Associate Professor David Shao

Digital innovation has revolutionized organizational processes and outcomes in several industries. Previous studies emphasize the transformational nature of digital innovation in redefining the nature of competition and exposing incumbent industry players to a dire fate. We focus on the retail sector, where platform sponsors leverage digital innovation to intermediate the exchanges between buyers and sellers and orchestrate complementary innovations to make the platform ecosystem more valuable to members. We investigate whether digital innovation in retail platform ecosystem renders established competencies of traditional intermediaries obsolete through a case study of Alibaba Lingshoutong, a business-to-business digital sourcing and distribution platform. The analysis shows the oppositional nature of digital innovation built from a transaction cost rationality and existing practices based upon social capital. This study reveals the incremental nature of digital innovation, a dimension which receives little attention thus far. Based on some preliminary empirical findings and conceptual rationale, we developed three propositions.

Complex media environments often intertwine multiple focal actors in media reports and different characteristics of media coverage. Credibility differences among various media sub-markets further exacerbate this complexity. Previous studies have limited understanding of the interactions between different focal subjects or media characteristics. This study focuses on the niche media market of official media coverage on corporate artificial intelligence (AI)-related activities to explore the impact of official media evaluations on the duration of corporate initial public offering (IPO) processes and the nonlinear moderating role of leadership media exposure on this impact. We examined the IPO review activities of 735 Chinese firms under the under the registration-based IPO system from 2019 to 2022, with a specific focus on media sentiment analysis through supervised machine learning techniques. Our findings are largely support our hypotheses: positive evaluations by official media of corporate AI activities are associated with shorter IPO process durations, with moderate leadership media exposure being most effective in expediting the process. Additionally, although official media can filter out firms with strong AI capabilities, these firms tend to exhibit poorer profitability after their IPOs. This study establishes connections between the relatively fragmented literature on different focal subjects and media characteristics.

This paper examines the impact of China's 2012 Green Credit Guidelines (GCG) on the green technological innovation of heavily polluting enterprises (HPEs). Using a panel dataset of 491 listed companies from 2009 to 2018, the study employs a Matched Difference-in-Differences (DID) model to analyse the policy's effects, complemented by Triple and Quantile DID models to explore underlying mechanisms. The results indicate that, contrary to expectations, the GCG negatively impacts green innovation among HPEs, particularly affecting private, smaller-scale, financially constrained, and low-profit firms in highly competitive markets. However, the adverse effect is short-term, with firms that have greater financial resilience, access to subsidies, stronger initial green innovation capabilities, and market power better able to withstand financial restrictions. The findings highlight the need for more tailored green credit policies that consider firm-specific characteristics to effectively promote green innovation.

In this paper, we analyze the economic impacts of the West-East Electricity Transmission Project (WEETP) project using the multi-period difference-in-difference (DID) method based on county-level data from 2000 to 2020. Our findings indicate that the WEETP project inhibits firm entry in electricity-exporting regions while encouraging firm entry in electricity-importing regions, thus hindering regional development equalization. Specifically, the initiation of WEETP discouraged firm activity in the southern and central corridors' exporting regions but boosted firm activity in the northern corridor. Additionally, WEETP exacerbated infrastructure overbuilding and environmental damage in energy-exporting regions, further weakening entrepreneurial dynamism (ED) and widening the development gap with energy-importing regions. Our study provides insights into the real impact of national energy strategies on regional development and highlights the institutional factors contributing to the resource curse. The findings demonstrate the limitations of non-market pricing approaches to resource allocation, thereby offering an empirical basis for narrowing the development gap and promoting developmental affirmative action.

The fairness of vocational contest scoring is key to generating reliable competency assessments. This study examined the performance impact of the motivation of English-as-a-foreign-language learners in contests with vocabulary knowledge antecedents in the contexts of artificial intelligence (AI) and blockchain (BC). The sample comprised 185 participants of an oral English contest at higher vocational institution in China. AI-powered scoring of learners' contest performance and a survey were used to collect data. The findings revealed that learners' intrinsic drive was the main positive factor, outweighing their extrinsic motivation, and that AI and BC increased the trustworthiness and integrity of contest records, thus providing new opportunities to build learner trust and form psychological incentives. This study enriches foreign language motivation theory in the context of contest research and highlights the importance of using AI and BC to enhance the scoring accuracy and credibility of contests as authoritative evaluation instruments in vocational education.

The composition-based view explains how emerging market firms creatively adopt compositional investment, compositional offerings, and compositional capabilities to gain a competitive advantage in the global marketplace. How the composition-based international strategy contributes to organizational resilience under the de-globalization world remains unclear. Using fuzzy-set qualitative comparative analysis, we explore how emerging market firms reconfigure compositional elements and the crucial role of firm heterogeneity in determining organizational resilience. Our analysis of 250 Chinese manufacturing firms revealed five distinct international compositional strategies and asymmetric outcomes. Multiple configurations of composition-based international strategies and firm heterogeneity were found to be related to high and low organizational resilience. Our findings confirm that emerging market firms must reconfigure their compositional elements to achieve sustained and resilient performance throughout the pandemic. Our findings extend the composition-based view by elucidating the multiple pathways and boundary conditions of compositional strategies leading to organizational resilience.

Uncertainty in the demand of individual customers increases demand volatility in the entire market. In this context, partial volume flexibility can help hedge against demand uncertainty and competition pressure, and subsequently help create value by means of high-quality production decisions and market responsiveness, within the assumption of transformation and applied knowledge incorporated into the production stages. The article aims to investigate this phenomenon. To achieve this, as part of the methodology, a unified measure, the flexibility degree, is taken to represent the level of partial volume flexibility. We find that under demand uncertainty, a firm goes through a process with four stages - capacity decision, stable production, flexible production, and pricing. In a monopoly model, we find that a slight change in the flexibility degree could significantly increase a firm's adjustable production range to respond to demand changes. Furthermore, we develop a general asymmetric duopoly model. We characterize the equilibrium and find that a firm with a higher flexibility degree is more sensitive to demand changes. Accordingly, in response to specific demand, a higher flexibility firm obtains more profit only when the demand is sufficiently high or low, otherwise, the lower flexibility firm wins. Finally, we observe that a firm's capacity increases in its flexibility degree and decreases in its rival's flexibility degree; this is because the firm balances the market share and product profitability.

We study the emerging customer-to-manufacturer (C2M) mode, in which the e-platform analyzes customer data and offers recommendations to manufacturers. We consider two types of C2M mode: the platform establishes its own brand (self-built mode) and the platform conducts joint development with the manufacturer (joint-development mode). We show that the manufacturer always benefits from a lower quality difference, whereas the platform does not. Interestingly, under the self-build mode, a smaller brand devaluation effect benefits not only the platform, but also the manufacturer. Further, we discover that a win-win situation exists if the manufacturer and the platform choose the same mode.

Exploring the role of artificial intelligence (AI) in renewable energy (RE) development is pivotal for seizing technological opportunities and achieving climate objectives. This study uses wavelet analysis to examine the correlation between AI and RE in China. Our findings indicate a co-movement between AI and RE from 2014 to 2016 and a positive influence from AI to RE emerging from late 2018 to 2022. This suggests that AI acts as a facilitator for China's energy transition. Nevertheless, this effect is not constant; it becomes more pronounced with advancements in AI technology. These outcomes align with the techno-economic paradigms framework, implying that China can benefit from AI breakthroughs to accelerate its energy transition. Future policy efforts may focus on fostering collaboration among the government, businesses, and universities to promote AI and RE development.

The dual role of artificial intelligence (AI) in carbon emissions has come under scrutiny. The feedback mechanism in the "AI-Carbon-Energy" system contains the enlightenment of coordinated development of environment and economy. Based on the dynamic connectedness index and network diagrams, we quantify how the AI industry is connected to the carbon market and the energy sector in the short-term and long-term. Our empirical findings suggest that the information spillover within the system changes over time and across frequency bands. The long-term component drives the overall information spillover. Both the carbon market and the energy sector are closely connected with the AI industry. Specifically, AI industry trading volume is a main information transmitter. Since the release of GPT-4, however, investor attention to the AI industry becomes more important. The carbon market receives a lot of information from the AI industry trading volume and investor attention to the AI industry, particularly since 2023. Nevertheless, the energy sector is only weakly connected to the other two markets. These findings have important implications for policy makers, investors, and producers.

This paper examines the asymmetric returns spillovers and time-frequency causality between the carbon emissions market and the energy market. To this end, we apply the time-varying asymmetry spillovers and Granger causality over the spectrum approaches. This research uses daily price indices of natural gas, gasoline, gas oil, heating oil, crude oil, coal, petroleum, kerosene, propane, and diesel to denote the energy market and the European Union Emissions Trading System (i.e., certificate prices for CO2 emissions) to represent the carbon market. Using historical time-series data from May 18, 2011, to September 23, 2020, the study reveals interesting and convincing empirical results showing that the carbon and energy markets are dynamically and asymmetrically connected. Further results show that the carbon market predominantly explains positive or negative returns in the energy market. Regarding volatility transmission, the study demonstrates that the carbon market is a primary net receiver of good or bad volatility transmitted from the energy market, such as crude oil, petroleum, heating oil, diesel, and kerosene.

Identifying the vital role of climate risk in developing technology is significant to promote the Fourth Industrial Revolution. The research utilises the full and sub-sample methodologies to capture the connection of the Southern Oscillation Index (SOI) and technological progress (TP) from the global perspective. In light of the quantitative discussion, we conclude that positive and adverse effects exist of SOI on TP, and the favourable one suggests that the La Nina phenomenon promotes technological progress. However, this opinion cannot be established in the adverse effect of SOI on TP that accompanies the La Nina phenomenon, which is primarily caused by the global financial crisis. Other adverse effects that accompany the El Nino phenomena point out that these climate risks are conducive to developing technology. In turn, the favourable and adverse influences from TP to SOI are accompanied by the El Nino phenomenon and La Nina event respectively, underlining that the development of technology is beneficial for alleviating climate risk. In the context of an increasingly severe climate crisis and a new round of scientific and technological revolution, this article will put forward valuable suggestions to tackle climate risk in the context of the Fourth Industrial Revolution.

The COVID-19 pandemic has severely disrupted travel behavior across diverse socio-economic areas, with a significant impact on transportation systems, public health, and the economy. As countries both recover and plan for future virus-driven stresses, it is crucial to identify the drivers of building travel behavior resilience, such as vaccination. Using an integrated dataset with over 150 million US county-level mobile device data from 01/01/2020 to 20/04/2021, we employ Bayesian structural time series (BSTS) models to infer the relative impact of the vaccination intervention on five types of travel behavior across Metropolitan, Micropolitan and Rural areas. Further, we develop partial least squares regression (PLSR) models to accurately estimate how COVID-19 vaccination rates, epidemiological indicators (i.e., COVID-19 incidence rates, death rates, and testing rates) and weather conditions (i.e., temperature, rain, and snow) would impact various travel behaviors across the diverse areas during the recovery period of the pandemic. The model results shed light on the positive role of vaccinations in fostering the recovery of travel behaviors and reveal the disparities in travel behavior resilience in response to vaccination rates, epidemiological indicators, and weather conditions across diverse areas. Our findings can offer evidential insights for policymakers, transport planners, and public health officials, guiding the development of equitable, sustainable, and resilient transportation systems prepared to adapt to future pandemics.

To uncover the secrets of creating competitive advantage for firms under demand uncertainty, we study the roles of technology level and capacity investment strategies. Specifically, we analyze the Nash equilibrium of two competing firms at different technology levels under two capacity investment strategies, namely flexible or inflexible. We examine both symmetrical competition where the two firms adopt a same capacity type (flexible or inflexible), and asymmetrical competition where the two firms invest different capacity types. We find that an advanced technology level incentivises capacity expansion regardless of the capacity strategy adopted. In addition, the technology-capacity relationship significantly depends on demand distribution when both firms adopt inflexible capacity investment, but is independent of demand distribution when both firms invest in flexible capacity. No overwhelming superiority is observed from either side when both firms adopt the same capacity investment, and firms always co-exist in a profitable market, despite significant differences in technology. However, overwhelming superiority emerges from one side when both firms have different capacity investments. A firm can squeeze out its competitor and capture the entire market by upgrading its technology. When the two firms adopt the same capacity strategy, each firm may increase or decrease its technology level and capacity volume proportionally in equilibrium, regardless of the competition scenarios. We further explore the endogenous capacity investment type and technology flexibility in competition. We show that the competition effect plays an important role when the flexible capacity cost is moderate.

We examine the effect of financial and manufacturing co-clustering on high-quality green development in China's 30 provinces from 2005 to 2020. The nexus between financial and manufacturing co-clustering and high-quality green development has been comprehensively investigated from linear and non-linear perspectives. We find that financial and manufacturing co-clustering significantly fosters high-quality green development. Mechanism analysis shows that formal environmental regulation has a significant negative moderating effect on high-quality green development, whereas informal environmental regulation plays a significantly positive moderating role. However, empirical results only show the mediation effect of formal environmental regulation in the incentive role of financial and manufacturing co-clustering to high-quality green development. A dynamic panel threshold model also certifies the non-linear effect between financial and manufacturing co-clustering and high-quality green development. Lastly, the promotion effect of financial and manufacturing co-clustering on high-quality green development creates significant heterogeneity.

Green manufacturing has become an important measure for energy conservation and emission reduction. The government has issued a series of policies, including carbon taxes and quotas, to promote green manufacturing. This study identifies effective green manufacturing modes under government carbon tax and carbon quota policies, considers the automobile manufacturing industry as the research object, and constructs three green manufacturing modes: (i) Product Remanufacturing Mode (PRM), (ii) Green Product Manufacturing Mode (GPMM), and (iii) Hybrid Manufacturing Mode. The analysis reveals that (i) Contrary to common sense, when the government carbon quota is larger, the carbon emission of PRM is lower than that of GPMM. However, when the government reduces carbon allowances, the carbon emissions of PRM are higher than that of GPMM. (ii) When the R&D of remanufacturing technology is more difficult than that of green product innovation technology (without considering price competition), if the government sets a low carbon tax, companies will generate more profit by choosing PRM. Otherwise, companies will obtain more profits by choosing GPMM. However, considering price competition, if the carbon tax is low, companies will obtain more profits by choosing GPMM. Or the company will obtain more profits by choosing PRM. (iii) However, when R&D of remanufacturing technology is less difficult than that of green product innovation technology contrary conclusions of (ii) follow.

New energy vehicles (NEVs) are considered to ease energy and environmental pressures. China actively formulates the implementation of NEVs development plans to promote sustainable development of the automotive industry. In view of the diversity of vehicle pollutants, NEV may show controversial environmental results. Therefore, this paper uses the quantile-on-quantile method to explore the relationship between NEVs and skewed patterns of pollutants distribution, aimed to comprehensively evaluate the role of new energy in the automotive industry in the environment improvement. The results show almost invisible environmental-friendly benefits from the current new energisation development. The main reason is that the industry is still growing, and technology and service have to be improved. Traditional and new energy power in the automotive market coexist. Hence, the final air pollution brought by the automotive is also changeable due to the scale of new energy vehicles, which the energy-environment model supports. In particular, the results evidence that the performance of battery electric vehicles (BEVs) is better than plug-in hybrid electric vehicles. As the total amount of BEVs increased, air pollution has effectively alleviated. This also confirms that NEVs play an indispensable role in exhaust emissions pollution prevention, although their effect remains to be displayed more obviously.

With the development of Industry 4.0 and the urgency of transitioning to a low-carbon economy, fintech and environmentally friendly financial instruments have been widely employed because they have played a crucial role in restoring investor confidence in the financial services sector since the global financial crisis in 2008. They not only help investors diversify their portfolios to hedge against risks and enhance returns, but they also help to reduce the negative impacts of climate change. In this study, we analyze the connections among financial technology stocks, green financial assets, and energy markets using nonparametric causality-in-quantile and cross-quantilogram approaches based on the financial contagion theory. We explore whether the performance of fintech prices across booms and busts affects the prices of eco-friendly assets and energy market prices. Our results indicate that in the short run, fintech is highly directionally predictable in all markets except that of green bonds in the lower quantile. Additionally, in the bullish state, the predictability of all lag lengths is negative. Thus, price movements in fintech markets contribute to the vulnerability of the price levels of renewable and non-renewable energy stocks, green bonds, green equities, and sustainable development. Because financial contagion is closely related to asset pricing, portfolio allocation, risk measurement, and monetary policy, the findings of this paper will be informative to investors, portfolio managers, and policy makers.

Exploring the connectedness and time-varying attributes of conventional and new energy markets is crucial to the green-oriented transition of energy. This investigation performs bootstrap full-and sub-sample techniques to explore the correlation between the oil and lithium markets, and further probe whether lithium could threaten the status of oil. The empirical outcomes reveal that oil price (OP) exerts positive influences on lithium price (LP). High OP increases the lithium demand and its price through taking new energy vehicles as substitutes for conventional automobiles, indicating that lithium may threaten the status of oil under the oil bull market. But the above opinion could not be supported under the oil bear market, low OP lessens the attraction of new energy vehicles and then reduces lithium demand and LP. In turn, low LP negatively affects OP since oil is also a vital engine for economic development. Thus, these outcomes are consistent with the price correlation model, and lithium can only partially replace oil as a vehicle fuel under certain situations, the latter's status could not be threatened completely. Against a backdrop of urgent green transition and potential oil and lithium bubbles, these conclusions bring meaningful inspirations to the public, enterprises and countries.

Blockchain hasbecome a widely used information system technology recently because of its effectiveness as an intermediary-free platform. While the use of blockchain in various fields, such as finance, supply chains, healthcare, education, and energy consumption, is increasingly enabling the development of Internet-enabled "distributed databases," there are not many exploratory studies available to provide an understanding of how the field is progressing. Therefore, it is imperative to explore the status quo of blockchain technology in the finance sector, particularly highlighting how blockchain architectures can aid the finance sector to gain competitive advantage. This systematic literature review analyzes the content of the 50 most relevant articles and professional industry reports through peer-reviewed relevant academic literature in the finance sector from 2008 to 2022 to identify several possible features of blockchain research in the financial sector. This study highlighted the dimensions of blockchain technology, blockchain in finance, its competitive advantages, the current status of finance, and various challenges that keep the implementation of blockchain-based financial information systems at the initial stage. We identified three main areas that require research attention in order for blockchain technology to become the "next-generation networks" that will revolutionize the financial sector.

Although the importance of artificial intelligence (AI) has often been highlighted in strategic agility and decision outcomes, whether it helps firms strengthen their competitiveness and the means firms use to achieve such competitiveness are still under-researched. Our research thus joins the recent discussion on digitalization trends and strategic responses to COVID-19 to better understand how firms strengthen their competitiveness during such challenging times. Namely, this study incorporates the strategic responses to COVID-19 into the technology¿organization¿environment (TOE) framework by investigating the impacts of different configurations of TOE contexts and strategic responses on a firm's competitive advantage. We used fuzzy-set qualitative comparative analysis to investigate how TOE contexts and strategic responses integrate into configurations and impact a firm's competiveness. By applying a configurational approach with data from 514 exporting firms in China, we find a strong indication of the equifinality of different strategies, indicating that multiple strategic paths can be used to respond to crises. The adoption of AI, while important, is not sufficient to enhance a firm's competitiveness. Our results stress the significance of data quality, organizational resources and capabilities, and digital business model innovation for AI adoption. We also identify successful strategic paths of AI adoption aversion and ambidextrous strategies. The findings have practical implications for firms seeking effective strategies to respond to future crises and sustain their competitive advantages.

Cross-border mergers and acquisitions by emerging multinational enterprises have significant failure rates due to difficulties in gaining legitimacy from local stakeholders. By incorporating the concept of strategic green marketing into marketing innovation via defensive green marketing and assertive green marketing, this study focuses on the green part of marketing innovation in the context of cross-border merger and acquisitions (M&As) by emerging multinational enterprises. This study examines the effects of defensive and assertive green marketing approaches on cross-border M&A completion as well as the boundary conditions of marketing channels of financial advisors. An analysis of 358 cross-border M&As conducted by Chinese firms over 9 years (2007 to 2015) reveals that conducting corporate social responsibility activities as a defensive green marketing approach, developing green patents as an assertive green marketing approach, and hiring financial advisors as a marketing channel can increase cross-border M&A completion rates. The main relationship between assertive green marketing approach and M&A completion is weakened by financial advisors due to the substitute effect between financial advisors and assertive green marketing innovation. This paper provides insights for international business studies by adopting the strategic green marketing perspective and linking the marketing innovation literature to the emerging multinational enterprises internationalization literature.

Carbon allowance is a market mechanism that is used to deal with climate change in the scientific community, and is gradually regarded as a financial asset. Uncertainty plays a vital role in affecting financial assets, and whether it affects carbon prices is an important question. Therefore, this article aims to examine the impact of four economic policy uncertainties (EPU) on China's carbon emissions trading (CET) market price from the year 2013 to 2021, by employing the nonlinear ARDL model and the asymmetric causality test. Empirical results show that the trade policy uncertainty (TPU) and monetary policy uncertainty (MPU), both positively affect the CET market price, while the exchange rate policy uncertainty (ECPU) comes with negative influences. Furthermore, the increase of TPU, MPU, and ECPU has a greater impact on the CET price. Our findings are consistent with the real effects of uncertainty shocks by Bloom (2009) who asserts that the uncertainty transfer mechanism has an asymmetric pattern. Therefore, policymakers should pay careful attention to the increasing fluctuations of different economic policies. Moreover, Chinese authorities should adopt firmer policy stances to avoid high volatility in the CET market price to achieve the goal of low-cost emission reductions through market mechanisms.

When taking into account the general terms on which the global green and low-carbon transition takes place, it can be affirmed that the use of clean and renewable energy, including wind, hydro, solar, etc., is an alternative to the traditional energy sources. The renewable energy industry possesses considerable potential, and has recently become the centre of the global energy landscape. Therefore, this article refers to the rolling-window Granger causality test, in order to explore the role of renewable energy (RE) in reducing the greenhouse gas emissions. By studying the interactions that take place between RE consumption and carbon dioxide (CO2) emissions, we find that the negative impact of RE on CO2 indicates that the replacement role of RE has become increasingly prominent, for it to effectively contribute towards the realization of carbon emission reduction. The results in this regard are consistent with the energy-environment model, suggesting that RE has an excellent performance in achieving carbon neutrality. In fact, CO2 usually exhibits a negative effect on RE, which indicates towards the predictability of environmental quality to the development potential of renewable energy. Carbon emission reduction has become a game of interest among the countries around the world. And hence, in relation to these turn of events in the last few decades, RE is now expected to usher in the required acceleration. While eventually it is believed that the green energy competition will reshape the geopolitics of the world.

As the most important foreign language in China, English poses great challenge for teachers to improve the performance of ESL (English as a second language) learners through in-class instruction. Blended, explicit, and implicit instruction are three widely used approaches in English class teaching, and it may be difficult to choose which one should be used in class to help the English language learners. With audio synthesis technology applied as an enhancement for teaching, this study aims to research the three effects on English pronunciation teaching in aspects of the performance and satisfaction of English language learners. 120 English learners in China were equally divided into three groups which were instructed by blended, explicit, and implicit instruction respectively. Based on the data collected by test and questionnaire in pre-test, in-test, and post-test, the results show that the blended instruction performs best in aspects of the improvement of performance and the class satisfaction. These findings indicate the great potential of blended instruction in English language teaching and more investment should be taken to promote its application to help the Chinese to better acquire this important language.

Recent research argues that robots could replace workers in dangerous work environments to reduce occupational injuries. However, robot development and usage in most developing countries remain at an infant stage, robots may increase the likelihood of occupational injuries due to conditions such as limited human capital and defects. By using a city-level dataset based on the Guangdong Province in China, we find that robot application is associated with an increase in the rate of occupational injuries in the first two years, and then exhibits nonsignificant and even negative effects afterwards. We also find that local governments can reduce or even eliminate the effect of robot application on occupational injuries by strengthening safety regulations. In addition, although local governments are keen on pushing robot application and industrial intelligence, the wide application of robots may impose a burden on the public health expenditure of local governments due to occupational injuries. This study extends our knowledge by challenging the traditional view that robot application can reduce the rate of occupational injuries, and also provides suggestions on the safety regulations of developing countries at the early stage of industrial intelligence.

Bitcoin is considered to be an exclusive marvel of the Fourth Industrial Revolution, and is one of the most sophisticated technological and financial products. It has long been a pivot point of attention for investors who are in pursuit of a safe haven asset. In this paper, we use the wavelet-based quantile-on-quantile method, and the quantile-based Granger causality method, in order to investigate the notion of Bitcoin in actually being a safe-haven asset, amid political and economic uncertainty in the US for the period between 2010:M06, and 2020:M10. Using the Partisan Conflict Index (PCI), and the Economic Policy Uncertainty Index (EPU) as proxies of uncertainties, we find that although Bitcoin effectively appears to be a safe haven asset when uncertainties are on the rise, however, this relationship tends to change during the short- to long-run. In this regard, our sample provides us with a unique opportunity to evaluate the safe haven hypothesis for Bitcoin, amid a time span with three Presidential elections in the US, and recently, an ongoing COVID-19 outbreak, which has been declared as a global pandemic. We have also supplemented our analysis with the bootstrap rolling window causality method, as a measure of robustness. In light of the recent COVID-19 pandemic, and the dynamic economic situation, our work provides valuable knowledge for investors, who wish to construct investment portfolios based on Bitcoin, and also provide insights for regulators about how to regulate the cryptocurrency speculation in an effective manner.

This paper investigates the heterogeneous effects of geopolitical risk (GPR) on enterprise cash holdings (CASH) in Chinese oil sectors. The empirical results are shown as follows: First, enterprises in oil exploration and exploitation sector and oil equipment sector tend to reserve more cash to confront high geopolitical risk. Second, the levels of CASH in oil refinery and sale sector enterprises are negatively related to GPR. Third, there is no significant causal link between GPR and CASH in oil storage and transportation sector. Therefore, oil enterprises should factor in geopolitical risks in differentiated cash management policies based on their sectors' character.

This review examines literature from 2011 to 2020 on information and communications technologies (ICTs) and blockchain technologies (BTs) in agriculture. To describe the status of the subjects and identify issues for future research, we utilized bibliometric and content analyses of leading journals. The main findings are: (1) ICT technique adoption is affected by application design and socio-demographic factors, while research in BTs in this area only focuses on application design factors; (2) ICTs and BTs have similar applications in agricultural production (information provision for efficient decisions and production improvement), logistics (tracing), and supply chain (trust-building); (3) ICT techniques are applied more to production improvement, while BTs focus more on using visibility, traceability, and automation to improve transparency and transaction efficiency in whole sectors; (4) in relation to consumption, ICTs focus on balancing supply and demand to achieve market stability, while BTs investigate effects on consumer purchasing decisions. Insights are also proposed for future research based on common challenges in the application of ICT and blockchain in agriculture in the following areas: (i) security and privacy protection; (ii) solutions for scalability and interoperability; (iii) solutions for high cost and high consumption, and demands for high knowledge in technology application; (iv) formulation of policies and regulations related to standard criteria for market stability. This review provides a basis for studying the combination of ICTs and BTs in agriculture and furthers the understanding of their usage by comparison; this has significant implications for technological development and agricultural sustainability.

The broad development of Internet has enabled the information and communications technology (ICT) and the blockchain technology (BT) to transform the traditional operational practices and customer behavior. In the context of the agricultural sector, it has prompted agricultural retailers to explore various online resources and communication channels. However, product information and physical experience in the brick-and-mortar stores influence the Internet pricing strategy and customers' online purchasing behaviors. Considering this cross-channel effect and sustainable digital agricultural (e-agriculture) development, this study constructed a theoretical analysis model to investigate the impact of the ICT- and BT-driven agricultural cross-channel information strategy on the retailer pricing strategy and customer behavior. The results indicate that, although ICT and BT tools provide agricultural retailers and customers with more information, a cross-channel information strategy does not continuously increase retailers' sales performance. Retailers tend to leverage this strategy when the devaluation coefficient (customers' quality perception for a product) is low. This channel information strategy is influenced by the offline channel cost and the devaluation coefficient in online trading. These findings on the devaluation effect can guide retailers in their channel information and pricing strategies and marketing-related decisions.

The recent years have been marked by the role of green tech innovation in decreasing carbon emissions worldwide to attain the carbon neutrality target. Despite many studies examining the nexus between the former and energy consumption, tech innovation's effects on CO2 releases have not been extensively researched, and the extant empirical findings are often contradictory. Also, a major concern regarding the available literature is the scarcity of papers that scan the impact of tourism on carbon emissions, even though the industry has a high potential to affect ambient air pollution. In this case, the evidence is mixed, and no consensus among academics on the relationships between the two. Therefore, this study seeks to investigate the relevance of green innovation and tourism in decreasing environmental damage in Thailand based on the bootstrapping ARDL causality model suggested by (McNown et al., 2018). This specification includes a new cointegration feature and conventional ARDL bounds tests, which increases the power of the t- and of the f-test and has several advantages, being more adequate for dynamic models with more than one explanatory variable. Our findings reveal that green innovation and tourism lead to lower environmental damage by reducing CO2 emissions, similar to foreign investments and that green tech innovation improves the environmental quality via lower carbon emissions.

The true potential of the industry 4.0, which is a byproduct of the fourth industrial revolution, cannot be actually realized. This is, of course true, until the smart factories in the supply chains get connected to each other, with their systems and the machines linked to a common networking system. The last few years have experienced an increase in the adoption and acceptance of the industry 4.0's components. However, the next stage of smart factories, which will be the smart supply chains, is still in its period of infancy. Moreover, there is a simultaneous need to maintain a focus on the supply chain level implementation of the concept that industry 4.0 puts forth. This is important in order to gain the end to end benefits, while also avoiding the organization to organization compatibility issues that may follow later on. When considering this concept, limited research exists on the issues related to the implementation of industry 4.0, at the supply chain level. Hence, keeping in mind this lack of literature and research available, on a phenomenon that will define the future of business and industry, this study uses an exploratory approach to capture the implementation of industry 4.0 concepts across multiple tiers of the supply chain. Based on this research, the study proposes a multistage implementation framework that highlights the organizational enablers such as culture, cross-functional approach, and the continuous improvement activities. Furthermore, it also highlights the staged implementation of the advanced tools, starting from the focal organization with the subsequent integration with the partner organizations.

In this paper we first studied the predictive power of economic policy uncertainty (EPU) by analyzing it in the traditional housing returns model, which included macroeconomic variables. Then we expanded the model to consider structure breaks and asymmetry and explored the optimal predictive model for housing returns. Last, based on the optimal model, we tested the predictive performance of specified EPU components. These analyses were employed on aggregate and different tier cities for the China housing market by using the flexible generalized least squares estimator to conduct in-sample and out-of-sample forecast evaluations. Empirical results revealed that our proposed model suggests EPU is superior to the traditional housing returns model in terms of in-sample and out-of-sample forecasts. We also found that extending the model to consider structure breaks and EPU asymmetry enhanced the prediction performance of the model. In addition, empirical results also showed that monetary policy uncertainty has the strongest predictability on housing returns in first-tier cities, while fiscal policy uncertainty has the strongest predictability on housing returns in the country, the second-tier, and third-tier cities.

A longstanding objective of managers is to reduce risk to their businesses. The conventional strategy for risk reduction is diversification; however, evidence for the effectiveness of diversification remains inconclusive. According to Organizational Portfolio Analysis, firms are viewed as portfolios of business units, and the key to risk reduction is both diversification and synchronization compensation. This study introduces "desynchronicity", a process that operationalizes synchronization compensation by assessing the degree of correlation between income streams of business units. Two samples of 737 and 332 firms (from COMPUSTAT) were used to empirically test the relationships between diversification and risk, and desynchronicity and risk. The results show that diversification alone will not always lead to a lower corporate risk. To reduce risk, firms also need to consider the desynchronicity of their business portfolios. Other practical implications include improved decisions on portfolio composition.