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We investigate the double-edged impacts of interorganizational knowledge flows in academia–industry collaboration on the innovation economic impacts of science-based firms. When a firm receives knowledge directly from more research institutes, its innovation economic impact improves subsequently. In contrast, when a firm receives knowledge indirectly from other firms through a research institute, its innovation economic impact declines subsequently. We also explore the moderation effects of firm technological originality and generality. We report empirical findings from a panel data of science-based firms in the United States. Our analysis yields theoretical implications for academia–industry collaborations in the science-based entrepreneurship literature and practical implications for management practitioners and policymakers in science-based firm creation and development.

Thanks to technological developments produced by scientists and engineers, franchising has grown to become a business model of choice for firm expansion in knowledge-intensive industries. We propose a formal model to explore to what degree franchisors should adapt their business practices or routines to successfully expand their franchises in newly targeted markets. By simultaneously considering the franchise's need to adapt locally in a new market and the level of business routine tacitness at the time of expansion, we integrate previously separate agency cost logics into one model. We offer refinements to the belief that expanding through a franchisee is the best when the business routines need adaptation, but expanding through a company-owned unit is best when these routines can be replicated.

The five papers in this special section explore the use of data analytics in current business and management decision making. Entrepreneurial ingenuity plays a crucial role in building new business enterprises, especially when resources are lacking, routines are nonexistent, a firm’s reputation is not established, and/or regulations are inadequate. Resources in the form of human capital are often the foundation of independent startups or new corporate business ventures. Routines in the form of organizational and technical processes are often key in building these new ventures. Reputation in terms of an entrepreneur’s accomplishments or network is essential for acquiring needed resources and developing fundamental routines to initiate, commit to, organize, and grow the startup. Examines the impacts of such shortages create threats or opportunities for independent startups and new business ventures spun off from established firms.

This article’s novelty lies within the modeling literature on competition in production quantity and product-launch timing, which has been silent regarding the impact of these interdependent decisions on firm survival. We rigorously address the competitive interplay between a startup and an established rival by developing a game-theoretic model that captures the startup’s vulnerability to failure through maximizing its survival likelihood. We allow the established rival to behave strategically by anticipating the startup’s timing and production decisions prior to making its own production decision. We propose that unless the market-entry investment is low, a survival maximizing startup should wait to launch its product, and do so with a larger production output than the established rival, when delaying the product launch enables the startup to charge a high price. Insights on the established firm involves when it profits from behaving strategically, which is only when competing with a survival-maximizing startup and not when competing with a profit-maximizing startup. If the market-entry investment is large, comparing a survival-maximizing startup to a profit-maximizing startup suggests that the former produces at a larger scale than the latter when either startup competes with an established rival, which in turn is forced to reduce its production level.

Technological innovation drives economic growth, and the pioneering activity of scientists and engineers produce technological innovation. We provide a mathematical model of pioneering strategic choice by adopting a perspective familiar to microeconomics, but less common in the engineering management literature. Instead of focusing on the specific features of a pioneer's technology, we focus on the malleability of the profit equation involved. By considering the arguments of the profit function (i.e., entry and variable costs and potential market demand) as strategic levers, we derive propositions that identify the ranges of actions (lever pulling) available to managers to protect (and even increase) entrepreneurial rents in a simple yet robust partial equilibrium case. For each lever, we show that there are several value ranges (intervals) and that the pioneer's incentives vary across these intervals. In addition, for each lever, we identify the existence of nontrivial profit discontinuities that change the pioneer's incentives in surprising ways and lead to counterintuitive strategic choices. Lastly, we show that for some range of each lever's values, welfare-improving transfer payments are possible and, therefore, pioneers and policy-makers both have an incentive to bargain. As in the case of patents, these transfers encourage the introduction of new technologies.

We use a principal-agent model to examine how venture capitalists can determine the ownership division when fund-seeking entrepreneurs possess private information on their disutility of effort. This situation is especially applicable to early-stage first-time entrepreneurs seeking funding, since no history exists on their potential performance. The venture capitalist must thus consider this private information by forming a belief on the entrepreneur's effort level toward the proposed investment opportunity. Formal modeling enables us to describe how the deal process unfolds and to build a simulation. We then identify a unique investor's belief and resulting ownership sharing that maximizes the return to the entrepreneur, one that maximizes the return to the venture capitalist, as well as one that maximizes the deal welfare. We also conjecture an ordering relationship between these critical beliefs and between their resulting ownership allocations. Furthermore, we identify conditions under which the venture capitalist should choose to revise the investment offer if rejected by the entrepreneur. This paper thus moves us closer to a comprehensive theory of venture investment decisions.

Empirical observation suggests that several industrial clusters originate from employees who break off and locate their new firms close to former employers. The reasons for such a choice are complex and include a variety of costs' considerations. We present a two-player three-stage simultaneous game with interdependent decisions concerning break-offs, deterrent compensations, location, and profit-maximizing production outputs. The structure of the game explains under what conditions a break-off is desirable, what location's choice makes it optimal, and why the break-off process may lead to the birth of a cluster. We demonstrate how marginal production/congestion cost, degree of product differentiation, R&D investment in a region, and market size, all influence the likelihood of a firm's break-off and its subsequent location decision. Our results provide a rationale for why, in industries in which technology plays a significant role, an increase in R&D investment in the region may encourage the break-off firm to locate away from the incumbent. We also show that subsidies aimed at increasing manufacturing activities and diffusing commercializable innovations can be ineffective in promoting clustering and unnecessary in larger markets, and their exact size is crucial in determining their effectiveness.

The problem focus is on startup decisions associated with staged venture financing, where both R&D and marketing are significant percentages of overall expenses. When should a startup owner acquire working capital, and how should she/he distribute that capital between R&D (to improve product quality) and marketing (to increase sales) to ultimately grow valuation? Also, should the startup owner cap the total R&D and marketing budgets to increase profitability during staged venture financing? We develop a model to study resource acquisition and allocation decisions across successive stages of startup growth. The model incorporates a funding process whereby the startup valuation is positively impacted by improved product quality and market growth. This model provides insights on optimal acquisition and allocation practices and characterizes the impact of changes in productivity, along with the evolution rate of R&D and marketing payoffs, on the underlying decisions. Our results also illustrate conditions for optimal capping of R&D and marketing expenses as a percentage of revenues.

An increasing number of decision makers are required to assess business opportunities, as new technologies have been developing at an astonishing rate and technology transfer has been facilitated in numerous institutions. We present a partially observed Markov decision process model of the assessment. At each point in time, a reward-maximizing decision maker chooses to either accept the opportunity, reject it, or gather costly, imperfect information and update her belief on the status of the opportunity. The resulting optimal policy balances the costs and benefits of decision accuracy, information gathering, and the window of opportunity closing. We show that the optimal policy is characterized by a pair of probability thresholds, derive bounds for these thresholds, and investigate their sensitivity to changes in information cost, the payoff structure, and information quality. Our approach unifies and contributes to three distinct streams of research: technology adoption, partially observed optimal stopping, and comparison of experiments. We also articulate how real decision makers can implement this opportunity assessment framework and offer implications for those who fund these opportunities.

The optimal stopping model presented here explores opportunity assessment with reference to a tradeoff between, on the one hand, reducing decision accuracy and, on the other hand, both increasing search costs and a higher probability that the opportunity is grasped by a competitor. From this model we propose a heuristic that specifies three threshold lines to define four decision areas. Decision area 1 prescribes the business opportunity be accepted, decision areas 2 and 4 prescribe that search continue, and decision area 3 prescribes that the business opportunity be rejected. We use a simulation to investigate the robustness of the heuristic beyond the model's boundary conditions.

Most entrepreneurs must rely on external financing when their new venture is based on the development of a new product. External financing, however, is usually uncertain in magnitude and timing, thus creating tradeoff between the advantage of waiting-to generate more money-and improve the product versus releasing the product before competition increases. This tradeoff is investigated through a dynamic programming framework for which a simple, easy-to-implement optimal stopping rule is identified for a special class of revenue functions. The model is utilized to characterize how the amount of funding and its variability are expected to affect the quality of the product at time to market. The model also studies how the release-time strategy is affected by the expectation and the variability of the effect that each unit of funding has on the quality of the product under development. The author shows that an increment in funding does not always lead to a product of higher quality. He also shows that an improvement in the effect of investment on quality does not necessarily lead to an enhanced product.