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This study used the literature review and the modified Delphi method to evaluate the importance of the catering quality indices of university canteens in China. In order to compile the catering quality indices of university canteens in China as reference for the subsequent improvement of Chinese canteens.

This study first analysed literature data to establish the preliminary quality indices and used the modified Delphi method for measurement. After three rounds of Delphi analysis by 35 experts, the results of the catering quality indices of university canteens in China are summarised.

The research results show that university canteen catering quality issues are divided into six dimensions, including catering safety management, employee hygiene management, catering service, food quality, environmental atmosphere and corporate social responsibility. Catering safety management is the most important index, followed by employee hygiene management.

The research results can be used as suggestions for follow-up improvements in the quality of university canteens in China and a basis of reference for amendments to relevant national or local laws and regulations. The food prices, food quality and whether food hygiene and safety standards are met by university canteens are all related to the health and vital interests of the teachers and students, as well as the stability of the university. Therefore, the government should increase supervision in these aspects to avoid decline in the quality of meals due to low profits and enforce strict requirements for food safety.

The concept of open innovation has captured the attention of both academics and practitioners alike. However, there is a dearth of research on how innovations can be diffused within open innovation ecosystems, a critical condition for the sustainability of such ecosystems. In this regard, the study advances a research agenda for guiding future inquiries into innovation diffusion within open innovation ecosystems.

Based on a systematic review of the extant literature on open innovation, this article identifies knowledge gaps in innovation diffusion, along with recommendations for bridging these gaps in the future. The study advocates that future research should consider not only innovation generation processes, but also innovation diffusion processes, especially in light of the growing application of open innovation in the context of digital goods and services.

Subscribing to an evolutionary view of innovation diffusion, the article draws on a five-phase framework – knowledge, persuasion, decision, implementation, and confirmation – to illustrate the roles played by three distinct yet interconnected parties (i.e. platforms, complementors, and individuals) within open innovation ecosystems as well as the research opportunities it brings.

The article examines the critical, yet underexplored role of innovation diffusion in sustaining open innovation ecosystems and outlines potential research avenues that can contribute to growing the understanding of the innovation diffusion process.

An individual’s driving style significantly affects overall traffic safety. However, driving style is difficult to identify due to temporal and spatial differences and scene heterogeneity of driving behavior data. As such, the study of real-time driving-style identification methods is of great significance for formulating personalized driving strategies, improving traffic safety and reducing fuel consumption. This study aims to establish a driving style recognition framework based on longitudinal driving operation conditions (DOCs) using a machine learning model and natural driving data collected by a vehicle equipped with an advanced driving assistance system (ADAS).

Specifically, a driving style recognition framework based on longitudinal DOCs was established. To train the model, a real-world driving experiment was conducted. First, the driving styles of 44 drivers were preliminarily identified through natural driving data and video data; drivers were categorized through a subjective evaluation as conservative, moderate or aggressive. Then, based on the ADAS driving data, a criterion for extracting longitudinal DOCs was developed. Third, taking the ADAS data from 47 Kms of the two test expressways as the research object, six DOCs were calibrated and the characteristic data sets of the different DOCs were extracted and constructed. Finally, four machine learning classification (MLC) models were used to classify and predict driving style based on the natural driving data.

The results showed that six longitudinal DOCs were calibrated according to the proposed calibration criterion. Cautious drivers undertook the largest proportion of the free cruise condition (FCC), while aggressive drivers primarily undertook the FCC, following steady condition and relative approximation condition. Compared with cautious and moderate drivers, aggressive drivers adopted a smaller time headway (THW) and distance headway (DHW). THW, time-to-collision (TTC) and DHW showed highly significant differences in driving style identification, while longitudinal acceleration (LA) showed no significant difference in driving style identification. Speed and TTC showed no significant difference between moderate and aggressive drivers. In consideration of the cross-validation results and model prediction results, the overall hierarchical prediction performance ranking of the four studied machine learning models under the current sample data set was extreme gradient boosting > multi-layer perceptron > logistic regression > support vector machine.

The contribution of this research is to propose a criterion and solution for using longitudinal driving behavior data to label longitudinal DOCs and rapidly identify driving styles based on those DOCs and MLC models. This study provides a reference for real-time online driving style identification in vehicles equipped with onboard data acquisition equipment, such as ADAS.

This study examines the relationship between demand forecasting error and retail inventory management in an uncertain supplier yield context. Replenishment is segmented into off-season and peak-season, with the former characterized by longer lead times and higher supply uncertainty. In contrast, the latter incurs higher acquisition costs but ensures certain supply, with the retailer's purchase volume aligning with the acquired volume. Retailers can replenish in both phases, receiving goods before the sales season. This paper focuses on the impact of the retailer's demand forecasting bias on their sales period profits for both phases.

This study adopts a data-driven research approach by drawing inspiration from real data provided by a cooperating enterprise to address research problems. Mathematical modeling is employed to solve the problems, and the resulting optimal strategies are tested and validated in real-world scenarios. Furthermore, the applicability of the optimal strategies is enhanced by incorporating numerical simulations under other general distributions.

The study's findings reveal that a greater disparity between predicted and actual demand distributions can significantly reduce the profits that a retailer-supplier system can earn, with the optimal purchase volume also being affected. Moreover, the paper shows that the mean of the forecasting error has a more substantial impact on system revenue than the variance of the forecasting error. Specifically, the larger the absolute difference between the predicted and actual means, the lower the system revenue. As a result, managers should focus on improving the quality of demand forecasting, especially the accuracy of mean forecasting, when making replenishment decisions.

This study established a two-stage inventory optimization model that simultaneously considers random yield and demand forecast quality, and provides explicit expressions for optimal strategies under two specific demand distributions. Furthermore, the authors focused on how forecast error affects the optimal inventory strategy and obtained interesting properties of the optimal solution. In particular, the property that the optimal procurement quantity no longer changes with increasing forecast error under certain conditions is noteworthy, and has not been previously noted by scholars. Therefore, the study fills a gap in the literature.

This study established a two-stage inventory optimization model that simultaneously considers random yield and demand forecast quality, and provides explicit expressions for optimal strategies under two specific demand distributions. Furthermore, the authors focused on how forecast error affects the optimal inventory strategy and obtained interesting properties of the optimal solution. In particular, the property that the optimal procurement quantity no longer changes with increasing forecast error under certain conditions is noteworthy, and has not been previously noted by scholars. Therefore, the study fills a gap in the literature.

The purpose of this paper is to investigate the total cost function of an inventory system with a reorder point/order quantity policy where the lead time is controllable based on the cost paid by the buyer for the service.

Cost functions are presented to investigate how the changes in lead time affect different components of inventory cost in the present of random demand. Two methods including an iteration technique and Simulated Annealing (SA) algorithm are presented to deal with the cost optimization issue. The application of proposed model is illustrated using numerical case scenarios.

The cost functions show that besides ordering cost, change in stochastic demand during lead time is the major factor that affects the other cost components such as holding and penalty costs. This finding is validated by numerical study. Results also show that performance of SA algorithm is highly similar to iteration methodology, while the former one is easier in application.

This paper develops less complex, more pragmatic methods, easily adoptable by logistics managers for cost minimization. This paper also analyzes and highlights the unique characteristics and features of these two approaches that can help practitioners in making the right choice when faced with the identified logistics issue.

This research explicitly investigate impacts of changing lead time on inventory cost components which enables informed decision making and inventory system planning for cost optimization by logistics practitioners. Two methodologies that can be easily used by practitioners without deep mathematical analysis and is cost effective are introduced to solve the optimization problem. Detailed roadmaps of how to implement proposed approaches have been illustrated by different case scenarios.

The global economy has been affected by the COVID-19 pandemic, which has placed greater responsibility on companies to fulfill their obligations to Corporate Social Responsibility (CSR) amid the crisis. This chapter investigates the role of a Chief Executive Officer (CEO) attributes in improving a firm's CSR in the emerging economy of Jordan and how the COVID-19 pandemic modifies this relationship. Using a Jordanian sample of 655 firm-year observations during the 2014–2021 period, the research results show that older CEOs, well-educated CEOs, CEOs' remuneration, and CEOs' ownership positively correlate with CSR reporting. However, long-tenured CEOs are associated with lower CSR initiatives. The subsample analysis findings also validate the significance of CEO attributes in improving CSR practice during the COVID-19 pandemic compared to the prepandemic period. These findings are beneficial for the regulatory setters to understand better whether CEO attributes are linked to engagement in CSR-related information. This research is among the limited number of studies that have explored how CEO attributes impact CSR reporting for the stakeholder's welfare. Moreover, it uniquely concentrated on contrasting the findings before and during the COVID-19 pandemic.

This study aims to focus on the preparation and characterization of the silver nanowire (AgNWs), as well as their application as antimicrobial and antivirus activities either with incorporation on the waterborne coating formulation or on their own.

Prepared AgNWs are characterized by different analytical instruments, such as ultraviolet-visible spectroscope, scanning electron microscope and X-ray diffraction spectrometer. All the paint formulation's physical and mechanical qualities were tested using American Society for Testing and Materials, a worldwide standard test procedure. The biological activities of the prepared AgNWs and the waterborne coating based on AgNWs were investigated. And, their effects on pathogenic bacteria, antioxidants, antiviral activity and cytotoxicity were also investigated.

The obtained results of the physical and mechanical characteristics of the paint formulation demonstrated the formulations' greatest performance, as well as giving good scrub resistance and film durability. In the antimicrobial activity, the paint did not have any activity against bacterial pathogen, whereas the AgNWs and AgNWs with paint have similar activity against bacterial pathogen with inhibition zone range from 10 to 14 mm. The development of antioxidant and cytotoxicity activity of the paint incorporated with AgNWs were also observed. The cytopathic effects of herpes simplex virus type 1 (HSV-1) were reduced in all three investigated modes of action when compared to the positive control group (HSV-1-infected cells), suggesting that these compounds have promising antiviral activity against a wide range of viruses, including DNA and RNA viruses.

The new waterborne coating based on nanoparticles has the potential to be promising in the manufacturing and development of paints, allowing them to function to prevent the spread of microbial infection, which is exactly what the world requires at this time.

This study aims to examine the risk-resistant role of environmental, social and governance (ESG) performance in the capital market, focusing on an organizational standpoint. Furthermore, it aims to offer management decision advice to companies seeking protection against stock market risks. Conclusions obtained through this research have the potential to enrich the economic consequences of ESG performance, provide practical implications for enhancing corporate ESG performance, improving corporate information quality and stabilizing capital market development.

Based on the data of Chinese A-share listed companies from 2009 to 2020, this study examines the risk-resistant function of ESG performance in the capital market. The impact of ESG performance on management behavior is analyzed from the perspective of organizational management and the three mechanisms of pre-event, during the event and post-event.

This paper demonstrates that companies that effectively implement ESG practices are capable of effectively mitigating risks associated with stock price crashes. Heterogeneity analysis reveals that the inhibitory effect of ESG performance on stock price crash risk is more pronounced in nonstate-owned enterprises and enterprises with higher levels of marketization. After controlling for issues such as endogeneity, the conclusions of this paper are still valid. The mechanism analysis indicates that ESG performance reduces the risk of stock price crash through three paths of organizational management: pre-event, during the event and post-event. That is, ESG performance plays the role of restraining managers’ opportunistic behavior, reducing information asymmetry and boosting investor sentiment.

This paper provides new insights into the relationship between ESG performance and stock price crash risk from an organizational management perspective. This study establishes three impact mechanisms (governance effect, information effect and insurance effect), offering a theoretical basis for strategic corporate decisions of risk management. Additionally, it comprehensively examines the contextual differences in the role of ESG performance, shedding light on the specific domains where ESG practices are influential. These findings offer valuable insights for promoting stable development in the capital market and fostering the healthy growth of the real economy.

Consumers often communicate with other consumers and perform impulse buying behavior on social commerce websites. Based on stimulus-organism-response framework and dual systems theory, the present study examines the effects of social interactions and self-control on consumers' impulse purchase.

An online survey consisting of 315 participants on social commerce websites was recruited to empirically examine the proposed research model. Partial Least Squares (PLS) was employed to analyze the research model.

Our main findings indicate that (1) source credibility, observational learning and review quality are important antecedents of perceived usefulness of online reviews, (2) source credibility, observational learning and perceived usefulness positively affect positive affect, which further results in urge to buy and impulse buying, (3) self-control weakens the effect of positive affect on urge to buy impulsively and also weakens the effect of urge to buy impulsively on impulse buying behavior.

The present study will bring more attention to social interactions in social networks in practice and encourage scholars to pay more attention to the reflective system in online impulse buying.