Skip to main content
SpringerLink
Log in

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

  • Published:
Electronic Commerce Research Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  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.

References

  1. Dellarocas, C. (2003). The digitization of word of mouth: Promise and challenges of online feedback mechanisms. Management Science, 49(10), 1407–1424.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Google Scholar 

  24. Singh, J., Irani, S., & Rana, N. (2016). Predicting the “helpfulness” of online consumer reviews. Journal of Business Research, 70, 346–355.

    Article  Google Scholar 

  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.

    Google Scholar 

  27. Liu, Z., & Park, S. (2015). What makes a useful online review? Implication for travel product websites. Tourism Management, 47(47), 140–151.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  30. Wu, J. (2017). Review popularity and review helpfulness: A model for user review effectiveness. Decision Support Systems, 97, 92–103.

    Article  Google Scholar 

  31. Guo, B., & Zhou, S. (2017). What makes population perception of review helpfulness: An information processing perspective. Electronic Commerce Research, 17(4), 1–24.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  50. Sangwon, P., & Nicolau, J. L. (2015). Asymmetric effects of online consumer reviews. Annals of Tourism Research, 50, 67–83.

    Article  Google Scholar 

  51. Wan, Y. (2015). The Matthew effect in social commerce: The case of online review helpfulness. Electronic Markets, 25(4), 313–324.

    Article  Google Scholar 

  52. Salehan, M., & Dan, J. K. (2016). Predicting the performance of online consumer reviews. Decision Support Systems, 81(C), 30–40.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  64. Lipsey, M. W., & Wilson, D. B. (2001). Practical meta-analysis. Beverly Hills: Sage.

    Google Scholar 

  65. Rosenthal, R. (1979). The ‘file drawer problem’ and tolerance for null results. Psychological Bulletin, 86, 638–641.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  67. Singer, J. D., & Willet, J. B. (2003). Applied longitudinal data analysis. Berlin: Springer.

    Book  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  72. Vessey, I., & Galletta, D. (1991). Cognitive fit: An empirical study of information acquisition. Information Systems Research, 2(1), 63–84.

    Article  Google Scholar 

  73. Jackson, T. W., & Farzaneh, P. (2012). Theory-based model of factors affecting information overload. International Journal of Information Management, 32(6), 523–532.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  75. Jiang, Z., & Benbasat, I. (2007). Investigating the influence of the functional mechanisms of online product presentations. Information Systems Research, 18(4), 221–244.

    Article  Google Scholar 

  76. Cohen, J. (1988). Statistical power analysis for the behavioral science. Journal of the American Statistical Association, 84(363), 19–74.

    Google Scholar 

  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.

    Article  Google Scholar 

  78. Zhang, Z. (2008). Weighing stars: Aggregating online product reviews for intelligent E-commerce applications. IEEE Intelligent Systems, 23(5), 42–49.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Google Scholar 

  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.

    Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Google Scholar 

  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.

    Article  Google Scholar 

  88. Karimi, S., & Wang, F. (2017). Online review helpfulness: Impact of reviewer profile image. Decision Support Systems, 96, 39–48.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

Download references

Acknowledgements

The work described in this paper is supported by National Natural Science Foundation of China (Grant Nos. 71531001 and 71572006).

Author information

Authors and Affiliations

  1. School of Economics and Management, Beihang University, Xueyuan Road, 37, Haidian District, Beijing, 100191, People’s Republic of China

    Yani Wang, Jun Wang & Tang Yao

  2. Key Laboratory of Complex System Analysis, Management and Decision, Beihang University, Ministry of Education, Beijing, 100191, People’s Republic of China

    Tang Yao

Authors
  1. Yani Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jun Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tang Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jun Wang.

Appendix A

Appendix A

See Table 6.

Table 6 Studies included in the meta-analysis
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Y., Wang, J. & Yao, T. What makes a helpful online review? A meta-analysis of review characteristics. Electron Commer Res 19, 257–284 (2019). https://doi.org/10.1007/s10660-018-9310-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10660-018-9310-2

Keywords

Search

Navigation