Tilburg University

About: Tilburg University is a education organization based out in Tilburg, Noord-Brabant, Netherlands. It is known for research contribution in the topics: Population & Context (language use). The organization has 5550 authors who have published 22330 publications receiving 791335 citations.

Value creation by knowledge‐based ecosystems: evidence from a field study

Abstract: A growing number of research and development-driven companies are located in knowledge-based ecosystems. Value creation by these ecosystems draws on the dynamics of single firms (interacting and partnering) as well as the ecosystem at large. Drawing on a field study of a Dutch high-tech campus, two key sources of value creation are identified: (1) facilitation of the innovation process for individual companies and (2) creation of an innovation community. Furthermore, the coevolution of the ecosystem's business model with firm-level business models explains why technology-based firms join, stay in, or leave the ecosystem at a certain point in time. A remarkable finding is that ecosystem managers have to deliberately facilitate exit routes for companies that no longer fit the ecosystem in order to enhance and reinforce its business model. As such, this study suggests a dynamic capability perspective on knowledge-based ecosystems that need to develop a business model at the ecosystem level to create sufficient innovative capacity and entrepreneurial fitness.

Japanese Investors' Choice of Joint Ventures Versus Wholly-owned Subsidiaries in the US: The Role of Market Barriers and Firm Capabilities

Abstract: This study analyzes how market barriers and firm capabilities affect multinationals' choice of joint ventures versus wholly-owned subsidiaries abroad. In the study, we compile a vector of variables that distinguish between industry-specific barriers and firm-specific capabilities to analyze Japanese investors' ownership decisions in the US. Our results in general support the hypothesis that Japanese investors facing high market barriers in the target industry are more likely to choose joint ventures, while those possessing strong competitive capabilities are more likely to set up wholly-owned subsidiaries. Specifically, marketing variables are more influential than technological factors in determining the choice of partial versus full ownership. These findings, however, vary across sub samples that represent low- versus high-tech industries and consumer versus industrial products.

Predicting Heart Failure With Preserved and Reduced Ejection Fraction The International Collaboration on Heart Failure Subtypes

Abstract: Background— Heart failure (HF) is a prevalent and deadly disease, and preventive strategies focused on at-risk individuals are needed. Current HF prediction models have not examined HF subtypes. We sought to develop and validate risk prediction models for HF with preserved and reduced ejection fraction (HFpEF, HFrEF). Methods and Results— Of 28,820 participants from 4 community-based cohorts, 982 developed incident HFpEF and 909 HFrEF during a median follow-up of 12 years. Three cohorts were combined, and a 2:1 random split was used for derivation and internal validation, with the fourth cohort as external validation. Models accounted for multiple competing risks (death, other HF subtype, and unclassified HF). The HFpEF-specific model included age, sex, systolic blood pressure, body mass index, antihypertensive treatment, and previous myocardial infarction; it had good discrimination in derivation (c-statistic 0.80; 95% confidence interval [CI], 0.78–0.82) and validation samples (internal: 0.79; 95% CI, 0.77–0.82 and external: 0.76; 95% CI: 0.71–0.80). The HFrEF-specific model additionally included smoking, left ventricular hypertrophy, left bundle branch block, and diabetes mellitus; it had good discrimination in derivation (c-statistic 0.82; 95% CI, 0.80–0.84) and validation samples (internal: 0.80; 95% CI, 0.78–0.83 and external: 0.76; 95% CI, 0.71–0.80). Age was more strongly associated with HFpEF, and male sex, left ventricular hypertrophy, bundle branch block, previous myocardial infarction, and smoking with HFrEF ( P value for each comparison ≤0.02). Conclusions— We describe and validate risk prediction models for HF subtypes and show good discrimination in a large sample. Some risk factors differed between HFpEF and HFrEF, supporting the notion of pathogenetic differences among HF subtypes.

Explaining the Level of Credit Spreads: Option-Implied Jump Risk Premia in a Firm Value Model

Abstract: Prices of equity index put options contain information on the price of systematic downward jump risk. We use a structural jump-diffusion firm value model to assess the level of credit spreads that is generated by option-implied jump risk premia. In our compound option pricing model, an equity index option is an option on a portfolio of call options on the underlying firm values. We calibrate the model parameters to historical information on default risk, the equity premium and equity return distribution, and S&P 500 index option prices. Our results show that a model without jumps fails to fit the equity return distribution and option prices, and generates a low out-of-sample prediction for credit spreads. Adding jumps and jump risk premia improves the fit of the model in terms of equity and option characteristics considerably and brings predicted credit spread levels much closer to observed levels.