Search results

This article aims to clarify the impact of stock market liberalization on corporate green technology innovation, analyze its mechanism from the perspectives of financing constraints and environmental management level and explore heterogeneity.

Using the panel data of Chinese enterprises from 2010 to 2020, this article adopts the multi-point difference-in-difference (DID) method to test the impact of stock market liberalization on enterprise green technology innovation and its conduction pathway.

The outcomes demonstrate that stock market liberalization contributes to the furthering of green technology innovation. The heterogeneity test reveals that this promotion is more pronounced for private companies, small-scale companies and companies with high information transparency. The mediating effect test shows that stock market liberalization boosts green technology innovation by alleviating corporate financing constraints and improving corporate environmental management.

This article elucidates the impact path of stock market liberalization on corporate green innovation based on alleviating corporate financing constraints and improving corporate environmental management levels. From the perspective of corporate green technology innovation, this article provides evidence from emerging market countries for the economic effects of capital market opening, which helps to further improve the level of green innovation.

The purpose of this article is to examine the impact of panic and hysteria news on the volatility of microchip stocks during Covid-19.

The authors use the P-GARCH (1,1) and random effects regression to model/examine the impact of Covid-19 panic and hysteria news on the overall microchip sector and individual firms. They further utilize the SVAR model to examine volatility spill-over from the microchip sector to the automobile and main technology sectors. Their time frame ranges from 6th January 2020 to 30th June 2021 to capture the effects of both waves of Covid-19.

The study results firstly reveal that Covid-19 panic and hysteria news have tremendous potential to model the volatility of microchip sector stock thus confirming the information discovery hypothesis. The authors secondly demonstrate the influence of Covid-19 cases, deaths and policy stringency on stock returns of individual microchip companies in different countries. Finally the authors confirm the presence of volatility spill-over from the microchip sector to other technology sectors.

The authors provide evidence to support the profundity of bad news in predicting stock behavior. The study results depict how Covid-19 has affected microchip stocks so that policy initiatives can be taken to protect the industry. The presence of volatility spill-over signifies the importance of diversifying portfolios by mixing technology and non-technology stocks.

The research strand on Covid-19 and individual sectoral stocks has received limited scholarly attention despite unparallel effects of the pandemic on different sectors.

This study aims to investigate the relationship between oil prices and stock returns of renewable energy firms in India under different market conditions.

The authors use the panel quantile framework with Fama–French–Carhart’s (1997) four-factor asset pricing model. All renewable energy firms listed in the National Stock Exchange of India are considered in this study. Three oil prices, such as West Texas Intermediate spot price, Europe Brent oil price and Indian basket oil price, are used in the regression. The analysis is done for the whole sample and its subgroups.

In the whole sample, stock returns of renewable energy firms respond positively to oil price changes in extreme market conditions only. In the subgroups of the renewable energy firms, the relationship between stock returns and oil price is positive and more robust in higher quantiles across all subgroup firms.

The contribution of the study is explained as follows. First, this study helps to explore the relationship between oil and stock returns of the renewable energy sector under different market conditions in the Indian context. Second, existing studies explore the effect of oil prices on stock returns of the renewable energy sector at the industry level, and most of the studies are in developed countries. To the best of the authors’ knowledge, this is the first study in the context of India. Third, this is a firm-level study

Over the last decade, investments in green energy companies have witnessed noticeable growth rates. However, the glacial pace of the world economic restoration due to COVID-19 pandemic placed a high degree of uncertainty over this market. Therefore, this study investigates the short- and long-term relationships between COVID-19 new cases and WilderHill New Energy Global Innovation Index (NEX) using daily data over the period from January 23, 2020 to February 1, 2023.

The authors utilize an autoregressive distributed lag bounds testing estimation technique.

The results show a significant positive impact of COVID-19 new cases on the returns of NEX index in the short run, whereas it has a significant negative impact in the long run. It is also found that the S&P Global Clean Energy Index has a significant positive impact on the returns of NEX index. Although oil has an influential effect on stock returns, the results show insignificant impact.

Governments have the chance to flip this trend by including investment in green energy in their economic growth stimulation policies. Governments should highlight the fundamental advantages of investing in this type of energy such as creating job vacancies while reducing emissions and promoting innovation.

First, as far as the authors are aware, the authors are the first to examine the effect of oil prices on clean energy stocks during COVID-19. Second, the authors contribute to studies on the relationship between oil prices and renewable energy. Third, the authors add to the emerging strand of literature on the impact of COVID-19 on various sectors of the economy. Fourth, the findings of the paper can add to the growing literature on sustainable development goals, in specific the papers related to energy sustainability.

Since 2008’s financial crisis, attention toward supply chain finance (SCF) has increased. However, most research investigates SCF considering single supply chain (SC) stages or buyer–supplier dyads and focuses on a single SCF solution. It is important to see how different solutions are adopted at different SC stages, by actors with different financing needs. This study aims to analyze SCF at different SC stages, to understand why different solutions are implemented at different SC stages and the contingency factors (regulation, SC stage, product category and size) influencing their adoption.

The paper is based on multiple exploratory case studies in the Italian agri-food industry, considering firms distributed at different SC stages and adopting multiple SCF solutions. The paper exploits a contingent approach (Sousa and Voss, 2008) to analyze how contingent factors influence SCF adoption at different SC stages.

Findings explain how and why different SC stages (producer, cooperative, processor and retailer) implement different SCF solutions (reverse factoring, dynamic discounting, inventory finance and Minibond), describing contingency variables’ impact on their adoption.

To the best of the authors’ knowledge, the research is original in its description of SCF at different SC stages, considering different SC actors’ drivers and barriers, and questioning the importance of a coordinated approach in SCF adoption along an entire SC. Moreover, the paper adopts a contingent approach, contributing to SCF research, seldomly based on theoretical lenses.

Considering rapid digitalization development, this study examines the impacts of digital technology innovation on social responsibility in platform enterprises.

The study applies the event study method and cross-sectional regression analysis, taking 168 digital technology innovations for social responsibility issued by 88 listed platform enterprises from 2011 to 2022 to study the impact of digital technology innovations for social responsibility announcements of different announcement content and platform attributes on the stock market value of platform enterprises.

The results show that, first, the positive stock market reaction is produced on the same day as the digital technology innovation announcement. Second, the announcement of the platform’s public social responsibility and the announcement of co-innovation and radical innovation bring more positive stock market reactions. In addition, the announcements mentioned above issued by trading platforms bring more positive stock market reactions. Finally, the social responsibility attribution characteristics of the announcement did not have a significant differentiated impact on the stock market reaction.

Most scholars have studied digital technology innovation for social responsibility through modeling rather than second-hand data to empirically examine. This study uses second-hand data with the instrumental stakeholder theory to provide a new research perspective on platform social responsibility. In addition, in order to explore the different impacts of digital technology innovation on social responsibility, this study has classified digital technology innovation for social responsibility according to its social responsibility and digital technology innovation characteristics.

This study examines the influence of various COVID-19 catastrophes variables on the stock market liquidity, considering the market depth and market tightness in the technology industry of the four biggest ASEAN capital markets.

The study utilised the panel data regression analysis obtained from 177 listed technology companies across the four ASEAN countries from March 2, 2020 to June 30, 2021 using the random effect and weighted least squares. The study also supported the result with robustness test, implementing the quantile regression to further present companies' segmentation within the variables.

The regression results indicate that daily growth COVID-19 confirmed cases and stringency that adversely impacted the stock market liquidity. Confirmed deaths were also found to have a detrimental effect on the stock market liquidity. On the other hand, recoveries and vaccination of COVID-19 enhance the stock market liquidity to escalate.

The study affirms that stock market liquidity is bound to be driven by the COVID-19 variables, but only to be limited to the technology industry observed in four major ASEAN capital markets. Awareness by investors and government could be shifted towards the rise of confirmed cases, recoveries, vaccination and stringency as it improves the liquidity of capital market in aggregate. However, rise of confirmed deaths negatively affect the liquidity. All in all, government and stock market regulator should promote transparency to boost investors' confidence in trading.

This study initiates the investigation in the four biggest ASEAN capital markets, particularly in the technology industry, regarding the COVID-19 catastrophes and stock market liquidity in terms of both market depth and market tightness. Further, this study enriches the impact of COVID-19 by taking the recovery cases and vaccination of COVID-19 as additional consideration.

This study aims to test the market anomaly of herding behavior driven by the response to supply chain disruptions in extreme market conditions such as those observed during COVID-19. The authors empirically test the response of the capital market participants for B2B firms, resulting in herding behavior.

Using the event study approach based on the market model, the authors test the impact of supply chain disruptions and resultant herding behavior across six sectors and among different B2B firms. The authors used cumulative average abnormal returns (CAAR) and cross-sectional absolute deviation (CSAD) to examine the significance of herding behavior across sectors.

The event study results show a significant effect of COVID-19 due to supply chain disruptions across specific sectors. Herding was detected across the automotive and pharmaceutical sectors. The authors also provide evidence of sector-specific disruption impact and herding behavior based on the black swan event and social learning theory.

The authors examine the impact of COVID-19 on herding in the stock market of an emerging economy due to extreme market conditions. This is one of the first studies analyzing lockdown-driven supply chain disruptions and subsequent sector-specific herding behavior. Investors and regulators should take sector-specific responses that are sophisticated during extreme market conditions, such as a pandemic, and update their responses as the situation unfolds.

Using social network theory (SNT), this study empirically examines the impact of digital supply chain announcements disclosing corporate social responsibility (CSR) information on stock market value.

Based on 172 digital supply chain announcements disclosing CSR information from Chinese A-share listed companies, this study uses event study method to test the hypotheses.

First, digital supply chain announcements disclosing CSR information generate positive and significant market reactions, which is timely. Second, strategic CSR and value-based CSR disclosed in digital supply chain announcements have a more positive impact on stock market, however there is no significant difference when the CSR orientation is either towards internal or external stakeholders. Third, in terms of digital supply chain network characteristics, announcements reflecting higher relationship embeddedness and higher digital breadth and depth lead to more positive increases of stock value.

First, the authors consider the value of CSR information in digital supply chain announcements, using an event study approach to fill the gap in the related area. This study is the first examination of the joint impact of digital supply chain and CSR on market reactions. Second, compared to the previous studies on the single dimension of digital supply chain technology application, the authors innovatively consider supply chain network relationship and network structure based on social network theory and integrate several factors that may affect the market reaction. This study improves the understanding of the mechanism between digital supply chain announcements disclosing CSR information and stock market, and informs future research.

Blockchain is a distributed ledger technology that uses cryptography to ensure transmission and access security, which provides solutions to numerous challenges to complex supply networks. The purpose of this paper is to empirically test the impact of blockchain implementation on shareholder value varying from internal and external complexity from the complex adaptive systems (CASs) perspective. It further explores how business diversification, supply chain (SC) concentration and environmental complexity affect the relationship between blockchain implementation and shareholder value.

Based on 138 blockchain implementation announcements of listed companies on the Chinese A-share stock market, the authors use event study methodology to evaluate the impact of blockchain implementation on shareholder value.

The results show that blockchain implementation has a positive impact on shareholder value, and this impact will be moderated by business diversification, SC concentration and environmental complexity. In addition, environmental complexity exerts a moderating effect on SC concentration. In the post hoc analysis, the authors further explore the impact of blockchain implementation on long-term operational performance.

This is the first research empirically examining the effect of blockchain implementation on shareholder value varying from internal and external complexity from the CASs perspective. This paper provides evidence of the different effects of blockchain implementation on short- and long-term performance. It adds to the interdisciplinary research of information systems (IS) and operations management (OM).