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Increased fraudulent practices have heightened the need for innovation in anti-fraud programs, necessitating the development of analytics techniques for detecting and preventing fraud. The subject of fraud analytics will continue to expand in the future for public-sector organizations; therefore, this research examined the progress of fraud analytics in public-sector transactions and offers suggestions for its future development.

This study systematically reviewed research on fraud analytics development in public-sector transactions. The review was conducted from June 2021 to June 2023 by identifying research objectives and questions, performing literature quality assessment and extraction, data synthesis and research reporting. The research mainly identified 43 relevant articles that were used as references.

This research examined fraud analytics development related to public-sector financial transactions. The results revealed that fraud analytics expansion has not spread equally, as most programs have been implemented by governments and healthcare organizations in developed countries. This research also exposed that the analytics optimization in fraud prevention is higher than for fraud detection. Such analytics help organizations detect fraud, improve business effectiveness and efficiency, and refine administrative systems and work standards.

This research offers comprehensive insights for researchers and public-sector professionals regarding current fraud analytics development in public-sector financial transactions and future trends.

This study presents the first systematic literature review to investigate the development of fraud analytics in public-sector transactions. The findings can aid scholars' and practitioners' future fraud analytics development.

The purpose of this study is to prepare a robust superhydrophobic coating on concrete substrate with remarkable chemical and mechanical durability through “all-covalent” strategy.

Amino-modified silica nano/micro-particles were prepared through two synthetic steps. “All-covalent” strategy was introduced to prepare a robust superhydrophobic coating on concrete surface via a “all-in-one” dispersion and a simple spraying method. The successful construction of the products was confirmed by Fourier transform infrared spectroscopy, water contact angles (WCA), X-ray photoelectron spectroscopy (XPS) and scanning electron microscope (SEM). The concrete protective properties were verified by solution immersion test, pull-off test and rapid chloride migration coefficient test. The mechanical durability was tested by falling sand impact.

Hierarchical structures combined with the low-surface-energy segments lead to typically superhydrophobic coating with a WCA of 156° and a sliding angle of 1.3°. The superhydrophobic coating prepared through “all-covalent” strategy not only improves chemical and mechanical durability but also achieves higher corrosion and wear resistance than the comparison sample prepared by physically blending strategy. More importantly, the robust superhydrophobic coating showed excellent adhesion and protective performance of concrete engineerings.

This new “all-covalent” superhydrophobic coating could be applied as a concrete protective layer with properties of self-cleaning, anti-graffiti, etc.

Introduction of both silica nanoparticles and silica microparticles to prepare a robust superhydrophobic coating on concrete surface through “all-covalent” strategy has not been systematically studied previously.

The purpose of this paper is to add expanded perlite (EP) immobilized microorganisms that replace part of the standard sand in mortar to improve the self-healing ability of mortar cracks and reduce the water absorption of mortar after healing.

Bacillus pseudofirmus spores were immobilized with EP particles as self-healing agents. The effects of adding self-healing agents on the compressive strength of mortar specimens were observed. The ability of mortar specimens to heal cracks was evaluated using crack microscopic observation and water absorption experiments. The filler at the cracks was microscopically analyzed by scanning electron microscope and X-ray diffraction experiments.

First, the internal curing effect of EP promotes the hydration of cement in mortar, which generates more amount and denser crystal structure of Ca(OH)2 at mortar cracks and improves the self-healing ability of mortar. Second, the self-healing ability of mortar improves with the increase of self-healing agent admixture. Adding a self-healing agent of high admixture makes the planar undulation of calcite crystal accumulation at mortar cracks more significant. Finally, the initial crack widths that can be completely healed by adding EP and self-healing agents to the mortar are 200 µm and 600 µm, respectively.

The innovation points of this study are as follows. (1) The mechanism of the internal curing effect of EP particles on the self-healing ability of mortar cracks was revealed by crack microscopic observation tests and microscopic experiments. (2) The effect of different self-healing agent amounts on the self-healing ability of mortar cracks has been studied. (3) The effects of EP particles and self-healing agents on healing different initial widths were elucidated by crack microscopic observation tests.

A cross-domain intelligent software-defined network (SDN) routing method based on a proposed multiagent deep reinforcement learning (MDRL) method is developed.

First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between the root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to acquire global network state information in real time. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a network traffic state prediction mechanism is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time.

Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and open shortest path first (OSPF) routing methods.

Message transmission and message synchronization for multicontroller interdomain routing in SDN have long adaptation times and slow convergence speeds, coupled with the shortcomings of traditional interdomain routing methods, such as cumbersome configuration and inflexible acquisition of network state information. These drawbacks make it difficult to obtain global state information about the network, and the optimal routing decision cannot be made in real time, affecting network performance. This paper proposes a cross-domain intelligent SDN routing method based on a proposed MDRL method. First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to realize the real-time acquisition of global network state information. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a prediction mechanism for the network traffic state is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time. Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and OSPF routing methods.

Although recent literature has examined diverse measures adopted by SMEs to navigate the COVID-19 turbulence, there is a shortage of evidence on how crisis-time strategy creation behaviour and digitalization activities increase (1) sales and (2) cash flow. Thus, predicated on a novel strategy creation perspective, this inquiry aims to investigate the crisis behaviour, sales and cash flow performance of 528 SMEs in Morocco.

Novel links between (1) aggregate wage cuts, (2) variable operating hours, (3) deferred payment to suppliers, (4) deferred payment to tax authorities and (5) sales performance are developed and tested. A further link between sales performance and cash flow is also examined and the analysis is conducted using a non-linear structural equation modelling technique.

While there is a significant association between strategy creation behaviours and sales performance, only variable operating hours have a positive effect. Also, sales performance increases cash flow and this relationship is substantially strengthened by e-commerce digitalization and innovation.

Theoretically, to the best of the authors’ knowledge, this is one of the first inquiries to espouse the strategy creation view to explain SMEs' crisis-time behaviour and digitalization. For practical purposes, to supplement Moroccan SMEs' propensity to seek tax deferrals, it is argued that debt and equity support measures are also needed to boost sales performance and cash flow.

This research aims to explore how consumers' local brand choices differ between air-polluted days and clean days, and why the difference occurs.

Two studies were conducted. Study 1 used the longitudinal consumption data of various yogurt brands and daily air quality indexes in 2014 and 2015. Study 2 conducted three rounds of surveys on a clean day, a general air-polluted day and a seriously air-polluted day.

The findings indicate that consumers show less tendency of attribution and compensatory consumption during air-polluted days, which in turn decrease their willingness to choose local brands.

Implications are provided for future research and marketing practice, especially for local companies that rely heavily on local consumers, and retailers in heavy air-polluted areas.

This paper is the first to illustrate the influence of air pollution on consumers' local brand choices, and it extends current understanding on air pollution and consumer choices by discovering psychological process underneath to explain the effect.

Few studies illustrate how contextual effects (e.g. assimilation and contrast) in pay-per-click ad design may impact consumers' attitudes and purchase intention. To fill this research gap, the authors provide theoretical predictions and empirical evidence on how ad design may prompt an assimilation and/or a contrast effect that may influence consumers' attitudes toward the ad and the brand and purchase intention. They also investigate whether the impact of contextual effects on consumers' decisions depends on the level of vividness in the ad.

A 2 (vividness: dynamic motion vs. static page) × 2 (information design: assimilation vs. contrast) × 2 (aesthetic design: assimilation vs. contrast) between-subjects experimental design is used to examine the effects of vividness, information design and aesthetic design. Conditional process analysis is used to assess the mediating role of attitudes toward the ad and the brand in the relationship between contextual effects and purchase intention.

For dynamic ads (i.e. high vividness) but not for static ads (i.e. low vividness), combined information contrast and aesthetic contrast designs generate a more favorable attitude toward the brand and a higher purchase intention than do combined information assimilation and aesthetic assimilation designs. Notably, combined information contrast and aesthetic contrast designs have the strongest effects than any other combination of assimilation and contrast designs of information and aesthetics. Attitudes toward the ad and the brand are significant mediators between contextual factors and intention to purchase.

The study examines the effectiveness of online ads from a new theoretical angle based on the attributes of pay-per-click ads.

The results suggest that when advertisers decide to use dynamic ads, they should adopt a contrast design for both the ad information and its aesthetics.

This study fills a research gap in the contextual effects literature, including providing evidence of an underlying process in the relationship between certain contextual effects and purchase intent. It also extends previous findings of assimilation/contrast in information design to aesthetics design and advances the literature on vividness by examining a moderation effect of vividness.

With the rise in wine consumption, accurate wine price forecasts have significantly impacted restaurant and hotel purchasing decisions and inventory management. This study aims to develop a precise and effective wine price point and interval forecasting model.

The proposed forecast model uses an improved hybrid kernel extreme learning machine with an attention mechanism and a multi-objective swarm intelligent optimization algorithm to produce more accurate price estimates. To the best of the authors’ knowledge, this is the first attempt at applying artificial intelligence techniques to improve wine price prediction. Additionally, an effective method for predicting price intervals was constructed by leveraging the characteristics of the error distribution. This approach facilitates quantifying the uncertainty of wine price fluctuations, thus rendering decision-making by relevant practitioners more reliable and controllable.

The empirical findings indicated that the proposed forecast model provides accurate wine price predictions and reliable uncertainty analysis results. Compared with the benchmark models, the proposed model exhibited superiority in both one-step- and multi-step-ahead forecasts. Meanwhile, the model provides new evidence from artificial intelligence to explain wine prices and understand their driving factors.

This study is a pioneering attempt to evaluate the applicability and effectiveness of advanced artificial intelligence techniques in wine price forecasts. The proposed forecast model not only provides useful options for wine price forecasting but also introduces an innovative addition to existing forecasting research methods and literature.

Risk factors related delay hinder the schedule performance of most construction projects in the world. It is a critical challenge to realize the advantages of prefabricated construction projects (PCPs) under the negative effect of schedule delay. This paper aims to propose an exhaustive list of risk factors impeding the progress of PCPs and evaluate the collected risk factors based on the cause–effect relations. The ultimate goal is to improve the understanding of the complex relations among various risk factors related delay in PCPs, and also offer managers a reference on aspect of schedule risk management.

This paper proposes a hybrid method of GT–DEMATEL–ISM, that is combing grounded theory, DEMATEL (decision-making trial and evaluation laboratory) and ISM (Interpretative Structural Modeling), to collect, evaluate and structure risk factors related delay for PCPs. The research procedure of this methodology is divided into three stages systematically involving qualitative and quantitative analysis. In the first stage, GT is utilized to implement qualitative analysis to collect the risk factors leading to schedule delay in PCPs. While, the quantitative analysis is to analyze and evaluate the collected risk factors based on the cause–effect relations in the next two stages evaluation by the DEMATEL focuses on quantifying the priority and intensity of the relations between factors. Additionally, ISM is employed to construct the hierarchical structure and graphically represent the pairwise relations between factors.

The outcome of qualitative investigation by grounded theory proposes a theoretical framework of risk factors related delay for PCPs. The framework contains three levels of category, namely, core category, main category and initial category and provides a list of risk factors related delay. Following this finding, evaluation results by the DEMATEL classify factors into cause and effect groups and determine 11 critical delay risk factors. Meanwhile, the findings show that risks referring to organizational management issue foremost impact the progress of PCPs. Furthermore, a systemic multilevel hierarchical structure model is visually constructed by ISM to present the pairwise linkages of critical factors. The model provides the risk transmission chains to map the spread path of delay impact in the system.

The contribution of the study involves twofold issues. Methodologically, this research proposes a hybrid method GT–DEMATEL–ISM used to identify and analyze factors for a complex system. It is also applicable to other fields facing similar problems that require collecting, evaluating and structuring certain elements as a whole in a comprehensive perspective. The theoretical contribution is to fill the relevant research gap of the existing body of knowledge. To the best knowledge of the authors, this paper is the first attempt to integrate qualitative and quantitative research for risk analysis related delay and take the insight into the whole process of PCPs covering off-site manufacture and on-site construction. Furthermore, the analysis of findings provided both a micro view focusing on individual risk factor and a managerial view from a systematic level. The findings also contribute the effective information to improve the risk management related schedule delay in PCPs.

The business world today is witnessing ever-growing disruption. This study highlights corporate social responsibility (CSR) as an effective strategy for firms in disrupted industries to consider in order to differentiate themselves and to increase their chance of survival facing disruption.

In this study, the authors test the hypotheses using a multilevel modeling (MLM) design to capture the group and intergroup effects at the industry level and at the firm level. The empirical analysis is based on a panel sample of 1,193 firms over the 10-year period from 2010 to 2019.

The empirical analysis indicates that CSR has a positive impact on corporate financial stability and the effect is especially significant for firms in disrupted industries. Further investigation suggests that this positive effect largely runs through traits of the social pillar, such as human rights, employee relations, customer protection, product responsibility and community impact. The results are robust after controlling for other firm-specific characteristics and after addressing endogeneity concerns.

This study examines whether, and through which channel, CSR helps enhance corporate financial stability and mitigate bankruptcy risk in disrupted industries. To the best of the authors' knowledge, this study is the first attempt to explore the use of CSR as an effective strategic response to disruption. Further analysis indicates that the social capital built through CSR plays an important role in helping enhance corporate financial stability.