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This paper investigates the profitability determinants of small- and medium-sized enterprises in high-tech industries. Literature review suggests that innovator position, market awareness, niche operation, and internationalization should have positive impacts on SMTEs' profitability. However, the empirical results partially agree with, and partially dissent from, the propositions.

University–Industry (U-I) cooperation is defined as “the interaction between any parts of the higher educational system and industry aiming mainly to encourage knowledge and technology exchange” (Ankrah and Al-Tabba 2015). In recent years, we have observed an intensification of this phenomenon based on the common belief that collaborative research by academia and industry may be a powerful source of innovation (Ambos et al. 2008; Mansfield 1998) and generate advantages for both partners. Consequently, in recent years, the phenomenon of U-I cooperation has increased greatly and has begun to include regions that are not at the frontier of innovation and sectors that are not traditionally high-tech. Nevertheless, extant studies overlook the important role of regional contexts in shaping the patterns of U-I cooperation in different regional contexts, including the analysis of how U-I cooperation patterns vary across low-tech and high-tech industries. This paper attempts to fill this gap by exploring patterns of U-I cooperation in four industry-specific clusters in the low-innovative region of Campania (Italy) with regard to three specific aspects (i.e., motivations, barriers and cooperation channels). Overall, our findings contrast the arguments made by previous literature focused on the sectoral effect of U-I collaboration in highly innovative regions by showing that in low-innovative regions, sectoral differences in motivations, channels and barriers of U-I cooperation are less clear-cut or come in different forms than in highly innovative regions. We believe that our study provides an original contribution to the existing literature by providing a more comprehensive view of of the phenomenon’s complexity by assessing the sectoral effect on three dimensions, considering both the perspectives of the university and the firm and by contextualizing the research in a low-innovative region, while mainstream literature tends to privilege the focus on highly innovative regions.

The high technology (high-tech) industry of China has gained a key strategic position in the Chinese economic goals. In this positioning, foreign direct investment (FDI) and technological innovation have emerged as strong pillars of the high-tech industry. However, there are growing concerns of carbon emission from this industry which is still debatable. In this context, this study measures the effect of FDI and technology innovation on carbon emissions in the high-tech industry from 28 provinces of China. The study uses the provincial data for China over the period 2000–2018. In addition to examining unit root properties, structural breaks, and cointegration, this study uses quantile regression for estimating long-run relationships among study variables. The findings reveal the negative impact of FDI on carbon emissions. Technology innovation positively impacts in the initial three quantiles, whereas negatively impacts in the next six quantiles. These results indicate that FDI and technology innovation have shaped the energy intensity in the high-tech industry, which causes fluctuation in carbon emissions over time. After controlling the effects of urbanization, energy intensity, and economic growth, this study recommends that policymakers should emphasize on the heterogeneous effects of FDI and technology-lead emissions at different quantiles during the process of CO 2 emission reduction.

The high-tech industry in China has largely developed in recent decades. To provide a basis for the sustainable development of high-tech industry, the government should evaluate its performance to find out its strengths and weaknesses that are critical for the future improvement of business operations. Dynamic network data envelopment analysis has received considerable attention from researchers evaluating the performance of a system during long-term production. However, studies on the issue of shared outputs caused by the lagged production effect of inputs are rare. In a real high-tech industry, the outputs during a production period are derived from the inputs in that production period and also from the inputs in the previous period. These intertemporal shared outputs in a system cannot be easily divided into different periods. Thus, a new dynamic two-stage data envelopment analysis approach is proposed to measure the efficiency of such system with a two-stage structure and shared outputs. We divide a high-tech activity system into two stages: technology research and development stage and technology digestion and absorption stage, where intertemporal shared outputs occur. Empirical results from our approach indicate that Chinese high-tech industries are weak in the technology digestion and absorption stage. Finally, suggestions are provided to improve the overall efficiency of Chinese high-tech industries.

Environmental regulation inhibits agglomeration innovation when industrial agglomeration encourages green technology innovation; when industrial agglomeration hampers green technology innovation, environmental planning enhances agglomeration innovation. In China, regional differences in cluster growth are evident, as is the impact of agglomeration innovation in different locations. To effectively support green technology innovation, it is vital to combine the existing status of cluster growth in applying environmental regulations for each region. The non-radial and non-angular SBM ML model was used to measure the green transformation level of the manufacturing industry using panel data from 30 provinces and cities in China from 2008 to 2020. This study investigated the characteristics of the impact of high-tech industrial agglomeration on the green transformation of the manufacturing industry in China, based on systematically sorting out the theoretical mechanism of high-tech industrial agglomeration promoting the green transformation of the manufacturing industry. According to the findings, the concentration of high-tech sectors helps to promote the transformation and upgrading of the green manufacturing industry, particularly in less developed areas, middle-stage industrialization areas, or industries with high pollution levels. Technological innovation is a buffer between high-tech industrial agglomeration and green transformation, upgrading manufacturing industries, particularly in the middle stages of industrialization and extremely polluting industries.

Innovation ability has become one of the core elements in the pursuit of China’s green growth, and high-tech industries are playing a leading role in technological innovation in China. With the rapid development of China’s high-tech industries, their innovation efficiency has attracted widespread attention. This article aims to illustrate a shared inputs two-stage network Data Envelopment Analysis (DEA), to measure the innovation efficiency of high-tech industries in China’s 29 provinces from 1999 to 2018. The results indicate that there are obvious differences in the innovation efficiency of the provinces. The technology development efficiency, the technical transformation efficiency, and the overall innovation efficiency of the developed east coast provinces are generally higher than those of the backward central and western provinces. This article further applies the spatial econometrics model to analyze the factors influencing the innovation efficiency of high-tech industries. We have found that government support, R&D input intensity, industries aggregation, economic extroversion, and the level of development of the modern service industries cause varying degrees of impact on innovation efficiency.

In emerging economies, organizational change is both a difficult challenge and a common phenomenon for high-tech firms. Change can enhance adaptability and leverage knowledge based on dynamic capability perspective, but it can also increase coordination costs and—according to the organizational inertia perspective—prompt conflict. Existing findings about the effect of organizational change on firm performance are inconsistent. Accordingly, this survey study of 213 firms in the Chinese high-tech industry investigates the curvilinear and differential effects of technical and administrative organizational change, as moderated by customer participation and innovation ambidexterity. The results reveal that the effects of technical and administrative change are both U-shaped. At a low level of change, increasing technical or administrative change hinders firm performance, but as the levels increase beyond a critical point, the effect of change becomes positive. Although customer participation strengthens the effect of technical change on firm performance, both customer participation and innovation ambidexterity attenuate the effect of administrative change on firm performance.

PurposeThis paper identifies the forces that drive digital adoption among SMEs from low and high-tech industries in Malaysia.Design/methodology/approachThis research uses multiple case analyses based on data gathered by in-depth interviews with key representatives of 20 firms from low and high-tech industries in Malaysia.FindingsThe findings suggest that digital adoption among SMEs derives by four fundamental forces, which are sales, marketing, process improvement and product development.Research limitations/implicationsThis study employed qualitative research, but lack of geographic diversity limits the generalisability of the case findings. This study provides several suggestions to policymakers and technology suppliers on how to encourage adoption of digitalisation among SMEs.Originality/valueThis study proposes a model that presents the critical forces that drive digital adoption for export-oriented firms, thus enriching the knowledge in SME digitalisation literature.

Industrial sulfur dioxide (SO 2 ) has become an important source of environmental pollution in China, and the regional SO 2 emission reduction capacity is a comprehensive reflection of cleaner production capacity, environmental regulation, and economic development. It is obvious that high-tech industries play a crucial role in promoting the cleaner production capacity of the whole industry. Simultaneously, only considering the regional emission and the development of high-tech industry in isolation may deviate from actual economic characteristics. Therefore, by using the panel data of 30 provinces in China from 2005 to 2016, this paper adopts spatial autoregression model (SAR), spatial error model (SEM), and spatial Durbin model (SDM) to analyze the effect of the high-tech industry development on SO 2 emission reduction under the spatial adjacency matrix (W1), geographic distance matrix (W2), and economic distance matrix (W3). In addition, this paper selects three indicators, which is SO 2 removal rate, SO 2 emission, and SO 2 removal quantity, as explanatory variables, and R&D investment and number of enterprises in high-tech industry are selected to represent the industrial development level. The major conclusions are as follows: (1) The ability of SO 2 emission reduction in the local province is significantly affected by the surrounding provinces, showing the agglomeration characteristics of “high-high” or “low-low.” (2) The R&D investment of high-tech industry has a negative impact on SO 2 removal rate and SO 2 removal quantity, but a positive effect on the SO 2 emissions for the local province, and has a positive effect on the emission reduction of surrounding provinces. (3) The expansion of high-tech industry has significantly improved the SO 2 emission reduction capacity of the local province and its surrounding provinces. The robustness test supports the empirical conclusions of this paper. Finally, this paper puts forward some policy suggestions for government in environmental governance, such as “joint prevention and control” and the promotion of cleaner production. Graphical abstract

The need for green innovation in the high-tech industry has become a critical path to sustainable economic development. However, evaluating green innovation efficiency (GIE) and its spatial characteristics within China’s high-tech industry remains underexplored. This study uses the three-stage undesirable SBM model to assess GIE in China’s high-tech industry from 2006 to 2022. Various spatial analysis methods, including the Theil index, Moran index, Standard Deviation Ellipse, Spatial Markov Chain, and β-convergence model, are applied to examine spatial differences, clustering patterns, and convergence trends of GIE across eight economic regions in China. The model adjusts input indicators to incorporate technological and environmental factors, providing a deeper understanding of the relationship between GIE and regional dynamics. The quantitative results show an increase in GIE from 0.350 to 0.566, with technological and environmental factors playing a significant role. The study highlights increasing spatial disparities in GIE, with the Northern Coastal Region achieving the highest levels. Spatial clustering analysis reveals distinct patterns: the Southern Coastal Region shows High-High clustering, while the Northeast Region exhibits Low-Low clustering. GIE demonstrates club convergence, β-convergence, and spatial spillover effects. These findings underscore the effectiveness of green innovation practices and offer insights into spatial dynamics, providing guidance for targeted interventions and promoting inclusive growth across regions.