Testing for Sources of Irreversible Consumer Demand

The existence of irreversible demand is tested, whereby price increases induce a different absolute magnitude of quantity change than price decreases. Irreversibility is potentially likely in retail food settings for storable products that are consumed regularly and can affect pricing strategy performance. If irreversibility exists, the subsequent research question for storable product demand is whether loss aversion effects dominate stockpiling effects, or vice versa. A two-period theoretical model is developed, which predicts more elastic responses to downward price movements via stockpiling, but empirical tests on secondary data are needed to evaluate offsetting loss aversion effects. A variant of the Rotterdam demand model is developed to allow differential response to price increases and decreases. The model is applied to scanner data of short periodicity （weekly in this case）, which are necessary to measure meaningful demand responses to food price changes. The products selected are U.S. cheeses and table spreads that are storable over multiple weeks. The results suggest that stockpiling dominates loss aversion. One potential cause of this behavior may be that marketers asymmetrically provide consumers with more reference price information when lowering prices, but not when raising prices. When stockpiling effects dominate, given the typically price-elastic store-level demand for food products, high-low pricing strategies should produce higher revenue. Regarding measurement of average demand response, reversible demand models applied to weekly data may overestimate own-price elasticities.

Micro Seasonal Fashion Impact in RMG Industry of Bangladesh

The Ready-Made Garments (RMG) industry is a major contributor to the economy of Bangladesh, accounting for over 80% of the country’s total exports. In recent years, the industry has faced challenges due to changing global fashion trends and increasing competition from other manufacturing countries. One of the latest trends in the fashion industry is micro seasonal fashion, which has significant impacts on the RMG industry of Bangladesh. The purpose of this study is to examine the impact of micro seasonal fashion on the RMG industry of Bangladesh. It will examine the changes in buyer and consumer behavior and demand patterns, the implications for manufacturers and suppliers, and the strategies adopted by industry players to adapt to this trend. The emergence of micro seasonal fashion has disorderly the traditional seasonal cycle of the fashion industry. Consumers are no longer satisfied with the two-season model of spring/summer and fall/winter, but instead demand new styles and trends every few weeks. This trend has created opportunities for the RMG industry of Bangladesh to cater to the fast-changing demands of consumers. Micro Seasonal Fashion also poses significant challenges for the industry. Manufacturers and suppliers must be able to produce and deliver garments quickly and efficiently, while ensuring high quality and sustainability standards. This requires investments in technology, supply chain management, and training of workers. To address these challenges, industry players in Bangladesh have adopted several strategies, such as diversifying product offerings, investing in technology and innovation, and enhancing sustainability practices. These strategies have helped the industry to remain competitive and meet the demands of fast-changing consumer preferences. The industry must be able to adapt quickly to the changing demands of consumers and invest in technology and sustainability practices to remain competitive. With the right strategies and investments, the RMG industry of Bangladesh can control the opportunities presented by micro seasonal fashion and continue to be a major player in the global fashion industry.

Week Ahead Electricity Power and Price Forecasting Using Improved DenseNet-121 Method

In the Smart Grid(SG)residential environment,consumers change their power consumption routine according to the price and incentives announced by the utility,which causes the prices to deviate from the initial pattern.Thereby,electricity demand and price forecasting play a significant role and can help in terms of reliability and sustainability.Due to the massive amount of data,big data analytics for forecasting becomes a hot topic in the SG domain.In this paper,the changing and non-linearity of consumer consumption pattern complex data is taken as input.To minimize the computational cost and complexity of the data,the average of the feature engineering approaches includes:Recursive Feature Eliminator(RFE),Extreme Gradient Boosting(XGboost),Random Forest(RF),and are upgraded to extract the most relevant and significant features.To this end,we have proposed the DensetNet-121 network and Support Vector Machine(SVM)ensemble with Aquila Optimizer(AO)to ensure adaptability and handle the complexity of data in the classification.Further,the AO method helps to tune the parameters of DensNet(121 layers)and SVM,which achieves less training loss,computational time,minimized overfitting problems and more training/test accuracy.Performance evaluation metrics and statistical analysis validate the proposed model results are better than the benchmark schemes.Our proposed method has achieved a minimal value of the Mean Average Percentage Error(MAPE)rate i.e.,8%by DenseNet-AO and 6%by SVM-AO and the maximum accurateness rate of 92%and 95%,respectively.

Nursing homes’social responsibility and competitive edge:a cross‐sectional study on elderly choices about care service and price levels in Zhejiang Province,China

Background In the context of China's aging population,meeting consumer demand is an essential way for nursing homes to fulfill social responsibilities and improve competitive advantages.However,since little is known about the elderly's service level and price choices for nursing home care,this study aims to explore the non-disabled elderly's nursing home admission intention,service level,and price choices.Methods A cross-sectional survey of 402 non-disabled respondents was conducted in three different income level cities of Zhejiang Province,in July and August 2018.Multinomial logistic regression and multiple linear regression were used to identify the determinants of admission intention,service level choice,and price choice.Results Education,residence,and number of children were significantly associated with nursing home admission intention.Compared to those with no intention,the elderly with higher income and household wealth were less likely to have conditional intentions,and those living with the family were less likely to have unconditional intentions.Compared to medium-level services,the elderly with higher monthly income(relative risk ratio[RRR]3.07,95%confidence interval[CI]:1.801 to 5.233),household wealth(RRR 5.451,95%CI:2.249 to 13.216),and age(RRR 1.528,95%CI:1.004 to 2.326)were more likely to prefer high-level services,while older adults with higher monthly income(RRR 0.516,95%CI:0.344 to 0.774),and those with pensions(RRR 0.267,95%CI:0.076 to 0.931)were less likely to prefer low-level services.The elderly's price preference increased by 398 CNY as monthly income increased by 1000 CNY,and by 270 CNY as the housing number increased by one.Having pensions increased price preference(468 CNY),whereas having health insurance decreased price preference(–690 CNY).Conclusion The elderly's intention of nursing home admission was primarily affected by sociodemographic factors,while price and service level choices were primarily affected by financial factors.Nursing homes should use the market segmentation method to provide precision nursing home care for different groups of non-disabled elderly.

An analysis of Chinese urban residents＇ consumer demand employing a dynamic semi-parametric panel data model

Using data for China for the years 1991 to 2005 by province and employing the semi- parametric panel data model estimation method developed by Horowitz （2004） and Henderson et al. （2006） and Hubler＇s non-parametric generalized method of moments （GMM） estimation （2005）, this article constructs a dynamic semi-parametric panel data model and describes the dynamic changing trajectory of the effect on consumption of income disparity among urban residents. Our findings show that there is a significant ＂ratchet effect＂ in the consumption of urban residents; that income disparity among urban residents has a clear negative influence on consumption; and that the trajectory of this influence shows a roughly bimodal curve.