Disconfirmation Effect: How do our product opinions change over time?

Firms of all sizes are known to spend large portions of their budget on marketing campaigns through which to attract consumers. It has been found that 20-50% of all purchasing decisions are made primarily as a result of word-of-mouth (WOM) (Bughin et al., 2010). Online WOM (eWOM) offers consumers and companies certain advantages; eWOM increases persuasion and willingness to pay (WTP) in consumers, while offering consumers informedness and a sense of trust in a company’s products or services (Healey, 2016). Companies are able to improve products through an enhanced understanding of the consumer, while profiting from free/cost-effective advertisement (Buttle, 1998).

Disconfirmation Effect

In their article, Ho et al. (2017) introduce a new take on studying rating/reviews and purchasing decisions in the digital marketplace, which is the concept of disconfirmation, (formally expectation-disconfirmation theory, or EDT for short), and is defined as ‘the discrepancy between the expected and experienced assessment of the same product’ (Ho et al., 2017; Anderson and Sullivan, 1993). The paper aims to study the effect disconfirmation (the difference in prepurchase expectation and postpurchase experience) on the behaviour of consumers leaving online product reviews. A conceptual framework outlining the author’s views on prepurchase and postpurchase influences can be seen in the figure below:

Screen Shot 2018-03-11 at 20.16.18


Ho et al. (2017) made use of a Bayesian learning framework to study consumer rating behaviour from a dynamic perspective (Wang and Yeung, 2016). In this framework, a shift in consumer opinion over time is incorporated into the analysis of the data. While previous works have focused on static points of view, this paper effectively studies rating effects over a longer period. Data was provided by ‘an online e-commerce website similar to Amazon’. Purchasing information came from 10 major product categories, and was very specific (Ho et al., 2017. Specific data was gathered between March 2006 and November 2011 and included:

  1. Order information (product name, price, shipping and handling time, order placement date): useful for finding out if a user is posting a review or only lurking
  2. Customer-reported reviews (with review title, review body, submission date, overall rating on 5-star scale): Useful for recovering rating information (valence, volume, variance) available to consumer upon purchase (Ho et al., 2017; Flanagin and Metzger, 2013).


The most essential finding applicable to the goal of the study are the fact that a consumer is more likely leave a review when the disconfirmation is more severe; the sign of the rating (positive or negative) depends on the sign of disconfirmation (see Figure 2). Key moderating factors are that the disconfirmation effect is attenuated by an increased period of time between purchase and receipt, and that dissension in evaluations among peers accentuates the disconfirmation effect (Ho et al., 2017).

Screen Shot 2018-03-11 at 20.31.55

Managerial Implications

The authors outline some actionable strategies resulting from the aforementioned findings. Firstly, managers should ensure quick order fulfilment. Consumers contribute more positive reviews when products are received quicker, so more eWOM van be achieved through efficient order fulfilment. Secondly, managers should maintain a high level of service. Consumers are likely to leave negative reviews when service is poor, which could decrease purchase intent (Imrie, 2005).


This paper is strong for a number of reasons aside from the econometric robustness of the study, the long time period studied (approx. 5.5 years), and the extent of data gathered.

  1. The study’s dynamic perspective differentiates it from previous works studying EDT or online reviews. This allows the learning framework to develop as necessary, which is essential as perceptions change depending on the reviews posted.
  2. The authors collected data at the micro (individual) level (Clark and Avery, 2010). Most articles about EDT and post-consumption WOM use insights that have been aggregated (product-focused), while this paper studies cases from the individual consumer perspective. This results in stronger insights into consumer behaviour and the drivers of disconfirmation, possibly allowing for more actionable recommendations.
  3. In their paper, Ho et al. (2017) consider the fact that (potential) consumers may not trust the review system, and therefore hold a perception of the review system This consists of two components, namely system biasedness (relating to pre-purchase expectations of the product) and system preciseness (relating to the level of disconfirmation experienced after purchase). The authors also highlight that not all shoppers are the same. A distinction is made between frequent and occasional raters/reviewers. Occasional raters are observed to be more biased and susceptible to disconfirmation; frequent raters are more objective and should be relied on more by both future consumers and managers.


A number of weaknesses also exist in this paper:

  1. There is no clear distinction between written reviews and discrete ratings despite a short mention and usage of a Bayesian model. The authors should discuss the potential influence a written review may have on the disconfirmation effect relative to a rating on a 5-point scale.
  2. Online spending habits may have changed significantly between March 2006 and December 2011 (statista.com, 2018). Also, the extent of the popularity of the website used to gather data will likely have changed in the timeframe. The authors could have done a better job evaluating the pros and cons of such a timeframe, and better distinguished between consumers, rather than only labelling them as ‘occasional’ or ‘frequent’ raters.
  3. There is no mention of the influence of geographical location on the study. The authors wrote the paper at American and Chinese universities, which could be indicative of vast cultural differences in purchasing or reviewing behaviour and may also have influenced the handling and shipping times observed. This warrants an extensive review of where reviewers came from and the generalisability of the study, including outside of an Amazon-like business.

Works Cited

Anderson, E. and Sullivan, M. (1993). The Antecedents and Consequences of Customer Satisfaction for Firms. Marketing Science, 12(2), pp.125-143.

Bughin, J., Doogan, J. and Vetvik, O. (2010). A new way to measure word-of-mouth marketing. [online] McKinsey & Company. Available at: https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/a-new-way-to-measure-word-of-mouth-marketing [Accessed 11 Mar. 2018].

Buttle, F. (1998). Word of mouth: understanding and managing referral marketing. Journal of Strategic Marketing, 6(3), pp.241-254.

Clark, W. and Avery, K. (2010). The Effects of Data Aggregation in Statistical Analysis. Geographical Analysis, 8(4), pp.428-438.

Flanagin, A. and Metzger, M. (2013). Trusting expert- versus user-generated ratings online: The role of information volume, valence, and consumer characteristics. Computers in Human Behavior, [online] 29(4), pp.1626-1634. Available at: https://www.sciencedirect.com/science/article/pii/S0747563213000575 [Accessed 11 Mar. 2018].

Healey, N. (2016). Electronic Word of Mouth (eWOM) – How can we can break it down?. [online] University of Brighton. Available at: http://blogs.brighton.ac.uk/nh165/2016/04/19/electronic-word-of-mouth-ewom-how-can-we-can-break-it-down/ [Accessed 11 Mar. 2018].

Ho, Y., Wu, J. and Tan, Y. (2017). Disconfirmation Effect on Online Rating Behavior: A Structural Model. Information Systems Research, [online] 28(3), pp.626-642. Available at: https://pubsonline.informs.org/doi/pdf/10.1287/isre.2017.0694 [Accessed 11 Mar. 2018].

Imrie, B. (2005). Beyond disconfirmation: The role of generosity and surprise. International Marketing Review, [online] 22(3), pp.369-383. Available at: https://www.emeraldinsight.com/doi/pdfplus/10.1108/02651330510602259 [Accessed 11 Mar. 2018].

Wang, H. and Yeung, D. (2016). Towards Bayesian Deep Learning: A Framework and Some Existing Methods. IEEE Transactions on Knowledge and Data Engineering, 28(12), pp.3395-3408.

http://www.statista.com. (2018). E-commerce Worldwide. [online] Available at: https://www.statista.com/topics/871/online-shopping/ [Accessed 11 Mar. 2018].

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Google+ photo

You are commenting using your Google+ account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )


Connecting to %s