Collaboration in pharmaceutical research: Exploration of country-level determinants.

In this paper we focus on proximity as one of the main determinants of international collaboration in pharmaceutical research. We use various count data specifications of the gravity model to estimate the intensity of collaboration between pairs of countries as explained by the geographical, cognitive, institutional, social, and cultural dimensions of proximity. Our results suggest that geographical distance has a significant negative relation to the collaboration intensity between countries. The amount of previous collaborations, as a proxy for social proximity, is positively related to the number of cross-country collaborations. We do not find robust significant associations between cognitive proximity or institutional proximity with the intensity of international research collaboration. Moreover, there is no robust and significant relation between the interaction terms of geographical distance with social, cognitive, or institutional proximity, and international research collaboration. Our findings for cultural proximity do not allow of unambiguous conclusions concerning their influence on the collaboration intensity between countries. Linguistic ties among countries are associated with a higher amount of cross-country research collaboration but we find no clear association for historical and colonial linkages.International Cooperation, Pharmaceuticals, Proximity

Unbundling dynamic capabilities for inter-organizational collaboration

Purpose The purpose of this paper is to explore two distinct subsets of dynamic capabilities that need to be deployed when pursuing innovation through inter-organizational activities, respectively, in the contexts of broad networks and specific alliances. The authors draw distinctions and explore potential interdependencies between these two dynamic capability reservoirs, by integrating concepts from the theoretical perspectives they are derived from, but which have until now largely ignored each other – the social network perspective and the dynamic capabilities view. Design/methodology/approach The authors investigate nanotechnology-driven R&D activities in the 1995–2005 period for 76 publicly traded firms in the electronics and electrical equipment industry and in the chemicals and pharmaceuticals industry, that applied for 580 nanotechnology-related patents and engaged in 2,459 alliances during the observation period. The authors used zero-truncated Poisson regression as the estimation method. Findings The findings support conceptualizing dynamic capabilities as four distinct subsets, deployed for sensing or seizing purposes, and across the two different inter-organizational contexts. The findings also suggest potential synergies between these subsets of dynamic capabilities, with two subsets being more macro-oriented (i.e. sensing and seizing opportunities within networks) and the two other ones more micro-oriented (i.e. sensing and seizing opportunities within specific alliances). Practical implications The authors show that firms differ in their subsets of dynamic capabilities for pursuing different types of inter-organizational, boundary-spanning relationships (such as alliances vs broader network relationships), which ultimately affects their innovation performance. Originality/value The authors contribute to the growing body of work on dynamic capabilities and firm-specific advantages by unbundling the dynamic capability subsets, and investigating their complex interdependencies for managing different types of inter-organizational linkages. The main new insight is that the “linear model” of generating more innovations through higher inter-firm collaboration in an emerging field paints an erroneous picture of how high innovation performance is actually achieved

Classification of Inter-Organizational Knowledge Mechanisms and their Effects on Networking Capability:A Multi-Layer Decision Making Approach

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Purpose – The role of inter-organizational knowledge mechanisms (IOKMs) in learning networks is increasing so that the competition of business networks in providing innovations is highly dependent on the effective selection and application of these mechanisms. This study aims to argue that recognizing the classification of IOKMs and understanding their impact on networking capability (NC) makes the selection of mechanismsmore effective. Design/methodology/approach – With a systematic review of literature, a comprehensive list of IOKMs, their main characteristics and NCs have been extracted. The authors have used a focus group for data gathering and a hybrid multi-layer decision-making approach for data analysis. Finally, the impact of IOKMs onNC was determined. Findings – By implementing a multi-layer decision-making approach, four categories of IOKMs including person-to-person, co-creation, team-oriented and informational are illustrated and their effects of NC are determined. Therefore, the findings of this research provide latecomer firms (LCFs) managers with a clear framework for selecting IOKMs. Originality/value – The literature review shows that the number of knowledge mechanisms, especially their inter-organizational types, is increasing. It has made it difficult for LCFs managers to select effective and efficient mechanisms. Most of these mechanisms are listed, and few studies have classified them. Besides, research shows that fewer studies have investigated how IOKMs relate to NC. Furthermore, most studies on IOKMs have been conducted in the context of leading firms and LCFs have been neglected

Social process of knowledge creation in science, The

2019 Summer.Includes bibliographical references.The Science of Team Science (SciTS) emerged as a field of study because 21st Century scientists are increasingly charged with solving complex societal and environmental challenges. This shift in the complexity of questions requires a shift in how knowledge is created. To solve the complex societal health and environmental challenges, scientific disciplines will have to work together, innovate new knowledge, and create new solutions. It is impossible for one person or one discipline to have the quantity of knowledge needed to solve these types of problems. Tackling these problems requires a team. My dissertation articles report on how knowledge is built and created on a spectrum of scientific teams from university students to long-standing teams. Collectively they answer: how is knowledge creation a social process? To answer this question, my dissertation used a mixed-methods approach that included: social network analysis, social surveys, participant observation, interviews, document analysis, and student reflections. The most important finding from my dissertation was that social relations and processes are key to knowledge creation. Historically, knowledge acquisition and creation have been thought of as individual tasks, but a growing body of literature has framed knowledge creation as a social product. This is a fundamental shift in how knowledge is created to solve complex problems. To work with scientists from other disciplines, individuals must develop personal mastery and build the necessary capacities for collaboration, collective cognitive responsibility, and knowledge building. Complex problems are solved when scientists co-evolve with teams, and individual knowledge and capacity grows alongside the ability for "team learning" Knowledge, then, is a collective product; it is not isolated or individual, but constructed and co-constructed through patterns of interactions