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The airline industry has been significantly hit by the occurrence of the new coronavirus SARS-CoV-2, facing one of its worst crises in history. In this context, the present paper analyses one of the well-known boarding methods used in practice by the airlines before and during the coronavirus outbreak, namely back-to-front and suggests which variations of this method to use when three passenger boarding groups are considered and a jet bridge connects the airport terminal with the airplane.

Based on the importance accorded by the airlines to operational performance, health risks, and passengers' comfort, the variations in three passenger groups back-to-front boarding are divided into three clusters using the grey clustering approach offered by the grey systems theory.

Having the clusters based on the selected metrics and considering the social distance among the passengers, airlines can better understand how the variations in back-to-front perform in the new conditions imposed by the novel coronavirus and choose the boarding approach that better fits its policy and goals.

The paper combines the advantages offered by grey clustering and agent-based modelling for offering to determine which are the best configurations that offer a reduced boarding time, while accounting for reduced passengers' health risk, measured through three indicators: aisle risk, seat risk and type-3 seat interferences and for an increased comfort for the passengers manifested through a continuous walking flow while boarding.

The cumulative sum (Cusum) operator, also referred to as accumulating generation operator, is the fundamental of grey system models and proves to be successful in various real-world applications. This paper aims to uncover the advantages of the Cusum operator from a parameter estimation perspective, i.e. comparing integral matching with classical gradient matching.

Grey system models are represented as a state space form to investigate the effect of measurement errors on estimation performance; subsequently, gradient matching and integral matching are respectively formulated to estimate parameters from noisy observations and, then, their quantitative relationships are established by using matrix computation tricks.

Extensive simulations, which are conducted on both linear and non-linear models under different sample size and noise level combinations, show that integral matching is superior to gradient matching, and, also the former is less sensitive to measurement error.

This paper explains why the Cusum operator is widely utilized in grey system models, thereby further solidifying the mathematical fundamentals of grey system models.

With the continuous increase in Web services, efficient identification of Web services that meet developers’ needs and understanding their relationships remains a challenge. Previous research has improved recommendation effectiveness by using correlations between Web services through graph neural networks (GNNs), while it has not fully leveraged service descriptions, limiting the depth and diversity of learning. To this end, a Web services recommendation method called LLMSARec, based on Large Language Model and semantic alignment, is proposed. This study aims to extract potential semantic information from services and learn deeper relationships between services.

This method consists of two core modules: profile generation and maximizing mutual information. The profile generation module uses LLM to analyze the descriptions of services, infer and construct service profiles. Concurrently, it uses LLM as text encoders to encode inferred service profiles for enhanced service representation learning. The maximizing mutual information model aims to align the semantic features of the services text inferred by LLM with structural semantic features of the services captured by GNNs, thus achieving a more comprehensive representation of services. The aligned representation serves as an input for the model to identify services with superior matching accuracy, thereby enhancing the service recommendation capability.

Experimental comparisons and analyses were conducted on the Programmable Web platform data set, and the results demonstrated that the effectiveness of Web service recommendations can be significantly improved by using LLMSARec.

In this study, the authors propose a Web service recommendation approach based on Large Language Model and semantic alignment. By extracting latent semantic information from services and effectively aligning semantic features with structural features, new representations can be generated to significantly enhance recommendation accuracy.

Recent accounting literature and Agency theory have predicted that corporate governance assists the convergence of interests between shareholders and managers, and thus enhances the quality of financial reporting. This chapter discusses some of the empirical studies on corporate governance in Saudi Arabia; it also elaborates on the corporate governance regulations introduced by Capital Market Authority in Saudi Arabia. Studies cover various subjects that interact with corporate governance, such as earnings management, corporate social responsibility disclosure, ownership structure, environmental disclosure and voluntary disclosure in annual reports of Saudi's listed firms. It also discusses the effectiveness and determinants of corporate governance structures, such as the board of directors, audit committee and other sub-committees. Results were generally in line with previous research from the developed countries, but sometimes there are contradictions, and these results have been discussed and explained, and implications to regulators and investors are drawn where possible.

When integrating smart elements offered by emergent technologies, libraries are facing the challenges of technological renovation and maintaining their operation using emerging technology. Given the importance of smart library, new technologies are needed in building new libraries or renovation of existing libraries. The purpose of this paper is to propose a risk warning system for library construction or renovation in the aspect of risk management.

The proposed Internet of Things (IoT)-based system consists of sensors that automatically monitor the status of materials, equipment and construction activities in real time. AI techniques including case-based reasoning and fuzzy sets are applied.

The proposed system can easily track material flow and visualize construction processes. The experiment shows that the proposed system can effectively detect, monitor and manage risks in construction projects including library construction.

Compared with existing risk warning systems, the proposed IoT-based system requires less data for making dynamic predictions. The proposed system can be applied to new builds and renovation of libraries.

This paper aims to propose a novel multiple transient modeling scheme for the 12-pulse phase-shifting reactor (PSR) rectifier to enhance the efficiency of full-cycle design evaluation.

The detailed time-domain method is adopted to model the rectifier at the behavioral layer. The diode bridges/transformer model at the architecture layer is established by using the switch function and Park transformation. The frequency domain model at the functional layer is derived with the time-varying Fourier decomposition and frequency-shifting. At the component layer, the magneto-thermal characteristics of the rectifier are analyzed with field-circuit and magnetic-thermal coupling methods. A computer-aided design program integrating multiple modeling is also developed for industrial product design.

The function layer modeling is preferred in the initial design stage, making up for the lack of modeling accuracy at the architectural layer and the lack of modeling rapidity at the behavioral layer. The component modeling is irreplaceable for the detailed evaluation in the latter design stage. The multiple modeling scheme based on the four-layer modeling helps the designers achieve high-quality products with a short development cycle.

The singular transient modeling cannot cover the needs of different stages in the full-cycle design evaluation. This paper fills this gap with a novel multiple modeling scheme. Meanwhile, the proposed multiple modeling scheme and developed computer-aided design program provide a great convenience for full cycle design evaluation of the 12-pulse PSR rectifier.

The purpose of this study is to investigate whether the convergence of linguistic features between physicians and patients with chronic diseases facilitates the effectiveness of physician–patient communication in online health communities (OHCs). Drawing on communication accommodation theory (CAT), the authors develop a research model that illustrates how the convergence of semantic features (language concreteness and emotional intensity) and stylistic features (language style) influence patient satisfaction and compliance. The model also incorporates the moderating effects of the physician's social status and the patients' complications.

The data, collected from a prominent online health platform in China, include 15,448 consultation records over five years. The logistic regression is leveraged to test the hypotheses.

The findings reveal that convergent semantic features, such as language concreteness and emotional intensity, along with stylistic features like language style, enhance patient satisfaction, which in turn leads to increased compliance. Additionally, the physician’s social status strengthens the effect of convergent emotional intensity but weakens the effect of convergent language concreteness. The physician’s social status has no significant impact on the link between convergent language style and satisfaction. Patients' complications weaken the effect of satisfaction on their compliance.

This study contributes to the CAT and OHC literature by enhancing the understanding of the role of linguistic convergence in the effectiveness of online physician–patient communication and provides managerial implications for physicians on how to accommodate their communicative styles toward chronic patients to improve patient satisfaction and compliance.

The purpose of this paper is to investigate the interaction effect between customer satisfaction and monetary incentives on online reviews and test the moderating effect of personal characteristics, filling the research gap in online review behavior from the senders.

Using a project role-playing technique that is widely applied in the marketing field, the authors conducted two experimental studies in a laboratory setting with student subjects and collected 390 and 362 acceptable samples for analysis in Studies 1 and 2, respectively.

This research confirms the positive effects of satisfaction and incentives on review scores and tests the interaction effect between satisfaction and incentives on review scores with the moderating effects of moral judgment and sensitivity of promotion. Incentives could strengthen customers’ review scores except under small incentives situation where dissatisfied customers decrease scores instead. Additionally, the moderating effects of moral judgment and sensitivity of promotion are more significant in the case of dissatisfaction.

As this study focuses exclusively on a single service context and uses student samples, limitations persist regarding the generalizability of the results.

The research provides new insights for marketers on designing effective incentive programs, as well as how to better balance costs and benefits in promotion strategies.

This is one of the first studies to explore the interaction effect between satisfaction and incentives on online reviews considering the moderating effects of moral judgment and sensitivity of promotion. As a result, a new model is forwarded.

The outdoor event market was devastated during the COVID-19 pandemic because of social distancing measures. Therefore, this study aimed to explore stereotyped tendencies and behavioral intentions associated with the prosocial and sustainable practices of outdoor event participants to assess shifts in industry paradigms.

This study adopted structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to relatively examine sequential and combined effects of cognitive (knowledge of COVID-19, awareness of consequences, ascribed responsibility and perceived threat of COVID-19), affective (positive and negative anticipated emotions) and normative drivers (social and moral norms) on intention to practice social distancing requirements. The impact of cultural differences was further explored by comparing attendees from China and USA.

The SEM results showed that most cognitive drivers significantly affected affective drivers and normative drivers, leading to the intention to practice social distancing requirements. In addition, China and the USA showed significant differences on six paths including the path from moral norm to intention to practice social distancing requirements. Further, fsQCA results revealed the important combination of the factors that affects social distancing intention.

This study provides meaningful theoretical and practical implications for outdoor events scholars and managers. The research suggests a changing direction in event studies and shares ideas on how to manage and make outdoor events a new success after the pandemic.

This is the first study to adopt a mixed method of SEM and fsQCA attempt to explore the driving forces of outdoor participants’ pro-social behavior from cognitive, affective and normative perspectives.