United Nations University – Maastricht Economic and Social Research Institute on Innovation and Technology

The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies. Results for the final phase of the 1000 Genomes Project are presented including whole-genome sequencing, targeted exome sequencing, and genotyping on high-density SNP arrays for 2,504 individuals across 26 populations, providing a global reference data set to support biomedical genetics. The 1000 Genomes Project has sought to comprehensively catalogue human genetic variation across populations, providing a valuable public genomic resource. The data obtained so far have found applications ranging from association studies and fine mapping studies to the filtering of likely neutral variants in rare-disease cohorts. The authors now report on the final phase of the project, phase 3, which covers previously uncharacterized areas of human genetic diversity in terms of the populations sampled and categories of characterized variation. The sample now includes more than 2,500 individuals from 26 global populations, with low coverage whole-genome and deep exome sequencing, as well as dense microarray genotyping. They find that while most common variants are shared across populations, rarer variants are often restricted to closely related populations. The authors also demonstrate the use of the phase 3 dataset as a reference panel for imputation to improve the resolution in genetic association studies.

Abstract Interfirm strategic alliances appear to have become more important as a part of (international) business. In this contribution an attempt is made to clarify our understanding of the motives that lead firms to cooperate in their innovative efforts. Going beyond general theoretical statements and case studies, attention is paid to both sectoral differences in the motivation for partnerships as well as to contrasts in interorganizational features of technology cooperation. Based on a large sample of alliances the analysis reveals some major differences regarding the research orientation of contractual arrangements and organizationally complex alliances.

Transitions are transformation processes in which society changes in a fundamental way over a generation or more. Although the goals of a transition are ultimately chosen by society, governments can play a role in bringing about structural change in a stepwise manner. Their management involves sensitivity to existing dynamics and regular adjustment of goals to overcome the conflict between long‐term ambition and short‐term concerns. This article uses the example of a transition to a low emission energy supply in the Netherlands to argue that transition management provides a basis for coherence and consistency in public policy and can be the spur to sustainable development.

The innovative performance of companies has been studied quite extensively and for a long period of time. However, the results of many studies have not yet led to a generally accepted indicator of innovative performance or a common set of indicators. So far the variety in terms of constructs, measurements, samples, industries and countries has been substantial. This paper studies the innovative performance of a large international sample of nearly 1200 companies in four high-tech industries, using a variety of indicators. These indicators range from R&D inputs, patent counts and patent citations to new product announcements. The study establishes that a composite construct based on these four indicators clearly catches a latent variable ‘innovative performance’. However, our findings also suggest that the statistical overlap between these indicators is that strong that future research might also consider using any of these indicators to measure the innovative performance of companies in high-tech industries.

This paper studies the links between productivity, innovation and research at the firm level. We introduce three new features: (i) A structural model that explains productivity by innovation output, and innovation output by research investment: (ii) New data on French manufacturing firms, including the number of European patents and the percentage share of innovative sales, as well as firm-level demand pull and technology push indicators; (iii) Econometric methods which correct for selectivity and simultaneity biases and take into account the statistical features of the available data: only a small proportion of firms engage in research activities and/or apply for patents; productivity, innovation and research are endogenously determined; research investment and capital are truncated variables, patents are count data and innovative sales are interval data. We find that using the more widespread methods, and the more usual data and model specification, may lead to sensibly different estimates. We find in particular that simultaneity tends to interact with selectivity, and that both sources of biases must be taken into account together. However our main results are consistent with many of the stylized facts of the empirical literature. The probability of engaging in research (R&D) for a firm increases with its size (number of employees), its market share and diversification, and with the demand pull and technology push indicators. The research effort (R&D capital intensity) of a firm engaged in research increases with the same variables, except for size (its research capital being strictly proportional to size). The firm innovation output, as measured by patent numbers or innovative sales, rises with its research effort and with the demand pull and technology indicators, either directly or indirectly through their effects on research. Finally, firm productivity correlates positively with a higher innovation output, even when controlling for the skill composition of labor as well as for physical capital intensity.

This paper provides an overview of the main perspectives and themes emerging in research on open innovation (OI). The paper is the result of a collaborative process among several OI scholars - having a common basis in the recurrent Professional Development Workshop on Researching Open Innovation' at the Annual Meeting of the Academy of Management. In this paper, we present opportunities for future research on OI, organised at different levels of analysis. We discuss some of the contingencies at these different levels, and argue that future research needs to study OI - originally an organisational-level phenomenon - across multiple levels of analysis. While our integrative framework allows comparing, contrasting and integrating various perspectives at different levels of analysis, further theorising will be needed to advance OI research. On this basis, we propose some new research categories as well as questions for future research - particularly those that span across research domains that have so far developed in isolation.

Abstract Strategic technology partnering between firms has become a growing subject of interest to both companies experimenting with this mode of economic organization and researchers from a wide variety of academic disciplines. In this study an effort is made to measure the effect of strategic technology partnering on companies engaged in such joint efforts. A study of the relevant literature on interfirm cooperation generates some basic understanding of this phenomenon, after which the empirical analysis is expanded with linear structural modeling of a number of relevant explanatory variables setting strategic partnering in a more complex environment.

This paper compares the role innovation plays in productivity across four European countries, France, Germany, Spain, and the UK, using firm-level data from the internationally harmonized Community Innovation Surveys (CIS3). Despite a considerable number of national firm-level studies analysing this relationship, cross-country comparisons using micro data are still rare. We apply a structural model that describes the link between R&D expenditure, innovation output, and productivity (CDM model). Our econometric results suggest that overall the systems driving innovation and productivity are remarkably similar across these four countries, although we also find interesting differences, particularly in the variation in productivity that is associated with more or less innovative activities.

textabstractSustainable development requires changes in socio-technical systems and wider societal change - in beliefs, values and governance that co-evolve with technology changes. In this article we present a practical model for managing processes of co-evolution: transition management. Transition management is a multilevel model of governance which shapes processes of co-evolution using visions, transition experiments and cycles of learning and adaptation. Transition management helps societies to transform themselves in a gradual, reflexive way through guided processes of variation and selection, the outcomes of which are stepping stones for further change. It shows that societies can break free from existing practices and technologies, by engaging in co-evolutionary steering. This is illustrated by the Dutch waste management transition. Perhaps transition management constitutes the third way that policy scientists have been looking for all the time, combining the advantages of incrementalism (based on mutual adaptation) with the advantages of planning (based on long-term objectives).

Image-based cell profiling is a high-throughput strategy for the quantification of phenotypic differences among a variety of cell populations. It paves the way to studying biological systems on a large scale by using chemical and genetic perturbations. The general workflow for this technology involves image acquisition with high-throughput microscopy systems and subsequent image processing and analysis. Here, we introduce the steps required to create high-quality image-based (i.e., morphological) profiles from a collection of microscopy images. We recommend techniques that have proven useful in each stage of the data analysis process, on the basis of the experience of 20 laboratories worldwide that are refining their image-based cell-profiling methodologies in pursuit of biological discovery. The recommended techniques cover alternatives that may suit various biological goals, experimental designs, and laboratories' preferences.

Purpose The purpose of this article is to provide an overview for those interested in the current state‐of‐the‐art in time management research. Design/methodology/approach This review includes 32 empirical studies on time management conducted between 1982 and 2004. Findings The review demonstrates that time management behaviours relate positively to perceived control of time, job satisfaction, and health, and negatively to stress. The relationship with work and academic performance is not clear. Time management training seems to enhance time management skills, but this does not automatically transfer to better performance. Research limitations/implications The reviewed research displays several limitations. First, time management has been defined and operationalised in a variety of ways. Some instruments were not reliable or valid, which could account for unstable findings. Second, many of the studies were based on cross‐sectional surveys and used self‐reports only. Third, very little attention was given to job and organizational factors. There is a need for more rigorous research into the mechanisms of time management and the factors that contribute to its effectiveness. The ways in which stable time management behaviours can be established also deserves further investigation. Practical implications This review makes clear which effects may be expected of time management, which aspects may be most useful for which individuals, and which work characteristics would enhance or hinder positive effects. Its outcomes may help to develop more effective time management practices. Originality/value This review is the first to offer an overview of empirical research on time management. Both practice and scientific research may benefit from the description of previous attempts to measure and test the popular notions of time management.

abstract This study centres around the way in which firms can enhance alliance performance through the development of alliance capabilities. Whereas most research has focused on inter‐firm antecedents of alliance performance, research on intra‐firm antecedents pointing to prior experience and internal mechanisms to foster knowledge transfer has only recently emerged. As little is known about how firms develop alliance capabilities, this study aims to uncover how differences in sources of alliance capabilities explain performance heterogeneity. The data are derived from a detailed survey held among alliance managers and Vice‐Presidents of 151 firms. The survey covers over 2600 alliances for the period 1997–2001. This study not only finds that alliance capabilities partially mediate between alliance experience and alliance performance, but also yields novel insights into the micro‐level building blocks underlying the process of alliance capability development.

Reports of malaria are increasing in many countries and in areas thought free of the disease. One of the factors contributing to the reemergence of malaria is human migration. People move for a number of reasons, including environmental deterioration, economic necessity, conflicts, and natural disasters. These factors are most likely to affect the poor, many of whom live in or near malarious areas. Identifying and understanding the influence of these population movements can improve prevention measures and malaria control programs.

A number of features of innovation diffusion are identified: appropriability, diversity, expectations, selection, learning, and spillover externalities. A dynamic model is formul ated to embed the diffusion question into a more general framework of disequilibrium competition. The model incorporates distinct vintage structures reflecting a change in technological trajectory, learning-by-using, a nd expectations-driven investment rules of thumb. Simulation studies reveal robust quasi-logistic curves, but a complicated pattern of net market share gains and losses. Uncertainty regarding the rationality of early or late adoption, it is argued, ensures that the requisite behavioral variety is present to generate these diffusion patterns. Copyright 1988 by Royal Economic Society.

We review the econometric literature on measuring the returns to R&D. The theoretical frameworks that have been used are outlined, followed by an extensive discussion of measurement and econometric issues that arise when estimating the models. We then provide a series of tables summarizing the major results that have been obtained and conclude with a presentation of R&D spillover returns measurement. In general, the private returns to R&D are strongly positive and somewhat higher than those for ordinary capital, while the social returns are even higher, although variable and imprecisely measured in many cases.

The school closures owing to the 2020 COVID‐19 crisis resulted in a significant disruption of education provision, leading to fears of learning losses and of an increase in educational inequality. This article evaluates the effects of school closures based on standardised tests in the last year of primary school in the Dutch‐speaking Flemish region of Belgium. Using a 6‐year panel, we find that students of the 2020 cohort experienced significant learning losses in three out of five tested subjects, with a decrease in school averages of mathematics scores of 0.17 standard deviations and Dutch scores (reading, writing, language) of 0.19 standard deviations as compared to previous cohorts. This finding holds when accounting for school characteristics, standardised tests in Grade 4 and school fixed effects. Given the large observed effect sizes, the effect of school closures appears to be a combination of lost learning progress and learning loss. Moreover, we observe that inequality both within schools and across schools rises by 7% for mathematics and 8% for Dutch. The learning losses are correlated with observed school characteristics, as schools with a more disadvantaged student population experience larger learning losses.

This paper combines general definitions of innovation applicable in all economic sectors with a systems approach, to develop a conceptual framework for the statistical measurement of innovation. The resulting indicators can be used for monitoring and evaluation of innovation policies that have been implemented, as well as for international comparisons. The extension of harmonised innovation measurement to all economic sectors has implications for innovation research and for policy learning.

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Accounting for Innovation and Measuring Innovativeness: An Illustrative Framework and an Application by Jacques Mairesse and Pierre Mohnen. Published in volume 92, issue 2, pages 226-230 of American Economic Review, May 2002