Journal of Management Science and Engineering最新文献

In this paper, we focus on small business enterprises (SBEs) that usually have low market power but can rely on retailers to transact sales and gain the ability to disclose quality information. Moreover, consumer loss aversion (CLA) is pronounced when buying from SBEs that have yet to develop a strong reputation and uncertain quality. We focus on two competing SBEs with heterogeneous quality levels and discuss their quality disclosure strategies— whether selling through a retailer— in the context of CLA. We study the interaction between consumers' prior belief in product quality and CLA and how these factors affect equilibrium outcomes. We show that a situation in which low-quality and high-quality SBEs both choose to disclose will not occur under a neutral consumer attitude, i.e., it happens only when the aversion level is significant. When the aversion level is low, either the low-quality SBE or the high-quality SBE will decide to disclose, and the disclosing party depends on the prior belief. In addition, CLA significantly impacts the monotonicity of both SBEs' and retailers' prices and profits relating to the consumers' prior beliefs.

Supply chain resilience (SCR) refers to a supply chain's (SC's) ability to recover from disruptions timely and effectively. This study uses a combination of contingency and configuration approaches to examine the direct and contingent relationships between SCR dimensions (i.e., internal, supplier, and customer resilience) and performance. It analyzes survey data collected from 206 Chinese manufacturers. The results show that the three SCR dimensions are positively related to customer satisfaction, whereas customer resilience has no direct contribution to financial performance. Internal resilience moderates the relationship between supplier resilience and performance (negative for customer satisfaction and positive for financial performance). A taxonomy for SCR was developed based on internal, supplier, and customer resilience, providing a holistic perspective for examining the performance discrepancies among four different SCR patterns: high external-leaning, high uniform, medium uniform, and low uniform. These findings offer insights for managers in building SCR from the crisis-management process.

The Conference of the Parties (COP26 and 27) placed significant emphasis on climate financing policies with the objective of achieving net zero emissions and carbon neutrality. However, studies on the implementation of this policy proposition are limited. To address this gap in the literature, this study employs machine learning techniques, specifically natural language processing (NLP), to examine 77 climate bond (CB) policies from 32 countries within the context of climate financing. The findings indicate that “sustainability” and “carbon emissions control” are the most outlined policy objectives in these CB policies. Additionally, the study highlights that most CB funds are invested toward energy projects (i.e., renewable, clean, and efficient initiatives). However, there has been a notable shift in the allocation of CB funds from climate-friendly energy projects to the construction sector between 2015 and 2019. This shift raises concerns about the potential redirection of funds from climate-focused investments to the real estate industry, potentially leading to the greenwashing of climate funds. Furthermore, policy sentiment analysis revealed that a minority of policies hold skeptical views on climate change, which may negatively influence climate actions. Thus, the findings highlight that the effective implementation of CB policies depends on policy goals, objectives, and sentiments. Finally, this study contributes to the literature by employing NLP techniques to understand policy sentiments in climate financing.

We consider a general framework of optimal contract design under the heterogeneity and short-termism of agents. Our research shows that the optimal contract must weigh the agent's information rent, incentive cost, and benefit to overcome the contract's adverse selection and moral hazards. Agents with higher moral levels were more likely to choose higher effort and lower manipulation. Simultaneously, the principal offers lower incentives and receives more significant payoff. We also extend our model to investigate the benefits of Bayesian learning. Furthermore, we compare the principal's returns in general and learning models and find that the learning contract can bring more profit to the principal.

Pedestrian group detection is a challenging but significant issue in pedestrian flow control and public safety management. To address the issue that most conventional pedestrian grouping models (PGMs) can only identify a pedestrian group at a limited distance of less than 2 m, this study extended the pedestrian distance constraint of conventional PGMs with a reconstruction of the normal group detection criterion and development of a novel group detection criterion suitable for long-span space. To measure the movement behavior similarity with normal distance, five necessary constraints: velocity difference, moving direction offset, distance limitation, distance fluctuation, and group-keeping duration were studied quantitatively to form the criterion to detect normal groups. Meanwhile, a long-span group detection criterion was proposed with extended distance and direction consistency constraints. Therefore, this study proposed an improved PGM that considers long-span spaces (PGMLS). In the PGMLS workflow, the MMTrack algorithm was used to obtain pedestrian trajectories. A difference measurement method based on sequential pattern analysis (SPA) was adopted to analyze the velocity similarity of pedestrians. To validate the proposed grouping model, experiments based on pedestrian movement videos in the exit hall of the Shanghai Hongqiao International Airport were conducted. The results indicate that the proposed model can detect both normal and widely separated pedestrian groups, with a long span range of 2–12 m.

In March 2018, the US used an immense trade deficit as an excuse to provoke trade friction with China. This study uses the EGARCH model and event study methods to study the impact of the major risk event of Sino-US trade friction on soybean futures markets in China and the United States. Results indicate that the Sino-US trade friction weakened the return spillover effect between the soybean futures markets in China and the US, and significantly increased market volatilities. As the scale of additional tariffs increased, the volatility of the Chinese soybean futures market declined; however, the volatility of the US soybean futures market did not weaken. In addition, expanding the sources of soybean imports helped ease the impact of tariffs on China’s soybean futures market, while the decline in US soybean exports to China intensified the volatility of the US soybean futures market. In addition, while the release of multiple tariff increases has had a short-term impact on the returns of soybean futures markets, the impact of trade friction has gradually decreased.

The rapid development of Chinese online loan platforms (OLPs), as well as their risks, has attracted widespread attention, increasing the demand for a complete credit rating mechanism. The present study establishes a credit rating indicator system for 130 mainstream Chinese OLPs that combines 12 quantitative metrics of online loan operations similar to commercial bank credit rating indicators, including platform transaction volume and average expected rate of return. We also consider two qualitative indicators of online loan background, namely platform background and guarantee mode, that reflect Chinese characteristics. Subsequently, a factor analysis was conducted to reduce the 14 indicators’ dimensions. The loads of the rating indicators in the resulting rotating component matrix were refined into an OLP operation scale factor, fund dispersion factor, security factor, and profitability factor. Finally, a K-means clustering algorithm was employed to cluster the factor scores of each OLP, thereby obtaining credit rating results. The empirical results indicate that the proposed machine learning–based credit rating method effectively provides early warnings of problem platforms, yielding more accurate credit ratings than those provided by two mainstream online loan rating websites in China, namely, Wangdaitianyan and Wangdaizhijia.