This handbook looks to provide academics and students with a comprehensive and holistic understanding of the phenomenon of innovation. Innovation spans a number of fields within the social sciences and humanities: Management, Economics, Geography, Sociology, Politics, Psychology, and History. Consequently, the rapidly increasing body of literature on innovation is characterized by a multitude of perspectives based on, or cutting across, existing disciplines and specializations. Scholars of innovation can come from such diverse starting points that much of this literature can be missed, and so constructive dialogues missed. The editors of The Oxford Handbook of Innovation have carefully selected and designed twenty-one contributions from leading academic experts within their particular field, each focusing on a specific aspect of innovation. These have been organized into four main sections, the first of which looks at the creation of innovations, with particular focus on firms and networks. Section Two provides an account of the wider systematic setting influencing innovation and the role of institutions and organizations in this context. Section Three explores some of the diversity in the working of innovation over time and across different sectors of the economy, and Section Four focuses on the consequences of innovation with respect to economic growth, international competitiveness, and employment. An introductory overview, concluding remarks, and guide to further reading for each chapter, make this handbook a key introduction and vital reference work for researchers, academics, and advanced students of innovation. Contributors to this volume - Jan Fagerberg, University of Oslo William Lazonick, INSEAD Walter W. Powell, Stanford University Keith Pavitt, SPRU Alice Lam, Brunel University Keith Smith, INTECH Charles Edquist, Linkoping David Mowery, University of California, Berkeley Mary O'Sullivan, INSEAD Ove Granstrand, Chalmers Bjorn Asheim, University of Lund Rajneesh Narula, Copenhagen Business School Antonello Zanfei, Urbino Kristine Bruland, University of Oslo Franco Malerba, University of Bocconi Nick Von Tunzelmann, SPRU Ian Miles, University of Manchester Bronwyn Hall, University of California, Berkeley Bart Verspagen , ECIS Francisco Louca, ISEG Manuel M. Godinho, ISEG Richard R. Nelson, Mario Pianta, Urbino Bengt-Ake Lundvall, Aalborg

The Oxford Handbook of Innovation

The Rise of the Creative Class

Economics Of Discrimination

Economists have long puzzled over the astounding differences in productivity between firms and countries. For example, looking at disaggregated data on U.S. manufacturing industries, Syverson (2004a) found that plants at the 90th percentile produced four times as much as the plant in the 10th percentile on a per-employee basis. Only half of this difference in labor productivity could be accounted for by differential inputs, such as capital intensity. Syverson looked at industries defined at the four-digit level in the Standard Industrial Classification (SIC) system (now the North American Industry Classification System or NAICS) like 'Bakeries and Tortilla Manufacturing' or 'Plastics Product Manufacturing.' Foster, Haltiwanger, and Syverson (2008) show large differences in total factor productivity even within very homogeneous goods industries such as boxes and block ice. Some of these productivity differences across firms and plants are temporary, but in large part they persist over time. At the country level, Hall and Jones (1999) and Jones and Romer (2009) show how the stark differences in productivity across countries account for a substantial fraction of the differences in average per capita income. Both at the plant level and at the national level, differences in productivity are typically calculated as a residual-that is, productivity is inferred as the gap between output and inputs that cannot be accounted for by conventionally measured inputs.

/pdf/why-do-management-practices-differ-across-firms-and-58rczn83xl.pdf

Why Do Management Practices Differ across Firms and Countries

We propose an independence criterion based on the eigen-spectrum of covariance operators in reproducing kernel Hilbert spaces (RKHSs), consisting of an empirical estimate of the Hilbert-Schmidt norm of the cross-covariance operator (we term this a Hilbert-Schmidt Independence Criterion, or HSIC). This approach has several advantages, compared with previous kernel-based independence criteria. First, the empirical estimate is simpler than any other kernel dependence test, and requires no user-defined regularisation. Second, there is a clearly defined population quantity which the empirical estimate approaches in the large sample limit, with exponential convergence guaranteed between the two: this ensures that independence tests based on HSIC do not suffer from slow learning rates. Finally, we show in the context of independent component analysis (ICA) that the performance of HSIC is competitive with that of previously published kernel-based criteria, and of other recently published ICA methods.

Measuring statistical dependence with Hilbert-Schmidt norms

Research has recently emphasized that the non-survival of entrepreneurial firms can be disaggregated into distinct exit routes such as merger and acquisition (M&A), voluntary closure, and failure. Firm performance is an alleged determinant of exit route. However, there is a lack of evidence linking exit routes to their previous growth performance. We contribute to this gap by analyzing a cohort of incorporated firms in Japan and find some puzzles for the standard view. Our empirical analysis suggests that sales growth generally reduces the probability of exit by merger, voluntary liquidation, and also bankruptcy. However, the relationship is U-shaped—such that rapid growth actually increases the probability of exit. More generally, each of the three exit routes can occur all across the growth rate distribution. Large firms are more likely to exit via merger or bankruptcy, while small firms are more likely to exit via voluntary liquidation.

Growth paths and routes to exit: 'shadow of death' effects for new firms in Japan

The Brighton Fuse

Work and life satisfaction depends on a number of pecuniary and nonpecuniary factors at the workplace and determines these in turn. We analyze these causal linkages using a structural vector autoregression approach for a sample of the German working populace collected from 1984 to 2008, finding that workplace autonomy plays an important causal role in determining well-being.

Causal linkages between work and life satisfaction and their determinants in a structural VAR approach

This paper is an empirical test of the hypothesis that the appropriateness of different business strategies is conditional on the firm's distance to the industry frontier. We use data on four 2-digit high-tech manufacturing industries in the US over the period 1972- 1999, and apply semi-parametric quantile regressions to investigate the contribution of firm behavior to market value at various points of the conditional distribution of Tobin's q. Among our results, we observe that innovative activity, measured in terms of R&D expenditure or patents, has a strong positive association with market value at the upper quantiles (corresponding to the leader firms) whereas the innovative efforts of laggard firms are valued significantly less. Laggard firms, we suggest, should instead achieve productivity growth through efficient exploitation of existing technologies and imitation of industry leaders. Employment growth in leader firms is encouraged whereas growth of backward firms is not as well received on the stock market.

https://www.econstor.eu/bitstream/10419/31801/1/572410638.pdf

Distance to Frontier and Appropriate Business Strategy

As a gentle introduction to quantitative foresight techniques we begin by providing a working definition of Future-Oriented Technology Analysis (FTA) and discussing its role, uses, and popularity over recent decades. We select 22 FTA techniques identified as the most important quantitative FTA techniques. We introduce these techniques, discuss their main contexts and uses and classify them into groups with common characteristics, positioning them along four key dimensions: descriptive/prescriptive; extrapolative/normative; data gathering/inference; and forecasting/foresight.

http://digital.csic.es/bitstream/10261/161559/1/QuannalysisOfTPart1.pdf

Alex Coad

Papers

Growth paths and routes to exit: 'shadow of death' effects for new firms in Japan

The Brighton Fuse

Causal linkages between work and life satisfaction and their determinants in a structural VAR approach

Distance to Frontier and Appropriate Business Strategy

Quantitative Analysis of Technology Futures. Part I: Techniques, Contexts, and Organizations