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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.

Inefficiency dilemmas in project governance are caused by various risks arising from the characteristic of construction supply chain projects, such as poor project performance, conflicts between stakeholders and cost overrun. This research aims to establish a fuzzy synthetic evaluation (FSE) model to analyze construction supply chain risk factors. Corresponding risk mitigation strategies are provided to facilitate the improvement performance of ongoing construction supply chain projects.

A literature review is utilized to reveal the deficiencies of construction supply chain risk management. Thus, a total of five hundred (500) questionnaires are distributed to construction professionals, and four hundred and thirty-five (435) questionnaires are recovered to obtain the evaluation data of construction professionals on critical risk factors. Additionally, the FSE is used to analyze and rank the significance of critical risk factors. Finally, this research discusses nine critical risk factors with high weight in the model, and explains the reason for the significance of critical risk factors in the construction supply chain.

The questionnaire results show that the thirty-one (31) identified critical risk factors are verified by related practitioners (government departments, universities and research institutions, owners, construction units, financial institutions, design units, consulting firms). Thirty-one (31) identified critical risk factors are divided into common risks, risks from contractors and risks from owners. The most significant factors in the three categories, respectively, are “political risks,” “owner's unprofessional” approach and “cash flow.” Managing these risks can facilitate the development of the construction supply chain.

This paper expands the research perspective of construction supply chain risk management and complements the risks in the construction supply chain. For practitioners, the research result provides some corresponding measures to deal with these risks. For researchers, the research result provides the direction of construction supply chain risk treatment.

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.

This study aims to develop a cross-level research model to explore the relationship between team-level contextual ambidexterity and employees' enterprise system (ES) ambidextrous use, and the mediating role of user empowerment in and moderating effect of leader–member exchange (LMX) on the relationship.

This study conducted a sequential mixed-methods approach, which included a quantitative survey and a qualitative case study. The survey, administered to 244 employees in 59 groups from a financial institution, analyzed the relationships between contextual ambidexterity and ES ambidextrous use. Furthermore, the cross-level mediation and moderation effects were explored. The case study, involving nine members in three groups from a manufacturing firm, served to reinforce the validity of the survey results.

Team-level contextual ambidexterity can affect ES ambidextrous use directly or through the partial mediator of user empowerment. Furthermore, this study highlights the moderating role of LMX in the relationship between contextual ambidexterity and user empowerment, thereby improving ES ambidextrous use.

This study contributes to the literature by uncovering the cross-level effect of contextual ambidexterity on ES ambidextrous use through user empowerment, thereby extending the ambidexterity perspective and self-determination theory to the ES context. Additionally, this study provides nuanced insights into how to enhance ES ambidextrous use by revealing the moderating role and moderated mediation effect of LMX anchoring on social exchange theory.

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.

Accurate and rapid tracking and counting of building materials are crucial in managing on-site construction processes and evaluating their progress. Such processes are typically conducted by visual inspection, making them time-consuming and error prone. This paper aims to propose a video-based deep-learning approach to the automated detection and counting of building materials.

A framework for accurately counting building materials at indoor construction sites with low light levels was developed using state-of-the-art deep learning methods. An existing object-detection model, the You Only Look Once version 4 (YOLO v4) algorithm, was adapted to achieve rapid convergence and accurate detection of materials and site operatives. Then, DenseNet was deployed to recognise these objects. Finally, a material-counting module based on morphology operations and the Hough transform was applied to automatically count stacks of building materials.

The proposed approach was tested by counting site operatives and stacks of elevated floor tiles in video footage from a real indoor construction site. The proposed YOLO v4 object-detection system provided higher average accuracy within a shorter time than the traditional YOLO v4 approach.

The proposed framework makes it feasible to separately monitor stockpiled, installed and waste materials in low-light construction environments. The improved YOLO v4 detection method is superior to the current YOLO v4 approach and advances the existing object detection algorithm. This framework can potentially reduce the time required to track construction progress and count materials, thereby increasing the efficiency of work-in-progress evaluation. It also exhibits great potential for developing a more reliable system for monitoring construction materials and activities.

Financial service providers are facing challenges in the acceptance of digital financial services. The study, therefore, intends to identify factors contributing towards the adoption of digital finance. It has worked on the influencers and demotivators of digital finance adoption by individuals. These influencers are labelled as perceived benefits and demotivators as perceived risks. In addition to perceived benefit and risk, the study has also included the difference in perception on the basis of generation cohort.

The data have been collected through a structured questionnaire from 411 respondents. Partial least squares structured equation modelling (PLS-SEM) has been used to analyse the proposed model on SmartPLS.

The findings suggested that the benefits were more influential in adoption behaviour than perceived risk. In addition to perceived benefit and risk, the study has also included the difference in perception on the basis of generation cohort. The results summarised that benefits had a more significant impact in Generation Z (Gen Z) than in Millennials.

The evaluation and categorisation of perceived risk and benefits into meaningful dimensions generate value to the adoption behaviour of digital finance. Thus, the findings are useful for the policymakers and researchers to contemplate the perception of individuals in digital finance based on the generation cohort.

The empirical findings of the present research contribute to limited evidence of a relationship between perceived risk, perceived benefit and digital finance adoption on the basis of generation cohort.

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.