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This study aims to investigate the influence of the financial system (financial development and financial structure) on firms' innovation efficiency in China.

This study employs country level data of capital markets and financial institutions along with innovation data from 18 high-tech industries in China spanning the 2009–2016 period, and the stochastic frontier analysis (SFA) is applied to explore how financial development and financial structure affect the innovation efficiency of these industries.

Results show that financial development influences firms' innovation efficiency positively and the capital-market-based financial structure has a positive impact on innovation efficiency of high-tech industries. Furthermore, when the high-tech industries are grouped into five sub-industries, the results show that financial structure had different effects on the innovation efficiency in each sub-industry.

This work contributes to the empirical research on considering the influential factors of innovation efficiency from the perspective of financial system. This paper also extends the existing literature by the different influences of financial system on innovation efficiency in each sub-industry of Chinese high-tech industries.

The purpose of this paper is twofold: to investigate the existence of different degrees of returns to scale in low-tech and high-tech manufacturing industries; and to examine whether the degrees of returns to scale change through time.

The empirical investigation implemented in the paper uses data from the EU KLEMS Database, covering a sample of 12 manufacturing industries in 11 OECD countries over the period 1976-2006. The investigation employed two different estimation methods: instrumental variables and system GMM. The robustness of the results was assessed by employing two different specifications of Kaldor-Verdoorn’s Law, by using lags and five-year averages to smooth business-cycle fluctuations, and by dividing the sample into two time periods.

The results reported in the paper provide strong evidence in support of the hypothesis of substantial increasing returns to scale in manufacturing. The investigation suggests that high-tech manufacturing industries exhibit larger degrees of returns to scale than low-tech manufacturing industries. Finally, the analysis revealed also that the magnitude of the returns to scale in manufacturing have increased in the last decades, driven by increases in the magnitude of returns to scale observed in high-tech industries.

No previous work has assessed the hypothesis that increasing returns to scale vary according to the technological content of industries. Moreover, no previous work has used system GMM or data from EU KLEMS to test Kaldor-Verdoorn’s Law. Most importantly, the findings of the paper present new evidence on the degree of returns to scale in high-tech and low-tech manufacturing industries.

The purpose of this paper is to empirically investigate the effectiveness of innovation protection mechanisms in four high-tech industries in Malaysia.

A questionnaire survey is administered to 339 managers working in four Malaysian high-tech industries.

The authors find that in most industries, patents are considered as the most effective innovation protection mechanism, while lead time is considered as least effective. In firms with private and foreign ownerships, patents and in firms categorized as “others” “moving quickly down the learning curve” are considered as the most effective protection mechanisms. It also emerges that “being first to market (lead time)” is considered as the least effective protection mechanism under all ownership structures. Further, young and middle-aged firms report patents, while old firms report secrecy as the most effective innovation protection mechanisms.

This paper restricted analysis to the high-tech sector in Malaysia, and thus, the findings are not generalizable to other industries. Second, this paper took into consideration only four innovation protection mechanisms. The authors suggest that future research should investigate the issue at hand in other industries and consider other innovation protection mechanisms to have a holistic view of how Malaysian firms view different innovation protection mechanisms.

In addition to providing inputs for policy-making, the results of this paper are expected to help practitioners in deciding on the right kind of innovation protection mechanisms for their innovations based on their industry, ownership structure and firm age.

This paper is the first of its kind conducted in the Malaysian high-tech sector and as such is expected to help policy-makers to design and implement effectively innovation protection policies.

High-tech industries play an important role in promoting economic and social development. The purpose of this paper is to accurately predict and analyze the output value of high-tech products in Guangdong Province, China, by using a multivariable grey model.

Based on the principle of fractional order accumulation, this study proposes a multivariable grey prediction model. To further enhance the prediction ability and accuracy of the model, an optimized model is established by reconstructing the background value. The optimal parameters are solved by minimizing the average relative error of the system characteristic sequence with the constraint of parameter relationships.

The results from the study show that the two proposed models exhibit better simulation and prediction performance than the traditional models, while the optimized model can significantly improve the modelling precision. In addition, it is predicted that the output value of high-tech products is 12,269.443bn yuan in 2021, which will approximately double from 2016 to 2021.

The two proposed models can be used to forecast the trend of the system and are grown as an effective extension and supplement of the traditional multivariable grey forecasting models.

The forecast and analysis of the development prospects of high-tech industries would be useful for the government departments of Guangdong Province and professional forecasters to grasp the future of high-tech industries and formulate decision planning.

A new multivariable grey prediction model based on fractional order accumulation and its optimized model obtained by reconstructing the background value, which can improve the modelling accuracy of the traditional model, is proposed in this paper.

A firm will seek an optimal balance between internal R&D and technology outsourcing when formulating its innovation strategy. This paper aims to provide a review of the determinants of firm's innovation strategy, and performs an empirical study on a sample from Chinese high‐tech industry, with the purpose of identifying two aspects of the issue: the choice patterns of Chinese firms over innovation strategy, and the innovation effect elasticity of different strategies.

The development of a multiple regression model supported by data from industry level and a statistic analysis.

Outsourcing is the major innovation strategy adopted by most Chinese high‐tech firms, especially technology import, which implies the imperfection of Chinese innovation service system. The empirical analysis also indicates the insufficiency of internal R&D expenditure and the weakness of absorptive capacity in Chinese high‐tech firms. Although, Chinese high‐tech firms prefer the outsourcing strategy in their innovation, the contribution of outsourcing is much smaller than that of internal R&D. When expenditures are increased by the same rate, the innovation output form internal R&D is twice the output of outsourcing. For improving Chinese firms' innovation efficiency, the reform of innovation service system is needed on the macro‐level, while on the micro‐level, it calls for firms to readjust their innovation strategy portfolio.

This paper will make up for the deficiency in current researches on innovation, which often apply firm samples in developed countries, and lack evidences from firm samples in developing countries. In addition, it will provide the decision‐making basis for Chinese Government's current actions in constructing and improving China's innovation service system.

The purpose of this study is to fill the gaps in previous literature and investigate the link between product launch performance and supply chain architecture and performance. During the past 20 years, most of the new product literature has focused on new product development and product innovation. Only a few product launches have been discussed in specific fields.

From the perspective of the manufacturer, interfunctional coordination, supply chain architecture and supply chain performance affect lean launch performance. Lean launches can also add value to product and marketing performance. A questionnaire was used to gather data from project, account and purchasing managers in the high-tech industry and to test the postulated research model and hypotheses. The conceptual model was tested using 242 usable questionnaires.

The results provide evidence that interfunctional coordination is the basis for improving supply chain architecture. The supply chain has a strong, positive effect on lean launch performance. Lean launch is vital to the successful performance of a new product. Although lean launch execution and supply chain performance affect marketing performance and new product performance, the direct effect on marketing performance is non-significant.

This study presents the characteristics of the supply chain architecture specific to the high-tech industry. The authors empirically tested and propose a model to explain how high-tech manufacturers build a solid supply chain and leverage the capabilities of suppliers to improve lean launch execution and new product performance.

Through the resource-based view (RBV) and contingency theory, this study empirically investigates the impacts of smart manufacturing systems' maturity levels on the performance of small and medium-sized enterprises (SMEs). Moreover, it aims to examine how industry types (i.e. high- and low-tech industries) and human-resource factors (i.e. the proportion of production workers to total workers) as contingency factors influence the effects of smart manufacturing systems.

The study conducted an empirical investigation of a sample of 163 Korean manufacturing SMEs. This study used an ordinary least squares regression to examine the impacts of the maturity levels of smart manufacturing systems on financial performance. Moreover, the impacts on operational efficiency were analysed using data envelopment analysis based on bootstrap methods and Tobit regression.

The RBV results indicate that the higher the maturity levels of smart manufacturing systems, the higher the financial performance and operational efficiency. Moreover, based on contingency theory, this study reveals that the effect of the maturity levels of smart manufacturing systems on financial performance and operational efficiency depends on firms' industry types and the proportion of production workers.

This study shows that the introduction of smart manufacturing systems can help SMEs achieve better financial performance and operational efficiency. However, their effectiveness is contingent on firms' industry types and the characteristics of their human resources.

Since the effects of the maturity levels of smart manufacturing systems on SME performance differ depending on their industries and the characteristics of human resources, managers need to consider them when introducing or investing in smart manufacturing systems.

Based on the RBV and contingency theory, this is the first empirical study to examine the moderating effects of industry types and the proportion of production workers on the impacts of the maturity levels of smart manufacturing systems on the financial performance and operational efficiency of SMEs.

The purpose of this paper is to examine the circumstances under which high quality audits reduce a firm's cost of debt. The paper extends previous research by Pittman and Fortin by considering how auditor quality relates to the capital cycle and industry of the firm.

The paper uses a sample of US initial public offerings (IPOs) from 1986 to 1998 to analyze a firm's debt costs for the five years following the IPO. The paper uses a firm's private age as a proxy for its capital cycle and existing banking relationships to capture the likely extent of debt dependence prior to IPO. The authors separately analyze technology firms from other firms.

Consistent with prior literature, it is found that firms that are young at the time of an IPO pay higher interest rates and auditor quality plays a significant role in lowering the cost of debt financing. Consistent with the hypotheses made, the authors also observe that the effect of auditor quality is larger for firms in the high tech industry sector. Further, the relationship between auditor quality and age depends on industry, with the benefits of hiring a high quality auditor primarily accruing to younger tech firms and older non‐tech firms.

While the issue of auditor quality and cost of debt has been examined by previous researchers, the additional insight that the effect of auditor quality depends on both capital cycle (age) and industry of a firm, increases understanding of the circumstances under which the audit of financial statements is socially desirable and economically valuable to investors and other stakeholders.

The purpose of this study is to offer explanations of the wide variation in the impact of market size on new market entry decisions – i.e. its positive impact lessens because of unreliable predictability of market size on post-entry profit and entry motivations other than post-entry profit.

On the basis of the two explanations, this paper builds a contingency frame that the impact of market size on new market entry depends on entry-context-specific variables. It validates the contingency frame, empirically analyzing 219 parameter estimates of the impact of market size on market entry obtained from 41 existing empirical studies.

The meta-analysis results reveal that the entry-context-specific variables used in this study – niche market entry, high-tech market entry, entry by industry incumbent firms and the year of market entry – notably moderate the impact of market size on new market entry decisions, as the research frame suggests.

This study examines the various literature and study outcomes in the areas of marketing, economics and strategy to elucidate whether and when market size is a critical driver of new market entry. In most cases, the greater the new market size, the greater is the propensity to enter the market. However, the contingency arguments stated in this paper suggest that firms may and do enter a new market even if the market size is not large at the time of entry.

This paper enhances the understanding of the relative importance of market size in market entry decisions, which depend on various entry contexts. It clarifies the direction and magnitude of the impact of such entry contexts.

This paper aims to examine whether foreign technology acquisition is complementary to internal technology development in the context of a developing country.

The selection model developed by Heckman (1979) was applied with the balanced panel data of manufacturing enterprises from the Annual Enterprise and Technology Surveys from 2012 to 2016 conducted by the Vietnamese General Statistics Organization.

The results indicate that foreign technology acquisition and internal technology development are complementary innovation options. Particularly, the number of patents granted for manufacturing enterprises positively affects the probability that enterprises acquire foreign technologies. This effect is stronger in cases of high-tech industries than in cases of low-tech industries.

Regarding the relationship between internal technology development and foreign technology acquisition, the findings suggest that adoption of foreign technology acquisition and priority in budget allocation for foreign technology acquisition are different in nature and that budget allocation is a more complex issue and may depend on other factors.

For developing countries, governments should adopt policies supporting domestic enterprises in acquiring technologies from advanced countries that could complement the locally developed technologies. These supports should focus on the high-tech or high-innovation rate industries.

In the context of a developing economy, the complementary effect of internal technology development and foreign technology acquisition is stronger in cases of the high-tech industries than in cases of the low-tech industries.