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).

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.

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。

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.

Live-streaming selling strategies for competitive firms

The booming live-streaming commerce has significantly changed the traditional e-commerce model,thus attracting much attention from both industry and academia.In recent years,an increasing number of scholars have applied analytical models to explore live-streaming strategies for firms in different scenarios.However,the previous literature mainly considers monopolists,while in the real world,competition is not rare.To fill this gap between the literature and practical observations,this paper applies a game theoretical model to study live-streaming adoption and pricing strategy for firms under competitive environments.The results show that,for competitive firms,the equilibrium strategy depends on the relation between the commission rate and the intensity of the market expansion effect.Additionally,compared to the case in which no firm adopts live-streaming,competitive firms do not always benefit from the adoption of live-streaming selling.The paper also shows that competition plays a negative role in inducing a firm to adopt live-streaming.

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.

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.

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.

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.

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.

An Improved Harris Hawk Optimization Algorithm for Flexible Job Shop Scheduling Problem

Flexible job shop scheduling problem(FJSP)is the core decision-making problem of intelligent manufacturing production management.The Harris hawk optimization(HHO)algorithm,as a typical metaheuristic algorithm,has been widely employed to solve scheduling problems.However,HHO suffers from premature convergence when solving NP-hard problems.Therefore,this paper proposes an improved HHO algorithm(GNHHO)to solve the FJSP.GNHHO introduces an elitism strategy,a chaotic mechanism,a nonlinear escaping energy update strategy,and a Gaussian random walk strategy to prevent premature convergence.A flexible job shop scheduling model is constructed,and the static and dynamic FJSP is investigated to minimize the makespan.This paper chooses a two-segment encoding mode based on the job and the machine of the FJSP.To verify the effectiveness of GNHHO,this study tests it in 23 benchmark functions,10 standard job shop scheduling problems(JSPs),and 5 standard FJSPs.Besides,this study collects data from an agricultural company and uses the GNHHO algorithm to optimize the company’s FJSP.The optimized scheduling scheme demonstrates significant improvements in makespan,with an advancement of 28.16%for static scheduling and 35.63%for dynamic scheduling.Moreover,it achieves an average increase of 21.50%in the on-time order delivery rate.The results demonstrate that the performance of the GNHHO algorithm in solving FJSP is superior to some existing algorithms.

Research on the Impact of Social Media Live-Streaming on Consumer Purchasing Behavior and Its Coping Strategies

Due to the rapid development of network technology and the popularity of live-streaming,more and more businesses and individuals are using live-streaming to promote their products,giving rise to a new marketing model of social media live-streaming.Based on the social media live-streaming market,businesses or enterprises focus on tapping into new consumer demand,and social media live-streaming marketing has gradually expanded into various fields.Based on the SOR theory,this paper discusses how to improve the coping strategies of consumer purchasing behavior through social media live-streaming by analyzing the representative factors affecting consumer purchasing behavior such as anchors,brands,and live-streaming content in social media live-streaming.The results of this study will contribute to a better understanding of the relationship between social media live-streaming marketing models and consumer purchasing behavior and will help to reveal the potentials and limitations of social media live-streaming,thus providing valuable ideas and scientific basis for social media live-streaming marketing strategies.

Deep Reinforcement Learning Solves Job-shop Scheduling Problems

To solve the sparse reward problem of job-shop scheduling by deep reinforcement learning,a deep reinforcement learning framework considering sparse reward problem is proposed.The job shop scheduling problem is transformed into Markov decision process,and six state features are designed to improve the state feature representation by using two-way scheduling method,including four state features that distinguish the optimal action and two state features that are related to the learning goal.An extended variant of graph isomorphic network GIN++is used to encode disjunction graphs to improve the performance and generalization ability of the model.Through iterative greedy algorithm,random strategy is generated as the initial strategy,and the action with the maximum information gain is selected to expand it to optimize the exploration ability of Actor-Critic algorithm.Through validation of the trained policy model on multiple public test data sets and comparison with other advanced DRL methods and scheduling rules,the proposed method reduces the minimum average gap by 3.49%,5.31%and 4.16%,respectively,compared with the priority rule-based method,and 5.34%compared with the learning-based method.11.97%and 5.02%,effectively improving the accuracy of DRL to solve the approximate solution of JSSP minimum completion time.

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。

商业建筑设计对购物环境舒适性的影响——以悉尼Burwood Westfield Shopping Center为例

该文分析了悉尼Burwood Westfield Shopping Center购物中心的建筑设计思路,提出商业购物环境对消费行为的影响。文章分析了商业建筑设计应如何更好地实现购物环境舒适性、更充分地实现以人为本、为顾客服务的目的。

Shopping mall演绎北京商业新境界

Shopping mall在欧美发达国家是一个成熟的业态,是各国经济发展到特定阶段后出现的商业高端集聚形式,在中国,在北京仍处于起步阶段。从北京现行社会条件和商业结构、水平看,北京已具备了有限度发展Shopping mall的基本条件。贯彻了新理念,具有新文化的Shopping mall将演绎北京商业新的境界。

“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。

网络Shopping,网住谁?

9月的北京秋高气爽。一个晴朗的周末,本刊几位年轻记者相约购物。走进北京最宽敞明亮的金源购物中心,或许是职业习惯,忽然萌发做个随机抽样调查的想法,并立即实施。调查虽然随意,但却很有意义。因为从结果看,大多都有网上购物经历,这大出我们意料。

乐汇城·石家庄的国际Shopping Mall

国际体验型shopping Mall（一站式购物中心）将首次亮相河北省省会石家庄。2011年8月18日，石家庄新源乐汇城（一期）招商大会暨签约仪式，在北京东方君悦大酒店盛大举行。来自沃尔玛、奥斯卡电影院线、C＆A、H＆M等众多国内外顶，尖品牌的代表参加了此次大会，并签约入驻。

我国Shopping Mal业态发展中的问题——以上海港汇广场为例

Shopping Mall是集购物、休闲、娱乐、餐饮等多种功能为一体,是目前最新的第四种商业形态,虽然在中国发展迅速,但是却喜忧参半。以上海为例,上海既有如港汇广场这样较为成功的Shopping Mall,但是也有处于艰难运行的Shopping Mall。那么Shopping Mall在中国究竟应该发展,本文从项目选址、功能定位、商务管理等方面提出了若干对策性思路。