Parallel Session Details

Track B: Firm Entrepreneurial Strategy

B1: To Reveal or Not to Reveal? Open Innovation Mechanisms Within an Emerging Personalized Medicine Innovation Ecosystem (Extended Abstract)

Andrew Park, Elicia Maine

Simon Fraser University, Canada

Drawing on and contributing to the Open Innovation literature, our study explores the boundary conditions of the effectiveness of Open Innovation strategies within emerging innovation ecosystems. Namely, we propose a relationship between the use of Open Innovation by emergent Personalized Medicine firms in British Columbia and their value outputs. We test our hypothesis empirically by quantifying the use of the Open Innovation mechanisms of Selective Revealing, Strategic Timing and Strategic Partnering by each of the firms in our sample, categorized by level of uncertainty, and comparing them to the value output of each of these firms. We find Open Innovation activities have a positive effect on value outputs for Personalized Medicine firms. Additionally, firms who operate in environments of high uncertainty and who employ these Open Innovation mechanisms enjoy higher value outputs than firms who operate in environments of low uncertainty. Thus, we argue that Open Innovation plays an important role in stimulating the economic performance of emerging science-based ecosystems.

B2: Parental knowledge transfer to entrepreneurial spin-offs: How do continued linkages influence performance? (Extended Abstract)

Daniela Bolzani1, Einar Rasmussen2, Riccardo Fini3

1University of Bologna, Italy; 2Nord University Business School, Norway; 3University of Bologna, Italy

This study explores the impact of parent university linkages on the market performance of university spin-off firms (USOs). We argue that spin-offs’ performance is not only affected by competencies inherited from their parent universities at start-up but also by linkages maintained over time. We longitudinally study 551 USOs established between 2000 and 2008 in Italy. Using estimations that account for attrition and endogeneity, we find that equity-based university linkages increase spin-offs’ market performance and that geographical proximity strengthens this effect. Furthermore, increasing technological ties between USOs’ entrepreneurial teams and their parent universities has a detrimental effect on performance, especially for companies that remain geographically proximate to their parent universities. The results have implications for theory and practice related to strategic linkages, alliances, and academic entrepreneurship.

B3: Choosing Technology: An Entrepreneurial Strategy Approach (Extended Abstract)

Joshua Gans1,3, Michael Kearney2, Erin L Scott2, Scott Stern2,3

1University of Toronto, Rotman School of Management; 2MIT Sloan School of Management, United States of America

A central premise of research in the strategic management of innovation is that start-ups are able to leverage emerging technological trajectories as a source of competitive advantage. But, if the potential for a technology is given by the fundamental character of a given technological trajectory, then why does entrepreneurial strategy matter? Or, put another way, if the evolution of technology is largely shaped by the strategic choices entrepreneurs make, then why do technological trajectories exhibit systematic patterns such as the Technology S-curve? Taking a choice-based perspective, this paper illuminates the choices confronting a start-up choosing their technology by resolving the paradox of the Technology S-curve through a reformulation of the foundations of the Technology S-curve. Specifically, we reconceptualize the Technology S-curve not as a technological given but as an envelope of potential outcomes reflecting differing strategic choices by the entrepreneur in exploration versus exploitation. Taking this lens, we are able to clarify the role of technological uncertainty on start-up technology strategy, the impact of constraints on technological evolution, and how technology choice is shaped by the possibility of imitation. Our findings suggest that staged exploration may stall innovation as a result the replacement effect, increasing the strategic importance of commitment.

B4: When Does Entrepreneurship Contribute to Productivity Growth? Experiment Capacity and the Entrepreneurship–Productivity Relationship (Extended Abstract)

Seongwuk Moon

Sogang University, Korea, Republic of (South Korea)

Motivated by less studies on when entrepreneurship contributes to the productivity growth across countries relative to whether it contributes, this paper examines why entrepreneurship affects the pro- ductivity growth in some countries but not in others. We examine the heterogeneity of incumbents across countries as a source of such effect heterogeneity of entrepreneurship on the productivity growth across countries. The incumbent firms can be a springboard for new startups to participate in value chains at  least in the initial period. If incumbents in some countries are better at selecting and managing the rela- tionship with new firms than those in other countries, new firms in the former countries are more likely to contribute to the productivity growth than in the latter countries. We  propose experiment capacity    of incumbent firms as an important source of effect heterogeneity that transforms the flow of startups into a national source of productivity growth. We define the experiment capacity as a capacity of an incumbent firm for selecting and learning new development. The capacity is embedded in organizational and management practices that encourage employees to work with new technology or new firms. We iden- tify the role of incumbents’ experiment capacity, distinctive from cluster and competitive environments, in the entrepreneurship–productivity growth relationship after controlling for the effects of institutions and infrastructure. Drawing on recently updated Penn World Table and international data collected by multiple institutions over last decades, we construct measures of total factor productivity, entrepreneur- ship, experiment capacity, business environments and institutions that influence the entrepreneurship– productivity growth relationship. Firms with high experiment capacity contribute to the productivity growth in two ways: the direct increase in productivity growth and the indirect increase through startups

B5: Typology of heuristics for entrepreneurial action in startups (Extended Abstract)

Simone Freitas, Mario Salerno

University of São Paulo, Brazil

A growing research stream relates heuristics to strategic decision-making. This stream concerns incumbent companies and does not explore conceptually and empirically the creation of entrepreneurial opportunities whether by new or established ventures. Based on the typology proposed by Bingham and Eisenhardt, which predicts that incumbent companies learn heuristics from the selection of opportunities, this article advances the theory of organizational heuristics by redefining this typology through the identifying a new type of heuristics, related to creation of opportunities by new ventures. To do so we went to six in-depth longitudinal case studies in startups, investigating along two years the decisions taken by their entrepreneurs, classifying the subjacent heuristics. We then propose a new category of heuristic, the heuristics for opportunity creation.

B6: The Emergence of Quantum Computing: Start-Up Companies and Internal Corporate Ventures (Extended Abstract)

Evan MacQuarrie1, Christoph Simon2, Stephanie Simmons1, Elicia Maine1

1Simon Fraser University, Canada; 2University of Calgary, Canada

The emerging quantum computing market still faces a high level of both technological and market uncertainty. The opportunity present in this technological uncertainty has led to a rapid growth in the number of ventures active in the sector. As the technical problems march towards their solutions, a variety of business strategies have emerged to tackle the market uncertainty. Here, we examine this recent growth in the quantum computing market in the context of dominant product designs and contrast emerging strategies for developing the quantum computing market.

B7: Financial Structure and Oligopoly: The R&D Effect (Extended Abstract)

Victor Song1, James Brander2

1Simon Fraser University, Canada; 2University of British Columbia, Canada

Financial structure is an important decision variable for Örms, and varies signiÖcantly across industries. Capital intensive industries such as manufacturing or utilities, which have substantial physical assets and typically generate stable cash áows, tend to have relatively high debt-equity ratios. In contrast, research intensive industries, such as those in the ìhigh techî sector, issue much less debt. Conventional wisdom is that Önancial distress costs are the most important explanatory factor for this variation in Önancial structure. That is, Örms with more uncertain prospects might seek to minimize the chance of Önancial distress and the associated costs by keeping debt levels low. However, the high tech giants with very low debt to equity ratios such as Apple, Google, Microsoft, Amazon, Facebook, and others do not seem vulnerable to Önancial distress risk. In this paper, we o§er a alternative theory to explain why Önancial structure varies across industries. We focus on the relationship between Önancial structure and R&D in an oligopoly context. There are two distinct types of R&D ñprocess R&D, which lowers the cost of producing a given product, and product R&D, which changes product characteristics and/or improves product quality. Our key insight is that process R&D is complementary with the strategic use of debt to improve a Örmís market position under oligopoly. As in Brander and Lewis (1986), Örms have an incentive to use debt to commit themselves to a more aggressive position in the output market. Process R&D strengthens this e§ect. Product R&D, on the other hand, increases product di§erentiation, weakens head-to-head competition between oligopolistic rivals, and reduces the incentive to use debt for strategic purposes. As manufacturing industries and utilities make relatively more use of process R&D, while high-tech industries undertake relatively more product R&D, we can explain why the high-tech industries make relatively less use of debt and have much lower debt to equity ratios.