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.     

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.     

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.     

Exploring the operating efficiency of Technology Development Programs by an intellectual capital perspective—A case study of Taiwan

Government supported technological research and development can help the private sector to compete globally. A more accurate evaluation system considering multi-factor performance is highly desired. This study offers an alternative perspective and characterization of the performance of Technology Development Programs (TDPs) via a two-stage process that emphasizes research and development (R&D) and technology diffusion. This study shall employ a sequential data envelopment analysis (DEA) with a non-parametric statistical analysis to analyze differences in intellectual capital variables among various TDPs. The results reveal that R&D performance is better than technology diffusion performance for the TDPs. In addition, the “Mechanical, Mechatronic, and Transportation field” is more efficient than the other fields in both R&D and technology diffusion performance models. The findings of this study point to the importance of intellectual capital in achieving high levels of TDP efficiency. The potential applications and strengths of DEA and intellectual capital in assessing the performance of TDP are also highlighted.     

Over-investment in corporate R&D,risk, and stock returns

Large-size firms which significantly increase their R&D expenditures experience subsequently three-year-long negative abnormal stock returns on the magnitude of 56 basis-points per month. We find no robust evidence of significant event-induced abnormal returns for small-size sample firms or any systematic risk changes for the small- and large- size firms. We also find that the large-size sample firms generate relatively much larger cash flows (i.e., have significantly greater over-investment discretion) and have significantly larger (over-) valuation multiples than the small-size firms. Moreover, some of their operating performance measures show signs of deterioration instead of improvement following these R&D programs. These findings are consistent with the view that investors initially underestimate the over-investment in R&D by some large-size firms that appear to be overvalued and have high cash flows at the time of the investment, only to be disappointed later.     

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.     

Assessing the technological innovation efficiency of China's high-tech industries with a two-stage network DEA approach

As significant strategic players in China's economy, high-tech industries need to evaluate and analyze the technological innovation activities from a system point of view to understand and improve their technological innovation efficiency and, thereby, promote their development. Different high-tech industries have different characteristics and thus benefit from different industrial development policies. However, few studies to date have discussed this issue from a systematic perspective. In this study, technological innovation activities are divided into a research and development (R&D) stage and a commercialization stage. A high-tech industrial evaluation framework of technological innovation efficiency based on two-stage network data envelopment analysis (DEA) is constructed with shared inputs, additional intermediate inputs, and free intermediate outputs. Our empirical results indicate that the overall efficiency of most industries is relatively low and the differences between the five high-tech industries (i.e., sub-sectors) we examined are large. The Spearman correlation shows that overall efficiency and R&D efficiency are more correlated than overall efficiency and commercialization efficiency. Additionally, R&D has better average efficiency. The sub-sector with the highest average efficiency is computers and office equipment, and the one with the lowest average efficiency is medicines. These findings indicate the inadequacy but potential for breakthroughs in the evolution of high-tech industries in China. The analysis proves that it is necessary to create different industrial policies to encourage effective progress in certain high-tech industries, and some guidelines for doing so are provided.     

Short-term R&D bias,competition on cost rather than innovation,and time to market

Mounting evidence indicates that capital markets often apply short-term pressure on firms to gain short-term results by focusing primarily on reported financial performance. As a result of short termism, it has been argued that companies are likely to cut expenditure on R&D which might otherwise improve longer-term performance. As there is a growing consensus that R&D is critically important to both organizational and national performance, short termism may have significant detrimental organizational consequences. One implication arising from a short-term R&D bias, and examined in this paper, is its effect on market time reduction. Arguments are examined that suggest a dominant R&D strategy is to reduce product time to market. Concerns have been expressed, however, that such a strategy is applicable in specific circumstances only. A review of the literature suggests that analyst and shareholder bias against high-risk, long-term research in favor of lower-risk, short-term product R&D influences organizations to reduce the time it takes to get a product to market when the emphasis in the marketplace is on cost competition rather than product innovation. The findings of the study suggest that when the emphasis on competition on cost rather than innovation is low, short-term R&D bias does not affect market time. In contrast, when the emphasis on competition on cost rather than innovation is high, the results indicate that short-term R&D bias positively influences market time reduction. The study concludes with suggestions for further research.     

An analysis of firm-level innovation strategies in the US biotechnology industry

This study examines factors that may affect innovation strategies and performance of firms in the biotechnology industry. Specifically, differences between factors common to firms with high R&D intensity and those to firms with low R&D intensity are investigated. Biotechnology firms with relatively higher levels of R&D intensity attribute their innovation performance to research-based innovation factors and strategies such as strengthening their own research capabilities, entering into research collaborations with universities, industry leaders and other biotech firms, and licensing their technology. These strategies can be summarized as alignment within the industry. Firms with relatively lower R&D intensity have a hybrid focus—they invest in R&D but may also have products on the market. These firms attribute their innovation performance more so to production-based innovation factors and strategies such as gaining market access and maintaining connections with customers. Their strategy focuses on competitiveness, marketing, and distribution channels, while not ignoring the importance of a strong research base and the need to advance technologically. In a sense, strategies employed to achieve successful innovation reflect the stage of innovation in which a firm is operating for a particular product or process.     

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.     

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.     

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.     

The dark side of stock market liberalization: Perspectives from corporate R&D activities in China

This study explores how stock market liberalization impacts corporate research and development (R&D) activities and examines the impact channels. Based on a quasi-natural experiment on the Shanghai-Hong Kong Stock Connect, we report a significantly negative relationship between stock market liberalization and R&D activities in non-state-owned enterprises (non-SOEs) using a difference-in-difference approach. For one thing, a decrease in analyst coverage will reduce the supervision of R&D activities; for another, the alleviation of financing constraints will heighten corporate financialization, resulting in a greater crowding-out effect on R&D activities. Furthermore, R&D activities in SOEs, which are guided by government policies, are not significantly affected by stock market liberalization.     

Recruitment and selection services: Efficiency and competitive reasons in the outsourcing of HR practices

While human resource (HR) activities have traditionally been performed internally, the outsourcing of HR practices is a rapidly increasing phenomenon. The accelerated rate of HR outsourcing also corresponds to a sweeping change in which non-transactional activities, such as recruitment, selection and training, are among the most outsourced HR practices. This article investigates the outsourcing decisions of recruiting and selection (R&S). It develops a predictive model based on efficiency drivers, rooted in transaction cost economy (TCE), and competitive motivations, derived from the resource-based view. The model has been tested in a sample of 276 medium and large enterprises in two specific contexts: the outsourcing of administrative R&S practices (job advertisement and pre-screening) and that of the more strategic R&S practices (colloquia and selection). Findings confirm the relevance of both categories of predictors, but they reveal how efficiency motivations are more important for the decisions to outsource administrative R&S practices while competitive issues matter more for the strategic side of R&S activity. Theoretical and managerial implications are offered on the basis of such evidence.     

Credit constraints and R&D over the boom and bust: Firm-level evidence from Central and Eastern Europe

This paper studies the effect of credit constraints on R&D over the recent boom and bust episode in Central and Eastern European countries (CEECs). Given that financial and venture capital markets in CEECs are thin in comparison to those in high-income economies, it is proposed that credit constraints have a significant adverse effect on R&D activity in these countries. The paper uses three waves of Business Environment and Enterprise Performance Survey (BEEPS) data between 2005 and 2013 on manufacturing firms from ten CEECs. We find that credit constraints have a substantial effect on R&D engagement, as the probability of credit constrained firms undertaking R&D activities is around 30 % lower than for other firms. The adverse effect of credit constraints for R&D emerges during the years of fast economic growth.     

The impact of open innovation on firm performance: The moderating effects of internal R&D and environmental turbulence

Researchers have identified open innovation as two dimensions, external technology acquisition and external technology exploitation. This study explores the direct and interactive effects of these two dimensions on firm performance and further examines the moderation effects of two factors (i.e., internal R&D and environmental turbulence) on the relationship between both types of open innovation and firm performance. Based on Chesbrough's open innovation model, multi-item scales were developed to measure two dimensions of firm-level open innovation. Survey results of 176 Taiwanese high tech manufacturing firms provide support for most hypotheses. The result shows that external technology acquisition positively affects firm performance, whereas external technology exploitation does not. This study also finds that external technology acquisition strengthens the relationship between external technology exploitation and firm performance. Both external technology acquisition and external technology exploitation are positively related to firm performance under high internal R&D investment and a turbulent market environment. However, technological turbulence only positively affects the relationship between external technological acquisition, but not external technology exploitation, and firm performance. The findings contribute to enhanced understanding of how the degree of leveraging open innovation dimensions depends on their complementarity, internal R&D, and environmental turbulence.     

What motivates R&D professionals? Evidence from decentralized laboratories in Greece

The challenge for research and development (R&D) professionals management is to meet the corporate objectives for effectiveness, productivity and profitability with the needs of technical employees for motivation, reward and satisfaction. Despite the continuous upgrading of the strategic importance of knowledge and technology, human resource management paid little attention to the distinctive characteristics of those highly educated knowledge workers, especially in an international perspective. Based on a large-scale survey of multinational enterprises' (MNEs') decentralized R&D laboratories in Greece, this paper offers insights on the perceived impact of intrinsic and extrinsic compensation on performance. Focused on three distinctive types of research implemented by MNEs, results record the existence of a multifaceted context of researchers' needs and satisfaction determinants, which is differentiated among the different roles of R&D laboratories. Our findings witness that in an EU peripheral economy, R&D professionals are motivated by extrinsic rewards and mainly by economic compensation.     

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.     

Estimating the multi-period efficiency of high-tech research institutes of the Chinese Academy of Sciences: A dynamic slacks-based measure

Although a large amount of past research has studied the measurement of institutional research and development (R&D) efficiency, there has been limited dynamic empirical investigation of this topic. This study differs from previous studies in its consideration of R&D capital stock, which is defined as a carry-over activity between two consecutive periods. To further test high-tech institutes’ R&D performance in the pre-reform and post-reform periods, we evaluate a panel of 29 high-tech institutes of the Chinese Academy of Sciences (CAS) for 2011–2017 with a dynamic slacks-based measure. (a) The empirical value of the average overall efficiency is 0.5559, and the clustering analysis shows that institutes operating above the average overall efficiency level follow an increasing and then decreasing trend, while those operating below the average overall efficiency level follow a decreasing and then increasing trend. (b) The efficiency of low-performing institutes has increased from 0.2259 in the pre-reform period to 0.2727 in the post-reform period, which does not reflect the efficiency improvements of high-performing institutes. (c) High-tech institutes selected as pilot institutes have an average efficiency level of 0.6524, which is higher than that of non-pilot institutes (0.4765), indicating the benchmarking effect of pilots. (d) The types of pilot institutes can be ordered as follows: feature institutes > centres of excellence > innovative academies. (e) The total-factor input efficiency level has decreased in the post-reform period. Only the total-factor efficiency of high-quality papers has increased in the post-reform period (by 0.0094).