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.     

R&D performance assessment of industrial enterprises in China: A two-stage DEA approach

With the growing expenditure on the R&D activities of industrial enterprises above designated size (IEDSs) in China, it is important to evaluate the R&D efficiency of the Chinese IEDSs. However, few studies about R&D efficiency measurement of Chinese IEDSs have considered the internal structure of the R&D production process. To fill this gap, this paper investigates the R&D performance of IEDSs of 30 sample provinces on China's mainland from 2009 to 2014, based on a two-stage data envelopment analysis (DEA) model. The major findings from the empirical results are shown as follows: (i) serious imbalance exists in the R&D resources among Chinese IEDSs of 30 provinces; (ii) there is a decline in the average overall efficiencies after 2012; (iii) there are great differences regarding the performances of R&D activities among the Chinese IEDSs of 30 provinces; (iv) high attention to the R&D activities or strong scientific research atmosphere may promote the R&D efficiency of Chinese IEDSs; and (v) the IEDSs with the relatively high profitability or high government support in terms of R&D activities have relatively poor performance. Based on these findings, several policy suggestions are proposed for the R&D activities of Chinese IEDSs.     

Environmental regulations in private and mixed duopolies: Taxes on emissions versus green R&D subsidies

In this article, we compare two kinds of environmental regulations—emissions taxes and green R&D subsidies—in private and mixed-duopoly markets in the presence of R&D spillovers. We show that a green R&D subsidy is better (worse) than an emissions tax when the green R&D is efficient (inefficient), irrespective of R&D spillovers, whereas the existence of a publicly owned firm encourages the government to adopt a subsidy policy. We also show that the optimal policy choice depends on R&D efficiency and spillovers. In particular, when green R&D is inefficient and the spillover rate is low (high), the government should choose an emissions tax and (not) privatize the state-owned firm. When green R&D is efficient, however, an R&D subsidy is better, but a privatization policy is not desirable for society, irrespective of spillovers.     

Performance evaluation of China's high-tech innovation process: Analysis based on the innovation value chain

The Chinese high-tech industry has developed greatly since the beginning of China's “National High-tech R&D (863) Program” and “China Torch Program”. This paper introduces a conceptual model extended from the innovation value chain model to simultaneously estimate the R&D and commercialization efficiencies for the high-tech industries of 29 provincial-level regions in China. To match reality, a network DEA incorporating both shared inputs and additional intermediate inputs is constructed to open the “black box” view of decision making units used in single-stage DEA. This study is the first attempt to link the R&D and commercialization with a solid theoretical foundation and feasible mathematical methods. The empirical findings show that most of the 29 regions have low efficiency in the commercialization sub-process compared to the R&D sub-process, although there are regional differences in China's high-tech industry. Pearson correlation shows that the R&D sub-process is not closely correlated to the commercialization sub-process in terms of efficiency. Our analysis can provide information for the formulation of policies to achieve high innovation efficiency.     

Reprint of "Performance evaluation of China's high-tech innovation process :Analysis based on the innovation value chain"

The Chinese high-tech industry has developed greatly since the beginning of China's “National High-tech R&D (863) Program” and “China Torch Program”. This paper introduces a conceptual model extended from the innovation value chain model to simultaneously estimate the R&D and commercialization efficiencies for the high-tech industries of 29 provincial-level regions in China. To match reality, a network DEA incorporating both shared inputs and additional intermediate inputs is constructed to open the “black box” view of decision making units used in single-stage DEA. This study is the first attempt to link the R&D and commercialization with a solid theoretical foundation and feasible mathematical methods. The empirical findings show that most of the 29 regions have low efficiency in the commercialization sub-process compared to the R&D sub-process, although there are regional differences in China's high-tech industry. Pearson correlation shows that the R&D sub-process is not closely correlated to the commercialization sub-process in terms of efficiency. Our analysis can provide information for the formulation of policies to achieve high innovation efficiency.     

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.     

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.     

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.     

Cooperative R&D ventures: Strategic behavior versus transactional efficiency

This paper examines a sample of 48 cooperative R&D ventures announced in the period 1983–1990 to determine their impact on the stock price of the announcing firms. It finds that the venturing firms earn statistically significant positive abnormal returns over periods surrounding the announcement date. These gains are greater than those that result from announcements of increases in expenditures on in-house R&D. The cross-sectional analysis reveals that abnormal returns to the venturing firms are independent of industry concentration, larger in international ventures than in domestic ventures, and equal in cooperative agreements and equity joint ventures. These results support the transactional efficiency perspective of cooperative R&D ventures and suggest that the relaxation of antitrust laws as they apply to cooperative R&D ventures in 1984 is a welfare-improving policy.     

Contributions and Channels of Interindustry R&D Spillovers: An Estimation for Japanese High-tech Industries

This paper estimates the contributions of R&D spillovers to four high-tech industries in Japan—general machinery, electrical machinery, transportation machinery and chemicals—by estimating the trans-log cost and share functions that include the R&D stock variables of own and spillover-source industries. The candidates for spillover- source industries are selected on the basis of large R&D flow or R&D proximity. The R&D flow measures the spillover embodied in purchased intermediate goods using input–output coefficients. The R&D proximity measures the extent of similarity between a pair of industries of the distribution of R&D expenditures across research fields, and is expected to show the likelihood of spillover at the R&D stage. The results suggest that electrical machinery benefited from R&D in the chemical industry, through the purchase of intermediate goods, whereas general machinery and transportation machinery benefited from R&D in the metal products industry, through R&D proximity. There was no evidence of the chemical industry benefiting from R&D spillovers. These results clearly imply that the contributions and the channels of R&D spillovers are diverse, casting doubt on earlier studies that used weighted sums of R&D expenditures (or their stocks) of other industries as aggregate spillover variables.     

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.     

Research collaboration and R&D outsourcing: Different R&D personnel requirements in SMEs

The literature on ‘open’ innovation emphasises the need to engage in external knowledge relations in order to innovate. Particularly for SMEs, research cooperation and R&D outsourcing can offer possibilities to complement the often limited internal research resources. However, they also bring in their wake requirements in terms of absorptive capacity and managerial skills of the internal R&D personnel.The paper focuses on the different requirements in terms of availability and training of research managers and R&D experts for research cooperation versus R&D outsourcing in SMEs. An empirical analysis of micro-level data provided by the OECD business R&D survey for Belgium reveals that the relation between R&D personnel requirements and research collaboration and R&D outsourcing depends upon the SME size. Therefore, to study this subject appropriately a distinction between very small, small, and medium-sized firms is relevant. Very small firms engage significantly less in research cooperation than medium-sized firms and the propensity to engage in research cooperation is positively associated with the share of PhD holders among the research managers and R&D experts. For R&D outsourcing a lower involvement is noted in medium-sized firms, and the propensity to outsource increases with the formal qualification level of the R&D personnel and with R&D training. Among the SME, small firms are most engaged in research cooperation and in R&D outsourcing. In the case of research cooperation they rely on highly qualified experts. For R&D outsourcing activities both the presence of research managers and R&D experts is important.     

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.     

Managerial incentives and R&D investments: The moderating effect of the directors’ and officers’ liability insurance

Literature regarding the impact of managerial incentives on firm’s research and development (R&D) investments suggests that due to the riskiness of R&D activities, firms need to provide managerial incentives to encourage managerial discretion on corporate long-term investments of R&D. In spite that managerial incentives influence corporate R&D spending, some also argue corporate R&D spending a function of managerial incentive schemes. This paper applies the simultaneous equation to investigate the association between managerial discretion on R&D investments and the incentive scheme of CEO compensations by using the sample firms listed in Taiwan Security Exchange and Taipei Exchange. The results indicate that the listed firms in Taiwan simultaneously determine corporate R&D investments and CEO compensations. They reward their CEOs in compliance with their efforts on R&D investments and CEO compensation motivates CEOs to align their interests with firms’ long-term investments on R&D. A further analysis of the protection effect from the directors’ and officers’ (D&O) liability insurance suggests that D&O protection intensifies the relationship between R&D investments and CEO compensation. It encourages CEOs to allocate resources on R&D activities and make CEO incentive contracts efficacious on corporate long-term investments. The result is robust in the electronic industry of Taiwan.     

The effect of R&D novelty and openness decision on firms' catch-up performance: Empirical evidence from China

This paper explores the strategic dimensions of R&D decisions toward novelty and openness in explaining the performance of latecomer firms in a developing economy. A structural equation model of R&D decision-making is formulated using survey data from 279 Chinese firms. The dimension of R&D novelty is defined as the degree of technological newness found in firms' R&D projects, while R&D openness describes the degree to which technologies are acquired from external sources. Our results indicate that firms' R&D decisions regarding novelty and openness are associated with demand opportunities, market competition, technological capability, and external networks. Greater R&D novelty contributes positively to innovative output but does not affect sales growth. Greater R&D openness contributes positively to sales growth but negatively to innovative output.     

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.     

Industry evolution and R&D externalities

R&D externalities can imply ranges of aggregate increasing returns to scale in R&D. As a consequence several equilibria can exist involving different numbers of firms and different R&D investment levels. In a theoretical model the equilibrium dynamics are analyzed, showing that cyclical fluctuations of the number of firms and R&D investment may be expected, and that the long-run equilibrium is highly sensitive to investors' initial beliefs. The model is tested empirically using a unique database comprising competing firms in various R&D races.     

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.     

Efficiency evaluation of China's high-tech industry with a multi-activity network data envelopment analysis approach

This paper employs a Russell multi-activity network DEA model and divides the overall innovation process into the upstream Research and Development (R&D) process and the downstream commercialization process to appraise the innovation performance of China's high-tech industries from 2009 to 2013. This model can deal with the problems of intermediates, shared inputs and slack-based measure in a unified framework, and the result can provide policy makers with process-specific information on how to improve the innovation performance of China's high-tech industries. The main findings are presented as follows. First, the overall efficiency of China's high-tech industries still remains at a low level, which has its roots mainly in commercialization inefficiencies other than R&D inefficiencies. Second, for most provinces, their R&D efficiencies do not match up with their commercialization efficiencies. Finally, the innovative activities of China's high-tech industries should be driven by the market demand -oriented for the improvement of innovation efficiency.     

Stratified network mapping decision making technique based decision support framework for R&D budget allocation in South Korea

In this research work, we proposed and developed a stratified network mapping (SNM) decision making method and used it to improve the industry–university specialization in R&D in each region selected in this study. The proposed method considers the influence of criteria on and their priority in alternatives performance evaluation process. By analyzing the influence of these criteria on decision-making, we can easily improve the performance of alternatives. The SNM gives a clear understanding of each alternatives performance efficiency level. It explores possible and inefficient states and high-level influence states in inefficient states. Narrowly using multi-criteria decision-making methods to rank alternatives does not improve the performance of alternatives. The proposed method helps rank alternatives and improve the performance level of alternatives in each state. We analyzed the R&D investment of central and local governments of South Korea. It is an attempt to invigorate and facilitate R&D collaboration using a decision support model. We analyzed industry–academia research networks and enhanced the efficiency of the research.