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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.

The innovation has been recognized as a competitive advantage for almost all organizations. Yet, it has been a rather elusive topic when it comes to implementation. Operations design and strategy are mostly concerned about effective implementation of ideas. Hence, in this paper we marry these two fields (operations design and innovation), and identify three key areas for evaluating the innovation strategy, based on our previous research on innovation management. We further offer three prescriptions for practical implementation, which may be of value to managers with their assessment of the value of their innovation strategies.

While innovation is on the agenda of almost all organizations, successful innovation management remains quite elusive for many managers. This paper on innovation management strategy identifies four key interrelated areas that an innovator needs to concurrently define and organize for a systemic and effective innovation: the innovation concept, the consumer context, the competitive context, and the collaborative context. We refer to the framework involving these four areas as the 4Cs of innovation. Careful consideration of each of these four areas addresses specific issues, regarding operations, marketing, finance, and regulations, for the organization to enhance the effectiveness of the innovation strategy. We offer a practical diagnostic tool to implement the 4Cs of innovation framework. Further, we discuss our research on 99 highly innovative clean technology projects funded by the Advanced Research Projects Agency-Energy as an example of how to utilize the 4Cs of innovation framework. Our analysis brings out important insights for managers of established firms and startups on how to prioritize innovation activities and highlights the effective use of 4Cs of innovation for guiding successful innovation strategies.

The climate change debate and economic recovery strategies in various industries demand highly innovative projects featuring stretched performance goals for developing clean technology. These projects face multiple sources of uncertainty in high risk situations, and require specialized know-how and longer periods for revenue growth than their counterparts in other industries. We use data from 207 clean technology projects funded by the U.S. Advanced Research Projects Agency-Energy to conduct a comparative study of how operations design can hedge risk and enhance project valuation in technology development and deployment stages. We find that deployment feasibility is significantly and positively related to project valuation. On the other hand, stretched technical performance goals, development feasibility and market growth targets are associated with lower valuation. We also find some significant differences for these results across institution types: mature firms, start-ups, universities, and research centers. We examine the risk profile of these projects by technology and institution type, and discuss the managerial and policy implications for these findings.