What fuzzy requests bring to frontline employees: An absorptive capacity theory perspective

This study uses absorptive capacity theory to construct a model to explain how frontline employees learn from customers’ fuzzy requests and then improve their performance. The research model is empirically examined using data collected from 364 hotel front desk service employees and further analyzed through a structural equation model. The study shows that request severity significantly influences need recognition, experience assimilation, concept transformation and inertia change. Request legitimacy has a positive effect on need recognition, experience assimilation, concept transformation, inertia change and cost assessment. Performance is positively affected by experience assimilation, concept transformation, inertia change and cost assessment. Some meditation and moderation effects are also identified. This study is the first to reveal the positive effects of fuzzy requests. These findings may help managers better handle and utilize fuzzy requests, with implications for organizational policy and for the support of frontline employees.     

Measuring financial performance through operating business efficiency in the global cruise industry: A fuzzy benchmarking study on the “big three”

Surprisingly, despite extensive research on the impacts of COVID-19 on the hospitality industry, no study develops financial performance benchmarks that reflect these effects. This research seeks to fill this gap by examining the financial performance of companies in the shipping sector, specifically the cruise sub-sector. The "three biggest players" in the sector are the focus of this analysis. Benchmarking is essential for both existing market participants and potential new entrants. Fuzzy methods are used in modeling situations that involve uncertainty. The findings indicate that the three companies' operations must be redesigned in order to produce income and pay off debts. Additionally, Carnival Corporation's resilience is evident in times of crisis, as it demonstrates the best growth prospects, remains dominant in terms of liquidity, presents the highest return on assets, and borrows less than the other two companies.     

流量可调燃气发生器压力闭环模糊控制算法

为了改进流量可调燃气发生器的调节精度,引入燃气发生器压力闭环控制,针对流量可调燃气发生器压力闭环控制特点,在压力闭环中引入了模糊积分控制,此控制算法响应速度快,超调量较小,不同工况及长时间工作下系统仍然有较好的动态特性。      

Strategies to develop a sustainable and resilient vaccine supply chain in the context of a developing economy

Developing a vaccine supply chain (VSC) is an intricate process due to product perishability issues and cross-border supply complexities. On top of that, developing a pandemic-driven VSC is more challenging due to having significant operational, infrastructural, and policy-related disruptions. From the perspective of a developing economy such as Bangladesh, handling the global COVID-19 pandemic through the proper establishment of a VSC has been disrupted by a multitude of organizational, economic, and policy barriers. This has hindered the process of establishing a resilient VSC let alone ensuring the sustainability of the supply chain (SC). Therefore, this study strives to identify the key VSC strategies and their interrelationships under four groups: Intra-organizational, Inter-organizational, Legislative, and Environmental, based on previous literature and the expert opinions of industrial practitioners and policymakers. 20 strategies are ranked, and their causal relationships are discussed using the fuzzy DEMATEL method. This study utilizes the fuzzy set theory to deal with the vagueness of human beings' perceptions, and the DEMATEL method to form a structural model to find out the cause (influencing and independent) and effect (influenced and dependent) relationships among different strategies. The outcome of this study shows that ‘developing local production facilities for vaccines’, ‘creating extensive governmental policy to ensure efficient distribution of vaccines’, ‘ensuring sustainable investment in vaccine manufacturing and distribution’, ‘integrating advanced data analytics for robust and resilient demand prediction’ and ‘promoting public-private-people partnership for sustainable investment’ are the most prominent strategies. The findings provide stakeholders and policymakers with a practical framework for developing a sustainable VSC prepared for any virus outbreak, such as COVID-19, while also achieving the Sustainable Development Goals (SDGs).     

Bankruptcy prediction using fuzzy convolutional neural networks

We propose a combined method for bankruptcy prediction based on fuzzy set qualitative comparative analysis (fsQCA) and convolutional neural networks (CNN). Currently, CNNs are being applied to various fields, and in some areas are providing higher performance than traditional models. In our proposed method, a CNN uses calibrated variables from fuzzy sets to improve performance accuracy. In addition, there are no published studies on the effect of feature selection at the input level of convolutional neural networks. Therefore, this study compares four well-known feature selection methods used in financial distress prediction, (t-test, stepdisc discriminant analysis, stepwise logistic regression and partial least square discriminant analysis) to investigate their effect on classification performance. The results show that fuzzy convolutional neural networks (FCNN) lead to better performance than when using traditional methods.     

Exploring the circular economy paradigm: A natural resource-based view on supplier selection criteria

The paradigm of circular economy and the transfer of its principles to supply chain management has recently received much attention from researchers and practitioners. Limited natural resources, governmental legislation, and social responsibility for environmental preservation are the main factors for the development of circular supply chains. Especially in the automotive industry, critical materials are used to produce electric vehicles, reinforcing the importance of circularity for the industry. As the first stage of the supply chain, suppliers have a considerable influence on creating self-sufficient production systems, and in the automotive sector, a low level of vertical integration is important. Therefore, selecting suppliers is an enabler for circular supply chains, but this decision is a complex process due to multiple, partly conflicting criteria. To contribute to the knowledge in this research area, this study applies a fuzzy decision-making trial and evaluation laboratory approach for supplier selection in a circular supply chain in a case study of electric vehicles. First, a set of criteria for supplier selection in circular supply chains was identified based on a literature review and was systematically categorized based on the natural resource-based view. Afterward, the criteria were assessed and refined by experts with a strong procurement background in the automotive industry. Second, the experts were interviewed for value collection, and finally, the mutual influence of the criteria was determined. The results show that the most important criteria for circular supplier selection in electric vehicle supply chains are environmental standards, environmental-related certifications, resource consumption, and waste generation. Regarding the natural resource-based view, short-term focused criteria addressing the capability of pollution prevention have a higher importance compared to long-term criteria that drive sustainable development. In addition, this study classifies the criteria into the categories of causes and effects, discusses the prominence of the criteria, and presents a strategic map showing the mutual influences of the criteria. The results contribute to the theoretical and practical discussion on circular supply chains by identifying the key criteria for circular supplier selection and providing decision-making support for procurement managers.     

Social commerce advertising avoidance and shopping cart abandonment: A fs/QCA analysis of German consumers

In recent years, due to the extensive use of social media advertisement and the development of social commerce tools, scholars and practitioners pay more attention to interaction with the digital channel, shopping intention, satisfaction, and retention. The objective of this study is to understand the shopping cart abandonment behavior as an outcome condition of ad avoidance on social media platforms. This research takes into consideration of cognitive, affective, and behavioral ad avoidance; alongside its antecedent conditions of perceived goal impediment, perceived add clutter, and prior negative experiences on social media. The research is conducted in Germany and Fuzzy sets/Qualitative Comparative Analysis (fs/QCA) is applied to understand the outcome condition of shopping cart abandonment. The findings imply that ad avoidance can create a learning mechanism for consumers about shopping cart abandonment of a brand or product if interrupted by a futile message or content. Theoretical and practical implications are discussed.     

基于GIS的黄土高原丘陵沟壑区的耕地地力评价研究

应用GIS技术,结合层次分析法和模糊数学综合评价方法等现代统计分析技术,在耕地地力调查的基础上,从土壤立地条件、土壤理化性状、剖面构型、气候、土壤管理等5个方面选取14项指标,对麦积区耕地地力进行综合评价,研究其影响该区耕地地力的主要指标。结果表明,麦积区耕地地力等级可分为5等,1～5等地,分别占总耕地面积的2.74%、9.18%、56.84%、30.45%、1.40%,其分别对应国家6～10等地;通过层次分析组合权重分析,灌溉能力、年降水量、有效磷、坡向、有机质为影响该区耕地地力最重要的指标。