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The purpose of this study is to investigate the impact of the accounting system reform on corporate innovation behavior and the heterogeneity and underlying mechanisms of this impact. This paper further aims to study the impact of accounting system reform on corporate value.

This study takes China's A-share listed corporates as a sample and uses the exogenous policy shock of the implementation of the New Accounting Standards in 2007 to design the identification strategy of propensity score matching and difference-in-differences method. By comparing the differences between the innovation level of corporates in high-tech industries and non-high-tech industries before and after the implementation of the New Accounting Standards, the impact of the accounting system reform on corporates' innovative behavior can be identified.

Results show that compared with corporates in traditional industries, high-tech corporates obtained higher patent output after the implementation of the New Accounting Standards. This reform mainly affects corporate innovation by improving corporate risk-taking. In addition, this paper finds that the reform of the accounting system has increased the market value of high-tech corporates in the long run.

This study provides new empirical evidence for addressing the insufficient innovation incentives for market entities and enriches the existing literature on the economic effects of the change of accounting systems and the influencing factors of corporate innovative behavior from the accounting system perspective.

This paper investigates the extent to which formal capital budgeting methods are used in small high-tech firms. We define high-tech firms by their R&D intensity. In addition, we define software industry as a special type of R&D-intensive firm. We focus on the methods that are used by the small high-tech firms in evaluating the profitability of investment projects, estimating the cost of capital and making decisions related to the capital structure. Our results based on two surveys of Finnish firms indicate that the high-tech firms use similar capital budgeting methods and estimate their cost of capital in a similar way to other small-sized firms in other industries. Moreover, high-tech firms seek external financing and co-owners.

This paper aims to promote the higher quality development of high-tech enterprises in China. While science and technology have greatly promoted human civilization, resources have been excessively consumed and the environment has been sharply polluted. Therefore, it is particularly important for current enterprises to make use of scientific and technological innovation to maximize the benefits of mankind, minimize the loss of nature, and promote the sustainable development of our country.

By using DEA-Banker-Charnes-Cooper (BCC) model and DEA-Malmquist model, this paper comprehensively examines the innovation efficiency of high-tech enterprises from both static and dynamic perspectives, and conducts a provincial comparative study with the panel data of ten representative provinces from 2011 to 2020.

The research findings are as follows: the rapid number increase of high-tech enterprises in most provinces (cities) is accompanied by an ineffective input–output efficiency; the quality of high-tech enterprises needs to comprehensively examine both input–output efficiency and total factor productivity; and there is not a positive correlation between element investment and innovation performance.

Because the DEA model used in this paper assumes that the improvement direction of invalid units is to ensure that the input ratio of various production factors remains unchanged but sometimes the proportion of scientific and technological activities personnel and the total research and development investment is not constant. In the future, the nonradial DEA model can be considered for further research. Due to historical data statistics, more provinces, cities and longer panel data are difficult to obtain. The samples studied in this paper mainly refer to the provinces and cities that ranked first in the number of national high-tech enterprises in 2020. Limited by the number of samples, DEA analysis failed to select more input and output indicators. In the future, with the accumulation of statistical data, the existing efficiency analysis will be further optimized.

Aiming at the misunderstanding of emphasizing quantity and neglecting quality in the cultivation of high-tech enterprises, this paper comprehensively uses DEA-BCC model and DEA Malmquist index decomposition method to make a comprehensive comparative study on the development of high-tech enterprises in ten representative provinces (cities) from two aspects of static efficiency evaluation and dynamic efficiency evaluation.

Efficiency and productivity has always being a key issue in economic science. The analysis of the impact of research and development (R&D) has been extensively studied in industries and countries of more or less aggregated level. This chapter aims to investigate the impact of corporate R&D in performance of low-tech industries, medium-tech, and high-tech in OECD countries.

This chapter aims to answer the questions: Is the impact of R&D significant for all types of industries? If so, what are the differences and the magnitude of these effects in each of these types of industries?

To this end, an unbalanced data set from 2000 to 2011 was collected for the main countries of Europe and the United States concerning low-, medium-, and high-tech to analyze the impact of the magnitude of corporate R&D and capital accumulation on productivity of these industries. The productivity of industries was measured by stochastic parametric frontier functions, in order to measure the efficiency of R&D and accumulation of capital on labor productivity.

The main results highlight the impact of corporate R&D on productivity of high-tech industries, but for other industries those relations are not clear. However, capital accumulation became crucial on low technology to improve their performance. These results, although needing to include a more extensive data set of industries across countries, refer the need for policy and decision makers to allocate public funds for R&D in high-tech industries, while the investment in capital seems crucial, particularly in low-tech industries to improve the productivity.

The purpose of this paper is to empirically analyze the impact of chief executive officer (CEO) turnover on strategic change and explore the mediating role of organizational slack between them, as well as the moderating role and joint moderating role of top management team (TMT) external social network, ownership nature and industry type.

Based on the upper echelons theory, resource allocation theory and structuration theory, this paper takes the unbalanced panel data of A-share listed companies in Shanghai and Shenzhen Stock Exchanges of China from 2001 to 2018 as the research sample, uses ordinary least squares (OLS) regression method and fixed effect model to study the relationship between CEO turnover and strategic change, and focuses on the mediating mechanism and moderating mechanism between them.

The authors find that CEO turnover is positively related to strategic change. When a CEO turns over, a new CEO will initiate strategic change. Precipitation organizational slack plays a mediating role between CEO turnover and strategic change. Non-precipitation organizational slack has no mediating effect between CEO turnover and strategic change, which is embodied as “suppressing effects.” When the non-precipitation organizational slack variable is controlled, the impact of CEO turnover on strategic change will be enhanced. TMT external social network, ownership nature and industry type all negatively moderate the relationship between CEO turnover and strategic change. TMT external social network and ownership nature have a joint moderating effect between CEO turnover and strategic change. When TMT external social network is small, CEO turnover has a positive effect on strategic change in both state-owned enterprises and non-state-owned enterprises, but the promotion effect is stronger in non-state-owned enterprises. When TMT external social network is large, the positive effect of CEO turnover on strategic change in state-owned enterprises is from strong to weak, but in the non-state-owned enterprises is from weak to strong. TMT external social network and industry type have a joint moderating effect between CEO turnover and strategic change. When TMT external social network is small, CEO turnover has a positive impact on strategic change in high-tech enterprises and non-high-tech enterprises, but the promotion effect is stronger in non-high-tech enterprises. When TMT external social network is large, the positive impact of CEO turnover on strategic change in high-tech enterprises is from strong to weak, but in the non-high-tech enterprises is from weak to strong.

On the basis of previous studies, this paper further expands the research scope of the mechanism of CEO turnover on strategic change, echoing the research arguments of relevant scholars. At the same time, the research results reveal the mechanism of organizational slack, TMT external social network, ownership nature and industry type in the relationship between CEO turnover and strategic change, and further deepen the application of upper echelons theory, resources allocation theory and structuration theory in China. In addition, the research conclusions of this paper also provide reference value for Chinese enterprises in carrying out strategic change, promoting enterprise transformation and improving the level of corporate governance, and help to enhance the understanding and attention of Chinese enterprises to CEO turnover, organizational slack, TMT external social network, strategic change and corporate governance under the background of high-quality economic development.

The purpose of this paper is to use high‐tech companies in Taiwan as research subjects to verify the fit of the commitment‐trust theory and explore the supply chain relationships among research variables.

The key mediating variables model (KMV) proposed by Morgan and Hunt is applied to construct the research structure, hypotheses, and questionnaire. The research hypotheses are validated through structural equation modelling and confirmatory factor analysis.

Research results show that for two parties of an exchange relationship, higher levels of trust can lead to better interactions and trust is an important factor affecting their supply chain partnerships. It helps increase interests of both parties, facilitate constant co‐operation and communication, and reduce uncertainties. Higher levels of commitment can also help increase value benefits, reduce a partner's propensity to leave, and enhance supply chain co‐operation efficiency.

Empirical results indicate that relationship marketing is a strategy that promotes trust and commitment of partners in high‐tech industries. While information sharing and communication can increase partners' intention of long‐term co‐operation, functional conflicts can facilitate positive interactions and reduce uncertainties. Through relationship marketing, high‐tech companies can create win‐win strategic alliances to develop their competitive advantages in the market.

This study aims to explore the strategic impact of R&D and export activity on the diverse dimensions of US manufacturing firms’ performance. It also explores, using a predictive analytic model, the interactive synergistic effect that R&D and exports have on firm performance.

This study presents an innovative two-stage regression-neural network approach. Complementing conventional statistical analysis, the predictive backpropagation neural network explores the relative impact of R&D and exports and their synergistic effect on firm performance.

This study demonstrates the significant and positive effect of R&D and export strategy/activity on the economic performance of leading US manufacturing firms, particularly on their market-based performance (i.e. sustained growth rate or SGR). Furthermore, this study finds that the synergistic effect of R&D and exports on short-term performance (i.e. return on investment) is positive in high-tech firms but negative in low-tech firms. However, the synergistic effect on SGR is increasingly positive regardless of the level of technology.

In addition to traditional statistical analysis, this study uniquely investigates the relative importance of selected strategic variables, along with R&D and export activity and their differential synergistic effects, for firms’ economic performance in contrasting industry settings (high-tech vs low-tech).

This paper empirically explored the influence of external and internal factors on technological and non-technological innovation of 5747 Indian firms. The study also explored novel insights about manufacturing firms by segregating them into high-technology and low-technology industries.

The study employed hierarchical regression analysis to analyse a cross-sectional dataset gathered from the World Bank enterprise survey. The firms are segregated into high-technology and low-technology industries based on the technology-intensity classification of the manufacturing industry given by the Organisation for Economic Co-operation and Development.

The main results highlight that technological and non-technological innovation was primarily driven by internal resources and capabilities rather than external factors. The authors found the highest effect of research and development spending on both forms of innovation. In both high-tech and low-tech industries, technology transfer is positively associated with technological innovation and negatively associated with non-technological innovation. Furthermore, external business support has substantially influenced non-technological innovation in low-tech industries.

This study used two-step hierarchical regression to explore the influence of external and internal factors on technological and non-technological innovation separately. Exploring determinants of innovation in high-technology and low-technology industries also brings the distinct prerequisites of enhancing innovation to the attention of policymakers and industry experts.

The purpose of this paper is to explore and compare the extent of intellectual capital (IC) and its four components in high-tech and non-high-tech small and medium-sized enterprises (SMEs) operating in China’s manufacturing sector, and to examine the relationship between IC and the performance of high-tech and non-high-tech SMEs.

The study uses the data of 116 high-tech SMEs and 380 non-high-tech SMEs listed on the Shenzhen stock exchanges during 2012–2016. The modified value added intellectual coefficient (MVAIC) model is used incorporating four components, namely, capital employed, human capital, structural capital and relational capital. Finally, multiple regression analysis is utilized to test the proposed research hypotheses.

The findings of this paper reveal that there is significant difference in MVAIC between high-tech and non-high-tech SMEs. The results further indicate a positive relationship between IC and financial performance of high-tech and non-high-tech SMEs. Specifically, IC is positively associated with firms’ earnings, profitability and operating efficiency. Additionally, capital employed efficiency, human capital efficiency and structural capital efficiency are found to be the most influential value drivers for the performance of two types of SMEs while relational capital efficiency possesses less importance.

This paper will provide a valuable framework for executives, managers and policy makers in managing IC within the Chinese context.

To the best knowledge of the authors, this is the first empirical study that has been conducted on high-tech and non-high-tech SMEs in the manufacturing sector in China.

The purpose of this study paper is to identify and prioritize the factors affecting the knowledge flow in high-tech industries.

This research is applied in terms of purpose and descriptive-survey in terms of data collection method. This research has been done in a qualitative–quantitative method. In the qualitative part, due to the nature of the data in this study, expert interviews have been used. The sample studied in this research includes 35 managers and expert professors with experience in the field of knowledge management working in universities and high-tech industries who have been selected by the method of snowball. In the quantitative part, the questionnaire tool and DANP multivariate decision-making method have been used.

In this study, a multicriteria decision-making technique using a combination of DEMATEL and ANP (DANP) was used to identify and prioritize the factors affecting the knowledge flow in high-tech industries. In this study, the factors affecting the knowledge flow, including 8 main factors and 31 subfactors, were selected. Human resources, organizational structure, organizational culture, knowledge communication, knowledge management tools, knowledge characteristics, laws, policies and regulations and financial resources were effective in improving knowledge flow, respectively.

By studying the research, it was found that the study area is limited, and the previous work has remained at the level of documentation and little practical use has been done. In previous research, the discussion of knowledge flow has not been very open, and doing incomplete work causes limited experiences and increases cost and time wastage, and parallel work may also occur. Therefore, to complete the knowledge management circle and fully achieve the research objectives, as well as to make available and transfer the experiences of people working in this field and also to save time and reduce costs, the contents and factors of previous models have been counted. It is designed for high-tech industries, a model for the flow of knowledge.