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.

An Elite-Class Teaching-Learning-Based Optimization for Reentrant Hybrid Flow Shop Scheduling with Bottleneck Stage

Bottleneck stage and reentrance often exist in real-life manufacturing processes;however,the previous research rarely addresses these two processing conditions in a scheduling problem.In this study,a reentrant hybrid flow shop scheduling problem(RHFSP)with a bottleneck stage is considered,and an elite-class teaching-learning-based optimization(ETLBO)algorithm is proposed to minimize maximum completion time.To produce high-quality solutions,teachers are divided into formal ones and substitute ones,and multiple classes are formed.The teacher phase is composed of teacher competition and teacher teaching.The learner phase is replaced with a reinforcement search of the elite class.Adaptive adjustment on teachers and classes is established based on class quality,which is determined by the number of elite solutions in class.Numerous experimental results demonstrate the effectiveness of new strategies,and ETLBO has a significant advantage in solving the considered RHFSP.

Cooperative-Guided Ant Colony Optimization with Knowledge Learning for Job Shop Scheduling Problem

With the advancement of the manufacturing industry,the investigation of the shop floor scheduling problem has gained increasing importance.The Job shop Scheduling Problem(JSP),as a fundamental scheduling problem,holds considerable theoretical research value.However,finding a satisfactory solution within a given time is difficult due to the NP-hard nature of the JSP.A co-operative-guided ant colony optimization algorithm with knowledge learning(namely KLCACO)is proposed to address this difficulty.This algorithm integrates a data-based swarm intelligence optimization algorithm with model-based JSP schedule knowledge.A solution construction scheme based on scheduling knowledge learning is proposed for KLCACO.The problem model and algorithm data are fused by merging scheduling and planning knowledge with individual scheme construction to enhance the quality of the generated individual solutions.A pheromone guidance mechanism,which is based on a collaborative machine strategy,is used to simplify information learning and the problem space by collaborating with different machine processing orders.Additionally,the KLCACO algorithm utilizes the classical neighborhood structure to optimize the solution,expanding the search space of the algorithm and accelerating its convergence.The KLCACO algorithm is compared with other highperformance intelligent optimization algorithms on four public benchmark datasets,comprising 48 benchmark test cases in total.The effectiveness of the proposed algorithm in addressing JSPs is validated,demonstrating the feasibility of the KLCACO algorithm for knowledge and data fusion in complex combinatorial optimization problems.

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.

China's E-commerce Operators Ready for Double 11 Shopping Festival

With one month until November 11,all major e-commerce platforms have started their preparations for this great event.The"Double 11"shopping festival was first initiated by Alibaba in 2009and is the world's largest online sales gala.This shopping festival is thus named due to the date of November11 and has extended from the original24 hours to several weeks in recent years.The pre-sales stage start from late October.Some new e-commerce companies like TikTok and Pinduoduo have also been involved in the event.

Correlation between psychological resilience and burnout among female employees in a shopping mall in Xi Xian new area,China:A cross-sectional survey-Retraction

In the article titled“Correlation between psychological resilience and burnout among female employees in a shopping mall in Xi Xian New Area,China:A cross-sectional survey”by Zhang Q and Liu L(J Integr Nurs 2021;3(3):117-121.doi:10.4103/jin.jin_14_21),[1]the content and results data of this article was questioned by International database(Web of Science)institution.This article was then investigated by the publisher and Journal of Integrative Nursing(JIN).

Spring Festival Celebrations Fire Maltese Fascination with Chinese Culture--An Exclusive Interview with Director of the China Cultural Center in Malta Yuan Yuan

THRONGS of shoppers,in a department store adorned with colorful Spring Festival posters,vying to try their hand either at Chinese calligraphy or traditional Chinese lantern-making,might seem incongruous in a European setting.The background to this vibrant scenario was The Point,Malta’s largest shopping mall,as part of the island country’s 2023 Chinese New Year celebrations,courtesy of the China Cultural Center in Malta.

Spatial Accessibility to Shopping Malls in Nanjing,China:Comparative Analysis with Multiple Transportation Modes

This study attempts to evaluate the rationality of the spatial layout of shopping malls and identify the urban area with poor shopping mall accessibility,as well as to provide a reference and assist decision-making for planning.Using the case of Nanjing,China,this study developed a method to evaluate the accessibility of shopping malls by three transport modes(car traffic,public transit,and bicycle).Specially,we divide the urban space into a regular hexagonal grid and harvest the total travel time from each of 7204 hexagon centroids to each of 129 shopping malls using the Baidu Internet map.The door-to-door travel time approach is used to evaluate all travel stages(walking,waiting,transfer,and transportation)based on travel time calculations.We further divide the shopping malls into two levels(super-regional and regional)based on the Dianping App’s information and develop the indicator of accessibility to shopping malls:the number of shopping malls within tolerance time thresholds and apply the closest facility and cumulative opportunities methods to measure accessibility scores.The results show that the accessibility estimations vary greatly with transport modes.The accessibility of shopping malls presents a concentric ring trend centered on the city center under the car traffic and bicycle modes.And public transit accessibility tends to axially extend due to the topology of bus routes and metro lines.In particular,we observe that the accessibility of shopping malls in Nanjing has an uneven spatial distribution pattern,with high accessibility values in the central urban areas and lots of underserved areas in urban fringe regions.Based on the accessibility measurements,we finally map the poor accessibility area and propose corresponding implications for urban planning.

A Novel Collaborative Evolutionary Algorithm with Two-Population for Multi-Objective Flexible Job Shop Scheduling

Job shop scheduling(JS)is an important technology for modern manufacturing.Flexible job shop scheduling(FJS)is critical in JS,and it has been widely employed in many industries,including aerospace and energy.FJS enables any machine from a certain set to handle an operation,and this is an NP-hard problem.Furthermore,due to the requirements in real-world cases,multi-objective FJS is increasingly widespread,thus increasing the challenge of solving the FJS problems.As a result,it is necessary to develop a novel method to address this challenge.To achieve this goal,a novel collaborative evolutionary algorithmwith two-population based on Pareto optimality is proposed for FJS,which improves the solutions of FJS by interacting in each generation.In addition,several experimental results have demonstrated that the proposed method is promising and effective for multi-objective FJS,which has discovered some new Pareto solutions in the well-known benchmark problems,and some solutions can dominate the solutions of some other methods.

We’ll go shopping without mobile phones

A UK supermarket has become the first in the world to let shoppers pay for groceries using just the veins(静脉)in their fingertips. Customers at the Costcutter store,at Brunei University in London,can now pay using their unique vein pattern to identify themselves. The firm behind the technology.

Hybrid Flow Shop with Setup Times Scheduling Problem

The two-stage hybridflow shop problem under setup times is addressed in this paper.This problem is NP-Hard.on the other hand,the studied problem is modeling different real-life applications especially in manufacturing and high performance-computing.Tackling this kind of problem requires the development of adapted algorithms.In this context,a metaheuristic using the genetic algorithm and three heuristics are proposed in this paper.These approximate solutions are using the optimal solution of the parallel machines under release and delivery times.Indeed,these solutions are iterative procedures focusing each time on a particular stage where a parallel machines problem is called to be solved.The general solution is then a concatenation of all the solutions in each stage.In addition,three lower bounds based on the relaxation method are provided.These lower bounds present a means to evaluate the efficiency of the developed algorithms throughout the measurement of the relative gap.An experimental result is discussed to evaluate the performance of the developed algorithms.In total,8960 instances are implemented and tested to show the results given by the proposed lower bounds and heuristics.Several indicators are given to compare between algorithms.The results illustrated in this paper show the performance of the developed algorithms in terms of gap and running time.

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.

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.

Lost in Translation Shopping online on Taobao can be tricky if not familiar with Chinese

There are a lot of online shopping platforms all over the globe, but one popular site that caught my attention since coming to China from Ghana is Taobao. Being China＇s largest online shopping platform, Taobao is known for being very user- friendly, especially if you can read Chinese characters.

TikTok Shop在东南亚五国的发展策略研究——基于SWOT-AHP模型的分析

伴随全球电商渗透率持续提升,我国的一批优秀企业纷纷开通跨境电商平台,以更好地助力品牌出海。字节跳动旗下的全球最大的短视频网站之一Tiktok就是其中的典型代表,其在东南亚五国开通了Titok Shop,为我国企业开拓东南亚市场搭建优质的跨境电商平台。首先总结了TikTok Shop在东南亚五国开展业务的现状,采用SWO T-AHP方法对TikTok Shop在东南亚五国的发展进行深入剖析,认为其战略发展方向应以SO战略为主;提出TikTok Shop应完善跨境电商物流体系、深耕本土化运营、完善品牌化建设、加强基础设施建设、培养跨境电商复合型人才等发展策略,以期为我国企业搭建优质的跨境电商平台,助力我国品牌出海提供有价值的参考。

基于零售业态集聚的Shopping Mall发展影响因素分析

研究零售业态集聚概念,对Shopping Mall的概念进行重构,揭示Shopping Mall是业态种类较多、空间分布紧促、且附带诸多服务功能的零售业态集聚的本质;构建Shopping Mall发展的影响因素模型,分析经济发展水平,区位选择、交通状况,服务设施与零售业态集聚的匹配、不同零售业态之间的耦合分别作为触发因素、环境因素、调节因素对Shopping Mall发展的影响。

“云消费”时代SHOPPING MALL的探索

当今我们正在进入一个新的消费时代——“云消费”时代，经历一场新的零售革命，不再是传统的业种业态之此消彼长，而是整个零售产业的重新洗牌。很多企业突然面对暴风疾雨式的零售革命，或是茫然找不到方向，或是盲动失去了自我。O2O、全渠道、互联网思维……各种概念满天飞，应当何去何从？观察全国各个知名零售商的经营业绩，诸多巨头不尽理想。

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.

Flexible Job Shop Composite Dispatching Rule Mining Approach Based on an Improved Genetic Programming Algorithm

To obtain a suitable scheduling scheme in an effective time range,the minimum completion time is taken as the objective of Flexible Job Shop scheduling Problems(FJSP)with different scales,and Composite Dispatching Rules(CDRs)are applied to generate feasible solutions.Firstly,the binary tree coding method is adopted,and the constructed function set is normalized.Secondly,a CDR mining approach based on an Improved Genetic Programming Algorithm(IGPA)is designed.Two population initialization methods are introduced to enrich the initial population,and a superior and inferior population separation strategy is designed to improve the global search ability of the algorithm.At the same time,two individual mutation methods are introduced to improve the algorithm’s local search ability,to achieve the balance between global search and local search.In addition,the effectiveness of the IGPA and the superiority of CDRs are verified through comparative analysis.Finally,Deep Reinforcement Learning(DRL)is employed to solve the FJSP by incorporating the CDRs as the action set,the selection times are counted to further verify the superiority of CDRs.

Strengthened Dominance Relation NSGA-Ⅲ Algorithm Based on Differential Evolution to Solve Job Shop Scheduling Problem

The job shop scheduling problem is a classical combinatorial optimization challenge frequently encountered in manufacturing systems.It involves determining the optimal execution sequences for a set of jobs on various machines to maximize production efficiency and meet multiple objectives.The Non-dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ)is an effective approach for solving the multi-objective job shop scheduling problem.Nevertheless,it has some limitations in solving scheduling problems,including inadequate global search capability,susceptibility to premature convergence,and challenges in balancing convergence and diversity.To enhance its performance,this paper introduces a strengthened dominance relation NSGA-Ⅲ algorithm based on differential evolution(NSGA-Ⅲ-SD).By incorporating constrained differential evolution and simulated binary crossover genetic operators,this algorithm effectively improves NSGA-Ⅲ’s global search capability while mitigating pre-mature convergence issues.Furthermore,it introduces a reinforced dominance relation to address the trade-off between convergence and diversity in NSGA-Ⅲ.Additionally,effective encoding and decoding methods for discrete job shop scheduling are proposed,which can improve the overall performance of the algorithm without complex computation.To validate the algorithm’s effectiveness,NSGA-Ⅲ-SD is extensively compared with other advanced multi-objective optimization algorithms using 20 job shop scheduling test instances.The experimental results demonstrate that NSGA-Ⅲ-SD achieves better solution quality and diversity,proving its effectiveness in solving the multi-objective job shop scheduling problem.