Abstract
User-generated content (UGC) is influential in reducing customer perceived risk and determining online store sales. E-sellers spend huge costs and efforts to improve UGC for it serves as a convenient and persuasive alternative for marketing and advertising purposes. Considering that consumers may set lower and/or upper limits (i.e., psychological thresholds) in which the good is expected to be, and purchase decisions are considered as a multi-stage decision process, yet models in previous research cannot uncover this decision-making process. Therefore, exploring the impact of UGC at each decision-making stage and detecting the psychological thresholds on various aspects of UGC (i.e., the fine-grained effects of UGC) contribute to optimizing the UGC with the best cost to boost sales. To this end, a fine-grained joint two-stage decision model, zero-inflated negative binomial regression (ZINB-P) model is proposed to support economical UGC marketing. Specifically, we compile a factors system composed of various types of aggregate-level statistics of UGC, which can impact risk perception. Afterward, change point analysis is used to find multi-level consumer psychological thresholds on UGC factors and consumers’ risk perception model is constructed to measure purchasing probabilities in the first decision-making stage. On the basis of consumers’ risk perception model, the ZINB-P model is built to fully capture the fine-grained effects of UGC factors on each stage of the consumer purchase decision. It integrates two stages of consumer decision: the consumer risk perception and non-compensatory choice in the first stage, and the second compensatory stage. A genetic algorithm is constructed to jointly estimate the parameters in ZINB-P model. Finally, an experiment on a kind of fresh produce from Taobao.com evidences the precision of our model. We demonstrate how our model can provide with economical UGC marketing strategies using a decision support table, in which some scenarios are identified. E-sellers can use this table to find the scenarios they are located in and identify the critical UGC factors that impede the sales in each scenario, and thus economical UGC marketing strategies can be obtained by improving these critical UGC factors.
Similar content being viewed by others
References
Mortimer, G., Fazal e Hasan, S., Andrews, L., & Martin, L. (2016). Online grocery shopping: The impact of shopping frequency on perceived risk. The International Review of Retail, Distribution and Consumer Research, 26(2), 202–223.
Hong, H., Xu, D., Wang, G. A., & Fan, W. (2017). Understanding the determinants of online review helpfulness: A meta-analytic investigation. Decision Support Systems, 102, 1–11.
Fader, P. S., & Winer, R. S. (2012). Introduction to the special issue on the emergence and impact of user-generated content. Marketing Science, 31(3), 369–371.
TurnTo. (2017). Hearing the Voice of the Consumer: UGC and the Commerce Experience. Retrieved February 4, 2020, from http://www2.turntonetworks.com/2017consumerstudy.
Cui, G., Lui, H. K., & Guo, X. (2010). Online reviews as a driver of new product sales. In 2010 International conference on management of e-Commerce and e-Government (pp. 20–25). IEEE.
Tirunillai, S., & Tellis, G. J. (2014). Mining marketing meaning from online chatter: Strategic brand analysis of big data using latent Dirichlet allocation. Journal of Marketing Research, 51(4), 463–479.
Xie, K., & Lee, Y. J. (2014). Quantifying the Impact of Earned and Owned Social Media Exposures in a Two-stage Decision Making Model of Brand Purchase. ICIS. Retrieved February 4, 2020, from https://aisel.aisnet.org/icis2014/proceedings/SocialMedia/25/.
De Maeyer, P. (2012). Impact of online consumer reviews on sales and price strategies: A review and directions for future research. Journal of Product & Brand Management, 21(2), 132–139.
Chintagunta, P. K., Gopinath, S., & Venkataraman, S. (2010). The effects of online user reviews on movie box-office performance: accounting for sequential rollout and aggregation across local markets. Marketing Science, 29(5), 944–957.
Duan, W., Gu, B., & Whinston, A. B. (2008). Do online reviews matter?—An empirical investigation of panel data. Decision Support Systems, 45(4), 1007–1016.
Chong, A. Y. L., Li, B., Ngai, E. W., Ch’ng, E., & Lee, F. (2016). Predicting online product sales via online reviews, sentiments, and promotion strategies: A big data architecture and neural network approach. International Journal of Operations & Production Management, 36(4), 358–383.
Roy, G., Datta, B., & Basu, R. (2017). Effect of eWOM valence on online retail sales. Global Business Review, 18(1), 198–209.
Tang, T., Fang, E., & Wang, F. (2014). Is neutral really neutral? The effects of neutral user-generated content on product sales. Journal of Marketing, 78(4), 41–58.
Chevalier, J. A., & Mayzlin, D. (2006). The effect of word of mouth on sales: Online book reviews. Journal of Marketing Research, 43(3), 345–354.
Li, X., Wu, C., & Mai, F. (2019). The effect of online reviews on product sales: A joint sentiment-topic analysis. Information & Management, 56(2), 172–184.
Ye, Q., Law, R., Gu, B., & Chen, W. (2011). The influence of user-generated content on traveler behavior: An empirical investigation on the effects of e-word-of-mouth to hotel online bookings. Computers in Human Behavior, 27(2), 634–639.
Ghose, A., & Ipeirotis, P. G. (2010). Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics. IEEE Transactions on Knowledge and Data Engineering, 23(10), 1498–1512.
Ghose, A., Ipeirotis, P. G., & Li, B. (2012). Designing ranking systems for hotels on travel search engines by mining user-generated and crowdsourced content. Marketing Science, 31(3), 493–520.
Scholz, M., Dorner, V., Landherr, A., & Probst, F. (2013). Awareness, interest, and purchase: The effects of user-and marketer-generated content on purchase decision processes. In 34th International conference on information systems (pp. 1–17).
Shocker, A. D., Ben-Akiva, M., Boccara, B., & Nedungadi, P. (1991). Consideration set influences on consumer decision-making and choice: Issues, models, and suggestions. Marketing Letters, 2(3), 181–197.
Drakopoulos, S. A. (1992). Psychological thresholds, demand and price rigidity. The Manchester School, 60(2), 152–168.
Monroe, K. B. (1973). Buyers’ subjective perceptions of price. Journal of Marketing Research, 10(1), 70–80.
Zou, P., Yu, B., & Hao, Y. (2011). Does the valence of online consumer reviews matter for consumer decision making? The moderating role of consumer expertise. Journal of computers, 6(3), 484–488.
Bauer, R. A. (1960). Consumer behavior as risk taking. In R. S. Hancock (Ed.), Dynamic marketing for a changing world, conference of the American marketing association (pp. 389–398). Chicago, IL: American Marketing Association.
Dillon, S., Buchanan, J., & Al-Otaibi, K. (2014). Perceived risk and online shopping intention: A study across gender and product type. International Journal of E-Business Research (IJEBR), 10(4), 17–38.
Taylor, J. W. (1974). The role of risk in consumer behavior: A comprehensive and operational theory of risk taking in consumer behavior. Journal of marketing, 38(2), 54–60.
Garbarino, E., & Strahilevitz, M. (2004). Gender differences in the perceived risk of buying online and the effects of receiving a site recommendation. Journal of Business Research, 57(7), 768–775.
Zeithaml, V. A., Gremler, D. D., & Bitner, M. J. (2018). Services marketing: Integrating customer focus across the firm (7th Edition). McGraw-Hill Education.
Dawes, J., & Nenycz-Thiel, M. (2014). Comparing retailer purchase patterns and brand metrics for in-store and online grocery purchasing. Journal of Marketing Management, 30(3–4), 364–382.
Eroglu, S. A., Machleit, K. A., & Davis, L. M. (2003). Empirical testing of a model of online store atmospherics and shopper responses. Psychology & Marketing, 20(2), 139–150.
Kim, J., & Lennon, S. J. (2013). Effects of reputation and website quality on online consumers’ emotion, perceived risk and purchase intention: Based on the stimulus-organism-response model. Journal of Research in Interactive Marketing, 7(1), 33–56.
Chunxia, W. U., & Ruihan, L. I. U. (2016). A study on consumers’ intentions and attitudes to fresh agricultural products for online shopping in China. International Journal of Simulation – Systems, Science & Technology, 17(45), 1–5.
Nelson, P. (1974). Advertising as information. Journal of Political Economy, 82(4), 729–754.
Lee, H. J., & Huddleston, P. (2006). Effects of e-tailer and product type on risk handling in online shopping. Journal of Marketing Channels, 13(3), 5–28.
Zhao, X., Wang, L., Guo, X., & Law, R. (2015). The influence of online reviews to online hotel booking intentions. International Journal of Contemporary Hospitality Management, 27(6), 1343–1364.
Simon, H. (1947). Administrative behavior. New York: Macmillan.
Erasmus, A. C., Boshoff, E., & Rousseau, G. G. (2001). Consumer decision-making models within the discipline of consumer science: A critical approach. Journal of Consumer Sciences, 29(1), 82–90.
Roberts, J. H., & Lattin, J. M. (1991). Development and testing of a model of consideration set composition. Journal of Marketing Research, 28(4), 429–440.
Moe, W. W. (2006). An empirical two-stage choice model with varying decision rules applied to internet clickstream data. Journal of Marketing Research, 43(4), 680–692.
Xie, K., & Lee, Y. J. (2015). Social media and brand purchase: Quantifying the effects of exposures to earned and owned social media activities in a two-stage decision making model. Journal of Management Information Systems, 32(2), 204–238.
Wu, J., & Rangaswamy, A. (2003). A fuzzy set model of search and consideration with an application to an online market. Marketing Science, 22(3), 411–434.
Zakay, D. (1990). The role of personal tendencies in the selection of decision-making strategies. The Psychological Record, 40(2), 207–213.
Anderson, C. J. (2003). The psychology of doing nothing: forms of decision avoidance result from reason and emotion. Psychological Bulletin, 129(1), 139–167.
Hauser, J. R., Ding, M., & Gaskin, S. P. (2009). Non-compensatory (and compensatory) models of consideration-set decisions. In Proceedings of the Sawtooth software conference (Vol. 14, pp. 207–232).
Gilbride, T. J., & Allenby, G. M. (2004). A choice model with conjunctive, disjunctive, and compensatory screening rules. Marketing Science, 23(3), 391–406.
Peters, R. C., Eeuwes, L. B., & Bretschneider, F. (2007). On the electrodetection threshold of aquatic vertebrates with ampullary or mucous gland electroreceptor organs. Biological Reviews, 82(3), 361–373.
Wallenius, J., Dyer, J. S., Fishburn, P. C., Steuer, R. E., Zionts, S., & Deb, K. (2008). Multiple criteria decision making, multiattribute utility theory: Recent accomplishments and what lies ahead. Management Science, 54(7), 1336–1349.
AliResearch. (2015). White paper on agricultural products electronic commerce of Ali. Retrieved February 4, 2020, from http://i.aliresearch.com/file/20150601/20150601222304.pdf.
Chen, X. (1991). Brief introduction to statistical analysis of change points: (II) least square method. Application of Statistics and Management, 1, 55–58.
Fryzlewicz, P. (2014). Wild binary segmentation for multiple change-point detection. The Annals of Statistics, 42(6), 2243–2281.
Sen, A., & Srivastava, M. S. (1975). On tests for detecting change in mean. The Annals of Statistics, 3(1), 98–108.
Greene, W. H. (2003). Econometric analysis. Bangalore: Pearson Education India.
Yao, L., & Sethares, W. A. (1994). Nonlinear parameter estimation via the genetic algorithm. IEEE Transactions on Signal Processing, 42(4), 927–935.
Hsu, C. J., Huang, C. Y., & Chen, T. Y. (2008). A modified genetic algorithm for parameter estimation of software reliability growth models. In 2008 19th International symposium on software reliability engineering (ISSRE) (pp. 281–282). IEEE.
Abo-Hammour, Z. E. S., Alsmadi, O. M., Al-Smadi, A. M., Zaqout, M. I., & Saraireh, M. S. (2012). ARMA model order and parameter estimation using genetic algorithms. Mathematical and Computer Modelling of Dynamical Systems, 18(2), 201–221.
Akaike, H.,(1998). Information theory and an extension of the maximum likelihood principle. In Selected papers of Hirotugu Akaike (pp. 199–213). New York, NY: Springer.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. Cambridge: MIT Press.
Moon, S., Park, Y., & Seog Kim, Y. (2014). The impact of text product reviews on sales. European Journal of Marketing, 48(11/12), 2176–2197.
Macdonald, E. K., & Sharp, B. M. (2000). Brand awareness effects on consumer decision making for a common, repeat purchase product: A replication. Journal of Business Research, 48(1), 5–15.
Chu, W., Choi, B., & Song, M. R. (2005). The role of on-line retailer brand and infomediary reputation in increasing consumer purchase intention. International Journal of Electronic Commerce, 9(3), 115–127.
Acknowledgements
This work was supported by the Chinese National Natural Science Foundation (No. 71871135).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Li, S.G., Zhang, Y.Q., Yu, Z.X. et al. Economical user-generated content (UGC) marketing for online stores based on a fine-grained joint model of the consumer purchase decision process. Electron Commer Res 21, 1083–1112 (2021). https://doi.org/10.1007/s10660-020-09401-8
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10660-020-09401-8