Thesis MSc. Business Administration: Marketing
Consumer behaviour in price discrepancy from Internet
differential pricing: The moderating effect of purchase
perishability
Keywords: Internet differential pricing, dynamic pricing, perceived price fairness,
perishability, behavioural intentions, transaction satisfaction, transaction similarity, price discrepancy
First Supervisor: Drs. Frank Slisser
Nick Beeks (5871417)
MSc Business Administration Track: Marketing
Amsterdam Business School
Faculty of Economics and Business University of Amsterdam
Statement of originality
This document is written by Student Nick Thomas Beeks, 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 and Business is responsible solely for the supervision of completion of the work, not for the contents.
Abstract
The development of Big Data-based pricing systems by e-merchants is likely to affect pricing strategies such as ‘Internet differential pricing’ (i.e. also referred to as ‘behavioural pricing’). It opposites static pricing and leads to a higher consumer surplus. Mixed views of the right implementation of this controversial pricing strategy are found in the literature. In this study it is researched how consumers respond to an (dis)advantageous outcome in Internet differential pricing (i.e. in terms of satisfaction, repurchase intention, and search intention). Also, it is tested if this pricing strategy leads to different outcomes when the purchase characteristics differ (i.e. the purchase of a perishable vs. non-perishable). A 2x2 scenario-based survey experiment was used, showing significant evidence for personal outcome (i.e. advantageous vs. disadvantageous) to influence consumer behaviour. Though, the place of perceived price fairness still seems subject to further research, as the analysis shows it only partially works as mediator, as was expected from the literature. The consumers’ outcome also directly influences transaction satisfaction and repurchase intention.
Content
Content 4
Content of tables and figures 5
Chapter 1 – Introduction 6
Chapter 2 – Literature review 9
2.1 Internet differential pricing 9
2.2 Perceived price fairness 11
2.3 Transaction (dis)similarity 14
2.4 Perishability 18
2.5 Satisfaction 19
2.6 Behavioural consequences 20
Chapter 3 – Research method and design 23
3.1 Survey-based scenario experiment 23
3.2 Measuring the variables 24
3.3 Manipulations 25
Chapter 4 – Results 27
4.1 Descriptive statistics 27
4.2 Reliability of the scales 28
4.3 Correlations 29
4.4 Multivariate analysis of variance 30
4.5 Testing the model 31
Chapter 5 – Discussion 35 5.1 Findings 35 5.2 Limitations 36 5.3 Managerial implications 37 Chapter 6 – Conclusion 38 References 39 Appendix 1: Vignettes 42 Appendix 2: Questionnaire 44
Content of tables and figures
List of Tables
2.1 – Research to perceived price fairness using perishables in the experiments 16 2.2 – Research to perceived price fairness using non-perishables in the experiment 17
4.1 – Cronbach’s alpha’s, means, and standard deviations 28
4.2 – Correlations of dependent variables and covariates with dependent variables 29
4.3 – PROCESS Model 4 significant coefficients 33
List of Figures
3.1 – Diagram of the proposed theoretical framework 21
3.2 – Distribution of the four scenarios 24
4.1 – PROCESS Model 1: Purchase perishability as moderator 32
4.2 – PROCESS Model 7: Perceived price fairness as mediator + purchase perishability as moderator 32
Chapter 1 – Introduction
E-commerce continues to grow the ultimate years, despite the economic recession to 5 billion in the Netherlands in the first half year of 2013 alone (Thuiswinkel Waarborg, 2013). More than ever consumers make purchases for both goods and services online. Along with the increasing time consumers spend online navigating through webstores and other websites, companies receive a lot of extra information about these customers as they leave a lot of traces. ‘Big Data’ is the term used for these structured and unstructured pieces of information. Information like browsing histories, information resulting from loyalty programmes, social media profiles, GPS tracking, commercial transactions, deliver a huge amount of valuable information to companies. Big Data is expected to stimulate innovation, competition and productivity and is assumed to have the ability to capture a bigger consumer surplus (McKinsey & Company, 2011). The price that a customer is willing to pay can be estimated more accurately due to more information. Therefore, analysing Big Data nowadays is a popular investment that makes e-merchants able to effectively capture sales and profit through personalised marketing.
The development of using Big Data is likely to affect the marketing mix, of which pricing is a part. Companies may use different pricing strategies, such as ‘Internet differential pricing’ (i.e. sometimes referred to as ‘behavioural pricing’), which opposites static pricing. The strategy is seen as closest to first-degree price discrimination1, as every person is targeted with a personified price, for instance, based on someone’s social media profiles. The concept of Internet differential pricing is defined as “the practice of charging customers different prices for essentially identical goods” (Hoffman et al., 2002, pp.). For instance, a lower price
1 First-degree price discrimination equals the willingness-to-pay. Every consumer pays the price that he or she
perceives as most fair. Second-degree price discrimination is when a different price is being offered because of a larger quantity that is purchased. Third-degree price discrimination, finally, is when the price is set according to segmentation (e.g. students pay a lower entrance fee in museums).
may be charged to acquire new customers and get higher sales. Customers that are seen as loyal and come back more often may pay a higher price than the market price instead (Daily Mail Online, 2012). This seems controversial. But there are positive effects as well; the pricing strategy can be used to diminish the income gap (e.g. it can be implemented in the pharmaceutical industries to make products accessible for every budget) (Ridley and Schulmann, 2004).
There are e-merchants that really believe in the future of Internet differential pricing. In fact, it already exists to a certain extent. Prices vary online and offline in some stores, and regional differences in price exists already. In e-commerce Amazon.com experimented with conditioning prices of DVDs based on prior purchase behaviour in 2000, which led to complaints by dissatisfied customers and bad media exposure. However, the experiment was rather based on third-degree price discrimination. Netflix experimented likewise using Big Data in combination with demographics to predict prices customers would pay for subscriptions (Washington Post, 2013). They reported a higher probability for purchase behaviour, though refrained from implementing the pricing strategy. More research is needed on the reactions of consumers to individual level pricing through the Internet, as possibilities arise to implement this in the e-commerce sector.
Less positive expectations for implementing Internet differential pricing derive from existing literature (Lii 2009, Acquisiti and Varian 2005, Huan 2005). But why is that some pricing innovations are accepted, whilst others are not (Garbarino and Maxwell, 2008, p. 1066)? Scientists state that customers’ awareness of price discrimination will lead to negative responses, whilst others reported acceptance. Mixed views could be partly because of the different experiments that have conducted in the literature (e.g. some researchers tested for flights and hotels that are already more often subject to dynamic pricing). It is said that differential pricing may work better for goods that are perishable and/or not transmittable
(e.g. gas, water, electricity, transport, and services) than for products that do not have these characteristics (Netseva-Porcheva 2013). It has not been tested, however, whether it is possible to address the acceptance to purchase characteristics.
Or maybe it really depends on the personal outcome, whether people accept the form of pricing or not. Maybe people like a bit of gambling and can accept it when they become the (dis)advantaged person in this win-loose situation. Or they just especially dislike their own disadvantaged situation, and perceive the price as more fair when they take advantage from Internet differential pricing. Equity theory suggests that people do care about their outcome, when they judge a price (Adams, 1965). Besides, people compare to other customers with similar transactions to determine their attitude on fairness (Xia et al., 2004).
In this research it will be tested how applicable Internet differential pricing through buyer identification is, and what the effects are on consumers’ perceived price fairness and behavioural consequences. The main question of this thesis is: What is the effect of the (dis)advantaged price discrepancy in Internet differential pricing on consumer behavioural responses, and is this effect moderated by purchase perishability?
Tested in this thesis will be whether perceived price fairness in such practices will vary: (1) when the consumer is in either the advantaged or disadvantaged situation of differential pricing, and (2) when the product/service that is purchased either perishable or non-perishable (i.e. assumed that differential pricing is perceived differently for perishable goods and services than for other non-perishable goods).
This thesis will be structured as follows: in Chapter 2 the existing literature on Internet differential pricing will be reviewed. In Chapter 3 the theoretical framework will be explained and the hypotheses will be stated. In Chapter 4 the research design and method that will be used for this study will be further explained.
Chapter 2 – Literature review
In this paragraph the existing literature upon Internet differential pricing and price fairness will be analysed. The gap in the literature will be rather explained and the research topic will be defined. The gap and the objective of this research will be stated here.
2.1 Internet differential pricing
The development of e-commerce makes it both easier for the customers as well as for merchants to gather information. Customers can find more easily what they are looking for, while e-merchants are able to collect more information about customers’ willingness-to-pay and price sensitivity (Garbarino and Maxwell, 2009, p. 1066). Internet differential pricing is “the practice of charging customers different prices for essentially identical goods” (Hoffman et al., 2002, pp.). This form of first-degree price discrimination tries to equal the maximum price that every buyer is willing to pay (Netseva-Porcheva, 2013). Chen and Sudhir (2004) state that companies should invest in personalisation of online shopping experiences. It may benefit sales and can be achieved using Big Data, such as customer information that the company gathers themselves or by using Big Data that derives from social media and browsing behaviour and that is acquired as being a third party. At the same time, researchers state as well that privacy concerns must be taken seriously. Trust is an important subject when it comes to online shopping (Ye et al., 2013), and people are concerned about privacy issues. Using private information for these commercial purposes may injure the social contract that customers have with the retailers, (i.e. webstores) (Jai et al., 2013). For retailers that want to engage in personalisation through Big Data it is the challenge to find the balance between using this information without harming this social contract. Instead, the firm should try to use the Big Data in such a fashion that it delivers more value to the customer. So, on the one hand
Internet differential pricing may seem like a solution for e-commerce firms to increase revenues, it may also be negatively received by customers. Though, it seems that e-merchants that have built up a certain reputation among a client base, can implement Internet differential pricing more easily. It will not destroy their trust among clients immediately (Garbarino and Lee, 2003). Perhaps that clarifies why some organisations started experimenting with implementing the strategy in the past years.
Even though some practical examples show that Internet differential pricing works smoothly (e.g. Netflix), others believe that it impossibly can exist in future e-commerce. The current literature especially describes negative scenarios. According to Acquisiti and Varian (2005) may Internet differential pricing only work if customers are myopic. But just like the e-merchants do, customers have also better access to better information about pricing nowadays. Therefore, they will most likely not perceive it is fair to charge different prices for different customers. At least, they will not think this is fair in all cases.
It is said that e-merchants nowadays possess a great amount of information, but so do customers themselves. The vast amount of comparison websites, for instance, will make it harder to differentiate prices for certain e-merchants. Huang et al. (2005) found that certain price discrimination strategies were perceived to be unfair as well. Yield management for instance, was perceived as unfair. This pricing strategy is often used for airline tickets. The perception of fairness seems important, as certain consequences of are investigated.
Internet differential pricing is seen as the pricing strategy closest to first-degree price discrimination as can be. It makes it possible to personalise the price according to the willingness-to-pay of a customer. Still, Internet differential pricing may be perceived as either fair or unfair under certain circumstances, and customers may evaluate prices differently in these cases (Bolton and Myers, 2003).
negative consequences such as searching behaviour (i.e. searching for another supplier of the (substitute) good or service) and complaints are the biggest risk for e-commerce merchants when customers react negatively towards this process (Lii and Sy, 2009). E-merchants benefit more from satisfied customers and an increasing retention rate. Lii and Sy (2009) found that perceived price fairness leads to positive emotions that result in especially in repurchase intentions. With the growing use of social media for consumers to share information, it is likely to assume that consumers will compare their prices of products with their social ties. This is based on the social comparison theory as used by Xia et al. (2004), where the transaction of another person is used as a reference point for comparison (e.g. instead of using personal previous experience as a reference point). Not only do they compare prices, but also compare transaction similarity (Weisstein, 2013). For instance, products might be strategically offered in a different way and therefore be priced differently. A transaction through the same website for the same product will be considered as similar transactions, though. In the case of differential pricing, a price discrepancy may come in either an advantageous or disadvantageous position for a customer, as they can be charged either more or less than the other customer. Similarity between transactions will result in lower perceived price fairness when consumers find out about differential pricing.
2.2 Perceived price fairness
The thing that makes Internet differential pricing so controversial is that customers may perceive such a form of price discrimination unfair. Perceived price fairness is an important attitude that determines the response of a consumer to a certain price for a product or service (Campbell, 1999, p. 187). Perceived fairness is “a consumer’s assessment and associated emotions of whether the difference (or lack of difference) between a seller’s price and the price of a comparative other party is reasonable, acceptable, or justifiable” (Xia et al., 2004, p.
3). Like explained before, the comparison can also be made with another customer’s transaction at the same store (Xia et al., 2004). Customer comparison has become more relevant since the rise of social media, and deserves more research (Bolton, 2003). Perceived price fairness has shown to be the variable that positively influences dependent variables such as trust and repurchase intention (Garbarino and Maxwell 2010, Grewal et al. 2004, Xia and Monroe 2010).
Campbell (1999) says that companies may have different motives to increase a price. The motive may either be good explainable for customers (i.e. there is a positive reason, such as a price increase of raw materials, or when the increase is benevolent not just only for the company) or just to increase its profits (i.e. which may be perceived as negative or greedy), which is mainly the case in Internet differential pricing. So, the extent to which a company is pursuing its own interest influences the extent to which customers perceive the increase in price as unfair (Campbell, 1999; Dickson and Karapurakal, 1994). But fact is that often consumers are not aware of the exact reason for a price increase. The perceived price unfairness will be moderated by a company’s reputation, whereas a higher reputation will have more credit to increase prices than companies with a lower reputation. Xia et al. (2004) emphasises that fairness and unfairness are different constructs in a manner that it is clearer to determine what consumers perceive as unfair. This, however, does not always explain that it is perceived as fair instead.
Different factors have shown to influence the concept of perceived price fairness: historic prices from previous purchases, reputation of the store, consciousness about the pricing strategy, the pricing strategy itself, and the advantaged position (Bolton et al., 2003). Prior purchases or historic prices are often used as a reference point for price judgement; the magnitude of a price increase determines price fairness (Bolton et al., 2003). And when this is not possible, customers will compare their price to the price other customers paid.
As mentioned before does reputation moderate the effect of price unfairness; whereas a good reputation makes customers less judge price changings as unfair (Campbell, 1999). The extent of consciousness about the price discrimination may also influence his or her perceived price fairness. When customers do not know what the other customer paid for the same product, they assume their price paid is fairer compared to a situation in which they do know there is a price difference (Collie et al., 2002). Also the pricing strategy itself results in different price fairness judgements (Huang et al., 2004). Auctions and group-buying discounts are perceived as fairer than random discounting, or dynamic pricing for instance.
It is interesting that people’s behaviour can differ when they find themselves in either an advantaged or disadvantaged position of first-degree price discrimination, and they are aware of the pricing strategy (Xia et al., 2004). When a price discrepancy makes the transaction dissimilar, it is likely to assume that people’s behaviour may be different in advantaged situations compared to disadvantaged situations (Gelbrich, 2011, Xia et al., 2004). The value for money, in these cases, is the main driver of consumers’ actions (i.e. price fairness judgement) (Xia et al., 2004). It may be questioned whether consumers might perceive price discrimination more fair only if it has worked in their benefit (Nguyen and Meng, 2012). Based on the equity theory by Adams (1965) it is possible to split up perceived price fairness into: (1) outcome fairness (i.e. refers to the outcome of the discrimination), and (2) procedural fairness (i.e. refers to the assessment of perceiving fairness of the procedure as a whole, without keeping in account the individual outcome). The first is more focused on the personal situation. Consumers might mind the differential pricing practices less when they took advantage of the pricing strategy and agreed so on the price (i.e. when they paid a lower price than the other).
Another reasonable point to consider is made by authors such as Netseva-Porcheva (2013), Bolton and Myers (2003) and Weisstein (2013). They state that the characteristics of
the product or service may influence the perceived price fairness in Internet differential practices. Netseva-Porcheva (2013) believes that especially purchases that are non-perishable will gain a lower fairness judgement than perishables. This is especially because perishables (e.g. gas, water, electricity to the biggest extent, services and transport)2 are not easily to resell to others for consumers (Netseva-Porcheva, 2013, p. 53). Besides, a lot of merchants that sell perishables already use discriminating strategies, such as yield management for example. This, however, does not apply for every perishable. For instance, airplane tickets are hard to resell as they are linked to an individual. Though concert tickets, which are also perishable purchases, are easier to resell. Bolton and Myers (2003) found some evidence for the distinction of goods vs. services; where a repeated price for a service was considered to be fairer than the repeated rental of a good.
The existing literature on research often omits the advantageous/disadvantageous position or the characteristics of the purchase. Table 2.1 and 2.2 give an overview of the literature on price discrimination and differential pricing, which are all based on survey-based experiments. The first gives an overview of perishables, whilst the second gives an overview of the non-perishables.
2.3 Transaction (dis)similarity
Internet differential pricing makes webstores able to offer each customer a personalised price for the exact same product. For this reason, whereas the transaction seems so similar, the only dissimilarity derives from a price discrepancy. This might lead to different attitudes among consumers, as some people really want every person to be treated equally and pay the same price (Garbarino and Lee, 2003, p. 510). It can be assumed that they might score lower in
2Note that perishables such as gas, water and electricity could be stored, and could therefore be seen as not
completely perishable. To most consumers it is assumed as a perishable, however, as they do not have the resources to store them. Services and transport are highly perishable, on the other hand.
process fairness, even when they were the one to take advantage of the situation. As said before is the price fairness judgement especially measurable when the consumers compare their own transaction to the one of another one and value for money becomes the main driver for judgement (Xia et al., 2004).
In this situation, customers might benefit from the controversial practice called Internet differential pricing by paying a lower price than another customer. Consumers care about what other customers paid (Gelbrich 2011, Feinberg et al. 2002, Haws and Bearden 2006, Tsai and Lee, 2007). The more similar the transactions are (e.g. for the same product and/or at the same store) the lower they would judge the fairness of the price when they are disadvantaged. Research has proven that more dissimilarity in transaction mediates the perceived price fairness. Taken into account the difference between outcome fairness and procedural fairness as well, the first hypothesis is:
H1: Consumers experience a higher (lower) degree of price fairness when they are in
advantaged (disadvantaged) situation of Internet differential pricing in a similar transaction with a price discrepancy.
Author Purchase Setting Pricing strategy Outcomes Reed Shiller (2013) Netflix subscription Online, experiment (real)
Differential pricing Formula is being developed to 90% precisely estimate the price equal to willingness-to-pay, resulting in a higher chance customers would subscribe.
Andréz-Martínez (2014)
4-star hotel Online, simulation
Yield management Customers use the reference price and familiarity to online bookings to determine perceived price fairness. It influences their satisfaction with the price mostly, whereas satisfaction is higher when it meets their reference price. Grewal et al. (2004) Vacation flight Online, questionnaire Dynamic pricing (both identification and purchase timing) of which customers are aware
Buyer identification to set a price is seen as less fair than adapting a price at different times of the day. Also, larger price differences are seen as less fair compared to small differences. All is more accepted in B2B e-commerce compared to consumer purchases. Heo and Lee (2011) Hotels (both luxury and budget hotels)
Online, survey Yield management, revenue
management (RM)
Customer characteristics (i.e. familiarity, education, gender, age) were tested. They found that persons with more familiarity, higher education and lower age found RM practices fairer, though income seemed not to have a significant influence. Especially
luxury hotels can implement these
practices, whereas budget hotels should be more cautious, because price
consciousness influenced fairness a lot.
Gender seems insignificant.
Huang (2004) Hotels in U.S. or Europe Offline vs. online, survey Price discrimination, yield management and other
If customers were informed and had the
advantageous outcome of random price
discrimination, they found it fairer. That loyal customers pay less (i.e. given a discount) is accepted (in Taiwanese culture at least). Martin (2009) Restaurants Offline, survey Price increase vs. revenue management
Only for small price increases people accept it when the reason is justifiable. In other cases people find it unfair.
Nguyen Meng (2012) Hotel Offline, scenario-based experiment
Pricing policies Outcome vs. procedural fairness.
Unambiguous information regarding the price increase procedural fairness has a stronger effect and outcome matters less.
Author Purchase Setting Pricing strategy Outcomes
Bolton et al. (2003)
Polo shirt, ice cream, clothing, children’s entertainment Offline, scenario-based experiment Price increase vs. revenue management
They broaden the view that perceptions of price fairness are mostly based on past purchases. Also competitor’s price and vendor costs are included. Besides, they make a distinction between single vs. multiple in combination with goods vs. services. Price rises in a time period make consumers suspicious about the vendor. Cross store price differences were seen as more fair. More insight in costs gives consumers a fairer
perception of price more. A repeated purchase of a (rental) good gained lower price fairness, than the repeated price of a service.
Campbell (1999a)
Gallon of water Offline, scenario-based experiment
Price increase with inferred motive (price increase right after an earthquake)
Consumers use contextual information to infer firms’ motives, even though this is not the real reason for price increase.
Campbell (1999b)
Barbie doll Offline, scenario-based experiment
Price increase with negative/positive motive
If the company makes clear it is not out on higher profits, the perceived price fairness is higher, positive motives help to increase as well. Reputation moderates the effect. Garbarino and Lee (2003) Jacket Online, scenario-based experiment Dynamic pricing (behavioural)
Since customers are aware of dynamic pricing practices their trust has become more important. Not clear yet, whether the magnitude of price difference influences the effect.
Garbarino and Maxwell (2008)
Canon Camera Online, scenario-based experiment
Dynamic pricing (behavioural)
They compare different prices at different stores with different prices at the same store. When different prices at different stores are asked, it does not violate a norm. Though, U.S.
customers belief that every customer in the same store should pay the same price.
Gelbrich (2011)
Winter coat Offline, scenario-based experiment
Price discrimination (based on geography)
This study compares the social effect of having an advantageous position in price discrimination situation. The advantage makes people happy, though their actual behaviour is determined by the relationship with the person to which they compare. For friends they could feel sorry.
Lii and Sy (2009)
Travel guide Online, scenario-based experiment
Differential pricing (buyer ID, quantity, timing, channels)
Buyer identification and Especially tactics that lead to positive emotions would result in word-of-mouth and repurchase intent. Negative emotions will lead to complaints and switching behaviour.
Weisstein (2013)
USB flash drive Online, scenario-based experiment
Price framing in with discounts and rewards
He studies the difference between discounts and rewards with regard to the framing of transaction similarity.
2.4 Perishability
The literature on (Internet) differential pricing shows different outcomes with respect to perceived price fairness. The majority of the studies that have been conducted use a scenario-based experiment in which a situation with price discrimination in both online or offline settings is suggested. Roughly, there is distinction to be made between a group of researches that described the scenario in a setting of a perishable (i.e. most often a service like a hotel booking or a flight) (Grewal 2004, Heo and Lee 2011, Nguyen Meng 2012, Andrez Martinez 2014) and a group that used a (retail) product for the purchase (Bolton 2003, Campbell 1999b, Garbarino and Lee 2003, Garbarino and Maxwell 2008, Gelbrich 2011), as shown in Tables 2.1 and 2.2.
Comparing the researches with each other is hard, as they all used different questionnaires to measure price fairness judgement and the other variables. But fact is that it is crucial to consider the characteristics of the product or service in a research on differential pricing. Bechwati et al. (2009) considered price fairness for different product- and service-categories based on customers’ perceptions of the price structure. Some products, such as hotels (Yelkur and DaCosta, 2001), seem to be better for online sales and price discrimination than for example retail products like DVDs, as was the case in the Amazon experiment.
Besides pricing for products and services are evaluated differently by consumers (Hoffmann, 2002). Especially, in perishables dynamic pricing exists in the form of yield management and is accepted more widely (Weisstein, 2013, p. 501). Especially for products that are consumed right after purchase (e.g. gas, electricity, water, and services – transport, medical, cultural) consumers seem to accept a dynamic way of pricing more than for other products (Netseva-Porcheva 2013, Weisstein 2013).
Purchase perishability can function in the model of Internet differential pricing as a moderator and diminish negative price fairness judgements by consumers on this pricing
strategy. It has been questioned in the literature, but not yet investigated. Therefore, the proposed hypothesis on perishability is:
H2: Purchase perishability will moderate the effect of H1, whereas (non)perishable
purchases will lead to a higher (lower) perceived price fairness.
2.5 Satisfaction
The consequences that derive from either the price fairness judgement are most relevant for managerial implications. For the long-term benefit of the company it seems best to retain customers, as attracting a new customer is more expensive than retaining one. Customers are likely to return to the store when they are satisfied with the services and brands provided by the (web)store, besides they will refer the store to acquaintances (Reichheld, 2003). Customer satisfaction is: “when actual performance exceeds or meets expectations” (Gelbrich, 2011, p. 211). Price strongly influences consumer satisfaction (Kung et al., 2002). The literature makes a distinction between overall satisfaction and transaction satisfaction. Measured will be transaction satisfaction, as it measures more specifically the performance during one occasion specific in time (Oliver, 1997, in: Olsen and Johnson, 2003, p. 185).
Prior research has not stated yet where to fit (customer) satisfaction should fit into the model of price fairness and differential pricing, but suggests it must be included somewhere at least (Lii and Sy 2009, Martin 2009). Satisfaction seems to be a good predictor for positive word-of-mouth and repurchase intentions (Lii and Sy 2009, Heo and Lee 2011). Even though it has not been proven, customer satisfaction has often been linked to customer loyalty as well (Andrés-Martinez et al., 2014). It is being expected that when customers become loyal to a (web)store they become less price sensitive (Anton et al., 2007). Perhaps, this might be the reason why differential pricing will work at the end.
Therefore, it seems urgent to include satisfaction, most logically as a consequence of perceived price fairness. Though, it may work as an antecedent at the same time, as Campbell (1999a) says that customer satisfaction influences the perceptions of price fairness in the next purchase. Building on the theory of Lii and Sy (2009), satisfaction may also be a mediator between perceived price fairness and behavioural intentions, as it derives from a positive emotion. In the model satisfaction will be presented as a consequence, resulting into the following hypothesis:
H3: Transaction satisfaction is positively related to perceived price fairness.
2.6 Behavioural consequences
Perhaps even more relevant than customer satisfaction are actual behavioural consequences that derive from perceived price fairness. Because of the controversy of Internet differential pricing, it is necessary to consider whether the behavioural responses are significantly different in different situations (i.e. transaction (dis)similarity and purchase characteristic differences). Most important for webstores is to know how they can retain customers and how customers do not dissolute the relationship with the supplier. Lii and Sy (2009) found that perceived price fairness could lead into either positive or negative emotions that consequently trigger actual behaviour that would impact the firm’s performance. Switching behaviour was measured when the extent of perceived price fairness was low. Also other researchers did include the behavioural responses into their models, such as: intentions to search for another supplier, and repurchase intentions at the same supplier (Garbarino and Maxwell 2010, Xia et al. 2010, Grewal et al. 2004, Xia and Monroe 2010, Weisstein 2013). Repurchase intention does not necessarily derive from price fairness judgements however, as it could also result from customer satisfaction.
Most behavioural consequences in this research will be in line with prior research (Lii and Sy 2009, Weisstein 2013). Expected is that in a transaction where the dissimilarity exists of either a higher or a lower price (i.e. advantageous or disadvantageous situation), that repurchase intention will be higher and lower respectively. Search intentions will be defined as: ‘the extent to which an individual is intending to visit other sites for information about the price of the purchase or alternatives’. For search intentions it will mean the opposite from repurchase intention; a disadvantageous position would rather lead to search intentions. The perishability of the purchase possibly is likely to moderate the effect, building on the thoughts by Netseva-Porcheva (2013). Non-perishable products and services are supposed to suffer more from perceived price unfairness, and therefore, negative consequences such as more search behaviour and less repurchase intention. In comparison will perishables suffer from less search behaviour and more repurchase intention. This results into the following hypothesis:
H4: A higher (lower) perceived price fairness will result in less (more) search intentions. H5: A higher (lower) perceived price fairness will result in more (less) repurchase
intentions.
All relations between the variables are visualised in Figure 3.1. Summarizing, this paragraph has given an overview of the existing literature upon Internet differential pricing and price fairness judgements. A lot of research has been done upon price fairness, but not too much has been done to the personal outcome of a dissimilar transaction and whether this influences the price fairness judgement. Also, the moderating effect of perishability has not been tested before. In this chapter the model of this research on Internet differential pricing and price fairness judgements has been proposed, and the complementing hypotheses were stated. The model also includes behavioural consequences and transaction satisfaction, as these are most important for managerial implications. The next chapter will discuss the research design and the method.
Chapter 3: Research method and design
In this research, consumers’ responses to Internet differential pricing will be investigated. In this paragraph the method that will be used to test these will be explained. Based on prior research and the needs of the current, the manner of elaborating the experiment will be clarified.
3.1 Scenario-based survey experiment
An experiment is the best manner to investigate the relation between Internet differential pricing and perceived price fairness. A simulation of an online purchase with manipulated price differences within scenarios (Weisstein et al, 2013, Campbell 1999, Lii and Sy, 2009). The study is a controlled experiment, as variables will be manipulated. For this reason, a scenario-based survey experiment will be conducted. The scenario-based experiment as a research method has some advantages and disadvantages. Scenario-based experiments can provide good internal validity, though they may lack external validity and generalizability (Martin et al., 2009, p. 592). The scenario describes a rather defined context. It is not guaranteed that the effect on perceived price fairness is the same in (slightly) different situations.
In this 2x2 vignette study two characteristics are will vary systematically: (1) the price discrepancy that works either as an advantage or disadvantage for the customer, and (2) the perishability of the product will be either high or low (i.e. two different purchases will be manipulated). In the scenario it is clearly stated that the webshop obviously uses buyer identification. After the purchase is made the consumer compares the price of the purchase with another customer that made a similar transaction, but just paid a different price, through the social media source that is responsible for the buyer identification. This is because the
other-comparison shows the strongest differences (Xia et al., 2004, p. 4). After learning that the friend paid another price, the scenario states that the person visits the webstore again. He or she sees that yet nothing has been changed to the price is paid for the product or service. This is to emphasize that the price is targeted personally.
The four scenarios are presented in Figure 3.1. The participants will be randomly assigned to one of the scenarios through an online survey. The amount of participants will be 50 for each scenario at minimum, to guarantee significant outcomes.
Figure 3.2 – Distribution of the four scenarios
3.2 Measuring the variables
Participants will be linked to the scenario description in which a purchase in a webstore is being made. The independent variable and mediator are manipulated as in Figure 3.1, and can be either high or low. Finally, participants will have to complete a set of questionnaire to test for the (dependent) variables. All scales that are used are existing scales that have been validated before and have shown reliability. The measurement tools used for measuring perceived price fairness and the behavioural intentions have shown to be both reliable and
valid in prior research. The concept of perceived price fairness is measured in several studies before using Likert scales varying on scales from 3-point to 9-point Likert scales (Weisstein 2013, Garbarino and Maxwell 2010, Lii and Sy 2009).
Nguyen and Meng (2012) tested the difference in perceived outcome price fairness (i.e. the individual benefits) and its mediator procedural fairness (i.e. the pricing strategy in general). This was done using a set of studies. The first was a scenario-based experiment in which was manipulated a price difference, and a manipulated procedure. So there were four scenarios tested. In study 2 was to check if the findings were affected by how procedural fairness was measured, and the price difference was bigger to measure the effect better. Also in this study will be tested whether the advantageous or disadvantageous position in price discrimination leads to a different perception of fairness. The mediator perceived price fairness will be measured overall.
The dependent variables will be: (1) (transaction) satisfaction; (2) search intentions; and (3) repurchase intention. For ‘search intentions’ and ‘repurchase intention’ existing, though recoded, 7-point Likert scales will be used (see Appendix 2). Transaction satisfaction will be measured with a bipolar adjective scale, as used by Oliver and Swan (1989). They state that transaction satisfaction is a better predictor for measuring satisfaction in a specific situation (i.e. in this case the online purchase). Transaction satisfaction differs from cumulative or overall satisfaction, which is also dependent of the experience and relationship with the store as a whole (Olsen and Johnson, 2003).
3.3 Manipulations
Bolton et al. (2003) states that consumers use historic prices and reputation also influence the perception of fairness in customers’ minds. For this research not all factors mentioned will be tested. Some must be held constant, such as the reputation of the store. For this reason, a
fictional website with a neutral name will be used and the purchase will include an unbranded product or service. Besides, no historic prices will be mentioned. The price of the product must seem perfect for the customer on first sight, as it must (almost) equal the willingness-to-pay of the customer.
This chapter has shown the research method of this study. A 2x2 scenario-based experiment with manipulations will be used. In the next chapter the results of the experiment are shown.
Chapter 4: Results
In the previous chapter the research method was explained. A 2x2 scenario-based survey was set up to find evidence in consumer behaviour in an advantageous vs. disadvantageous price discrepancy setting and to control for a moderating effect of purchase perishability. In this section the results of the survey will be clarified. The section will start off with descriptive statistics. Afterwards, the internal reliability of the scales will be enhanced and the means of the scenarios will be compared, resulting in conclusions for the hypotheses.
4.1 Descriptive statistics
The survey was Internet based in the software called Qualtrics. Respondents were being approached through Facebook (i.e. both my personal network as well as Facebook-groups). The participants were randomly assigned to either one of the four scenarios. Facebook was used as medium to find respondents as the person in the scenario was using Facebook as well as a mean of communication. Besides of that, the medium makes it easy to find participants, and most of the Internet-users are experienced in Internet-shopping (refer!).
A total of 200 persons started off with the survey, of whom 8 dropped off in the first question (i.e. the terms of agreement). Even though it was not possible to proceed to the next page when not answering all questions (i.e. due to obligatory answering settings in Qualtrics), some respondents might have left the survey without completing it. The sample size useful for analysis resulted in N = 171 (scenario 1: N = 44; scenario 2: N = 42; scenario 3: N = 43; scenario 4: N = 42). The age was ranging from 18 to 60, with an average of 27.9 years.
The control variables ‘gender’ and ‘student’ had to be recoded into dummy variables. Also education level was separated into two dummies (i.e. ‘Bachelor’ and ‘Master’), as there were only three different education levels measured in the sample size. The fifth category in
the income variable (i.e. ‘not relevant’) had to be eliminated in order to avoid that it would have been ranked as highest income category. Some participants did not complete the control questions (e.g. age, gender, education level etc.), resulting in a N = 164 concerning these results and N = 131 when income was included.
4.2 Reliability of the scales
For the variables perceived price fairness, search intention and repurchase intention 7-point Likert scales were used. For transaction satisfaction a bipolar adjective scale was used. All scales were (recoded) items from prior research (Garbarino and Maxwell, 2008; Westbrook and Oliver, 1989). None of them showed any remarkable outcomes in a principal component analysis. To make sure the several items that make a single variable it is critical to control for internal reliability by assessing the Cronbach’s alpha of each scale with a minimum of .700 as acceptable (Field, 2009, p. 675).
The Cronbach’s alphas for the variables are listed in Table 4.1. The 5-item scale for measuring perceived price fairness seemed reliable after reverse-coding the negatively keyed item question 3 (Cronbach’s alpha = 0.905). Satisfaction showed a value of 0.963. The behavioural responses variables repurchase intention and search intention (N = 165) of 0.908 and 0.875 consecutively. The results allow it to create reliable variables of the items for further analysis.
Cronbach’s alpha N Minimum Maximum M SD
Perceived price fairness .905 171 1.00 7.00 2.6842 1.38323
Transaction satisfaction .963 171 1.00 7.00 3.7222 1.68121
Repurchase intention .908 165 1.00 7.00 3.3661 1.63134
Search intention .875 165 2.00 7.00 6.1576 1.05958
4.3 Correlations
The correlations, as exhibited in Table 4.2, show significant (p < 0.000) effects between the measured (dependent) variables. All correlations are in the directions they were expected to be (i.e. perceived price fairness , transaction satisfaction , and repurchase intention correlate positively with each other, whilst search intention shows negative correlations). Also, the control variables show some significant correlations with the dependent variables. Age correlates with transaction satisfaction (r = 0.160, p < 0.05). Education level is the only control variable that seems to have an effect on one of the dependent variables, repurchase intention (r = -0.165, p < 0.05).
The correlations suggest a relation between the variables, and show support for H3, H4, and H5. Transaction satisfaction is positively related to perceived price fairness (r = 0.595, p < 0.01), and so is repurchase intention (r = 0.652, p < 0.01). Search intention is negatively related to perceived price fairness (r = -0.496, p < 0.01). Yet, it is no support for the entire hypothesised model.
P er cei ved p ri ce T rans ac ti on sa tis fa cti o n R epur chas e in te n ti o n Se ar ch in te n ti o n G en d er A g e St u de nt E duc a ti o n level Inc om e Perceived price fairness 1 .595** .000 169 .652** .000 169 -.496** .000 165 .086 .273 164 .080 .306 164 .031 .694 164 -.070 .375 164 .034 .699 131 Transaction satisfaction 1 .763** .000 165 -.368** .000 165 .011 .894 164 .160* .040 164 -.050 .523 164 .012 .881 164 .049 .577 131 Repurchase intention 1 -.433** .000 165 .047 .548 164 .108 .168 164 -.044 .573 164 -.165* .035 164 -.011 .904 131 Search intention 1 -.117 .134 164 -.021 .790 164 -.085 .280 164 -.103 .189 164 .023 .796 131 Table 4.2 – Correlations of dependent variables and covariates with dependent variables
4.4 Multivariate analysis of variance
A series of three factorial MANOVAs was run to measure the different means, as it is an effective analysis when there are multiple dependent variables that are correlating with each other. Perceived price fairness was included as dependent variable, and the control variables were included as covariates. The first MANOVA was run with advantage vs. disadvantage (i.e. the dummy ‘Advantage’) as fixed factor. It showed a significant (p < 0.001) Box’s M, allowing it only to report Pillai’s trace to confirm effects (Tabachnik and Fidell, 2001). Advantage showed a significant effect: F(4, 117) = 22.531, p < 0.01; Pillai’s Trace = 0.435. And so did it in the univariate analysis on perceived price fairness (F = 21.899, p < 0.01), satisfaction (F = 21.899, p < 0.01), and repurchase intention (F = 21.899, p < 0.01). Only for search intention it did not show a significant result (F = 2.750, p > 0.10).
The second was run with perishable vs. non-perishable (i.e. the dummy ‘Perishability) as fixed factor and showed a marginally non-significant effect on the model (F (4, 117) = 2.140, p = 0.080; Wilk’s Λ = 0.932), but does not show a big explanation of the model. The univariate test shows a significant effect of perishability on the dependent variable perceived price fairness (F = 6.567; p < 0.05).
The third analysis was run including an interaction between ‘Advantage’ and ‘Perishability’. ‘Advantage’ showed significant results in this analysis (F (4, 115) = 22.032, p < 0.01; Wilk’s Λ = 0.566). ‘Perishability’ slightly improved from the inclusion of ‘Advantage’ in the same analysis (F (4, 115) = 2.344, p = 0.059; Wilk’s Λ = 0.925). The interaction ‘Advantage’*’Perishability’, however, seemed non-significant (F (4, 115) = 0.444, p = 0.776; Wilk’s Λ = 0.985). In the univariate analyses ‘Advantage’ showed significant results for all the dependents, apart from search intention (F = 2.970; p = 0.87). On the other hand, perishability showed even more significance for perceived price fairness compared to
the second run (F = 7.950; p < 0.01), and also for repurchase intention (F = 4.454; p < 0.05). The interaction, however, did not result in significant results here.
The control variable education showed significant outcomes in each of the analyses. The education level was separated into dummy variables ‘Bachelor’ and ‘Master’, and showed significant results in the third run (Bachelor: F (4, 115) = 2.636; p < 0.05; Wilk’s Λ = 0.916; Master: F(4, 115) = 4.949; p < 0.01 Wilk’s Λ = 0.853). Both showed an effect on repurchase intention, ((Bachelor: F = 7.324; p < 0.05; Master: F = 8.944; p < 0.05).
The results of the MANOVA show no support for ‘Perishability’ to have an effect in this model. An advantageous or disadvantageous price discrepancy, however, seems to seriously influence the perceived price fairness. At the same time, does these analyses also show support for a direct effect on the dependent variables. This raises the question if the perceived price fairness is placed correctly in the hypothesised model.
4.5 Testing the model
The multivariate analysis of variance was used to search for variables that were explained by the model. Perceived price fairness, however, was considered to be a dependent variable in this analysis. In order to find support for this variable and the other variables to be explained by each other in the model, the PROCESS macro for SPSS was used (Hayes, 2013). Model 7 and 4 were used (see Figure 4.2 and 4.3), as they contain both a mediator and Model 7 does also contain a moderator (i.e. like the proposed model). Though, first a model 1 analysis was run to control for a moderating effect of purchase perishability on perceived price fairness (see Figure 4.1).
Model 1 suggests no significant moderation effect of purchase perishability on the relation between advantageous vs. disadvantageous price discrepancy on perceived price fairness (F (1, 118) = 0.0631, p = 0.8021).
Figure 4.1 – PROCESS Model 1: Purchase perishability as moderator
Figure 4.2 – PROCESS Model 7: Perceived price fairness as mediator + purchase perishability as moderator
Analysing Model 7 (see Figure 4.2) suggests that perceived price fairness and (dis)advantageous price discrepancy seem to be very good predictors for all three dependent variables. There is a significant direct effect of ‘Advantage’ on satisfaction (β = 1.4208; p < 0.01), repurchase intention (β = 1.2275; p < 0.01), though not for search intention. Consumers will be less satisfied and be less willing to make a repurchase when they found themselves to be in a situation of disadvantageous price discrepancy. ‘Perceived price fairness’ also shows a significant direct effect on the dependent variables: satisfaction (β = 0.5679; p < 0.01), repurchase intention (β = 0.6518; p < 0.01), and search intention (β = -0.4303; p < 0.01). Not a significant interaction effect between ‘Advantage’ and ‘Perishability’ was found. For this reason, this moderation effect was left out in the further analysis and H2 is rejected.
After analysing Model 4 (see Figure 4.3) ‘Advantage’ still seems a good predictor for two of the dependent variables, reporting significant total effect for satisfaction (β = 2.0070; p < 0.01) and repurchase intention (β = 1.9209; p < 0.01) (see Table 4.3). Search intention only seems to have a significant with perceived price value as mediator (β = -0.4577; p < 0.01). Both direct, as well as indirect, effects were significant, suggesting that perceived price fairness is a good predictor for the outcome variables and explains the model partially. As well, it provides support for H1; the advantaged situation of the price discrepancy indeed leads to higher perceived price fairness.
(Transaction) satisfaction Repurchase intention Search intention
Direct effect IV on DV 1.4028 1.2275 -
Indirect effect IV on DV 0.6041 0.6935 -0.4577
Effect of mediator on DV 0.5679 0.6518 -0.4303
Total effect of IV on DV 2.0070 1.9209 -
Concluding, the analyses suggest that the model is partially explained with perceived price fairness as mediator. An advantageous or disadvantageous price discrepancy has both a direct effect on the dependent variables: satisfaction and repurchase intention. It also shows an indirect effect. But the outcomes also suggest that ‘perceived price fairness’ could be an outcome variable. For search intention, perceived price fairness was explained as a mediator, as no direct effect from advantage was found. In the next section the supported and non-supported hypotheses will be further explained. As well the limitations to this study and the managerial implications will be mentioned.
Chapter 5: Discussion
In the last section the results of the survey-based experiment were exposed. In this paragraph the results will be interpreted and they will be linked to the theory. The managerial implications of the results will be further explained and the limitations to this study will be mentioned.
5.1 Findings
The purpose of this study was twofold: (1) to find the differences in consumer behaviour through perceived price fairness between an advantageous vs. disadvantageous price discrepancy in a situation of Internet differential pricing where personal data was used to determine the price; (2) to find support for a moderating effect in this situation for the purchase of a perishable vs. non-perishable. Internet differential pricing is the method to determine the price closest to a consumer’s willingness-to-pay (Netseva-Porcheva, 2013), which is ought to be perceived as a fair price by the consumer. However, in this research a social comparison was included on which the consumer based its perceived price fairness (Xia et al., 2004). Their own transaction became the main driver to determine fairness (Xia et al., 2004). Taking benefit from an Internet differential pricing situation seems to influence price fairness according to the results of this study. Also, does fairness influence satisfaction, repurchase intention, and search behaviour. Though, perceived price fairness only seemed to partially work as a mediator in this model, as the advantageous vs. disadvantageous price discrepancy also influences (transaction) satisfaction and repurchase intention directly in a positive manner. It makes the position of perceived price fairness in the model open to further investigation.
Search intention was the measured variable hardest to predict. Presumably, consumers become suspicious no matter their situation (i.e. advantageous or disadvantageous). People compare their transactions to other people to determine fairness (Xia et al., 2004). Even though they might have perceived their outcome as fair (i.e. being in the advantageous position), they also might have taken this event of comparing prices with their friend as a warning to be more aware of differential pricing methods and consumer tracking on the Internet. Trusting the company, therefore, might be important to consider in further research (Ye et al., 2013).
Finally, consumers did not respond differently on behalf of the purchase’s characteristic. This study shows no support for product characteristics perishable vs. non-perishable as a moderator on the perceived price fairness, as was expected building on arguments from the literature (Netseva-Porcheva, 2013; Bolton and Myers, 2003; Weisstein, 2013). No differences in consumers’ reactions were found through measuring the same situation, only changing the characteristics of the purchase.
5.2 Limitations
Although most of the hypotheses that were tested in this study were supported, there are some limitations to this study. Some researchers warn for the risks of implementing first-degree price discrimination, as it may negatively affect repurchase intention (Zhang et al, 2013), but this study has shown that it positively influences repurchase intention as long as the consumer pays a better price than the one they compare it with. Zhang et al. (2013) states that personal characteristics may influence the perceived fairness and moderate the effect. However, this still makes it hard to state that Internet differential pricing indeed works or not. A distinction in dividing perceived price fairness into two, and measure both outcome fairness and procedural fairness could explain this further.
Second, customer comparison has become more important since social media are widely used by consumers. In this research a friend, or a Facebook-friend, was suspect for other-comparison. Further research could focus more on social ties, as Gelbrich states that people also consider the importance of the relationship in other-comparison (Gelbrich, 2011, p. 220). People may have a different perception of price fairness in another social comparison (Bolton, 2003).
5.3 Managerial implications
This research was written to give practical support that may be implemented. This study has shown that the product type does not have to determine whether e-merchants should implement Internet differential pricing to their webstores or not. Even though, after all, consumers seem to respond equally in a transaction for perishables vs. non-perishables. Still, after successful implementations of Internet differential pricing (Washington Post, 2013), there is scientific prove that should warn e-merchants for negative consequences (Lii 2009, Acquisiti and Varian 2005, Huan 2005). Also, this study shows that when consumers notice that they are mistreated, this results in less repurchases and a lower satisfaction.
E-merchants nowadays do have a great amount of information to treat their customers more personally. Since Internet differential pricing seems to work positively for repurchase intention when the consumer thinks that he made a good deal in comparison to another consumer. So, Internet differential pricing could be implemented successfully when the consumer thinks that he or she is in an advantageous position. Though, consumers have a larger amount of information as well, and as the results have shown, they will search for more information when they find out that an e-merchant is using differential pricing.
Chapter 6: Conclusion
The development of using Big Data seems to affect pricing strategies, as e-merchants nowadays can implement Internet differential pricing, and target a customers’ price closest to willingness-to-pay (Netseva-Porcheva, 2013). The literature warns for negative responses of consumers (Lii 2009, Acquisiti and Varian 2005, Huan 2005). In this research it has been tested whether perceived price fairness varies: (1) when the consumer is in either the advantaged or disadvantaged situation of differential pricing, and (2) when the purchase concerns a perishable or non-perishable.
A simulation of an online purchase with manipulated price differences within scenarios was used to address the effects of differential pricing and purchase characteristics on price fairness (Weisstein et al, 2013, Campbell 1999, Lii and Sy, 2009). Also, consumer reactions were measured (i.e. transaction satisfaction, repurchase intention, and search behaviour). Results have shown that the suggested model is partially explained with perceived price fairness as mediator. But the consumers’ outcome also determines behaviour directly (i.e. satisfaction and repurchase intention). From the outcomes it can also be concluded that Internet differential pricing automatically leads to more satisfaction when the outcome is beneficial for the consumer, and simultaneously they will be keener to make another purchase. Not all hypotheses were supported, however, as the purchase characteristic perishability seemed not to have a moderation effect as was expected from the literature (Netseva-Porcheva, 2013; Bolton and Myers, 2003; Weisstein, 2013). Consumers tend to respond in the same way to first-degree price discrimination for both perishables as non-perishables.
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