Southwestern University of Finance and Economics

The global population at risk from mosquito-borne diseases-including dengue, yellow fever, chikungunya and Zika-is expanding in concert with changes in the distribution of two key vectors: Aedes aegypti and Aedes albopictus. The distribution of these species is largely driven by both human movement and the presence of suitable climate. Using statistical mapping techniques, we show that human movement patterns explain the spread of both species in Europe and the United States following their introduction. We find that the spread of Ae. aegypti is characterized by long distance importations, while Ae. albopictus has expanded more along the fringes of its distribution. We describe these processes and predict the future distributions of both species in response to accelerating urbanization, connectivity and climate change. Global surveillance and control efforts that aim to mitigate the spread of chikungunya, dengue, yellow fever and Zika viruses must consider the so far unabated spread of these mosquitos. Our maps and predictions offer an opportunity to strategically target surveillance and control programmes and thereby augment efforts to reduce arbovirus burden in human populations globally.

Previous studies on green human resource management ( GHRM ) are mainly positioned at theoretical or qualitative level. There is urgent need to develop a valid measurement of GHRM and then to offer more insights into the implication of it on individual or organizational performance. The aim of this study was to propose and validate an instrument to measure GHRM . Based on exploratory analysis (study 1), it was established that GHRM includes five dimensions: green recruitment and selection, green training, green performance management, green pay and reward, and green involvement. Confirmatory factor analysis (study 2) was used to confirm the factor structure of study 1. The results indicated that the proposed measurement is valid. This study is the first and also the most comprehensive one to measure main human resource practices for environmental management, which can provide broader focus for further research and for practitioners.

ABSTRACT Since 2009, blockchain has served as a potentially transformative information technology expected to be as revolutionary as the Internet. Originally developed as a methodology to record cryptocurrency transactions, blockchain's functionality has evolved into a large number of applications, such as banking, financial markets, insurance, voting systems, leasing contracts, and government service. Despite such advancements, the application of blockchain to accounting and assurance remains under-explored. This paper aims to provide an initial discussion on how blockchain could enable a real-time, verifiable, and transparent accounting ecosystem. Additionally, blockchain has the potential to transform current auditing practices, resulting in a more precise and timely automatic assurance system.

Abstract With the implementation of China’s top-down CO 2 emissions reduction strategy, the regional differences should be considered. As the most basic governmental unit in China, counties could better capture the regional heterogeneity than provinces and prefecture-level city, and county-level CO 2 emissions could be used for the development of strategic policies tailored to local conditions. However, most of the previous accounts of CO 2 emissions in China have only focused on the national, provincial, or city levels, owing to limited methods and smaller-scale data. In this study, a particle swarm optimization-back propagation (PSO-BP) algorithm was employed to unify the scale of DMSP/OLS and NPP/VIIRS satellite imagery and estimate the CO 2 emissions in 2,735 Chinese counties during 1997–2017. Moreover, as vegetation has a significant ability to sequester and reduce CO 2 emissions, we calculated the county-level carbon sequestration value of terrestrial vegetation. The results presented here can contribute to existing data gaps and enable the development of strategies to reduce CO 2 emissions in China.

Abstract Extant literature, while often suggesting a positive link between green innovation and firm performance, is inconclusive. Moreover, the possibly moderating role of management has not been sufficiently considered. Using a unique dataset sampling 188 manufacturing firms in China, we examine how managerial concern (for green issues) moderates the relationship between green innovation and firm performance. We find that green process innovation and green product innovation both significantly (positively) predict firm performance, when not considering managerial concern for the environment. Once managerial concern is included, we observe that it compounds the positive effect of green process innovation on firm performance – but not product innovation, which no longer explains significant unique variance in firm performance. The findings hold various implications for future research and business policy. Copyright © 2017 John Wiley & Sons, Ltd and ERP Environment

This article aims to evaluate the impact of urbanization and different urbanization modes on cultivated land changes using an econometric model that incorporates socio-economic and policy factors in the eastern China, which experience the great urbanization in recent years. Based on land-use remote sensing data interpreted from Landsat Thematic Mapper/Enhanced Thematic Mapper digital images of Chinese Academy of Sciences and a unique set of socio-economic data, an econometric model is developed to empirically estimate the impacts on cultivated land changes. Although urbanization has an effect on the changes of cultivated land, its effect is marginal. Moreover, the expansion of built-up areas in different urbanization modes causes varying impacts on changes in cultivated land use in different regions. Assuming that other factors remain constant, compared with the expansion of villages or the development of small towns, in the periods of 1995–2000, the urbanization in the more developed eastern region alleviates the loss of cultivated land by 7%, while during 2000–2008 the rapid urbanization lead to the cultivated land loss increase by 29.2%. The policies designed to protect cultivated land by encouraging people move to small towns may actually accelerate the occupation of cultivated land.

Purpose – The purpose of this paper is to investigate the factors associated with consumer’s intention to adopt wearable technology in healthcare, and to examine the moderating effects of product type on consumer’s adoption intention. Design/methodology/approach – An integrated acceptance model was developed based on unified theory of acceptance and use of technology 2 (UTAUT2), protection motivation theory (PMT), and privacy calculus theory. The model was tested with 462 respondents using a survey. Findings – Consumer’s decision to adopt healthcare wearable technology is affected by factors from technology, health, and privacy perspectives. Specially, fitness device users care more about hedonic motivation, functional congruence, social influence, perceived privacy risk, and perceived vulnerability, but medical device users pay more attention to perceived expectancy, self-efficacy, effort expectancy, and perceived severity. Originality/value – This study is among the first to investigate healthcare wearable device from behavioral perspective. It also helps to comprehensively understand emerging health information technology (HIT) acceptance from technology, health, and privacy perspectives.

Blockchain is considered by many to be a disruptive core technology. Although many researchers have realized the importance of blockchain, the research of blockchain is still in its infancy. Consequently, this study reviews the current academic research on blockchain, especially in the subject area of business and economics. Based on a systematic review of the literature retrieved from the Web of Science service, we explore the top-cited articles, most productive countries, and most common keywords. Additionally, we conduct a clustering analysis and identify the following five research themes: “economic benefit,” “blockchain technology,” “initial coin offerings,” “fintech revolution,” and “sharing economy.” Recommendations on future research directions and practical applications are also provided in this paper.

In social network group decision making (SNGDM), the consensus reaching process (CRP) is used to help decision makers with social relationships reach consensus. Many CRP studies have been conducted in SNGDM until now. This paper provides a review of CRPs in SNGDM, and as a result it classifies them into two paradigms: (i) the CRP paradigm based on trust relationships, and (ii) the CRP paradigm based on opinion evolution. Furthermore, identified research challenges are put forward to advance this area of research.

The business value of information technology (IT) has been one of the top concerns of both practitioners and scholars for decades. Numerous studies have documented the positive effects of IT capability on organizational performance but our knowledge of the processes through which such gains are achieved remains limited due to a lack of focus on the business environment. Such a linkage therefore remains the subject of debate in the information systems literature. In this study, we fill this gap by investigating the mediating role of business process agility and the moderating roles of environmental factors. On the basis of matched survey data obtained from 214 IT and business executives from manufacturing firms in China, our analyses show that even though firm-wide IT capability presents the characteristics of rarity, appropriability, non-reproducibility, and non-substitutability, its impact on organizational performance is fully mediated by business process agility. Our results also show that the impact of the environment is multifaceted and nuanced. In particular, environmental hostility weakens the effect of IT capability on business process agility, while environmental complexity strengthens it. The theoretical and practical implications of this study, and its limitations, are also discussed. © 2014 Operational Research Society Ltd. All rights reserved.

Firms can seek innovative external ideas and solutions to business tasks by sponsoring co-creation activities such as crowdsourcing. To get optimal solutions from crowdsourcing contest participants, firms need to improve task design and motivate contest solvers' participation in the co-creation process. Based on the theory of extrinsic and intrinsic motivation as well as the theory of job design, we developed a research model to explain participation in crowdsourcing contests, as well as the effects of task attributes on intrinsic motivation. Subjective and objective data were collected from 283 contest solvers at two different time points. We found that intrinsic motivation was more important than extrinsic motivation in inducing participation. Contest autonomy, variety, and analyzability were positively associated with intrinsic motivation, whereas contest tacitness was negatively associated with intrinsic motivation. The findings suggest a balanced view of extrinsic and intrinsic motivation in order to encourage participation in crowdsourcing. We also suggest that crowdsourcing contest tasks should preferably be highly autonomous, explicitly specified, and less complex, as well as require a variety of skills.

The electrocardiogram (ECG) is one of the most important techniques for heart disease diagnosis. Many traditional methodologies of feature extraction and classification have been widely applied to ECG analysis. However, the effectiveness and efficiency of such methodologies remain to be improved, and much existing research did not consider the separation of training and testing samples from the same set of patients (so called inter-patient scheme). To cope with these issues, in this paper, we propose a method to classify ECG signals using wavelet packet entropy (WPE) and random forests (RF) following the Association for the Advancement of Medical Instrumentation (AAMI) recommendations and the inter-patient scheme. Specifically, we firstly decompose the ECG signals by wavelet packet decomposition (WPD), and then calculate entropy from the decomposed coefficients as representative features, and finally use RF to build an ECG classification model. To the best of our knowledge, it is the first time that WPE and RF are used to classify ECG following the AAMI recommendations and the inter-patient scheme. Extensive experiments are conducted on the publicly available MIT–BIH Arrhythmia database and influence of mother wavelets and level of decomposition for WPD, type of entropy and the number of base learners in RF on the performance are also discussed. The experimental results are superior to those by several state-of-the-art competing methods, showing that WPE and RF is promising for ECG classification.

Emissions trading scheme (ETS) has been adopted by an increasing number of countries and regions for carbon mitigation, but its actual effect depends on specific program design and institutional context. Before launching the world largest ETS, China experimented with seven independent regional pilots, whose effects are only indirectly explored. Here we provide firm-level evidence of the innovation effect directly from China's pilot emissions trading, based on latest patenting information and a quasi-experimental design. China's pilots increase low-carbon innovation of ETS firms by 5-10% without crowding out their other technology innovation. The increase from ETS firms accounts for about 1% increase of the regional low-carbon patents, while a similar increase from large non-ETS firms is also induced by the ETS. Most importantly, the effect is not associated with permit price, auction, or firm characteristics, but is driven by mass-based allowance allocation. A rate-based approach, however, is adopted by China's national market.

As fundamental data, gross domestic product (GDP) and electricity consumption can be used to effectively evaluate economic status and living standards of residents. Some scholars have estimated gridded GDP and electricity consumption. However, such gridded data have shortcomings, including overestimating real GDP growth, ignoring the heterogeneity of the spatiotemporal dynamics of the grid, and limited time-span. Simultaneously, the Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS) and National Polar-orbiting Partnership's Visible Infrared Imaging Radiometer (NPP/VIIRS) nighttime light data, adopted in these studies as a proxy tool, still facing shortcomings, such as imperfect matching results, discontinuity in temporal and spatial changes. In this study, we employed a series of methods, such as a particle swarm optimization-back propagation (PSO-BP) algorithm, to unify the scales of DMSP/OLS and NPP/VIIRS images and obtain continuous 1 km × 1 km gridded nighttime light data during 1992-2019. Subsequently, from a revised real growth perspective, we employed a top-down method to calculate global 1 km × 1 km gridded revised real GDP and electricity consumption during 1992-2019 based on our calibrated nighttime light data.

The measurement scales, consistency index, inconsistency issues, missing judgment estimation and priority derivation methods have been extensively studied in the pairwise comparison matrix (PCM). Various approaches have been proposed to handle these problems, and made great contributions to the decision making. This paper reviews the literature of the main developments of the PCM. There are plenty of literature related to these issues, thus we mainly focus on the literature published in 37 peer reviewed international journals from 2010 to 2015 (searched via ISI Web of science). We attempt to analyze and classify these literatures so as to find the current hot research topics and research techniques in the PCM, and point out the future directions on the PCM. It is hoped that this paper will provide a comprehensive literature review on PCM, and act as informative summary of the main developments of the PCM for the researchers for their future research.

On-line platforms foster the communication capabilities of the Internet to develop large-scale influence networks in which the quality of the interactions can be evaluated based on trust and reputation. So far, this technology is well known for building trust and harnessing cooperation in on-line marketplaces, such as Amazon (www.amazon.com) and eBay (www.ebay.es). However, these mechanisms are poised to have a broader impact on a wide range of scenarios, from large scale decision making procedures, such as the ones implied in e-democracy, to trust based recommendations on e-health context or influence and performance assessment in e-marketing and e-learning systems. This contribution surveys the progress in understanding the new possibilities and challenges that trust and reputation systems pose. To do so, it discusses trust, reputation and influence which are important measures in networked based communication mechanisms to support the worthiness of information, products, services opinions and recommendations. The existent mechanisms to estimate and propagate trust and reputation, in distributed networked scenarios, and how these measures can be integrated in decision making to reach consensus among the agents are analysed. Furthermore, it also provides an overview of the relevant work in opinion dynamics and influence assessment, as part of social networks. Finally, it identifies challenges and research opportunities on how the so called trust based network can be leveraged as an influence measure to foster decision making processes and recommendation mechanisms in complex social networks scenarios with uncertain knowledge, like the mentioned in e-health and e-marketing frameworks.

The digital economy is an important engine to promote sustainable economic growth. Exploring the mechanism by which the digital economy promotes economic development, industrial upgrading and environmental improvement is an issue worth studying. This paper takes China as an example for study and uses the data of 286 cities from 2011 to 2019. In the empirical analysis, the direction distance function (DDF) and the Global Malmquist-Luenberger (GML) productivity index methods are used to measure the green total factor productivity (GTFP), while Tobit, quantile regression, impulse response function and intermediary effect models are used to study the relationship among digital economy development, industrial structure upgrading and GTFP. The results show that: (1) The digital economy can significantly improve China's GTFP; however, there are clear regional differences. (2) The higher the GTFP, the greater the promotion effect of the digital economy on the city's GTFP. (3) From a dynamic long-term perspective, the digital economy has indeed positively promoted China's GTFP. (4) The upgrading of industrial structures is an intermediary transmission mechanism for the digital economy to promote GTFP. This paper provides a good reference for driving green economic growth and promoting the environment.

Financial technology (Fintech) makes a significant contribution to the financial system by reducing costs, providing higher quality services and increasing customer satisfaction. Hence, new studies play an essential role to improve Fintech investments. This study evaluates Fintech-based investments of European banking services with an application of an original methodology that considers interval type-2 (IT2) fuzzy decision-making trial and evaluation laboratory and IT2 fuzzy TOPSIS models. Empirical findings are controlled for consistency by applying the VIKOR method. Moreover, we conduct a sensitivity analysis by considering six distinct cases. This study contributes to the existing literature by identifying the most important Fintech-based investment alternatives to improve the financial performance of European banks. Our empirical findings illustrate that results are coherent, reliable, and identify "competitive advantage" as the most important factor among Fintech-based determinants. Moreover, "payment and money transferring systems" are the most important Fintech-based investment alternatives. It is recommended that, among Fintech-based investments, European banks should mainly focus on payment and money transferring alternatives to attract the attention of customers and satisfy their expectations. This is also believed to have a positive impact on the ease of bank' receivable collection. Another important point is that Fintech-based investments in money transferring systems could help to decrease costs.

In a group decision-making (GDM) process, experts reach a consensus after discussion and persuasion, which requires a moderator to spend time and resource to persuade experts to change their original opinions. Since a unanimous agreement is hard to achieve and costly, a consensus degree or soft consensus was used and various approaches have been proposed to measure the level of consensus in GDM. Though cost is an important factor in GDM, few works have calculated consensus cost occurred during the process. Moreover, the degree of consensus was not considered in the minimum cost consensus study. The objective of this paper is to propose consensus models under a certain degree of consensus, which considers both consensus degree and cost in GDM. To do this, we develop a generalized soft cost consensus model under a certain degree of consensus, which is built by defining a consensus level function and a generalized aggregation operator. A soft minimum cost consensus model is constructed based on arithmetic weighted average operator (AWAO), and the maximum return model is constructed through its dual model. The cost (compensation) is studied from both the perspectives of a moderator and individual experts. The relationships between the two soft consensus cost models are analyzed, and the economic significance of the models are also discussed. Numerical examples are used to explain the proposed models. In addition, to show the usability of the proposed models in real-world context, we apply the proposed models to a loan consensus scenario using data from an online peer-to-peer lending platform.

There is now growing interest in employee resilience in the organizational context and its contribution to organizational performance. However, little is known the extent to which high-performance work systems (HPWS) contributes towards enhancing employee’s resilience as well as their levels of engagement. This study examines the relationships among HPWS, employee resilience and engagement, using a sample of 2040 employees in the Chinese banking industry. Drawing on the job demands-resources model and strategic/high-performance human resource management theory, we develop three hypotheses to test the relationship between HPWS and employee resilience, resilience and employee engagement, and the mediating effect of resilience on the relationship between HPWS and engagement. All hypotheses are supported and suggest that HPWS can be used as a job resource to positively affect resilience and subsequently employee engagement. The key message of the paper is that employee resilience can be viewed as a set of skills and attributes that can be developed through the effective use of HPWS to benefit both individuals and the organization.