Portfolio Entrepreneurs’ Behavior and Performance: A Resource Redeployment Perspective

Prior research suggests portfolio entrepreneurs—businesspeople who run more than one firm simultaneously—launch more successful ventures than their single-business counterparts. However, their ventures are less likely to survive. In an attempt to reconcile this paradox, this paper presents a framework in which portfolio entrepreneurs’ main advantage is not a superior ability to select the best business opportunities ex ante, but rather the ability to redeploy human and capital resources across businesses ex post, which reduces the sunkenness of their investments in new projects. This redeployment option facilitates their exit from new businesses that fail initial market tests. Thus, portfolio entrepreneurs’ heterogeneous termination decisions explain a greater portion of new firm performance differential than ex ante opportunity selection. We test these ideas using a longitudinal data set of more than 5,700 entrepreneurs and find consistent evidence. Portfolio companies do not show systematically higher performance at the time of entry; a performance difference emerges only over time, as the selection effect and resource redeployment occur. This paper was accepted by Ashish Arora, entrepreneurship and innovation.

The Life Cycle of Corporate Venture Capital

This paper investigates why industrial firms conduct Corporate Venture Capital (CVC) investment in entrepreneurial companies. I test alternative views on CVC by exploiting the entry, investment, and termination decisions of CVC divisions. CVC entry concentrates in firms that experience deteriorations of internal innovation. At the investment stage, CVCs select startups with a similar technological focus but that have a non-overlapping knowledge base, and they integrate technologies generated from these ventures that create strategic value. CVCs are terminated when parent firms’ innovation recovers. Overall, the strategic desire to fix innovation weaknesses after adverse shocks motivates firms to adopt CVCs. Received November 15, 2017; editorial decision March 2, 2019 by Editor Francesca Cornelli. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

Projection-Based Stopping Rules for Computerized Adaptive Testing in Licensure Testing

The confidence interval (CI) stopping rule is commonly used in licensure settings to make classification decisions with fewer items in computerized adaptive testing (CAT). However, it tends to be less efficient in the near-cut regions of the θ scale, as the CI often fails to be narrow enough for an early termination decision prior to reaching the maximum test length. To solve this problem, this study proposed the projection-based stopping rules that base the termination decisions on the algorithmically projected range of the final θ estimate at the hypothetical completion of the CAT. A simulation study and an empirical study were conducted to show the advantages of the projection-based rules over the CI rule, in which the projection-based rules reduced the test length without jeopardizing critical psychometric qualities of the test, such as the θ and classification precision. Operationally, these rules do not require additional regularization parameters, because the projection is simply a hypothetical extension of the current test within the existing CAT environment. Because these new rules are specifically designed to address the decreased efficiency in the near-cut regions as opposed to for the entire scale, the authors recommend using them in conjunction with the CI rule in practice.