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.     

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.     

How does Internationalisation affect the productivity of R&D activities in large innovative firms? A conditional nonparametric investigation

Quality & Quantity - This work explores the relationship between multinational R&D and innovation productivity among top corporate knowledge and R&D producers by adopting a...     

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.     

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.     

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.     

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 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.     

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.     

Making sense of innovation by R&D and non-R&D innovators in low technology contexts: A forgotten lesson for policymakers

This paper attempts to use an integrated theory based on the framework of a firm's internal and external sources of knowledge to analyze how R&D activities differ in innovation from non-R&D activities, especially in the context of low and medium-low tech (LMT) sectors where most of the firms are SMEs. Simultaneously, the paper also explores the key differences between R&D and non-R&D innovators. The empirical analysis is based on a representative panel of 2023 Spanish manufacturing firms from the Spanish Ministry of Industry for 2005 and 2006. Innovation in product and process is explained using non-R&D variables such as marketing, design or the hiring of tertiary degree employees. Only innovation in product is explained by R&D expenditures. Regarding innovation in process, the R&D variables work in a few specific cases. Therefore, innovation can be explained using non-R&D variables. The firms with more internal resources, those which conduct R&D activities, present a better absorptive capacity (AC) and this leads them to engage in cooperation agreements and to access external flows of knowledge. The paper has important implications for policymakers due to the fact that most policies for R&D are based just on R&D programmes.     

TPL参与的V MI供应链集成模式研究

VMI的实施对买方而言益处是显而易见的,而对卖方或者供应商而言则是有利有弊的。供应商物流能力的制约是产生弊端的主要原因之一。鉴于此,TPL被引入到VMI模式中。文中首先从多角度分析了VMI＆TPL的集成动因,其次构建了引入TPL后的VMI＆TPL集成模型,并分析了在不同模型中TPL所承担的责任和所起的作用。根据TPL在VMI＆TPL模型中角色和作用的不同,将VMI＆TPL集成模式分为分散型、协调型和集中型VMI＆TPL模式,并分别阐述了其适用环境。最后讨论了VMI＆TPL的运作流程并提出了VMI＆TPL集成模式有效运作的基本原则。     

Language and interaction: applying sociolinguistics to social network analysis

Quality & Quantity - In recent years social network analysis, influenced by relational sociology, has taken a cultural turn. One result has been a growing interest in...     

Cross-border mergers and acquisitions with heterogeneous firms: Technology vs. market motives

The most common form of foreign direct investment (FDI) is cross-border mergers and acquisitions (M&A). A common explanation for M&A activity identified in the industrial organization literature is that firms seek technological expertise. However, this has not been examined in the FDI literature. In this paper, I develop and estimate a model of cross-border M&A and focus on the technology seeking explanation. In particular, I develop a general equilibrium model of exporting, greenfield FDI, technology-seeking cross-border M&A, and market-seeking cross-border M&A with heterogeneous firms. The model predicts that firms from a larger country are more likely to acquire in a smaller country when M&A activity is driven by a technology-seeking motive, but the opposite is true when it is driven by a market-seeking motive. Using detailed data on worldwide M&A activity from 1985 to 2007, I find empirical evidence that cross-border M&A activity exhibits behavior consistent with this prediction.     

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.     

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.     

To invest or not to invest: strategic decision making toward investing in training and development in Korean manufacturing firms

This study draws on the literature on strategic choice theory and training and development (T&D) to explore the theoretical mechanisms that explain the strategic decisions of top management, thereby leading to the T&D investment of firms. The current theoretical framework was examined using cross-lagged data collected from 163 Korean manufacturing companies at three time points over a five-year period. Results confirmed that firm performance and environmental change exert significant effects on top management strategic orientation toward T&D, which in turn, lead to financial resource allocation to T&D. The analysis also demonstrated that the effect of top management strategic orientation toward T&D on actual financial investment in T&D is stronger when the current level of human capital is high but not when it is low. This study provides meaningful practical and theoretical insights into the firm-level strategic decisions on T&D investment in organizations.     

Mining the R&D innovation performance processes for high-tech firms based on rough set theory

The research and development (R&D) innovation of firms continues to be viewed as an important source of competitive advantage to academics and practitioners. To explore and extract the R&D innovation decision rules, it is important to understand how the R&D innovation rule-base works. However, many studies have not yet adequately induced and extracted the decision rule of R&D innovation and performance based on the characteristics and components of the original data rather than on post-determination models. The analysis of this study is grounded in the taxonomy of induction-related activities using a rough set theory approach or rule-based decision-making technique to infer R&D innovation decision rules and models linking R&D innovation to sales growth. The rules developed using rough set theory can be directly translated into a path-dependent flow network to infer decision paths and parameters. The flow network graph and cause-and-effect relationship of decision rules are heavily exploited in R&D innovation characteristics. In addition, an empirical case of R&D innovation performance will be illustrated to show that the rough sets model and the flow network graph are useful and efficient tools for building R&D innovation decision rules and providing predictions. We will then illustrate that integrating the flow network graph with rough set theory can fully reflect the characteristics of R&D innovation, and, through the established model, we can obtain a more reasonable result than with artificial influence.     

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.     

Optimal R&D investment for a risk-averse entrepreneur

Entrepreneurs investing in R&D projects face technical uncertainty associated with the cost to completion of the project, which is idiosyncratic and inherently unhedgeable. We extend existing real options models of R&D investment to incorporate the cost of bearing this unhedgeable risk and find it decreases risk-averse entrepreneurs’ valuations of R&D projects and increases the minimum NPVs required for continued investment in R&D (threshold NPVs) relative to ‘unpriced risk’ values and threshold NPVs. As in the ‘unpriced risk’ case, for less risk-averse entrepreneurs with small R&D projects, threshold NPVs remain negative and decrease with technical uncertainty. However, for sufficiently risk-averse entrepreneurs with sufficiently large R&D projects, threshold NPVs can become positive and increase with technical uncertainty.