【摘要】：近年來,在電商網(wǎng)站的帶動下,推薦系統(tǒng)不斷發(fā)展。目前關(guān)于推薦系統(tǒng)的研究多集中于推薦系統(tǒng)算法設(shè)計優(yōu)化方面,從消費(fèi)者角度研究推薦系統(tǒng)的相關(guān)文獻(xiàn)較少,本研究從消費(fèi)者角度出發(fā)研究推薦系統(tǒng)對消費(fèi)者采納意向的影響機(jī)理,將有助于完善推薦系統(tǒng)設(shè)計,提高推薦服務(wù)質(zhì)量。本文基于消費(fèi)者視角,結(jié)合技術(shù)接受模型和信息系統(tǒng)成功模型,以推薦系統(tǒng)特征為自變量,構(gòu)建推薦系統(tǒng)對消費(fèi)者采納意向影響模型,分別以手機(jī)和圖書為搜尋品和體驗(yàn)品的代表,通過問卷調(diào)查法收集有效問卷386份,并借助smart PLS軟件對研究模型進(jìn)行結(jié)構(gòu)方程檢驗(yàn)分析,最后對完善推薦系統(tǒng)提出建議。主要結(jié)論如下:(1)推薦系統(tǒng)可以從信息質(zhì)量、系統(tǒng)質(zhì)量和交互質(zhì)量三個方面進(jìn)行評價,信息質(zhì)量可以從推薦準(zhǔn)確性、推薦多樣性、推薦新穎性、推薦關(guān)聯(lián)性等方面進(jìn)行衡量,系統(tǒng)質(zhì)量可以從界面設(shè)計和推薦解釋等方面進(jìn)行衡量,交互質(zhì)量可以從系統(tǒng)交互質(zhì)量和用戶間交互質(zhì)量等方面進(jìn)行衡量;(2)對于搜尋品來說,推薦準(zhǔn)確性、系統(tǒng)交互質(zhì)量、推薦多樣性、推薦關(guān)聯(lián)性、界面設(shè)計對采納意向既有間接影響,也有直接影響。而對于體驗(yàn)品來講,推薦準(zhǔn)確性、推薦解釋、界面設(shè)計、系統(tǒng)交互質(zhì)量、用戶間交互質(zhì)量對采納意向有間接影響,而且也有直接影響;(3)對于搜尋品來說,推薦系統(tǒng)特征對采納意向的影響程度由大到小依次是系統(tǒng)交互質(zhì)量、推薦準(zhǔn)確性、推薦多樣性、界面設(shè)計、推薦關(guān)聯(lián)性。對于體驗(yàn)品,推薦系統(tǒng)特征對采納意向的影響程度由大到小依次是用戶間交互質(zhì)量、界面設(shè)計、推薦解釋、推薦準(zhǔn)確性、系統(tǒng)交互質(zhì)量;(4)推薦新穎性對采納意向的影響作用在兩類產(chǎn)品中都不顯著;(5)建議商家加深對推薦系統(tǒng)角色的認(rèn)知,根據(jù)不同的產(chǎn)品類別設(shè)計推薦系統(tǒng),并拓展推薦系統(tǒng)的交互功能。
[Abstract]:In recent years, under the impetus of ecommerce website, recommendation system develops continuously. At present, most of the researches on recommendation system are focused on the optimization of the algorithm design of the recommendation system, and there are few related documents to study the recommendation system from the consumer's point of view. This study studies the influence mechanism of recommendation system on consumers' intention from the perspective of consumers, which will help to perfect the design of recommendation system and improve the quality of recommendation service. In this paper, based on the consumer perspective, combining the technology acceptance model and the information system success model, taking the characteristics of the recommendation system as the independent variable, this paper constructs the model of the impact of the recommendation system on the consumer's intention to adopt. Taking mobile phone and books as the representatives of search and experience, 386 valid questionnaires were collected by questionnaire, and the structural equation of the research model was tested and analyzed by smart PLS software. Finally, some suggestions were put forward to improve the recommendation system. The main conclusions are as follows: (1) recommendation system can be evaluated from three aspects: information quality, system quality and interaction quality. System quality can be measured from interface design and recommendation interpretation, interaction quality can be measured from system interaction quality and user interaction quality. (2) for search products, recommended accuracy, system interaction quality, etc. Recommendation diversity, recommendation relevance and interface design have both indirect and direct effects on the adoption intention. For experience products, recommendation accuracy, recommended interpretation, interface design, system interaction quality, and user interaction quality have indirect effects on the adoption intention, and also have a direct impact on the adoption intention; (3) for search products, The influence of the features of the recommendation system on the intention of adoption is followed by the quality of system interaction, the accuracy of recommendation, the diversity of recommendation, the design of interface and the relevance of recommendation. For the experience, the influence of the features of the recommendation system on the intention of adoption is in order of the interaction quality, interface design, recommendation interpretation, recommendation accuracy between the users. System interaction quality; (4) the effect of recommendation novelty on adoption intention is not significant in both categories of products; (5) it is suggested that merchants deepen their understanding of the role of recommendation system and design the recommendation system according to different product categories. And expand the interactive function of recommendation system.
【學(xué)位授予單位】：北方工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.3

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