The Psychology of Shopping Addiction in Consumer Behaviour

This comprehensive article examines the phenomenon of consumer addiction,primarily focusing on shopping addiction and its dimensions,including brand addiction.It delves into the underlying causes,manifestations,and consequences of consumer addiction from both consumer and marketer perspectives,shedding light on the ethical and cultural considerations within today's society.Consumer addiction is characterized by recurrent,irresistible purchasing behaviors driven by negative emotions such as anxiety and impulsivity.It is recognized as a behavioral addiction closely intertwined with consumerism.The article emphasizes the imperative for ethical marketing practices to mitigate the exacerbation of addictive behaviors while acknowledging the impact of culture on consumer choices.The article also discusses the crucial role of research in understanding the implications of consumer addiction on the economy,and it suggests that marketers should focus on fostering positive brand addiction rather than exploiting consumerism.It underscores the influence of cultural factors on addictive consumption and calls for responsible marketing practices and governmental regulations.In conclusion,this article highlights the critical significance of consumer addiction in the field of marketing and its multifaceted implications for both consumers and businesses.It underscores the need for ethical marketing strategies,cultural awareness,and responsible brand management to address this complex phenomenon in contemporary society.

The influence of different types of satisfaction on loyalty on C2C online shopping platform:From the perspective of sellers and the platform

With the rise and development of major types of platforms,the competition for resources has become extremely fierce,and the market share of C2C platforms has been seriously threatened by the loss of resources.Therefore,building and maintaining buyers’satisfaction and loyalty to C2C platforms is critical to the survival and sustainability of C2C platforms in China.However,the current knowledge on how platform satisfaction and loyalty are constructed in the C2C e-commerce environment is incomplete.In this study,seller-based satisfaction and platform-based satisfaction are constructed separately.We further distinguish seller-based transaction satisfaction into economic and social satisfaction and explore their antecedents and consequences.To test our research hypotheses,we conduct a survey and collect data from a real online market(Taobao website).The results show that seller-based transaction satisfaction positively affects platform-based overall satisfaction and loyalty,and that perceived product quality,perceived assurance,and perceived price fairness all have a significant effect on economic satisfaction,whereas perceived relationship quality and perceived empathy significantly influence social satisfaction.These findings help us understand the literature related to customer satisfaction in the context of C2C in China and provide inspiration for online sellers and platforms.

The Continuum Stored Energy for Constitutive Modeling Finite Deformations of Polymeric Materials

With symmetries measured by the Lie group and curvatures revealed by differential geometry, the continuum stored energy function possesses a translational deformation component, a rotational deformation component, and an ellipsoidal volumetric deformation component. The function, originally developed for elastomeric polymers, has been extended to model brittle and ductile polymers. The function fits uniaxial tension testing data for brittle, ductile, and elastomeric polymers, and elucidates deformation mechanisms. A clear distinction in damage modes between brittle and ductile deformations has been captured. The von Mises equivalent stress has been evaluated by the function and the newly discovered break-even stretch. Common practices of constitutive modeling, relevant features of existing models and testing methods, and a new perspective on the finite elasticity-plasticity theory have also been offered.

Proposed Caching Scheme for Optimizing Trade-off between Freshness and Energy Consumption in Name Data Networking Based IoT

Over the last few years, the Internet of Things (IoT) has become an omnipresent term. The IoT expands the existing common concepts, anytime and anyplace to the connectivity for anything. The proliferation in IoT offers opportunities but may also bear risks. A hitherto neglected aspect is the possible increase in power consumption as smart devices in IoT applications are expected to be reachable by other devices at all times. This implies that the device is consuming electrical energy even when it is not in use for its primary function. Many researchers’ communities have started addressing storage ability like cache memory of smart devices using the concept called—Named Data Networking (NDN) to achieve better energy efficient communication model. In NDN, memory or buffer overflow is the common challenge especially when internal memory of node exceeds its limit and data with highest degree of freshness may not be accommodated and entire scenarios behaves like a traditional network. In such case, Data Caching is not performed by intermediate nodes to guarantee highest degree of freshness. On the periodical updates sent from data producers, it is exceedingly demanded that data consumers must get up to date information at cost of lease energy. Consequently, there is challenge in maintaining tradeoff between freshness energy consumption during Publisher-Subscriber interaction. In our work, we proposed the architecture to overcome cache strategy issue by Smart Caching Algorithm for improvement in memory management and data freshness. The smart caching strategy updates the data at precise interval by keeping garbage data into consideration. It is also observed from experiment that data redundancy can be easily obtained by ignoring/dropping data packets for the information which is not of interest by other participating nodes in network, ultimately leading to optimizing tradeoff between freshness and energy required.

Efficiency of Bio-Fertilizing as One of the Natural Alternatives to Improve the Growth of Khaya senegalensis and Swietenia mahagoni Trees and for Sustainability

A field experiment was carried out at Ismailia Research Station, Ismailia Governorate from 2020-2022 to improve the growth of Khaya senegalensis and Swietenia mahagoni by using a combination of mineral fertilizer (NPK) and biological fertilizer (Azotobacter chroococcum, Bacillus megatherium, and Bacillus circulant) as recommended dose under new sandy soils conditions. Split plot designed with four treatments (Control, (50% Mineral fertilizer (M.) + 50% Biological fertilizer (Bio.)), 100% M. and 100% Bio.) of each species. Vegetative growth, leaf area, tree biomass, stored carbon, basal area, tree volume, and in the soil both of microbial account and mineral content were determined. The experimental results showed no significant differences between studied species among the most studied parameters except for Khaya senegalensis which gave the highest significant difference in root biomass and below-stored carbon than Swietenia mahagoni. Evidently, the highest significant growth parameters were 100% mineral fertilizer followed by (50% M. + 50% Bio.) as compared with control. No significant difference between 100% M. and (50% M. + 50% Bio.) of shoot dry biomass (15.19 and 12.02 kg, respectively) and above-stored carbon (0.28 and 0.22 Mt, respectively). Microbial account and mineral content in soil were improved after cultivation of tree species compared to before planting and control, especially with 50% mineral fertilizer and 50% bio-fertilizer treatment. In conclusion, a treatment containing 50% mineral fertilizer and 50% bio-fertilizer has led to the ideal Khaya senegalensis and Swietenia mahagoni growth in sandy soil for cheaper and sustainable.

Smart Location-Based Mobile Shopping Android Application

In the paper, a smart location-based mobile shopping application for Android devices is proposed. The Geo-position of the user’s mobile device is utilized to produce location information in shopping application (SAGO). The flow of the application is that user searches a product, and then SAGO identifies the location and searches the product on the closest electronic local stores. The idea is to get the prices from each local store with in stock information and smartly listed product list. With the proposed smart filtering algorithm, mobile shopping application achieves precise and minimum error based on searching and listing results.

Prevalence and Antimicrobial Susceptibility Status of Gram-Negative and Gram-Positive Bacteria on Handheld Shopping Trolleys and Baskets in Supermarkets in Ndola, Zambia

Background: Supermarkets are a place visited by individuals with different health conditions daily where microbiological contaminants through touch onto fomites such as trolleys and baskets can be passed on to other people hence potentially spreading infectious diseases. This study aimed to investigate the presence of Gram-negative and Gram-positive bacteria on handheld shopping trolleys and baskets and their antimicrobial susceptibility status against commonly used antibiotics in Zambia. Methods: A cross-sectional study was conducted. Trolleys and basket handles were swabbed and standard microbiological methods were used to identify the bacteria and disc diffusion to determine their antimicrobial susceptibility status. Data was collected from December 2021 to April 2022. Data was analysed using IBM Statistical Package for Social Sciences (SPSS) Version 22. Results: Twenty-eight percent of the 200 total samples were found to be culture-positive and predominant isolates were Staphylococcus aureus (17.3%), Pseudomonas species (4.5%), Escherichia coli (2%), Corynebacterium species (2%), Staphylococcus species (1.5%) and Enterobacter aerogenes (0.5%). Staphylococcus aureus showed the most resistance to azithromycin (17%) followed by ciprofloxacin (2.8%), nitrofurantoin (2.8%) and chloramphenicol (2.8%). Escherichia coli showed 100% resistance to amoxicillin, cloxacillin and ampicillin, 75% resistance to ciprofloxacin and the least resistance to azithromycin (25%) while it was susceptible to nitrofurantoin. Staphylococcus species, Corynebacterium species, Enterobacter aerogenes and Pseudomonas species showed no resistance to any antibiotics. Conclusion: The study showed the presence of microorganisms with considerable antimicrobial resistance to antibiotics in Zambia on trolley and basket handles indicating the need for more initiatives to address proper hygiene in public environmental sites for better infection prevention and control.

中国城市老年人日常购物行为的空间特征——以北京、深圳和上海为例

以中国城市老年人日常消费行为状况的问卷调查为基础,主要运用经验行为主义方法,从宏观层面揭示出中国城市老年人日常购物行为的空间特征,通过对北京、深圳和上海三个城市的老年人购物空间圈层结构的比较研究,发现北京市老年人购物活动随距离衰减的规律比较规则;深圳老年人购物行为空间则随距离增加而波动式衰减;上海老年人购物活动空间的收敛性最强。进一步从微观和实证的角度分析了居住区位因素对老年人购物空间的影响,得出小区级商业设施的完善程度和可达性直接决定了老年人购物空间的收敛程度的结论。

Behavior Targeting Based on Hierarchical Taxonomy Aggregation for Heterogeneous Online Shopping Applications

Behavior targeting(BT)based on individual web-browsing history has become more valuable in precision marketing for many companies through capturing users’interest and preference.It is common in practice that the behavior data collected from different online shopping applications are inconsistent since they are labelled by different item taxonomy,where the same behavior could have different representations and therefore analysis confusion arises.To address this issue,we propose a semantic similarity based strategy to transform the heterogeneous behavior extracted from deep packet inspection(DPI)data of a telecommunication operator into a unique standard one.The Word Mover’s Distance algorithm is exploited to evaluate the semantic similarity of the distributed representations of two web-browsing histories.Moreover,the architecture of the behavior targeting platform on Hadoop is implemented,which is capable of processing data with size of PB level every day.

THE PERIODIC VARIATION OF MOTION OF ^(171)Yb^+STORED ION IN PAUL RF TRAP

The periodic variation of motion of 171Yb+ stored ion in a Paul r.f.trap is observed.The reason of which and its influence to the uncertainty and stability of stored ton frequency standard are going to be studied.

When Chinese Manufacturing Met On-line shopping Carnival

After fast development in the past four year,"11.11"has become an online shopping carnival for Chinese netizens.On the single day of November11,2013,Tmall reaped an amazing daily sales volume of RMB 35 billion.Afterwards,such e-suppliers as JD.com,Dangdang,Suning.com,Gome.com and Vipshop joined the"11.11"shopping carnival with considerable sales volume.Products of the traditional manufacturing

浅谈医药高职高专执业药师后备力量培养模式的构建

目前零售药店执业药师数量不足,严重影响了药学服务质量。医药高职高专毕业生将是零售药店执业药师的主要后备军,因此高职高专有必要建立培养执业药师后备力量的模式。笔者根据21世纪中国的零售药房营销模式论述了高职高专培养执业药师后备力量的总体目标,需达到此目标应具备的技术能力,理论知识和应具有的职业道德、外语水平、自学能力、创新意识等方面的综合素质,并提出了要达到总目标和这些要求需构建理论教学,实践教学,素质教育三大教学体系,以培养适应21世纪中国零售药店发展的执业药师后备军。

Numerical Simulation about the Characteristics of the Store Released from the Internal Bay in Supersonic Flow

To understand the influence of the initial release conditions on the separation characteristics of the store and improve it under high Mach number(Ma=4)flight conditions,the overset grid method and the Realizable k−εturbulence model coupled with an equation with six degrees of freedom are used to simulate the store released from the internal bay.The motion trajectory and the attitude angle of the store separation under the conditions of different centroid,velocity,height and control measures are given by the calculated result.Through analysis,the position of the centroid will affect the separation of the store,which needs to be considered in the design.Increasing the launching height is conducive to the separation of the store.If the store has an initial velocity,it can leave the internal bay more quickly and reduce the probability of collision with the wall.Cylindrical rod and slanted aft wall control measures can improve the attitude of the store and make the store fall more smoothly.

Shopping :Men’s and Women’s

Men say they hate to shop," says Zhukin, City University of New York Sociologyprofessor. "Yet when you ask them deeper questions, it turns out that they like to shop. Men generally like to shop for books, music and hardware . But if you ask them about the shopping they do for books or music, theyll sa y, ’Well, thats not shopping. Thats research.’"

Q-Learning-Assisted Meta-Heuristics for Scheduling Distributed Hybrid Flow Shop Problems

The flow shop scheduling problem is important for the manufacturing industry.Effective flow shop scheduling can bring great benefits to the industry.However,there are few types of research on Distributed Hybrid Flow Shop Problems(DHFSP)by learning assisted meta-heuristics.This work addresses a DHFSP with minimizing the maximum completion time(Makespan).First,a mathematical model is developed for the concerned DHFSP.Second,four Q-learning-assisted meta-heuristics,e.g.,genetic algorithm(GA),artificial bee colony algorithm(ABC),particle swarm optimization(PSO),and differential evolution(DE),are proposed.According to the nature of DHFSP,six local search operations are designed for finding high-quality solutions in local space.Instead of randomselection,Q-learning assists meta-heuristics in choosing the appropriate local search operations during iterations.Finally,based on 60 cases,comprehensive numerical experiments are conducted to assess the effectiveness of the proposed algorithms.The experimental results and discussions prove that using Q-learning to select appropriate local search operations is more effective than the random strategy.To verify the competitiveness of the Q-learning assistedmeta-heuristics,they are compared with the improved iterated greedy algorithm(IIG),which is also for solving DHFSP.The Friedman test is executed on the results by five algorithms.It is concluded that the performance of four Q-learning-assisted meta-heuristics are better than IIG,and the Q-learning-assisted PSO shows the best competitiveness.

SQL or NoSQL—Practical Aspect and Rational behind Choosing Data Stores

Data storage solutions are a crucial aspect of any application, significantly impacting data management and system performance. This article explores the rationale behind utilizing both SQL and NoSQL databases, addressing key questions about when each type is preferable. The background emphasizes the importance of selecting the appropriate database technology to meet specific application requirements. The purpose of this research is to provide a comprehensive guide for choosing between SQL and NoSQL databases based on various factors, including workload characteristics, scalability needs, and consistency requirements. To achieve this, we examine different strategies for implementing SQL and NoSQL databases in large-scale distributed applications and systems. The research method involves a comparative analysis of the features, advantages, and limitations of both database types. We specifically focus on scenarios involving read-heavy versus write-heavy systems and the trade-offs between availability and consistency. The results of this research indicate that SQL databases, with their relational structure and ACID compliance, are ideal for applications requiring complex queries and data integrity. In contrast, NoSQL databases, offering schema flexibility and horizontal scalability, are better suited for managing extensive datasets and high-velocity data ingestion. In conclusion, the selection of a database depends on the specific needs of the application. SQL databases are preferred for transactional systems with complex relationships, while NoSQL databases excel in scenarios demanding flexibility and scalability. The study provides insights into hybrid approaches, leveraging both database types to optimize system performance.

Energy-Saving Distributed Flexible Job Shop Scheduling Optimization with Dual Resource Constraints Based on Integrated Q-Learning Multi-Objective Grey Wolf Optimizer

The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worker constraints.As one critical factor of production,effective utilization of worker resources can increase productivity.Meanwhile,energy consumption is a growing concern due to the increasingly serious environmental issues.Therefore,the distributed flexible job shop scheduling problem with dual resource constraints(DFJSP-DRC)for minimizing makespan and total energy consumption is studied in this paper.To solve the problem,we present a multi-objective mathematical model for DFJSP-DRC and propose a Q-learning-based multi-objective grey wolf optimizer(Q-MOGWO).In Q-MOGWO,high-quality initial solutions are generated by a hybrid initialization strategy,and an improved active decoding strategy is designed to obtain the scheduling schemes.To further enhance the local search capability and expand the solution space,two wolf predation strategies and three critical factory neighborhood structures based on Q-learning are proposed.These strategies and structures enable Q-MOGWO to explore the solution space more efficiently and thus find better Pareto solutions.The effectiveness of Q-MOGWO in addressing DFJSP-DRC is verified through comparison with four algorithms using 45 instances.The results reveal that Q-MOGWO outperforms comparison algorithms in terms of solution quality.