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The existence of long memory and persistent volatility characteristics of cryptocurrencies justifies the investigation of return and volatility/shock spillovers between traditional financial market asset classes and cryptocurrencies. The purpose of this paper is to investigate the dynamic relationship between the cryptocurrencies, namely Bitcoin and Ethereum, and stock market indices of G7 and E7 countries to analyze the return and volatility spillover patterns among these markets by means of multivariate (MGARCH) approach.

Applying the newly developed VAR-GARCH-in mean framework with the BEKK representation, the empirical results reveal that there exists an evidence of mean and volatility spillover effects among Bitcoin and Ethereum as the proxies for the cryptocurrencies, and stock markets reviewed.

Interestingly, the direction of the return and volatility spillover effects is unidirectional in most E7 countries, but bidirectional relationship was found in most G7 countries. This can be explained as the presence of a strong return and volatility interaction among G7 stock markets and crypto market.

Overall, the results of this study are of particular interest for portfolio management since it provides insights for financial market participants to make better portfolio allocation decisions. It is also increasingly important to understand the volatility transmission mechanism across these markets to provide policymakers and regulatory bodies with guidance to eliminate the negative impact of cryptocurrency's volatility on the stability of financial markets.

This paper aims to investigate the antecedents of recombinant innovation from the perspective of ego–network dynamics, and further disentangle whether ego–network stability or ego–network expansion is more conducive to recombinant innovation under heterogeneous knowledge base.

This paper uses 1,801 patent data in China’s biotechnology field as a sample and adopts fixed effects regression model to examine the effects of ego–network dynamics on recombinant innovation and further uses the Wald tests to discern which ego–network dynamic is more conducive to recombinant innovation under heterogeneous knowledge base.

The empirical results indicate that ego–network dynamics have a positive impact on recombinant innovation. Specifically, for firms with high knowledge breadth and high knowledge depth as well as high knowledge breadth and low knowledge depth, ego–network stability is more conducive to recombinant innovation. By contrast, for firms with low knowledge breadth and high knowledge depth, recombinant innovation benefits more from ego–network expansion. As for firms with low knowledge breadth and low knowledge depth, both ego–network stability and ego–network expansion can promote recombinant innovation, while the effects are not significant.

This research may enlighten managers to choose suitable ego–network dynamics strategies for recombinant innovation based on their knowledge base.

This research not only contributes to the literature on recombinant innovation by revealing the impact of different ego–network dynamics on recombinant innovation but also contributes to network dynamics theory by exploring whether ego–network stability or ego–network expansion is more conducive to recombinant innovation under a heterogeneous knowledge base.

This study aims to examine how the knowledge breadth and depth of a patent affect its likelihood of being pledged. It also seeks to explore whether these relationships change diversely in different technological environments.

A complementary log-log model with random effects was conducted to test the hypotheses using a unique data set consisting of 348,927 invention patents granted by the China National Intellectual Property Administration from 1985 to 2015 belonging to 74,996 firms.

The findings reveal that both knowledge breadth and depth of a patent positively affect its likelihood of being pledged. Furthermore, the knowledge breadth and depth entail different degrees of superiority in different technological environments.

This study focuses on the effect of an individual patent’s knowledge base on its likelihood of being selected as collateral. It does not consider the influence of the overall knowledge characteristics of the selected patent portfolio.

Managers need to pay attention to patents’ knowledge characteristics and the changes in technological environments to select the most suitable patents as collateral and thus improve the success rate of pledge financing.

This study explores the impact of multidimensional characteristics of knowledge base on patent pledge financing within a systematic theoretical framework and incorporates technological environments into this framework.

The purpose of this paper is to analyse the institutional threshold effects of foreign aid on foreign direct investment (FDI).

This paper develops a theoretical model from an extended Solow model that introduces the conductive effect of institutions in an aid recipient country towards the capacity of attracting FDI. This study evidences threshold effects with the most recent panel threshold models that consider endogeneity issues. The data on economic institutions and foreign aid are decomposed into disaggregated level to reveal the detailed threshold pattern. Several sample subsets are used for a heterogeneity analysis.

Conducting empirical research on a sample of 62 countries during the period 2003–2016, this study finds robust evidence of the existence of an institutional threshold in the aid–FDI nexus which a country must attain to reap the full attraction of FDI by foreign aid providing financial resources. Furthermore, foreign aid tends to promote FDI in institutions characterized by a right-sized government, a strengthened legal system and an appropriate regulatory environment. On the other hand, aid may crowd out FDI. The results are robust to regional combinations and a subset of low and lower-middle-income countries. In addition, this study finds that aid targeted at social infrastructure and services has a positive effect regardless of institutional threshold.

This paper contributes to the literature by introducing a non-linear and discontinuous effect of aid on FDI, i.e. a threshold effect, highlighting the relevance of legal systems and regulations and the possibility of a crowding-out effect on FDI for specific institutional regimes. The thresholds provide a guide for donor countries to ensure aid effectiveness at the risk of being counterproductive and for recipient countries to better assess the institutional dimensions that need to be improved.

The rapid, yet low-profit, expansion of the production capacity of state-owned enterprises (SOEs) represents a remarkable phenomenon. However, the motivation behind this key operational decision remains underexplored, especially concerning the prioritization of sociopolitical and financial goals in operations management. Drawing on the multiple-goal model in the behavioral theory of the firm (BTOF), the authors' study aims to examine how SOE capacity expansion is driven by performance feedback regarding the sociopolitical goal of employment provision and how SOEs differently prioritize sociopolitical and financial goals based on negative versus positive feedback on the sociopolitical goal.

The authors' study uses panel data on 826 Chinese SOEs in manufacturing industries from 2011 to 2019. The authors employ the fixed-effects model with Driscoll–Kraay standard errors, which are robust to heteroscedasticity, autocorrelation and cross-sectional dependence.

The authors find that SOEs increase capacity expansion as sociopolitical feedback becomes more negative, but they may not increase capacity expansion in response to positive sociopolitical feedback. Moreover, negative profitability feedback strengthens SOEs' capacity expansion in response to negative sociopolitical feedback. In contrast, negative profitability feedback weakens their response to positive sociopolitical feedback.

The authors' study offers a novel behavioral explanation of SOEs' operational decisions regarding capacity expansion. While the literature has traditionally assumed multiple goals as either hierarchical or compatible, the authors extend the BTOF's multiple-goal model to illuminate when firms pursue sociopolitical and financial goals as compatible (i.e. the activation rule) versus hierarchical (i.e. the sequential rule), thereby reconciling their tension in distinct performance situations. Practically, the authors provide fine-grained insights into how operations managers can prioritize multiple goals when making operational decisions. The authors' study also shows how policymakers can influence SOE operations to pursue sociopolitical goals for public benefit.

This study aims to explore the impact mechanism of the degree of innovation failure on breakthrough innovation in Chinese listed companies, and examines the moderating effect of the company’s own knowledge-based capabilities.

Based on organizational learning theory and using the innovation failure data of invention patents from Chinese A-share listed companies on the main board from 2003 to 2017 as research samples, this study constructs and examines a comprehensive framework and its impact on breakthrough innovation regarding “what kind of innovation failure will promote breakthrough innovation”, focusing on innovation failure, enterprise knowledge base, and breakthrough innovation.

Empirical research has found a U-shaped relationship between innovation failure and breakthrough innovation. In other words, both a low level of failure and an extremely high level of failure can significantly promote breakthrough innovation in enterprises. Furthermore, when the depth of enterprise knowledge is high, it further strengthens the non-linear relationship between innovation failure and breakthrough innovation.

The research results enrich the study of the failure predicament and breakthrough innovation of Chinese technology innovation enterprises, revealing effective paths for Chinese technology innovation enterprises to get rid of the passive situation of innovation failure, and providing theoretical support and practical reference for “breaking new ground and achieving breakthrough innovation”.

This study investigates the profound impact of artificial intelligence (AI) capabilities on decision-making processes and organizational performance, addressing a crucial gap in the literature by exploring the mediating role of decision-making speed and quality.

Drawing upon resource-based theory and prior research, this study constructs a comprehensive model and hypotheses to illuminate the influence of AI capabilities within organizations on decision-making speed, decision quality, and, ultimately, organizational performance. A dataset comprising 230 responses from diverse organizations forms the basis of the analysis, with the study employing a partial least squares structural equation model (PLS-SEM) for robust data examination.

The results demonstrate the pivotal role of AI capabilities in shaping organizational decision-making processes and performance. AI capability significantly and positively affects decision-making speed, decision quality, and overall organizational performance. Notably, decision-making speed is a critical factor contributing significantly to enhanced organizational performance. The study further uncovered partial mediation effects, suggesting that decision-making processes partially mediate the relationship between AI capabilities and organizational performance through decision-making speed.

This study contributes to the existing body of literature by providing empirical evidence of the multifaceted impact of AI capabilities on organizational decision-making and performance. Elucidating the mediating role of decision-making processes advances our understanding of the complex mechanisms through which AI capabilities drive organizational success.

The purpose of this study is to empirically explore how firms configure centrifugal and centripetal forces in promoting breakthrough innovation (BI), thus improving their strategic performance (SP) in the artificial intelligence (AI) context.

This study applies the centrifugal and centripetal forces model to a survey sample of 285 Chinese AI firms. Fuzzy-set qualitative comparative analysis (fsQCA) and propensity score matching (PSM) are integrated to explore the configurational effects of three centrifugal forces—the autonomy of technical experts, knowledge search and alliance network—and two centripetal forces—strictness of organisational institutions (SOI) and human–human–AI collaboration (HHAC)—on BI, examining whether the configurations that enhance BI can further improve SP.

The results indicate that the strictness of innovation institutions (SII) and strictness of ethical institutions (SEI) are equally important for determining SOI. Three configurations can improve BI when SOI and HHAC are the core conditions; only one of three configurations can further improve SP significantly.

By introducing SOI composed of equally important levels of SII and SEI and HHAC, this research is one of the few empirical studies to explore the mechanisms behind the impact of centrifugal and centripetal forces on BI and SP, which may help researchers and managers address innovation challenges in the AI context.

This study investigates the effect of business cycles on school enrollment in Turkey. During recessions, school enrollment might increase as opportunity cost of schooling declines, yet it might also decrease because of reduced income households have for education. Which effect dominates depends on the context. We empirically explore this in a context displaying canonical features of developing countries.

Using the Turkish Household Labor Force Survey data for a period covering the Great Recession, we estimate the effect of unemployment rate separately for enrollments in general and vocational high schools and in undergraduate programs. To understand the cyclicality, we use a probit model with the regional and time variations in unemployment rates. We also build a simple theoretical model of work-schooling choice to interpret the findings.

We find that the likelihood of enrolling in general high schools and undergraduate programs declines with higher adult unemployment rates, but the likelihood of enrollment in vocational high schools increases. Confronting these empirical findings with the theoretical model suggests that the major factor in enrollment cyclicality in Turkey is how parental resources allocated to education change during recessions by schooling type.

Our finding of pro-cyclical enrollment in academically oriented programs is in contrast with counter-cyclicality documented for similar programs in developed countries, which highlights the importance of income related factors in developing-country contexts. Our heterogeneous findings for general and vocational high schools are also novel.