本文選題：網(wǎng)絡(luò)購買 + 數(shù)據(jù)挖掘　； 參考：《東華大學(xué)》2011年碩士論文

【摘要】：隨著以電子信息技術(shù)為基礎(chǔ)的電子商務(wù)在國內(nèi)的快速發(fā)展,網(wǎng)絡(luò)購物已經(jīng)成為中國網(wǎng)民工作生活的一部分,網(wǎng)上購物成為一種新型的商業(yè)模式已經(jīng)逐漸被中國消費(fèi)者接受,并逐步成為一種日常化的購物方式。這也吸引了越來越多的專家學(xué)者把其作為研究對象,其中對于網(wǎng)絡(luò)環(huán)境下消費(fèi)者的消費(fèi)心理、動機(jī)和行為等方面有較多論述研究,然而對于網(wǎng)絡(luò)消費(fèi)者購買決策影響因素的研究并不多見。本研究在總結(jié)和吸取以往消費(fèi)者購買相關(guān)研究成果的基礎(chǔ)上,結(jié)合當(dāng)前電子商務(wù)與網(wǎng)絡(luò)營銷理論實(shí)踐的新發(fā)展,通過實(shí)驗與實(shí)證相結(jié)合的研究方法,研究我國網(wǎng)民網(wǎng)絡(luò)購買決策的典型影響因素,通過數(shù)據(jù)挖掘方法對其進(jìn)行研究。先通過系統(tǒng)聚類算法從47個網(wǎng)絡(luò)購買決策的影響因素中聚類出網(wǎng)站個性化功能定制、網(wǎng)站娛樂性、網(wǎng)站網(wǎng)址記憶容易、有網(wǎng)絡(luò)廣告宣傳、店鋪交易信息公開透明、店鋪商品為知名品牌、店鋪信譽(yù)7個因素是網(wǎng)絡(luò)購買的典型影響因素,然后利用決策樹算法通過實(shí)驗研究方法對此7個典型影響因素進(jìn)行挖掘,挖掘出各個影響因素的重要性大小。以期研究成果可以為網(wǎng)絡(luò)賣家已經(jīng)相關(guān)研究領(lǐng)域提供參照與借鑒。 本文的創(chuàng)新點(diǎn)主要體現(xiàn)在以下兩個方面： 內(nèi)容創(chuàng)新：目前,國內(nèi)外學(xué)者對于網(wǎng)絡(luò)環(huán)境下消費(fèi)者的消費(fèi)心理、動機(jī)和行為等方面有較多論述,然而對于網(wǎng)絡(luò)消費(fèi)者購買決策關(guān)鍵影響因素的研究并不多見。把網(wǎng)絡(luò)購買決策影響因素作為本研究的對象,可以說是對該領(lǐng)域研究的一個探索、一個嘗試。目前少有的關(guān)于網(wǎng)絡(luò)購買決策影響因素的研究中,大多是針對網(wǎng)絡(luò)購買決策影響因素中的一個或數(shù)個進(jìn)行研究,本研究嘗試著能系統(tǒng)地對網(wǎng)絡(luò)購買決策影響因素進(jìn)行整體性研究,更具合理性和接近實(shí)際。 方法創(chuàng)新：R型系統(tǒng)聚類算法常應(yīng)用于解決復(fù)雜的、難以量化的且相關(guān)聯(lián)的多個因素間的聚類問題。本研究先通過R型系統(tǒng)聚類算法對網(wǎng)絡(luò)購買的所有影響因素進(jìn)行聚類,篩選出網(wǎng)絡(luò)購買決策典型影響因素,以此為基礎(chǔ),設(shè)計實(shí)驗獲取實(shí)驗數(shù)據(jù)作為決策樹數(shù)據(jù)挖掘算法的輸入,得到對網(wǎng)絡(luò)購買決策典型影響因素按重要性的排序。
[Abstract]:With the rapid development of electronic commerce based on electronic information technology in China, online shopping has become a part of the working life of Chinese Internet users, online shopping has become a new business model has been gradually accepted by Chinese consumers. And gradually become a daily way of shopping. This has also attracted more and more experts and scholars to take it as the object of study, among which there are more exposition and research on consumer consumption psychology, motivation and behavior in the network environment. However, the research on the influencing factors of online consumer purchase decision is rare. On the basis of summing up and absorbing the previous research results of consumer purchase, combined with the new development of electronic commerce and network marketing theory and practice, this study combines the experimental and empirical research methods. This paper studies the typical influencing factors of purchase decision of Internet users in China, and studies them by data mining method. Firstly, through the systematic clustering algorithm, we cluster out the personalized function customization of the website, the entertainment of the website, the easy memory of the website address, the publicity of the network advertisement, the transparency of the shop transaction information, among the influencing factors of 47 network purchase decisions. Store merchandise is a well-known brand, and store reputation is a typical influence factor of network purchase. Then the decision tree algorithm is used to excavate the seven typical influencing factors through experimental research method. Dig out the importance of each factor. With a view to the research results for online sellers have been related to provide reference and reference. The innovation of this paper is mainly reflected in the following two aspects: content innovation: at present, domestic and foreign scholars have more exposition on consumer consumption psychology, motivation and behavior in the network environment. However, there are few researches on the key factors of online consumer purchase decision. Taking the influencing factors of network purchase decision as the object of this study can be said to be an exploration and an attempt in this field. At present, most of the few researches on the influencing factors of network purchase decision are aimed at one or more of the influencing factors of network purchase decision. This study attempts to systematically study the influencing factors of network purchasing decision, which is more reasonable and close to reality. Methods the innovation of class R clustering algorithm is often applied to solve the complex, difficult to quantify and related multiple factors clustering problem. In this study, we first cluster all the influencing factors of network purchase through R-type system clustering algorithm, and screen out the typical influencing factors of network purchase decision. The experimental data are obtained as the input of the decision tree data mining algorithm, and the order of the typical influencing factors of the network purchase decision is obtained according to the importance of the data mining algorithm.
【學(xué)位授予單位】：東華大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2011
【分類號】：F724.6;F224

【引證文獻(xiàn)】

相關(guān)碩士學(xué)位論文 前3條

1 郭麗麗;展覽會專業(yè)觀眾參觀決策行為影響因素研究[D];東北財經(jīng)大學(xué);2011年

2 劉馨予;網(wǎng)購決策效率影響因素的實(shí)驗研究[D];西南財經(jīng)大學(xué);2012年

3 鐘玉潔;基于CHINA-VALS模型的網(wǎng)絡(luò)團(tuán)購消費(fèi)者分群研究[D];江南大學(xué);2013年

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本文編號：2044348