How much is a star worth? : the influence of customer ratings on the customer's willingness-to-pay

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How much is a star worth?

The influence of customer ratings on the customer's

willingness-to-pay

Master thesis author:

Tim Benjamin Hasenkampf (11374349) Under the supervision of:

Dr. Umut Konuș

M.Sc. Business Administration – Digital Business Track Amsterdam Business School

University of Amsterdam

Key words: Online User Review, Star Rating, Willingness-to-pay 23th June, 2017

Statement of originality

This document is written by Tim Benjamin Hasenkampf who declares to take full responsibility for the contents of this document. I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it. The Faculty of Economics

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The influence of star ratings on willingness-to-pay: How much is a star worth?

Abstract

Previous research regarding online user reviews has shown the significant effect of these on customer behavior, such as the purchase intention.

One of the more sparsely investigated areas effected by online user reviews is the

willingness-to-pay of the customer, despite the far-reaching implications of this measure for profitability in the online shopping business.

This thesis aims to quantify the relationship and the influence of star ratings on the willingness-to-pay of potential customers by means of a field experiment.

After emphasizing the relevance of this topic in the introduction, a comprehensive literature review will describe previous research about the core concepts and components of the conceptual framework as well as their interrelations. With the help of the conceptual framework, hypotheses about the relationships of its components will be established. These hypotheses are tested in a field experiment, providing the data needed to draw conclusions regarding the influence of online user reviews, in the form of star ratings, on the willingness-to-pay, while simultaneously demonstrating the influence and/or role of possible moderators in these transactions.

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Acknowledgements

I would like to express my gratitude towards a number of supporters, who made thesis possible in its scope and quality. First and foremost I would like to thank my supervisor Dr Umut Kunuș for his undying support, useful thoughts and engagement far beyond the required. I would like to thank Stephan Thun, CEO of MaritzCX Europe, for the inspiration for this topic. Next, I would like to thank Anouar El Haji, CEO of Veylinx, and Tessa

Kammeren, senior researcher of Veylinx, for their support with the conduct of the experiment of this thesis. Lastly, I would like to thank Eline van Oostveen for the translation of the questionnaire and Ken Makowino, as well as Amaris Enid Montest, for editing and quality assurance.

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List of Figures

Figure 1 - Means plot star rating ... 65

Figure 2 - Means Plot Age Groups ... 66

Figure 3 - Means plot gender ... 67

Figure 4- Means Plot Risk Orientation ... 68

Figure 5 - Frequencies Bids per Treatment... 81

Figure 6 - Boxplots with outliers per Product ... 81

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List of Tables

Table 1 - Overview Types of Online User Reviews ... 15

Table 2 - Overview Literature on Online Reviews ... 18

Table 3 - Overview Literature concerning Willingness-to-Pay ... 23

Table 4 - Summary Table possible influence factors on the relationship of star ratings and willingness-to-pay ... 34

Table 5 - Overview Hypotheses ... 42

Table 6 - Overview Treatments ... 47

Table 7 - Descriptive Statistics Sample ... 52

Table 8 - Summery Table Variables ... 58

Table 9 - One-Way ANOVA Star Rating ... 59

Table 10 - Univariate ANOVA Results ... 62

Table 11 - Overview of Hypotheses with results ... 71

Table 12 - Frequencies Bids ... 82

Table 13 - Further Descriptive Statistics Sample ... 83

Table 14 - Means and Standard Deviations of Star Rating Manipulation ... 83

Table 15 - T-Test Product Type Descriptives ... 83

Table 16 - Independent Samples Test Product Type ... 84

Table 17 - Means Gender vs. Star Ratings ... 84

Table 18 - Means Risk-Behavior vs. Star Rating ... 84

Table 19 - Descriptive Statistics Bids for Star Ratings... 85

Table 20 - Test for Normality ... 85

Table 21 - Levene's Test of Equality of Error Variances... 85

Table 22 - Skewness and Kurtosis Treatment groups ... 86

Table 23 - One-Way ANOVA Absolute Difference Mean Ranks ... 86

Table 24 - Rank Scores Star Ratings ... 86

Table 25 - Omnibus Kruskal-Wallis Test ... 87

Table 26 - Kruskal-Wallis Test 4 Stars vs. 4,5 Stars ... 87

Table 27 - Kruskal-Wallis Test 4 Stars vs. 5 Stars ... 88

Table 28 - Kruskal-Wallis Test 4,5 Stars vs. 5 Stars ... 88

Table 29 - Univariate ANOVA with Dummie-Variables ... 89

List of Illustrations

Illustration 1 - Typical Star Rating ... 37

Illustration 2 - Conceptual Framework ... 39

Illustration 3 - Visualization of methodology ... 45

Illustration 4 - Start of Auction ... 56

Illustration 5 - Rules of Auction ... 57

Illustration 6 - Veylinx Auction ... 58

Illustration 7 - Confirmation Bid Veylinx Auction... 59

Illustration 8 - Survey Question about Risk-Behavior with 1 to 7 scale ... 60

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1. Introduction

As a society we have reached the time of the so-called information age (Castells, 1999). This development has occurred due to the widespread use of the internet as source of information. At this moment, over 3.5 billion people are connected to the internet, amounting to 45 percent of the world's population (Statista, 2016).

Along with the rise of global internet usage, online shopping and e-commerce have become an essential part of the business model of many companies and established itself as growth driver. A recent global study about online shopping shows that over 31% consumers shop online at least once a month, with 39% shopping online more frequently (Statista, 2016). This fact emphasizes the potential and importance of online shopping for retail businesses and companies seeking to sell their products online.

Despite the growing popularity of online shopping, the online environment still has one major disadvantage in comparison with traditional brick-and-mortar stores. Since the customer cannot physically examine the quality of the product during his or her decision making process, the customer is confronted with uncertainty. Logically, the customer seeks effective ways to reduce the uncertainty about the product considered for purchase and attempts to obtain further information.

e-WOM in form of online user reviews and ratings provided by customers has emerged as one of the most important sources of information for potential customers. The relevance of such reviews has been shown by a Deloitte survey (2007) which found out that nearly all customers read customer-written product reviews. Adding to this, the great majority of those customers (82 percent) say that their purchase decisions have been influenced by thes (Li & Hitt, 2010).

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Moreover, a study by Nielsen (2012) shows that online customer reviews play an important role in purchase decision making, especially for goods such as cars, electronics and travels. Another Nielsen study conducted in 2012 illustrates that online customer opinion is a highly relevant and trustworthy form of advertisement, being ranked second in both of categories (trustworthiness and relevance). Overall, word-of-mouth is the most trusted form of advertisement (Nielsen, 2009).

All of these studies emphasize the great and still growing influence of online user reviews on customer behavior, especially on the purchase decisions of potential customers - an influence that calls on managers and companies to manage the online reputation of their product

carefully. The management of an online reputation can only be successful once managers acknowledge which factors and conditions are going to have an impact on reviews and ratings, and how these in turn impact customer decision-making.

In online shopping, the most common form of expressing one’s opinion about a product is through a rating which is often made visual by a number of stars; the more stars the customer gives, the better his or her experience was.

Star ratings are a simple but effective way to express the perceived quality of a product or service; moreover, they prove impactful in online shopping. Content marketing experts from Yotpo.com have investigated one million reviews over multiple e-commerce websites and have found evidence for the tremendous effect star rating have on purchase behavior.

It was brought to light that overwhelming 94% of all products bought online had a star rating of 4 stars or more. Additionally, the number of stars given had a strong influence on review behavior. Again, 94% of reviews were written about products with 4 stars or more on

average. The reviews written about products that received a 5 star rating on average exceeded the amount of reviews of products with a 4 star rating by 174% (Yotpo, 2015).

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These findings demonstrate the relevance of star ratings as a form of e-WOM for customers, and therefore for managers of companies with e-commerce channels.

This master thesis seeks to understand how star ratings influence the customer’s willingness-to-pay for a product that has been rated and which factors might affect this relationship. Willingness-to-pay is defined as the maximum amount of money a customer is willing to pay for a service or product (Wertenbroch & Skiera, 2002). The willingness-to-pay of potential customers has been identified as interesting field of inquiry because of its far-reaching implications for managers and their companies. When managers are aware of the willingness-to-pay and demand curves of their customers, they are able to set the price that guarantees them maximum margin. Moreover, they can predict the revenue from sales made in the future. This is of great value to companies since they are often confronted with uncertainty about future sales of their products and the accuracy of their pricing, both of which have great influence on the profitability of sales.

One of the challenges one has to overcome when investigating this topic is the variety of customers’ responses towards online user reviews.

Previous research has shown that the amount and magnitude of the influence of e-WOM, like online user reviews, differs from individual to individual. Therefore, it is important to

understand which factors might moderate the relationship between a product’s star ratings and the customer’s willingness-to-pay.

Moderators of this relationship could lie in the individual itself (e.g. risk-averseness,

involvement or motivation), in the product characteristics (e.g. type of product), or in factors of the rating itself (e.g. volume of reviews).

For example, a star rating about a search product, which’s quality can be accessed by

physical properties, could be less influential than about an experience good, that can only be accessed regarding quality by consumption. For a customer, who is very active and

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experienced in online shopping, the impact of a star rating might differ in comparison to someone who does not have these characteristics.

Due to the potential influence of these factors, this master thesis is going to test variables in respect to product, rating and customer characteristics.

The proven influence of online user reviews on customers and the subsequent importance of these for companies as resulted in a great amount of research on this particular topic.

However, little research has been done on the specific relationship between the two concepts of e-WOM (in form of star ratings) and customer’s willingness-to-pay.

This master thesis seeks to contribute to this scarcely researched topic and to provide a comprehensive understanding of the influence of star ratings on customer’s willingness-to-pay, as well as identifying factors that may moderate this relationship.

Therefore, the research question:

What is the influence of a product’s star ratings on willingness-to-pay of the customer, and which factors influence this relationship?

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2. Literature Review

The literature review of this thesis aims to provide an impression about the research that has been conducted regarding the influence of online user reviews on customer behavior, on the concept of willingness-to-pay and on the link between these two important topics, as well as the factors that influence their relationship.

2.1. Word-of-Mouth and Online User Reviews

Before being able to provide well-grounded insights about the research that has been carried out in relation to the topic of this master thesis, I will define two important concepts:

online user reviews and word-of-mouth.

Word-of-mouth (or WOM), in a very elementary definition, is communication or advice about services, products or companies that is shared by one former customer to another person, in person or via a communication medium (East et al., 2007).

This “informal advice” (East et al., 2007), due to its perceived trustworthiness (Zhang, Ma, & Cartwright, 2013), has a significantly strong influence on customer decision making and behavior, which has led many researchers to investigate word-of-mouth (Anderson, 1998; Goldenberg et al., 2001; Huang et al., 2011; Stokes & Lomax, 2002).

As the internet became to one of the most significant means of communication of our time (Newhagen & Rafaeli, 1996), customers have been granted the opportunity to share their advice and experiences with companies as well as products online.

Online user reviews can be perceived as a means of sharing experiences with a company, of articulating experiences of product consumption or satisfaction with a service (Hennig-Thurau & Walsh, 2003). Examples of such reviews is customer feedback on online shopping site, like amazon.com or review sites like yelp.com.

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2.2. Online User Reviews as e-WOM

Online user reviews are an important source of information throughout the customer’s

purchase decision making process, substituting and complementing more traditional forms of word-of-mouth (like verbal communication) concerning product quality and service

(Chevalier & Mayzlin, 2006; Clemons et al., 2006).

With the rise of the internet and e-commerce, e-WOM has emerged as a new and extremely relevant form of WOM. e-WOM is defined as “any positive or negative statement made by potential, actual, or former customers about a product or company, which is made available to a multitude of people and institutions via the Internet” (Hennig-Thurau & Walsh, 2003). Following this definition, online user reviews can be classified as e-WOM. Consumers can share their experiences with a product, service or company via e-WOM with millions of other potential customers. This is the most significant differences to traditional WOM. The

combined power of the trustworthiness of e-WOM and its large reach make customer reviews and ratings a powerful tool for sales and customer engagement, while simultaneously posing a potentially devastating threat for e-commerce managers.

2.2.1. The impact of e-WOM and online reviews on customer behavior and attitudes

Previous research found that e-WOM has various effects on customer behavior and attitudes. For example, it has been shown that e-WOM influences the decision making process of customers (Hennig-Thuran & Walsh, 2003; Gupta & Harris, 2009), product sales (Chevalier & Mayzlin, 2006; Basuroy et al., 2003; Chintagunta et al., 2010; Clemons et al., 2006; Zhu & Zhang, 2010), and customer’s trust and purchase intention (Chaterjee, 2001). This part of the literature review will examine to the most relevant and interesting findings surrounding the topic of this thesis.

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One of these findings was made by Hennig-Thuran and Walsh (2003), who discovered two main motives that drive customers to consider e-WOM during their purchase decision making process. Customers in the online shopping setting seek the diminution of risk and the

lowering of search time by means of customer reviews. This is because customers are confronted with more risk, uncertainty and a higher amount of available information concerning the product compared to an offline setting.

An interesting example of how e-WOM influences the decision making process is provided by the research of Gupta and Harris (2009), who found in a laboratory experiment that e-WOM influences the time spent in different phases of the decision making process. The results of their study indicated that customers, who are motivated to process information, spent more time in the buying process (Gupta & Harris, 2009). This finding is especially compelling for businesses that sell products over the internet that require intensive research of before purchase such as electronics.

Next to aiding the customer in their decision making, it has also been shown that e-WOM in form of online user reviews is directly related with product sales, which should interest any manager seeking revenue online (Chevalier & Mayzlin, 2006).

Online user reviews typically include review volume (the number of reviews a product or a seller receives), review valence (the average review rating), and review variance (usually shown through the distribution of reviews). All of these review related characteristics have been subject to research.

The valence of a review has been shown to positively affect product price and sales (Chevalier & Mayzlin, 2006; Moe & Trusov, 2011 ; Wu & Gaytán, 2012), while review volume and variance can have a positive, insignificant, or even negative influence on marketing outcomes (Basuroy et al., 2003; Chintagunta et al., 2010; Clemons et al., 2006; Zhu & Zhang, 2010).

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Another way to categorize fields of research on the subject of online user reviews is to differentiate by the product which has been rated or reviewed by the customer.

The influence of online user reviews in combination with experience goods has been widely investigated (Chevalier & Mayzlin, 2006; Clemons et al., 2006; Duan et al., 2008; Huang et al., 2009; Mudambi & Schuff, 2010; Ye et al., 2009). As mentioned before, experience goods are products that can only be evaluated by subjective experience, therefore customers require sampling or purchase in order to assess product quality (Nelson, 1970, 1974).

Major examples of research about the influence of reviews on the sales of experience goods concern movie box performance (Chintagunta, Gopinath, & Venkataraman, 2010), hotel bookings (Ye, Law & Gu, 2009) and book sales (Chevalier & Mayzlin, 2006).

Generally, researchers find a positive influence of online user reviews and ratings on the commercial performance of products.

The effect of online user reviews, in combination with search goods, like electronics, has also been subject to investigation by researchers. As said, search goods are mostly evaluated by objective properties and the customers do not require interaction in order to evaluate the product (Nelson, 1970, 1974).

In 2008, Chen and Xie found that online customer reviews of high technology products are likely to be more relevant to customers than seller-created information. Furthermore, Zhang, Ma and Cartwright (2013) investigated the influence of online user reviews and found that they affect the number of sales of search goods. The authors found that both the average online customer review ratings and the count of total online user

reviews influence the sales of search goods. Additionally, like Chevalier and Mayzlin (2006), they also demonstrated that extremely negative ratings have a stronger negative effect on sales than extremely positive ratings.

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In line with this, the strong effect of negative user reviews is also explored by Chaterjee (2001), who investigated the effects of e-WOM and found that negative e-WOM strongly decreases a customer’s trust and purchase intention. The effect is even more significant on bargain shoppers who are looking for low-priced products. This suggests that online shoppers are especially sensitive to negative e-WOM in low-price regions. Therefore, companies that are specializing on price as a competitive advantage should pay close attention to managing negative e-WOM.

The direct influence of e-WOM on product sales and customer behavior urges managers of businesses, that use the internet as a sales or marketing channel to pay close attention to reviews and ratings of their products or business. Due to the importance of this topic for businesses, researchers have vested interest in the effects of e-WOM on consumers.

User reviews can take different forms and visualizations; they can be text based, in form of a like or dislike or be expressed in some sort of scale (see Table 1). As mentioned before, the impact of such ratings is of importance to managers and therefore businesses are regularly confronted with the question of which ratings or review system should be implemented in their online shop or company website. It is not within the scope of this master thesis to elaborate this question. Nevertheless, it is helpful for the general understanding to gain an overview of the forms in which customer reviews and ratings appear (see Table 1).

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Table 1- Overview Types of Online User Reviews

Type of Review Description Found on Text-based

review

A review consisting of a user-generated story about the experience with the product and purchase

Amazon Product Reviews, Ebay, Trip Advisor and many more

1-5 Scale Rating

A review in form of a rating of the experience with the product visualized by a 1 to 5 scale (often in form of stars)

Amazon, Ebay, Trip Advisor and many more

1-10 Scale rating

A review in form of a rating of the experience with the product visualized by a 1 to 10 scale

NPS score survey

Binary A review given about the experience with a product given with a simple good or bad, like or dislike, etc.

Netflix, Ebay Seller/Buyer Review

Notation Based Rating System

A review using notations to express opinion about a product or another review, i.e. not helpful,

somewhat helpful, helpful, very helpful

Epinions, Amazon

2.2.2. Online Star Ratings

This part of the literature review is dedicated to the concept of star ratings as form of e-WOM and the research that has been done about online customer ratings of this form.

Online star rating, in the setting of this master thesis, are the visualization of review valence or sentiment of the customer in form of a number of stars, mostly on a Likert-scale from 1 to 5 (see Illustration 1).

The simplicity of star ratings seems to provide an explicit way of communicating a customer’s assessment of a product.

One might think that star ratings increase in effect the more positive (more stars) they get. Nevertheless, a person’s perception of star ratings and their meaning can differ. A three star rating could be a good rating for one customer and a bad one for another.

Research about the impact of star ratings and the varying conditions that determine their usefulness have brought up some interesting findings.

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In 2015, a study conducted by Park and Nicolau found that extreme ratings (positive and negative) are more useful and enjoyable to the customer than moderate ratings. This indicates the amount of influence the star rating has on the customer depends whether they are

(extremely) positive or negative.

Additionally, a study by Chevalier and Mayzlin (2006) found that both the average online user review rating and the number of online reviews have a significant influence on book sales. Interestingly, they found that the negative effect of extremely negative review ratings (1 star) on book sales is greater than the positive effect of extremely positive ratings (5 stars) on purchases.

Mudambi and Schuff (2010) examined the helpfulness of ratings across different product types. Interestingly, they found that for experience goods, reviews with extreme ratings are less helpful than reviews with moderate ratings. This provides evidence that the influence of star ratings on the customer could differ across different product-types.

Often, in rating systems, stars ratings are accompanied by a review text.

The situation of misalignment between review text and star rating was investigated by Mudambi, Schuff and Zhang (2014). It was found that this misalignment occurs most often with experience goods and with goods that have extremely positive star ratings.

This shows that star ratings are not necessarily always in line with the actual attitude or sentiment of the customer about the product that was rated. This phenomenon seems to be influenced by the product type as well.

All of these studies show that especially for experience goods, e-WOM in the form of star ratings is relevant to customers.

Previous research has shown that e-WOM influences the purchase intention of potential customers (Chaterjee, 2001). Consequently, research about online star ratings, as form of e-WOM, has justified these findings as they also found an influence of star ratings on product

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sales (Chevalier and Mayzlin, 2006) and prove these reviews are useful to customers during the decision making process (Mudambi and Schuff, 2010; Park and Nicolau, 2015). The size of the effect of star ratings on customers seems to differ depending on product type and polarity of the rating.

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Table 2 Overview Literature on Online Reviews

Article Research Question Main Finding

Chevalier, J. A., & Mayzlin, D. (2006) What is the effect of consumer reviews on relative sales of books at Amazon.com and

Barnesandnoble.com?

Positive book reviews lead to improved sales. Impact of 1-star rating is greater than 5-star review. Review text more important to customer than summary statistics.

Clemons, E., Gao, G., & Hitt, L. (2006) How are online reviews used to evaluate the effectiveness of product differentiation strategies based on the theories of hyper differentiation and resonance marketing?

Variance and strength of ratings play a significant role in determining sales growth of new products in the market-place.

Zhang, L., Ma, B. & Cartwright, D.K. (2013) What is the impact of online user reviews on sales of search goods?

Average online customer review and the number of online reviews have significant influence on digital camera sales.

Hennig-Thurau, T., & Walsh, G. (2003) Why do customer retrieve information from other customers online articulation?

Customers consider other customers’ online articulations to reduce decision making time and make better decisions when faced with uncertainty. Wu, J. & Gaytán, E. A. A. (2012). What is the role of online seller reviews and product

price in buyers’ WTP?

Differences in effects of online seller reviews, particularly how these influence in buyers’ risk attitudes (averse, neutral, or seeking).

Moe, W. W., Smith, R. H., & Trusov, M. (2011) What is the impact of social dynamics in the ratings environment on both subsequent rating behavior and product sales?

Rating behavior is influenced by previously posted ratings and can directly improve sales.

Chintagunta, P. K., Gopinath, S., & Venkataraman, S. (2010) What is the impact (valence, volume, and variance) of national online user reviews on designated market area (DMA)-level local geographic box office performance of movies?

Valence of reviews are the most important influence factor concerning reviews on movie ticket sales.

Zhu, F., & Zhang, X.. (2010) How do product and consumer characteristics moderate the influence of online consumer reviews on product sales?

Experienced customers are more influenced by online reviews. The influence of online reviews is greater for less popular products.

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Huang, P., Lurie, N.H., & Mitra, S. (2009) What are the differences in customer behavior while searching for information for search and experience goods online?

The presence of product reviews by other

consumers has a greater effect on consumer search and purchase behavior for experience than for search goods.

Duan W., Gu B., & Whinston A. B. (2008) What is the effect of online WOM as both a precursor to and an outcome of retail sales?

Movie's box office revenue and WOM valence significantly influence WOM volume. WOM volume in turn leads to improved box office performance.

Ye, Q., Law, R., & Gu, B. (2009) What is the impact of online consumer-generated reviews on hotel room sales?

Positive online reviews significantly increase sales. Variance or polarity of reviews have a negative impact on sales.

Mudambi, S. & Schuff, D. (2010) What makes a helpful online review for search and experience goods on Amazon.com?

For experience goods, reviews with extreme ratings are less helpful than reviews with moderate ratings. The product type moderates the effect of review depth as well as the helpfulness of the review.

Gupta, P., & Harris, J. (2010) How do e-WOM recommendations influence product consideration and quality of choice?

e-WOM increases the time considering the

recommended product. For motivated consumers, e-WOM leads to more time spent on choice-making task.

Mudambi, S.M., Schuff, D., & Zhang. Z. (2014) When is misalignment of star ratings and review text most likely to occur?

Misalignment is noted for experience goods and goods with high star ratings, a positive interaction is found between them.

Park, S., & Nicolau, J.L. (2015) What is the effect of review ratings on usefulness and enjoyment of the review?

Extremely polarized ratings (either positive or negative) are more useful moderate ratings. The extent of the effect of online reviews depends on whether they are positive or negative.

Sridhar, S., & Srinivasan, R. (2012) How do other consumers’ online product ratings moderate the reviewer’s online product rating?

Consumers who influence others are themselves influenced by other consumers. Other consumers’ online ratings weaken positive or negative aspects product experiences.

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2.2. Willingness-to-Pay

A consumer's willingness-to-pay denotes the maximum price a consumer is willing to pay for a product (Ajzen, & Driver, 1992).

A potential costumer’s willingness-to-pay is an important measure for estimating the demand of a good and helps determine the optimal pricing strategy (Wertebbroch & Skiera, 2002). This measure can be an important hint for the profitability of a product and provides

managers important advice on where to position a product within a competitive market, given that willingness-to-pay has proven essential to pricing decisions (Wertenbroch & Skiera, 2002). Accurate information about the degree of willingness-to-pay is particularly useful for new products, since managers cannot estimate base profit or cost-benefit calculations derived from real/historic demand curves (Hoffmann, Menkhaus, Chakravarti, Field, & Whipple, 1993). For these reasons, this aspect of customer attitude towards a product or a service has been subject to a great amount of research.

Miller, Hofstetter, Krohmer and Zhang (2011) suggested how willingness-to-pay should be measured. They compared four commonly used approaches to measure consumers'

willingness-to-pay with real purchase data that provided a baseline comparison of the real willingness-to-pay of the customers. The research settings investigated were the open-ended (OE) question format, choice based conjoint (CBC) analysis, Becker, DeGroot, and

Marschak’s (BDM) incentive-compatible mechanism, and incentive-aligned choice-based conjoint (ICBC) analysis. Their results revealed that consumers are more price sensitive in incentive-aligned settings (BDM and ICBC) than in non-incentive-aligned settings and the real-life setting, but they also found that an incentive-aligned approach may be better for the purpose of value audition. Moreover, even when the OE format and CBC analysis generate

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hypothetical bias, they may still lead to the approximate demand curves and thus towards correct pricing decisions.

In respect to the usefulness of the Vickrey auction mechanism this master thesis will utilize, Noussair, Robin and Ruffieux (2004) wrote a paper that compared Becker, DeGroot and Marschak’s (BDM) with the Vickrey auction mechanism.

The Vickrey auction is a sealed-bid and second-price auction, in which bidders state their willingness-to-pay for a good, unknowing of the bids of other participants, and, in the case of winning the auction, pay the price of the second highest bid (Ausubel & Cramton, 2004). Measuring the bias and dispersion of bids relative to valuations from a sample of French customers, Noussair, Robin and Ruffieux (2004) find the Vickrey auction to be a better elicitation device than the BDM process. This important finding evidences why the Vickrey auction may be a suitable mechanism for detecting potential customers’ willingness-to-pay, and will thus be employed in this master thesis.

Supporting this conclusion, a study conducted by Sichtmann and Stingel (2007) compared the Vickrey auction and the limit conjoint analysis. Their results showed that Vickrey auctions perform better than limit conjoint analysis when customers are in a low involvement state, while there was no such difference in high involvement situation. This finding could be especially compelling, because the involvement of the recipient during the data collection of this thesis could influence the results.

Earlier in this literature review, it was mentioned that willingness-to-pay is important information for managers, especially when introducing innovative products with no demand precedent form customers. In another study Hofstetter, Miller, Krohmer, and Zhang (2013) researched how managers can account for the bias incorporated in most of approaches in order to estimate willingness-to-pay, when measuring for innovative product. They found that the customers’ ability to assess the new products’ quality and his or her buying intention

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influenced this bias the most. Interestingly, the bias of the measurement increases with the ability of the potential customer to access innovativeness of the product, which means that measurements taken from these customers have to be handled with care. The study suggests that involvement and customer characteristics should be taken in account when measuring a potential customer’s willingness-to-pay, especially when the product is new.

In another study, Gracia, de Magistris and Nayga (2012) explored the question whether or not social influences have an effect on willingness-to-pay as well as the question whether the gender of the customer plays a role. In an experimental auction for a local food product they found that factors related to perceived public benefits, such as sustainability, have a positive effect on willingness-to-pay of women; the effect is negative for men. This indicates that customers’ willingness-to-pay for the same product can differ depending on demographic variables such as gender.

Another academic work about willingness-to-pay during purchase decision making process was conducted by Bertini, Wathieu and Iyengar (2012), who examined how discriminate between products of different quality. Their research found that people are prepared to pay more for high-quality products and less for low-quality products when faced with a large variety of products to choose from. This implies that customers are willing to pay more for products with higher quality, especially when they are overwhelmed with information and large quantity of products to choose from. This context is especially applicable in online shopping, because the online shopper is typically bombarded with information and choice. To conclude this part of the literature review, it was demonstrated which methods are well suited to measure a potential customers’ willingness-to-pay, why this measure is important to managers and which conditions have to be taken into account when measuring customer’s willingness-to-pay. For a comprehensive overview of the literature please see Table 3.

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Table 3 Overview Literature concerning Willingness-to-Pay

Article Main Research Focus Main Finding

K. Wertebbroch & B. Skiera (2002) Empirical comparison of several procedures for eliciting WTP that are applicable directly at the point of purchase.

Incentive-based procedures like BDM and yield lower WTP estimates than do non-incentive-compatible. K. Miller, R. Hofstetter, H. Krohmer, & J. Zhang (2011) Comparing the performance of four commonly used

approaches to measure consumers' willingness to pay with real purchase data (REAL): the open-ended (OE) question format; choice based conjoint (CBC) analysis; Becker, DeGroot, and Marschak's (BDM)

incentive-compatible mechanism; and incentive-aligned choice-based conjoint (ICBC) analysis.

Respondents are more price sensitive in incentive-aligned settings than in non-incentive-incentive-aligned settings or the real setting.

Noussair, R., Robin, S., & Ruffieux, B. (2004) Comparison of the BDM mechanism and the Vickrey auction.

Vickrey auction, for the setting of the experiment, is more effective as a willingness-to-pay elicitation device than the BDM process.

Sichtmann, C., & Stingel. S. (2007) Comparison of Limit conjoint analysis and Vickrey auction as methods to elicit consumers' willingness-to-pay.

For low involvement products, Vickrey auction performs better than limit conjoint analysis.

For high involvement situations, the results are not as clear.

Gracia, A., De Magistris, T., & Nayga, R. (2012) How do social influences affect the consumers’ willingness-to-pay and are there any differences?

Social influence, like sustainability, indeed affects WTP values, but the effects differ between men and women.

Bertini, M., Wathieu, L., & Iyengar, S. S. (2012). How do customer discriminate between products in a crowded product space and does this affect their willingness-to-pay?

Customers are prepared to pay more for high-quality products but less for low-quality products when they are considered in the context of a dense, as opposed to a sparse, set of alternatives.

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2.3. The Impact of Star Ratings on Willingness-to-Pay

The influence of online user ratings on customer behavior and especially on product sales has been a major subject to research in the past. Previous research mainly focused on explaining or predicting sales, revenue, or sales growth through the consideration of online user reviews. Research that specifically targets the influence of online user reviews and ratings on the willingness-to-pay is sparse.

Previous research has shown that experiences and opinions from other customers can provide information about the quality and value of a product and can therefore reduce customer’s choice risk (Zhu & Zhang, 2010; Cui, Lui, & Guo, 2012).

Wu, Wu, Sun and Yang (2013) have studied the influence of online user reviews on the risk perception of customers, and ultimately, on the potential customers’ willingness-to-pay in the online shopping context. The authors explored how product and seller reviews influence the assessment of uncertainty about the product and seller, while shopping online. The authors unveiled that the influence of online user reviews differs depending on the customer's risk attitude. For example, the influence of review volume and variance is positive for risk-averse customers, but may be insignificant for risk-neutral ones.

The fact that the impact of review volume on the willingness-to-pay varies depending on uncertainty preferences of the individual provides evidence that customer characteristics such as risk-attitude could be important influence factors when measuring willingness-to-pay in the online shopping setting.

Supporting the prior studies, Sun (2012) focused in his research on the informative role of the distribution of product ratings by focusing on the variance of ratings. The study finds that the interaction of the average rating and the standard deviation of ratings plays a significant role on market outcomes. One key finding of the study is that with products with low average

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ratings a higher variance of ratings indicates to potential buyers that the product is suited for a special clientele of customers (niche product). Even low average ratings, given high

variance, increase demand for the product. Interestingly, in products with a high average rating, a higher variance of ratings reduces the demand for the product.

This study indicates that, in line with Wu and Gaytán (2012) and Wu et al. (2013), rating variance has an impact on customer behavior and could therefore also have an influence on willingness-to-pay of potential customers.

Following up on this, a more recent study by Wu and Wu (2016) considered the varying impact of review volume on customer’s willingness-to-pay. Their experimental study unveiled that the effect size of online user reviews on an individual’s willingness-to-pay differs. The authors argue that the impact of review volume and variance on willingness-to-pay does not only differ across individuals, but also changes depending on review valence, within individuals. This has an important implication: Preferences towards review statistics should be accounted for with pricing models that take into consideration the individual or segment level, meaning that the pricing of products could be adjusted to appeal to different individuals or segments based on characteristics of the star rating of the product.

The research discussed in this segment demonstrates that online user reviews and ratings have a significant influence on the customer's risk perception (Wu, Wu, Sun & Yang , 2013), their ability to evaluate products (Zhu & Zhang, 2010; Cui, Lui, & Guo, 2012) and their purchase behavior (Sun, 2012).

All of this implies that e-WOM, in the form of star ratings, will have an impact on the willingness-to-pay of the customer, because it has an effect on the level of uncertainty and risk the customer is confronted with when shopping online. It has been proven that risk and uncertainty have an effect on willingness-to-pay of consumers (Huang, 1993; Wu and Wu, 2016).

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Based on the literature presented, it can be expected that with increasing positive star rating, the willingness-to-pay of the customer will increase, because the customer is assured by the opinion of previous customers about the quality of the product.

For this reason, it is important for researchers and managers to understand the magnitude and properties of the relationship between star ratings and a customer’s willingness-to-pay. As mentioned earlier in the introduction of this master thesis, pricing based on the customer’s willingness-to-pay can not only influence the demand of products, but also improve margins, which ultimately leads to higher profitability.

This master thesis aims to provide researchers and managers with the necessary insights for understanding the impact star ratings have on their customers’ willingness-to-pay. Only then will managers be able to optimally manage their products reputation and brand online.

2.4. Factors influencing the impact of ratings on willingness-to-pay

Throughout this literature review we have seen relevant research that has been done on the effect of online user reviews and ratings on customer behavior, the research that has been conducted on willingness-to-pay and research that connected the two concepts. It has been demonstrated, specifically how reviews could influence customer’s willingness-to-pay. The following part of the literature review is dedicated to factors that could have an influence on customers’ responsiveness towards online user reviews and ratings, as well as their willingness-to-pay online. Therefore, the following aspects could moderate the impact of star ratings on the willingness-to-pay of potential customers.

2.4.1. Rating Valence

Earlier in this literature review it was stated that online user ratings typically consist of review volume (the number of reviews a product or a seller receives), review valence (the average review rating), and review variance (usually shown through the distribution of

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reviews). Although previous research has shown that the volume and variance of online user reviews and ratings are interesting moderators that influence the effect of a star rating on the willingness-to-pay of the customer (Wu & Wu, 2016), investigating these rating-related influence factors would go beyond the scope of a master thesis; these aspects would not be independent influence factors but rather endogenous variables, which would add a great amount of complexity to this research.

Since this master thesis aims to examine the influence of a product’s star ratings on the willingness-to-pay of potential customers in online shopping settings, and the number of stars in a rating that can be seen as the review valence, this thesis will focus on the rating valence as a rating related variable.

In this literature review, it was already established that the valence of a review has a positive effect on product price and sales (Chevalier & Mayzlin, 2006; Moe & Trusov, 2011; Wu & Gaytán, 2012, Chen, Wu & Yoon, 2004). Moreover, it has been shown that extremely negative ratings have a stronger effect on sales than extremely positive ratings (Chevalier & Mayzlin, 2006) and that negative user reviews have a devastating effect on the customer’s trust and purchase intention (Chaterjee, 2001).

Based on the evidence drawn from previous research, it can be concluded that the valence of a rating will have an impact on the way the rating is perceived by the customer and how strong the effect of such a rating affects his or her willingness-to-pay.

Furthermore, it can be expected that low or negative rating will have a strong negative effect on the customer’s willingness-to-pay, while extremely positive ratings will have a more significant effect than moderate ratings.

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2.4.2. Customer Characteristics: Uncertainty - Activity - Involvement

In the previous section of this literature review it was already mentioned that customer characteristics such as the customers attitude towards risk and uncertainty (Wu, Wu, Sun, & Yang, 2013), motivation to process information (Gupta & Harris, 2010) and involvement (Lee, Park, & Han, 2008) can have an effect on the customer's perception of online reviews and ratings.

As noted earlier, Wu, Wu, Sun and Yang (2013) have investigated the influence of online user reviews on the risk perception of customers. The authors demonstrated that review volume and rating variance positively influences risk-averse customers, while the influence is insignificant for risk-neutral ones. This implies that the customers’ attitude towards

uncertainty affects the customers’ responsiveness towards online user reviews.

In a study by Gupta and Harris (2010) it was shown that customers who are more motivated to process information spent more time evaluating products during their buying process. This indicates that the effect of online user reviews could differ depending on the customer’s motivation to make a purchase.

In line with this, Lee, Park and Han (2008), in their research concerning the effect of negative customer reviews on customer attitude towards products, contend that high-involvement consumers tend to inspect review more thoughtfully and conform depending on the quality of the review. In contrast, consumers with low-involvement agree with negative reviews

regardless of the quality of the review.

This finding shows that the effect of negative reviews is moderated by the involvement of the consumer exposed to the review.

Additionally, Alba and Hutchinson (1987) found that less involved individuals tend to process reviews with simple, heuristic processing strategies, while individuals with higher involvement examine stimuli, like WOM, at the attribute level. Therefore, it can be expected

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that involvement and motivation of the customer in the buying process could be an influence factor.

The literature presented here indicates that the characteristics of the reader of the rating could have an influence on the extent of the impact of star ratings of a product on potential

customers. The way the customer perceives risk seems to influence the way he or she values the volume and variance aspect of a rating (Wu, Wu, Sun, & Yang, 2013); motivated

customers take more time evaluating products and their ratings (Gupta & Harris, 2010); customers, who are highly involved in the buying process behave differently towards reviews than low-involvement customers (Lee, Park, & Han, 2008). All of these findings suggest that the characteristics of the recipient, exposed to a review could influence the way customers perceive a review or rating. Thus, the characteristics of the customer could also moderate the impact of star ratings on the customers’ willingness-to-pay.

2.4.3. Product Characteristics - Search or experience good

Next to characteristics related to the online user review, and to the recipient exposed to the review, the characteristics of the product rated can also play a role in the responsiveness of the customer towards the product rating.

An important product characteristic that has been investigated in research concerning e-WOM and its influence on customer behavior is the categorization of product as experience or as a search good. As mentioned earlier in this literature review, experience goods are goods that are evaluated by subjective experience; customers require sampling or purchase in order to evaluate product quality. Search goods, on the other hand, are evaluated according to their objective properties (Nelson, 1970, 1974).

Mainstream research on the influence of customer reviews on sales performance was focused on experience goods.

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Jiménez and Mendoza (2013), who studied the influence of online reviews on purchase intentions of both search and experience products. They found that, the more credible a customer finds a review, the more his or her purchase intention will increase. Intriguingly, the study also revealed that credibility of an review is assessed differently for search and

experience goods.

When it comes to search goods, potential customers find reviews with more detailed information more credible, while for experience goods consumer credibility is more depending on the level of agreement with the review, which is the degree of perceived agreement among reviewers regarding the evaluation of a product.

Research suggest that customers respond differently to reviews and ratings depending on different product types, signaling that customers’ responsiveness towards online user reviews can be influenced by whether the product in question is an experience or search good, which could therefore influence the impact of star ratings on the customer’s willingness-to-pay.

2.4.4. Demographic Variables – Gender and Age

Adding to rating related, customer and product characteristics, the demographic aspects of the customer could also play a role in the perception of star rating and therefore influence the effect of star ratings on willingness-to-pay.

2.4.4.1. Gender

As first demographic variable that is of interest for this thesis is the gender of the individual reading the review. This factor could influence the potential customers’ receptivity to online user review or rating and thus their willingness-to-pay, because e-WOM, in the form of ratings, can be perceived as social influences (Sridhar & Srinivasan, 2012).

In a study about the effect of social influences on the willingness-to-pay of customers, Gracia, de Magistris and Nayga (2012) found that the degree of social influence is different

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for man and women. This finding suggest that man and women could have a different

perception of online user reviews, and thus a different repose towards them. In line with this, we have already seen that customer rating behavior is influenced by the rating provided by other customers (Sridhar & Srinivasan, 2012). This implies that ratings function as social influences. Combining these two findings, one can conclude that online user ratings are perceived differently by man and women.

Additionally, a recent study by Punj (2015) concerning the influence of customer

characteristics on willingness-to-pay for online subscription services has shown that gender is a significantly influential factor in willingness-to-pay. The study found that women are more likely to be willing to pay for such a product, but man have a higher willingness-to-pay on average. In contrast, Goyanes (2014) found that gender had no significant influence.

2.4.4.2. Age

Research on the online behavior of customers found that demographics play a role in the time an individual spends online (Chyi & Lasorsa, 2002; Dimmick, Chen, & Li, 2004; Pew

Internet and American Life Project, 2003; Stemple & Hargrove, 1996; Stemple, Hargrove & Bernt, 2000). This suggests, that demographics, such as age, could influence the behavior of online customers.

In 2005, Chyi conducted a study about the willingness-to-pay for online news. The author’s research showed that among the demographic variables considered (gender, age and

education), only age had a significant effect on the willingness-to-pay for online news. The results indicated that younger users (15-34 years old) were more likely to pay for online news access.

This finding is compelling since it suggests that willingness-to-pay of customers for products or services could differ depending on the age of the customer.

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Research conducted by Wan, Nakayama and Sutcliffe (2012) regarding the impact of age and online shopping experience on the perception of uncertainty about goods found that age is a significant factor. The researchers found that older customers (40+ years old ) were less uncertain about the quality of the product than young customers (18-39 years old). This finding is relevant for the purpose of this thesis, firstly, because experience and search goods are considered and secondly, because it reveals that older customers face less uncertainty. Thus, a product’s star ratings as means of risk reduction would have less influence on this segment.

Because age seems to play a role in the way the rating of a product is considered, this factor should be considered when assessing a user’s/customer’s willingness-to-pay.

In relation to the research question of this thesis and based on previous literature, it can be expected that younger customers (18-34 or 18-39 years old) will have a higher willingness-to-pay overall and that the influence of star ratings will be greater for this age group.

2.5. Conclusion Literature Review

In this literature review we saw that there is a comprehensive body of research about online user reviews (see Table 2,3 & 4). Research has mainly focused on the influence of e-WOM on purchase behavior and products sales. The research on willingness-to-pay has focused on how different methods of measurement perform and what consumer characteristics influence these measurements. Some literature connected these two concepts (e-WOM in the form of online user reviews/ratings and willingness-to-pay) and focused on the effect online user reviews have on the risk- and uncertainty-attitude of consumer. Nevertheless, research on the specific influence of star ratings on the willingness-to-pay of potential customers is scarce.

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This master thesis aims to contribute to fill this knowledge gap and demonstrate how online user reviews, in form of star ratings, influence willingness-to-pay and what factors influence this relationship.

The gap in the literature and research leads us to the main research question of this thesis: What is the influence of product star ratings on the willingness-to-pay of consumers? Alongside this main research question, sub research questions appear, which arose when thinking of factors that influence the relationship between star ratings and the willingness-to-pay of a potential customer.

What is the influence of rating-related characteristics on consumer responsiveness towards a product’s star ratings?

What is the influence of consumer-related characteristics on consumer responsiveness towards a product’s star ratings?

What is the influence of product-related characteristics on consumer responsiveness towards a product’s star ratings?

This master thesis seeks to answer these questions in order to provide comprehensive

knowledge to managers and researchers who are interested in understanding the influence of star ratings on consumers and what circumstances mitigate this influence.

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Table 4 Summary Table possible influence factors on the relationship of star ratings and willingness-to-pay

Article Influence Factor Finding

Clemons, E. K., Gao, G. G., & Hitt, L. M. (2006).

Volume High review volumes have a positive effect on product sales.

Chen, P. Y., Wu, S. Y., & Yoon, J. (2004).

Volume and Valence The combination of high average rating, together with high volume of reviews, serves as a good predictor for high product sales.

Chevalier, J. A., & Mayzlin, D. (2006).

Volume High review volumes have a positive effect on product sales.

Wu, J., Wu, Y., Sun, J., & Yang, Z. (2013).

Risk perception of customers

The influence of customer rating volume and variance is positive for risk-averse customers, while the influence is insignificant for risk-neutral ones.

Gupta, P., & Harris, J. (2010). Motivation of customer Customers who are more motivated to process information spent more time evaluating products for purchase.

Lee, J., Park, D. H., & Han, I. (2008).

Involvement of the customer

High-involvement consumers scrutinize the reviews more critically.

Gracia, A., De Magistris, T., & Nayga, R. M. (2012).

Gender of customer Social influence is different for man and women. Women are more influenced by social influences than males.

Goyanes, M. (2014). Gender of customer Gender has no significant effect on willingness-to-pay for online news.

Jiménez, F. R., & Mendoza, N. A. (2013).

Product type The more credible a customer finds a review, purchase intention increases. For search goods, more detailed information is more credible, while for experience goods, credibility depends on the level of agreement with the review.

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3. Conceptual Framework

In the literature review we have seen that previous research identified a multitude of potential influence factors that could moderate the effect of online user reviews on the potential

customers’ willingness-to-pay.

This master thesis will focus on the relationship between the independent variable star rating and a few moderators (see Illustration 2) which have been identified as most relevant to a potential customer’s perception and responsiveness to star ratings.

This section of the thesis will focus on explaining the main constructs of the conceptual framework (see Illustration 2) in which this master thesis is going to investigate the research question.

3.1. Star rating

One of the most popular methods of reflecting user review valence on e-commerce website like Amazon.com is using a Likert-scaled star system (1 to 5 star system in which 5 is the best score) (see Illustration 1). Within this framework, star rating will refer to the amount of stars that are shown to the recipient.

As mentioned in the introduction of this thesis, the vast majority of products have a product rating of 4 stars or more (Yotpo, 2015). Additionally, we have already concluded in the literature review that low ratings would have a devastating effect on the willingness-to-pay of the customer. This would harm the relevance of this thesis’ results and its managerial value. As a result, ratings between 4 and 5 stars will be the most suitable towards the purpose of this thesis and will help investigate the relationship between star ratings and willingness-to-pay.

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Illustration 1 Typical Star Rating on Amazon.com with review valence, volume and variance

3.2. Product Type

The second moderator in this conceptual framework is the product type of the product that has been rated. Product type in this setting refers to whether the product shown to the recipient, with a star rating attached to it, is a search good or an experience good.

Search and experience goods were chosen as the product type variable because most research concerning the influence of online user reviews and rating has been done about either search goods or experience goods. Research that compares these two types of products is relatively scarce. One of the aims of this thesis is to contribute knowledge of how the influence of a product’s star ratings differs depending on product types in the online setting.

3.3. Gender

Third moderator of the conceptual framework is the gender of the potential customer. Gender in this setting refers to whether the recipient is a man or a women.

As already mentioned in part 2.4.4.1., the gender of the recipient might influence the way the recipient perceives a product’s star rating in the setting of this thesis. Also, a user review or rating (e-WOM) can be seen as a social influence. In previous research we have seen that social influences have a stronger effect on females than on male customers (Sridhar & Srinivasan, 2012).

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This thesis aims to find out if the same applies for star ratings as social influence factor and whether female are affected differently, or more significantly, than male customers.

3.4. Age

The fourth moderator in the conceptual framework is age. The participants of the field experiment could be classified in age groups. Customers could be categorized in one “old” and one “young” group.

The reason for including the age of the customer in the conceptual framework of this thesis is because previous research indicates, in an online shopping environment, younger customers have a higher willingness-to-pay in comparison to older customers when it comes to online services like online news (Chyi, 2005) and the higher uncertainty perception towards the quality of goods of younger customers (Wan, Nakayama, & Sutcliffe, 2012) . Since this thesis also investigates willingness-to-pay in online shopping settings, it would be an important contribution to test if the influence of star ratings differs among age groups.

3.5. Risk Orientation

The fifth and last moderator of the relationship between star rating and willingness-to-pay of the customer is the attitude of the consumer towards risk and uncertainty. Risk-attitude will be codified as risk-averse and risk-neutral attitude.

In the literature review, we saw that one of the main motives for assessing e-WOM like customer reviews and ratings during the purchase decision process is the reduction of risk and uncertainty about the product (Hennig-Thuran & Walsh, 2003). Also, it has been shown that the influence of rating related characteristics, such as volume and variance, differs depending on whether the customer is risk-averse or risk-neutral (Wu, Wu, Sun, & Yang, 2013). For

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these reasons, it would be relevant to see whether this effect also holds for rating valence in form of star ratings.

3.6. Willingness-to-pay

Willingness-to-pay of the participant for a product displayed alongside its corresponding rating is the dependent variable that is being investigated in this study.

Willingness-to-pay will be assessed with bids in a Vickrey-auction system and will stand for the maximum amount a customer is to pay in Euro for the product shown to the recipient.

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4. Hypotheses

Based on the empirical evidence discussed in the literature review and our conceptual framework, the hypotheses (see Table 5) can be formulated, which are going to be put to the test in the result section of this thesis. The foregrounded hypotheses are the following:

4.1. H1 - Star Ratings have a significant positive influence on

willingness-to-pay of the customer.

This thesis’ main hypothesis is that online user reviews in form of star ratings have a positive effect on the willingness-to-pay of the customers who have been exposed to the rating. This would mean that a recipient’s willingness-to-pay will increase depending on the increasing amount of the stars the product has been given in the experiment. It is thus expected that an increase in rating will lead to an increase in the participant’s willingness-to-pay for the product.

This effect is expected because previous research has proven that ratings reduce the perceived uncertainty about the quality of the product (Wu, Wu, Sun, & Yang, 2013). Given that

customers are willing to pay more for products with higher quality, it can be expected that products with high ratings (therefore with higher perceived quality), will have higher bids than products with lower ratings.

4.2. H2 - The age of the customer has a negative moderating effect on the

impact of the product’s star rating on the willingness-pay of the customer.

The second hypothesis of this research, states that the influence of star ratings on the

customer’s willingness-to-pay will decrease as the age of the customer increases. In the same vein, it is expected that younger participants of the experiment will have a higher willingness-to-pay than older participants, and that the influence of star ratings is going to be more significant in this group.

How much is a star worth? : the influence of customer ratings on the customer's willingness-to-pay