知识付费产品销量影响因素研究:以知乎Live为例
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摘要
伴随着在线知识付费平台的蓬勃发展,基于互联网的知识付费市场正在迅速成长。通过知识付费平台,用户可以轻松便捷的分享知识,藉此获得收入;用户也可以付费或者免费收听他人分享的知识。目前在线虚拟社区中的知识付费研究主要集中探究知识提供者生产意愿的影响因素。从知识消费者角度出发,探究其对新兴知识产品付费意愿及知识产品销量影响因素的研究还相对较少。本文以社区直播类知识付费平台知乎Live为研究对象,根据其实际运营数据,构建基于信号理论的模型并提出假设,探究知识产品(如Live)价格对其销量的影响。本文研究发现,价格对知识产品销量有负向影响,但是当产品评论数量足够多时,价格对销量的负向影响减弱。对于知识这种特殊的商品,在不同的情境下价格对知识消费者是成本信号与质量信号的混合。本研究对现有文献做出了补充,同时可帮助知识付费平台更精准地对产品销量进行预测;同时对知识分享者、消费者提供建议,引导其更高效的完成知识交易。
With the rapid development of online knowledge sharing platforms which provide paid knowledge content, the Internet-based knowledge exchange market is growing rapidly. The development of web 2.0 makes it easier for people to share their experience and skills. On knowledge-trading platforms, these experience and skills can conveniently be traded as knowledge commodity and bring profit to knowledge providers, which forms the current era of "knowledge realization". However, it is difficult to evaluate the utility function of knowledge products as typical experience goods. In consumers' decision-making process,consumers usually judge the quality of experience products by observing multiple cues in the transaction, such as product price, characteristic of online reviews(including review valence and volume), popularity cues(such as the number of likes) and reputation of product providers.Prior research on signaling theory suggested that cost and difficulty to obtain could influence signal properties. Although quality cues like reviews characteristic(review valence and volume) have been intensively studied in the literature, how these review characteristics and reputation of knowledge providers may interact with price under the circumstance of knowledge paid content is still scant. Drawn upon signaling theory, this work proposes and empirically tests a conceptual framework to reveal how price cues interact with other cues and influence sales volume of online knowledge products. We mainly seek to answer the following research questions:(1) What role does price play in consumers' purchase process of online knowledge paid products?(2) How does review volume moderate the effect of price for online knowledge paid products?(3) How does provider reputation moderate the effect of price for online knowledge paid products?Zhihu, built in Jan 2011, is one of the largest online social-network-based Q&A community in China. It aims at building a knowledge repository of questions and answers. In May 2016, Zhihu launched a new knowledge sharing project named Zhihu Live. Everyone can easily create Lives using Powerpoint, voice,video or text message to share their unique opinions and skills. Others can pay to participant. Up to December 2017, Zhihu has attracted 120 million registers,generated 150 million questions, 55 million answers and 250 thousand topics. More than 5 million people paid to participate Live and the repurchase rate reached 42%. Nearly 3000 people served as speaker, and the average income for Live speaker exceeded 10000 yuan(RMB)/hour.A python crawler was designed to collect data from Zhihu Live, transaction data from 2016.05.17 to 2017.09.17 was collected, including Live product information, sales information and user information. After the data pre-processing, we retained 4581 Live records. We employed multiple linear regression in SPSS 22.0 to verify our research models. The results show a positive relationship between review valence, review volume, number of likes, reputation of product provider and the sales volume of knowledge paid products, whereas price has a negative effect on the sales volume. However, the negative effect of price can be moderated by review volume, that is, the negative effect of price on sales of knowledge paid product decrease when there are many reviews. Unlike review volume, provider reputations don't have similar moderating effect.Several limitations need to be addressed when interpreting the findings of this research. First, we collected data from only one online knowledge sharing platform. Future studies should use various kinds of online knowledge products data to corroborate with our findings. Second, Within the parameters of this research, it was not possible to account for all exogenous variables. Historical sales may be an important factor for a specific consumer, while our cross-sectional data could not reflect the herding effect. Our future study will examine dynamic panel data because they may help understand complex consumer decision process. Besides, review information only can be generated after Live starts, which means influential factors for sales of knowledge paid products may be different before and after Live starts. This is also a promising direction for our further study. Finally, even though we investigated price interact with reviews, we did not examine the content of the reviews. A sentiment analysis of online reviews and its effect on purchase decisions for online knowledge products would be an interesting endeavor for future work in this domain.
With the rapid development of online knowledge sharing platforms which provide paid knowledge content, the Internet-based knowledge exchange market is growing rapidly. The development of web 2.0 makes it easier for people to share their experience and skills. On knowledge-trading platforms, these experience and skills can conveniently be traded as knowledge commodity and bring profit to knowledge providers, which forms the current era of "knowledge realization". However, it is difficult to evaluate the utility function of knowledge products as typical experience goods. In consumers' decision-making process,consumers usually judge the quality of experience products by observing multiple cues in the transaction, such as product price, characteristic of online reviews(including review valence and volume), popularity cues(such as the number of likes) and reputation of product providers.Prior research on signaling theory suggested that cost and difficulty to obtain could influence signal properties. Although quality cues like reviews characteristic(review valence and volume) have been intensively studied in the literature, how these review characteristics and reputation of knowledge providers may interact with price under the circumstance of knowledge paid content is still scant. Drawn upon signaling theory, this work proposes and empirically tests a conceptual framework to reveal how price cues interact with other cues and influence sales volume of online knowledge products. We mainly seek to answer the following research questions:(1) What role does price play in consumers' purchase process of online knowledge paid products?(2) How does review volume moderate the effect of price for online knowledge paid products?(3) How does provider reputation moderate the effect of price for online knowledge paid products?Zhihu, built in Jan 2011, is one of the largest online social-network-based Q&A community in China. It aims at building a knowledge repository of questions and answers. In May 2016, Zhihu launched a new knowledge sharing project named Zhihu Live. Everyone can easily create Lives using Powerpoint, voice,video or text message to share their unique opinions and skills. Others can pay to participant. Up to December 2017, Zhihu has attracted 120 million registers,generated 150 million questions, 55 million answers and 250 thousand topics. More than 5 million people paid to participate Live and the repurchase rate reached 42%. Nearly 3000 people served as speaker, and the average income for Live speaker exceeded 10000 yuan(RMB)/hour.A python crawler was designed to collect data from Zhihu Live, transaction data from 2016.05.17 to 2017.09.17 was collected, including Live product information, sales information and user information. After the data pre-processing, we retained 4581 Live records. We employed multiple linear regression in SPSS 22.0 to verify our research models. The results show a positive relationship between review valence, review volume, number of likes, reputation of product provider and the sales volume of knowledge paid products, whereas price has a negative effect on the sales volume. However, the negative effect of price can be moderated by review volume, that is, the negative effect of price on sales of knowledge paid product decrease when there are many reviews. Unlike review volume, provider reputations don't have similar moderating effect.Several limitations need to be addressed when interpreting the findings of this research. First, we collected data from only one online knowledge sharing platform. Future studies should use various kinds of online knowledge products data to corroborate with our findings. Second, Within the parameters of this research, it was not possible to account for all exogenous variables. Historical sales may be an important factor for a specific consumer, while our cross-sectional data could not reflect the herding effect. Our future study will examine dynamic panel data because they may help understand complex consumer decision process. Besides, review information only can be generated after Live starts, which means influential factors for sales of knowledge paid products may be different before and after Live starts. This is also a promising direction for our further study. Finally, even though we investigated price interact with reviews, we did not examine the content of the reviews. A sentiment analysis of online reviews and its effect on purchase decisions for online knowledge products would be an interesting endeavor for future work in this domain.
引文
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(1)数据来源:http://m.sohu.com/a/213450030_115980
[2]Iimedia.2017 China Pay-to-Read Research Report[R].2017.
[3]艾瑞咨询.2018中国在线知识付费市场研究报告[R].2018.
[4]OECD.The knowledge based ecomony[R].Paris:Organization for Economic Co-operation and Development,1996:229-256.
[5]国家信息中心分享经济研究中心.中国共享经济发展年度报告(2018)[R].2018.
[6]艾媒咨询.2017年中国知识付费市场研究报告[R].2017.
[7]艾瑞咨询.中国B2B2C在线教育平台行业研究报告[R].2017.
[8]知识经济这一年,内容开始赚钱了,但它是一门持续的生意吗?年终盘点[EB/OL].http://www.ifanr.com/780052.
[9]Yan Z,Kuang L,Huang H,et al.Do Financial Incentives Induce More Online Participatory Behaviors?[C].ICIS,2017.
[10]刘子齐,吴鼎,郭讯华,et al.社会化问答社区用户知识贡献的影响因素探究[C].CNAIS,2017.
[11]Chyi HI.Willingness to Pay for Online News:An Empirical Study on the Viability of the Subscription Model[J].Journal of Media Economics,2005,18(2):131-142.
[12]Wagner TM,Hess T.What Drives Users to Pay for Freemium Services?Examining People’s Willingness to Pay for Music Services[J],2013.
[13]36氪.我们研究了28家平台,为你揭开知识付费的现状与未来|36氪知识新经济报告[R].2017.
[14]Dou W.Will Internet Users Pay for Online Content?[J].Journal of Advertising Research,2004,44(44):349-359.
[15]Dodds WB,Monroe KB,Grewal D.Effects of price,brand,and store information on buyers'product evaluations[J].Journal of Marketing Research,1991,28(3):307-319.
[16]Zeithaml VA.Consumer Perceptions of Price,Quality,and Value:AMeans-End Model and Synthesis of Evidence[J].Journal of Marketing,1988,52(3):2-22.
[17]Bearden WO,Shimp TA.The Use of Extrinsic Cues to Facilitate Product Adoption[J].Journal of Marketing Research,1982,19(2):229-239.
[18]Agarwal S,Teas RK.Perceived Value:Mediating Role of Perceived Risk[J].Journal of Marketing Theory&Practice,2001,9(4):1-14.
[19]Sweeney JC,Soutar GN,Johnson LW.The role of perceived risk in the quality-value relationship:A study in a retail environment[J].Journal of Retailing,1999,75(1):77-105.
[20]Galletta DF.Consumer Perceptions and Willingness to Pay for Intrinsically Motivated Online Content[J].Journal of Management Information Systems,2006,23(2):203-231.
[21]Goyanes M.An Empirical Study of Factors that Influence the Willingness to Pay for Online News[J].Journalism Practice,2014,8(6):742-757.
[22]Wang CL,Zhang Y,Ye LR,et al.Subscription to fee-based online services:What makes consumer pay for online content?[J].Journal of Electronic Commerce Research,2005,6(4):304.
[23]Dutta S.Analyzing consumer intention to pay for online content:Asystematic approach[J].Journal of Theoretical and Applied Information Technology,2012,38(1):89-102.
[24]Chu CW,Lu HP.Factors influencing online music purchase intention in Taiwan:An empirical study based on the value-intention framework[J].Internet Research,2007,17(2):139-155.
[25]Hidding GJ,Catterall SM.Anatomy of a learning organization:Turning knowledge into capital at Andersen Consulting[J].Knowledge and Process Management,1998,5(1):3-13.
[26]周波.知识交易的定价[J].经济研究,2007,(4):79-89.
[27]Hui KL,Chau PYK.Classifying digital products[M].ACM,2002:73-79.
[28]Arrow KJ.Economic Welfare and the Allocation of Resources for Invention[M].Macmillan Education UK,1959:609-626.
[29]Bhargava HK,Sundaresan S.Contingency Pricing for Information Goods and Services Under Industrywide Performance Standard[J].Journal of Management Information Systems,2003,20(2):113-136.
[30]Milgrom PR,Roberts JM.Price and Advertising Signals of New Product Quality[J].Journal of Political Economy,1984,94(4).
[31]Clow KE,Kurtz DL,Ozment J.A Longitudinal Study of the Stability of Consumer Expectations of Services[J].Journal of Business Research,1998,42(1):63-73.
[32]Balachander S,Ghose S.Reciprocal Spillover Effects:A Strategic Benefit of Brand Extensions[J].Journal of Marketing,2003,67(1):4-13.
[33]Mavlanova T,Benbunan-Fich R,Koufaris M.Signaling theory and information asymmetry in online commerce[J].Information&Management,2012,49(5):240-247.
[34]Xu Y,Cai S,Kim HW.Cue consistency and page value perception:Implications for web-based catalog design[J].Information&Management,2013,50(1):33-42.
[35]Nam S,Manchanda P,Chintagunta PK.The Effect of Signal Quality and Contiguous Word of Mouth on Customer Acquisition for a Video-on-Demand Service[J].Marketing Science,2010:690-700.
[36]Cai S,Lin X,Xu D,et al.Judging online peer-to-peer lending behavior[J].Information&Management,2016,53(7):857-867.
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(1)数据来源:http://m.sohu.com/a/213450030_115980