Does Partnering Pay Off? - Stock Market Reactions to Inter-Firm Collaboration Announcements in Germany

The dramatic increase in interorganizational partnering in the last two decades raises questions for scholars and managers regarding the value impact of inter-firm collaborations. Using event study methodology, this paper tests whether stock market reactions differ when a collaboration formation or termination is announced. In addition, the study provides an in-depth analysis of potential determinants of stock market reactions to collaboration formation announcements. The sample consists of 1037 announcements in German stock markets from 1997 to 2002. The results show that an unexpected termination announcement decreases firm valuation, and a formation announcement increases firm valuation. Further, certain collaborations are more favorable than others, depending on firm industry, age, size, collaboration constellations, and equity versus non-equity investment in partner firm. The results open avenues for further research on partnering strategies

Statistical Sources of Variable Selection Bias in Classification Tree Algorithms Based on the Gini Index

Evidence for variable selection bias in classification tree algorithms based on the Gini Index is reviewed from the literature and embedded into a broader explanatory scheme: Variable selection bias in classification tree algorithms based on the Gini Index can be caused not only by the statistical effect of multiple comparisons, but also by an increasing estimation bias and variance of the splitting criterion when plug-in estimates of entropy measures like the Gini Index are employed. The relevance of these sources of variable selection bias in the different simulation study designs is examined. Variable selection bias due to the explored sources applies to all classification tree algorithms based on empirical entropy measures like the Gini Index, Deviance and Information Gain, and to both binary and multiway splitting algorithms

Can’t Buy Me Rights! The Contractual Structure of Asymmetrical Inter-firm Collaborations

The efficient allocation of control rights in inter-firm collaborations is a widely emphasized issue. In this paper, I empirically identify control rights and the allocation of these rights using a unique survey data set on collaborations between biotechnology and pharmaceutical firms. Fifteen control rights are identified to make up the structure of deals with five rights being the items of contention in deal making (ownership of patents, production, further development of the technology, the right to manage the collaboration, and the right to market universally). I find that the assignment of control rights is related to the bargaining position of firms and incentive issues. Hence, goliaths –pharmaceutical incumbents–subrogate critical rights to the new ventures when the final outcome of the project is depending on the venture’s effort

The Seven Day Weekend/Le Weekend de Sept Jours

The exhibition The Seven Day Weekend / La Weekend de Sept Jours was the second collaboration between the research program La Seine at the Ecole Nationale Superieure des Beaux Arts, Paris, the Research Department of the Royal College of Art, London and LASALLE College of the Arts, Singapore, launched in February 2009 in Singapore

Variable Selection Bias in Classification Trees Based on Imprecise Probabilities

Classification trees based on imprecise probabilities provide an advancement of classical classification trees. The Gini Index is the default splitting criterion in classical classification trees, while in classification trees based on imprecise probabilities, an extension of the Shannon entropy has been introduced as the splitting criterion. However, the use of these empirical entropy measures as split selection criteria can lead to a bias in variable selection, such that variables are preferred for features other than their information content. This bias is not eliminated by the imprecise probability approach. The source of variable selection bias for the estimated Shannon entropy, as well as possible corrections, are outlined. The variable selection performance of the biased and corrected estimators are evaluated in a simulation study. Additional results from research on variable selection bias in classical classification trees are incorporated, implying further investigation of alternative split selection criteria in classification trees based on imprecise probabilities

Stakeholders' Impact on Turnaround Performance: The Case of German Savings Banks

This study explores how savings banks as powerful stakeholders of SMEs in Germany assess turn-around performance. It tests the impact of the support provided by German savings banks and distressed SMEs’ actions with survey data from corporate advisors. The results show that structural and continuing support foster turnaround performance. This support is conducive in the initial stage of turnaround but negligible in the recovery stage. Contributing to stakeholder theory and turnaround management, the findings shed light on the factors that motivate a selected stakeholder’s involvement and SMEs’ ability to engage in actions fostering this stakeholder’s support for a turnaround

The Economics of Knowledge Regulation: An Empirical Analysis of Knowledge Flows

Successful innovation depends on the management of a firm’s knowledge base. This paper empirically investigates the determinants of knowledge regulation. Using a unique survey dataset, the analysis suggests that R&D managers do not leak knowledge randomly, but rather regulate knowledge consciously. We find that the source and the channel of knowledge inflows impact knowledge regulation. The findings reveal that the more a firm profits from knowledge inflows from competitors, the fewer actions it takes to regulate outgoing knowledge. We do not find that the extent of knowledge inflows from collaborating firms impacts knowledge regulation. However, the type of channel being used to acquire knowledge matters. Compared to public channels, the different types of private channels used to access knowledge inflow and the type of the competitive relationship influence the firms’ decision to regulate knowledge outflow in the following way: concerning relationships with competitors, firms regulate knowledge outflow more when using formal channels, but less when using informal channels (although a significant difference is not found with the latter); concerning collaborative relationships, firms regulate knowledge outflow less regardless of whether they are using formal or informal private channels compared to using public channels. Presumably firms that acquire knowledge from competing firms through formal private channels compared to public channels, try to establish opaque and soundproof fences to surround them, whereas firms that acquire knowledge from collaborating firms through formal or informal private channels do not want to restrict circulation, but rather facilitate inter-firm knowledge exchange. Our results have important implications for academics and R&D managers alike