A Study on the Factors Influencing Consumer Purchase Decision Under the Live-Streaming Sales Model

In recent years,with the rapid development and popularization of Internet information technology,many new media platforms have risen rapidly,and major e-commerce companies have begun to explore the mode of livestreaming.Especially during the COVID-19 pandemic,due to the lockdown,live-streaming has become an important means of economic development in many places.Owing to its remarkable characteristics of timeliness,entertainment,and interactivity,it has become the latest and trendiest sales mode of e-commerce channels,reflecting huge economic potential and commercial value.This article analyzes two models and their characteristics of live-streaming sales from a practical perspective.Based on this,it outlines consumer purchasing decisions and the factors that affect consumer purchasing decisions under the live-streaming sales model.Finally,it discusses targeted suggestions for using the live-streaming sales model to expand the consumer market,hoping to promote the healthy and steady development of the live-streaming sales industry.

Prediction of NFT Sale Price Fluctuations on OpenSea Using Machine Learning Approaches

The rapid expansion of the non-fungible token(NFT)market has attracted many investors.However,studies on the NFT price fluctuations have been relatively limited.To date,the machine learning approach has not been used to demonstrate a specific error in NFT sale price fluctuation prediction.The aim of this study was to develop a prediction model for NFT price fluctuations using the NFT trading information obtained from OpenSea,the world’s largest NFT marketplace.We used Python programs to collect data and summarized them as:NFT information,collection information,and related account information.AdaBoost and Random Forest(RF)algorithms were employed to predict the sale price and price fluctuation of NFTs using regression and classification models,respectively.We found that the NFT related account information,especially the number of favorites and activity status of creators,confer a good predictive power to both the models.AdaBoost in the regression model had more accurate predictions,the root mean square error(RMSE)in predicting NFT sale price was 0.047.In predicting NFT sale price fluctuations,RF performed better,which the area under the curve(AUC)reached 0.956.We suggest that investors should pay more attention to the information of NFT creators.We anticipate that these prediction models will reduce the number of investment failures for the investors.

Clothing Sales Forecast Considering Weather Information: An Empirical Study in Brick-and-Mortar Stores by Machine-Learning

Reliable sales forecasts are important to the garment industry. In recent years, the global climate is warming, the weather changes frequently, and clothing sales are affected by weather fluctuations. The purpose of this study is to investigate whether weather data can improve the accuracy of product sales and to establish a corresponding clothing sales forecasting model. This model uses the basic attributes of clothing product data, historical sales data, and weather data. It is based on a random forest, XGB, and GBDT adopting a stacking strategy. We found that weather information is not useful for basic clothing sales forecasts, but it did improve the accuracy of seasonal clothing sales forecasts. The MSE of the dresses, down jackets, and shirts are reduced by 86.03%, 80.14%, and 41.49% on average. In addition, we found that the stacking strategy model outperformed the voting strategy model, with an average MSE reduction of 49.28%. Clothing managers can use this model to forecast their sales when they make sales plans based on weather information.

Product Specification Analysis for Modular Product Design Using Big Sales Data

Big data on product sales are an emerging resource for supporting modular product design to meet diversified customers’requirements of product specification combinations.To better facilitate decision-making of modular product design,correlations among specifications and components originated from customers’conscious and subconscious preferences can be investigated by using big data on product sales.This study proposes a framework and the associated methods for supporting modular product design decisions based on correlation analysis of product specifications and components using big sales data.The correlations of the product specifications are determined by analyzing the collected product sales data.By building the relations between the product components and specifications,a matrix for measuring the correlation among product components is formed for component clustering.Six rules for supporting the decision making of modular product design are proposed based on the frequency analysis of the specification values per component cluster.A case study of electric vehicles illustrates the application of the proposed method.

Optimization of Electricity Purchase and Sales Strategies of Electricity Retailers under the Condition of Limited Clean Energy Consumption

In the process of my country’s energy transition,the clean energy of hydropower,wind power and photovoltaic power generation has ushered in great development,but due to the randomness and volatility of its output,it has caused a certain waste of clean energy power generation resources.Regarding the purchase and sale of electricity by electricity retailers under the condition of limited clean energy consumption,this paper establishes a quantitative model of clean energy restricted electricity fromthe perspective of power system supply and demand balance.Then it analyzes the source-charge dual uncertain factors in the electricity retailer purchasing and selling scenarios in the mid-to long-term electricity market and the day-ahead market.Through the multi-scenario analysis method,the uncertain clean energy consumption and the user’s power demand are combined to form the electricity retailer’s electricity purchase and sales scene,and the typical scene is obtained by using the hierarchical clustering algorithm.This paper establishes a electricity retailer’s risk decisionmodel for purchasing and selling electricity in themid-and long-term market and reduce-abandonment market,and takes the maximum profit expectation of the electricity retailer frompurchasing and selling electricity as the objective function.At the same time,in themediumand longterm electricity market and the day-ahead market,the electricity retailer’s purchase cost,electricity sales income,deviation assessment cost and electricity purchase and sale risk are considered.The molecular results show that electricity retailers can obtain considerable profits in the reduce-abandonment market by optimizing their own electricity purchase and sales strategies,on the premise of balancing profits and risks.

Comparison of Sales Prediction in Conventional Insights and Machine Learning Perspective

With the integration of global economy development and the rapid growth of science knowledge and technology,the needs of people’s consumption are increasingly personalized and diversified.Such a market background makes sales forecasting become an indispensable part of enterprise management and development.The definition of the sales forecasting is that based on the past few years’sales situation,the enterprises through systematic sales forecasting models estimate of the quantity and amount of all or some specific sales products and services in a specific time in the future.Accurate sales forecasting can promote enterprises to do better in future revenue,and can also encourage enterprises to set and keep an efficient sales management team.This paper will analyze traditional sales forecasting methods and sales forecasting methods based on big data models related to the perspective of machine learning,and then compare them.The research shows that the two sales forecasting methods have their own advantages and disadvantages.In the future,enterprises can adopt the two sales forecasting methods in parallel to maximize the utilization advantage of sales forecasting for enterprises.

Did weekly economic index and volatility index impact US food sales during the first year of the pandemic?

We explore the impacts of economic and financial dislocations caused by COVID-19 pandemic shocks on food sales in the United States from January 2020 to January 2021.We use the US weekly economic index(WEI)to measure economic dislocations and the Chicago Board Options Exchange volatility index(VIX)to capture the broader stock market dislocations.We validate the NARDL model by testing a battery of models using the autoregressive distributed lags(ARDL)methodology(ARDL,NARDL,and QARDL specifications).Our study postulates that an increase in WEI has a significant negative long-term effect on food sales,whereas a decrease in WEI has no statistically significant(long-run)effect.Thus,policy responses that ignore asymmetric effects and hidden cointegration may fail to promote food security during pandemics.

Production and sales growth of China's knitting industry slows down,export hits a record high

In 2022,the international economic and trade environment is in turmoil,singed by the pandemic,while domestic sales are weak.In the face of complex and severe situation at home and abroad,China textile industry has shown strong resilience.In 2022,36,000 enterprises in China's textile industry achieved more than 5,200 billion yuan in main business revenue and 200 billion yuan in profit,with operating margin of 3.9 percent.In 2022,China's textile and apparel exports reached a new record high of 340.95 bilion US dollars,up 2.5 percent year-on-year.

NEV Sales Surge In April

Retail sales of new energy vehicles(NEVs)in China jumped 85.6 percent year on year in April,data from the China Passenger Car Association(CPCA)showed on 9 May.A total of 527,000 NEVs were sold in China in April,down 3.6 percent from March,according to the CPCA.NEV sales of major domestic brands accounted for 70.5 percent of the total NEV sales in the country,the data revealed.

Research on the Effect of R&D Investment Intensity and Sales Expense on the Performance of Biomedical Enterprises

Objective To explore the influence of new drug R&D investment and sales expense on the performance of biomedical enterprises.Methods The financial statements of 76 listed biomedical enterprises for 5 consecutive years were selected,and the data were modeled to study the effect of R&D investment and sales expense on the performance of biomedical enterprises by using financial indicators as tools and statistical methods of multiple linear regression.Results and Conclusion Under the premise that the weak related factors such as enterprise scale,life cycle and asset-liability ratio are set as unrelated variables,the R&D investment intensity of biomedical enterprises is negatively correlated with the current performance,which also shows that the R&D of biomedical enterprises has the characteristics of high risk.Besides,the influence of early R&D investment is delayed.However,the sales expense of leading biomedical enterprises with large scales have higher proportion.Meanwhile the greater sales expense of the same enterprise in different periods,the better the enterprise performance is.Biomedical enterprises should consider their own development stage to develop more patented drugs.Besides,they must formulate plans for allocating reasonable sales personnel and cost expense to ensure that enterprises can obtain better benefits.

The Contribution Margin due to a Limiting Factor in the Presence of Several Sales Options: Actuality Is Not Always As It Appears at the Beginning of the Analysis

The analysis of company data useful for economic decisions,if not interpreted in an overall view of the company situation,can lead to wrong conclusions.This is the case when a company has to choose between several sales options for one or more products in the presence of a limiting factor.The continuation of the investigation often denies the initial analysis.Not everything is as it appears,therefore,at the beginning of the deepening of the data useful for economic decisions.As it is well known,the choices of profitability concerning the planning of the sale of company products take place,at least in the majority of cases,through the determination of the contribution margin,i.e.the profitability margin connected to the individual goods/services sold by the companies(selling price net of variable costs).The contribution margin can be determined with four objectives:(1)Determination of the yield of the single product,net of variable costs only.In this case,the margin defines unitary,from net product yield to unitary contribution margin.(2)Determination of the return on total sales of an individual product,net of variable costs.In this hypothesis,reference is made to the first level(or gross)contribution margin.(3)Determination of the ability of the individual product to contribute to the coverage of fixed costs common to the company.This margin is determined net of special product variable and fixed costs.This aggregate is defined as a Level II(or semi-gross)margin.(4)Determination of the useful value in the planning choices in case of presence of scarce productive factors.In this case,it must identify the so-called unitary margin for low factor.Here we will only deal with the problem of the use of the contribution margin in the presence of rare factors.To complete the analysis,below are some very brief considerations regarding,respectively,the unitary,level I,and level II contribution margin in order to better understand where the problem of the most convenient choice of income is located in the event of the presence of rare production factors,especially in an environment characterized by a plurality of sales options.

油气企业销售业务和加油站转型路径设计

“双碳”目标背景下,国内能源结构加速调整,新能源汽车开始发力,对传统油气企业业务影响深远,尤其是给终端销售环节带来的冲击和影响巨大,油气企业的销售业务与加油站经营亟待转型。文章分析光伏发电、充电、换电、加氢四大赛道无法承载销售业务转型的原因;并探讨油气企业打造电氢新能源产业链的设想,销售业务以补能为中心纵向延伸、横向拓展构建培育“车—能—路—云”融合发展新生态服务模式。销售业务转型需要以加油站和站外站为依托,站点通过分步、分类、分层、有序转型推动销售业务转型。提出了智慧化建设、投资策略、考核引领、机构设施、人才培养以及跟踪研究等方面的建议。

高速铁路预售期旅客购票量分布预测

在预售前(相隔31 d)预测高速铁路预售期旅客购票量分布是铁路企业精准进行收益管理的前提。基于高速铁路预售模式和旅客售票数据,分析预售期内各预售日旅客购票量的相关性,探究预售期旅客购票量分布的影响因素。综合考虑出发日特征以及旅客购票量分布时序特征的影响,构建了考虑多输出间关联性的最小二乘支持向量回归-卷积长短期记忆网络(MLSSVR-ConvLSTM)模型。以京沪高铁线路中上海虹桥站至北京南站、上海虹桥站至徐州东站、上海虹桥站至无锡东站这3种不同距离OD旅客为例,进行预售期旅客购票量分布预测实例分析。研究结果显示:MLSSVR-ConvLSTM模型预测结果较好地反映了真实的预售期旅客购票量分布的变化趋势,平均绝对百分比误差为6.7%~11.0%,预测效果优于多元线性回归(MLR)、K近邻回归(KN)、极致梯度提升算法(XGBoost)、支持向量回归机(SVM)、多输出最小二乘支持向量回归(MLSSVR)和卷积长短期记忆网络(ConvLSTM)等模型,验证了所提出模型的合理性和有效性。进而表明,在构建预售期旅客购票量分布预测模型时,考虑预售期旅客购票量分布整体性以及各类因素的综合影响可有效地提高模型预测精度。所提出的预售期旅客购票量分布预测模型可以为铁路企业制定动态票额分配和浮动票价等政策提供理论支撑。

1+X证书背景下药品经营人才培养研究

目的探讨“1+X”证书背景下药品经营人才培养路径。方法以“药品购销”职业技能等级标准为指南,以“1+X”药品购销员考试内容为依据进行改革。结果修订和完善人才培养方案、课程标准,同时进行课程“三教”改革。结论“1+X”证书制度背景下进行药学专业(群)教育改革必要可行。

基于卷积神经网络的电力市场短期售电量预测方法

电力市场短期售电量预测的精度对优化用电结构以及提高供电可靠性具有重要意义,传统短期售电量预测方法没有考虑偏差电量考核影响、用电行为差异导致电预测精度低,提出基于卷积神经网络的电力市场短期售电量预测方法,首先根据用户的用电负荷率进行分类,获取不同行业的用电特征和需求模式,然后考虑正负偏差电量的影响,设计基于CNN-ResNet的短期售电量预测方法,通过实验分析表明,该方法能够有效提高多因素影响下售电量预测的准确率。

基于六西格玛对汽车销售服务质量提升的研究

如何提高销售服务质量成为汽车4S店当前面临的新问题。本论文以X汽车4S店为研究对象,采用minitab分析出影响汽车销售服务质量的关键因素,在此基础上采用六西格玛管理方法加以改善,从而促进汽车公司提高销售服务水平,为提高销售服务质量提供一定的理论依据。

1+X证书背景下药品经营与管理专业三融通课程体系构建研究——以药品购销职业技能等级证书为例

“1+X”证书背景下,在进行专业核心课程建设的同时,要体现职普融通、产教融通和课证融通,即“三融通”。而“三融通”的课程体系构建的最终落脚点为有效的课证融通。文章利用PDCA循环,形成了药品经营与管理相关专业核心课程的“三融通”课程构建路径,在课程建设过程中注重课证充分融通,将药品购销职业技能等级证书的技能点与课程思政元素有效融入专业的各级各类课程中,并进行多元客观评价以及本土化人才培养。

基于集成学习的电商销量预测研究分析

针对电商销量数据的复杂性和现有预测模型的稳定性及泛化能力不足问题,本研究基于大样本多变量数据,应用随机森林和渐进梯度回归树等机器学习模型进行分析。研究结果表明,相比于广义线性回归、弹性网络、支持向量回归、KNN回归树、决策树、多层感知机、AdaBoost,随机森林和渐进梯度回归树对电商销售数据预测拟合更加精确。相比于广义线性回归、弹性网络等7种传统机器学习算法,随机森林和渐进梯度回归树这两种集成学习的方法对电商销量预测更加精确,且渐进梯度回归树算法拟合效果更好、均方根误差更小,是一种更加有效的电商销量预测方法。

高速铁路旅客购票时间选择行为研究

研究高速铁路旅客的购票行为对铁路企业预测需求、优化票价、分配票额等具有重要意义。考虑旅客个体和群体层面的异质性,分别构建混合和潜在类别Logit模型,研究出行时段、票种和供需关系等因素对旅客购票时间选择的影响。利用售票数据估计模型参数,得到高速铁路旅客购票时间选择的规律:一天中下午和晚上出行的旅客在出行当日或前一天购票的可能性更高;购买全价票、儿童票、半价票的旅客偏向于在距离出发日9~30 d内购票,免票旅客往往选择随到随走;供需关系会显著影响旅客的购票时间选择而且越临近出发日影响越显著。进一步结合预售期购票量预测值和旅客购票选择概率,计算各预售阶段的购票量,可为铁路企业制定票额预分和浮动票价等策略提供决策依据。

某医院2019—2022年麻醉药品使用情况分析

目的对某医院2019—2022年麻醉药品使用情况进行分析,以期加强麻醉药品的规范管理,保障麻醉药品在临床中合理使用。方法调取该医院信息系统中2019—2022年麻醉药品的使用数据,包括药品的名称、规格、数量、使用金额,计算各品规销售金额占比、用药频率(DDDs)、限定日费用(DDC)、使用金额排序与DDDs的排序比(B/A)。并对2019—2022年该医院麻醉药品的处方点评结果进行汇总分析。结果4年间,该院麻醉药品共使用18个品规;使用金额总体呈增长趋势,且每种麻醉药品的使用金额每年排序相对稳定;DDDs稳居前三位的是吗啡缓释片(30 mg)、芬太尼贴剂(8.4 mg)和芬太尼贴剂(4.2 mg);DDC相对稳定,前3名分别是羟考酮注射液、注射用瑞芬太尼(1 mg)和注射用瑞芬太尼(2 mg);所有麻醉药品的B/A处于0.1~3.5之间。该院麻醉药品处方医嘱点评合格率在97%以上。结论该院麻醉药品的临床使用基本合理,用药符合医院学科特色,能够全面、认真地贯彻并落实麻醉药品相关法律法规,符合麻醉药品的安全性、有效性、经济性和适宜性原则。