Using the fuzzy associative memory (FAM) computation to explore the R&;D project performance

This study found out there were nonlinear relationships between the R&D project performance and its determinants—R&D project manager’s skills, quality of project environment, and teamwork effectiveness—by using the Fuzzy Associative Memory (FAM) technique in the IC design industry of Taiwan. The results showed that R&D project manager’s skills and quality of project environment had significantly inverse U-shaped effects on the R&D project performance, while teamwork effectiveness had almost monotonic positive influence upon it. Therefore, there were optimal values for R&D project manager’s skills and quality of project environment for the R&D project performance in the Taiwanese IC design companies, although they can raise teamwork effectiveness as much as possible.     

Building the Knowledge Stock: Lags,Depreciation, and Uncertainty in R&D Investment and Link with Productivity Growth

The search for an appropriate methodology to investigate the relation between R&D investment, knowledge stock and productivity growth is the main purpose of the paper. In analogy with physical assets, we present a model of knowledge capital formation which allows the calculation of the relevant user cost, as well. The proposed model accumulates R&D investment based on a stochastic gestation lag and a geometric depreciation of the stock. The basic parameters underlying the lag structure differ according to the types of research expenditure. The approach is applied to public R&D investment in Italian agriculture; the results provide interesting information about the economic structure of public research effort in Italian agriculture and plausible estimates of its internal rate of return.     

Selecting balanced portfolios of R&D projects with interdependencies: A Cross-Entropy based methodology

The intensive growth of technology makes firms rely on research and development (R&D) activities in order to adapt to technology changes in an ever-changing and uncertain environment. Due to R&D budget constraints and limited resources, firms are often forced to select a subset of all candidate projects by means of project portfolio selection techniques mitigating the corresponding risks and enhancing the overall value of portfolio. Projects' interdependencies and types were rarely considered in existing models of R&D portfolio selection that may result in selecting wrong projects. This flaw hinders the projects alignment with corporate objectives and strategy and leads to excessive risk and missing the promised values. In this paper, a balanced set of R&D project evaluation criteria was proposed. Next, to construct R&D project portfolio, a 0–1 nonlinear mathematical programming method for balancing portfolio values and risks was proposed, in which research projects' interdependencies, types and other constraints were all considered. Finally, a Cross-Entropy algorithm was developed to solve the proposed model and results were reported. The algorithm proved to be very effective in terms of solution quality and computational time. The proposed algorithm especially suits large scale instances while exact approaches are doomed to fail.     

Corporate R&D and firm efficiency: evidence from Europe’s top R&D investors

The main objective of this study is to investigate the impact of corporate research and development (R&D) activities on firm performance, measured by labour productivity. To this end, the stochastic frontier technique is used on a unique unbalanced longitudinal dataset comprising top European R&D investors over the period 2000–2005. In this framework, this study quantifies technical inefficiency of individual firms. From a policy perspective, the results of this study suggest that if the aim is to leverage firms’ productivity, the emphasis should be put on supporting corporate R&D in high-tech sectors and, to some extent, in medium-tech sectors. On the other hand, corporate R&D in the low-tech sector is found to have a minor effect in explaining productivity. Instead, encouraging investment in fixed assets appears important for the productivity of low-tech industries. Hence, the allocation of support for corporate R&D seems to be as important as its overall increase and an ‘erga omnes’ approach across all sectors appears inappropriate. However, with regard to technical efficiency, R&D intensity is found to be a pivotal factor in explaining firm efficiency and this turns out to be true for all industries.     

Governance and effects of public R&D subsidies: Evidence from China

This study examines the effects of public research and development (R&D) subsidies and the governance of such subsidies on firm productivity based on the analysis of a firm-level panel dataset between 1998 and 2007 in China. It is found that public R&D subsidies tend to support more productive firms and the productivity of these government-backed firms is improved further after they get the government support. Less attention is paid to the observable or measurable performance measurements in ex-ante project selection, and the ex-post effects are stronger when the governance of the public R&D subsidies becomes more decentralized due to an exogenous policy change. In other words, better decentralization of governance is associated with more pronounced effects of R&D subsidies. Identification concerns are addressed with various approaches to confirm the treatment effect of public R&D subsidies and the governance of such subsidies.     

The R&D knowledge base in city-agglomerations and knowledge searching in product innovative SMEs

ABSTRACT

This paper looks at the relation between the R&D knowledge base of city-agglomerations and knowledge sourcing in product innovative small and medium-sized enterprises (SMEs). The small open Belgian economy is used as a test case. The characteristics of the city-agglomeration’s R&D knowledge are posited to be instrumental for SMEs’ reliance on particular sources of information for innovation. The R&D knowledge base is studied as a multidimensional concept consisting of R&D capacity, R&D specialization and R&D diversification. A representative sample of product innovative SMEs drawn from two waves of the Community Innovation Survey between 2008 and 2012 reveals that a strong R&D capacity at city-agglomeration level favours private external information sources for innovation, but has no influence on the likelihood to rely on public sources for innovation. Accordance between specialization of the private R&D knowledge base and the SME’s activities positively influences the use of clients as information sources for innovation, whereas under these circumstances supplier responsiveness turns out to be less frequently solicited for. A more diversified private R&D environment reduces the reliance on universities and public research organizations as information sources for innovation. A public R&D knowledge base specialized in natural sciences or engineering favours information sources from universities.     

The negative impact of R&D capitalization: A value relevance approach

Abstract

Accounting for R&D costs is an open issue. SFAS N°2 mandates that all R&D costs must be immediately expensed. IAS 38 requires capitalization of R&D costs if they meet certain criteria. Recent research papers show the value relevance of capitalized R&D. We test the value relevance of R&D reporting in a sample of 197 French firms between 1993 and 2002. The French context provides an interesting field for R&D value relevance studies because both accounting treatments of R&D costs (expensing and capitalization) are allowed. Unlike previous studies, we find that capitalized R&D is negatively associated with stock prices and returns. This negative coefficient on capitalized R&D implies that investors are concerned with and react negatively to capitalization of R&D. We also find that the firms choosing to capitalize (successful) R&D are smaller, more highly leveraged, less profitable and have less growth opportunities. Taking into account these characteristics, our robustness checks confirm that capitalized R&D is not associated with higher prices and is related to lower returns.     

On robust asymmetric equilibria in asymmetric R&;D-driven growth economies

In R&D-driven growth models with asymmetric fundamentals, the steady-state equilibrium R&D investments are industry-specific, and they are such that R&D returns are equalized across industries. Return equalization, however, makes investors indifferent as to where to target research and, hence, the problem of allocation of R&D investments across industries is indeterminate. Agents’ indifference creates an ambiguous investment scenario. We assume that agents hold “ambiguous” beliefs on the per-industry profitability of their R&D investments. Investors’ aversion towards ambiguity eliminates the indeterminacy of the investment problem. In particular, the asymmetric return-equalizing equilibrium is robust against a however small degree of investors’ ambiguity aversion.     

The impact of cluster connectedness on firm innovation: R&D effort and outcomes in the textile industry

Recent research into the clustering effect on firms has moved away from a simplistic view to a more complex approach. More realistic and complex causal relationships are now considered when analysing these territorial networks. Specifically, this paper attempts to analyse how cluster connectedness moderates the relationship of a firm's innovation effort and the results obtained from this effort. We want to question the commonly accepted direct and positive impact of R&D effort, and moreover, we suggest the existence of a saturation effect and that the level of cluster's inter-connectedness in the cluster moderates this effect. We have developed our empirical study focusing on the Spanish textile industrial cluster. This is a complex manufacturing industry that uses relatively low-technology manufacturing and R&D. Our findings suggest that the degree to which a firm is involved with, or connected to, other firms in the cluster can moderate the effect of the R&D effort on its innovation results. More generally, we aim to contribute to the discussion on the degree to which firms should be involved in the cluster network in order to operate efficiently and gain the maximum competitive advantages. Our findings have implications both in recent cluster and network literature as well for institutional policy.     

Coordination in global R&D organizations: An examination of the role of subsidiary mandate and modular product architectures in dispersed R&D organizations

International research and development (R&D) operations require a significant amount of coordination between the headquarters and the subsidiaries in order to integrate the dispersed activities in one final product. This article explores what mechanisms multinational companies (MNCs) use to coordinate their overseas R&D units. Based on a multiple case study involving nine MNCs with overseas R&D subsidiaries of varying mandates, we find that R&D sites with high technology and/or market orientation tend to be coordinated by informal mechanisms while sites with little technology and/or market orientation tend to be coordinated by formal mechanisms. Furthermore, it appears that this relationship is strongly affected by the product’s architecture: while rather complex R&D activities are conducted at the systems level and at sites with high technology orientation, less complex R&D activities are conducted at the component level at sites with low technology and market orientation. Finally, the findings suggest that modular product architectures have a coordinating effect in global R&D activities which have the power to lower firms’ overall coordination effort. The findings bear important implications for the effective coordination of MNCs’ international R&D subsidiaries.     

Information Sharing in Contests

We study the incentives to share private information ahead of contests, such as markets with promotional competition, procurement contests, or research and development (R&D). We consider the cases where firms have (i) independent values and (ii) common values of winning the contest. In both cases, when decisions to share information are made independently, sharing information is strictly dominated. With independent values, an industry‐wide agreement to share information can arise in equilibrium. Expected effort is lower with than without information sharing. With common values, an industry‐wide agreement to share information never arises in equilibrium. Expected effort is higher with than without information sharing.     

A performance-oriented risk management framework for innovative R&D projects

Uncertainty is one of the major inherent difficulties in developing innovative products, due to their highly dynamic markets and technologies. The presence of a large degree of uncertainty leads to high R&D risks, resulting in many R&D failures. Therefore, it is important to manage R&D risks through all R&D stages to improve R&D project success rates. This paper proposes a new risk management framework that aligns project risk management with corporate strategy and a performance measurement system to increase success rates of R&D projects and to accomplish corporate strategic goals. The balanced scorecard is used to identify major performance measures of an R&D organization based on the firm vision and strategy. Quality function deployment is adapted to transform organizational performance measures into project performance measures and a systematic procedure is developed for risk identification, assessment, response planning, and control. The proposed risk management framework enables an R&D project to be focused on achieving the corporate goals and provides a more effective way to identify, assess, analyze, and monitor R&D risks along the project cycle. The proposed methodology is illustrated with a drug development project.     

How do firm characteristics affect behavioural additionalities of public R&D subsidies? Evidence for the Austrian transport sector

Interest of STI policies to influence the innovation behaviour of firms has been increased considerably. This gives rise to the notion of behavioural additionality, broadening traditional evaluation concepts of input and output additionality. Though there is empirical work measuring behavioural additionalities, we know little about what role distinct firm characteristics play for their occurrence. The objective is to estimate how distinct firm characteristics influence the realisation of behavioural additionalities. We use survey data on 155 firms, considering the behavioural additionalities stimulated by the Austrian R&D funding scheme in the field of intelligent transport systems in 2006. We focus on three different forms of behavioural additionality—project additionality, scale additionality and cooperation additionality—and employ binary regression models to address this question. Results indicate that R&D related firm characteristics significantly affect the realisation of behavioural additionality. R&D intensive firms are less likely to substantiate behavioural additionalities, while small, young and technologically specialised firms more likely realise behavioural additionalities. From a policy perspective, it may be concluded that direct R&D promotion of firms with a high R&D intensity may be misallocated. Attention of public support should be shifted to smaller, technologically specialised firms with lower R&D experience. The findings deliver important insights into interactions of public R&D support and R&D behaviour of firms operating in the Austrian transport sector, bearing significant implications for future policy designs.     

Leadership and innovation: relations between leadership,individual characteristics and the functioning of R&D teams

The main focus of this paper is the functioning of R&D teams, the role of the team leader and the characteristics of individual team members. After a brief overview of recent literature on leadership and innovation, some research results are presented from a study of leadership in self-managing teams; these teams have a number of characteristics in common with R&D teams, such as dependent tasks and job autonomy. The results lead to an integrated model for the effective functioning of R&D teams, with a focus on the relations between leadership behaviour, individual characteristics, team functioning and outcome variables.     

How to optimize the allocation of research resources? An empirical study based on output and substitution elasticities of universities in Chinese provincial level

Although significant progress has been made in China's basic research in recent years, there remains a wide gap between research in China and that from developed countries. How to optimize the allocative efficiency of research resources is of great importance for increasing research output. In this paper, using the fixed effect stochastic frontier model based on the translog production function, we estimate output and substitution elasticities of research and development (R&D) inputs at universities in China's provincial level during 2009–2016. We find that the R&D technical efficiency of China's universities, after a rapid growth, has tended to become relatively stable. Improvements of internationalization degree and exogenous R&D capabilities are conducive to promoting R&D technical efficiency, whereas expenditures from government grants inhibit the promotion of R&D technical efficiency; the effects of R&D capital deepening and internet penetration are not evident. The output elasticity of R&D capital is much higher than that of R&D personnel, suggesting that R&D capital is the main driving force of research output. The substitution elasticity between R&D capital and personnel has experienced a change from substitution to complementary since 2014. To realize sustained growth of research output, we should increase R&D input with positive output elasticity or reduce R&D input with negative output elasticity, making the necessary trade-offs according to the substitution relationship between the two R&D inputs.     

A micro-econometric analysis of the role of R&D spillovers using a nonlinear translog specification

This paper assesses the impact of Research and Development (R&D) spillovers on production for a panel of 1,203 Italian manufacturing firms over the period 1998–2003.The estimations are based on a nonlinear translog production function augmented by a measure of R&D spillovers which combines the geographical distance between firms, the technological similarity within each pair of firms and the technical efficiency of each firm. The estimation method takes into account the endogeneity of regressors and the potential sample selection issue regarding the decision by firms to invest in R&D. Results show that the translog production function is more suitable than the Cobb-Douglas for modelling firm behaviour and that returns to scale are increasing. Moreover, the internal and external stocks of technology exert a significant impact on firms’ production. Finally, it emerges that, for Italian manufacturing firms, R&D capital and R&D spillovers are highly substitutes.     

Strategic Disclosure of Intermediate Research Results

We analyze the incentives to disclose intermediate research results during the course of a patent contest. Despite knowledge spillovers, the leading innovator sometimes discloses to signal commitment to the project, and so potentially inducing a rival's exit. Surprisingly, when development costs are low the leading innovator does not need to disclose to induce the same strategic deterrence effect as that which arises from disclosure. Taking into account wasteful duplication of R&D effort, a patent office can increase welfare by choosing the probability of granting a contested patent and so altering the proportion of rivals that the leading innovator deters.     

Learning from R&D outsourcing vs. learning by R&D outsourcing

We analyze how research and development (R&D) outsourcing influences product innovation. We propose a separation between learning from R&D outsourcing, whereby the firm improves its ability to innovate by using outsourced R&D directly in new products, from learning by R&D outsourcing, whereby the firm indirectly uses outsourced R&D by integrating it with internal R&D to create new products. Building on the knowledge-based view, we argue that learning from R&D outsourcing is likely to have an inverse U-shaped relationship with product innovation, because the initial benefits of using outsourced component R&D knowledge to innovate products is eventually outweighed by the hollowing out of the firm's ability to innovate. In contrast, we propose that learning by R&D outsourcing is likely to have a U-shaped relationship with product innovation, because the initial challenges of integrating internal and external R&D are eventually overcome, resulting in more innovations. Finally, we distinguish between domestic and foreign R&D outsourcing and propose a liability of foreignness in R&D outsourcing as it has a lower impact on new products than domestic R&D outsourcing. The empirical analysis shows that outsourced R&D has an inverted U-shaped relationship with the number of new products, while the interaction between outsourced R&D and internal R&D has a U-shaped relationship with the number of new products. It also shows that domestic outsourced R&D has a higher positive impact on the number of new products than foreign outsourced R&D.     

The differential effects of public R&D support on firm R&D: Theory and evidence from multi-country data

This paper aims to evaluate the effects of various forms of public research and development (R&D) support on firms’ incentives to invest in R&D. First, in order to identify potential channels through which public R&D support influences firm R&D, a formal model of firm R&D with public R&D support is developed and analyzed. Four potential channels are identified: the technological-competence-enhancing effect, the demand-creating effect, the R&D-cost-reducing effect and the (project) overlap (or duplication) effect. These multiple channels indicate that it is difficult to evaluate the aggregate effect of public R&D support and that there are differential effects of public R&D support on firm R&D, depending on various firm- or industry-specific characteristics. Second, the differential effects of public R&D support are empirically tested using unique firm-level data for nine industries across six countries. Public support tends to have a complementarity effect on private R&D for firms with low technological competence, for firms in industries with high technological opportunities and for firms facing intense market competition. In contrast, firms with high technological competence and firms that have enjoyed fast demand growth in recent years show a crowding-out effect, and firm size and age do not show any discernible differential effect.     

Technological Opportunity and Productivity of R&D Activities

Economists have managed to find a positive impact of R&D efforts on productivity. However, the empirical results of their studies have not explained the observed sectoral differences in this important impact. With due reference to three global industries, namely, chemical, computer, and electrical/electronic, the objective of this study is to evaluate the impact of technological opportunity on the productivity of R&D activities. Technological opportunity refers to the ease of achievement of innovations and technical improvements, which could be jointly represented by the intensities of knowledge spillovers, inter-firm research overlap and scope of research. In this study, the degree of technological opportunity is quantified by patent statistics. The empirical findings confirm a positive relationship between technological opportunity and the productivity of R&D effort, and the estimated rate of return falls within the range as reported by past studies.