Tag Archives: customer reviews

From e-commerce to social commerce


A matter of trust

The advancement of Web 2.0 social networks brought new developments to e-commerce. In recent years, e-commerce has transformed to social commerce. Social commerce is a new stream and subset of traditional e-commerce, which combines e-commerce with Web 2.0 social networks.

Social commerce, trust & buying intention

Thanks to social networks consumers can now communicate, rate other products, review others’ opinions, participate in forums, share their experiences and recommend products and services. By bringing the features of Web 2.0 social networks to e-commerce, consumers can support each other in the acquisition of products and services in an online context. This results in more customer-oriented business models where customers can share knowledge, experiences and information about their products and services.

Social commerce has three main constructs that empower customers and increase the sociability of e-commerce:

  • Forums and communities: Online discussion sites that support information sharing;
  • Ratings and reviews: Provide comprehensive information about a product for potential customers;
  • Referrals and recommendations: Unlike brick and mortar stores, in online stores it is not possible to interact with staff, so customers rely more on other customers’ recommendations.

Trust is a central issue in e-commerce. Social commerce has helped to establish more trust in e-commerce platforms. Customers experience higher levels of trust as they can support each other through information exchange. This is because interactions and interconnectivity reduce the perceived risk in online transactions. Reviews, ratings and recommendations can indicate the trustworthiness of an online seller as customers consider reviews from other customers to be more reliable than information from a commercial website.

Hajli (2015) found that the three social commerce constructs significantly positively influence the user’s intention to buy. Trust appeared to be a mediating variable. Social commerce constructs have a positive effect on user’s trust, which in turn positively influences the intention to buy (Figure 1). To arrive at these findings, Hajli (2015) conducted a survey study with four constructs: intention to buy, social commerce constructs, perceived usefulness and trust. A five point Likert-scale was used in the questionnaire. Data was collected at universities in the UK. The final sample consisted of 243 completed and usable questionnaires. Next, Structural Equation Modelling (SEM) was used for data analysis. The hypotheses were tested with the Partial Least Squares (PLS) method. The findings underline that social commerce constructs, like customer reviews, are more likely to increase trust, and in turn increase customers’ intention to buy.

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Amazon customer reviews

From a practical perspective, this study encourages online businesses to make a plan for reviews and to manage social networks effectively as it significantly impacts customers’ purchasing decisions. It recommends them to engage with their customers through reviews to develop trust. Other research indeed shows that 91 percent of customers read online reviews and that 84 percent trusts online reviews as much as a personal recommendation (Bloem, 2017) In practice, this implies that not offering customer reviews is similar to ignoring 84 percent of your buying population by not giving them the information they want to support them in their buying decision (DeMers, 2015).

To illustrate, Amazon optimised its business model based on customer reviews and ratings. Customer reviews are one of the most important ranking factors in Amazon’s A9 algorithm. It ranks product search results based on the positivity of customer reviews and rating. (Grosman, 2017)

Fake review problem

A weakness in the study of Hajli (2015) is that it does not consider that information related to the identity of the reviewers influences the perceived trustworthiness of a review.  The paper simply finds that more reviews increases trust, which in turn increases the buying intention.  However, in reality, it might not be that straight forward anymore with the rise of fake product reviews. Nowadays, it is hard for customers to decide which reviews to trust. There is looming crisis of confidence in online product reviews, which used to be a key factor in customers’ buying decision. (Silverman, 2017) As customers cannot trust reviews anymore, it can be questioned whether the positive relation between the number of reviews, trust and buying decision still holds.

Increasingly, customers pay careful attention to reviews, e.g. looking for reviews with a Verified Purchase tag. Nearly 66.3 percent of Amazon reviews are five-star ratings, which is highly unrealistic. Reviews on Amazon are a key factor when making a purchasing decision and without reviews it is difficult for online retailers to gain sales. In an attempt to boost sales, retailers offer reviewers free or discounted samples in return for a positive customer review. So, it is no surprise that 96 percent of paid reviews on Amazon is rated four- or five-star.  (Cipriani, 2016)

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Source credibility

Many authors have investigated the positive impact of online reviews on sales of products and services. However, given the importance of source credibility, I believe more research is needed on trustworthiness of reviewers as an important construct. The source credibility theory explains how a recommendation persuasiveness is affected by the perceived credibility of its source. Actually, customers accept reviews depending on the perceived trustworthiness of the reviewer, which consequently impacts the buying decision. Reviewer trustworthiness is therefore an important moderating variable that positively moderates the impact of review-based online reputation. (Banerjee, Bhattacharyya, & Bose, 2017)

Concluding, instead of solely increasing the number of (positive) customer reviews, online retailers should also build a good review-based online reputation that encourages and identifies top trustworthy reviewers and that ranks reviews based on reviewer trustworthiness.

This post was inspired by: Hajli, N. (2015). Social commerce constructs and consumer’s intention to buy. International Journal of Information Management, 183-191

References:

Banerjee, S., Bhattacharyya, S., & Bose, I. (2017). Whose online reviews to trust? Understanding reviewer trustworthiness. Decision Support Systems, 17-26.

Bloem, C. (2017, July 31). 84 Percent of People Trust Online Reviews As Much As Friends. Here’s How to Manage What They See. Opgehaald van Inc.: https://www.inc.com/craig-bloem/84-percent-of-people-trust-online-reviews-as-much-.html

Cipriani, J. (2016, March 14). Why You Shouldn’t Trust All Amazon Reviews. Opgehaald van Fortune: http://fortune.com/2016/03/14/paid-amazon-reviews/

DeMers, J. (2015, December 28). How Important Are Customer Reviews For Online Marketing? Opgehaald van Forbes: https://www.forbes.com/sites/jaysondemers/2015/12/28/how-important-are-customer-reviews-for-online-marketing/#35eccc711928

Grosman, L. (2017, February 28). Five Tips To Improve Your Ranking On Amazon. Opgehaald van Forbes: https://www.forbes.com/sites/forbescommunicationscouncil/2017/02/28/five-tips-to-improve-your-ranking-on-amazon/#3079c5f89fed

Hajli, N. (2015). Social commerce constructs and consumer’s intention to buy. International Journal of Information Management, 183-191.

Silverman, D. (2017, April 20). A Matter of Trust: Amazon Declares War on Fake Product Reviews. Opgehaald van Clavis Insight: https://www.clavisinsight.com/blog/matter-trust-amazon-declares-war-fake-product-reviews

To Keep Or Not To Keep: Effects of Online Customer Reviews on Product Returns


By Madeleine van Spaendonck (365543ms)

In the US, the current average return rate for products bought online is approximately around 30% of purchases (The Economist, 2013). Most returns take place due to customers’ negative post-purchase product evaluation rather than product defects. One factor that is found to have an impact on this is the role of Online Consumer Reviews.

This is what Minnema et al. (2016) investigated in their study “To Keep or Not to Keep: Effects of Online Customer Reviews on Product Returns”. It uses a multi-year (2011-2013) dataset from a European online retailer that offers both electronics and furniture products. The paper examines the impact of three OCR characteristics (valence, volume and variance) on return decisions (figure 1). The researchers evaluate the net effect of OCRs, looking at its influence on both purchase and return decisions.

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Theory

The hypotheses examined are based on the ‘expectation disconfirmation mechanism’. Post-purchase satisfaction results from the combination of customer expectations formed at the purchase-moment, product performance, and the difference between them. Negative expectation disconfirmation therefore decreases satisfaction, leading to a higher return probability. Therefore, higher expectation levels should lead to higher purchase and return probabilities, while higher expectation uncertainty should lower these.

Main results

Figure 2 presents a summary of the results of the study.

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A particularly counterintuitive insight is that overly positive review valence (whereby the current OCR valence is higher than the long-term product average) leads to not only more sales but also a higher return probability. A potential reason for this is that OCRs induce the customer to form product expectations at the moment of purchase, leading to higher purchase probability. However, high expectations due to overly positive reviews may not be met. This leads to negative expectation confirmation, which then leads to higher return probability. Review volume and variance mostly affect purchase decisions, having little to no effect on product returns.

Strengths, Weaknesses and Suggested Improvements

While the majority of scholarly work in this field focuses on OCRs effects on product sales, this paper also addresses the lack of understanding of its effects on product returns. Taking into account both aspects is vital, because the prediction of OCR effects on retailer performance may be overly optimistic or pessimistic if only its effects on sales are considered. The study also shows that OCR effects advance beyond the moment of purchase and have the power to affect the decision to return a product. However, the model did not incorporate other information sources available at the purchase-moment that affect return-likelihood, such as product descriptions and pictures provided by the retailer. A comparative analysis could be used to evaluate whether reviews or retailer-provided information have the strongest impact on returns.

Managerial Implications

The study highlights the importance of considering product returns when evaluating OCR effects, as overly positive reviews may have negative consequences for retailers’ financial performance. Overly positive reviews, leading to more product returns, result in large reverse logistics costs. To reduce negative expectation disconfirmation, retailers should provide information and tools (besides OCRs) that allow consumers to set the right expectations and see if the product really meets their needs.

Sources:

Minnema, A., Bijmolt, T.H.A., Gensler, S., Wiesel, T. (2016). “To Keep or Not to Keep: Effects of Online Customer Reviews on Product Returns.” Journal of Retailing, 92(3), pp. 253–267.

The Economist. (2013). Return to Santa. December 21, (latest accessed March 8, 2017), http://www.economist.com/news/business/21591874-e- commerce-firms-have-hard-core-costly-impossible-please-customers- return-santa

Source for cover photo:

Ministry Ideaz, (2016), How do I return a product I no longer want? [ONLINE]. Available at: http://support.ministryideaz.com/customer/portal/articles/1022650-how-do-i-return-a-product-i-no-longer-want- [Accessed 8 March 2017].