Empirical Analysis Of The Integration Impact Of Online Recommendation And Online Reviews On The Consumption Of Digital Content Products

Digital content product is an important field of information consumption. In the Several Opinions on Promoting Information Consumption to Expand Domestic Demand (2013), the State Council has explicitly put forward to enrich the content of information consumption, and to develop the emerging of digital content industry such as digital publishing. But because It is deeply affected by the network consumption environment, and it has the typical nature of experience goods, the consumption of digital content products is faced with challenges such as information overload, quality uncertainty. This leads to difficulty to choose, delayed purchase, dissatisfied for the choice, and lower willingness for continuous use.Online recommendation and online reviews are important means to propel the consumption of the digital content goods. Online recommendation is an effective tools to help consumers solve the information overload, filter and find the target products and improve the quality of decisions, but scholars remain suspicious of its effect. Online reviews are important source of information for online consumers to find out information about the quality of the product, to realize indirect experience and to reduce uncertainty, but there are many inconsistent opinions about its effect on sales. What’s more important, the existing researches ignores the interaction between online recommendation online reviews, and pays little attention to the mutual effect among these two and other factors when consumers are in the process of observational learning.In addition, there is a process of experience and continuous consumption, thus the influence and effect as mentioned above will become more complicated. In this case, this paper studies into the integration effect of online recommendation and online reviews to the experience and consumption of digital content products, explores the influential factors and mechanism, and aims to gives better guidance for the management of the online recommendation and online word-of-mouth marketing.This paper takes China Mobile Reading as research object; it starts from the whole process of access, experience and continuous consumption of the digital books, studies into the recommend research network and online comments on experience and the impact of online recommendation and online reviews on the basic process of experience and consumption of digital content products. It Includes the following contents:(1) A literature review for the relationship of online recommendation, online reviews, online experience and online marketing.(2) As the lack of existing related research, this paper, on the base of a case study of China Mobile Reading, concludes the basic process of experience and consumption of digital content products from the two aspects of website and books, analyzes and concludes the impact of online recommendation and online reviews.(3) Based on the theoretical and case analysis, our study combines the influences of online recommendation and online reviews, constructs a three-stage research model which includes "discovery and access--observe and select--continuous experience and consumption", and then proposes21hypotheses.(4) For the first stage (discovery and access stage), we empirically examine the effects of online recommendations. We analyse the effect of ranking、intelligent recommendation and artificial recommendation on the visits of digital book details page.(5) For the second stage (observation and selection stage), we empirically assess the effects of online reviews and observational learning. Our study analyses the effect of online reviews, online recommendation and observations learning on reading select, and the changes of the effects in different stages of the life cycle.(6) For the third stage (continuous experience and consumption stage),we empirically research the effect of online reviews and observational learning on continuous experience consumption.Based on the combination of theoretical and empirical analysis, we draw the following important conclusions:Firstly, Our study propose a three-stage integrated model for the particularity of digital content products. Three phases include "discovery and access,""observe and select","continuous experience and consumption", and we use the number of users to access the details page, reading rate and retention rate of users as the dependent variable. Meanwhile, online recommendation, online reviews observational learning, consumer expectation and perceived experience are involved in the integrated model system for analysis.Secondly, our research examines the effect of three types of recommendations on book visits through empirical ways. The study has found that there exists a cascade effect of ranking information and Pareto distribution, and the strength of intelligent recommendation has a significant positive impact on the details page visits, but to a certain extent diminishing marginal benefit. Further study using AB field test has found that sources of information in the artificial recommendation have a significant impact, and the impact of the experts recommendation is more than the impact of the users recommendation while the impact of the users recommendation is more than the recommendation with no source recommendation. For the TOPS ranking books. there are significant more book visitings when providing selling information labels than the same content but without these information.In this case the impact of the recommendation source was not significant, which fully explains that the top selling information has high diagnostic power and information cascade effect.Thirdly, we research the integrated impact of the online recommendation、online reviews and observational learning, finding that online reviews and poor rates have a significant effect on the reading rate. The quantity of rates is influential in the beginning, but becomes insignificant after observational learning variables are added, which indicates that observational learning has the partly substitute effect. Research finds that, as an observational learning variable, the accumulated PV (Page View) has positive influence on reading rate, and there is a complementary mechanism between online reviews and observational learning. In the meantime, we also find that, online recommendation can not only influence the starting rate of reading, but also have moderating effect, which enhances as the time goes from the books’putting on sales. Fourthly, the essay has done some research about the influence of consumption expectation and perceived quality on continuous experience consumption. We innovatively measured the consumption expectation factor showing the influence of online reviews and observational learning on next stage behaviors was constructed. Research finds that, as the number of pages read increase, the positive effect of perceived quality decreases but is still significant, while the positive effect of consumption expectation weakens and becomes non-significant in the end. One book’ s first few chapters are vital to decide whether the consumer will stay.Lastly, the essay has done some innovation on data collection and research methods. Using big data method, we collect a large sample of deep panel data on the Mobile Reading platform, like user’s visiting route, detail pages’quantity of visiting users, staying rate in every chapter of a book and so on. With these we construct the big-data foundation which surpasses previous researches. In the meantime, this essay does multiple regression analysis based mainly on panel data, and controls the inner difference of books using fixed effects method. In the research of artificial recommendation on a website, the essay uses the method of A/B Test field experiment innovatively to improve the inner and external effect. In the research of continuous experience consumption stage, the essay constructs a creative method of segmented multiple regression.