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This paper aims to examine the different impacts of six variables on firm technological innovation performance in different high-tech industries in China. Through a comparative analysis of data about growth enterprises market board (GEM)-listed companies, this study attempts to get some conclusions, to help firms in different high-tech industries use resources more rationally and to improve technological innovation performance more effectively.

This paper constructs semi-parametric models based on the relevant data of GEM-listed companies during 2010 to 2015 for different high-tech industries. These models can ensure that the influencing factors of firm technological innovation performance are no longer restricted to a particular aspect but can provide a comprehensive comparative analysis of the effects of factors on firm technological innovation performance in different high-tech industries.

The empirical results show that R&D expenditures have a significant positive impact on firm technological innovation performance in most high-tech industries, but not in electronic and communication equipment manufacturing industry; R&D personnel investment and government subsidies have significant positive impacts on firm technological innovation performance in knowledge-oriented industries; technology diversity has a significant positive impact on firm technological innovation performance in technology-oriented industries; the proportion of exports shows an inverted U-shaped relationship with firm technological innovation performance in electronic and communication equipment manufacturing industry, while firm size shows an inverted U-shaped relationship with firm technological innovation performance in general equipment manufacturing industry; and the effect of semi-parametric model fit is superior to the general parameters model.

Drawing on the resource dependence perspective, this paper is the first to consider a comprehensive treatment of differential effects of internal resources (R&D personnel, R&D expenditure), external resources (government subsides) and firm characteristics (firm size, export ratio) on firm technological innovation performance in different high-tech industries in an emerging country, in particular in contrast to previous studies that have focused on a single industry or taken the type of industry as a control variable. In addition, most studies about the determinants of firm innovation performance are based on survey questionnaires, which may introduce large subjective errors. Setting the relationship between variables in advance may also introduce fit error when using a general-parameter model. Semi-parametric regression which is used in this paper is able to prevent this shortcoming effectively. When constructing a regression model, this can be exempted from the formal constraints, thus estimating data more accurately and ensuring superior fit.

The purpose of this paper is to propose an economic cybernetics model based on the grey differential equation GM(1,N) for China's high-tech industries and provide the necessary support to assist high-tech industries management departments with their policy making.

Based on the principle of grey differential equation GM(1,N), the grey differential equations of five high-tech industries in China are established using the net fixed assets, labor quantity and patent application quantity as cybernetics variables. After the discretization and first-order subtraction reduction to the simultaneous equation of the five grey models, a linear cybernetics model is resulted in. The structure parameters in the cybernetics system show explicit economic significance and can be identified through least square principle. At last, the actual data in 2004-2010 are introduced to empirically analyze the high-tech industrial system in China.

The cybernetics system for China's high-tech industries are stable, observable, and controllable. On the whole, China's high-tech industries show higher output coefficients of the patent application quantity than those of net fixed assets and labor quantity. This suggests that China's industry development mainly depends on technological innovation rather than capital or labor inputs. It is expected that the total output value of China's high-tech industries will grow at an average annual rate of 15 percent in 2011-2015, with contributions of pharmaceuticals, aircraft and spacecraft, electronic and telecommunication equipments, computers and office equipments, medical equipments and meters by 21, 16, 13, 10, and 28 percent, respectively. In addition, pharmaceuticals, as well as medical equipments and meters, present upward proportions in the gross of Chinese high-tech industries significantly. Electronic and telecommunication equipments, plus computers and office equipments exhibit an obvious decreasing proportion. The proportion of the output value of aircraft and spacecraft is basically stable.

Empirical analysis results are helpful for related management departments to formulate reasonable industrial policies to keep the sustained and stable development of the high-tech industries in China.

Based on the grey differential equation GM(1,N), this research puts forward an economic cybernetics model for the high-tech industries in China. This model is applicable to the economic system with small sample data set.

This study explores how to develop high-tech industries in Hunan province and enhance regional competitiveness. Through the comprehensive quantitative analysis of the development status of the high-tech industry in Hunan province, this paper provides a reference for the development of the high-tech industry in China and the world.

This study constructs a comprehensive evaluation index system of regional competitiveness in Hunan province from the five dimensions of innovation, coordination, green, openness and sharing of the “new development concept.” Through the screening and analysis of relevant economic indicators in Hunan province from 2011 to 2020, the principal component analysis method is used to measure the five development dimensions, verify the hypotheses in the study and finally draw the conclusion.

Hunan's high-tech industry is positively correlated with Hunan's regional innovation development competitiveness, regional coordinated development competitiveness, regional green development competitiveness, regional open development competitiveness and regional shared development competitiveness. Among them, the promotion effect on innovation development is the best, followed by the promotion effect on green development, coordinated development and shared development dimension. In contrast, the promotion effect on the open development dimension is relatively weak.

The statistical data selected in this study have certain timeliness. At the same time, the current economic environment is affected by the new corona pneumonia epidemic, showing specific particularity. In this context, it is bound to cause changes in the impact of high-tech industries on regional competitiveness. In addition, this paper studies the regional competitiveness of Hunan's high-tech industry from a macro perspective. Although relevant studies are conducted from five dimensions, there is a lack of micro-level research.

From five aspects of the new development concept, this study provides suggestions for developing high-tech industries in Hunan province and even China and the path to enhance regional competitiveness.

Up to now, no article measures regional competitiveness with the five development dimensions of new development: innovation, coordination, green, openness and sharing, and quantitatively analyses regional competitiveness on this basis.

High‐tech industry transferring shows different features to the transferring of the traditional industries. In order to explain those special features in China, the purpose of this paper is to build a new conceptual model for high‐tech industry transferring and explore some empirical evidence.

This paper employs a perspective of specialization to analyze high‐tech industry transferring and a primary conceptual analytical model is discussed. Key models and methodologies are the industrial gravity center model, dynamic modes analysis, character induction and mechanism deduction method.

The features of high‐tech industry transferring within the China context include fast, surge, inverse‐gradient, as well as agglomeration and specification transferring. Based on the transfer pattern, high‐tech industry transferring modes are classified into two categories: vibration transferring and surge transferring.

Using a conceptual model, this paper analyzes the features of high‐tech industry transferring in China and proposes two typical patterns to explain how the industries transfer and what are the dynamic mechanisms. In order to improve high‐tech industry transferring in China, five policy implications are recommended.

For the studies whose purposes are to evaluate the relationship between industrial characteristics and innovation activities of the enterprises, there are some limitations in the measures of industrial characteristics and using traditional statistical techniques. The purpose of this paper is to investigate the relationship between industrial characteristics and innovation capabilities within Chinese high-tech industries using grey system theory. The research results show that grey system theory is suitable to investigate the relationship between industrial characteristics and innovation capabilities within Chinese high-tech industries.

This paper proposes the measures of industrial characteristics and innovation capabilities of high-tech enterprises. First, based on the data on Chinese large and medium-sized high-tech enterprises for the period of 2011-2013, this paper applies grey relational analysis to identify the relatively most important indexes on affecting innovation capabilities of Chinese high-tech enterprises. Second, based on the results from grey relational analysis, this study draws a ranking of the five Chinese high-tech industries in terms of innovation capabilities by grey decision making. Finally, based on the results from grey decision making, this study applies GM (0, N) model to investigate the relationship between industrial characteristics and innovation capabilities within Chinese high-tech industries.

The results of this study show that in the evaluation indexes system of innovation capabilities of high-tech enterprises, personnel in R & D institutions, R & D personnel, internal expenditure on R & D, expenditure on new product development, expenditure on technology imports, expenditure on technology renovation, and expenditure on technology assimilation and absorption are relatively most important elements affecting innovation capabilities of Chinese high-tech enterprises. In addition, the two top ranking on innovation capabilities are manufacture of electronic equipment and communication equipment, and manufacture of medicines. At last, the findings indicate that in the measures of industrial characteristics, the three top ranking on affecting innovation capabilities of Chinese high-tech enterprises are R & D intensity, technology absorption intensity of indigenous high-tech enterprises and foreign-invested enterprises size. The opening level is in the middle position. Technology intensity, market concentration, and state-owned enterprises size are the three bottom ranking on affecting innovation capabilities of Chinese high-tech enterprises.

This study has some limitations. First, this study is limited to Chinese high-tech industries. The findings may not be applicable to other countries’ high-tech industries. Further studies with other countries’ high-tech industries could be extended and examined how industrial characteristics affect innovation capabilities of the firms in these industries. Second, the measures of industrial characteristics proposed in this study are somewhat theoretically weak. In the future, the authors will further improve the current analysis, and develop the measures of industrial characteristics. Finally, with the advent of the more data with the consistent statistical coverage released by China’s National Bureau of Statistics during the more continuous years, other methods, such as panel data regression model in econometrics could be used to evaluate the relationship between industrial characteristics and innovation capabilities within Chinese high-tech industries. By then, the scholars can compare the results from grey system theory and those from panel data regression model in econometrics.

Appropriate industrial environment is favorable for Chinese high-tech enterprises to feed their innovation capabilities. Scientific evaluation on the relationship between industrial characteristics and innovation capabilities within Chinese high-tech industries is of great significance for Chinese high-tech enterprises in exerting technological catch-up and promoting their competitive advantage. The purposed measures of industrial characteristics and innovation capabilities of high-tech enterprises in this paper, and combined methodology based on grey system theory could be applied to evaluate the relationship between industrial characteristics and innovation capabilities of Chinese high-tech enterprises.

This paper proposes the measures of industrial characteristics and innovation capabilities of high-tech enterprises, and uses grey system theory to evaluate the relationship between industrial characteristics and innovation capabilities within Chinese high-tech industries.

Because of the rapid progress of global value chains (GVCs), it is worthwhile to study their impact on innovation. This study aims to explore the impact of GVC position of high-tech industries in the developing-country context of China on innovation performance; it also aims to explore the moderating effects of industrial agglomeration (specialization agglomeration and diversification agglomeration) on the relationship between GVC position and innovation performance.

The study is based on data gathered on Chinese high-tech industries in 30 provinces from the 2005–2015 period. The econometric analysis relies on merged data from the China Premium Database and the Trade in Value Added 2018 Database.

The regression results show that GVC position of China’s high-tech industries significantly affects their innovation performance, and both specialization agglomeration and diversification agglomeration significantly enhance the positive relationship between GVC position and innovation performance of China’s high-tech industries. After dividing the country into coastal and inland regions, new findings appear.

This study highlights the importance of GVC position and its effect on innovation performance of China’s high-tech industries. It contributes to the literature on the relationship between GVCs and innovation by elaborating on the moderating effects of industrial agglomeration on this relationship.

The Chief Financial Officer (CFO), despite being a critical organization member responsible for ensuring quality of financial reporting, audit and compliance, is under-researched. Grouped as a member of top management teams (TMS) in studies, factors influencing decision making in this group rely on static measures of characteristics without regard for dynamic and longitudinal influences of career trajectories and industry occupational group memberships. The relationship between the high-tech industry as a site of notable reported internal control (IC) weakness and influences on CFOs requires closer examination. The paper aims to discuss these issues.

The study draws together the upper echelons theory and occupational communities (OCs) to explore the impact of shared values and behavioral norms from different sources on executive decision making. Internal and external sources of OC are proposed and their influence on activities with respect to corporate IC is tested. The sample of 1,573 firm/year observations includes high-tech firms listed on major US exchanges was developed using data from five distinct databases. Executives’ biographic information was manually collected.

Results indicate that senior financial executives belong not only to their firm and its culture but also to OCs that extend beyond the firm. Membership in professional credential granting occupational groups has less impact on effective IC than experience in the high-tech industry. In combination, multiple OCs show evidence of compound and counteracting effects on IC. The OC that arises in the high-tech industry makes a measurable positive difference in the quality of IC in sample firms, in contrast with the OC among credentialed accounting and financial professionals.

This quantitative study of OC reveals the differential impact of different sources of OC and contributes to the literature on TMS a new framework for examining decision making. OC is typically studied through qualitative methods and, thus, potential exists to further explore the specific nature and dynamics of the OCs identified in this study.

The study highlights the role of broad affiliations and networks among senior financial executives which may have bearing on their ability to effectively manage IC. The role of these networks may also partially explain instances of CFO failure and thus dismissal. Knowledge of the role of OC may help boards of directors in the selection and promotion of senior financial officers of the firm.

The paper offers a different perspective on professional accounting expertise in one specific industry where incidence of IC weakness is high relative to other industries. Study results expand recent research on TMS to include sociological impacts of cohort groups. Despite generally weaker IC in the high-tech sector, this study demonstrates the value of exploring group membership within the industry as an important predictor of behavior. The result is a new perspective to CFO decision making which illustrates the relevance of OCs among upper echelons. The implications of findings for CFO recruitment and promotion are borne out in recent instances of senior financial executive failure in the sector.

This paper compares four external technology acquisition channels' (foreign technology transfer, exporting, inter-industry R&D spillover and domestic technology transfer) contributions to Chinese high-tech enterprises' innovation in the moderating role of absorptive capacity (AC).

Using technological catch-up theory and China Statistics Yearbook on High Technology Industry, this research investigates 16 Chinese high-tech industries from 2004 to 2015 via negative binomial regression.

The results show that indigenous knowledge transfer improves foreign-funded enterprises' (FFEs) innovation, while transnational knowledge transfer is the main channel for state-owned enterprises' (SOEs) technology acquisition. AC strengthens the positive relationship between transnational knowledge transfer and innovation in SOEs and improves FFEs' innovation and exported tacit knowledge absorption.

This study contributes to the literature on external technology acquisition (ETA) and innovation by examining the different mechanism of ETA instead of isolated strategy considering the AC and ownership structure of Chinese high-tech enterprises, which enrich the technological catch-up theory.

The purpose of this paper is to examine the effects of parameter uncertainty in the returns process with regime shifts on optimal portfolio choice over the long run for a static buy-and-hold investor who is investing in industry portfolios.

This paper uses a Markov switching model to model returns on industry portfolios and propose a Gibbs sampling approach to take into account parameter uncertainty. This paper compares the results with a linear benchmark model and estimates without taking into account parameter uncertainty. This paper also checks the predictive power of additional predictive variables.

Incorporating parameter uncertainty and taking into account the possible regime shifts in the returns process, this paper finds that such investors can allocate less in the long run to stocks. This holds true for both the NASDAQ portfolio and the individual high tech and manufacturing industry portfolios. Along with regime switching returns, this paper examines dividend yields and Treasury bill rates as potential predictor variables, and conclude that their predictive effect is minimal: the allocation to stocks in the long run is still generally smaller.

Studying the effect of regime switching behavior in returns on the optimal portfolio choice with parameter uncertainty is our main contribution.

The purpose of this paper is to understand the practices and policies unique to high-tech manufacturing start-ups in emerging economies, such as India. The study analyzes the main features and challenges of the high-tech manufacturing sector, and the way in which enabling environment including policy making, supply chain capability and related technologies through Industry 4.0 could be leveraged to foster growth.

The paper undertakes an exploratory approach through in-depth semi-structured interviews conducted with high-tech manufacturing firms in various stages of their growth. The paper provides evidence of the challenges that high-tech manufacturing firms face in India, the strategies they adopt and highlights the role of institutional structures and policies.

Findings show that high-tech manufacturing start-ups in India face various challenges in the upstream, production and downstream supply chain processes. Further, issues related to availability of quality material, quality suppliers, contracts, funding and access to markets remain. Results also show that enabling operational and financial levers could be created by policy makers and other stakeholders to help the high-tech manufacturing start-ups scale faster and create value.

This paper contributes to the R&D intensive industry and high-tech manufacturing literature in the context of emerging economies, such as India, and provides a rigorous overview of the start-up ecosystem in high-tech manufacturing.