This paper conducted an in-depth study to elucidate the impact of corporate intelligence transformation and regional financial technology on green economic growth, particularly the role of credit resource allocation. We developed a multi-sector general equilibrium model, integrating the heterogeneity of intelligent transformation in production sectors and accounting for the influence of Fintech on financial institutions. Within this model framework, panel data from 2011 to 2021 at the provincial, municipal, and micro-enterprise levels in China were used to validate the theoretical model through a mixed regression approach. The findings indicate that as intelligent transformation firms receive more credit resources, their potential for green economic growth increases, contributing to reduced regional carbon emissions. Additionally, the excess productivity of intelligent transformation firms has a significant positive impact on regional carbon reduction efforts. Moreover, the advancement of Fintech reduces financial institutional costs, further optimizing credit allocation and lowering overall market interest rates, thereby promoting green development within the region. However, advancements in Fintech may also redirect more credit resources toward low-risk general enterprises, resulting in a credit crowding-out effect for intelligent transformation firms. These findings indicate that, while promoting intelligent transformation, policy measures should also balance the resource allocation effects of Fintech across different types of enterprises.

Adopting textual analysis, we examine the links between corporate social responsibility (CSR) and the Sustainable Development Goals (SDGs) for 30 foreign subsidiaries in Myanmar, a Country of Concern (CoC). The analysis covers the period from 2001 up to 2020. Our work contributes to the literature on SDGs and CSR in a CoC. We find that although multinational enterprises (MNEs) address community issues via philanthropy unrelated to their principal business, in some cases CSR is related to their core capability. Despite some diversity in CSR processes, we find that MNEs tackle limited CSR issues. Furthermore, MNEs’ CSR generates positive externalities rather than reduce negative externalities. This finding confirms the discourse in international business policy suggesting MNEs focus on only positive externalities and ignoring negative externalities, and this neglect of negative externalities could result in a net-negative impact from their CSR. Nevertheless, we observe that CSR activities map onto all but one SDG, thus demonstrating the potential for further investment in CSR in Myanmar. Our study highlights that given a CoC is all about institutional weakness, MNEs’ CSR must focus on strengthening institutions to bring about systemic changes in these contexts, as opposed to short-term ‘bandage’ approaches, otherwise, gains to SDGs will be short-lived.

Green finance is critical to promoting carbon neutralisation and is an essential part of the carbon emission reduction policy framework. This paper applies quantile connectedness to analyze the overall situation and dynamic evolution of information spillover in the green and grey financial markets system and the financial roles in coordinating clean and traditional fossil fuels. The results show that fossil fuel is the primary source of risk in the information network, and their fluctuations have intensified the risk spillover effects in the system. The spillover level is more prominent in extreme cases, which means the information linkage in the system is integrated. The spillover effects of each variable fluctuate with time. In addition, green assets can be treated as a risk diversifier against fossil fuel investment shocks due to the weak connectedness in the average market. The risk infection path can provide a reference for governments to prevent the risk of infection in financial markets and guide the sustainability of the green investment.

Climate change and resource degradation is the critical challenge the world is experiencing at this moment. The sharp resource consumption by manufacturing firms predominantly causes them to meet public demands and maximize their revenue. The United Nations (UN) has urged businesses to follow eco-friendly practices to protect and revive nature. In this regard, it is imperative to investigate which factors facilitate firms in achieving the UN sustainable development goals. Using the lens of the stakeholder theory, this study delves into investigating the role of environmental and organizational factors in firms' green innovation and sustainable development activities. Considering the imperative role of organizational capabilities to absorb and capitalize on the knowledge, the authors took knowledge absorptive capacity (KAC) as the moderating variable in the main model. The authors received data from manufacturing firms in Turkey and analyzed it through the PLS-SEM technique. It is found that organizational and environmental factors significantly help firms achieve green innovation and sustainable development goals. Moreover, the organizational capacity to absorb knowledge bolsters the relationship between the main hypotheses. However, an insignificant moderating role of KAC was identified between environmental factors and sustainable development. Considering environmental and organizational factors as driving forces of firm performance, this study links them with green innovation and sustainable development activities by integrating KAC as an acute boundary condition. The findings provide practical implications to industrialists and stakeholders for complying with the United Nations' sustainable development goals by capitalizing on studied variables.

This research has analyzed how natural resource abundance impacts economic growth and carbon dioxide (CO2) emissions in a sample of Middle East and North Africa (MENA) and Next Eleven (N-11) countries for the period from 2011 to 2020. Employing Fixed Effects and Auto Regressive Distributed Lag (ARDL) models, the results obtained suggest that natural resource abundance has a positive effect on economic growth, natural resource abundance leads to a positive impact on CO2 emissions and natural resource abundance with CO2 emissions positively impact economic growth. This study has also showed the importance of technology and environmental regulation in this area. Results have provided important policy implications. Economic policy should incorporate improved utilization of natural resources to achieve higher economic growth. Businesses can get financial gains and lower their costs through corporate social responsibility and sustainability standards. Government authorities should implement environmental regulations and ensure compliance, in order to promote sustainable economies.

This paper studies the interrelationship between Chinese consumer confidence (CC) and the oil market by testing the full-sample and sub-sample. Some empirical conclusions show that CC can positively impact oil price (OP) manifested as high confidence may boost oil demand and lead to higher prices. However, the negative effects of CC on OP cannot support this view, mainly due to the lack of confidence that will lead to lower demand and excess oil supply, which can cause OP to decline while CC remains high. This can be explained by the fact that low OP leads consumers to spend more money on non-essential goods and boost CC. These negative impacts emphasise that economic turmoil will affect OP fluctuation, which makes it different from the intertemporal capital asset pricing model. Under the uncertain oil market, these conclusions benefit Chinese consumers and the government by adjusting oil reserves and steadying national economic growth. It inspires enterprises and the government to prevent sudden changes in OP and stabilise CC.

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.

The hospitality and tourism sector has long played a significant role in Australia’s economy, especially in regional areas. Due to the onslaught of COVID-19, numerous businesses have experienced lockdowns, restrictions, and closures due to the fact that people’s activity in restaurants, shopping centers, and recreational destinations was restricted, and many other places went into hibernation. After about 2 years since the outbreak, businesses in this sector are gradually starting to reopen and revitalize themselves, but in order to have better decision support about the future of this sector, thus being able to plan, businesses are suffering from an effective analytics solution due to the lack of broken data trends. Starting from fresh day-to-day real-time big data, the study aims to develop a new data analytics model, adopting the design science research methodology, which can provide invaluable options and techniques to make prediction easier from immediate past datasets. This study introduces an innovative design artifact as a big data solution for hospitality managers to utilize analytics for predictive strategic decision-making in post-COVID situation. The artifact can also be generalized for other sectors with tailoring aspects which are subject to further studies. The proposed artifact is then compared with other design artifacts related to big data solutions where it outperforms them in terms of comprehensiveness. The proposed artifact also shows promises for primarily available UGC in managers’ decision support aids.

The purpose of this study is to investigate the influence of renewable energy research and development (R&D) investment and the environment-related technological innovations, on the quality of the environment of the G7 economies over a period spanning from 1990 to 2020. The role of economic growth, R&D, and human capital has also been examined. For this very purpose, Panel data approaches, such as slope heterogeneity and cross-section dependence, have been used. All the variables taken into account are found in the long run cointegration as- sociation. The study also uses the novel Method of Moment Quantile Regression to determine the individual impact of each variable. The examined results have asserted that economic growth and human capital are positively associated with carbon emissions. Whereas the development of environment related technologies significantly reduce the level of emissions. On the other hand, R&D and renewable energy R&D exhibit a U- shaped influence on carbon emissions. The Dumitrescu and Hurlin Granger panel causality test reveals a bidi- rectional causal association of carbon and all the other variables taken into consideration. These results are found robust, as validated by the quantile regression. Based on the empirical results, this study suggests enhancing the financing efforts towards R&D, renewable energy R&D, and environment-related technological innovations, in order to attain environmental sustainability.

With the deep integration of Internet technology into online education in recent years, the online education model has gained increasing recognition, while there has also been a problem with students' lack of interest in continuing their online education. Using the structural equation model as an analytical tool, this paper starts from three aspects, learning interaction, teacher support, and flow experience, and studies the influencing factors of online learning continuation willingness, and constructs a theoretical model of these factors to understand students' continuation willingness to learn online and the mechanism of related influencing factors. The findings of the study demonstrate that the continuation willingness of online learning is affected by learning interaction and teacher support via the mediation effect of flow experience.

Sustainable development pursues equilibrium between the environment, economic growth, and quality of life. Currently, in many economies, environmental pollution has become a critical issue. The financial sector development has played a crucial role in developing every sector of the economy by providing necessary funds, and the environment sector is no exception. Therefore, we aim to investigate the impact of green finance and financial innovation on the environmental status in China from 1996 to 2020. To analyze the finance-environment nexus, we have employed the ARDL model. Findings of the ARDL model confirm that the long-run estimates attached to green finance are significantly negative in both the CO2 emissions and GHGs models. Similarly, the long-run estimates of financial innovation are negative and significant in the CO2 emissions and GHGs models. These results imply that an increase in green finance and financial innovation reduces China's CO2 emissions and GHGs emissions. Thus, environmental performance improves. In the short run, only the green finance impact is significant and negative on CO2 emissions and GHGs models. The results recommend some vital policy implications.

This paper investigates the heterogeneous responses of the carbon emission trading price (CETP) to different time frequencies of economic policy uncertainty (EPU) through a wavelet-based quantile-on-quantile regression approach. The empirical results indicate that when EPU is in different quantiles and frequencies, the coefficients between EPU and CETP are dynamic and even change in opposite directions, which demonstrates that their relationship is unstable. The major contribution of this paper is that it fully considers the heterogeneity of EPU with different frequencies, distinctive carbon emission markets, and the varying relationship between EPU and CETP, providing more valuable and accurate conclusions. Based on these findings, policies are proposed to reduce negative shocks from EPU volatility on the carbon emission trading market. The government needs to construct platforms for encouraging innovation and accelerating the energy transition. The auction-based mode of allocating carbon emission rights should gradually replace free issuance. Enterprises should also actively join the carbon emission trading market and undertake social responsibilities, complying with environmental regulations.

Online forms are widely used to collect data from human and have a multi-billion market. Many software products provide online services for creating semi-structured forms where questions and descriptions are organized by pre-defined structures. However, the design and creation process of forms is still tedious and requires expert knowledge. To assist form designers, in this work we present FormLM to model online forms (by enhancing pre-trained language model with form structural information) and recommend form creation ideas (including question / options recommendations and block type suggestion). For model training and evaluation, we collect the first public online form dataset with 62K online forms. Experiment results show that FormLM significantly outperforms general-purpose language models on all tasks, with an improvement by 4.71 on Question Recommendation and 10.6 on Block Type Suggestion in terms of ROUGE-1 and Macro-F1, respectively.

This investigation explores whether sustainable finance and renewable energy could facilitate U.S. carbon neutrality. We perform the time-varying parameter-stochastic volatility-vector auto-regression (TVP-SV-VAR) model to obtain the changing relations among U.S. sustainable finance (SF), renewable energy (RE) and carbon dioxide emission (CO2). The empirical outcomes reveal a short-term negative effect from RE to CO2, indicating that renewable energy consumption could promote U.S. carbon neutrality. This effect is asymmetrical, and it could be observed that RE increase has a greater effect on CO2 than RE reduction. Also, the development of sustainable finance could facilitate U.S. carbon neutrality, and the direct impact is longer and more significant than RE, but the indirect effect of SF on CO2 by influencing RE is hysteretic. Besides, the asymmetric effect reveals that the negative direct impact of SF increase on CO2 is smaller than SF reduction, and the latter's indirect effect is more rapid than the former. Against the backdrop of global warming and frequent extreme weather, the above conclusions have meaningful practical applications for the U.S. to achieve carbon neutrality targets through developing sustainable finance and renewable energy.

The past two decades have witnessed the significant growth of emerging markets and the rise of emerging market multinational enterprises (EMNEs) (Luo and Tung 2007) [...]

The unanticipated coronavirus disease 2019 (COVID-19) pandemic has hit global business heavily, disrupting the management of human resources across numerous industries. More than 500 articles (indexed in Scopus and the Web of Science) on the impact of the COVID-19 outbreak on emerging human resources issues and related practices were published from 1 January 2020 to 31 January 2021. In this study, we conduct a systematic literature review on emerging studies in the business and management field to explore what the emerging human resource issues are during the COVID-19 pandemic and propose related practices to solve these issues. The analysis of the published literature identifies nine main human resource issues across 13 industries. The findings of this study suggest that COVID-19 has enormous impact on conventional human resource management and requires the theoretical and empirical attention of researchers. The propositions nominate related human resource practices to deal with emerging human resources issues and identify several research venues for future studies in this field.

Speech processing systems currently do not support the vast majority of languages, in part due to the lack of data in low-resource languages. Cross-lingual transfer offers a compelling way to help bridge this digital divide by incorporating high-resource data into low-resource systems. Current cross-lingual algorithms have shown success in text-based tasks and speech-related tasks over some low-resource languages. However, scaling up speech systems to support hundreds of low-resource languages remains unsolved. To help bridge this gap, we propose a language similarity approach that can efficiently identify acoustic cross-lingual transfer pairs across hundreds of languages. We demonstrate the effectiveness of our approach in language family classification, speech recognition, and speech synthesis tasks.

The current study investigates carbon neutrality targets for the US's case while analyzing the role of environmental-related research and development (ERR&D) and renewable energy research and development (RER&D). This study also considered economic growth (GDP) and energy productivity (EP) as controlled variables. Utilizing the time series data over the period from 1990 to 2019, this study used various econometric approaches, such as unit root tests and cointegration tests for stationarity and the long-run association between variables, respectively. This study's main econometric regression tools, such as dynamic ordinary least square (DOLS) and fully modified ordinary least square (FMOLS), are utilized. The empirical findings reveal that economic growth played a negative role in achieving carbon neutrality targets. However, EP, RER&D, and ERR&D positively contribute to carbon neutrality target achievement by reducing atmospheric CO2 emissions. Moreover, this study found a cointegration relationship between the study variables. The bidirectional causality is found between ERR&D and CO2 emissions, while a unidirectional causality is observed, running from exogenous variables towards CO2 emissions. Based on the empirical findings, this study recommends expanding the investment and expenditures in both ERR&D and RER&D sectors to attain carbon neutrality.

Expatriate management has evolved through the practices of developed economy multinational enterprises (DMNEs), with the aim of improving expatriate adaptability, cross-cultural adjustment, and performance. However, most of these studies focus on expatriates from developed countries and try to help DMNEs instead of emerging market MNEs (EMNEs). In a turbulent global economy, how EMNEs manage their expatriates when conducting business through their outward foreign direct investment (FDI) is understudied. This empirical study aims to address this research gap by utilising a qualitative approach and a multiple case study. It has conducted semi-structured interviews with expatriates, executives, and middle managers of Chinese MNEs in 2014. It contributes as one of the few to systematically examine expatriate related issues in the context of EMNEs with first-hand empirical evidence. The findings show that EMNEs are leapfrogging with their internationalisation and hence their expatriate policies are often ad hoc without systematic planning. Moreover, this study has contributed to practice, especially to EMNEs, regarding the way they need to improve their expatriate policies and practices.

After the Paris Climate Conference (Conference of the Paris COP: 21), most developing countries face challenges to attain a sustainable economy and carbon neutrality targets with minimum CO2 emission. The next eleven (N-11) economies are in line with the global phenomena of environmental degradation; very few studies have analyzed the effects of green technology innovation on environmental degradation in N-11 countries. Therefore, the present study addresses the gap and examines green technology innovation and renewable energy with CO2 emission from 1980 to 2018. The present study considers all the issues related to panel data analysis, such as cross-sectional dependence, stationarity, heterogeneity in slope parameters, and structural break with advanced panel estimators. Moreover, the cross-sectional augmented autoregressive distributed lags (CS-ARDL) test results show the negative and significant impact of green technology innovation and renewable energy with CO2 emission in the long run. However, the short-run association of green technology innovation is not significant-further, the results endorsed by the robustness tests such as AMG and CCEMG. To reduce environmental deterioration in N-11 countries, governments are suggested implementing some policies to support green innovation technologies and renewable energy resources.