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High market uncertainty impedes an entrepreneur's ability to evaluate the state of the market for a business opportunity. For many entrepreneurial ventures, data collection and analysis techniques and technologies are becoming an important source to manage uncertainty. This trend is often referred to as “data-driven entrepreneurship.” We consider a dynamic approach using data to overcome market uncertainty for business opportunity-related evaluations. In particular, we examine the entrepreneur's investment portfolio in which each investment generates expected returns and some information about a specific aspect of the market for a single business opportunity. We develop a model that analyzes imperfect market data (e.g., financial, social, regulatory), while factoring in the entrepreneur's risk preference and operational shortages of resources, routines, reputation, and regulations. Our numerical findings show that, rather than pursuing the highest expected returns, an entrepreneur may choose perfect information, risk hedging, or market-controlling investments based on his/her cash level and risk preference. Hence, the entrepreneur, fueled by the availability of data analysis, could overcome uncertainties and obtain better insights for business opportunity decisions.

We draw upon stewardship theory to formally derive bounds on the investment amount in a business prospect, and to characterize ownership sharing when investors offer two-stage financing along with know-how to increase the prospect’s valuation. In the early-development stage, we show that the direct effect of investor know-how increases the entrepreneur’s share while the indirect effect from that know-how due to its interaction with the investment size, decreases it. In the subsequent growth stage, the direct effect decreases the entrepreneur’s share while the indirect effect increases it. These tradeoffs offer theoretical and practical implications for writing investment contracts involving investor know-how.

Problem definition: We examine how the presence of capital market frictions influences the decision to invest in production cost reduction and the resultant production volume. This investment can increase the firm’s cash flow by increasing the profit margin, but it can also decrease the firm’s risk-free cash reserves and thus affect its exposure to capital market frictions. Academic/practical relevance: Process improvement aimed at production cost reduction has generated myriad of theoretical questions about efficient investment options and capacity choices. From a managerial perspective, process improvement is a fundamental concern in operations strategy. Nevertheless, its analysis typically excludes financial constraints by assuming a perfect capital market. Methodology: We formulate a two-stage profit maximization model in which a capital-constrained firm commits to a cost-reduction investment in the first stage in anticipation of its production decision in the second stage of this two-stage decision process. The firm considers capital market frictions when making decisions at each stage, while considering uncertainty in demand for its offering and in reducing its unit production cost. Results: When a firm faces small initial capital and low preinvestment unit production costs, it can benefit from investing in production cost reduction in the presence of capital market frictions more so than in their absence. Moreover, uncertainty in the production cost reduction mitigates the impact of market frictions on the net benefit (i.e., additional profit), whereas demand uncertainty decreases the feasible parameter space, where investing in production cost reduction is optimal. Managerial implications: A firm’s decision to invest in production cost reduction affects its operational and financial capabilities. Managers should thus consider this investment as an operational hedge not only against the uncertainty of matching supply and demand but also against exposure to capital market frictions and the resultant financial risk.

Clean technology startups face multiple sources of uncertainty, and require specialized knowhow and longer periods for revenue growth than their counterparts in other industries. These startups require large investments and have been hit hard during the current credit squeeze. On the other hand, clean technologies create important positive externalities for the economy. Hence, loan guarantees and other incentive schemes are being developed that are conditioned upon operational benchmarks. The authors offer a framework to establish the extent wherein operational hedging can reduce risk and increase the probability of obtaining financing. They examine a variety of evidence, ranging from production outsourcing to creation of joint ventures, to posit that operational hedging may affect both the marginal cost of capital and the marginal return on investment through mitigating the informational problems in the market. However, operational hedging may not be an effective strategy in all settings: the decision for creation of such hedges ought to weigh the benefits of reduced marginal cost of capital and the opportunity cost of reduced future growth potential against a status quo.

Whether to invest in process development that can reduce the unit cost and thereby raise future profits or to conserve cash and reduce the likelihood of bankruptcy is a key trade-off faced by many startup firms that have taken on debt. We explore this trade-off by examining the production quantity and cost reducing R&D investment decisions in a two period model wherein a startup firm must make a minimum level of profit at the end of the first period to survive and operate in the second period. We specify a probabilistic survival measure as a function of production and investment decisions to track and manage the risk exposure of the startup depending on three key market factors: technology, demand, and competitor's cost. We develop managerial insights by characterizing how to create operational hedges against the bankruptcy risk: if a startup makes a "conservative" investment decision, then it also selects an optimal quantity that is less than the monopoly level and hence sacrifices some of first period expected profits to increase its survival chances. If it decides to invest "aggressively," then it produces more than the monopoly level to cover the higher bankruptcy risk. We also illustrate that debt constraint shrinks the decision space, wherein such process investments are viable.

This article addresses the challenge of generating cash investment and business knowhow through the design of a contingent contract between an investor and an entrepreneur. We first develop a two-s...

The impact of an investor’s cash and knowhow on an entrepreneur’s performance has been debated. Investors may argue that their knowhow can substitute for cash, whereas entrepreneurs view knowhow to be a complement for cash, asking for more knowhow when they seek more cash. We model the impact of cash and knowhow in two-stage contingent contracts, subject to the entrepreneur meeting performance goals at the end of the first stage. Analytical findings are tested using angel investment data. Results reveal a nuanced relationship between cash and knowhow. Increasing the level of knowhow provision in the first stage of the contract makes the investor’s offer more attractive to the entrepreneur. However, contracts for such knowhow can create a capability trap, such that insufficient knowhow transfer results in failure to secure second stage financing. Analyzing contract parameters illustrates a tradeoff between the capability trap risk and the amount of second stage investment.