【摘要】：互聯(lián)網(wǎng)的普及帶動(dòng)了電子商務(wù)的蓬勃發(fā)展,網(wǎng)絡(luò)購物越來越受到消費(fèi)者的青睞。與傳統(tǒng)的線下購物相比,網(wǎng)上購物增加了消費(fèi)者對(duì)產(chǎn)品和服務(wù)質(zhì)量的不確定性,為了降低消費(fèi)者的不確定感,購物網(wǎng)站紛紛建立了用戶評(píng)價(jià)系統(tǒng),鼓勵(lì)購買過產(chǎn)品的用戶對(duì)產(chǎn)品質(zhì)量以及購物過程進(jìn)行評(píng)價(jià)。CNNIC調(diào)查表明在線評(píng)論成為影響消費(fèi)者購買決策最重要的因素。與此同時(shí)在線評(píng)論引起學(xué)術(shù)界高度關(guān)注,對(duì)在線評(píng)論的研究大致可以分為兩大類,一類是在線評(píng)論在個(gè)體層面對(duì)消費(fèi)者購買意愿的影響研究,另一類是在線評(píng)論在市場層面對(duì)產(chǎn)品銷售情況的影響研究。本文在大量閱讀國內(nèi)外相關(guān)文獻(xiàn)的基礎(chǔ)上,對(duì)在線評(píng)論相關(guān)的研究進(jìn)行了綜述。由于針對(duì)處于不同銷售階段的產(chǎn)品,用戶對(duì)在線評(píng)論的依賴程度不同,本文試圖探索在線評(píng)論對(duì)新舊產(chǎn)品銷量的影響及其差異。 基于消費(fèi)者購買決策行為理論、創(chuàng)新擴(kuò)散理論、信息過載理論和信息價(jià)值理論,作者提出了本文的研究假設(shè)。結(jié)合文獻(xiàn)研究,識(shí)別出評(píng)論數(shù)量大小、評(píng)論數(shù)量分布均勻程度、好評(píng)率、差評(píng)率作為解釋變量,產(chǎn)品價(jià)格作為控制變量,共同影響交易網(wǎng)站產(chǎn)品銷量,除了評(píng)論數(shù)和評(píng)論情感傾向這些常規(guī)的變量,本文首次關(guān)注評(píng)論數(shù)量分布情況對(duì)銷量的影響。本文使用爬蟲軟件抓取京東商城手機(jī)類產(chǎn)品的產(chǎn)品信息和評(píng)論信息,按照上架時(shí)間不同,將手機(jī)劃分為新產(chǎn)品和早期上架產(chǎn)品(舊產(chǎn)品),具體分析由三部分組成,首先是消費(fèi)者新舊產(chǎn)品評(píng)論行為對(duì)比分析,其次是在線評(píng)論對(duì)產(chǎn)品銷量影響研究,最后是在線評(píng)論對(duì)新舊產(chǎn)品銷量影響的對(duì)比分析。 本文通過實(shí)證數(shù)據(jù)分析,發(fā)現(xiàn)在線評(píng)論數(shù)量、在線評(píng)論離散系數(shù)和價(jià)格對(duì)產(chǎn)品銷量有顯著影響,好評(píng)率在低于0.9時(shí),對(duì)產(chǎn)品銷量有影響,在高于0.9時(shí)無顯著影響,評(píng)論數(shù)對(duì)新產(chǎn)品的影響大于對(duì)舊產(chǎn)品即舊產(chǎn)品的影響。在數(shù)據(jù)分析的基礎(chǔ)上本文提出了在線評(píng)論系統(tǒng)設(shè)計(jì)以及評(píng)論管理相關(guān)建議。
[Abstract]:The popularity of the Internet has led to the vigorous development of e-commerce, online shopping more and more favored by consumers. Compared with traditional offline shopping, online shopping increases consumers' uncertainty about the quality of products and services. In order to reduce the uncertainty of consumers, shopping websites have established user evaluation systems. The CNNIC survey shows that online reviews are the most important factors affecting consumers' purchasing decisions. At the same time, online reviews have attracted great attention in academic circles. The research on online reviews can be divided into two categories, one is the impact of online reviews on consumers' willingness to buy at the individual level. The other is the impact of online reviews on product sales at the market level. On the basis of reading a lot of relevant literature at home and abroad, this paper summarizes the research on online review. Due to the different degree of dependence of users on online reviews, this paper attempts to explore the impact of online reviews on the sales of new and old products and their differences. Based on the consumer purchase decision behavior theory, innovation diffusion theory, information overload theory and information value theory, the author puts forward the research hypothesis of this paper. Combined with the literature research, we can identify the number of comments, the uniform distribution of the number of comments, the rate of praise, the rate of poor evaluation as the explanatory variable, the price of the product as the control variable, which together affect the sales volume of the products on the trading website. In addition to the conventional variables such as the number of comments and the tendency of comment emotion, this paper focuses for the first time on the impact of the distribution of the number of comments on sales. In this paper, crawler software is used to grab the product information and comment information of mobile phone products in JingDong Mall. According to the different time on shelves, the mobile phone is divided into new products and early products (old products). The detailed analysis is made up of three parts. The first is the comparative analysis of the behavior of consumers' reviews of new and old products, the second is the impact of online reviews on product sales, and the last is the comparative analysis of the impact of online reviews on the sales of new and old products. Through the empirical data analysis, it is found that the quantity of online reviews, the dispersion coefficient and price of online reviews have significant effects on the sales volume of products. When the praise rate is lower than 0.9, it has an impact on the sales volume of products, but it has no significant effect on the sales when it is higher than 0.9. The effect of the number of comments on the new product is greater than on the old product, that is, the old product. On the basis of data analysis, this paper puts forward some suggestions on the design of online comment system and comment management.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：F724.6;F224

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本文編號(hào)：2186667