Research on the time-varying effects among green finance markets in China: A fresh evidence from multi-frequency scale perspective

Green finance is an essential instrument for achieving sustainable development. Objectively addressing correlations among different green finance markets is conducive to the risk management of investors and regulators. This paper presents evidence on the time-varying correlation effects and causality among the green bond market, green stock market, carbon market, and clean energy market in China at multi-frequency scales by combining the methods of Ensemble Empirical Mode Decomposition Method (EEMD), Dynamic Conditional Correlation (DCC) GARCH model, Time-Varying Parameter Vector Autoregression with Stochastic Volatility Model (TVP-VAR-SV), and Time-varying Causality Test. In general, the significant negative time-varying correlations among most green finance markets indicate a prominent benefit of risk hedging and portfolio diversification among green financial assets. In specific, for different time points and lag periods, the green finance market shock has obvious time-varying, positive and negative alternating effects in the short-term scales, while its time delay and persistence are more pronounced in the medium-term and long-term scales. Interestingly, a positive event shock will generate positive connectivity among most green finance markets, whereas a negative event including the China/U.S. trade friction and the COVID-19 pandemic may exacerbate the reverse linkage among green finance markets. Furthermore, the unidirectional causality of “green bond market - carbon market - green stock and clean energy markets” was established during 2018–2019.     

Advancing purchasing as a design science: Publication guidelines to shift towards more relevant purchasing research

Due to rapidly changing business environments, purchasing and supply management (PSM) organisations are constantly confronted with new problems impacting organisational performance. PSM research can address these problems through design science research. Design science is also regarded as the science of the artificial. Design science research is a methodology that aims to systematically generate knowledge for the design, synthesis, testing, and evaluation of human-made artefacts (e.g., tools, interventions, policies) that solve practical problems. PSM artefacts such as the purchasing portfolio matrix invented by Kraljic (1983) represent a valuable opportunity to solve problems in the PSM discipline. However, our artificial-intelligence (AI)-based analysis of the discipline's flagship journal, the Journal of Purchasing and Supply Management (JPSM), indicates that design-oriented publications in PSM are underrepresented, accounting for less than 4% of the total publications. We argue that existing PSM research should be complemented with more design-oriented research, and address the following research question: How can PSM scholars publish more design-oriented research? Our objectives are to (1) provide arguments for advancing PSM as a design science, (2) nurture a better understanding of design science research as a methodology, and (3) propose publication guidelines that enable researchers to present design-oriented research in a management journal.     

Assessment of the sustainable development of rural minority settlements based on multidimensional data and geographical detector method: A case study in Dehong,China

In a developing country, paying attention to the sustainable development of rural areas is conducive to the development of the entire country. Ethnic minority areas are an important part of China's economic and social development. Owing to a lack of relevant statistical data, most previous studies in this area have focused on the sustainable development of rural areas or the development of ethnic minorities, but have not studied the sustainable development of rural ethnic minorities. The development of rural ethnic minorities is worthy of attention. In this study, we took Dehong as the study area. First used toponyms to accurately identify the rural minority areas and then calculated a grid of settlement density. Second, we considered the digital number (DN) value of the visible infrared imaging radiometer suite (VIIRS) as a measure of the development of the region and digital elevation model (DEM), net primary productivity (NPP), normalized difference vegetation index (NDVI), and gross domestic product (GDP) data as the indicators of terrain, climate, ecological, and economic factors, respectively. Finally, linear regression and the geographical detector method were used to determine the weight of the factors for constructing a sustainable development index (SDI) to quantitatively analyze the sustainable development and influencing factors of each minority nationality. The factors evaluated using linear regression and the geographical detector method were ranked as follows: NDVI > elevation > GDP > slope > NPP > settlement density. The results demonstrate that of the five main ethnic minorities in Dehong, Dai and Jingpo have higher SDI, followed by Achang, Lisu and De'ang. In addition, we provide some suggestions for ethnic minorities in Dehong.     

The era of Omni-learning: Frameworks and practices of the expanded human resource development

According to the ever-changing organizational environment, we also adopt an ever-expanding HRD in contents and scope. Focusing on the drivers of the recent HRD reforms, the growing demand for organizational agility and holistic capabilities of human resources is driving the need for change, and the pandemic crisis is pushing the revolutionary changes of HRD. Such trends of the expanded HRD can be characterized as a ‘march toward Omni-learning’. In specific, there are at least four noticeable and intertwined waves of HRD reforms toward Omni-learning: (1) embracing holistic capabilities such as benchmarking, modeling, forecasting, and backcasting (BMFB); (2) integrating working and learning by promoting on-the-job learning (OJL), on-the-life learning (OLL), and on-the-life training (OLT); (3) standardizing communication tools such as LMF (logic tree; multi-dimensional matrix/map; flowchart) and EEOSP (everything/everyone on the same page); and (4) diversifying communication space-time across diverse places (close; remote) and times (synchronized; a-synchronized). And all the HRD waves are commonly facilitated and promoted by technological breakthroughs of artificial intelligence (AI) and the metaverse. Beyond the current innovations of HRD, no one would be certain about the answer to the question “What’s next?”. But what is certain is that HRD will continue to be deepened and widened as long as human resources are needed to respond to the ever-changing organizational environment.     

A data-driven decision support system with smart packaging in grocery store supply chains during outbreaks

The unexpected emergence of the COVID-19 pandemic has changed how grocery shopping is done. The grocery retail stores need to ensure hygiene, quality, and safety concerns in-store shopping by providing “no-touch” smart packaging solutions for agri-food products. The benefit of smart packaging is to inform consumers about the freshness level of a packaged product without having direct contact. This paper proposes a data-driven decision support system that uses smart packaging as a smart product-service system to manage the sustainable grocery store supply chain during outbreaks to prevent food waste. The proposed model dynamically updates the price of a packaged perishable product depending on freshness level while reducing food waste and the number of rejected customers and maximising profit by increasing the inventory turnover rate of grocery stores. The model was tested on a hypothetical but realistic case study of a single product. The results of this study showed that stock capacities, freshness discount rate, freshness period, and quantity discounts significantly affect the performance of a grocery store supply chain during outbreaks.     

The effect of stabilization fund to rescue stock market based on expected return-capita circulation equation

This paper builds an expected return-capital circulation equation to explore the impact of using the stabilization fund to rescue the stock market when it crashes. We find that the stock stabilization fund buying the underlying stocks can restrain the sharp decline of the stock index, but it is also easy to promote the fluctuation in prices of small-cap stocks. Therefore, we suggest that the government in use of stock stabilization fund to take control of the stock market crash should not only buy underlying stocks in the index but also pay attention to prevent implicit stock disaster in the market.     

Performance of microfinance institutions in Europe—Does social capital matter?

This paper investigates performance drivers of microfinance suppliers in Europe. As such suppliers, in contrast to advanced microfinance suppliers in developing economies, typically focus on uncollateralized microcredit services to individuals at the margins of society and of labor markets, we draw on the theory of social capital and empirically investigate the role that social capital may play in the overall performance of European microfinance suppliers. We build a unique, unbalanced panel data set of 302 microfinance service providers in Europe covering the years 2008–2015, and measure their performance in terms of credit risk, financial and social performance, and efficiency. Pursuing an econometric approach, we test a series of hypotheses using various measures of conditions conducive to building social capital on both the institutional and the country level, such as the client base of a microfinance supplier and the level of cultural fractionalization in a society. Our findings confirm that a higher intensity of social capital is positively associated with all areas of the performance of microfinance suppliers in Europe. Our conclusions could help in the design and launch of microfinance institutions in those European countries in which microfinance markets are developed not at all or only to a very limited extent. Our paper thus contributes to the nascent literature on microfinance in developed economies by applying and extending the theoretical framework and empirical models on social capital and microfinance that were originally elaborated for developing economies.     

Orchestrating distributed data governance in open social innovation

Open Social Innovation (OSI) involves the collaboration of multiple stakeholders to generate ideas, and develop and scale solutions to make progress on societal challenges. In an OSI project, stakeholders share data and information, utilize it to better understand a problem, and combine data with digital technologies to create digitally-enabled solutions. Consequently, data governance is essential for orchestrating an OSI project to facilitate the coordination of innovation. Because OSI brings multiple stakeholders together, and each stakeholder participates voluntarily, data governance in OSI has a distributed nature. In this essay we put forward a framework consisting of three dimensions allowing an inquiry into the effectiveness of such distributed data governance: (1) openness (i.e., freely sharing data and information), (2) accountability (i.e., willingness to be held responsible and provide justifications for one's conduct) and (3) power (i.e., resourceful actors' ability to impact other stakeholder's actions). We apply this framework to reflect on the OSI project #WirVsVirus (“We versus virus” in English), to illustrate the challenges in organizing effective distributed data governance, and derive implications for research and practice.     

Twitter’s daily happiness sentiment,economic policy uncertainty,and stock index fluctuations

This research applies quantile Granger causality and impulse-response analyses to evaluate the causal linkages among Twitter’s daily happiness sentiment, economic policy uncertainty (EPU), and S&P 500 indices across the U.S. stock market cycles. We present notable evidence of bi-directional causality among cyclical components of Twitter’s daily happiness sentiment, economic policy uncertainty, and S&P 500 indices for most quantiles. The causal linkage of Twitter’s daily happiness sentiment and S&P 500 indices identified in this study reconciles the so-called Easterlin Paradox and Easterlin Illusion arguments from previous studies on income-happiness relationship. Moreover, given a high (low) EPU level, the positive (negative) impulse-response effects between the Twitter’s daily happiness sentiment and the S&P 500 indices are justified during a stock market bust cycle, but the signs of these correlations change to negative (positive) during a stock market boom cycle. These findings imply that investors’ hedging strategies can be linked to the surveillance of the Twitter’s daily happiness sentiment index.