Quality management practices have become increasingly important as firms seek to obtain quality certifications to dominate markets. To date, adequate research evaluating the effects of quality management is lacking. In this work, we used Chinese quality awards to evaluate a firm’s quality level. A PSM-DiD (propensity score matching and difference-in-difference) model describing the relationship between quality award effects and financial benefits in terms of return on assets was developed. We further used a hierarchical regression to examine the influence of operational performance on financial benefits. The results show that quality awards cannot assure their winners a higher return on asset. Indicators of operating performance, such as less lead time and higher inventory turnover, can significantly enhance firms’ profitability. The moderating effects of operational performance suggest that firms may focus on how to translate quality management practices into business improvement. This study also contributes to the operation management literature by describing how firms need outstanding financial performance for sustainable development and continuous improvement.

In light of the growing demand for quality certifications, quality management practices have become crucial in the quest for profitability and market share. To date, the research into the firm performance of quality management is very limited. This paper aims to investigate the effects of quality management practices on the operational and financial performance. An event study methodology describing the year-to-year and cumulative effects of quality awards is developed. The data were collected from a sample of 148 Chinese manufacturing companies that won Chinese national and provincial quality awards. The results indicate that while quality award winners have advantages in terms of their profitability and operating efficiency, their sales growth is not improved as expected. In terms of innovation capability, award winners strongly underperform compared with their counterparts. This study also highlights that the positive impact on profitability may disappear after the certification and that the most significantly positive stimulus occurs in the certification year. This study provides evidence that quality certification cannot assure companies of competitive advantages. It also enlightens managers regarding the existence of obstacles from quality management systems to business performance.

This paper studies maintenance policies for multi-component systems in which failure interactions and opportunistic maintenance (OM) involve. This maintenance problem can be formulated as a Markov decision process (MDP). However, since an action set and state space in MDP exponentially expand as the number of components increase, traditional approaches are computationally intractable. To deal with curse of dimensionality, we decompose such a multi-component system into mutually influential single-component systems. Each single-component system is formulated as an MDP with the objective of minimising its long-run average maintenance cost. Under some reasonable assumptions, we prove the existence of the optimal (n, N) type policy for a single-component system. An algorithm to obtain the optimal (n, N) type policy is also proposed. Based on the proposed algorithm, we develop an iterative approximation algorithm to obtain an acceptable maintenance policy for a multi-component system. Numerical examples find that failure interactions and OM pose significant effects on a maintenance policy.

It is paramount to ensure the resilience of networked critical infrastructures, which are vital for the economic development and our daily requirements but threatened by intentional attacks. For this purpose, their protection before and restoration after intentional attacks are of great significance. Moreover, in order to keep the prescribed demand continuously satisfied, postdisruption flow redistribution is also necessary. However, due to the severity of intentional attacks, certain components may need to be overloaded as a result of the redistribution and, thus, the network reliability is influenced. To minimize such influence, we first present a new metric of resilience to reflect the incurred reliability changes. Then, a trilevel optimization model integrating protection, restoration, and adaptive flow redistribution to maximize the network resilience is proposed. To deal with the complexity of the proposed problem, an evolutionary algorithm is employed with necessary adaption.

Quality awards are widely considered symbols of successful quality management. The purpose of this paper is to empirically test the effects of the China Quality Award (CQA) on firms' performance. The study further explores how the benefits due to CQA are affected by contextual factors. Using the data of CQA winners from 2001 to 2016, the event study method is applied to analyze the abnormal performance of winners. Furthermore, multiple regression models are proposed to evaluate the effects of contextual factors on the relationship between the award and profitability. The findings show that CQA has positive impacts on profitability and fixed asset efficiency but not on labor productivity. Besides, state-owned firms and firms with high innovation intensity obtain more profitability benefits than others. This is the first study to explain the relationship between quality awards and firm performance from a theoretical perspective, providing new insights into the quality management and performance literature. Furthermore, this study deepens the understanding of the relationship between quality awards and performance and reveals new implications. Some of the contextual factors examined, such as innovation intensity, are considered for the first time in quality award research.

Plague, caused by the highly lethal bacterium Yersinia pestis, remains a critical public health threat. Tibet's unique alpine environment fosters sensitive ecosystems, yet plague dynamics there are understudied. This study reveals environmental drivers and distribution evolution of plague foci in Tibet. Using Tibet plague surveillance data (2000−2021), we integrated eight environmental variables (maximum temperature, precipitation, normalized difference vegetation index, land use and land cover, soil moisture, slope, aspect, and population spatial distribution). Ten algorithms via BIOMOD2 modeled plague risk across four phases (2000–2004, 2005–2009, 2010–2015, 2016–2021), validated with 2022–2023 data. We simulated risk distribution, calculated area/centroid changes, and elucidated spatiotemporal evolution. The ensemble model (EM) showed excellent external validation (2022: AUC = 0.98, TSS = 0.92, Kappa = 0.88; 2023: AUC = 0.99, TSS = 0.99, Kappa = 0.99). Key drivers were population, precipitation, max temperature, and NDVI. The EM revealed stage-wise centroid migration. High-risk areas concentrated in Lhasa, eastern Shigatse, and eastern Nagqu during 2000–2004, contracting spatially 2005–2009 with northeast centroid shift. Post-2010, the centroid shifted southeast then northwest, linked to anthropogenic activities and climate variations. Multi-model integration and spatiotemporal simulation revealed plague foci distribution and centroid evolution in Tibet, capturing human-climate impacts, providing a scientific basis for targeted prevention in high-risk areas.