Search results

Although the theoretical arguments provide several channels through which innovation affects export, empirical validation of this relationship is scarce. Further, the impact of the diverse channels of domestic and foreign research and development (R&D) on export is assessed in isolation by previous studies. This paper empirically investigates the impact of technological innovation on export capacity and intensity of industrial enterprises in emerging countries by considering three channels of domestic innovation and foreign R&D spillovers, namely internal R&D, embodied knowledge and disembodied knowledge in a unified framework.

Data on China's industrial enterprises in the manufacturing sector are extracted from the China National Bureau of Statistics (NBSC), the Ministry of Science and Technology of China (MST) and the UN Comtrade database for the period from 1998 to 2020. The instrumental variables two-stage least squares (IV-2SLS) and three-stage least squares (IV-3SLS) methods are used to control for the possible endogeneity bias and the problem of cross-equation correlation between residuals.

The results show that internal R&D is a critical factor to enhance the export performance of enterprises in emerging countries, while the effect of embodied spillovers and public–private collaboration on export capacity and intensity of industrial enterprises is substantial. Further, disembodied knowledge that is acquired through licensing of technology from advanced countries does not directly contribute to the export performance of enterprises but requires a threshold level of internal R&D capability. This study’s results also report a greater effect of embodied knowledge spillovers on export capacity and export intensity than internal R&D in emerging countries. The results are consistent to changes in the sample period and the estimation methods. The findings of the paper suggest that developing countries can speed up the process of export upgrading by relying on both domestic and foreign R&D efforts.

The findings would help policymakers to keep in mind the relative importance of internal R&D and embodied and disembodied knowledge spillovers for export performance before formulating a catch-up strategy and the outcome would encourage them to consider prior related knowledge in terms of internal R&D capability while acquiring external technology.

This study fills the gap in the existing literature by providing empirical validation of the innovation–export interplay and simultaneously assessing the effect of three diverse channels of technological innovation on the export performance of industrial enterprises. This paper enunciates important policy lessons for emerging countries' smooth transition to a knowledge-based economy.

This study aims to examine the complex effects of foreign direct investment (FDI) on China’s agricultural total factor productivity (TFP) from 2005 to 2020. It also explores the role of absorptive capacity as a moderating factor during this period.

Employing provincial panel data from China, this research measures agricultural TFP using the Stochastic Frontier Approach (SFA)-Malmquist method. The impact of FDI on agricultural productivity is further analyzed using a nondynamic panel threshold model.

The results highlight technological progress as the main driver of agricultural TFP growth in China. Agricultural FDI (AFDI) seems to impede TFP development, whereas nonagricultural FDI (NAFDI) shows a distinct positive spillover effect. The study reveals a threshold in absorptive capacity that affects both the direct and spillover impacts of FDI. Provinces with higher absorptive capacity are less negatively impacted by AFDI and more likely to benefit from FDI spillovers (FDISs).

This study provides new insights into the intricate relationship between FDI, absorptive capacity and agricultural productivity. It underscores the importance of optimizing technological progress and research and development (R&D) to enhance agricultural productivity in China.

This study aims to explore the risk spillover effects among different sectors of the Chinese stock market after the outbreak of COVID-19 from both Internet sentiment and price fluctuations.

The authors develop four indicators used for risk contagion analysis, including Internet investors and news sentiments constructed by the FinBERT model, together with realized and jump volatilities yielded by high-frequency data. The authors also apply the time-varying parameter vector autoregressive (TVP-VAR) model-based and the tail-based connectedness framework to investigate the interdependence of tail risk during catastrophic events.

The empirical analysis provides meaningful results related to the COVID-19 pandemic, stock market conditions and tail behavior. The results show that after the outbreak of COVID-19, the connectivity between risk spillovers in China's stock market has grown, indicating the increased instability of the connected system and enhanced connectivity in the tail. The changes in network structure during COVID-19 pandemic are not only reflected by the increased spillover connectivity but also by the closer relationships between some industries. The authors also found that major public events could significantly impact total connectedness. In addition, spillovers and network structures vary with market conditions and tend to exhibit a highly connected network structure during extreme market status.

The results confirm the connectivity between sentiments and volatilities spillovers in China's stock market, especially in the tails. The conclusion further expands the practical application and theoretical framework of behavioral finance and also lays a theoretical basis for investors to focus on the practical application of volatility prediction and risk management across stock sectors.

This study examines the differences in the economic stimulus effects, transmission mechanisms, and output multipliers of government consumption, government traditional investment, and government science and technology investment.

This study constructs and estimates a New Keynesian model of endogenous technological progress embedded in the research and development (R&D) and technology transfer sectors. Using Chinese macroeconomic time series data from 1996 to 2019, this study calibrates and estimates the model and analyzes the impulse response function and a counterfactual simulation of expenditure structure adjustment.

The results show that compared with the traditional dynamic stochastic general equilibrium (DSGE) model, the endogenous process of technological progress amplifies the impact of government consumption shock and traditional government investment shock on the macroeconomy, leading to greater economic cycle fluctuations. As government investment in science and technology has positive external spillover effects on firm R&D activities and the application of innovation achievements, it can promote more sustainable economic growth than government consumption and traditional investment in the long run.

This study constructs an extended New Keynesian model with different types of government spending, which includes endogenous technological progress within the R&D and technology transfer sectors, thereby linking fiscal policy, business cycle fluctuations and long-term economic growth. This model can study the macroeconomic impact of fiscal expenditure structure adjustment when fiscal expansion is limited. In the Bayesian estimation of model parameters, this study not only uses macroeconomic variables but also adds a sequence of private R&D investment.

In the context of constructing an integrated national strategic system, collaborative innovation among enterprises is the current social focus. Therefore, in order to find the interest relationship between multiple game subjects, to explore the influencing factors of collaborative innovation of civil-military integration enterprises. This paper constructs a collaborative innovation mechanism for military–civilian integration involving four game subjects (military enterprises, private enterprises, local governments, and science and technology intermediaries). It aims to solve and reveal the evolutionary game relationship among the four parties.

To explore the mechanism of military–civilian collaborative innovation involving four players, this study employs game theory and constructs an evolutionary game model for collaborative innovation with the participation of military enterprises, civilian enterprises, local governments, and technology intermediaries. The model reveals the evolutionary game patterns among these four entities, analyzes the impact of various parameters on the evolutionary process of the game system, and numerical simulation is used to show these changes more specifically.

The research findings demonstrate that active government subsidies promote cooperation throughout the system. Moreover, increasing the input-output ratio of research and development (R&D), the rate of technological spillovers, and the R&D investment of civilian enterprises all facilitate the tendency toward cooperation within the system. However, when the government chooses to actively provide subsidies, increasing R&D investment in military enterprises may hinder the tendency toward cooperation. Furthermore, central transfer payments, government punishment from the central government, and an increase in the information conversion rate of technology intermediaries may suppress the rate of cooperation within the system.

Most of the previous studies on the collaborative innovation of military–civilian integration have been tripartite game models between military enterprises, private enterprises, and local governments. In contrast, this study adds science and technology intermediaries on this basis, reveals the evolution mechanism of collaborative innovation of civil-military integration enterprises from the perspective of four-party participation, and analyzes the factors influencing the cooperation of the whole system. The conclusion of this study not only enriches the collaborative innovation evolution mechanism of military–civilian integration enterprises from the perspective of multiple agents but also provides practical guidance for the innovation-driven development of military–civilian integration enterprises.

This study aims to explore the effects of internal financial technology (FinTech) integration within Jordanian banks on their performance metrics, specifically focusing on profitability, risk-taking and stock returns.

Using panel data analysis, this study investigates the financial performance of 13 listed commercial banks in Jordan over a decade, from 2010 to 2019, to examine the hypothesized impacts of bank FinTech developments. In addition, several robustness tests addressing potential issues of endogeneity and autocorrelation are conducted to enhance the reliability of the results.

The results reveal that the bank FinTech development significantly enhances bank profitability and inversely affects risk-taking levels, indicating a substantial and positive impact on financial performance and stability. However, the results suggest no significant evidence of the effect of bank FinTech development on stock return.

The findings advocate for Jordanian commercial banks to continue and expand their investment in FinTech innovations, highlighting the crucial role these technologies play in enhancing financial performance and reducing bank risks. Additionally, these findings suggest that regulatory bodies and policymakers should develop and enhance institutional and regulatory environments to support and guide the FinTech evolution within the banking sector.

This study sheds light on the relatively under-researched area of internal bank FinTech. It provides critical insights into how FinTech integration within banks contributes to their profitability and stability, offering another perspective that enriches the FinTech literature. This contribution is essential for devising future strategies, developing theoretical frameworks and informing policy decisions in the FinTech domain.

This study aims to examine connections between five variables, including innovation in environment-related technology (EI), trade openness (TRADE), CO₂ emissions (CO₂) and foreign direct investment (FDI) from 1994 to 2019.

This study used an extended joint connectedness technique and the time-varying parameter vector autoregression (TVP-VAR) method. The analysis focuses on the variables of innovation in environment-related technology (EI), trade openness (TRADE), CO₂ emissions (CO₂) and foreign direct investment (FDI) using data from 1994 to 2019.

The results demonstrate that innovation in environment-related technology and an openness to the global network captured by FDI are identified as crucial net transmitters of shocks. In addition, an openness to the global trade network captured by TRADE turns from a transmitter to a receiver of shocks and vice versa. Moreover, it can be seen that the impact of EI was significant in the first five years of the observed period, and it transmitted the largest shock in 1997.

With regard to policy implications, the findings offer valuable insights for investors and policymakers. As the tradeoff between business efficiency and environmental sustainability diminishes, it is essential for Vietnam’s economy and enterprises to embrace green and sustainable growth in line with global trends. In a world characterized by uncertainties and risks, enterprises need to develop strategies to manage risks and shocks arising from geopolitical tensions, input material supply, financial–monetary instability and natural disasters.

This study contributes to the existing literature in two significant ways. First, as previously emphasized, this paper represents the first attempt to investigate the relationship between economic globalization and environmental innovation. Second, this study proposes a novel methodology that is better suited for analyzing volatility interlinkages across different market types.

The study aims to examine the joint effects of foreign ownership (FO) and involvement in global value chains (GVCs) on the productivity performance of firms from a catching-up country (Poland) and a leader economy (Germany).

The authors use micro-level data on firms combined with several sector-level GVC participation measures. The authors investigate whether the link between productivity and the overall sectoral degree of involvement in global production structures depends on a firm's ownership. The authors verify the robustness of the obtained results by using an instrumental variables approach and weighted regression.

The results show that domestically owned firms are less productive than foreign ones, which is particularly true at low GVC participation levels. However, as GVC involvement increases, the FO productivity premium decreases, leading to productivity catching up between foreign and domestically owned firms. This mechanism is similar in Poland and Germany. However, in the leader country (Germany), the productivity performance of domestically owned firms is more stable along the distribution of GVC involvement.

This study contributes to the foreign direct investment (FDI)–productivity literature by comparing the catching-up and developed countries' perspectives and incorporating the productivity–GVC relationship into the FDI analysis. The authors show that the FO premium is not confined to the developing context but is also present in a leader country. Moreover, the link between productivity and the overall sectoral degree of involvement in global production structures depends on a firm's ownership.

The study evaluates the effects of GVC participation on firm productivity and profitability. Hence this study aims to find evidence whether there is a clear difference between the productivity and profitability effects of simple and complex backward and forward participations for Turkish firms.

The authors employ a firm level data from the Türkiye's both first and second top 500 industrial enterprises from 1993 to 2019. In addition, the authors calculate country-sector level both backward and forward GVC participation indices with their simple and complex sub-indices for each year from 1990 to 2015 from the Full Eora data of the Eora Global Supply Chain Database. The authors estimate the model with OLS and fixed effects. To understand the role of the 2008 global crisis, the authors also undertake estimations for the pre-crisis and post-crisis. The authors also divide the data by R&D intensity of sectors.

While backward GVC participation lowers both labor productivity and profitability growth, forward GVC participation promotes both. Moreover, simple and complex backward participation have similarly negative effects on productivity and profitability growth, simple and complex forward participation have the completely opposite effects though. The authors then provide substantial evidence for the differing effects of participation on productivity and profitability growth between pre-crisis and post-crisis periods. Interestingly, backward participation has a negative impact for both hi-tech and low-tech firms while forward participation boosts the productivity growth only for low-tech firms, probably due to the relatively more upstream position of low-tech firms.

To the best of the knowledge, no previous study has yet examined the profitability effects of GVC for firms. Second, in addition to overall backward and forward GVC participation rates, the authors also calculate and utilize simple and complex GVC measures in the estimations. Third, to reveal whether the global financial crisis leads to a shift in the productivity and profitability effects of GVCs, the authors separately run the regressions for the pre- and post-crisis periods. Fourth, the authors then investigate the argument that hi-tech sectors/firms could benefit more from joining GVCs compared to firms in low-tech technology sectors.

Prior research from transaction costs economics argued that central firms perform better because they have superior access to information to discipline their alliance partners. Central firms may also, however, face higher costs and risks of unintentional learning and weaken their competence through structural inertia. We propose that these costs and risks are influenced by the learning capacities of the firms in the network and can explain different outcomes for focal firm performance.

To test our predictions, we use instrumental variable–generalized method of moments estimation techniques on 15,517 firm-year observations from equity alliance portfolios in the global food industry across a 21-year window.

We find support for our predictions and show that the relationship between network degree centrality and firm performance is negatively influenced by partners’ learning capacity and positively influenced by focal firms’ learning capacity, while firms with low network degree centrality benefit less from their learning capacity.

Future developments in transaction cost economics may consider partner and focal firms’ learning capacity as moderators of the network degree centrality – firm performance relationship.

In alliance decisions, managers must consider that the combination of high network degree centrality and partners’ learning capacity can lead to high costs, risks of unintentional learning, and structural inertia, all of which have negative consequences for performance. In concentrated industries where network positions are controlled by a few large firms, policymakers must acknowledge that firms may face substantial barriers to collaboration with learning-intensive firms.

This study is the first to develop and test a comprehensive transaction cost analysis of the central firm’s unintended knowledge flows and structural inertia in alliance networks. It is also the first to incorporate theoretically and empirically the hazards of complex and unintended information flows on the relationship of network degree centrality to performance in equity alliance portfolios.