Showing papers by "College of Management and Economics published in 2019"
TL;DR: It is found that the number of tweets is a significant driver of next day trading volume and realized volatility which is supported by linear and nonlinear Granger causality tests.
233 citations
TL;DR: Drawing upon the trust heuristic, a psychological model to explain three acceptance measures of fully AD: general acceptance, willingness to pay (WTP), and behavioral intention (BI) was tested and social trust retained a direct effect as well as an indirect effect on all FAD acceptance measures.
Abstract: Automated driving (AD) is one of the most significant technical advances in the transportation industry. Its safety, economic, and environmental benefits cannot be realized if it is not used. To explain, predict, and increase its acceptance, we need to understand how people perceive and why they accept or reject AD technology. Drawing upon the trust heuristic, we tested a psychological model to explain three acceptance measures of fully AD (FAD): general acceptance, willingness to pay (WTP), and behavioral intention (BI). This heuristic suggests that social trust can directly affect acceptance or indirectly affect acceptance through perceived benefits and risks. Using a survey (N = 441), we found that social trust retained a direct effect as well as an indirect effect on all FAD acceptance measures. The indirect effect of social trust was more prominent in forming general acceptance; the direct effect of social trust was more prominent in explaining WTP and BI. Compared to perceived risk, perceived benefit was a stronger predictor of all FAD acceptance measures and also a stronger mediator of the trust-acceptance relationship. Predictive ability of the proposed model for the three acceptance measures was confirmed. We discuss the implications of our results for theory and practice.
161 citations
TL;DR: The research reveals that the three members of the dark triad have different effects on EI in different cultural contexts, and the research findings have certain reference value for further improvement of entrepreneurship education and entrepreneurial practice.
Abstract: The driving factors behind the exploration and search for entrepreneurial intention (EI) are critical to entrepreneurship education and entrepreneurial practice. To reveal in depth the influence of personality traits on EI, our study introduces the opposite of proactive personality—the dark triad that consists of narcissism, psychopathy and Machiavellianism. Our study used the MBA students of Tianjin University as a sample to analyze the relationship between the dark triad, entrepreneurial self-efficacy (ESE) and EI. From the overall perspective of the dark triad, the results show that the dark triad positively predicts EI, and ESE has a partial mediating effect on the dark triad and EI. From the perspective of the three members of the dark triad, the study found that narcissism/psychopathy has a negative effect on ESE and EI; narcissism/psychopathy has a nonlinear effect on EI; Machiavellianism has a positive effect on ESE and EI; and ESE has a mediating effect on the three members of the dark triad and EI. In short, our research reveals that the three members of the dark triad have different effects on EI in different cultural contexts, and the research findings have certain reference value for further improvement of entrepreneurship education and entrepreneurial practice.
137 citations
TL;DR: In this article, the authors investigated whether gender diversity on the board of directors in the United States is associated with firms' environmental performance and found that gender diversity brings a greater variety of skills to the board.
Abstract: This study investigates whether gender diversity on the board of directors in the United States is associated with firms' environmental performance. Under the theoretical framework of resource dependence theory, we argue that gender diversity brings a greater variety of skills to the board. Diversity allows for a healthy mix of knowledge and experience to improve the decision‐making process of the board. Using propensity score matching and controlling for endogeneity, this study uses a more rigorous statistical model than previous work. It also uses content analysis of directors' biographies to provide evidence of the role that gender diversity plays. We find gender diversity is positively associated with firms' environmental performance scores primarily in the more environmentally impacting industries. Therefore, our research provides valuable direction for those firms working to improve both their boards' gender diversity and their environmental performance. Our findings also offer insight into the mixed results of previous studies.
135 citations
TL;DR: In this article, a detailed observation by including 973 forms of cryptocurrency and 30 international indices from a dynamic perspective was made. And the empirical results mainly show that cryptocurrency is a safe haven but not a hedge for most of the international indices.
131 citations
TL;DR: Investigating manufacturer encroachment with both endogenous quality decision and asymmetric demand information to examine the effects of encroachment and information structure on quality and profits for chain members shows that encroachment leads to a lower quality when the manufacturer’s direct selling cost is intermediate.
122 citations
TL;DR: Examination of willingness to pay in China found trust and perceived benefit were positive predictors of WTP and perceived risk and perceived dread were negative predictorsOf WTP.
Abstract: Research on willingness to pay (WTP) can provide practical insights for assessing the value of self-driving vehicle (SDV) technology in the vehicle market. Are people willing to pay extra for the technology? What demographic and psychological factors can influence people’s WTP for this technology? These questions are not yet well investigated. We conducted surveys in two cities in China (total N = 1355) and examined WTP and its potential demographic determinants (familiarity, age, gender, education, and income) and psychological determinants (perceived benefit and risk of SDVs, anticipated perceived dread riding in SDVs, and trust in SDVs). About 26.3% of participants were unwilling to pay extra, 39.3% were willing to pay less than $2900, and the remaining 34.3% were willing to pay more than $2900. Younger and highly educated participants with higher-income were willing to pay more. Participants who had heard about SDVs before the survey reported higher WTP and higher trust and perceived higher benefits, lower risks, and lower dread. Trust and perceived benefit were positive predictors of WTP and perceived risk and perceived dread were negative predictors of WTP. Our results may offer practical implications for increasing the public’s acceptance and WTP of SDVs.
120 citations
TL;DR: In this paper, the authors explored the causal relationship between environmental regulations and air quality, and the effectiveness of the New Ambient Air Quality Standards (New Standards) was evaluated using the Difference-in-Differences (DID) method to weaken the endogeneity problems.
120 citations
TL;DR: A Social network analysis-based Conflict Relationship Investigation Process (S-CRIP) is presented to detect the conflict relationships among DMs for LSDM events, in which sparse representation is used and three processes constitute the S-CRIP and CD-CRIP-based LSDM model, which is suitable for any numerical representations.
113 citations
TL;DR: In this paper, the effects of environmental orientation on supplier green management and financial performance under different levels of relational capital using a survey method were empirically examined using Hierarchical regression.
108 citations
TL;DR: In this article, the authors explore the retailer's strategies to deter the manufacturer from encroaching in a retailer-led setting where the manufacturer keeps his own direct selling cost private, and show that the dominant retailer is always worse off while the manufacturer is always better off by manufacturer encroachment in a retailers-led supply chain when the fixed encroaching cost is negligible.
Abstract: Noting the rise of dominant retailers, we explore the retailer’s strategies to deter the manufacturer from encroaching in a retailer-led setting where the manufacturer keeps his own direct selling cost private. Our results show that the dominant retailer is always worse off while the manufacturer is always better off by manufacturer encroachment in a retailer-led supply chain when the fixed encroaching cost is negligible. This gives rise to a question that whether there exist effective anti-encroachment strategies for the retailer. We investigate a noted and prevailing strategy of retailers, retail service investing, to examine if it can help the retailer to prevent encroachment. Results show that the retail service level is reduced by encroachment. Retail service investing may actually be an effective anti-encroachment measure for the dominant retailer, especially when retail service investing is highly efficient and the retailer holds a great downward estimation deviation on the direct selling cost of the manufacturer. Retail service investing may lead to Pareto improvement for both the supply chain members and consumers. Additionally, the manufacturer may have incentives to share cost information with the retailer, depending on the retailer’s estimation deviation on the direct selling cost. Finally, we find that a prisoner’s dilemma may occur for a moderate fixed cost of encroachment.
TL;DR: Wang et al. as discussed by the authors explored the influence of prefabricated construction from the perspectives of technology promotion and cleaner production, and established an influencing factor model composed of industry factors, company factors, technology factors, government factors and market factors.
TL;DR: This paper focuses on hesitant fuzzy LSGDM problems where decision makers (DMs) use hesitant fuzzy preference relations (HFPRs) to express their assessment information, and proposes an unreliable DM management method to be used in the RI-CRP, based on the computation of DM's opinion reliability index.
Abstract: Recently, large scale group decision making (LSGDM) problems have become a hotspot. This paper focuses on hesitant fuzzy LSGDM problems where decision makers (DMs) use hesitant fuzzy preference relations (HFPRs) to express their assessment information. HFPRs can represent the fuzziness and hesitancy of DM assessment information well. To improve the efficiency of hesitant fuzzy LSGDM problems, we first propose a reliability index-based consensus reaching process (RI-CRP). By assessing the ordinal consistency of DM's assessment information and measuring the deviation from collective opinion, the DM's opinion reliability index is given. To avoid unreliable information, we propose an unreliable DM management method to be used in the RI-CRP, based on the computation of DM's opinion reliability index. Moreover, an alternative ranking-based clustering (ARC) method with HFPRs is proposed to improve the efficiency of the RI-CRP. The similarity index between two DMs’ opinions is provided to ensure the ARC method can be effectively implemented. Compared with those clustering methods which need to preset several correlated parameters, the presented ARC method is more objective with a different approach based on the alternative ranking. A numerical example shows that the proposed ARC method and the RI-CRP are feasible and effective for hesitant fuzzy LSGDM problems.
TL;DR: It is presented an interesting insight that the marketplace channel should be introduced under not only a low degree but also a high degree of upstream sales inefficiency, which also means that a weak direct channel would not necessarily become a burden for the two.
TL;DR: In this paper, the authors explore the evolution of different participants' behavior and their evolutionary stable strategy in line with the duplication of dynamic equations, enabling a robust, quantitative analysis of this iterative, interactive, three-player game.
TL;DR: A new expressed-preference approach was proposed for the first time to determine the socially acceptable risk of SDVs, and it showed that SDVs were required to be safer than HDVs.
Abstract: Self-driving vehicles (SDVs) promise to considerably reduce traffic crashes. One pressing concern facing the public, automakers, and governments is "How safe is safe enough for SDVs?" To answer this question, a new expressed-preference approach was proposed for the first time to determine the socially acceptable risk of SDVs. In our between-subject survey (N = 499), we determined the respondents' risk-acceptance rate of scenarios with varying traffic-risk frequencies to examine the logarithmic relationships between the traffic-risk frequency and risk-acceptance rate. Logarithmic regression models of SDVs were compared to those of human-driven vehicles (HDVs); the results showed that SDVs were required to be safer than HDVs. Given the same traffic-risk-acceptance rates for SDVs and HDVs, their associated acceptable risk frequencies of SDVs and HDVs were predicted and compared. Two risk-acceptance criteria emerged: the tolerable risk criterion, which indicates that SDVs should be four to five times as safe as HDVs, and the broadly acceptable risk criterion, which suggests that half of the respondents hoped that the traffic risk of SDVs would be two orders of magnitude lower than the current estimated traffic risk. The approach and these results could provide insights for government regulatory authorities for establishing clear safety requirements for SDVs.
TL;DR: In this paper, the authors investigated how lean and green processes in manufacturer-customer (customer side) and manufacturer-supplier interfaces (supply side) in the supply chain influence sustainability in environmental, social, and economic performance.
TL;DR: In this paper, an 11-level hierarchal model was developed by implementing interpretive structural modelling (ISM) methodology to delineate these barriers into the categories of "driving", "linkage", and "dependent".
TL;DR: In this paper, the authors consider manufacturer encroachment with the cost reduction decision under either asymmetric or symmetric demand information and find that encroachment motivates the manufacturer to invest more in cost reduction if and only if the direct selling channel is relatively efficient.
Abstract: This paper considers manufacturer encroachment with the cost reduction decision under either asymmetric or symmetric demand information. By solving a signaling game, we find that encroachment motivates the manufacturer to invest more in cost reduction if and only if the direct selling channel is relatively efficient. Furthermore, both members benefit from the cost reduction action without encroachment, while encroachment allows the manufacturer to monopolize all of the benefit. In addition, encroachment benefits the manufacturer when the direct selling cost is sufficiently low, while it benefits the retailer when this cost is sufficiently high. Finally, we obtain some insights into information management.
TL;DR: In this article, 30 provinces are divided into three areas (advantageous area, potential area and backward area) from 1996 to 2015 by factor analysis and cluster analysis according to the different social development which is measured by urbanization, economy, energy utilization, industry and technology.
TL;DR: In this article, the authors compared the environmental impacts of wind power, nuclear power, and hydropower in terms of global warming potential, acidification potential, eutrophication potential, photochemical ozone creation potential, and human toxicity potential.
TL;DR: In this article, an inverted U-shaped relationship between the amount of subsidies and four indicators of technology innovation was investigated, leading to three effects: resource allocation, information efficiency, risk control and regional economic development.
TL;DR: Wang et al. as mentioned in this paper employed the geographically weighted regression (GWR) model to examine the impact of the urbanization quality on CO2 emissions and revealed the spatial differences of 30 provinces in 2000, 2005, 2010, and 2015.
Abstract: China is facing increasingly severe challenges in its quest to achieve urbanization and mitigate CO2 emissions. The existing studies have usually introduced a single indicator to describe urbanization and have ignored the complexity and multi-dimensionality of urbanization. This study establishes an evaluation system of urbanization quality to estimate the urbanization development level. The geographically weighted regression (GWR) model is employed to examine the impact of the urbanization quality on CO2 emissions and reveals the spatial differences of 30 provinces in 2000, 2005, 2010, and 2015. The results show that there are significant temporal and spatial differences in the effects of the urbanization quality on CO2 emissions between provinces. Improvements in the urbanization quality have contributed to cutting CO2 emissions in most provinces. The impact of the urbanization quality on CO2 emissions in the central region and western region is greater than that in the eastern region. The energy intensity has the largest positive impact on CO2 emissions, which indicates that technical progress can effectively reduce CO2 emissions. The industrial structure has a positive impact on CO2 emissions in 2000 and 2015, whereas it has a negative impact on the CO2 emissions of some provinces in 2005 and 2010. This paper provides valuable findings and conclusions of the relationship between urbanization quality and CO2 emissions. Differentiated policy implications are proposed according to geographical differences.
TL;DR: The proposed hybrid algorithm can not only help to reduce the complexity of SAGASW algorithm and effectively extracting the optimal feature subset to a certain extent, but it can also obtain the maximum classification accuracy and minimum misclassification cost.
Abstract: Breast cancer is one of the leading causes of death among women worldwide. Accurate and early detection of breast cancer can ensure long-term surviving for the patients. However, traditional classification algorithms usually aim only to maximize the classification accuracy, failing to take into consideration the misclassification costs between different categories. Furthermore, the costs associated with missing a cancer case (false negative) are clearly much higher than those of mislabeling a benign one (false positive). To overcome this drawback and further improving the classification accuracy of the breast cancer diagnosis, in this work, a novel breast cancer intelligent diagnosis approach has been proposed, which employed information gain directed simulated annealing genetic algorithm wrapper (IGSAGAW) for feature selection, in this process, we performs the ranking of features according to IG algorithm, and extracting the top m optimal feature utilized the cost sensitive support vector machine (CSSVM) learning algorithm. Our proposed feature selection approach which can not only help to reduce the complexity of SAGASW algorithm and effectively extracting the optimal feature subset to a certain extent, but it can also obtain the maximum classification accuracy and minimum misclassification cost. The efficacy of our proposed approach is tested on Wisconsin Original Breast Cancer (WBC) and Wisconsin Diagnostic Breast Cancer (WDBC) breast cancer data sets, and the results demonstrate that our proposed hybrid algorithm outperforms other comparison methods. The main objective of this study was to apply our research in real clinical diagnostic system and thereby assist clinical physicians in making correct and effective decisions in the future. Moreover our proposed method could also be applied to other illness diagnosis.
TL;DR: In this paper, the authors explored the coordination mechanism regarding whether to coordinate, when to adopt the optimal coordinated strategy and how such a strategy can perform well in a sustainable humanitarian supply chain.
TL;DR: A psychological model was developed and developed from the conversation on trust and developed a psychological model to explain three acceptance measures, namely, general acceptance, behavioral intention to use, and willingness to pay (WTP).
Abstract: The autonomous vehicle (AV) is expected to dramatically increase road safety. Understanding the public’s initial perceptions and acceptance of AV is imperative because these aspects are likely to d...
TL;DR: The purpose of the presented SRIFC approach is to investigate the group intra-relations among DMs and to detect the group leaders for each interest group during the clustering process.
Abstract: In this paper, a sparse representation-based intuitionistic fuzzy clustering (SRIFC) approach is presented for solving the large-scale decision making (LSDM) problem. It consists of two algorithms: the sparse representation-based intuitionistic fuzzy clustering-exactly precision algorithm (which is presented for an exactly precision requirement), and the sparse representation-based intuitionistic fuzzy clustering-soft precision and scalable algorithm (which is proposed for soft precision and scalable requirements). In the proposed SRIFC approach, decision makers are clustered into several interest groups according to their interest preferences and relation sparsity of their intuitionistic fuzzy assessment information. The purpose of the presented SRIFC approach is to investigate the group intra-relations among DMs and to detect the group leaders for each interest group during the clustering process. According to the illustrative experiment results, the presented SRIFC approach is an adaptive and the unsupervised clustering method and presents more robust and efficient for LSDM problems.
TL;DR: Experimental results on different datasets show that the proposed clustering algorithm outperforms other compared methods in various evaluation metrics; this approach enhances the prediction accuracy and effectively deals with the sparsity problem.
Abstract: Data sparsity is a widespread problem of collaborative filtering (CF) recommendation algorithms. However, some common CF methods cannot adequately utilize all user rating information; they are only able to use a small part of the rating data, depending on the co-rated items, which leads to low prediction accuracy. To alleviate this problem, a novel K-medoids clustering recommendation algorithm based on probability distribution for CF is proposed. The proposed scheme makes full use of all rating information based on Kullback–Leibler (KL) divergence from the perspective of item rating probability distribution, and distinguishes different items efficiently when selecting the cluster centers. Meanwhile, the distance model breaks the symmetric mode of classic geometric distance methods (such as Euclidean distance) and considers the effects of different rating numbers between items to emphasize their asymmetric relationship. Experimental results on different datasets show that the proposed clustering algorithm outperforms other compared methods in various evaluation metrics; this approach enhances the prediction accuracy and effectively deals with the sparsity problem.
TL;DR: An urgent need to consider trade types and water scarcity when developing water resource allocation and conservation policies is revealed.
TL;DR: Models with a profit-maximizing platform that considers the pricing decision effects of the provider’s threshold participating quantity, value-added service (VAS) and matching ability are developed and it is shown that the thresholdparticipation quantity significantly affects pricing decisions when the basic demand is relatively low.
Abstract: Affected by the online supply-demand matching, traditional pricing decisions cannot be applied to recent ‘online-to-offline’ (O2O) platforms, which should consider more about the features of the demander side, provider side and platform matching. Models with a profit-maximizing platform that considers the pricing decision effects of the provider’s threshold participating quantity, value-added service (VAS) and matching ability are developed in this study. Specifically, the main conclusions are divided into two parts: low-demand state and high-demand state. In the low-demand state, we show that the threshold participating quantity significantly affects pricing decisions when the basic demand is relatively low. There are two different critical values that make the pricing decisions into three cases. Second, regardless of the platform’s capital and the basic demand, the VAS always benefits the platform. Third, when the basic demand is relatively low and the threshold participating quantity is relatively high, the platform will not benefit from a higher matching ability, which is counter-intuitive. In the high-demand state, we show that the threshold participating quantity will not affect the pricing decisions. Second, developing the VAS still contributes to the improvement of platform’s profit. Third, different from the low-demand state, the platform’s profit always increases with the matching ability.