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Electronic Commerce Research
Erschienen in: Electronic Commerce Research 2/2019

28.06.2018

What makes a helpful online review? A meta-analysis of review characteristics

verfasst von: Yani Wang, Jun Wang, Tang Yao

Erschienen in: Electronic Commerce Research | Ausgabe 2/2019

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Abstract

In this study, we aim to clarify the determinants of online review helpfulness concerning review depth, extremity and timeliness. Based on a meta-analysis, we examine the effects of important characteristics of reviews employing 53 empirical studies yielding 191 effect sizes. Findings reveal that review depth has a greater impact on helpfulness than review extremity and timeliness with the exception of its sub-metric of review volume, which exerts the negative influence on review helpfulness. Specifically, readability is the most important factor in evaluating review helpfulness. Furthermore, we discuss important moderators of the relationships and find interesting insights regarding website and culture background. In accordance with the results, we propose several implications for researchers and E-business firms. Our study provides a much needed quantitative synthesis of this burgeoning stream of research.
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Fußnoten
1
We convert t value to the correlation coefficient effect size by using the formula suggested by Rosenthal [46]: \({\text{r}} = {\raise0.7ex\hbox{${\text{t}}$} \!\mathord{\left/ {\vphantom {{\text{t}} {\sqrt {\left( {{\text{t}}^{2} + {\text{d}}.{\text{f}}.} \right)} }}}\right.\kern-0pt} \!\lower0.7ex\hbox{${\sqrt {\left( {{\text{t}}^{2} + {\text{d}}.{\text{f}}.} \right)} }$}}\) where \({\text{t}}\) is the t-value associated with the regression parameter that captures the effect and \({\text{d}}. {\text{f}}.\) is the degree of freedom of the reported regression model; We convert \(\beta\) to coefficient effect size by using the formula suggested by Peterson and Brown [47]: \(r = 0.98\beta + 0.05\lambda\), where \(\lambda\) is a variable that equals 1 when \(\beta\) is non-negative and 0 when \(\beta\) is negative.
 
2
We use the following formulae for Fisher’s Z: (1) transformation: \(z_{r} = 0.5{ \ln }\left( {{\raise0.7ex\hbox{${\left( {1 + r} \right)}$} \!\mathord{\left/ {\vphantom {{\left( {1 + r} \right)} {1 - r}}}\right.\kern-0pt} \!\lower0.7ex\hbox{${1 - r}$}}} \right)\), (2) Weighted average: \(\overline{{z_{r} }} = {\raise0.7ex\hbox{${\sum \left( {n_{i} - 3} \right)*z_{r} }$} \!\mathord{\left/ {\vphantom {{\sum \left( {n_{i} - 3} \right)*z_{r} } {\sum \left( {n_{i} - 3} \right)}}}\right.\kern-0pt} \!\lower0.7ex\hbox{${\sum \left( {n_{i} - 3} \right)}$}}\) and (3) back-transformation to correlation units:
$$\bar{r} = {\raise0.7ex\hbox{${\left( {e^{{2\overline{{z_{r} }} }} - 1} \right)}$} \!\mathord{\left/ {\vphantom {{\left( {e^{{2\overline{{z_{r} }} }} - 1} \right)} {\left( {e^{{2\overline{{z_{r} }} }} + 1} \right)}}}\right.\kern-0pt} \!\lower0.7ex\hbox{${\left( {e^{{2\overline{{z_{r} }} }} + 1} \right)}$}}\;[49].$$
 
3
We calculate the 95% confidence interval as: lower \(CI = \overline{{z_{r} }} - {\raise0.7ex\hbox{${1.96}$} \!\mathord{\left/ {\vphantom {{1.96} {\sqrt {\sum \left( {n_{i} - 3} \right)} }}}\right.\kern-0pt} \!\lower0.7ex\hbox{${\sqrt {\sum \left( {n_{i} - 3} \right)} }$}}\), upper \(CI = \overline{{z_{r} }} + {\raise0.7ex\hbox{${1.96}$} \!\mathord{\left/ {\vphantom {{1.96} {\sqrt {\sum \left( {n_{i} - 3} \right)} }}}\right.\kern-0pt} \!\lower0.7ex\hbox{${\sqrt {\sum \left( {n_{i} - 3} \right)} }$}}\); the variance of effect size as: \(S_{r}^{2} = {\raise0.7ex\hbox{${\sum n_{i} \left( {r_{i} - \bar{r}} \right)^{2} }$} \!\mathord{\left/ {\vphantom {{\sum n_{i} \left( {r_{i} - \bar{r}} \right)^{2} } {\sum n_{i} }}}\right.\kern-0pt} \!\lower0.7ex\hbox{${\sum n_{i} }$}}\) and variation caused by sampling error as: \(S_{e}^{2} = {\raise0.7ex\hbox{${\sum n_{i} \left( {1 - \bar{r}} \right)^{2} }$} \!\mathord{\left/ {\vphantom {{\sum n_{i} \left( {1 - \bar{r}} \right)^{2} } {\sum n_{i} }}}\right.\kern-0pt} \!\lower0.7ex\hbox{${\sum n_{i} }$}}\); Q-value is calculated as: \(Q = \sum \left( {n_{i} - 3} \right)*\left( {z_{r} - \overline{{z_{r} }} } \right)^{2}\) and the fail safe N is as: \(N = k*\left( {{\raise0.7ex\hbox{${\bar{r}}$} \!\mathord{\left/ {\vphantom {{\bar{r}} {r_{c} }}}\right.\kern-0pt} \!\lower0.7ex\hbox{${r_{c} }$}} - 1} \right)\), where \(r_{c}\) is the “just significant” level or critical effect size which usually use 0.01.
 
Literatur
1.
Dellarocas, C. (2003). The digitization of word of mouth: Promise and challenges of online feedback mechanisms. Management Science, 49(10), 1407–1424.CrossRef
2.
Malik, M., & Hussain, A. (2018). An analysis of review content and reviewer variables that contribute to review helpfulness. Information Processing and Management, 54(1), 88–104.CrossRef
3.
Mudambi, S. M., & Schuff, D. (2010). What makes a helpful online review? A study of customer reviews on amazon.com. MIS Quarterly, 34(1), 185–200.CrossRef
4.
Ghose, A., & Ipeirotis, P. G. (2011). 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.CrossRef
5.
Yin, G., Wei, L., Xu, W., & Chen, M. (2014). Exploring heuristic cues for consumer perceptions of online reviews helpfulness: The case of yelp.com. In Pacific Asia conference on information systems.
6.
Farley, J. U., Lehmann, D. R., & Sawyer, A. (1995). Empirical marketing generalizations using meta-analysis. Marketing Science, 14(3), G36–G46.CrossRef
7.
Floyd, K., Freling, R., Alhoqail, S., Cho, H. Y., & Freling, T. (2014). How online product reviews affect retail sales: A meta-analysis. Journal of Retailing, 90(2), 217–232.CrossRef
8.
You, Y., Vadakkepatt, G., & Joshi, A. M. (2015). A meta-analysis of electronic word-of-mouth elasticity. Journal of Marketing, 79(2), 19–39.CrossRef
9.
Rosario, A. B., Sotgiu, F., Valck, K. D., & Bijmolt, T. H. A. (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.CrossRef
10.
Purnawirawan, N., Eisend, M., Pelsmacker, P. D., & Dens, N. (2015). A meta-analytic investigation of the role of valence in online reviews. Journal of Interactive Marketing, 31, 17–27.CrossRef
11.
Pan, Y., & Zhang, J. Q. (2011). Born unequal: A study of the helpfulness of user-generated product reviews. Journal of Retailing, 87(4), 598–612.CrossRef
12.
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.CrossRef
13.
Ogut, H., & Tas, B. (2012). The influence of internet customer reviews on the online sales and prices in hotel industry. The Service Industry Journal, 32(2), 197–214.CrossRef
14.
Chu, W., & Roh, M. (2014). Exploring the role of preference heterogeneity and causal attribution in online ratings dynamics. Asia Marketing Journal, 15(4), 61–101.
15.
Duan, W., Gu, B., & Whinston, A. (2008). Do online reviews matter?—An empirical investigation of panel data. Decision Support Systems, 45(4), 1007–1016.CrossRef
16.
Hu, N., Liu, L., & Zhang, J. (2008). Do online reviews affect product sales? The role of reviewer characteristics and temporal effects. Information Technology Management, 9(3), 201–214.CrossRef
17.
Cui, G., Lui, H. K., & Guo, X. (2012). The effect of online consumer reviews on new product sales[M]. International Journal of Electronic Commerce, 17(1), 39–57.CrossRef
18.
Cao, Q., Duan, W., & Gan, Q. (2011). Exploring determinants of voting for the “helpfulness” of online user reviews: A text mining approach. Decision Support Systems, 50(2), 511–521.CrossRef
19.
Korfiatis, N., García, B. E., & Sánchez, A. S. (2012). Evaluating content quality and helpfulness of online product reviews: The interplay of review helpfulness versus review content. Electronic Commerce Research and Applications, 11(3), 205–217.CrossRef
20.
Baek, H., Ahn, J., & Choi, Y. (2012). Helpfulness of online consumer reviews: Readers’ objectives and review cues. International Journal of Electronic Commerce, 17(2), 99–126.CrossRef
21.
Lee, S., & Choeh, J. Y. (2014). Predicting the helpfulness of online reviews using multilayer perceptron neural networks. Expert Systems with Applications, 41(6), 3041–3046.CrossRef
22.
Huang, A. H., Chen, K., Yen, D. C., & Tran, T. P. (2015). A study of factors that contribute to online review helpfulness. Computers in Human Behavior, 48(C), 17–27.CrossRef
23.
Fu, D., Hong, Y., Wang, K., & Fan, W. (2017). Effects of membership tier on user content generation behaviors: Evidence from online reviews. Electronic Commerce Research, 7, 1–27.
24.
Singh, J., Irani, S., & Rana, N. (2016). Predicting the “helpfulness” of online consumer reviews. Journal of Business Research, 70, 346–355.CrossRef
25.
Otterbacher, J. (2009). ‘Helpfulness’ in online communities: a measure of message quality. In Sigchi conference on human factors in computing systems (pp. 955–964).
26.
Baek, H., Lee, S., Oh, S., & Ahn, J. (2015). Normative social influence and online review helpfulness: Polynomial modeling and response surface analysis. Journal of Electronic Commerce Research, 16(4), 290–306.
27.
Liu, Z., & Park, S. (2015). What makes a useful online review? Implication for travel product websites. Tourism Management, 47(47), 140–151.CrossRef
28.
Ngoye, T. L., & Sinha, A. P. (2014). The influence of reviewer engagement characteristics on online review helpfulness: A text regression model. Decision Support Systems, 61(4), 47–58.CrossRef
29.
Xiang, Z., Du, Q., Ma, Y., & Fan, W. (2017). A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism. Tourism Management, 58, 51–65.CrossRef
30.
Wu, J. (2017). Review popularity and review helpfulness: A model for user review effectiveness. Decision Support Systems, 97, 92–103.CrossRef
31.
Guo, B., & Zhou, S. (2017). What makes population perception of review helpfulness: An information processing perspective. Electronic Commerce Research, 17(4), 1–24.CrossRef
32.
Chen, Z., & Lurie, N. (2013). Temporal contiguity and negativity bias in the impact of online word of mouth. Journal of Marketing Research, 50(4), 463–476.CrossRef
33.
Kohli, R., Devaraj, S., & Mahmood, M. A. (2004). Understanding determinants of online consumer satisfaction: A decision process perspective. Journal of Management Information Systems, 21(1), 115–135.CrossRef
34.
Qazi, A., Syed, K., Raj, R. G., & Cambria, E. (2016). A concept-level approach to the analysis of online review helpfulness. Computers in Human Behavior, 58(C), 75–81.CrossRef
35.
Racherla, P., & Friske, W. (2012). Perceived usefulness of online consumer reviews: An exploratory investigation across three services categories. Electronic Commerce Research and Applications, 11(6), 548–559.CrossRef
36.
Schindler, R. M., & Bickart, B. (2012). Perceived helpfulness of online consumer reviews: The role of message content and style. Journal of Consumer Behavior, 11(3), 234–342.CrossRef
37.
Kuan, K., & Hui, K. (2015). What makes a review voted? An empirical investigation of review voting in online review systems. Journal of the Association for Information Systems, 16(1), 48–71.CrossRef
38.
Anderson, W. E., & Salisbury, C. L. (2003). The formation of market-level expectations and its covariates. Journal of Consumer Research, 30(1), 115–124.CrossRef
39.
Khare, A., Lauren, I. L., & Anthony, K. A. (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.CrossRef
40.
Salganik, M. J., Peter, S. D., & Duncan, J. W. (2006). Experimental study of inequality and unpredictability in an artificial cultural market. Science, 311(5762), 854–856.CrossRef
41.
Forman, C., Ghose, A., & Wiesenfeld, B. (2008). Examining the relationship between reviews and sales: The role of reviewer identity disclosure in electronic markets. Information Systems Research, 19(3), 291–313.CrossRef
42.
Zhou, S., & Guo, B. (2015). The interactive effect of review rating and text sentiment on review helpfulness. In E-Commerce and web technologies (pp. 100–111).
43.
Zhu, L., Yin, G., & He, W. (2014). Is this opinion leader’s review useful? Peripheral cues for online review helpfulness. Journal of Electronic Commerce Research, 15(4), 267–280.
44.
Li, J., & Zhan, L. (2011). Online persuasion: How the written word drives WOM—Evidence from consumer-generated product reviews. Journal of Advertising Research, 51(1), 239–257.CrossRef
45.
Chua, A. Y. K., & Banerjee, S. (2016). Helpfulness of user-generated reviews as a function of review sentiment, product type and information quality. Computers in Human Behavior, 54(C), 547–554.CrossRef
46.
Kim, S. M., Pantel, P., Chklovski, T., & Pennacchiotti, M. (2006). Automatically assessing review helpfulness. In Conference on empirical methods in natural language processing (pp. 423–430). Association for Computational Linguistics.
47.
Wu, P. F., Hans, V. D. H., & Korfiatis, N. (2011). The influences of negativity and review quality on the helpfulness of online reviews. In International conference on information systems. Shanghai, China.
48.
Yin, D., Bond, S. D., & Zhang, H. (2014). Anxious or angry? Effects of discrete emotions on the perceived helpfulness of online reviews. MIS Quarterly, 38(2), 539–560.CrossRef
49.
Chua, A. Y. K., & Banerjee, S. (2015). Understanding review helpfulness as a function of reviewer reputation, review rating, and review depth. Journal of the Association for Information Science and Technology, 66(2), 354–362.CrossRef
50.
Sangwon, P., & Nicolau, J. L. (2015). Asymmetric effects of online consumer reviews. Annals of Tourism Research, 50, 67–83.CrossRef
51.
Wan, Y. (2015). The Matthew effect in social commerce: The case of online review helpfulness. Electronic Markets, 25(4), 313–324.CrossRef
52.
Salehan, M., & Dan, J. K. (2016). Predicting the performance of online consumer reviews. Decision Support Systems, 81(C), 30–40.CrossRef
53.
Koh, N. S., Hu, N., & Clemons, E. K. (2010). Do online reviews reflect a product’s true perceived quality? An investigation of online movie reviews across cultures. Electronic Commerce Research and Applications, 9(5), 374–385.CrossRef
54.
Mcginnies, E., & Ward, D. C. (1980). Better liked than right: Trustworthiness and expertise as factors in credibility. Personality and Social Psychology Bulletin, 6(3), 467–472.CrossRef
55.
Grewal, D., Gotlieb, J., & Marmorstein, H. (1994). The moderating effects of message framing and source credibility on the price-perceived risk relationship. Journal of Consumer Research, 21(1), 145–153.CrossRef
56.
Schweidel, D., & Moe, W. (2014). Listening in on social media: A joint model of sentiment and venue format choice. Journal of Marketing Research, 51(4), 387–402.CrossRef
57.
Johnson, D. W., Maruyama, G., Johnson, R., Nelson, D., & Skon, L. (1981). Effects of cooperative, competitive and individualistic goal structures on achievement: A meta-analysis. Psychological Bulletin, 89(1), 47–62.CrossRef
58.
Assmus, G., Farley, J. U., & Lehmann, D. R. (1984). How advertising affects sales: Meta-analysis of econometric results. Journal of Marketing Research, 21(1), 65–74.CrossRef
59.
Lee, J. (2013). What makes people read an online review? The relative effects of posting time and helpfulness on review readership. Cyberpsychology Behavior & Social Networking, 16(7), 529.CrossRef
60.
Völckner, F., & Hofmann, J. (2007). The price-perceived quality relationship: A meta-analytic review and assessment of its determinants. Marketing Letters, 18(3), 181–196.CrossRef
61.
Rosenthal, R. (1994). Parametric measures of effect size. In H. Cooper & L. V. Hedges (Eds.), The handbook of research synthesis (pp. 231–244). New York: Russell Sage Foundation.
62.
Peterson, R. A., & Brown, S. P. (2005). On the use of beta coefficients in meta-analysis. Journal of Applied Psychology, 90(1), 175–181.CrossRef
63.
Hunter, J. E., & Schmidt, F. L. (2006). Methods of meta-analysis: Correcting error and bias in research findings. Evaluation & Program Planning, 29(3), 236–237.CrossRef
64.
Lipsey, M. W., & Wilson, D. B. (2001). Practical meta-analysis. Beverly Hills: Sage.
65.
Rosenthal, R. (1979). The ‘file drawer problem’ and tolerance for null results. Psychological Bulletin, 86, 638–641.CrossRef
66.
Tellis, G. J. (1988). The price elasticity of selective demand: A meta-analysis of econometric models of sales. Journal of Marketing Research, 25(4), 331–341.CrossRef
67.
Singer, J. D., & Willet, J. B. (2003). Applied longitudinal data analysis. Berlin: Springer.CrossRef
68.
Bijmolt, T. H. A., & Pieters, R. G. M. (2001). Meta-analysis in marketing when studies contain multiple measurements. Marketing Letters, 12(2), 157–169.CrossRef
69.
Hair, J. F., Anderson, R. E., Tatham, R. L., & Black W. C. (1998). Multivariate data analysis (5th ed.). All Publications.
70.
Huang, H. (2005). A cross-cultural test of the spiral of silence. International Journal of Public Opinion Research, 17(3), 324–345.CrossRef
71.
Park, D. H., & Kim, S. (2009). 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.CrossRef
72.
Vessey, I., & Galletta, D. (1991). Cognitive fit: An empirical study of information acquisition. Information Systems Research, 2(1), 63–84.CrossRef
73.
Jackson, T. W., & Farzaneh, P. (2012). Theory-based model of factors affecting information overload. International Journal of Information Management, 32(6), 523–532.CrossRef
74.
Schultz, C., Schreyoegg, J., & Von, R. C. (2013). The moderating role of internal and external resources on the performance effect of multitasking: Evidence from the R&D performance of surgeons. Research Policy, 42(8), 1356–1365.CrossRef
75.
Jiang, Z., & Benbasat, I. (2007). Investigating the influence of the functional mechanisms of online product presentations. Information Systems Research, 18(4), 221–244.CrossRef
76.
Cohen, J. (1988). Statistical power analysis for the behavioral science. Journal of the American Statistical Association, 84(363), 19–74.
77.
Huang, H. A., & Yen, C. D. (2013). Predicting the helpfulness of online reviews—A replication. International Journal of Human-computer Interaction, 29(2), 129–138.CrossRef
78.
Zhang, Z. (2008). Weighing stars: Aggregating online product reviews for intelligent E-commerce applications. IEEE Intelligent Systems, 23(5), 42–49.CrossRef
79.
Willemsen, L. M., Neijens, P. C., Bronner, F., & Ridder, J. (2011). “Highly Recommended!” The content characteristics and perceived usefulness of online consumer reviews. Journal of Computer-Mediated Communication, 17(1), 19–38.CrossRef
80.
Siering, M., Muntermann, J., & Rajagopalan, B. (2018). Explaining and predicting online review helpfulness: The role of content and reviewer-related signals. Decision Support Systems, 108, 1–12.CrossRef
81.
Zhang, J. Q., Craciun, G., & Shin, D. (2010). When does electronic word-of-mouth matter? A study of consumer product reviews. Journal of Business Research, 63(12), 1336–1341.CrossRef
82.
Einar, B., Havro, L. J., & Moen, O. (2015). An empirical investigation of self-selection bias and factors influencing review helpfulness. International Journal of Business & Management, 10(7), 16–30.
83.
Siering, M., & Muntermann, J. (2013). What drives the helpfulness of online product reviews? From stars to facts and emotions. In 11th international conference on Wirtschaft sinformatik.
84.
Wang, C. C., Li, M. Z., & Yang, Y. H. (2015). Perceived usefulness of word-of-mouth: An analysis of sentimentality in product reviews (pp. 448–459). Berlin: Springer.
85.
Wu, P. F. (2013). In search of negativity bias: An empirical study of perceived helpfulness of online reviews. Psychology & Marketing, 30(11), 971–984.CrossRef
86.
Chen, X., Sheng, J., Wang, X., & Deng, J. S. (2016). Exploring determinants of attraction and helpfulness of online product review: A consumer behavior perspective. Discrete Dynamics in Nature and Society, 1, 1–19.
87.
Lee, S., & Choeh, J. Y. (2017). Exploring the determinants of and predicting the helpfulness of online user reviews. Management Decision, 40(3), 316–332.CrossRef
88.
Karimi, S., & Wang, F. (2017). Online review helpfulness: Impact of reviewer profile image. Decision Support Systems, 96, 39–48.CrossRef
89.
Yu, X., Liu, Y., Huang, X., & An, A. (2010). Mining online reviews for predicting sales performance: A case study in the movie domain. IEEE Transactions on Knowledge and Data Engineering, 24(4), 720–734.CrossRef
90.
Hong, H., & Xu, D. (2015). Research of online review helpfulness based on negative binary regress model the mediator role of reader participation. In International conference on service systems and service management (pp. 1–5). IEEE.
91.
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.CrossRef
92.
Ullah, R., Zeb, A., & Kim, W. (2015). The impact of emotions on the helpfulness of movie reviews. Journal of Applied Research & Technology, 13(3), 359–363.CrossRef
93.
Cheng, Y. H., & Ho, H. Y. (2015). Social influence’s impact on reader perceptions of online reviews. Journal of Business Research, 68(4), 883–887.CrossRef
94.
Hu, Y. H., & Chen, K. (2016). Predicting hotel review helpfulness: The impact of review visibility, and interaction between hotel stars and review ratings. International Journal of Information Management, 36(6), 929–944.CrossRef
95.
Fang, B., Ye, Q., Kucukusta, D., & Law, R. (2016). Analysis of the perceived value of online tourism reviews: Influence of readability and reviewer characteristics. Tourism Management, 52, 498–506.CrossRef
96.
Kwok, L., & Xie, K. L. (2016). Factors contributing to the helpfulness of online hotel reviews: does manager response play a role? International Journal of Contemporary Hospitality Management, 28(10), 2156–2177.CrossRef
97.
Yin, G., Zhang, Q., & Li, Y. (2014). Effects of emotional valence and arousal on consumer perceptions of online review helpfulness. In Twentieth Americas conference on information systems, Savannah.
98.
Li, H., Zhang, Z., Janakiraman, R. & Fang, M. (2016) How review sentiment and readability affect online peer evaluation votes?—An examination combining reviewer’s social identity and social network. In Tourism travel and research association: Advancing tourism research globally (p. 29).
99.
Ghose, A., & Ipeirotis, P. G. (2007). Designing novel review ranking systems: Predicting the usefulness and impact of reviews. In International conference on electronic commerce (pp. 303–310).
100.
Zhang, Y. (2014). Automatically predicting the helpfulness of online reviews. In The fifteenth IEEE international conference on information reuse and integration (pp. 662–668). IEEE.
Metadaten
Titel
What makes a helpful online review? A meta-analysis of review characteristics
verfasst von
Yani Wang
Jun Wang
Tang Yao
Publikationsdatum
28.06.2018
Verlag
Springer US
Erschienen in
Electronic Commerce Research / Ausgabe 2/2019
Print ISSN: 1389-5753
Elektronische ISSN: 1572-9362
DOI
https://doi.org/10.1007/s10660-018-9310-2

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Incorporating facial attractiveness in photos for online dating recommendation