Innovation Effects of Science-Related Technological Opportunities - Theoretical Considerations and Empirical Findings for Firms in the German Manufacturing Industry -

This paper investigates the innovation effects of science-related technological opportunities. Against the background of theoretical considerations about the interrelation of innovation and the adaptation of external (knowledge) resources, the impacts of technological opportunities stemming from scientific institutions on firms' innovation input and output are empirically analyzed for the German manufacturing industry. The investigations focus on the question whether science-related technological opportunities are used as complements or substitutes in the innovation process. The estimations indicate complementary relationships between firms' innovation input and technological opportunities stemming from scientific institutions. The adaptation of science-related knowledge resources has stimulating effects on the intensity of inhouse R&D. The results for the innovation output effects are ambiguous. On the one hand, empirical evidence for complementary impacts on the realisation of improved products could be found. On the other hand, science-related technological opportunities have no enhancing effects on the probability of realizing new products. Obviously, knowledge from universities and research institutes stimulates the development of new products more indirectly by increasing inhouse capacities and enhancing R&D efficiency.innovation activities, technological opportunities, scientific institutions, manufacturing industry

A secondary analyses of Bradac et al. s prototype process-monitoring experiment

We report on the secondary analyses of some conjectures and empirical evidence presented in Bradac et al. s prototype process-monitoring experiment, published previously in IEEE Transactions on Software Engineering. We identify 13 conjectures in the original paper, and re-analyse six of these conjectures using the original evidence. Rather than rejecting any of the original conjectures, we identify assumptions underlying those conjectures, identify alternative interpretations of the conjectures, and also propose a number of new conjectures. Bradac et al. s study focused on reducing the project schedule interval. Some of our re-analysis has--considered improving software quality. We note that our analyses were only possible because of the quality and quantity of evidence presented in the original paper. Reflecting on our analyses leads us to speculate about the value of descriptive papers --that seek to present empirical material (together with an explicit statement of goals, assumptions and constraints) separate from the analyses that proceeds from that material. Such descriptive papers could improve the public scrutiny of software engineering research and may respond, in part, to some researchers criticisms concerning the small amount of software engineering research that is actually--evaluated. We also consider opportunities for further research, in particular opportunities for relating individual actions to project outcomes

Evidence-Informed Criminal Justice

The American criminal justice system is at a turning point. For decades, as the rate of incarceration exploded, observers of the American criminal justice system criticized the enormous discretion wielded by key actors, particularly police and prosecutors, and the lack of empirical evidence that has informed that discretion. Since the 1967 President’s Commission on Law Enforcement and Administration of Justice report, The Challenge of Crime in a Free Society, there has been broad awareness that the criminal system lacks empirically informed approaches. That report unsuccessfully called for a national research strategy, with an independent national criminal justice research institute, along the lines of the National Institutes of Health. Following the report, police agencies continued to base their practices on conventional wisdom or “tried-and-true” methods. Prosecutors retained broad discretion, relying on their judgment as lawyers and elected officials. Lawmakers enacted new criminal statutes, largely reacting to the politics of crime and not empirical evidence concerning what measures make for effective crime control. Judges interpreted traditional constitutional criminal procedure rules in deference to the exercise of discretion by each of these actors. Very little data existed to test what worked for police or prosecutors, or to protect individual defendants’ rights. Today, criminal justice actors are embracing more data-driven approaches. This raises new opportunities and challenges. A deep concern is whether the same institutional arrangements that produced mass incarceration will use data collection to maintain the status quo. Important concerns remain with relying on data, selectively produced and used by officials and analyzed in nontransparent ways, without sufficient review by the larger research and policy community. Efforts to evaluate research in a systematic and interdisciplinary fashion in the field of medicine offer useful lessons for criminal justice. This Article explores the opportunities and concerns raised by a law, policy, and research agenda for an evidence-informed criminal justice system

Long-run marketing inferences from scanner data.

Good marketing decisions require managers' understanding of the nature of the market-response function relating performance measures such as sales and market share to variations in the marketing mix (product, price, distribution and communications efforts). Our paper focuses on the dynamic aspect of market-response functions, i.e. how current marketing actions affect current and future market response. While conventional econometrics has been the dominant methodology in empirical market-response analyses, time-series analysis offers unique opportunities for pushing the frontier in dynamic research. This paper examines the contributions an d the future outlook of time-series analysis in market-response modeling. We conclude first, that time series analysis has made a relatively limited overall contribution to the discipline, and investigate reasons why that has been the case. However, major advances in data (transactions-based databases and in modeling technology (long-term time-series modeling) create new opportunities for time-series techniques in marketing, in particular for the study of long-run marketing effectiveness. We discuss four major aspects of long -term time-series modeling, relate them to substantive marketing problems, and describe some early applications. Combining the new data with the new methods, we then present original empirical results on the long-term behavior of brand sales and category sales for four consumer products. We discuss the implications of our findings for future research in market response. Our observations lead us to identify three areas where additional research could enhance the diffusion of the identified time-series concepts in marketing.Data; Marketing;

The dynamics of managing people in the diverse cultural and institutional context of Africa

Purpose The purpose of this article is to introduce the special issue which considers some of the contemporary debates in managing people in Africa. Design/methodology/approach The papers that constitute this special issue were selected from submissions to various events hosted by the Africa Research Group, a community of scholars committed to researching Africa, and from a more general call for submissions. Findings The papers highlight the changing picture of the African organisational landscape and provide both theoretical and empirical insights about the opportunities and challenges of managing people in a culturally complex continent. Originality/value Taken together, the papers make an important contribution by engaging current debates and demonstrating potential new areas for further research

Exploring the Knowledge Filter - How Entrepreneurship and University-Industry Relations Drive Economic Growth

Why do regions post different growth rates and differences in technological progress? Knowledge creation and knowledge spillovers are an important element in stimulating economic development. Recent empirical studies have shown that knowledge spillovers positively affect technological change and economic growth. Other studies have shown that knowledge spillovers do not occur automatically, hence, it is less clear which mechanisms facilitate and foster knowledge flows. This paper focuses on the exploitation of opportunities and commercialization of knowledge, namely the transformation of knowledge into products, processes and organizations, and their contribution to regional economic growth. The degree of knowledge exploitation may differ across regions because the level of research and development activities varies, incumbent firms might not exploit new opportunities to the full extent, and new knowledge generated in research institutions and universities is hardly translated into new products or services . It may be argued that a knowledge filter exists limiting the total conversion of knowledge into new products, processes and organizations (for details, see Acs, Audretsch, Braunerhjelm & Carlsson, 2003). This paper introduces entrepreneurship and university-industry relations as mechanisms for knowledge spillovers and determinants of economic growth. Entrepreneurial activity can be assumed as a mechanism by which knowledge spillover occurs. Many radical innovations have been introduced by new firms rather than by incumbents, because the set-up of one’s own business might be the most promising possibility to commercialize knowledge (Audretsch, 1995). University-industry relations may be another mechanism facilitating the exploitation of knowledge and the flow of ideas (Mansfield, 1991, 1998). If the generated knowledge at universities is transferred via research partnerships it may accelerate technology transfer and enable firms to develop new products and process (Cohen, Nelsen & Walsh, 2002, Mansfield 1991 and 1998). The empirical modeling framework develops a regional model of economic growth and analyzes if the region’s absorptive capacity, entrepreneurship and university-industry relations drive economic growth. The results of the empirical analysis suggest that new ventures and partnerships between university and industry amplify the permeability of the knowledge filter and thus spur economic growth. The paper also gives an outlook and discusses implications for public policy to stimulate entrepreneurship and university-industry relations.

Collaboration and Research Practice in Intelligence

Close, intensive research collaboration between universities, companies, and the public sector can open up new and different opportunities for qualitative research, and provide analytic and empirical insights that otherwise might be difficult to obtain. The aim of this paper is to explore collaboration as a means of doing research with the intelligence community. Experiences from a research project concerning dilemmas the practitioners face in their organization within the Swedish Armed Forces, serve as a starting point for this reflective discussion. It is argued here that collaboration is suitable when change is required. The mutual learning between the actors feeds into change processes. However, such collaboration raises fundamental ethical issues that are complex and highlight various academic, institutional, and personal perspectives. Collaborations should not be a set of “how-to” recipes, but rather a research activity that can have substantial rewards for researchers and practitioners alike

THE ROLE OF THE INDUSTRIAL POLICY IN ITALY

The aim of this paper is to match the Italian small-medium firms’ (SMEs) need for technological innovation and the state and regional aid programs aimed at supporting innovation and technology. The purpose is to highlight existing capabilities and new opportunities in support of Italian SMEs requirements in innovation. The paper reports the results of two empirical research projects recently carried out at Ceris-Cnr (Institute of Economic Research on Firms and Growth – Italian National Research Council). After a framework of the most important innovation policies the Italian aid programmes for innovation and technology are described. In particular the role of the Italian Regions is analysed in depth. The empirical research confirmed that the approach to innovation of Italian SMEs tends to satisfy the demand of existing market in the best possible way ompared with competitors. Product improvement follows incremental processes. The most common way of introducing new technology is the purchase of new machines and equipment to reduce costs and improve quality. All the industrialised countries tend to favour the linking of the SMEs with external sources of knowledge. The research shows that such a policy clashes with the SMEs’ capacity for absorbing innovation. Most of them lack the technical structures (technical office, design department, R&D laboratory, prototype department, etc.) and graduate staff capable of interfacing with the research world.

Learning, Internal Research, and Spillovers Evidence from a Sample of R&D Laboratories

This paper presents new evidence on the practice of industrial Research and Development (R&D), especially the allocation between learning and internal research, and the role of outside knowledge, as represented by R&D spillovers, in reshaping this allocation. The evidence describes the sources of outside knowledge, portrays the flow of that knowledge into firms, and interprets the channels by which outside knowledge influences R&D. The empirical work is based on a sample of 220 R&D laboratories owned by 115 firms in the U.S. chemicals, machinery, electrical equipment, and motor vehicles industries. The findings are consistent with the view that universities and firms generate technological opportunities in R&D laboratories. In addition to partnerships that define rather strict channels of opportunity, the paper uncovers broader effects of R&D spillovers. The results also suggest that academic spillovers drive learning about universities, and that industrial spillovers drive learning about industry. In this way externally derived opportunities reshape the rate and direction of R&D. Overall the findings paint an image of practitioners of industrial R&D reaching aggressively for opportunities, rather than waiting for opportunities to come to them.

The best of times and the worst of times: empirical operations and supply chain management research

We assess the current state of empirical research in operations and supply chain management (OSM), using Dickens’ contrast between the best of times and the worst of times as a frame. The best of times refers to the future that empirical OSM research is now entering, with exciting opportunities available using big data and other new data sources, new empirical approaches and analytical techniques and innovative tools for developing theory. These are well aligned with new research questions related to the digital economy, Industry 4.0, the impact of the millennial generation as consumers, social media, 3D printing, etc. However, we also explore how it is the worst of times, focusing on the challenges and problems that plague empirical OSM research. Our goal is to show how OSM researchers can learn from the worst of times, in order to be poised to take advantage of the best of times. We introduce the research diamond as a vehicle for emphasising the importance of a balanced research perspective that treats the research problem, theory, data collection and data analysis as equally important, requiring alignment between them. By learning and addressing the issues in this period of the best of times and the worst of times, we can take advantage of the opportunities facing our field to generate research that is balanced, insightful, rigorous, relevant, impactful and interesting