The dynamic effects of online product reviews on purchase decisions
Abstract
Previous studies have demonstrated that online reviews play an important role in the purchase decision process. Though the effects of positive and negative reviews to consumers’ purchase decisions have been analyzed, they were examined statically and separately. In reality, online review community allows everyone to express and receive opinions and individuals can reexamine their opinions after receiving messages from others. The goal of this paper is to study how potential customers form their opinions dynamically under the effects of both positive and negative reviews using a numerical simulation. The results show that consumers with different membership levels have different information sensitivities to online reviews. Consumers at low and medium membership levels are often persuaded by online reviews, regardless of their initial opinion about a product. On the other hand, online reviews have less effect on consumers at higher membership levels, who often make purchase decisions based on their initial impressions of a product.
Keyword : opinion evaluation, online reviews, membership level, purchase decision
How to Cite
Chen, J., Kou, G., & Peng, Y. (2018). The dynamic effects of online product reviews on purchase decisions. Technological and Economic Development of Economy, 24(5), 2045-2064. https://doi.org/10.3846/tede.2018.4545
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behaviour. New York: Prentice Hall.
Banerjee, S., Bhattacharyya, S., & Bose, I. (2017). Whose online reviews to trust? Understanding reviewer trustworthiness and its impact on business. Decision Support Systems, 96, 17-26. https://doi.org/10.1016/j.dss.2017.01.006
Babic, R., Sotgiu, F., De Valck, K., & Bijmolt, T. H. (2016). The effect of electronic word of mouth on sales: a meta-analytic review of platform, product, and metric factors. Journal of Marketing Research, 53(3), 297-318. https://doi.org/10.1509/jmr.14.0380
Berger, J., Sorensen, A. T., & Rasmussen S. J. (2010). Positive effects of negative publicity: when negative reviews increase sales. Marketing Science, 29(5), 815-827. https://doi.org/10.1287/mksc.1090.0557
Cai, Y., & Zhu, D. (2016). Fraud detections for online businesses: a perspective from blockchain technology. Financial Innovation, 2, 20. https://doi.org/10.1186/s40854-016-0039-4
Chan, O., & Ma, A. (2016). Stochastic cost flow system for stock markets with an application in behavioral finance. International Journal of Financial Engineering, 3(4), 1650026. https://doi.org/10.1142/S2424786316500262
Channel Advisor. (2011). Consumer survey: global consumer shopping habits. Channel Advisor, 2011.
Cheng, Y., & Ho, H. (2015). Social influence’s impact on reader perceptions of online reviews. Journal of Business Research, 68, 883-887. https://doi.org/10.1016/j.jbusres.2014.11.046
Cheung, C. M. K., & Lee, M. K. O. (2012). What drives consumers to spread electronic word of mouth in online consumer-opinion platforms. Decision Support Systems, 53, 218-225. https://doi.org/10.1016/j.dss.2012.01.015
Cheung, M. K., & Thadani, R. (2012). The impact of electronic word-of-mouth communication: a literature analysis an integrative model. Decision Support Systems, 54, 461-470. https://doi.org/10.1016/j.dss.2012.06.008
Cheung, M., Luo, C., Sia, C., & Chen, H. (2009). Credibility of electronic word-of-mouth: informational and normative determinants of on-line consumer recommendations. International Journal of Electronic Commerce, 13(4), 9-38. https://doi.org/10.2753/JEC1086-4415130402
Deborah, R., John, C., & Whitney, Jr. (1986). The development of consumer knowledge in children: a cognitive structure approach. Journal of Consumer Research, 12(March), 406-417.
Deffuant, G., Neau, D., Amblard, F., & Weisbuc, G. (2000). Mixing beliefs among interacting agents. Advances in Complex Systems, 3, 87-98. https://doi.org/10.1142/S0219525900000078
Deng L., & Liu Y. (2011). Research on opinion formation and extremists modeling in internet virtual community. Journal of Beijing Jiao Tong University, 35, 66-71.
Deng, L., Liu, Y., & Zeng, Q. (2012). How information influences an individual opinion evolution. Physica, A 391, 6409-6417. https://doi.org/10.1016/j.physa.2012.07.037
Dellarocas, C., Zhang, X., & Awad, N. (2007). Exploring the value of online product reviews in forecasting sales. Journal of Interactive Marketing, 21, 23-45. https://doi.org/10.1002/dir.20087
Eagly, A. H., & Chaiken, S. (1993). The psychology of attitudes. Harcourt Brace Jovanovich College Publishers, Fort Worth, TX.
Eldomiaty, T., Rashwan, M., Din, M., & Tayel,W. (2016). Firm, industry and economic determinants of working capital at risk. International Journal of Financial Engineering, 3(4), 1650031. https://doi.org/10.1142/S2424786316500316
Fazio, R. H. (1990). Multiple processes by which attitudes guide behavior: The MODE model as an integrative framework. Advances in Experimental Social Psychology, 23, 75-109. https://doi.org/10.1016/S0065-2601(08)60318-4
Floyd, K., Freling, R., Alhoqail, S., Cho, H., & Freling, T. (2014). How online product reviews affect retail sales: a meta-analysis. Journal of Retailing, 90(2), 217-232. https://doi.org/10.1016/j.jretai.2014.04.004
Filieri, R. (2015). What makes online reviews helpful? A diagnosticity-adoption framework to explain informational and normative influences in e-WOM. Journal of Business Research, 68(6), 1261-1270. https://doi.org/10.1016/j.jbusres.2014.11.006
Fu, D., & Wang, K. (2013). Does customer membership level affect online reviews? A study of online reviews from 360buy.Com in China. Proceedings – Pacific Asia Conference on Information Systems, PACIS 2013 01/2013, 1-12.
Friestad, M., & Wright, P. (1994). The persuasion knowledge model: how people cope with persuasion attempts. Journal of Consumer Research, 21, 1-31. https://doi.org/10.1086/209380
Hsu, C. L., & Liao, Y. C. (2014). Exploring the linkages between perceived information accessibility and microblog stickiness: the moderating role of a sense of community. Information & Management, 51, 833-844. https://doi.org/10.1016/j.im.2014.08.005
Khare, A., Labrecque, L. I., & Asare, A. K. (2011). The assimilative and contrastive effects of word-of-mouth volume: an experimental examination of online consumer ratings. Journal of Retailing, 87(1), 111-126. https://doi.org/10.1016/j.jretai.2011.01.005
Kim, D., Jang, S., & Adler, H. (2015). What drives café customers to spread eWOM?. International Journal of Contemporary Hospitality Management, 27(2), 261-282. https://doi.org/10.1108/IJCHM-06-2013-0269
Kou, G., Ergu, D., Lin, C., & Chen, Y. (2016). Pairwise comparison Matrix in multiple criteria decision making. Technological and Economic Development of Economy, 22(5), 738-765. https://doi.org/10.3846/20294913.2016.1210694
Kou, G., Peng, Y., & Wang, G. (2014a). Evaluation of clustering algorithms for financial risk analysis using MCDM methods. Information Sciences, 27, 1-12. https://doi.org/10.1016/j.ins.2014.02.137
Kou, G., & Lin, C. (2014b). A cosine maximization method for the priority vector derivation in AHP. European Journal of Operational Research, 235, 225-232. https://doi.org/10.1016/j.ejor.2013.10.019
Kou, G., Ergu, D., & Shang, J. (2014c). Enhancing data consistency in decision matrix: adapting Hadamard model to mitigate judgment contradiction. European Journal of Operational Research, (1), 261-271. https://doi.org/10.1016/j.ejor.2013.11.035
Kou, G., Lu, Y., Peng, Y., & Shi, Y. (2012). Evaluation of classification algorithms using MCDM and Rank Correlation. International Journal of Information Technology & Decision Making, 11(1), 197-225. https://doi.org/10.1142/S0219622012500095
Kulakowski, K. (2009). Opinion polarization in tie Receipt-Accept-Sample Model. Physica A: Statistical Mechanics and its Applications, 2(388), 469-476. https://doi.org/10.1016/j.physa.2008.10.037
Lee, J., Park, D., & Han, I. (2008). The effect of negative online consumer reviews on product attitude: an information processing view. Electronic Commerce Research and Applications, 7, 341-352. https://doi.org/10.1016/j.elerap.2007.05.004
Lee, J., & Lee, J. N. (2009). Understanding the product information inference process in electronic word-of-mouth: an objectivity–subjectivity dichotomy perspective. Information & Management, 46(5), 302-311. https://doi.org/10.1016/j.im.2009.05.004
Lee, K., & Koo, D. (2012). Effects of attribute and valence of e-WOM on message adoption: moderating roles of subjective knowledge and regulatory focus. Computers in Human Behavior, 28, 1974-1984. https://doi.org/10.1016/j.chb.2012.05.018
Libai, B., Bolton, R., Bügel, M. S., Ruyter, K., Götz, O., Risselada, H., & Stephen A. T. (2010). Customer-to-customer interactions: broadening the scope of word of mouth research. Journal of Service Research, 13(3), 267-282. https://doi.org/10.1177/1094670510375600
Liu, Y. (2006). Word of mouth for movies: its dynamics and impact on box office revenue. Journal of Marketing, 70(7), 74-89. https://doi.org/10.1509/jmkg.70.3.74
Luo, C., Luo, X., Xu, Y., Warkentin, M., & Sia, C. (2015). Examining the moderating role of sense of membership in online review evaluations. Information & Management, 52, 305-316. https://doi.org/10.1016/j.im.2014.12.008
Matlab. (2013). Version 8.0 [Software]. Natick, MA: The Math Works Inc.
Muchnik, L., Aral, S., & Taylor, S. J. (2013). Social influence bias: a randomized experiment. Science, 341, 647. https://doi.org/10.1126/science.1240466
Mukhopadhyay, B. (2016). Understanding cashless payments in India. Financial Innovation, 2, 27. https://doi.org/10.1186/s40854-016-0023-z
Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2, 1-135. https://doi.org/10.1561/1500000011
Park, D., & Kim, S. (2008). The effects of consumer knowledge on message processing of electronic word-of-mouth via online consumer reviews. Electronic Commerce Research and Applications, 7(4), 399-410. https://doi.org/10.1016/j.elerap.2007.12.001
Park, D. H., Lee, J., & Han, I. (2007). The effect of on-line consumer reviews on consumer purchasing intention: the moderating role of involvement. International Journal of Electronic Commerce, 11(4), 125-148. https://doi.org/10.2753/JEC1086-4415110405
Park, D., & Lee, J. (2008). eWOM overload and its effect on consumer behavioral intention depending on consumer involvement. Electronic Commerce Research and Applications, 7(4), 386-398. https://doi.org/10.1016/j.elerap.2007.11.004
Petty, R. E., Tormala, Z. L., Briüol, P., & Jarvis, W. B. G. (2006). Implicit ambivalence from attitude change: an exploration of the PAST model. Journal of personality and Social Psychology, 90, 21-41. https://doi.org/10.1037/0022-3514.90.1.21
Petty, R. E., & Cacioppo, J. T. (1984). Motivational factors in consumer response to advertisements. In W. Beatty, R. Geen, & R. Arkin (Eds.), Human motivation (pp. 418-454). New York: Allyn & Bacon.
Pietri, E. S., & Shook F. J. (2013). Weighting positive versus negative: the fundamental nature of valence asymmetry. Journal of Personality, 81(2), 196-208. https://doi.org/10.1111/j.1467-6494.2012.00800.x
Ridings, C. M., Gefen, D., & Arinze, B. (2002). Some antecedents and effects of trust in virtual communities. The Journal of Strategic Information Systems, 1(3), 271-295. https://doi.org/10.1016/S0963-8687(02)00021-5
Tee, H., & Ong, H. (2016). Cashless payment and economic growth. Financial Innovation, 2, 4. https://doi.org/10.1186/s40854-016-0023-z
Ullah, R., Amblee, N., Kim, W., & Lee, H. (2016). From valence to emotions: exploring the distribution of emotions in online product reviews. Decision Support Systems, 81, 41-53. https://doi.org/10.1016/j.dss.2015.10.007
Wang, X., Teo, H., & Wei, K. (2015). Simultaneity and interactivity of the effects of communication elements on consumers’ decision making in eWOM Systems. Journal of Electronic Commerce Research, 16(3), 153-174.
Wu, W., & Kou, G. (2016). A group consensus model for evaluating real estate investment alternatives. Financial Innovation, 2, 8. https://doi.org/10.1186/s40854-016-0027-8
Wu, X., Wang, X., Ma, S., & Ye, Q. (2017). The influence of social media on stock volatility. Frontiers of Engineering Management, 4(2), 201-211. https://doi.org/10.15302/J-FEM-2017018
Wu, J. (2017). Review popularity and review helpfulness: a model for user review effectiveness. Decision Support Systems, 97, 92-103. https://doi.org/10.1016/j.dss.2017.03.008
Xia, H., & Hou, Z. (2016). Consumer use intention of online financial products: the Yuebao example. Financial Innovation, 2, 18. https://doi.org/10.1186/s40854-016-0041-x
Ye, M., & Li, G. (2017). Internet big data and capital markets: a literature review. Financial Innovation, 3, 6. https://doi.org/10.1186/s40854-017-0056-y
Zhang, Z. K., Zhao, J., Cheung, M. K., & Lee, K. O. (2014). Examining the influence of online reviews on consumers’ decision-making: a heuristic-systematic model. Decision Support Systems, 67, 78-89. https://doi.org/10.1016/j.dss.2014.08.005
Zaller, J. (2005). The nature and origins of mass opinion (pp. 32-44). Cambridge: Cambridge University Press.
Banerjee, S., Bhattacharyya, S., & Bose, I. (2017). Whose online reviews to trust? Understanding reviewer trustworthiness and its impact on business. Decision Support Systems, 96, 17-26. https://doi.org/10.1016/j.dss.2017.01.006
Babic, R., Sotgiu, F., De Valck, K., & Bijmolt, T. H. (2016). The effect of electronic word of mouth on sales: a meta-analytic review of platform, product, and metric factors. Journal of Marketing Research, 53(3), 297-318. https://doi.org/10.1509/jmr.14.0380
Berger, J., Sorensen, A. T., & Rasmussen S. J. (2010). Positive effects of negative publicity: when negative reviews increase sales. Marketing Science, 29(5), 815-827. https://doi.org/10.1287/mksc.1090.0557
Cai, Y., & Zhu, D. (2016). Fraud detections for online businesses: a perspective from blockchain technology. Financial Innovation, 2, 20. https://doi.org/10.1186/s40854-016-0039-4
Chan, O., & Ma, A. (2016). Stochastic cost flow system for stock markets with an application in behavioral finance. International Journal of Financial Engineering, 3(4), 1650026. https://doi.org/10.1142/S2424786316500262
Channel Advisor. (2011). Consumer survey: global consumer shopping habits. Channel Advisor, 2011.
Cheng, Y., & Ho, H. (2015). Social influence’s impact on reader perceptions of online reviews. Journal of Business Research, 68, 883-887. https://doi.org/10.1016/j.jbusres.2014.11.046
Cheung, C. M. K., & Lee, M. K. O. (2012). What drives consumers to spread electronic word of mouth in online consumer-opinion platforms. Decision Support Systems, 53, 218-225. https://doi.org/10.1016/j.dss.2012.01.015
Cheung, M. K., & Thadani, R. (2012). The impact of electronic word-of-mouth communication: a literature analysis an integrative model. Decision Support Systems, 54, 461-470. https://doi.org/10.1016/j.dss.2012.06.008
Cheung, M., Luo, C., Sia, C., & Chen, H. (2009). Credibility of electronic word-of-mouth: informational and normative determinants of on-line consumer recommendations. International Journal of Electronic Commerce, 13(4), 9-38. https://doi.org/10.2753/JEC1086-4415130402
Deborah, R., John, C., & Whitney, Jr. (1986). The development of consumer knowledge in children: a cognitive structure approach. Journal of Consumer Research, 12(March), 406-417.
Deffuant, G., Neau, D., Amblard, F., & Weisbuc, G. (2000). Mixing beliefs among interacting agents. Advances in Complex Systems, 3, 87-98. https://doi.org/10.1142/S0219525900000078
Deng L., & Liu Y. (2011). Research on opinion formation and extremists modeling in internet virtual community. Journal of Beijing Jiao Tong University, 35, 66-71.
Deng, L., Liu, Y., & Zeng, Q. (2012). How information influences an individual opinion evolution. Physica, A 391, 6409-6417. https://doi.org/10.1016/j.physa.2012.07.037
Dellarocas, C., Zhang, X., & Awad, N. (2007). Exploring the value of online product reviews in forecasting sales. Journal of Interactive Marketing, 21, 23-45. https://doi.org/10.1002/dir.20087
Eagly, A. H., & Chaiken, S. (1993). The psychology of attitudes. Harcourt Brace Jovanovich College Publishers, Fort Worth, TX.
Eldomiaty, T., Rashwan, M., Din, M., & Tayel,W. (2016). Firm, industry and economic determinants of working capital at risk. International Journal of Financial Engineering, 3(4), 1650031. https://doi.org/10.1142/S2424786316500316
Fazio, R. H. (1990). Multiple processes by which attitudes guide behavior: The MODE model as an integrative framework. Advances in Experimental Social Psychology, 23, 75-109. https://doi.org/10.1016/S0065-2601(08)60318-4
Floyd, K., Freling, R., Alhoqail, S., Cho, H., & Freling, T. (2014). How online product reviews affect retail sales: a meta-analysis. Journal of Retailing, 90(2), 217-232. https://doi.org/10.1016/j.jretai.2014.04.004
Filieri, R. (2015). What makes online reviews helpful? A diagnosticity-adoption framework to explain informational and normative influences in e-WOM. Journal of Business Research, 68(6), 1261-1270. https://doi.org/10.1016/j.jbusres.2014.11.006
Fu, D., & Wang, K. (2013). Does customer membership level affect online reviews? A study of online reviews from 360buy.Com in China. Proceedings – Pacific Asia Conference on Information Systems, PACIS 2013 01/2013, 1-12.
Friestad, M., & Wright, P. (1994). The persuasion knowledge model: how people cope with persuasion attempts. Journal of Consumer Research, 21, 1-31. https://doi.org/10.1086/209380
Hsu, C. L., & Liao, Y. C. (2014). Exploring the linkages between perceived information accessibility and microblog stickiness: the moderating role of a sense of community. Information & Management, 51, 833-844. https://doi.org/10.1016/j.im.2014.08.005
Khare, A., Labrecque, L. I., & Asare, A. K. (2011). The assimilative and contrastive effects of word-of-mouth volume: an experimental examination of online consumer ratings. Journal of Retailing, 87(1), 111-126. https://doi.org/10.1016/j.jretai.2011.01.005
Kim, D., Jang, S., & Adler, H. (2015). What drives café customers to spread eWOM?. International Journal of Contemporary Hospitality Management, 27(2), 261-282. https://doi.org/10.1108/IJCHM-06-2013-0269
Kou, G., Ergu, D., Lin, C., & Chen, Y. (2016). Pairwise comparison Matrix in multiple criteria decision making. Technological and Economic Development of Economy, 22(5), 738-765. https://doi.org/10.3846/20294913.2016.1210694
Kou, G., Peng, Y., & Wang, G. (2014a). Evaluation of clustering algorithms for financial risk analysis using MCDM methods. Information Sciences, 27, 1-12. https://doi.org/10.1016/j.ins.2014.02.137
Kou, G., & Lin, C. (2014b). A cosine maximization method for the priority vector derivation in AHP. European Journal of Operational Research, 235, 225-232. https://doi.org/10.1016/j.ejor.2013.10.019
Kou, G., Ergu, D., & Shang, J. (2014c). Enhancing data consistency in decision matrix: adapting Hadamard model to mitigate judgment contradiction. European Journal of Operational Research, (1), 261-271. https://doi.org/10.1016/j.ejor.2013.11.035
Kou, G., Lu, Y., Peng, Y., & Shi, Y. (2012). Evaluation of classification algorithms using MCDM and Rank Correlation. International Journal of Information Technology & Decision Making, 11(1), 197-225. https://doi.org/10.1142/S0219622012500095
Kulakowski, K. (2009). Opinion polarization in tie Receipt-Accept-Sample Model. Physica A: Statistical Mechanics and its Applications, 2(388), 469-476. https://doi.org/10.1016/j.physa.2008.10.037
Lee, J., Park, D., & Han, I. (2008). The effect of negative online consumer reviews on product attitude: an information processing view. Electronic Commerce Research and Applications, 7, 341-352. https://doi.org/10.1016/j.elerap.2007.05.004
Lee, J., & Lee, J. N. (2009). Understanding the product information inference process in electronic word-of-mouth: an objectivity–subjectivity dichotomy perspective. Information & Management, 46(5), 302-311. https://doi.org/10.1016/j.im.2009.05.004
Lee, K., & Koo, D. (2012). Effects of attribute and valence of e-WOM on message adoption: moderating roles of subjective knowledge and regulatory focus. Computers in Human Behavior, 28, 1974-1984. https://doi.org/10.1016/j.chb.2012.05.018
Libai, B., Bolton, R., Bügel, M. S., Ruyter, K., Götz, O., Risselada, H., & Stephen A. T. (2010). Customer-to-customer interactions: broadening the scope of word of mouth research. Journal of Service Research, 13(3), 267-282. https://doi.org/10.1177/1094670510375600
Liu, Y. (2006). Word of mouth for movies: its dynamics and impact on box office revenue. Journal of Marketing, 70(7), 74-89. https://doi.org/10.1509/jmkg.70.3.74
Luo, C., Luo, X., Xu, Y., Warkentin, M., & Sia, C. (2015). Examining the moderating role of sense of membership in online review evaluations. Information & Management, 52, 305-316. https://doi.org/10.1016/j.im.2014.12.008
Matlab. (2013). Version 8.0 [Software]. Natick, MA: The Math Works Inc.
Muchnik, L., Aral, S., & Taylor, S. J. (2013). Social influence bias: a randomized experiment. Science, 341, 647. https://doi.org/10.1126/science.1240466
Mukhopadhyay, B. (2016). Understanding cashless payments in India. Financial Innovation, 2, 27. https://doi.org/10.1186/s40854-016-0023-z
Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2, 1-135. https://doi.org/10.1561/1500000011
Park, D., & Kim, S. (2008). The effects of consumer knowledge on message processing of electronic word-of-mouth via online consumer reviews. Electronic Commerce Research and Applications, 7(4), 399-410. https://doi.org/10.1016/j.elerap.2007.12.001
Park, D. H., Lee, J., & Han, I. (2007). The effect of on-line consumer reviews on consumer purchasing intention: the moderating role of involvement. International Journal of Electronic Commerce, 11(4), 125-148. https://doi.org/10.2753/JEC1086-4415110405
Park, D., & Lee, J. (2008). eWOM overload and its effect on consumer behavioral intention depending on consumer involvement. Electronic Commerce Research and Applications, 7(4), 386-398. https://doi.org/10.1016/j.elerap.2007.11.004
Petty, R. E., Tormala, Z. L., Briüol, P., & Jarvis, W. B. G. (2006). Implicit ambivalence from attitude change: an exploration of the PAST model. Journal of personality and Social Psychology, 90, 21-41. https://doi.org/10.1037/0022-3514.90.1.21
Petty, R. E., & Cacioppo, J. T. (1984). Motivational factors in consumer response to advertisements. In W. Beatty, R. Geen, & R. Arkin (Eds.), Human motivation (pp. 418-454). New York: Allyn & Bacon.
Pietri, E. S., & Shook F. J. (2013). Weighting positive versus negative: the fundamental nature of valence asymmetry. Journal of Personality, 81(2), 196-208. https://doi.org/10.1111/j.1467-6494.2012.00800.x
Ridings, C. M., Gefen, D., & Arinze, B. (2002). Some antecedents and effects of trust in virtual communities. The Journal of Strategic Information Systems, 1(3), 271-295. https://doi.org/10.1016/S0963-8687(02)00021-5
Tee, H., & Ong, H. (2016). Cashless payment and economic growth. Financial Innovation, 2, 4. https://doi.org/10.1186/s40854-016-0023-z
Ullah, R., Amblee, N., Kim, W., & Lee, H. (2016). From valence to emotions: exploring the distribution of emotions in online product reviews. Decision Support Systems, 81, 41-53. https://doi.org/10.1016/j.dss.2015.10.007
Wang, X., Teo, H., & Wei, K. (2015). Simultaneity and interactivity of the effects of communication elements on consumers’ decision making in eWOM Systems. Journal of Electronic Commerce Research, 16(3), 153-174.
Wu, W., & Kou, G. (2016). A group consensus model for evaluating real estate investment alternatives. Financial Innovation, 2, 8. https://doi.org/10.1186/s40854-016-0027-8
Wu, X., Wang, X., Ma, S., & Ye, Q. (2017). The influence of social media on stock volatility. Frontiers of Engineering Management, 4(2), 201-211. https://doi.org/10.15302/J-FEM-2017018
Wu, J. (2017). Review popularity and review helpfulness: a model for user review effectiveness. Decision Support Systems, 97, 92-103. https://doi.org/10.1016/j.dss.2017.03.008
Xia, H., & Hou, Z. (2016). Consumer use intention of online financial products: the Yuebao example. Financial Innovation, 2, 18. https://doi.org/10.1186/s40854-016-0041-x
Ye, M., & Li, G. (2017). Internet big data and capital markets: a literature review. Financial Innovation, 3, 6. https://doi.org/10.1186/s40854-017-0056-y
Zhang, Z. K., Zhao, J., Cheung, M. K., & Lee, K. O. (2014). Examining the influence of online reviews on consumers’ decision-making: a heuristic-systematic model. Decision Support Systems, 67, 78-89. https://doi.org/10.1016/j.dss.2014.08.005
Zaller, J. (2005). The nature and origins of mass opinion (pp. 32-44). Cambridge: Cambridge University Press.