperform the market’s near-term earnings expectations. The market also seems unable to accurately incorporate innovation strategy information. We find that firms with exploitation strategies are undervalued relative to firms with exploration strategies and that this return differential is incremental to standard risk and innovation-based pricing factors examined in the prior literature. This result provides a more nuanced view on whether stock market pressure hampers innovation.


Professor Lee Fleming joined the IEOR Department at UC Berkeley in Fall 2011 and is the Faculty Director of the Coleman Fung Institute of Engineering Leadership. He teaches engineering leadership and a capstone lab within the Masters of Engineering curriculum. His research applies machine learning and NLP techniques on large datasets with causal inference models from management and social science. His work investigates how mobility influences creative output, innovative search strategy drives firm performance, non-competes change labor mobility and entrepreneurship, and crowdfunding might reduce regional inequality.

 kmcaleer@berkeley.edu, 510-642-6222