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Every day, small and big incidents happen all over the world, and given the human, financial and spiritual damage they cause, proper planning should be sought to deal with them so they can be appropriately managed in times of crisis. This study aims to examine humanitarian supply chain models.

A new model is developed to pursue the necessary relations in an optimal way that will minimize human, financial and moral losses. In this developed model, in order to optimize the problem and minimize the amount of human and financial losses, the following subjects have been applied: magnitude of the areas in which an accident may occur as obtained by multiple attribute decision-making methods, the distances between relief centers, the number of available rescuers, the number of rescuers required and the risk level of each patient which is determined using previous data and machine learning (ML) algorithms.

For this purpose, a case study in the east of Tehran has been conducted. According to the results obtained from the algorithms, problem modeling and case study, the accuracy of the proposed model is evaluated very well.

Obtaining each injured person's priority using ML techniques and each area's importance or risk level, besides developing a bi-objective mathematical model and using multiple attribute decision-making methods, make this study unique among very few studies that concern ML in the humanitarian supply chain. Moreover, the findings validate the results and the model's functionality very well.

Data-driven marketing is replacing conventional marketing strategies. The modern marketing strategy is based on insights derived from customer behavior information gathered from their facial expressions and neuro-signals. This study explores the potential for face recognition and neuro-marketing in modern-day marketing.

The study conducts an in-depth examination of the extant literature on neuro-marketing and facial recognition marketing. The articles for review are downloaded from the Scopus database, and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) is then used to screen and choose the relevant papers. The systematic literature review method is applied to conduct the study.

An extensive review of the literature reveals that the domains of neuro-marketing and face recognition marketing remain understudied. The authors’ review of selected papers delivers five neuro-marketing and facial recognition marketing themes that are essential to modern marketing concepts.

Neuro-marketing and facial recognition marketing are artificial intelligence (AI)-enabled marketing techniques that assist in gaining cognitive insights into human behavior. The findings would be of use to managers in designing marketing strategies to enhance their marketing approach and boost conversion rates.

The uniqueness of this study lies in that it provides an updated review on neuro-marketing and face recognition marketing.

This paper aims to solve the major assessment problem in matching the satisfaction of psychological gratification and mission accomplishment pertaining to volunteers with the disaster rescue and recovery tasks.

An extended belief rule-based (EBRB) method is applied with the method's input and output parameters classified based on expert knowledge and data from literature. These parameters include volunteer self-satisfaction, experience, peer-recognition, and cooperation. First, the model parameters are set; then, the parameters are optimized through data envelopment analysis (DEA) and differential evolution (DE) algorithm. Finally, a numerical mountain rescue example and comparative analysis between with-DEA and without-DEA are presented to demonstrate the efficiency of the proposed method. The proposed model is suitable for a two-way matching evaluation between rescue tasks and volunteers.

Disasters are unexpected events in which emergency rescue is crucial to human survival. When a disaster occurs, volunteers provide crucial assistance to official rescue teams. This paper finds that decision-makers have a better understanding of two-sided match objects through bilateral feedback over time. With the changing of the matching preference information between rescue tasks and volunteers, the satisfaction of volunteer's psychological gratification and mission accomplishment are also constantly changing. Therefore, considering matching preference information and satisfaction at two-sided match objects simultaneously is necessary to get reasonable target values of matching results for rescue tasks and volunteers.

Based on the authors' novel EBRB method, a matching assessment model is constructed, with two-sided matching of volunteers to rescue tasks. This method will provide matching suggestions in the field of emergency dispatch and contribute to the assessment of emergency plans around the world.

By introducing the mass customization service mode into the cloud logistics environment, this paper studies the joint optimization of service provider selection and customer order decoupling point (CODP) positioning based on the mass customization service mode to provide customers with more diversified and personalized service content with lower total logistics service cost.

This paper addresses the general process of service composition optimization based on the mass customization mode in a cloud logistics service environment and constructs a joint decision model for service provider selection and CODP positioning. In the model, the two objective functions of minimum service cost and most satisfactory delivery time are considered, and the Pareto optimal solution of the model is obtained via the NSGA-II algorithm. Then, a numerical case is used to verify the superiority of the service composition scheme based on the mass customization mode over the general scheme and to verify the significant impact of the scale effect coefficient on the optimal CODP location.

(1) Under the cloud logistics mode, the implementation of the logistics service mode based on mass customization can not only reduce the total cost of logistics services by means of the scale effect of massive orders on the cloud platform but also make more efficient use of a large number of logistics service providers gathered on the cloud platform to provide customers with more customized and diversified service content. (2) The scale effect coefficient directly affects the total cost of logistics services and significantly affects the location of the CODP. Therefore, before implementing the mass customization logistics service mode, the most reasonable clustering of orders on the cloud logistics platform is very important for the follow-up service combination.

The originality of this paper includes two aspects. One is to introduce the mass customization mode in the cloud logistics service environment for the first time and summarize the operation process of implementing the mass customization mode in the cloud logistics environment. Second, in order to solve the joint decision optimization model of provider selection and CODP positioning, this paper designs a method for solving a mixed-integer nonlinear programming model using a multi-layer coding genetic algorithm.

The purpose of this paper is to design and manufacture an intelligent 3D printed sensor to monitor the re-occurrence of diaphragmatic hernia (DH; after surgery) in bovines as an Internet of Things (IOT)-based solution.

The approach used in this study is based on a bibliographic analysis for the re-occurrence of DH in the bovine after surgery. Using SolidWorks and ANSYS, the computer-aided design model of the implant was 3D printed based on literature and discussions on surgical techniques with a veterinarian. To ensure the error-proof design, load test and strain–stress rate analyses with boundary distortion have been carried out for the implant sub-assembly.

An innovative IOT-based additive manufacturing solution has been presented for the construction of a mesh-type sensor (for the health monitoring of bovine after surgery).

An innovative mesh-type sensor has been fabricated by integration of metal and polymer 3D printing (comprising 17–4 precipitate hardened stainless steel and polyvinylidene fluoride-hydroxyapatite-chitosan) without sacrificing strength and specific absorption ratio value.

This study aims to examine the antecedents of career decision self-efficacy (CDSE) and provide a comprehensive understanding of the factors that influence this critical construct in career development and decision-making.

This study employed a meta-analysis of 43 independent studies, comprising 90 correlations and 17,143 participants. The Hunter-Schmidt method meta-analysis was used to analyze the data and identify the factors associated with CDSE. Random-effect meta-regression analysis was applied to detect the potential moderators.

The study found that CDSE is positively associated with social support (ρ = 0.41), age (ρ = 0.05), agreeableness (ρ = 0.23), conscientiousness (ρ = 0.48), emotional intelligence (ρ = 0.48), extraversion (ρ = 0.41), openness (ρ = 0.35) and proactive personality (ρ = 0.68), while negatively related to neuroticism (ρ = −0.33). Furthermore, the results indicate that sample gender (%female) and mean age partially moderate the relationship between CDSE and age, core-self evaluations and neuroticism.

In this study, the authors have contributed significantly to the existing research on CDSE antecedents by conducting a thorough analysis of the various factors associated with this critical construct. The findings offer an accurate understanding of the factors that influence CDSE, and this paper's moderation analysis sheds light on the boundary conditions in the CDSE literature. Moreover, this research has practical implications for practitioners such as teachers, parents and career counselors. By leveraging the insights gained from this study, practitioners can provide more effective career support and intervention to young people, which can help increase their CDSE and improve their overall career development and well-being.

From the signaling theory perspective, the current study explores various drivers of brand transparency and its effect on users' interactions with food delivery apps.

First, a set of precursors of brand transparency of food delivery apps from focus group discussions was identified. Next, an integrated model tests the impact of brand transparency, perceived risk and brand trust on users' ordering frequency. Data collected from 522 users were analyzed using the partial least squares structural equation modeling method.

The outcomes showed the effectiveness of brand communications as the strongest indicator of brand transparency. Moreover, brand transparency favorably influences users' brand trust and ordering intention and negatively influences perceived risk. Hygiene rating attenuates the adverse effects of perceived risk.

The current study is a pioneering attempt that offers ways for online food delivery providers to build brand transparency, lessen users' risk perceptions and foster greater use of apps in the post-pandemic scenario.

Due to the influence of complex and intersecting factors, self-governed public open spaces (POSs) (managed by local communities) are subject to collective action dilemmas such as tragedy of the commons (overexploitation), free-riding, underinvestment and mismanagement. This review paper adopts a multi-dimensional and multi-tier social-ecological system (SES) framework proposed by McGinnis and Ostrom, drawing on collective action theory to explore the key institutional-social-ecological factors that impact POS self-governance.

In this paper, Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) was utilized to systematically screen and review the relevant literature for the period from 2000 to 2023 in three databases: Web of Science, Scopus and Google Scholar. A total of 57 papers were chosen for in-depth analysis.

The literature review identified and categorized several variables associated with the self-organizing system of POS; consequently, an SES-based POS management framework was developed for the first time, consisting of 114 institutional-social-ecological sub-variables from different dimensions and three levels. Compared to ecological factors, among others, governance organizations, property-rights systems, socioeconomic attributes and actors' knowledge of SES have been commonly and primarily studied.

There is still room for the refinement of the conceptual SES-based POS collective action framework over the time (by adding in new factors), and indefinitely empirical research validating those identified factors is also worth to be undertaken, particularly testing how SES factors and interaction variables affect the POS quality (collective action).

The findings of this study can provide local policy insights and POS management strategies based on the identification of specific SES factors for relevant managers. Moreover, this research makes significant theoretical contributions to the integration of the SES framework and collective action theory with POS governance studies.

Channel coordination has become an essential part of researching hotel supply chain management practices. This paper develops an improved channel coordination approach to coordinate the profit distribution between hotels and online travel agencies (OTAs) achieved through an introduction of advertising fees. This direction further improves the decentralization of cooperation and achieves Pareto improvement to achieve mutual profitability.

The methodology used in this study involves Stackelberg game theory employed for the decision-making and analysis of both the hotel and OTA. The OTA, acting as the leader, offers a hotel a contract specifying the commission rate that the hotel will pay to the respective OTA. The hotel, acting as a follower, sets a self-interested room rate as a given response. A deterministic, price-sensitive linear demand function is utilized to derive possible analytical solutions once centralized, noncooperative decentralization and cooperative decentralized channel occurs.

Results show that a new channel coordination approach is possible, namely via advertising fees. Prior to channel coordination, the OTA tends to set a higher commission rate, and the hotel sets a higher room rate in response under noncooperative decentralization. As such, this results in a lower channel-wide profit for all. One way to reduce channel-wide profit loss is to use a method of cooperative decentralization, which can, and will result in optimal profit as centralization takes place. However, the lack of incentives makes cooperative decentralization unfeasible. Further improvement is possible by using advertising fees based on a cooperative decentralization agreement, which can reach Pareto improvement.

This paper helps the OTA industry and hotel owners cooperate by way of smoother coordination. This study provides practitioners with two important practical implications. The first one is that the coordination between the hotel industry and OTA through cooperative decentralization allows for the achievement of higher profitability than that of noncooperative decentralization. The second one is that this paper solves the outstanding problem of insufficient incentives characteristic of cooperative decentralization by means of an advertising fee as a new supply chain coordination approach.

This paper offers both the problem and solution regarding the lack of incentives that hamper cooperative decentralization without the use of advertising fees. This paper is unique in that it derives analytical solutions regarding commissions levied in a typical hotel supply chain under noncooperative decentralization.

The purpose of this research is to conduct a theoretical prediction study exploring the effectiveness of different content marketing strategies in expanding the second-hand market for fashion brands, comparing the costs and risks involved in these strategies in practice.

First, the expert interview method is employed to extract the content marketing strategies of the fashion second-hand market. Then, a descriptive space that is able to identify various fashion brand images is established. Then, experts' perceptions of the relationships between content marketing strategies and fashion brand image dimensions are obtained through a subjective evaluation procedure. Data of semantic evaluation were quantified and analyzed using the fuzzy logic method.

When fashion brands expand to the second-hand market, they not only need to focus on improving the individual differentiation of products but also give priority to the quality of products and services and the overall customer experience. Exploring the “social impact strategy” will become an important direction for the development of fashion brands in the future.

The research methodology employed herein exhibits a noteworthy degree of novelty. This study introduces a pioneering theoretical prediction approach utilizing fuzzy logic, marking the inaugural exploration of this emerging and captivating dimension within the context of the study. Simultaneously, the study provides comparative results among content marketing strategies for expanding the fashion second-hand market, offering guidance for market expansion.