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2015 | OriginalPaper | Buchkapitel

The Role of Management Capacity in the Innovation Process for Firm Profitability

verfasst von : Giovanni Cerulli, Bianca Potì

Erschienen in: The Evolution of Economic and Innovation Systems

Verlag: Springer International Publishing

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Abstract

This paper studies the relation between firm managerial capacity in doing innovation and firm profitability. The approach taken is at the intersection of evolutionary/neo-Schumpeterian theory and the resource-based view of the firm. Utilizing a stochastic frontier analysis, we provide a direct measure of the innovation management capacity which is then plugged into a profit margin equation, augmented by the traditional Schumpeterian drivers of profitability. We run both ordinary least squares and quantile regressions.

Results show evidence of an average positive effect of the innovation managerial capacity on firm profitability, although quantile regressions show that this mean effect is mainly driven by the stronger magnitude of the effect for lower quantiles. This means that less profitable firms (i.e. the smaller ones in our sample) could gain more from increasing managerial efficiency for innovation in comparison to more profitable (larger) businesses.

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Vorheriges Kapitel Restless Knowledge, Capabilities and the Nature of the Mega-Firm

Nächstes Kapitel Industrial Growth and Productivity Change in German Cities: A Multilevel Investigation

“Innovativeness is to innovation what TFP (Total Factor Productivity) is to production. […] Both correspond to omitted factors of performance such as technological, organizational, cultural, or environmental factors (and to other sources of misspecification errors), although TFP is commonly interpreted as being mainly an indicator of technology” (Mairesse and Mohnen 2002, p. 226).

An important evolution of the firm capability theory deals with dynamic capabilities. Teece (1987, p. 516) define firm dynamic capabilities as the ability to integrate and reconfigure internal and external competences/resources. These capabilities are what matters also in the case of R&D collaboration and joint ventures.

Over time, low productivity firms are selected out and the better ones survive and prosper. But in the steady state there will always be some dispersion of productivity, as cost factors limit the number of new firms that enter the market (Bloom and van Reenen 2010).

These scholars reported that, overall, variables representing workplace organization show highly significant positive associations with innovation propensity, and that some of them seem to be more important than other “standard” determinants of innovation, such as demand development, competition conditions or human capital assets.

They control the impact on: (a) the firm’s innovation propensity (whether or not a firm has introduced innovations in a certain period), and (b) innovation success as measured by the firm’s innovative product sales in relation to total turnover.

Another problem indicated by Arvanitis et~al. (2013) is reverse causality: innovative firms could in turn be more likely to adopt innovative organizational practices.

One way to summarize firm-specific quality is to z-score each individual question and take an average across all 18 questions. Another is to take the principal factor component. This in fact provides extremely similar results to the average z-score, since they are correlated (see Van Reenen and Bloom 2007).

Other scholars have used data envelopment analysis (DEA) to estimate the innovation frontier. Zhang et~al. (2003) and Coelli and Rao (2005) provide a discussion of the differences between DEA and stochastic frontier analysis (SFA). Kumbhakar and Lovell (2000) give an elaborate discussion of the development and application of SFA to efficiency measurement.

The general aim of their paper is to examine at micro level the impact of external technology acquisition on the achievement of innovative efficiency and productivity, i.e. on a firm’s innovation performance.

For the sake of simplicity we do not assume other forms of the production function (e.g., the translog), or that the Cobb-Douglas regression coefficients vary across sectors.

AIDA: Information Analysis of Italian Companies.

The OLS results in Tables 5 and 6 are numerically different only because Table 3 reports standardized Beta coefficients (i.e., coefficients measured in standard deviation units), while Table 3 sets out OLS coefficients. In effect the difference is only in the unit of measurement employed.

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Titel: The Role of Management Capacity in the Innovation Process for Firm Profitability
verfasst von: Giovanni Cerulli
Bianca Potì
Verlag: Springer International Publishing
Buch: The Evolution of Economic and Innovation Systems
Print ISBN: 978-3-319-13298-3

Electronic ISBN: 978-3-319-13299-0

Copyright-Jahr: 2015
DOI: https://doi.org/10.1007/978-3-319-13299-0_19

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