Statistical Analytical Study of the Impact of Consumer Awareness Among the Students of the Faculty of Business Administration at the University of Kuwait in Consumer Protection and Its Relationship to Some Economic and Social Characteristics

Kuwaiti youth is an importam element in the field of development in the Kuwaiti society, especially in the field of self- consciousness and social consciousness incident. As always said, that young Gulf inherently consumerist element of first degree because of abundant liquidity and reasonable level of income compared to others of non - Gulf societies. Therefore, this research aims to study and analyze the impact of consumer awareness among the students of the Faculty of Business Administration in consumer protection in terms of the current role and hoped to guide consumption and its relationship to some economic and social characteristics of the consumers. In particular it has been studying the impact of some of the qualifications for heads of Kuwaiti families, their total monthly income and their place of residency inhabited by the expressive factors of consumer awareness in college students as well as their impact on the degree of preference for quality of consumer goods in being a low-price, median-price or expensive-price. In addition, it has been studying the relationship of these social variables noted with the role of government and consumer protection associations through what they are doing to develop laws and policies directed to develop and educate consumers. To achieve these goals, a special questionnaire has been prepared especially for this purpose has been met from a random sample of students from the University of Kuwait. I have been using the appropriate statistical analysis to study these goals. Thus, some of the appropriate suggestions and recommendations are obtained to achieve the objectives of this research.

Supply Chain Management Analysis and Design for a Variety of Economic Scenarios, Including Data and System Administration

Due to the rapid progress of information technology, organizations anticipate significant changes in the planning, scheduling, and optimization aspects of operation and supply chain management (SCM) shortly. Two primary types of risk have an impact on supply chain management and design. The first group deals with the difficulties in matching supply and demand, whereas the second group deals with disruptions to regular business operations. The essay offers a theoretical framework that combines the cooperative efforts of risk assessment and mitigation, which are critical for effectively handling potential supply chain interruptions. This content provides insightful viewpoints on the strategic resources and operational structure needed to improve organizational success. We utilized the partial least squares (PLS) method to address the problem of multicollinearity and measurement mistakes in examining cause-and-effect constructs. The statistical method, Least Squares (PLS), used in structural equation modeling, is based on partial variance. The Partial Least Squares (PLS) strategy uses a two-stage estimate procedure to calculate weights, loadings, and route estimations. Initially, several simple and complex regressions were performed with the provided model. The procedure was repeated until a solution was found, resulting in a set of weights used to determine the latent variable scores. In the second step, non-iterative PLS regression yields loadings, path coefficients, mean scores, and location parameters. According to the structural study, implementing Sustainable Supply Chain Management (SSCM) can significantly improve a business’s operational and financial performance. The findings offer a comprehensive understanding of several elements of supply chain management (SSCM), including information systems, organizational configurations, supply chain network architecture (SCND), and supply chain strategy (SCS). The supply chain is essential for effectively moving goods over great distances and encouraging cooperation between parties. Therefore, these connections are established precisely, quickly, and cheaply via a knowledgeable and efficient supply chain. Two key components are necessary for a supply chain (SC) to be successful: efficient collaboration and the smooth integration of information-sharing platforms.

Integrating Blockchain Innovation: A Sustainable Adoption Model for Business

Blockchain technology holds significant promise for driving innovations across diverse industries, businesses, and applications. Recognized as a crucial source of competitive advantage in a fast-evolving environment, blockchain is anticipated to contribute substantially to sustainable economic and social development. Despite these high expectations, many blockchain projects currently face high failure rates, leading to negative impacts on various aspects of economic and social sustainability, including corporate governance, risk management, financial management, human resources, culture management, and competitiveness. This paper evaluates adoption models, identifying both risk and success factors. It introduces an integrated adoption model designed to operationalize, measure, and manage blockchain-driven business innovation sustainably. An empirical study involving 20 industry sectors and 125 business leaders was conducted to assess the model’s applicability. The findings indicate that the adoption model has the potential to support the sustainable implementation of blockchain technology for business innovations across various industries and applications. Future research and industry activities should continue validating this model through further case studies.

A Comprehensive Survey on Federated Learning Applications in Computational Mental Healthcare

Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing trend.This aims to alleviate the workload on healthcare professionals and aid individuals.Numerous applications have been developed to support the challenges in intelligent healthcare systems.However,because mental health data is sensitive,privacy concerns have emerged.Federated learning has gotten some attention.This research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare systems.It explores various dimensions of federated learning in mental health,such as datasets(their types and sources),applications categorized based on mental health symptoms,federated mental health frameworks,federated machine learning,federated deep learning,and the benefits of federated learning in mental health applications.This research conducts surveys to evaluate the current state of mental health applications,mainly focusing on the role of Federated Learning(FL)and related privacy and data security concerns.The survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact.

Opinion dynamics in finance and business: a literature review and research opportunities

Opinion dynamics is an opinion evolution process of a group of agents,where the final opinion distribution tends to three stable states:consensus,polarization,and fragmentation.At present,the opinion dynamics models have been extensively studied in differrent fields.This paper provides a review of opinion dynamics in finance and business,such as,finance,marketing,e-commerce,politics,and group decision making.Furthermore,identified research challenges have been proposed to promote the future research of this topic.

Business Model Innovation of Small and Medium-sized Enterprises on the Basis of Value Creation Logic of Balanced Score Card

Small and medium-sized enterprises have developed rapidly under the guidance and support of the national industrial policy.Because of the influence of the post financial crisis,the rate of the global economy development decreased and the market competition increased,the profit of small and medium-sized enterprises is on the decline or even in a loss.The paper,based on the value creation logic of Balanced Score Card,put forward that we should,with the Differentiation Strategy as the basis,using Strategy Map and Cascade Strategy,integrate the key internal procedures to increase the strategic preparation of intangible assets to guide the business model innovation of small and medium-sized enterprises and promote the rapid development of small and medium-sized enterprises.

Determining winner in multi-attribute procurement auction:a method based on technical and business experts' evaluation information

A method for solving the winner determination problem （WDP） in multi-attribute procurement auctions is proposed, based on technical and business experts＇ evaluation information. Firstly, on the background of procurements in China, a multi-attribute pro- curement auction mechanism is presented, where technical and business experts participate in the bid evaluation. Then, the concept of TOPSIS is used to determine the positive and negative ideal points of the WDP according to bid prices, the technical and business experts＇ evaluation information. Further, the closeness coefficient of each bidder （candidate supplier） is obtained by calculating the distances to the positive and negative ideal points. Thus, the winning supplier can be determined according to the closeness coefficients. Finally, a numerical example is used to illustrate the use of the proposed method.

Security Threats to Business Information Systems Using NFC Read/Write Mode

Radio Frequency IDentification(RFID)and related technologies such as Near Field Communication(NFC)are becoming essential in industrial contexts thanks to their ability to perform contactless data exchange,either device-to-device or tag-to-device.One of the three main operation modes of NFC,called read/write mode,makes use of the latter type of interaction.It is extensively used in business information systems that make use of NFC tags to provide the end-user with augmented information in one of several available NFC data exchange formats,such as plain text,simple URLs or enriched URLs.Using a wide variety of physical form factors,NFC-compatible tags(wireless transponders)are currently available in many locations with applications going from smart posters,contactless tokens,tap-and-go payments or transport ticketing to automated device configuration,patient identification at hospitals or inventory management within supply chains.Most of these applications handle sensitive processes or data.This paper proposes a complete security threat model for the read/write operation mode of NFC used in Next Generation Industrial IoT(Nx-IIoT)contexts.This model,based on a wellknown methodology,STRIDE,allows developers and users to identify NFC applications vulnerabilities or weaknesses,analyze potential threats,propose risk management strategies,and design mitigation mechanisms to mention only some significant examples.

Research on SAP Business One Implementation Risk Factors with Interpretive Structural Model

The possible risk factors during SAP Business One implementation were studied with depth interview. The results are then adjusted by experts. 20 categories of risk factors that are totally 49 factors were found. Based on the risk factors during the SAP Business One implementation, questionnaire was used to study the key risk factors of SAP Business One implementation. Results illustrate ten key risk factors, these are risk of senior managers leadership, risk of project management, risk of process improvement, risk of implementation team organization, risk of process analysis, risk of based data, risk of personnel coordination, risk of change management, risk of secondary development, and risk of data import. Focus on the key risks of SAP Business One implementation, the interpretative structural modeling approach is used to study the relationship between these factors and establish a seven-level hierarchical structure. The study illustrates that the structure is olive-like, in which the risk of data import is on the top, and the risk of senior managers is on the bottom. They are the most important risk factors.

Research on the Training Model of Electronic Business Professionals in Higher Vocational Colleges Based on Modern Apprenticeship

For vocational education, modern apprenticeship is of great importance to its teaching quality, and the application of modern apprenticeship in training e-commerce professionals in higher vocational colleges can promote the training of e-commerce professionals. This paper briefly introduces the modern apprenticeship system, and has analyzed the existing problems in the teaching of e-commerce specialty in higher vocational colleges and the training model of modern apprenticeship for e-commerce professionals in higher vocational colleges, and finally, it puts forward several problems which we should pay attention to in the application of the modern apprenticeship to improve the teaching level of the major of e-commerce and promote the development of contemporary vocational education.

Analysis on Customer Behavior and Interest in Mining on WeChat Business

In recent years,with the increasing number of WeChat users,a new shopping mode has emerged in the social platform of WeChat,which is what we call WeChat business.It can share goods and publicly display goods information to social friends.Since WeChat’s origin is social rather than marketing tools,WeChat business can find user groups and interconnected big data more than traditional e-commerce.However,with the advent of the era of big data,WeChat business has generated huge customer behavior data,and with the strong demand of microbusiness dealers to convert massive amounts of data into useful information,how to dig out users’shopping in huge behavioral data.Interest,and thus the personalized recommendation service in the microbusiness model,is particularly urgent.

Business valuation methods selection that based on the life cycle

Based on modem financial markets becoming more and more perfect, in company stock, bonds, represented by the investment banking has permeated all aspects of life. Business valuation is the core of value management.However, companies in different stages of development have a certain stage characteristics, and therefore from the features and characteristics of the different stages of business valuation methods and the factors that affect the value of different businesses created to start, can provide investors assess the business value of new ideas.

The Current Context and Future Trends of the Photovoltaic Business Models in Central and Eastern Europe:Case Study—Romania

The evolution of the PV industry has represented one of the main focus points of economic and political debates and research of the last ten years in Europe. However, these discussions have only presented general aspects of the industry development and accomplishments with little information about the companies’ actual practices. The present study aims to inquire the PV field in Romania further, by researching the currently implemented and the future business models of firms producing solar energy. Using the Delphi method based on questionnaires and interviews with experts of this field, this paper shows the key elements of business models in the PV field that reflect the companies’ practices and future trends and expectations regarding the internal activity of the firm and the environmental changes from the external environment of the company, which influence the directions of the PV domain. The relevance of this study is based on the importance of the applied PV business model elements in order to ensure the survival and expansion of the company in a dynamic field that faces continuous legal, economic and technological changes and challenges. The results of this study have some directions that could be applied in most of the European countries where the PV industry is developing.

Implementation of Business Game Activity Support System

Business game can be used not only as an educational tool for the development of decision making ability, but also can be used for supporting the knowledge creation activity in organizations. In this paper, some conceptual considerations to meanings of the business game in the knowledge creation activity by using the knowledge creation theory and other related theories are given, and business game activity concept which refers to game play and development is proposed. Then focusing on the business game activity as an instantiation of the knowledge creation activity, and a Web based gaming activity support system based on the former system called YBG that enables us to play and develop many business games through the standard web browser is proposed. This system also provides us a lot of opportunities to play and develop the business games over business game communities.

Analysis of factors influencing carbon emissions in the Yangtze River Delta region and projections of carbon peak scenarios

Based on the supply-side perspective,the improved STIRPAT model is applied to reveal the mechanisms of supply-side factors such as human,capital,technology,industrial synergy,institutions and economic growth on carbon emissions in the Yangtze River Delta(YRD)through path analysis,and to forecast carbon emissions in the YRD from the baseline scenario,factor regulation scenario and integrated scenario to reach the peak.The results show that:(1)Jiangsu's high carbon emission pattern is the main reason for the YRD hindering the synergistic regulation of carbon emissions.(2)Human factors,institutional factors and economic growth factors can all contribute to carbon emissions in the YRD region,while technological and industrial factors can generally suppress carbon emissions in the YRD region.(3)Under the capital regulation scenario,the YRD region has the highest level of carbon emission synergy,with Jiangsu reaching its peak five years earlier.Under the balanced regulation scenario,the YRD region as a whole,Jiangsu,Zhejiang and Anhui reach the peak as scheduled.

How can technology and efficiency alleviate the dilemma of economic growth and carbon emissions in China's industrial economy? A metafrontier decoupling decomposition analysis

This paper attempts to explore the decoupling relationship and its drivers between industrial economic increase and energy-related CO_(2) emissions(ICE). Firstly, the decoupling relationship was evaluated by Tapio index. Then, based on the DEA meta-frontier theory framework which taking into account the regional and industrial heterogeneity and index decomposition method, the driving factors of decoupling process were explored mainly from the view of technology and efficiency. The results show that during2000-2019, weak decoupling was the primary state. Investment scale expansion was the largest reason hindering decoupling process of industrial increase from ICE. Both energy saving and production technology achieved significant progress, which facilitated the decoupling process. Simultaneously, the energy technology gap and production technology gap among regions have been narrowed, and played a role in promoting decoupling process. On the contrary, both scale economy efficiency and pure technical efficiency have inhibiting effects on decoupling process. The former indicates that the scale economy of China's industry was not conducive to improve energy efficiency and production efficiency, while the latter indicates that resource misallocation problem may exist in both energy market and product market.

How does network intermediary affect collaborative innovation?Evidence from Chinese listed companies

Purpose:This study aims to explore how network intermediaries influence collaborative innovation performance within inter-organizational technological collaboration networks.Design/methodology/approach:This study employs a mixed-method approach,combining quantitative social network analysis with regression techniques to investigate the role of network intermediaries in collaborative innovation performance.Using a patent dataset of Chinese industrial enterprises,the research constructs the collaboration networks and analyzes their structural positions,particularly focusing on their role as intermediaries,characterized by betweenness centrality.Negative binomial regression analysis is employed to assess how these network characteristics shape innovation outcomes.Findings:The study reveals that firms in intermediary positions enhance collaborative innovation performance,but this effect is nuanced.A key finding is that network clustering negatively moderates the intermediary-innovation relationship.Highly clustered networks,while fostering local collaboration,may limit the innovation potential of intermediaries.On the other hand,relationship strength,measured by collaboration intensity and trust among firms,positively moderates the intermediary-innovation link.Research limitations:This study has several limitations that present opportunities for further research.The reliance on quantitative social network analysis may overlook the complexity of intermediaries’roles,and future studies could benefit from incorporating qualitative methods to better understand cultural and institutional factors.Additionally,cross-country comparisons are needed to assess the consistency of these dynamics in different contexts.Practical implications:The study offers practical insights for firms and policymakers.Organizations should strategically position themselves as network intermediaries to access diverse information and resources,thereby improving innovation performance.Building strong trust helps using network intermediary advantages.For firms in highly clustered networks,it is important to seek external partners to avoid limiting their exposure to new ideas and technologies.This research emphasizes the need to balance network diversity with relationship strength for sustained innovation.Originality/value:This research contributes to the literature by offering new insights into the role of network intermediaries,presenting a comprehensive framework for understanding the interaction between network dynamics and firm innovation.

Dynamic analysis of major public health emergency transmission considering the dual-layer coupling of community–resident complex networks

We construct a dual-layer coupled complex network of communities and residents to represent the interconnected risk transmission network between communities and the disease transmission network among residents. It characterizes the process of infectious disease transmission among residents between communities through the SE2IHR model considering two types of infectors. By depicting a more fine-grained social structure and combining further simulation experiments, the study validates the crucial role of various prevention and control measures implemented by communities as primary executors in controlling the epidemic. Research shows that the geographical boundaries of communities and the social interaction patterns of residents have a significant impact on the spread of the epidemic, where early detection, isolation and treatment strategies at community level are essential for controlling the spread of the epidemic. In addition, the study explores the collaborative governance model and institutional advantages of communities and residents in epidemic prevention and control.

Posture Detection of Heart Disease Using Multi-Head Attention Vision Hybrid(MHAVH)Model

Cardiovascular disease is the leading cause of death globally.This disease causes loss of heart muscles and is also responsible for the death of heart cells,sometimes damaging their functionality.A person’s life may depend on receiving timely assistance as soon as possible.Thus,minimizing the death ratio can be achieved by early detection of heart attack(HA)symptoms.In the United States alone,an estimated 610,000 people die fromheart attacks each year,accounting for one in every four fatalities.However,by identifying and reporting heart attack symptoms early on,it is possible to reduce damage and save many lives significantly.Our objective is to devise an algorithm aimed at helping individuals,particularly elderly individuals living independently,to safeguard their lives.To address these challenges,we employ deep learning techniques.We have utilized a vision transformer(ViT)to address this problem.However,it has a significant overhead cost due to its memory consumption and computational complexity because of scaling dot-product attention.Also,since transformer performance typically relies on large-scale or adequate data,adapting ViT for smaller datasets is more challenging.In response,we propose a three-in-one steam model,theMulti-Head Attention Vision Hybrid(MHAVH).Thismodel integrates a real-time posture recognition framework to identify chest pain postures indicative of heart attacks using transfer learning techniques,such as ResNet-50 and VGG-16,renowned for their robust feature extraction capabilities.By incorporatingmultiple heads into the vision transformer to generate additional metrics and enhance heart-detection capabilities,we leverage a 2019 posture-based dataset comprising RGB images,a novel creation by the author that marks the first dataset tailored for posture-based heart attack detection.Given the limited online data availability,we segmented this dataset into gender categories(male and female)and conducted testing on both segmented and original datasets.The training accuracy of our model reached an impressive 99.77%.Upon testing,the accuracy for male and female datasets was recorded at 92.87%and 75.47%,respectively.The combined dataset accuracy is 93.96%,showcasing a commendable performance overall.Our proposed approach demonstrates versatility in accommodating small and large datasets,offering promising prospects for real-world applications.

A Quarterly High RFM Mining Algorithm for Big Data Management

In today’s highly competitive retail industry,offline stores face increasing pressure on profitability.They hope to improve their ability in shelf management with the help of big data technology.For this,on-shelf availability is an essential indicator of shelf data management and closely relates to customer purchase behavior.RFM(recency,frequency,andmonetary)patternmining is a powerful tool to evaluate the value of customer behavior.However,the existing RFM patternmining algorithms do not consider the quarterly nature of goods,resulting in unreasonable shelf availability and difficulty in profit-making.To solve this problem,we propose a quarterly RFM mining algorithmfor On-shelf products named OS-RFM.Our algorithmmines the high recency,high frequency,and high monetary patterns and considers the period of the on-shelf goods in quarterly units.We conducted experiments using two real datasets for numerical and graphical analysis to prove the algorithm’s effectiveness.Compared with the state-of-the-art RFM mining algorithm,our algorithm can identify more patterns and performs well in terms of precision,recall,and F1-score,with the recall rate nearing 100%.Also,the novel algorithm operates with significantly shorter running times and more stable memory usage than existing mining algorithms.Additionally,we analyze the sales trends of products in different quarters and seasonal variations.The analysis assists businesses in maintaining reasonable on-shelf availability and achieving greater profitability.