Online Word-of-Mouth

Research Curation Team:
Wael Jabr (Pennsylvania State University)
Ben Liu (City University of Hong Kong)
Dezhi Yin (University of South Florida)
Han Zhang (Georgia Institute of Technology)

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Online word-of-mouth (WOM) has been an important area of scholarly inquiry at the intersection between Internet technologies and marketing (where the concept of offline WOM was born). Though there is no consensus around what it refers to, we define online (or electronic) WOM as any statements publicly available on the Internet that are made by individuals about any object of their interest (e.g., a product, a seller, stock market, a political figure or event).

1. Focus of the Research Curation

Online WOM started drawing attention from information systems (IS) researchers in the early 2000s. Since then, it has become a major IS research topic. This curation identifies 31 articles published in MIS Quarterly (MISQ) that focus on online WOM (see Table 1). Articles included in this curation meet the following criteria: the content of WOM 1) contains valenced statements (that can be positive or negative), 2) is created by Internet users, not by companies or platforms, and 3) is widely available through the Internet. We exclude articles where online WOM is not central to the research framework.

2. Progression of Research in MIS Quarterly

While WOM has been researched in offline settings, the advent of the Internet and the emergence of Web 2.0 at the turn of the century opened the door to its online counterpart. Right around that time, the first online WOM article in MISQ was published (Ba and Pavlou 2002). While this article did not expressly use the term online WOM, it focused on feedback ratings and their impact on trust building in e-commerce. It was not until 2010 that the next article appeared. It explored the question of what constitutes a helpful consumer review (Mudambi and Schuff 2010). Subsequently, the research progressed to investigating the content of WOM that can be collected and analyzed to derive business intelligence (Chau and Xu 2012), and the interaction of price with ratings to influence subsequent pricing and ratings (Li and Hitt 2010). While most studies focused on the context of business-to-customer e-commerce (Chau and Xu 2012; Yin et al. 2014; Jabr and Zheng 2014; Ye et al. 2014), some examined the role of online WOM in other domains such as IT ventures (Aggarwal and Singh 2013), music (Dewan and Ramaprasad 2014), health (Gao et al. 2015), and journalism (Oh et al. 2016).

Early research relied in their analyses on numeric metrics directly available from e-commerce websites or social media platforms (e.g., average ratings, number of reviews). With the growing ability to collect large amounts of unstructured data (e.g., review texts) and to implement newer techniques (e.g., text mining, machine learning), research expanded to examine a variety of WOM such as tweets (Gunarathne et al. 2018) and Facebook likes (Li and Wu 2018), situated in diverse contexts such as politics (Shore et al. 2018) and the stock market (Deng et al. 2018). Besides the two widely used dimensions of WOM, namely valence and volume, researchers also explored the format of WOM (Adomavicius et al. 2019) and the semantic and lexical content of WOM (Huang et al. 2017; Shore et al. 2018). Others explored optimal ways to design WOM systems (Xu et al. 2018; Adomavicius et al. 2019).

A variety of research methodologies were employed, including econometric analysis of observational data which was also the most used (e.g., Mudambi and Schuff 2010; Jabr and Zheng 2014), experiments (e.g., Yin et al. 2014; Liu and Karahanna 2017), analytical modeling (e.g., Li 2017; Xu et al. 2018), design science approach (e.g., Chau and Xu 2012; Venkatesh et al. 2017), with many articles combining multiple methods (e.g., Ba and Pavlou 2002; Li and Hitt 2010; Chau and Xu 2012; Yin et al. 2014; Hu et al. 2017) (see Table 1 for details).

3. Thematic Advances in Knowledge

We identify four themes based on the articles identified: (1) generation of online WOM, (2) evaluation and dynamics of online WOM, (3) impact of online WOM, and (4) design of online WOM systems. Whenever possible, we coded each article into one primary theme. However, a few articles were coded into two themes, as these themes are not mutually exclusive.

Generation of online WOM: These articles examine factors driving people’s decision to post WOM. Several articles explored the role of extrinsic drivers with a focus on the hosting platforms. Shen et al. (2015) investigated the impact of reviewer rankings on reviewers’ decision of choosing which products to review and what ratings to post to gain attention. Oh et al. (2016) investigated the role of a paywall restricting access to content and found that the paywall decreases WOM volume and impacts disproportionately popular contents. Huang et al. (2017) investigated the role of social network integration and its effect on the characteristics of WOM. They found that these characteristics increase the volume of WOM but reduce its quality.

Evaluation and dynamics of online WOM: These articles focus on online WOM itself, including its perceived value, its reliability and biases, its dynamics, and its use for other research and practice purposes.

(a) Articles on the perceived value examined how rating and review characteristics (such as its extremity and expressed emotions) shape consumers’ perception of review helpfulness (Mudambi and Schuff 2010; Yin et al. 2014), and how consumers make use of online reviews in information search (Li et al. 2017). These studies identified ingredients of helpful reviews and demonstrated the value of distinct genres of product reviews at different stages of the online shopping process.

(b) Another set of articles challenged the assumption that consumer-generated ratings reflect the quality of a product or service by showing that ratings are vulnerable to self-selection biases (Gao et al. 2015; Hu et al. 2017) and can be biased by price (Li and Hitt 2010). Relatedly, the design of reputation markets to eliminate biases has been examined (Xu et al. 2018).

(c) A third set of articles examined dynamic changes of online WOM, such as how early online WOM can stimulate subsequent WOM (Luo et al. 2017; Hu et al. 2017) and how social media opinions can become polarized (Shore et al. 2018). These findings demonstrated how WOM opinions influence each other over time and over social networks.

(d) Finally, a few articles used online WOM to quantify other theoretical constructs (Wu et al. 2019) and gather business intelligence (Chau and Xu 2012).

Impact of online WOM: These articles pertain to the consequences of WOM in market contexts where firms sell products to consumers and where firms and/or consumers use WOM for certain purposes. We characterize these articles based on sources of WOM, dimensions of WOM, and whether WOM is examined from a seller or consumer perspective.

(a) A main source of WOM is reviews and ratings from e-commerce websites (Ba and Pavlou 2002; Jabr and Zheng 2014; Venkatesh et al. 2017; Liu and Karahanna 2017; Hu et al. 2017; Jensen and Yetgin 2017; Kwark et al. 2017; Li 2017; Li et al. 2019). Other sources of WOM include blog posts (Aggarwal and Singh 2013; Dewan and Ramaprasad 2014; Luo et al. 2017), tweets (Gunarathne et al. 2018; Li and Wu 2018), Facebook likes (Li and Wu 2018), online forum posts (Geva et al. 2017), and messages from other social media platforms (Deng et al. 2018; Rhue and Sundararajan 2019).

(b) While a few articles considered the consequences of the availability of WOM (Venkatesh et al. 2017; Rhue and Sundararajan 2019), others looked at the impact of more granular dimensions including volume (Ba and Pavlou 2002; Aggarwal and Singh 2013; Dewan and Ramaprasad 2014; Jabr and Zheng 2014; Geva et al. 2017; Luo et al. 2017; Gunarathne et al. 2018; Li and Wu 2018), valence (Ba and Pavlou 2002; Jabr and Zheng 2014; Geva et al. 2017; Luo et al. 2017; Deng et al. 2018; Li et al. 2019), variance (Jabr and Zheng 2014), and distribution of ratings (Hu et al. 2017). Researchers also examined a few reviewer- and product-related dimensions including agreement among reviewers and visibility of reviewers (Jabr and Zheng 2014), amount of product attribute information, degree of information conflict, and coherence between attribute and product-level assessment (Liu and Karahanna 2017).

(c) The online WOM articles examined the impact of reviews from both the seller and consumer perspectives. From the seller perspective, the articles examined the impact of online WOM on the product or the brand, the firm, and the market outcomes, including product sales (Dewan and Ramaprasad 2014; Jabr and Zheng 2014; Geva et al. 2017; Li and Wu 2018; Li et al. 2019), price premium (Ba and Pavlou 2002), firms’ product pricing strategy (Hu et al. 2017), firms’ responses to online WOM (Gunarathne et al. 2018), and stock market return (Deng et al. 2018). From the consumer perspective, the focus was on the impact of online WOM on consumer beliefs, judgments, decision processes, and post-purchase behaviors, such as beliefs towards shopping and perceived shopping outcomes (Venkatesh et al. 2017), funding decisions by venture capitalists (Venkatesh et al. 2017), importance weight assigned to product attributes (Liu and Karahanna 2017), and purchase disclosure (Rhue and Sundararajan 2019).

Design of online WOM systems: These articles examine different design features in WOM systems and their impact on users, firms and markets.

(a) The first set of articles examined user behavior resulting from different WOM system designs. User behaviors can be induced by a policy change (banning buyers from revoking their negative feedback regarding sellers) in eBay’s reputation systems (Ye et al. 2014), the presence of a feature (a reviewer ranking system) on Amazon (Shen et al. 2015), and a mechanism design (auditing) in a reputation market (Xu et al. 2018). These findings revealed the importance of online WOM system design and its role in shaping users’ strategic behaviors.

(b) The second set of articles explored biases generated by different WOM systems. Li and Hitt (2010) found that unidimensional ratings can be biased by price effects to a greater degree than multidimensional ratings. Adomavicius et al. (2019) found that graphical rating display designs are more advantageous than numerical designs in reducing biases. These articles compared different WOM systems and illustrated the advantages and disadvantages of different WOM system designs under different scenarios.

4. Conclusion

Across the articles identified in this curation, researchers examined a variety of online WOM types, a broad range of dimensions, and various links between online WOM and other constructs in the nomological network. They also used a rich set of theoretical perspectives and empirical methods. The research not only advances our theoretical understanding of online WOM, but also generates useful practical insights for individuals, firms, and platforms.

1 Table: MIS Quarterly Papers on Online Word-of-mouth

1 Figure: Infographic

Please cite this curation as follows: Jabr, W., Liu, B., Yin, D., Zhang, H. “Online Word-of-Mouth,” in MIS Quarterly Research Curations, Ashley Bush and Arun Rai, Eds., http://misq.org/research-curations, November 23, 2020.

References

Adomavicius, G., Bockstedt, J. C., Curley, S. P., and Zhang, J. 2019. “Reducing Recommender System Biases: An Investigation of Rating Display Designs,” MIS Quarterly (43:4), pp. 1321–1341. Also available at https://aisel.aisnet.org/misq/vol43/iss4/16/.

Aggarwal, R., and Singh, H. 2013. “Differential Influence of Blogs Across Different Stages of Decision Making: The Case of Venture Capitalists,” MIS Quarterly (37:4), pp. 1093–1112. Also available at https://aisel.aisnet.org/misq/vol37/iss4/7/.

Ba, S., and Pavlou, P. A. 2002. “Evidence of the Effect of Trust Building Technology in Electronic Markets: Price Premiums and Buyer Behavior,” MIS Quarterly (26:3), pp. 243–268. Also available at https://aisel.aisnet.org/misq/vol26/iss3/6/.

Chau, M., and Xu, J. 2012. “Business Intelligence in Blogs: Understanding Consumer Interactions and Communities,” MIS Quarterly (36:4), pp. 1189–1216. Also available at https://aisel.aisnet.org/misq/vol36/iss4/17/.

Deng, S., Huang, Z., Sinha, A. P., and Zhao, H. 2018. “The Interaction Between Microblog Sentiment and Stock Returns: An Empirical Examination,” MIS Quarterly (42:3), pp. 895–918. Also available at https://aisel.aisnet.org/misq/vol42/iss3/12/.

Dewan, S., and Ramaprasad, J. 2014. “Social Media, Traditional Media, and Music Sales,” MIS Quarterly (38:1), , pp. 101–121. Also available at https://aisel.aisnet.org/misq/vol38/iss1/7/.

Gao, G., Greenwood, B. N., Agarwal, R., and McCullough, J. S. 2015. “Vocal Minority and Silent Majority: How Do Online Ratings Reflect Population Perceptions of Quality?,” MIS Quarterly (39:3), pp. 565–590. Also available at https://aisel.aisnet.org/misq/vol39/iss3/5/.

Geva, T., Oestreicher-Singer, G., Efron, N., and Shimshoni, Y. 2017. “Using Forum and Search Data for Sales Prediction of High-Involvement Products,” MIS Quarterly (41:1), pp. 65–82. Also available at https://aisel.aisnet.org/misq/vol41/iss1/6/.

Gunarathne, P., Rui, H., and Seidmann, A. 2018. “When Social Media Delivers Customer Service: Differential Customer Treatment in the Airline Industry,” MIS Quarterly (42:2), pp. 489–520. Also available at https://aisel.aisnet.org/misq/vol42/iss2/8/.

Hu, N., Pavlou, P. A., and Zhang, J. 2017. “On Self-Selection Biases in Online Product Reviews,” MIS Quarterly (41:2), pp. 449–471. Also available at https://aisel.aisnet.org/misq/vol41/iss2/8/.

Huang, N., Hong, Y., and Burtch, G. 2017. “Social Network Integration and User Content Generation: Evidence from Natural Experiments,” MIS Quarterly (41:4), pp. 1035–1058. Also available at https://aisel.aisnet.org/misq/vol41/iss4/4/.

Jabr, W., and Zheng, Z. 2014. “Know Yourself and Know Your Enemy: An Analysis of Firm Recommendations and Consumer Reviews in a Competitive Environment,” MIS Quarterly (38:3), pp. 635–654. Also available at https://aisel.aisnet.org/misq/vol38/iss3/3/.

Jensen, M. L., and Yetgin, E. 2017. “Prominence and Interpretation of Online Conflict of Interest Disclosures,” MIS Quarterly (41:2), pp. 629–643. Also available at https://aisel.aisnet.org/misq/vol41/iss2/15/.

Kwark, Y., Chen, J., and Raghunathan, S. 2017. “Platform or Wholesale? A Strategic Tool for Online Retailers to Benefit from Third-Party Information,” MIS Quarterly (41:3), pp. 763–785. Also available at https://aisel.aisnet.org/misq/vol41/iss3/7/.

Li, H., Fang, Y., Lim, K. H., and Wang, Y. 2019. “Platform-Based Function Repertoire, Reputation, and Sales Performance of E-Marketplace Sellers,” MIS Quarterly (43:1), pp. 207–236. Also available at https://aisel.aisnet.org/misq/vol43/iss1/12/.

Li, M., Tan, C.-H., Wei, K.-K., and Wang, K. 2017. “Sequentiality of Product Review Information Provision: An Information Foraging Perspective,” MIS Quarterly (41:3), pp. 867–892. Also available at https://aisel.aisnet.org/misq/vol41/iss3/11/.

Li, X. 2017. “Revealing or Non-Revealing: The Impact of Review Disclosure Policy on Firm Profitability,” MIS Quarterly (41:4), pp. 1335–1345. Also available at https://aisel.aisnet.org/misq/vol41/iss4/16/.

Li, X., and Hitt, L. M. 2010. “Price Effects in Online Product Reviews: An Analytical Model and Empirical Analysis,” MIS Quarterly (34:4), pp. 809–831. Also available at https://aisel.aisnet.org/misq/vol34/iss4/11/.

Li, X., and Wu, L. 2018. “Herding and Social Media Word-of-Mouth: Evidence from Groupon,” MIS Quarterly (42:4), pp. 1331–1351. Also available at https://aisel.aisnet.org/misq/vol42/iss4/16/.

Liu, Q. Ben, and Karahanna, E. 2017. “The Dark Side of Reviews: The Swaying Effects of Online Product Reviews on Attribute Preference Construction,” MIS Quarterly (41:2), pp. 427–448. Also available at https://aisel.aisnet.org/misq/vol41/iss2/7/.

Luo, X., Gu, B., Zhang, J., and Phang, C. W. 2017. “Expert Blogs and Consumer Perceptions of Competing Brands,” MIS Quarterly (41:2), pp. 371–395. Also available at https://aisel.aisnet.org/misq/vol41/iss2/5/.

Mudambi, S. M., and Schuff, D. 2010. “What Makes a Helpful Online Review? A Study of Customer Reviews on Amazon.Com,” MIS Quarterly (34:1), pp. 185–200. Also available at https://aisel.aisnet.org/misq/vol34/iss1/11/.

Oh, H., Animesh, A., and Pinsonneault, A. 2016. “Free Versus For-a-Fee: The Impact of a Paywall on the Pattern and Effectiveness of Word-of-Mouth via Social Media,” MIS Quarterly (40:1), pp. 31–56. Also available at https://aisel.aisnet.org/misq/vol40/iss1/4/.

Rhue, L., and Sundararajan, A. 2019. “Playing to the Crowd: Digital Visibility and the Social Dynamics of Purchase Disclosure,” MIS Quarterly (43:4), pp. 1127–1141. Also available at https://aisel.aisnet.org/misq/vol43/iss4/7/.

Shen, W., Hu, Y. J., and Ulmer, J. R. 2015. “Competing for Attention: An Empirical Study of Online Reviewers’ Strategic Behavior,” MIS Quarterly (39:3), pp. 683–696. Also available at https://aisel.aisnet.org/misq/vol39/iss3/10/.

Shore, J., Baek, J., and Dellarocas, C. 2018. “Network Structure and Patterns of Information Diversity on Twitter,” MIS Quarterly (42:3), pp. 849–872. Also available at https://aisel.aisnet.org/misq/vol42/iss3/10/.

Venkatesh, V., Aloysius, J. A., Hoehle, H., and Burton, S. 2017. “Design and Evaluation of Auto-ID Enabled Shopping Assistance Artifacts in Customers’ Mobile Phones: Two Retail Store Laboratory Experiments,” MIS Quarterly (41:1), pp. 83–114. Also available at https://aisel.aisnet.org/misq/vol41/iss1/7/.

Wu, J., Huang, L., and Zhao, J. L. 2019. “Operationalizing Regulatory Focus in the Digital Age: Evidence from an E-Commerce Context,” MIS Quarterly (43:3), pp. 745–764. Also available at https://aisel.aisnet.org/misq/vol43/iss3/5/.

Xu, H., Chen, J., and Whinston, A. B. 2018. “Identity Management and Tradable Reputation,” MIS Quarterly (42:2), pp. 577–593. Also available at https://aisel.aisnet.org/misq/vol42/iss2/11/.

Ye, S., Gao, G., and Viswanathan, S. 2014. “Strategic Behavior in Online Reputation Systems: Evidence from Revoking on EBay,” MIS Quarterly (38:4), pp. 1033–1056. Also available at https://aisel.aisnet.org/misq/vol38/iss4/7/.

Yin, D., Bond, S. D., and Zhang, H. 2014. “Anxious or Angry? Effects of Discrete Emotions on the Perceived Helpfulness of Online Reviews,” MIS Quarterly (38:2), pp. 539–560. Also available at https://aisel.aisnet.org/misq/vol38/iss2/12/.

Figure 1. Online Word-of-Mouth Infographic