Knowledge that Transforms

A channel rebate is a payment from a manufacturer to a retailer based on retailer sales to end consumers. Two common forms of channel rebates are linear rebates, in which the rebate is paid for each unit sold, and target rebates, in which the rebate is paid for each unit sold beyond a specified target level. When demand is not influenced by sales effort, a properly designed target rebate achieves channel coordination and a win-win outcome. Coordination cannot be achieved by a linear rebate in a way that is implementable. When demand is influenced by retailer sales effort, a properly designed target rebate and returns contract achieves coordination and a win-win outcome. Other contracts, such as linear rebate and returns or target rebate alone, cannot achieve coordination in a way that is implementable. Contrary to the view expressed in the literature that accepting returns weakens incentives for retailer sales effort, we find that the provision of returns strengthens incentives for effort.

This paper examines the incentives for firms to share information vertically in a two-level supply chain in which there are an upstream firm (a manufacturer) and many downstream firms (retailers). The retailers are engaged in a Cournot competition and are endowed with some private information. Vertical information sharing has two effects: "direct effect" due to the changes in strategy by the parties involved in sharing the information and "indirect effect" (or "leakage effect") due to the changes in strategy by other competing firms (who may infer the information from the actions of the informed parties). Both changes would affect the profitability of the firms. We show that the leakage effect discourages the retailers from sharing their demand information with the manufacturer while encouraging them to share their cost information. On the other hand, the direct effect always discourages the retailers from sharing their information. When voluntary information sharing is not possible, we identify conditions under which information can be traded and show how price should be determined to facilitate such information exchange. We also examine the impact of vertical information sharing on the total supply chain profits and social benefits.

Many research universities engage in efforts to license inventions developed by university-affiliated inventors. However, no systematic explanation of the conditions under which university inventions will be licensed or commercialized has been provided. Drawing on transaction cost economics, I provide a conceptual framework to explain which university inventions are most likely to be licensed, commercialized, and generate royalties, and who will undertake that commercialization. I test this framework on data on the 1,397 patents assigned to the Massachusetts Institute of Technology during the 1980-1996 period. The results showthat (1) university inventions are more likely to be licensed when patents are effective; (2) when patents are effective, university technology is generally licensed to noninventors; (3) when patents are effective, licensing back to inventors increases the likelihood of license termination and reduces the likelihood of invention commercialization; and (4) the effectiveness of patents increases royalties earned for inventions licensed to noninventors. The implications of these findings for innovation management and strategy, entrepreneurship, and university technology commercialization are discussed.

While “most” decision makers may prefer one uncertain prospect over another, stochastic dominance rules as well as other investment criteria, will not reveal this preference due to some extreme utility functions in the case of even a very small violation of these rules. Such strict rules relate to “all” utility functions in a given class including extreme ones which presumably rarely represents investors' preference. In this paper we establish almost stochastic dominance (ASD) rules which formally reveal a preference for “most” decision makers, but not for “all” of them. The ASD rules reveal that choices which probably conform with “most” decision makers also solve some debates, e.g., showing, as practitioners claim, an ASD preference for a higher proportion of stocks in the portfolio as the investment horizon increases, a conclusion which is not implied by the well-known stochastic dominance rules.

We draw on diverse data sets to compare the institutional organization of upstream life science research across the United States and Europe. Understanding cross-national differences in the organization of innovative labor in the life sciences requires attention to the structure and evolution of biomedical networks involving public research organizations (universities, government laboratories, nonprofit research institutes, and research hospitals), science-based biotechnology firms, and multinational pharmaceutical corporations. We use network visualization methods and correspondence analyses to demonstrate that innovative research in biomedicine has its origins in regional clusters in the United States and in European nations. But the scientific and organizational composition of these regions varies in consequential ways. In the United States, public research organizations and small firms conduct R&D across multiple therapeutic areas and stages of the development process. Ties within and across these regions link small firms and diverse public institutions, contributing to the development of a robust national network. In contrast, the European story is one of regional specialization with a less diverse group of public research organizations working in a smaller number of therapeutic areas. European institutes develop local connections to small firms working on similar scientific problems, while cross-national linkages of European regional clusters typically involve large pharmaceutical corporations. We show that the roles of large and small firms differ in the United States and Europe, arguing that the greater heterogeneity of the U.S. system is based on much closer integration of basic science and clinical development.

In this paper, we investigate the relationship between complementary product integration and the initial technology strategy of a high-technology new venture. With customers placing considerable emphasis on cross-product integration, the success of a new venture is dependent as much on its ability to integrate its product with relevant complementary products as on the core product functionality itself. We identify three types of complementary product integration: value-added internal, add-on module, and data interface. We argue that the adoption of proactive initial technology strategy critically determines the ability of a high-technology new venture to rapidly and efficiently integrate its product with new and emerging complementary products. More specifically, we offer hypotheses that relate initial design and development strategies to the number and the type of complementary product integrations achieved by a new venture in the initial years. The hypotheses are tested using data from a set of U.S.-based software new ventures. The results support our arguments and imply the need for high-technology new ventures to adopt an explicit complementary product focus during initial product design.

In this paper, we consider a supply-chain model consisting of a single product, one supplier, and multiple retailers. Demand at the retailers is random, but stationary. Each retailer places her orders to the supplier according to the well-known (Q,R) policy. We assume that the supplier has online information about the demand, as well as inventory activities of the product at each retailer, and uses this information when making order/replenishment decisions. We first propose a replenishment policy for the supplier, which incorporates information about the inventory position of the retailers. Then, we provide an exact analysis of the operating measures of such systems. Assuming the inventory/replenishment decisions are made centrally for the system, we compare the performance of our model with those that do not use information in their decision making, namely, systems that use installation stock policies via a numerical experiment. Based on our numerical results, we identify the parameter settings under which information sharing is most beneficial.

In this paper, we use data from the Carnegie Mellon Survey on industrial R&D to evaluate for the U.S. manufacturing sector the influence of “public”(i.e., university and government R&D lab) research on industrial R&D, the role that public research plays in industrial R&D, and the pathways through which that effect is exercised. We find that public research is critical to industrial R&D in a small number of industries and importantly affects industrial R&D across much of the manufacturing sector. Contrary to the notion that university research largely generates new ideas for industrial R&D projects, the survey responses demonstrate that public research both suggests new R&D projects and contributes to the completion of existing projects in roughly equal measure overall. The results also indicate that the key channels through which university research impacts industrial R&D include published papers and reports, public conferences and meetings, informal information exchange, and consulting. We also finnd that, after controlling for industry, the influence of public research on industrial R&D is disproportionately greater for larger firms as well as start-ups.

We develop a rule-based contingency misfit model and related hypotheses to test empirically the Burton and Obel (1998) multi contingency model for strategic organizational design. The model is a set of “if-then” misfit rules, in which misfits lead to a loss in performance; they are complements to the strategy and organizational contingency theory fit rules. Using data from 224 small- and medium-sized Danish firms, misfits are categorized and identified. Then, performance hypotheses are developed and tested using regression models. We confirm the hypotheses that firms with situational misfits or contingency misfits, or both, incur performance losses in return on assets compared with firms with no misfits. Contrary to our hypotheses, we did not find that additional misfits lead to increased performance loss. Our results suggest that just one misfit of any kind may significantly compromise performance. These results yield a deeper understanding of organizational contingency theory, as well as implications for the rule-based fit-misfit organizational design model.

We model a firm's decisions about product innovation, focusing on the extent to which features should be improved or changed in the succession of models that comprise a life cycle. We show that the structure of the internal and external environment in which a firm operates suggests when to innovate to the technology frontier. The criterion is maximization of the expected present value of profits during the life cycle. Computational studies complement the theoretical results and lead to insights about when to bundle innovations across features. The formalization was influenced by extensive interviews with managers in a high-technology firm that dominates its industry.