Research Article

Customers’ asymmetrical responses to variable pricing Received (in revised form): 20th November 2013

Lingjing Zhan and Alison E. Lloyd Department of Management and Marketing, The Hong Kong Polytechnic University, Kowloon, Hong Kong Lingjing Zhan is an assistant professor of Marketing at the Hong Kong Polytechnic University. She holds a PhD in marketing from University of Alberta. Her research interests include marketing communication, consumer judgment and decision making, and cultural differences in consumer behaviour. Her work has been published in Journal of Advertising Research and Journal of Business Research. Alison E. Lloyd is a senior teaching fellow at the Hong Kong Polytechnic University. She holds a PhD in marketing from The Hong Kong Polytechnic University. Her research interests include services marketing and customer perceived value. Her work has appeared in Journal of Services Marketing, Tourism Management, Journal of International Business Studies and Journal of Global Fashion Marketing. Correspondence: Lingjing Zhan, Department of Management and Marketing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

ABSTRACT This study investigates customer reactions to variable pricing (that is, being charged different prices for the same goods or services at one point in time across different markets) and finds that customers show asymmetrical responses to an increase in price difference. Specifically, customers who pay a higher price show stronger intentions to switch stores, to complain and to spread negative word-of-mouth when price difference gets larger, while the same amount of increase in price difference shows no effect on those who pay a lower price. In addition, negative emotions experienced by customers mediate the relationship between unfairness perception and behavioural responses. Results suggest that managers should exercise extra caution when introducing a relatively large price difference, and they should try to manage customers’ in-store emotional state to reduce the negative effects of variable pricing. Journal of Revenue and Pricing Management (2014) 13, 183–198. doi:10.1057/rpm.2013.39; published online 3 January 2014 Keywords: variable pricing; price fairness; price favourability; price difference; emotions

Pricing is one of the most powerful tools available to marketers because price has a significant influence on consumers’ purchase behaviour, and consequently on firm sales and profits (Han et al, 2001). As a result, marketers strive to find optimal prices for their goods to maximise profits and retain customers. Research indicates that variable pricing, the practice of charging different prices for the same goods or services across different markets at one point in

time, is gaining popularity among practitioners (Sinha, 2000; Levy et al, 2004). Large retailers such as Wal-Mart, The Gap and Home Depot utilise variable pricing by implementing ‘localised markdowns’ rather than slashing prices across the board (Cheng, 2009). This upward trend is expected to further intensify as more marketers begin to invest and embrace profit optimisation software to determine optimal prices (Grewal and Levy, 2007; Cheng, 2009).

© 2014 Macmillan Publishers Ltd. 1476-6930 Journal of Revenue and Pricing Management www.palgrave-journals.com/rpm/

Vol. 13, 3, 183–198

Zhan and Lloyd

However, related studies also show that marketers often behave without sufficient understanding of customers’ behavioural responses to pricing (Bearden and Urbany, 1998). As variable pricing becomes one of the most prevalent techniques in retailing, marketers are calling for more information about customer reaction to this pricing method, particularly in the grocery context where variable pricing is common (Levy et al, 2004). A key criticism of variable pricing is its fairness. Previous research suggests that when customers compare their prices to historical prices, prices paid by other buyers or competitor prices, they perceive only equitable exchange as fair (Bechwati et al, 2009). In this article, we followed Xia et al (2004) and defined a price as unfair when customers assess it as unreasonable, unacceptable or unjustifiable. For most customers, fixed prices ensure fair treatment because each customer is treated equally, or at least treated no worse than the next customer (Sinha, 2000). In this light, variable pricing can be perceived as an unfair means to acquire surplus from selected customers, and therefore can potentially reduce customer trust and threaten the customer–firm relationship (Mattila and Choi, 2005; McMahon-Beattie et al, 2010; McMahon-Beattie, 2011). Moreover, prior studies suggest that unfairness perception leads to unfavourable behavioural responses, which are of particular importance to retailers, such as store switching, complaining and negative word-of-mouth (Campbell, 1999; Xia et al, 2004; Homburg et al, 2005a; Schoefer and Diamantopoulos, 2008a). Unlike one-way price changes induced by other pricing strategies in variable pricing, some customers pay a lower price whereas at the same time others pay a higher price. Hence, both favourable and unfavourable results can possibly occur to customers. The purpose of this study is to examine the effect of price favourability on customers’ cognitive, affective and behavioural responses to variable pricing. Specifically, the first research question that this study attempts to address is: (i) would an increase in the magnitude of price difference strengthen

184

© 2014 Macmillan Publishers Ltd. 1476-6930

customer reactions to the same extent regardless of whether a lower price or a higher price is paid? If customer reactions are always linearly related to the amount of price difference, retailers may not need to worry too much when imposing a large price difference, because the negative responses from customers who pay high are accompanied with the positive responses from those who pay low, and hence overall customer evaluations would remain relatively stable. However, if the linear relationship does not hold, retailers would need to know where to draw the line. Our second research question is: (ii) what are the underlying process that translates cognitive processes (that is, unfairness perception) into behavioural responses? Understanding this issue would enable retailers to better cope with customers’ unfavourable responses towards variable pricing and thus minimise the possible negative consequences. This study probes customers’ affective responses to price unfairness and examines the role of the experienced negative emotions in mediating the relationships between unfairness perception and various behavioural intentions. To date, much of the marketing literature on price fairness has focused primarily on the cognitive processes and, comparatively, overlooks the important role of affect in price evaluation and the related decision-making process (O’Neill and Lambert, 2001). With affect introduced into the framework, the current study extends knowledge by integrating customers’ cognitive, affective and behavioural responses to price changes in variable pricing.

LITERATURE REVIEW AND HYPOTHESES DEVELOPMENT Variable pricing Charging different prices for the same goods or services is not new. In the 1960s, economists introduced the concept of consumer surplus, which refers to the difference between what consumers are willing to pay for a good or service and what they actually pay (Baran and Sweezy, 1966).

Journal of Revenue and Pricing Management

Vol. 13, 3, 183–198

Customers’ asymmetrical responses to variable pricing

Dynamic pricing, which can be broadly defined as charging each customer based on his/her implied willingness to pay, enables sellers to capture consumer surplus from buyers. Industries with high fixed costs and expiring capacity, such as airlines, hotels and car rentals, have long incorporated dynamic (or optimal) pricing into revenue management (RM) (Mattila and Choi, 2005). For these industries, dynamic pricing is critical to profitability because charging fixed prices fails to effectively respond to disparities in demand, competition, seasonality or cost of operations. The rapid advances in information technology have also contributed to a wide acceptance of dynamic pricing in many other industries (Levy et al, 2004). In 2000, for instance, Amazon.com sold the same DVD movies at different prices to different customers (Adamy, 2000). Among dynamic pricing strategies, there is one strategy, namely, variable pricing, involving charging different customers different prices for the same goods or services at one point in time. Grocery retailers have shown increasing interest in variable pricing in recent years (Cataluña, 2004). Grocery stores belonging to the same chain use different pricing and promotional policy because of the difference in customer profiles and competitors at different locations. Geographical distance between different stores also serves as a barrier to prevent customers from switching from one market to another. Otherwise, retailers do not find it prudent to use different prices in stores within the same trade area because customers may easily find out the price difference in contiguous stores and switch stores (Levy et al, 2004). However, recent evidence suggests that variable pricing is not limited to geographically disperse markets. For example, over the past few years, Hong Kong newspapers often reported the price discrepancies of the same product at different stores belonging to the same supermarket chain, with the difference as high as 110 per cent (Sing Tao Daily, 2008). Similarly, at a Banana Republic store in New York’s World Financial Center, a white pleated skirt was on sale for US$39.99, while the same skirt was discounted to $33.99 at Banana

© 2014 Macmillan Publishers Ltd. 1476-6930

Republic’s SoHo store, just 2 miles away (Cheng, 2009). This trend is attributable to the development of advanced pricing software that enables practitioners to precisely capture differences between any two markets. However, such pricing practices can increase tension between customers and firms and impose ethical concerns about fairness on practitioners.

Perceived price unfairness and its consequences Customers are highly sensitive to inequity (or unfairness). According to equity theory, individuals gauge the fairness of an exchange by comparing the ratio of the inputs they bring to a particular exchange over the outcome against the perceived inputs and outcomes of others (Adams, 1963). If buyers perceive themselves to be similar to one another and find out that they purchase the same product or service at different prices, a judgment of unfairness will likely occur. On the basis of a review of previous discussions on price fairness, Elegido (2009) concludes that a fair price is the price that a product commonly fetches in an open market. Prior research suggests that unfairness perceptions can lead to various unfavourable behaviours directed towards the firm. The current study focuses on three independent behaviours that are demonstrated to be most relevant to price unfairness (Schoefer and Diamantopoulos, 2008a). The first is store switching. Prior studies indicate that perceived price unfairness is associated with lower value perceptions and reduced repurchase intention (Campbell, 1999; Homburg et al, 2005a). In addition, customers who perceive unfair treatment are likely to engage in various voicing behaviours directed towards the company or a third-party agency, including complaining to the store manager, a consumer agency or a media such as newspapers (Xia et al, 2004; Schoefer and Diamantopoulos, 2008a). The third type of behaviours associated with price unfairness is negative word-of-mouth (Xia et al, 2004). For instance, Amazon.com customers flooded chat boards, expressing anger

Journal of Revenue and Pricing Management

Vol. 13, 3, 183–198

185

Zhan and Lloyd

Theoretical Framework Magnitude of price difference Perceived price unfairness

Negative emotions

Behavioural responses: store switching, complaining, and negative word-of-mouth

Price favourability

Figure 1: Theoretical framework.

and resentment when they discovered that the company charged different prices depending on the buyer’s price sensitivity (Adamy, 2000). Next we develop hypotheses to explore how perceived unfairness and consequent behavioural intentions are influenced by magnitude of price difference and price favourability. Because behavioural responses can be viewed as coping mechanisms to restore the desired fair situation (Xia et al, 2004), we propose that the two factors exhibit similar effects on behavioural intentions as they do on unfairness perception. We also probe the underlying process of translating cognitive judgments into behaviours. The theoretical framework is presented in Figure 1.

Effects of magnitude of price difference Empirical evidence suggests that customers who evaluate a new price typically compare the new price with a reference price (Mazumdar et al, 2005; D’Andrea and Schleicher, 2006; Gbadamosi, 2009). Either the price encountered on a past purchase occasion or the lowest price available in the market can serve as a reference price and influence the outcome of a price comparison. If there is a discrepancy between the current price and the reference price, customers will take into account the similarity between the two transactions to evaluate the price fairness (Xia et al, 2004). When the transaction similarity is low (for example, buying a bottle of water in a supermarket or in a convenience store located at a luxury hotel), the difference between the two

186

© 2014 Macmillan Publishers Ltd. 1476-6930

transactions may explain the price difference, and as a result, customers will judge the price as less unfair. Conversely, when the transactions are perceived to be highly similar, according to equity theory, perceived price unfairness is positively related to the magnitude of price difference. A recent study by Homburg et al (2005a) controlled the similarity between transactions, and results show that the magnitude of a price increase negatively impacts repurchase intention. Hence, given the research context of this current study in which transaction similarity is perceived to be high, we expect a price to be judged as more unfair as the price difference increases. We also hypothesise a positive effect of magnitude of price difference on customer intentions for the three negative behaviours. The first set of hypotheses is presented below: Hypothesis 1a: The magnitude of price difference has a positive effect on perceived price unfairness. Hypothesis 1b: The magnitude of price difference has a positive effect on propensity of store switching. Hypothesis 1c: The magnitude of price difference has a positive effect on complaining intention. Hypothesis 1d: The magnitude of price difference has a positive effect on negative word-of-mouth.

Journal of Revenue and Pricing Management

Vol. 13, 3, 183–198

Customers’ asymmetrical responses to variable pricing

Effects of price favourability Most of the extant research on consumer reactions to price changes focuses on a one-way direction of price change, either an increase or decrease; however, with variable pricing, a customer has the chance of paying a higher or lower price, so both favourable and unfavourable comparison outcomes can possibly occur. Many prior studies suggest that people react more positively when outcomes are relatively favourable rather than unfavourable and that the former leads to less unfair perceptions and higher outcome satisfaction (D’Andrea and Schleicher, 2006; Anderson and Patterson, 2008). These results are consistent with self-interest theory, which states that, in general, people prefer outcomes that provide them with the greatest benefit (Ng and Allen, 2005). Hence, we hypothesise a negative effect of price favourability (favourable versus unfavourable) on perceived price unfairness and behavioural intentions. We expect to find that customers paying a lower price (that is, a favourable outcome) will tend to judge the price as less unfair and will be less likely to switch stores, complain or spread negative word-of-mouth. The effects of price favourability are listed below: Hypothesis 2a: Price favourability has a negative effect on perceived price unfairness. Hypothesis 2b: Price favourability has a negative effect on propensity of store switching. Hypothesis 2c: Price favourability has a negative effect on complaining intention. Hypothesis 2d: Price favourability has a negative effect on negative word-of-mouth.

Interacting effects between price difference and price favourability Paying a price that is lower than the reference price is perceived as a gain. Conversely, paying a higher price is perceived as a loss. Prospect theory posits that people react differently to perceived gains versus losses and that, in general, people react more negatively to losses than they

© 2014 Macmillan Publishers Ltd. 1476-6930

react positively to gains (Kahneman and Tversky, 1979). Prior studies provide support for this proposition by showing that customers react more strongly to price increases than to price decreases. Kalyanaram and Winer (1995) reviewed eight empirical studies that identified asymmetric price responses by customers. For instance, Putler (1992) analysed egg sales data to find that the decrease in sales caused by a price increase was nearly 2.5 times greater than the increase in sales caused by a price decrease. More recent studies also lend support to the asymmetry of gains and losses. Moon et al (2006) separated customers into three categories: (i) those who do not use reference prices in any way, (ii) those who use past prices recalled from memory as reference prices and (iii) those who use the current price of a reference brand. The study results aligned with prospect theory: both groups who used reference prices showed loss aversion in their responses to price changes. Another study by Wangenheim and Bayón (2007) analysed customers’ behavioural responses to airline overbooking experiences, such as denied service, downgrading or upgrading. They found that customers who experienced negative consequences significantly reduced the number of their transactions with the airline, whereas upgraded customers exhibited only weak positive responses. In short, prior studies suggest that customers are generally more sensitive to losses in price changes than to gains. In other words, if price changes are likely to have an impact only when the price difference is above a threshold, customers have a higher threshold for price decreases than price increases (Han et al, 2001). Therefore, we expect that the same increase in price difference will show a stronger effect on customer reactions when the price is unfavourable than when the price is favourable. Therefore, we hypothesise interacting effects between the magnitude of price difference and price favourability on perceived price fairness and behavioural intentions as follows: Hypothesis 3a: The effect of the magnitude of price difference on perceived price

Journal of Revenue and Pricing Management

Vol. 13, 3, 183–198

187

Zhan and Lloyd

unfairness is moderated by price favourability, such that the effect is stronger for an unfavourable price (i.e., when a higher price is paid). Hypothesis 3b: The effect of the magnitude of price difference on propensity of store switching is moderated by price favourability, such that the effect is stronger for an unfavourable price. Hypothesis 3c: The effect of the magnitude of price difference on complaining intention is moderated by price favourability, such that the effect is stronger for an unfavourable price. Hypothesis 3d: The effect of the magnitude of price difference on negative word-ofmouth is moderated by price favourability, such that the effect is stronger for an unfavourable price.

Role of experienced emotions The consequences of unfairness perception are not only behavioural but also emotional (O’Neill and Lambert, 2001; Schoefer and Diamantopoulos, 2008a). According to appraisal theory, different emotions are elicited based on a person’s subjective evaluation or appraisal of the personal significance of a situation, object or event (Scherer, 1999). The result of cognitive appraisal of price inequality, unfairness perception, is typically accompanied by negative emotions such as anger, annoyance and discontentment (Schoefer and Diamantopoulos, 2008a, b). Xia et al (2004) argue that these strong negative emotions are important elements that distinguish unfairness from fairness. When negative emotions accompanying unfairness perceptions become strong, individuals feel an urge to press for action or redress (Finkel, 2001). Bagozzi et al (1999) suggest that emotions arising from a particular purchase situation may be more strongly associated with repurchase intention, complaint behaviours or

188

© 2014 Macmillan Publishers Ltd. 1476-6930

negative word-of-mouth than with cognitive evaluations of the situation. Empirical studies on fairness perception also lend support to the direct relationship between emotions and consequent behaviours. For instance, Schoefer and Diamantopoulos (2008a) investigated customer experiences of service failure and showed that negative emotions played a role in translating unfairness perception into reduced repurchase intention and negative word-of-mouth. On the basis of the above results, we hypothesise three mediation effects of experienced emotions: Hypothesis 4a: The experienced negative emotions mediate the relationship between perceived price unfairness and propensity of store switching. Hypothesis 4b: The experienced negative emotions mediate the relationship between perceived price unfairness and complaining intention. Hypothesis 4c: The experienced negative emotions mediate the relationship between perceived price unfairness and negative word-of-mouth. Prior studies also suggest that customer satisfaction with the firm serves to ‘buffer’ customer reactions to price increases. For instance, customer satisfaction moderates the effect of the magnitude of a price increase on repurchase intention, such that the effect is attenuated when customer satisfaction is high (Homburg et al, 2005a). Another recent study showed that satisfied customers are willing to pay more than unsatisfied customers (Homburg et al, 2005b). In light of these findings, we also measure customer satisfaction in the current study and include it as a covariate in our analysis to control for its effect.

METHOD Research design and data collection To test the above hypotheses, we employed a 2 (magnitude of price difference: small versus

Journal of Revenue and Pricing Management

Vol. 13, 3, 183–198

Customers’ asymmetrical responses to variable pricing

large) × 2 (price favourability: paying a lower versus higher price) between-subjects design. Because the scenario-based approach is deemed to be a viable method for research on fairnessrelated issues (Collie et al, 2002; Xia et al, 2010), respondents were asked to imagine that s/he bought a packet of biscuits at Price 1 in a store and later found that the price for the same biscuits at another store of the same chain was Price 2. Biscuits were chosen because they are a frequently purchased snack in Hong Kong, where the study was conducted. A filter question was provided at the beginning of the questionnaire to determine whether or not the respondents had ever purchased biscuits from one of the two major Hong Kong supermarket chains that each has over 200 stores located across the territory and serves millions of customers each month. Respondents were also asked to identify the chain from which they most frequently purchased biscuits so that they could be given the scenario that was related to the indicated chain store. The crux of the scenario entailed finding out that the price paid for a packet of biscuits (Price 1) was different from the price being offered at another store of the same chain (Price 2). Depending on the condition, the respondent received different information about Price 1 and Price 2. When the price difference was small, the two prices were $3.00 and $3.75, respectively; when the price difference was large, the two prices were $3.00 and $4.50, respectively. Price favourability was manipulated in the scenario by creating a situation in which half of the respondents paid a lower Price 1 than Price 2; and the remaining half paid a higher Price 1 than Price 2. After reading the scenario, respondents completed a questionnaire containing the dependent measures. Respondents were each randomly assigned to one of four conditions and the number of respondents for each condition ranged from 47 to 59.

Sample Trained interviewers recruited respondents from several shopping districts in Hong Kong.

© 2014 Macmillan Publishers Ltd. 1476-6930

Table 1: Demographic profile of respondents

Sample size (N)

207

Gender (in percentage) Male Female

33.8 66.2

Age group (in percentage) 25 or less 26–35 36–45 46–55 56–65 66 or above

20.5 24.6 24.2 20.3 6.8 3.6

Disposable monthly income per capita (in percentage) Less than $387 11.8 $388–$645 3.9 $646–$1288 18.4 $1289–$1933 22.2 $1934–$2577 25.1 More than $2578 18.6 Education level (in percentage) High school or below College or university Postgraduate degree or above Other

48.8 35.3 10.6 5.3

Data were collected using street intercept techniques over a 2-week period. All respondents were screened to ensure they were local residents and had prior experience purchasing biscuits in at least one of the two supermarket chains. In total, 207 respondents provided data usable for analysis. Table 1 provides the general demographic composition of the sample.

Measures All measurement items utilised established and validated scales, although minor changes were made in some scales to suit the context of the study (Table 2). Following Vaidyanathan and Aggarwal (2003) and Xia et al (2010), we

Journal of Revenue and Pricing Management

Vol. 13, 3, 183–198

189

Zhan and Lloyd

Table 2: Items used to measure the constructs

Construct

Questions/itemsa

Sources

Price unfairness (α = 0.90) In your opinion, the price is 1. extremely unfair/extremely fair 2. extremely unreasonable/extremely reasonable 3. extremely unacceptable/extremely acceptable Negative emotions To what extent do you feel (not at all/ (α = 0.91) very much) 1. annoyed 2. disappointed 3. discontented Switching intention Likert scale (α = 0.75) 1. I will shop in this supermarket less 2. I will switch to another supermarket 3. I will still shop in this supermarket next time Complaining intention Likert scale (α = 0.84) 1. I will complain to store manager 2. I will call the supermarket’s service hotline to complain 3. I will ask for a refund Negative word-of-mouth Likert scale intention (α = 0.85) 1. I will tell others about the experience 2. I will warn friends and relatives not to buy from this supermarket 3. I will complain to friends and relatives about this experience Customer satisfaction Likert scale (α = 0.84) 1. I am satisfied with purchases I have made in this supermarket 2. I like shopping in this supermarket 3. I feel pleased to shop in this supermarket a

— — — Schoefer and Diamantopoulos (2008b); Zeelenberg and Pieters (2004) — — — Zeelenberg and Pieters (2004) — — — Zeelenberg and Pieters (2004) — — — Schoefer and Diamantopoulos (2008a) — — — Jones et al (2000). — — —

All items used a 1–7 scale.

measured unfairness perception using three items. Respondents classified the prices in the scenario as fair or unfair on a 7-point Likert-type scale (extremely unfair–extremely fair; extremely unreasonable–extremely reasonable; extremely unacceptable– extremely acceptable). We captured negative

190

Xia et al (2004)

© 2014 Macmillan Publishers Ltd. 1476-6930

emotions using the three emotion items borrowed from Schoefer and Diamantopoulos (2008b) and Zeelenberg and Pieters (2004). Respondents indicated the extent they felt annoyed, disappointed and discontented in light of their scenario on a 7-point scale with the

Journal of Revenue and Pricing Management

Vol. 13, 3, 183–198

Customers’ asymmetrical responses to variable pricing

Table 3: Descriptive statistics and discriminant validity analyses

Construct

1

2

3

4

5

Perceived unfairness Negative emotions Propensity of store switching Complaining intention Negative word-of-mouth

0.719 0.350 0.073 0.080 0.160

— 0.829 0.130 0.137 0.185

— — 0.430 0.106 0.118

— — — 0.618 0.155

— — — — 0.655

Mean Standard deviation

2.936 1.323

4.385 1.442

3.746 0.985

3.512 1.565

4.626 1.327

Note: AVE bolded in the diagonal and squared correlations off-diagonal.

endpoints labelled as not at all and very much, respectively. We measured switching and complaining intention with three items for each, adapted from Zeelenberg and Pieters (2004). Negative word-of-mouth used a 3-item scale adopted from Schoefer and Diamantopoulous (2008a). For all the behavioural response items, respondents indicated the likelihood they would engage in each specified behaviour based on the scenario provided. These items were all measured using 7-point Likert scales with 1 indicating not likely at all and 7 indicating extremely likely. The control variable, customer satisfaction, was measured with three items adapted from Jones et al (2000).

was satisfactory: χ2 = 166.32; df = 80; NFI = 0.917; IFI = 0.955; CFI = 0.954; RMSEA = 0.072 (Hair et al, 1995). In addition, each construct’s composite reliability was greater than the recommended threshold value of 0.6 (Bagozzi and Yi, 1988), and all standardised factor loadings reached statistical significance at P<0.01. Moreover, as noted in Table 3, the average variance extracted (AVE) for each construct was greater than 0.5, thus providing support for convergent validity. The data also demonstrated discriminant validity, as the variance shared among the constructs was smaller than AVE of each construct (Fornell and Larcker, 1981).

RESULTS

Tests of hypotheses

The scale items were averaged to generate measure indexes for the constructs. We first assessed the validity of measures using structural equation modeling (SEM), and then tested the hypotheses with MANOVA and ANOVA analyses, followed by mediation analysis using SEM.

Measurement model Confirmatory factor analysis was conducted with AMOS 17.0 to assess the measurement properties of the scales. All factor loadings were significant (P<0.001), and standardised loadings ranged from 0.65 to 0.93. Scale reliability fell between 0.75 and 0.91. The overall model fit

© 2014 Macmillan Publishers Ltd. 1476-6930

We first tested whether the two manipulated factors showed the same effects on the four dependent variables. We conducted a repeated measures ANOVA analysis with perceived unfairness, store switching, complaining and negative word-of-mouth as repeated measures, and the magnitude of price difference and price favourability as between-subject factors. Results suggested that magnitude showed different effects on the four dependent measures, as evidenced by a significant interaction between magnitude and the within-subject factor (F(3, 609) = 5.96, P<0.01). Similarly, the effects of price favourability also differed among the four dependent measures (F(3, 609) = 8.09, P<0.001). The interaction

Journal of Revenue and Pricing Management

Vol. 13, 3, 183–198

191

Zhan and Lloyd

Table 4: ANOVA overview

Dependent variable: Perceived unfairness F-value

P-value

Model (adjusted R = 0.090)

6.086

<0.001

—

Independent variables Magnitude of price difference Price favourability MagnitudexFavourability Customer satisfaction

8.097 11.903 1.084 3.459

<0.01 <0.01 >0.10 <0.07

Hypothesis 1a Hypothesis 2a Hypothesis 3a —

8.102

<0.001

7.376 5.512 4.574 12.313

<0.01 <0.05 <0.05 <0.01

Hypothesis 1b Hypothesis 2b Hypothesis 3b

Dependent variable: Complaining intention Model (adjusted R2 = 0.065)

4.580

<0.01

—

Independent variables Magnitude of price difference Price favourability MagnitudexFavourability Customer satisfaction

1.374 7.340 3.686 4.988

>0.10 <0.01 <0.06 <0.05

Hypothesis 1c Hypothesis 2c Hypothesis 3c

Dependent variable: Negative word-of-mouth Model (adjusted R2 = 0.063)

4.450

<0.01

Independent variables Magnitude of price difference Price favourability MagnitudexFavourability Customer satisfaction

3.370 3.244 4.919 4.613

<0.07 <0.08 <0.05 <0.05

2

Dependent variable: Propensity of store switching Model (adjusted R2 = 0.121) Independent variables Magnitude of price difference Price favourability MagnitudexFavourability Customer satisfaction

between magnitude and price favourability was also significant (F(1, 203) = 12.14, P<0.01). To further examine the effect of magnitude and price favourability on each of the four dependent measures, we conducted a series of 2 (magnitude of price difference) x 2 (price favourability) ANOVA analyses and included customer satisfaction in the model as

192

© 2014 Macmillan Publishers Ltd. 1476-6930

Hypothesis

Hypothesis 1d Hypothesis 2d Hypothesis 3d —

a covariate. Table 4 provides an overview of the results.

Perceived unfairness The ANOVA analysis revealed a significant main effect of magnitude, suggesting that a larger difference yielded higher perceived

Journal of Revenue and Pricing Management

Vol. 13, 3, 183–198

Customers’ asymmetrical responses to variable pricing

unfairness. The main effect of price favourability was also significant, implying that paying a higher price led to a greater perception of price unfairness, compared with paying a lower price. Therefore, Hypotheses 1a and 2a were supported. It should be noted that the mean unfairness perception from the respondents who paid a lower price was significantly different from the mid-point of a 7-point scale (Mpaying-low = 3.26, t(94) = −5.17, P<0.001), which indicates that those who actually benefited from this pricing practice also perceived the prices as unfair. Inconsistent with Hypothesis 3a, the interaction between magnitude and price favourability was not significant.

Propensity of store switching Supporting Hypotheses 1b and 2b, both price favourability and magnitude of price difference showed significant effects on store-switching intention. Their interaction was also significant, suggesting price difference showed different effects on store switching, depending on price favourability. To further understand the interaction effect, an independent sample t-test examined the effect of magnitude under the two favourability conditions. As hypothesised (Hypothesis 3b), respondents who paid a higher price were more likely to switch to another store as the magnitude of price difference increased (Msmall-difference = 3.52, Mlarge-difference = 4.20, t(110) = −3.72, P<0.001), while the difference in switching likelihood between the two magnitude conditions fell below significance when the respondent paid a lower price (Msmall-difference = 3.55, Mlarge-difference = 3.62, t(93) = −0.39, n.s.).

Complaining intention The ANOVA results revealed a main effect of price favourability, which supported Hypothesis 2c, but main effect of magnitude (Hypothesis 1c) did not reach significance. However, the effect of magnitude was qualified by a (marginally) significant interaction between

© 2014 Macmillan Publishers Ltd. 1476-6930

the two factors (P<0.06). Independent sample t-tests further confirmed our predictions. Magnitude of price difference had a significant effect on complaining intention when the paid price was higher (Msmall-difference = 3.40, Mlarge-difference = 4.10, t(110) = −2.54, P = 0.012), but the effect fell below significance when the price was lower (Msmall-difference = 3.28, Mlarge-difference = 3.13, t(93) = 0.47, n.s.).

Negative word-of-mouth Finally, both magnitude and price favourability showed marginally significant effects on negative word-of-mouth in expected directions, supporting Hypotheses 1d and 2d. Respondents were more likely to spread negative word-of-mouth when the price difference was higher (versus lower) or when they paid a higher (versus lower) price. More importantly, the interaction was also significant. As expected (Hypothesis 3d), the effect of magnitude on negative word-of-mouth was significant when the respondent paid a higher price (Msmall-difference = 4.37, Mlarge-difference = 5.14, t(110) = −3.21, P = 0.002), but the effect was mitigated when the customer paid a lower price (Msmall-difference = 4.49, Mlarge-difference = 4.42, t(93) = 0.25, n.s.). In the above ANOVA models, the covariate, customer satisfaction with the store, was significant at P<0.05 for all the dependent variables except unfairness perception, for which the effect of customer satisfaction was marginally significant (P<0.07, see Table 4).

Mediation analysis SEM was used to test the mediating role of negative emotions. The SEM approach to mediation analysis builds on Baron and Kenny’s (1986) original recommendation and is demonstrated to be superior to tests using linear regressions (Iacobucci et al, 2007). SEM is a preferred method for examining mediation because it provides information about the degree of fit for the whole model after controlling for measurement error (Holmbeck, 1997,

Journal of Revenue and Pricing Management

Vol. 13, 3, 183–198

193

Zhan and Lloyd

p. 600). Following the procedure recommended by James et al (2006), we simultaneously compared the direct and indirect effects within one structural model. Evidence for full mediation occurs when the indirect path from the independent to the ultimate dependent variable is significant and when the direct path between those two constructs is not significant. Partial mediation occurs when both the direct path and the indirect path are significant, but the strength of the direct path reduces in magnitude after the indirect path is added. As noted in Table 5, before negative emotions entered the model, the direct paths between unfairness perception and the behavioural intentions were all significant and negative. When the construct of negative emotions was added into the model, the direct path between unfairness perception and store switching fell below significance, suggesting that negative emotions fully mediated the relationship between the two constructs. Similarly, negative emotions fully mediated the effect of unfairness perception on complaining. In addition, the relationship between unfairness perception and negative word-of-mouth was still significant when negative emotions were included in the model, but the effect was reduced in magnitude, suggesting that negative

affect partially mediated the relationship. Therefore, Hypotheses 4a and 4b were supported, and Hypothesis 4c attained only partial support.

DISCUSSION Variable pricing is becoming more prevalent in different industries, yet the empirical research on its behavioural effects remains quite limited. This study shows that the perceived fairness of variable pricing and the resulting behavioural intentions are influenced by both the magnitude of the price difference and the perception of the price as a gain or a loss. More interestingly, customers display asymmetrical responses to losses versus gains, in that the same amount of increase in price difference shows a more pronounced effect on behavioural intentions for a higher price than a lower price. In other words, customers who benefit from paying a lower price do not necessarily respond more favourably to a larger price difference, whereas customers who pay a higher price tend to show much stronger negative responses. Although variable pricing creates both favourable and unfavourable outcomes, this study suggests that the overall consequences of this pricing practice are not positive; it may cause unfairness perceptions, induce negative emotions and lead to

Table 5: Mediating analysis

Total indirect effect Estimate Perceived unfairness →Store switching Perceived unfairness →Complaining Perceived unfairness →Negative word-ofmouth Perceived unfairness →Negative emotions Negative emotions →Store switching Negative emotions→Complaining Negative emotions →Negative word-ofmouth

t-value

Direct effect Estimate

−0.237*** −4.544 −0.047 −0.444*** −4.758 −0.148 −0.565*** −7.283 −0.195* — — — —

— — — —

Evidence of mediation

t-value −0.776 Full mediation −1.24 Full mediation −2.146 Partial mediation

−0.703*** −9.567 0.245*** 4.044 0.375*** 3.372 0.479*** 5.534

* P<0.05, *** P<0.001.

194

© 2014 Macmillan Publishers Ltd. 1476-6930

Journal of Revenue and Pricing Management

Vol. 13, 3, 183–198

— — — —

Customers’ asymmetrical responses to variable pricing

coping strategies that could possibly produce detrimental effects on customer well-being and the bottom line of a firm. Although previous research has examined how customers react to dynamic pricing, this work contributes to the literature by integrating cognitive, emotional and behavioural responses to variable pricing that involves a low-involvement product. We hope this article can provide a more complete picture on how customers respond to variable pricing, and therefore provide practical suggestions on how to better control the negative effects of variable pricing on customer relationship management (CRM). Our findings are consistent with prior research in that emotions mediate the relationship between cognitive perceptions and behavioural responses. However, some unexpected results were revealed when we experimentally manipulated price favourability and magnitude of price difference to investigate their effects on customer reactions. First, the interacting effect of price favourability and magnitude of price difference is shown for behavioural intentions, but not for unfairness perception. Customers perceive a price to be more unfair as the price difference increases, regardless of whether the new price is lower or higher than the reference price. This finding lends empirical support to the conceptual understanding of ‘fairness’ solely based on the ground of ‘exchange equitability’ (for example, Bechwati et al, 2009). In addition, in light of the mediating role of emotions, our findings suggest that the asymmetrical responses to losses versus gains are likely to be evident only when affect comes into play. This speculation supports the notion that emotional reactions often diverge from cognitive assessments and become the main drivers of behaviours (for example, Bagozzi et al, 1999; Loewenstein et al, 2001). However, the testing of this proposition is beyond the scope of this study and it presents only one possible explanation for our findings. Another unpredicted finding is that negative emotions fully mediate the relationships between perceived unfairness and behavioural intentions of store switching and complaining, while only

© 2014 Macmillan Publishers Ltd. 1476-6930

partially mediating the effect of perceived unfairness on negative word-of-mouth. This result aligns with Watson and Spence’s (2007) speculation that cognitive appraisals may influence behaviours through two paths: (i) an indirect path through emotion and (ii) a direct path from cognitive perceptions to behaviours. Thus, negative word-of-mouth can be triggered not only indirectly through emotion, but also directly through cognitive appraisal, which implies that customers who perceive price unfairness may spread negative word-of-mouth without having to experience a negative affective state. This effect may be explained by the relatively lower level of effort required to engage in word-of-mouth in comparison to other behaviours such as store switching or asking for justification from the store manager.

Managerial implications This study presents several implications for managers. In general, it suggests that managers should adopt a customer perspective instead of a profit perspective in the design of future pricing strategies. Managers often face challenges when they engage in RM and CRM simultaneously, because CRM centres on profitable relationships with customers with the goal to maximise the lifetime values of current and potential customers, whereas RM aims to maximise financial returns by charging different customers different prices (McMahon-Beattie et al, 2010). Our results suggest that firms are advised to exercise caution when practicing variable pricing because charging different prices for an identical product may disappoint customers, induce their negative emotions and cause harm to both parties of the exchange, especially when the price difference is relatively large. Some retailers have already realised the possible negative consequences of price variations and have begun to make changes. For example, the Australian supermarket chain, Coles, announced in January 2010 that it started to charge the same prices for about 97 per cent of its products in all of its 192 stores (Collier, 2010).

Journal of Revenue and Pricing Management

Vol. 13, 3, 183–198

195

Zhan and Lloyd

The prevalence of the online environment means consumers can access price information easily, including any price differences between stores. The research has demonstrated that customers are concerned about price differences, and the behavioural effects are amplified particularly when faced with higher prices. An important managerial implication arising from this is to increase the level of transaction dissimilarity when practicing variable pricing. This can be achieved through framing price differences in various ways. Retailers can use price framing to make two transactions dissimilar, such as when a price of a product is altered by using a percentage off discount, and in another store the same product could have a different price by using a dollar off discount. An alternative way to arrive at a lowered price could include the use of quantity discounts. By using different price frames, it makes the direct comparison of prices more difficult for consumers, and hence lowers the negative affect caused by variable pricing. Conversely, if retailers were to raise the price of the product compared with another store, the offering of a free gift or premium item upon purchase would help increase perceptions of gain. Alternatively, segmented pricing could be employed by retailers whereby price charged could be based upon customer lifetime value. If the retailer has comprehensive enterprise data management systems such as Oracle or SAP, it becomes possible to capture detailed customer relationship information. Pricing could then be altered by giving greater value to those more loyal customers as a way to appreciate their repeated patronage. Another way to increase the level of transaction dissimilarity is to use retail sub-brands or different retail formats. Some supermarket retailers use different sub-brands to differentiate the retail format. For instance, a retailer could have one supermarket sub-brand that is designed around a type of high-end specialty gourmet offering, where aspects of the retail mix including the merchandise mix and store environment are emphasised. In this type of format, it is not

196

© 2014 Macmillan Publishers Ltd. 1476-6930

uncommon to find some items that are also available in conventional supermarkets, being sold at higher prices. Here by using different retail sub-brands or retail formats, it serves to increase transaction dissimilarity and makes direct comparisons by consumers more difficult. Given the location of high-end gourmet food, specialty formats are often in high-rental areas such as prime shopping mall or commercial facilities, consumers are often more understanding about paying higher prices. Hence, differentiating prices based on store format could be a way for retailers to charge different prices for the same products. The prominent role of negative affect in driving negative behavioural intentions suggests that retailers should try to manage customers’ instore emotional state to reduce the potentially harmful effects of negative affect. Retailers may consider improving customers’ shopping experiences by providing a more pleasant store environment. Prior research has shown that many atmospheric elements in a store can affect customer mood and purchase behaviour, such as colour, lighting, music, scents and store layout (see Turley and Milliman, 2000 for a review). For instance, music can produce significant effects on customer emotions (Hui et al, 1997) and ambient odours also arouse customers and therefore influence their perceptions of shopping environment and shopping behaviour (Chebat and Michon, 2003). Furthermore, retailers can provide good customer service by adopting a lenient return policy or investing more on salesperson training. To alleviate perceived unfairness, retailers may consider providing reasons for price difference. Attribution theory indicates that individuals are likely to search for causal explanations for an event when it is surprising or negative. When facing unexpected price increases, customers are inclined to infer motives for price changes, and these inferred motives in turn influence price fairness (Campbell, 1999). Therefore, when retailers have reasons to introduce a price difference, such as a stock clearance or special promotion at particular stores, they

Journal of Revenue and Pricing Management

Vol. 13, 3, 183–198

Customers’ asymmetrical responses to variable pricing

should explicitly provide such information to avoid unnecessary misconceptions.

behind the price change and explore how such inferences are influenced by customer satisfaction with the firm or by customer characteristics such as shopper orientation.

Limitations and future research The current study has only considered the context of grocery shopping, so further testing is needed before the findings can be generalised to other settings. The stimulus employed in this study is biscuits, which may not trigger strong emotional arousal because of the low involvement usually attached to this product category. However, even with this product category, significant effects of price difference and price favourability were observed, and emotions played a prominent role in translating unfairness perceptions into behavioural intentions; hence, similar effects can be expected for decisions with higher involvement such as shopping for clothes or hotel and airline reservations for vacations. Another limitation of the study is that it only considers one form of variable pricing where different prices are charged for the same product in different stores. However, retailers can utilise other pricing tactics to implement variable pricing, such as using mark-downs or in-store coupons. Future research needs to investigate how these ‘facades’ of variable pricing can influence customer reactions. It is likely that customers may perceive the price difference in an alternative light under such circumstances. For instance, customers may form a justification for a price difference, and thus perceive it as less unfair, if they know that the price offered in a store is a mark-down of the original price offered in another store. Finally, inferred motives are important antecedents to perceived unfairness (Campbell, 1999), but this study did not provide respondents with the reason behind the price change, nor did it ask respondents to infer possible reasons for the price difference. Future investigations could provide specific reasons for a price change and assess the impact of these reasons on unfairness perceptions and resultant behavioural intentions. Another option is to allow customers to infer the motives

© 2014 Macmillan Publishers Ltd. 1476-6930

ACKNOWLEDGEMENTS Financial support from Hong Kong SAR RGC General Research Fund (PolyU 552210H) is gratefully acknowledged.

REFERENCES Adams, J.S. (1963) Toward an understanding of inequity. Journal of Abnormal and Social Psychology 67(5): 422–436. Adamy, J. (2000) E-tailer price tailoring may be wave of future. Chicago Tribune, 25 September, http://articles.chicagotribune .com/2000-09-25/business/0009250017_1_prices-amazonspokesman-bill-curry-don-harter, accessed 2 December 2013. Anderson, W.D. and Patterson, M.L. (2008) Effects of social value orientations on fairness judgments. Journal of Social Psychology 148(2): 223–245. Bagozzi, R.P., Gopinath, M. and Nyer, P.U. (1999) The role of emotions in marketing. Journal of the Academy of Marketing Science 27(2): 184–206. Bagozzi, R.P. and Yi, Y. (1988) On the evaluation of structural equation models. Journal of the Academy of Marketing Science 16(1): 74–94. Baran, P. and Sweezy, P.M. (1966) Monopoly Capital: An Essay on the American Economic and Social Order. New York: Monthly Review Press. Baron, R.M. and Kenny, D.A. (1986) The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology 51(6): 1173–1182. Bearden, W.O. and Urbany, J.E. (1998) Introduction to the special issue. Journal of Retailing 74(3): 305–309. Bechwati, N.N., Sisodia, R.S. and Sheth, J.N. (2009) Developing a model of antecedents to consumers’ perceptions and evaluations of price unfairness. Journal of Business Research 62(8): 761–767. Campbell, M.C. (1999) Perceptions of price unfairness: Antecedents and consequences. Journal of Marketing Research 36(2): 187–199. Cataluña, F.J.R. (2004) Price discrimination in retailing. International Journal of Retail & Distribution Management 32(4): 205–215. Chebat, J.C. and Michon, R. (2003) Impact of ambient odors on mall shoppers’ emotions, cognition, and spending: A test of competitive causal theories. Journal of Business Research 56(7): 529–539. Cheng, A. (2009) Retailers find new ways to fine tune discounts. Marketwatch, 2 July, http://www.marketwatch.com/story/ retailers-find-new-ways-to-fine-tune-discounts, accessed 2 December 2013. Collie, T., Bradley, G. and Sparks, B.A. (2002) Fair process revisited: Differential effects of interactional and procedural justice in the presence of social comparison information. Journal of Experimental Social Psychology 38(6): 545–555.

Journal of Revenue and Pricing Management

Vol. 13, 3, 183–198

197

Zhan and Lloyd

Collier, K. (2010) Coles to standardise prices across the State. The Age, 26 January, http://www.theage.com.au/business/colesto-standardise-prices-across-the-state-20100127-mwkh.html, accessed 2 December 2013. D’Andrea, G. and Schleicher, M. (2006) The role of promotions and other factors affecting overall store price image in Latin America. International Journal of Retail & Distribution Management 34(9): 688–700. Elegido, J.M. (2009) The just price: Three insights from the Salamanca school. Journal of Business Ethics 90(1): 29–46. Finkel, N.J. (2001) Not Fair! The Typology of Commonsense Unfairness. Washington DC: American Psychological Association. Fornell, C. and Larcker, D.F. (1981) Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research 18(3): 1–19. Gbadamosi, A. (2009) Cognitive dissonance: The implicit explication in low-income consumers’ shopping behaviour for ‘low-involvement’ grocery products. International Journal of Retail & Distribution Management 37(12): 1077–1095. Grewal, D. and Levy, M. (2007) Retailing research: Past, present, and future. Journal of Retailing 83(4): 447–464. Hair, J.F., Anderson, R.E., Tatham, R.L. and Black, W.C. (1995) Multivariate Data Analysis. Englewood Cliffs, NJ: Prentice-Hall. Han, S., Gupta, S. and Lehmann, D.R. (2001) Consumer price sensitivity and price thresholds. Journal of Retailing 77(4): 435–456. Holmbeck, G.N. (1997) Toward terminological, conceptual, and statistical clarity in the study of mediators and moderators: Examples from the child-clinical and pediatric psychology literatures. Journal of Consulting and Clinical Psychology 65(4): 599–610. Homburg, C., Hoyer, W.D. and Koschate, N. (2005a) Customers’ reactions to price increases: Do customer satisfaction and perceived motive fairness matter? Journal of the Academy of Marketing Science 33(1): 36–49. Homburg, C., Koschate, N. and Hoyer, W.D. (2005b) Do satisfied customers really pay more? A study of the relationship between customer satisfaction and willingness to pay. Journal of Marketing 69(2): 84–96. Hui, M.K., Dube, L. and Chebat, J. (1997) The impact of music on consumers’ reactions to waiting for services. Journal of Retailing 73(1): 87–104. Iacobucci, D., Saldanha, N. and Deng, X. (2007) A mediation on mediation: Evidence that structural equations models perform better than regressions. Journal of Consumer Psychology 17(2): 139–153. James, L.R., Mulaik, S.A. and Brett, J.M. (2006) A tale of two methods. Organizational Research Methods 9(2): 233–244. Jones, M.A., Mothersbaugh, D.L. and Beatty, S.E. (2000) Switching barriers and repurchase intentions in services. Journal of Retailing 76(2): 259–274. Kahneman, D. and Tversky, A. (1979) Prospect theory: An analysis of decision under risk. Econometrica 47(2): 263–291. Kalyanaram, G. and Winer, R.S. (1995) Empirical generalization from reference price research. Marketing Science 14(3): 161–169. Levy, M., Grewal, D., Kopalle, P.K. and Hess, J.D. (2004) Emerging trends in retail pricing practice: Implications for research. Journal of Retailing 80(3): 13–21. Loewenstein, G.F., Weber, E.U., Hsee, C.K. and Welch, N. (2001) Risks as feelings. Psychological Bulletin 127(2): 267–286.

198

© 2014 Macmillan Publishers Ltd. 1476-6930

Mattila, A.S. and Choi, S. (2005) The impact of hotel pricing policies on perceived fairness and satisfaction with the reservation process. Journal of Hospitality and Leisure Marketing 13(1): 25–39. Mazumdar, T., Raj, S.P. and Sinha, I. (2005) Reference price research: Review and propositions. Journal of Marketing 69(4): 84–102. McMahon-Beattie, U., Palmer, I. and Yeoman, I. (2010) Does the customer trust you? In: I. Yeoman and U. McMahonBeattie (eds.) Revenue Management: A Practical Pricing Perspective. Basingstoke, UK: Palgrave Macmillian. McMahon-Beattie, U. (2011) Trust, fairness and justice in revenue management: Creating value for the consumer. Journal of Revenue and Pricing Management 10(1): 44–46. Moon, S., Russell, G.J. and Duvvuri, S.D. (2006) Profiling the reference price consumer. Journal of Retailing 82(1): 1–11. Ng, S.H. and Allen, M.W. (2005) Perception of economic distributive justice: Exploring leading theories. Social Behaviour and Personality 33(5): 435–454. O’Neill, R.M. and Lambert, D.R. (2001) The emotional side of price. Psychology & Marketing 18(3): 217–237. Putler, D.S. (1992) Incorporating reference price effects into a theory of consumer choice. Marketing Science 11(3): 287–309. Scherer, K.R. (1999) Araisal theory. In: T. Dalgleish and M. Power (ed.) Handbook of Cognition and Emotion. Sussex, UK: John Wiley & Sons, pp. 637–663. Schoefer, K. and Diamantopoulos, A. (2008a) The role of emotions in translating perceptions of (in)justice into postcomplaint behavioural responses. Journal of Service Research 11(1): 91–103. Schoefer, K. and Diamantopoulos, A. (2008b) Measuring experienced emotions during service recovery encounters: Construction and assessment of the ESRE scale. Service Business: An International Journal 2(1): 65–81. Sing Tao Daily (2008) Housewife corps is comparing prices across Hong Kong supermarkets. 5 May. Sinha, I. (2000) Cost transparency: The net’s real threat to prices and brands. Harvard Business Review 78(3/4): 3–8. Turley, L.W. and Milliman, R.E. (2000) Atmospheric effects on shopping behaviour: A review of the experimental evidence. Journal of Business Research 49(2): 193–211. Vaidyanathan, R. and Aggarwal, P. (2003) Who is the fairest of them all? An attributional approach to price fairness perceptions. Journal of Business Research 56(6): 453–463. Xia, L., Monroe, K.B. and Cox, J.L. (2004) The price is unfair! A conceptual framework of price fairness perceptions. Journal of Marketing 68(4): 1–15. Xia, L., Kukar-Kinney, M. and Monroe, K.B. (2010) Effects of consumers’ efforts on price and promotion fairness perceptions. Journal of Retailing 86(1): 1–10. Wangenheim, F. and Bayón, T. (2007) Behavioural consequences of overbooking service capacity. Journal of Marketing 71(4): 36–47. Watson, L. and Spence, M.T. (2007) Causes and consequences of emotions on consumer behaviour: A review and integrative cognitive appraisal theory. European Journal of Marketing 41(5/6): 487–511. Zeelenberg, M. and Pieters, R. (2004) Beyond valence in customer dissatisfaction: A review and new findings on behavioural responses to regret and disappointment in failed services. Journal of Business Research 57(4): 445–455.

Journal of Revenue and Pricing Management

Vol. 13, 3, 183–198