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Studies concentrating on digitalization and interconnected capabilities have increased over the past several decades. Digitalization capability and open innovation are perceived as sources of sustained competitiveness across disciplines. This study investigated how digitalization capability and coopetition strategy affect the sustainable performance of firms by exploring the role of internal and external factors in influencing the adoption and success of open innovation in emerging markets.

To test the hypothesis, the authors conducted a structural equation model analysis on 509 firm datasets from the hub cities in China, an innovative battlefield where multilateral cooperation and competition are interwoven for globalization, clean development and the enhancement of economic growth.

The authors found that a firm's digitalization capability positively impacts outbound/inbound open innovation, coopetition strategy and sustainable performance. This study’s results support a series of mediating effects through outbound/inbound open innovation and coopetition strategy. Also, it provides a nuanced understanding of how digitalization capability and open innovation can affect sustainable performance in emerging markets.

The present study provides a nuanced understanding of how digitalization capability and in/out-bound open innovation can affect sustainable performance in emerging markets. The authors believe this model contributes to current knowledge by filling several research gaps, and this study’s findings offer valuable and practical implications for achieving open innovation and creating sustainable performance.

While prior studies have explored the relationship between visual appeal and purchasing decisions, the role of bookmarking has largely been underemphasized. This research aims to address this gap by focusing on the impact of bookmarking on consumer behavior, guided by the cognitive load theory and dual-system theory.

The authors executed a controlled experiment and analyzed the results using a two-stage regression method that linked visual appeal, bookmarking and purchase intent. Further empirical analysis was conducted to authenticate the authors' proposed model, utilizing real-world mobile commerce data from a clothing company.

This study's findings suggest that visual appeal influences purchase intent primarily through the full mediation of bookmarking, rather than exerting a direct influence. Furthermore, an increase in colorfulness corresponds positively with visual appeal, while visual complexity exhibits an inverted U-shaped relationship with it.

This study provides novel insights into the choice-set formation process through the theoretical lens of dual-system theory. Additionally, the authors employed an image processing technique to quantify a product's visual appeal as depicted in a photograph. This study also incorporates a comprehensive econometric analysis to connect the objective aspects of visual appeal with subjective responses.

The crude oil market plays a key role in addressing the issue of energy economics. This paper aims to detect the causality relationship between the crude oil market and economy based on the financial system.

This paper used the static and dynamic Hatemi-J Bootstrap Toda–Yamamoto and Diebold–Yilmaz connectedness index. The Hatemi-J Bootstrap Toda-Yamamoto approach allows researchers to use nonstationary data and that method is robust to nonnormal distribution and heteroscedasticity. The Diebold–Yilmaz connectedness index model provides researchers to detect the power of connectedness besides linkage direction. The analyzed period is the span from January 3, 2005 to October 3, 2022.

The results show bidirectional causality in the full sample but unidirectional causality before and after the 2008 financial crisis. During the 2008 financial crisis period and the COVID-19 period, there was a bidirectional and unidirectional causality, respectively. The connectedness approach indicates that the crude oil market affects financial stress through investors’ risk preferences.

The Diebold–Yilmaz spillover index model is based on vector autoregression methods with a stationarity precondition. However, some of the five dimensions that constitute the financial stress index (FSI) are nonstationary in level. Therefore, the authors takes the first difference of the nonstationary data.

The linkage between the crude oil market and the FSI provides useful information for investors and policymakers. For instance, this paper indicates that an investor wanted to forecast future value of the crude oil (financial stress) should consider the current and past values of financial stress (crude oil). Moreover, policymaker should consider the crude oil market (FSI) to make a policy proposal for financial system (crude oil market).

Recently, indicators of economic activity levels (economic policy uncertainty, implied volatility index) have begun to be considered to analyze the relationship between energy and the economy but very little is known in the literature about the leading and lagging roles of data in subsample periods and the linkage channel. The other originality of this research is using the new econometric approaches.

Might the impact of the financial stress on exchange markets be asymmetric and exposed to regime changes? Departing from the existing literature, highlighting that the domestic and foreign financial stress in terms of money market have substantial effects on exchange market, this paper aims to investigate the impacts of the bond yield spreads of three emerging countries (Mexico, Russia, and South Korea) on their exchange market pressure indices using monthly observations for the period 2010:01–2019:12. Additionally, the paper analyses the impact of bond yield spread of the US on the exchange market pressure indices of the three mentioned emerging countries. The authors hypothesized whether the negative and positive changes in the bond yield spreads have varying effects on exchange market pressure indices.

To address the research question, we measure the bond yield spread of the selected countries by using the interest rate spread between 10-year and 3-month treasury bills. At the same time, the exchange market pressure index is proxied by the index introduced by Desai et al. (2017). We base the empirical analysis on nonlinear vector autoregression (VAR) models and an asymmetric quantile-based approach.

The results of the impulse response functions indicate that increases/decreases in the bond yield spreads of Mexico, Russia and South Korea raise/lower their exchange market pressure, and the effects of shocks in the bond yield spreads of the US also lead to depreciation/appreciation pressures in the local currencies of the emerging countries. The quantile connectedness analysis, which allows for the role of regimes, reveals that the weights of the domestic and foreign bond yield spread in explaining variations of exchange market pressure indices are higher when exchange market pressure indices are not in a normal regime, indicating the role of extreme development conditions in the exchange market. The quantile regression model underlines that an increase in the domestic bond yield spread leads to a rise in its exchange market pressure index during all exchange market pressure periods in Mexico, and the relevant effects are valid during periods of high exchange market pressure in Russia. Our results also show that Russia differs from Mexico and South Korea in terms of the factors influencing the demand for domestic currency, and we have demonstrated the role of domestic macroeconomic and financial conditions in surpassing the effects of US financial stress. More specifically, the impacts of the domestic and foreign financial stress vary across regimes and are asymmetric.

This study enriches the literature on factors affecting the exchange market pressure of emerging countries. The results have significant economic implications for policymakers, indicating that the exchange market pressure index may trigger a financial crisis and economic recession.

India started economic reforms at a rapid pace to catch up the world economy by following the services-led-growth model during the post-liberalisation period. Over the years, the growing unemployment rate posits a re-look into the dynamics of growth model for wider work force participation. In this backdrop, the paper aims to examine the dynamics of structural changes in employment pattern in view of economic growth led by services-led growth model in India.

The study employs a non-linear autoregressive model (NARDL) to examine the effect of the growth rates in three broad economic sectors namely agriculture and allied, services and industry on work force participation representing the employment opportunities in India.

The results highlight that the rapid expansion of the service sector has not occurred with enough employment opportunities by the same rate. By contrast, the growth in the industrial sector significantly creates employment opportunities in the short and long run. These results support the industry led growth model over the services for sustainable and inclusive economic growth in the country.

The study relies on combined labour force participation rates rather than gender-specific rates. Further, the regulatory, working conditions and economic incentives may affect the gender-specific engagement of the labour force in three broad sectors.

The results offer important insight into changing patterns in employment with policy lessons. A wider workforce force participation calls for expansion of manufacturing activities through pro-industry programmes.

The study makes pioneer efforts to examine the dynamics of labour force participation with respect to the growth of three broad economic sectors of the Indian economy. The results provide new insights with policy implications for the changing employment pattern and policy response.

Considering the frequency of extreme events, enhancing the global financial system's stability has become crucial. This study aims to investigate the contagion effects of extreme risk events in the international commodity market on China's financial industry. It highlights the significance of comprehending the origins, severity and potential impacts of extreme risks within China's financial market.

This study uses the tail-event driven network risk (TENET) model to construct a tail risk spillover network between China's financial market and the international commodity market. Combining with the characteristics of the network, this study employs an autoregressive distributed lag (ARDL) model to examine the factors influencing systemic risks in China's financial market and to explore the early identification of indicators for systemic risks in China's financial market.

The research reveals a strong tail risk contagion effect between China's financial market and the international commodity market, with a more pronounced impact from the latter to the former. Industrial raw materials, food, metals, oils, livestock and textiles notably influence China's currency market. The systemic risk in China's financial market is driven by systemic risks in the international commodity market and network centrality and can be accurately predicted with the ARDL-error correction model (ECM) model. Based on these, Chinese regulatory authorities can establish a monitoring and early warning mechanism to promptly identify contagion signs, issue timely warnings and adjust regulatory measures.

This study provides new insights into predicting systemic risk in China's financial market by revealing the tail risk spillover network structure between China's financial and international commodity markets.

The authors contribute to the literature on leadership by investigating how characteristics of principal investigators (PIs) affect innovation performance, and how collaborative and non-collaborative projects moderate this relationship within the context of inter-organisational research projects.

The authors analysed panel data from the National Science and Technology Information Service on 171 research projects within a biomedical and regenerative medicines programme overseen by the Korea Health Industry Development Institute. The authors used a hierarchical regression model, based on the ordinary least squares method, to examine the relationship between PI characteristics and performance, considering both quantity and quality.

The results show that the characteristics of PIs have diverse effects on the quantity and quality of innovation performance. Gender diversity within PIs negatively affects the quality of innovation performance, while the capacity of PIs positively influences it. Moreover, the degree of PI’s engagement is positively associated with the quantity of innovation performance but does not have a significant relationship with the quality of performance. In terms of moderating effects, collaborative projects with multiple leaders seem less reliant on PI capacity than non-collaborative projects led by a single leader, in terms of innovation performance.

The results contribute significantly to the literature on innovation management by examining the role of leadership in collaborative environments to enhance innovation performance, addressing the need for empirical evidence in this area. Analyses of PI characteristics in government R&D management can lead to improved team performance, more efficient processes and effective resource allocation, ultimately fostering innovation.

Present study aimed to integrate Geographic Information Systems (GIS) and Building Information Modeling (BIM) in conjunction with multicriteria decision-making (MCDM) to enhance infrastructure investment planning.

This analysis combines GIS databases with BIM simulations for a novel highway project. Around 150 potential alternatives were simulated, narrowed to 25 more effective routes and 3 options underwent in-depth analysis using PROMETHEE method for decision-making, based on environmental, cost and safety criteria, allowing for comprehensive cross-perspective comparisons.

A comprehensive framework proposed was validated through a case study. Demonstrating its adaptability with customizable parameters. It aids decision-making, cost estimation, environmental impact analysis and outcome prediction. Considering these critical factors, this study holds the potential to advance new techniques for assessment and planning railways, power lines, gas and water.

The study acknowledges limitations in GIS data quality, particularly in underdeveloped areas or regions with limited technology access. It also overlooks other pertinent variables, like social, economic, political and cultural issues. Thus, conclusions from these simulations may not entirely represent reality or diverse potential scenarios.

The proposed method automates decision-making, reducing subjectivity, aids in selecting effective alternatives and considers environmental criteria to mitigate negative impacts. Additionally, it minimizes costs and risks while demonstrating adaptability for assessing diverse infrastructures.

By integrating GIS and BIM data to support a MCDM workflow, this study proposes to fill the existing research gap in decision-making prioritization and mitigate subjective biases.

This study aims to investigate the effects of the historical prevalence of infectious diseases on contemporary entrepreneurship. Previous studies reveal numerous proximate causes of entrepreneurship, but little is known about the fundamental determinants of this widespread economic concern.

The central hypothesis is that historical pathogens exert persistent impacts on present-day entrepreneurship. The authors provide support for the underlying hypothesis using ordinary least squares and two-stage least squares with cross-sectional data from 125 countries consisting of the averages between 2006 and 2018.

Past diseases reduce entrepreneurship both directly and indirectly. The strongest indirect effects occur through GDP per capita, property rights, innovation, entrepreneurial attitudes, entrepreneurial abilities, entrepreneurial aspirations and skills. This result is robust to many sensitivity tests. Policymakers may take these findings into account and incorporate disease pathogens into the design of entrepreneurship.

The novelty of this paper lies in the adoption of a historical approach that sheds light on the deep historical roots of cross-country differences in entrepreneurship.

Despite the huge financial resources associated with oil, Nigeria has consistently recorded poor growth performance. Therefore, this study aims to examine how corruption and oil rent influence Nigeria’s economic performance during the 1996–2021 period.

Various estimation techniques were used. These include the bootstrap autoregressive distributed lag (ARDL) bounds-testing, dynamic ordinary least squares (DOLS), the fully modified OLS (FMOLS) and the canonical cointegration regression (CCR) estimators and the Toda–Yamamoto causality.

The bounds testing results provide evidence of a cointegrating relationship between the variables. In addition, the results of the ARDL, DOLS, CCR and FMOLS estimators demonstrate that oil rent and corruption have a significant positive impact on growth. Further, the results indicate that human capital and financial development enhance economic growth, whereas domestic investment and unemployment rates slow down long-term growth. Additionally, the causality test results illustrate the presence of a one-way causality from oil rent to economic growth and a bi-directional causal relationship between corruption and economic growth.

Existing studies focused on the effects of either oil rent or corruption on growth in Nigeria. Little attention has been paid to the exploration of how the rent from oil and the pervasiveness of corruption contribute to the performance of the Nigerian economy. Based on the outcome of this study, strategies and policies geared towards reducing oil dependence and the pervasiveness of corruption, enhancing human capital and financial development and reducing unemployment are recommended.