Systematic and Unsystematic Time-Based Dynamic
Pricing, Do Customers Prefer a Pattern?
Purchase Satisfaction and Switching Intention regarding Systematic
and Unsystematic Time-Based Dynamic Pricing in The Supermarket
Industry: An Experimental Study
by
Peter Neef
2
Systematic and Unsystematic Time-Based Dynamic
Pricing, Do Customers Prefer a Pattern?
Purchase Satisfaction and Switching Intention regarding Systematic
and Unsystematic Time-Based Dynamic Pricing in The Supermarket
Industry: An Experimental Study
University of Groningen
Faculty of Economics and Business
MSc Marketing Management & Marketing Intelligence
Master Thesis
January 13, 2020
Peter Neef
p.neef@student.rug.nl
3
ABSTRACT
Supermarkets use dynamic pricing on close to use-by date products, this technique can reduce food waste and increase profits. The author investigates purchase satisfaction and switching intention of customers on systematic (consistent price development) and unsystematic (inconsistent price development) time-based dynamic pricing. The author uses a story-based experiment to uncover these effects. Switching intention is higher for customers in the systematic condition compared to those in the unsystematic condition and is mediated by perceived price unfairness. Furthermore, perceived price unfairness also mediates the effect of the type of dynamic pricing on purchase satisfaction. Perceived price uncertainty creates marginal significantly mediates the effects on purchase satisfaction and switching intention. Furthermore, there is a marginal significant moderating effect of fresh products relative to non-fresh products on purchase satisfaction. This research offers a new view on time-based dynamic pricing in the supermarket industry and is the first step on understanding customer attitudes and responses on systematic relative to unsystematic time-based dynamic pricing.
4
PREFACE
This thesis represents my final work for the Master Marketing Intelligence and Management at the University of Groningen. I have worked on numerous projects and followed several courses during my period as a student in Groningen and I am proud that I can conclude this period with this research.
I would like to thank my supervisor, Arnd Vomberg, for helping me find an interesting topic and for the sessions where we discussed the progress of my thesis. During these session I received useful feedback that helped me to improve my thesis. Furthermore, I want to thank my friends and family for motivating and supporting me during the past period when I was writing my thesis.
5
TABLE OF CONTENTS
1. INTRODUCTION ... 7 2. CONCEPTUAL BACKGROUND ... 9 2.1 Dynamic Pricing ... 9 2.2 Price Unfairness ... 9 2.3 Price Uncertainty ... 10 2.4 Perishable Products ... 102.5 Development of Conceptual Model ... 11
3. METHODOLOGY ... 14 3.1 Research Design ... 14 3.1.1 Procedure ... 14 3.1.2 Independent Variables ... 14 3.1.3 Pre-test ... 15 3.1.4 Dependent Variables ... 16 3.1.5 Mediating Variables ... 16
3.1.6 Demographics, Manipulation Check and Debrief ... 16
3.2 Measurement Evaluation ... 16 3.2.1 Sample ... 16 3.2.2 Data Cleaning ... 17 3.2.3 Recoding ... 17 3.2.4 Reliability Analysis ... 17 3.2.5 Descriptives ... 18 4. RESULTS ... 20 4.1 Direct Effects ... 20 4.2 Mediation ... 20 4.3 Moderation ... 22 4.4 Moderated Mediation ... 23 4.5 Hypotheses ... 25 5. ADDITIONAL ANALYSIS ... 26 5.1 Post-hoc Results ... 26 5.1.1 Direct Effects ... 26 5.1.2 Mediation ... 26 5.1.3 Moderation ... 27
5.2 Tests of Regression Assumptions ... 28
5.2.1 Multicollinearity ... 28
6 5.2.3 Normality ... 29 5.2.4 Heteroscedasticity... 29 5.2.5 Endogeneity ... 29 6. DISCUSSION ... 30 6.1 Theoratical Implications ... 32 6.2 Managerial Implications ... 32
6.3 Limitations and Future Research ... 33
6.4 Conclusion ... 34
REFERENCE LIST ... 35
APPENDIX A: Experiment ... 39
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1. INTRODUCTION
About 1.3 billion ton of food is globally wasted per year, a part is wasted in the supermarket industry (Gustavsson, Cederberg, Van Otterdijk, & Meybeck, 2011). Supermarkets implement discounts on less favorable, close to use-by date products to reduce waste (Buisman & Haijema, 2019), a form of dynamic pricing. According to Weisstein, Monroe & Kukar-kinney (2013), implementing dynamic pricing can increase the revenue and profit of firms up to 25%. However, according to Schur, Gönsch, & Hassler (2019), prices were rarely adjusted two decades ago in the retail industry due to the high costs associated with price changes. They say that this situation has changed due to the internet, digital price tags and the development of IT software and hardware. Despite the growing implementation of dynamic pricing, the difference in attitudes and responses of customers regarding forms of dynamic pricing is still uncovered. Do customers care what kind of dynamic pricing retailers work with?
Dynamic pricing for fresh products in the supermarket industry has been studied before (e.g. Buisman & Haijema, 2019; Herbon et al., 2012; Tsiros & Heilman, 2005; Wang & Li, 2012), however they did not cover the difference between systematic (consistent price development) and unsystematic (inconsistent price development) discounts on food over time. This study focuses on time-based dynamic pricing because this form is often tested while focusing on perishable products (e.g. Buisman & Haijema, 2019; Herbon, Levner, & Cheng, 2012). Therefore, this research builds upon the following research question:
What is the influence of systematic relative to unsystematic time-based dynamic pricing on purchase satisfaction and switching intention in the supermarket industry?
Price (un)fairness related to dynamic pricing has been investigated many times and is linked to dynamic pricing (e.g. Garbarino & Lee, 2003; Garbarino & Maxwell, 2010; Gelbrich, 2011; Haws & Bearden, 2006; Li & Jain, 2015; Weisstein, Monroe & Kukar-kinney, 2013; Xia, Monroe & Cox, 2004). Furthermore researchers found that unfairness is linked to purchase satisfaction (Haws & Bearden, 2006) and related to customer attitudes and responses (Xia et al., 2004). However, none of the studies include the difference between systematic dynamic pricing relative to unsystematic dynamic pricing. Based on the link between dynamic pricing, (un)fairness and customer attitudes and responses, it is logical to include unfairness in this study.
8 dynamic pricing has not been empirically studied before. Because perceived price unfairness and perceived price uncertainty have not been studied in this context, the following research question is proposed:
How do perceived price unfairness and perceived price uncertainty influence the effect of systematic relative to unsystematic time-based dynamic pricing on purchase satisfaction and switching intention in the supermarket industry?
Retailers regularly use discounts as a technique in order to sell less favorable products and reduce food waste (Buisman & Haijema, 2019). No studies have come to light with regards to dynamic pricing that look at the difference between fresh and non-fresh products and distinguish systematic and unsystematic price variation types. The final research question therefore is:
How does the purchase of fresh relative to non-fresh products influence the effect of systematic relative to unsystematic time-based dynamic pricing on purchase satisfaction and switching intention in the supermarket industry?
This research contributes to the current research of dynamic pricing in multiple ways. First the distinction between systematic and unsystematic time-based dynamic pricing gives insights in the difference in attitudes and responses from customers regarding these types. Second, it sheds light on the underlying factors of customers regarding dynamic pricing. Lastly, the difference in products helps to understand the customers’ attitudes and responses of forms of dynamic pricing for fresh and non-fresh products in the supermarket industry.
Managers who work or want to work with dynamic pricing in the supermarket industry can use the findings to optimize their pricing strategy. Even managers outside the supermarket industry could use the findings to uncover differences in time-based dynamic pricing and use these in their own sector. They can take the relevant customer attitudes and responses on different forms of dynamic pricing into account and implement it on relevant products.
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2. CONCEPTUAL BACKGROUND
2.1 Dynamic Pricing
Dynamic pricing can be defined as “a strategy in which prices vary over time, consumers and/or circumstances” (Haws & Bearden, 2006: 305). This paper distinguishes systematic and unsystematic time-based dynamic pricing in order to find the difference in purchase satisfaction and switching intention for these types in the supermarket industry. Systematic time-based dynamic pricing is considered as a form where prices of products decrease or increase consistently over time. In contrast to unsystematic time-based dynamic pricing, where prices of products decrease and increase inconsistently over time. According to Spreng, Mackenzie, & Olshavsky (1996), purchase satisfaction arises when a customers’ expectations are exceeded regarding a purchase. Based on the research of Antón, Camarero, & Carrero (2007), switching intention is the intention to exit the relationship with the supermarket.
Economic theory argues that dynamic pricing on the individual level is good for the profitability of the firm, because it allows the retailer to capture a larger share of customer surplus (Garbarino & Lee, 2003). For perishable services, like airplane seats and hotel rooms, dynamic pricing is used, expected and accepted (Kannan & Kopalle, 2001). However, there are examples where unsystematic dynamic pricing has a negative impact (Cox, 2001). Li & Jain (2015) state that dynamic pricing can backfire when customers find out after the purchase that they have paid more than others. Therefore, not every form of dynamic pricing is accepted. Companies have been experimenting with online unsystematic dynamic prices for several years, some online retailers change prices even multiple times during the day (Kannan & Kopalle, 2001; Weisstein et al., 2013), so dynamic pricing is not new to customers. Retailers of physical stores change prices based on the time of the year (Cox, 2001), so customers already deal with time-based dynamic pricing in offline stores for a long time.
2.2 Price Unfairness
10 pricing (Garbarino & Maxwell, 2010). Besides person-based dynamic pricing, people also consider temporally proximal time-based price changes as unfair (Haws & Bearden, 2006). This finding insinuates that people find it unfair when they are discriminated based on time.
2.3 Price Uncertainty
Uncertainty in a buying context is the difficulty of making decisions that match the purchasing goals as well as the possible negative consequences related to costs to achieve these goals (Huang, Schrank, & Dubinsky, 2004; Wu, Huang, & Fu, 2011). Uncertainty can be classified as aleatory or epistemic. Aleatory uncertainty is primarily due to structural instability, epistemic uncertainty is the form of uncertainty that occurs due to a lack of knowledge (Fontana & Gerrard, 2004). This research focuses purely on epistemic uncertainty because a lack of price knowledge is the type of uncertainty that occurs for customers when firms vary with prices.
Decision theory states that people prefer choices with information about the probability of the outcomes over choices with no probability of the outcomes (Eichberger & Jürgen, 2018). Ellsberg (1961) showed in an experiment that people rather want to take a risk with a given probability than without knowing the probabilities. Furthermore, he argues that a person’s willingness to act in the presence of uncertainty depends partly on its vagueness or ambiguity (Fox & Tversky, 1995). Ambiguity is a type of uncertainty that often exists in decision making situations (Frisch & Baron, 1988). It can be defined as “the subjective experience of missing information relevant to a prediction” (Frisch & Baron, 1988: 152).
2.4 Perishable Products
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2.5 Development of Conceptual Model
This section states the hypotheses and the expected effects. Based on the literature, a conceptual model is formed that can be found in Figure 1.
FIGURE 1 Conceptual Model
As has been explained regarding general economic theory, dynamic pricing can be beneficial for the profitability, however, not every form is accepted (Li & Jain, 2015). In the case of bundled dynamic pricing, customers are more likely to accept prices that are easy to understand and transparent (Kannan & Kopalle, 2001). This statement insinuates understandable prices have a higher likelihood of acceptance from customers. Systematic dynamic pricing is likely to be easier understood based on the pattern in comparison to unsystematic dynamic pricing. Norms are expectations from a group about behavior from a group (Heide & John, 1992). Dynamic pricing that breaks a social norm leads to lower customer satisfaction and a higher likelihood of punishment actions from the customer towards the retailer, switching is such a punishment (Garbarino & Maxwell, 2010). Based on theory and empirical findings, the following hypothesis for the main effect is stated:
H1: Systematic time-based dynamic pricing relative to unsystematic time-based dynamic pricing has a less negative and maybe even a positive effect on purchase satisfaction (a) and a less positive and maybe even a negative effect on switching intention (b).
12 customers expect that a product is offered to other customers for the same price and trust that the retailer is not price discriminating against them. Furthermore, they say that when a customer learns that a retailer has price-discriminated against him, this person can feel it as a breach of trust and can harm the relationship. Garbarino & Maxwell (2010) found that dynamic pricing that breaks a social norm leads to this lower perceived fairness, they say that pricing norms are the rules that customers agree on sellers should follow when setting their prices, the community expects retailers to follow these norms. Haws & Bearden (2006) found that customers find it less fair when prices change within very short time periods (i.e. one hour) than over a more extended period (i.e. one month). This finding is relevant because the prices will vary within very short periods of time in this research.
Price unfairness can have a negative impact on the firm because it can result in a negative word of mouth (Jin, He, & Zhang, 2014). Furthermore, fairness is closely related to trust, a violation of a pricing norm is likely to have a major impact on trust of customers (Garbarino & Maxwell, 2010). The positive attitudes of customers are lower towards a firm when they perceive that prices of this firm are unfair (Campbell, 1999). Perceived price unfairness that stems from disadvantaged price inequality decreases satisfaction (Haws & Bearden, 2006) and increases switching intention of customers (Antón et al., 2007). Based on the existing research regarding dynamic pricing and price unfairness, the following hypothesis is stated:
H2: The effect of systematic relative to unsystematic time-based dynamic pricing on purchase satisfaction (a) and switching intention (b) is mediated by perceived price unfairness.
13 avoid ambiguity, one of them is that blame, responsibility and regret are more salient in ambiguous situations. Based on the theory and findings regarding uncertainty, the following hypothesis is formulated:
H3: The effect of systematic relative to unsystematic time-based dynamic pricing on purchase satisfaction (a) and switching intention (b) is mediated by perceived price uncertainty.
Researchers found that simple discounts on fresh products based on the quality of the product increases revenue (Herbon et al., 2012) and reduces waste (Buisman & Haijema, 2019). Kannan & Kopalle (2001) proposed that for non-perishable products it is more likely that a dynamic pricing strategy has a negative impact than for perishable products. This proposition is partly based on the impact of dynamic pricing on retailers like Amazon. The company learned that implementing dynamic pricing on DVD’s, a non-perishable product, can harm the company. Furthermore, according to Weisstein et al. (2013), implementing dynamic pricing on non-perishable products might increase financial benefits, but also increases negative reactions. Therefore the hypothesis for the moderating effect is:
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3. METHODOLOGY
3.1 Research Design
This experiment used a 2 (systematic time-based dynamic pricing vs. unsystematic time-based dynamic pricing) x 2 (fresh products vs. non-fresh products) between-participants ANOVA design. Potential participants were approached online via social media platforms and offline via leaflets that are distributed in the Faculty of Economics and Business at the University of Groningen. Therefore the overall sample is relative young, representative for students, but not for the overall population in the Netherlands. However, it was convenient to collect the data. In order to trigger potential participants to take part in the experiment, potential participants were told they had the chance to win a Bol.com (Dutch online retailer) voucher of €10 if they completed the experiment. The data is collected in English in an online experiment via Qualtrics from week 45 until week 47 in 2019. The full experiment can be found in Appendix A.
3.1.1 Procedure
Participants were instructed to read a story where they imagined that they decide to go to a new supermarket in town, ‘Supermarché’. They imagined buying a basket of products for €14 at 10 AM on a random Tuesday. After the purchase, they found out that Supermarché introduced a new pricing system. A picture showed how the prices of the basket that they bought developed on days during the same week. The story clarified that the price of the same basket at 6PM on the same day was €10 instead of €14 at 10AM.
3.1.2 Independent Variables
In order to minimize the risk that the treatment is correlated with subject characteristics, people were randomly divided in an experimental and a control group (Moffat, 2016). The participants in the experimental group were shown a systematic decreasing price of the basket, participants in the control group were shown an unsystematic increasing and decreasing price of the basket without any pattern. Red circles highlighted the moment that the participant bought the basket for €14 at 10AM and the moment that the price was €10 at 6PM. Both the illustration of the systematic price and the unsystematic price can be found in Figure 2. The price of the basket varied in both conditions between €9 and €15.
15 and detergent). A picture of either the fresh or the non-fresh products was shown in both conditions and can be found in Figure 3.
FIGURE 2
Dynamic Price Type in Systematic (above) and Unsystematic (below) Condition
FIGURE 3
Basket in Fresh (left) and Non-fresh Condition (right)
3.1.3 Pre-test
16 3.1.4 Dependent Variables
After the manipulation, participants received four questions based on a research of Darke & Dahl (2003) regarding purchase satisfaction on a 11-point Likert scale (1 = “dissatisfied” and 11 = “satisfied”, 1 = “unhappy” and 11 = “happy”, 1 = “disappointed” and 11 = “delighted”, 1 = “displeased” and 11 = “pleased”). Thereafter, participants received two questions regarding switching intention on a seven-point Likert scale (1 = “strongly disagree”, 7 = “strongly agree”) based on a research of Antón et al. (2007).
3.1.5 Mediating Variables
Perceived price unfairness is measured using five questions on a seven-point Likert scale based on Garbarino & Maxwell (2010). Three items were anchored based on how likely a situation would be for them (1 = “very unlikely”, 7 = “very likely”). The two other items were anchored on perceived fairness (1 = “very unfair” 7 = “very fair”). Participants could fill in how certain they were about the prices on a given moment by filling in three questions based on the research of Shiu, Walsh, Hassan, & Shaw (2011). Participants could answer the questions based on how confident or sure they were. (1 = “not at all confident”/”not at all sure”) to (7 = “completely confident”/”completely sure”).
3.1.6 Demographics, Manipulation Check and Debrief
Before the manipulation, the participants received questions regarding their gender and age in order to get a description of the sample. After the experiment, the participants received two questions were there was asked if they observed a pattern in the price development (“yes”/”no”) and what kind of products they bought (“fresh”/”fresh”/”fresh & non-fresh”) to test if the manipulation worked. Thereafter, the participants were able to fill in their email address if they wanted more information about the research or have a chance in winning the Bol.com voucher. Subsequently, people had the option to comment on the experiment. On the final page, participants were thanked and asked not to talk about the experiment until the 1st of December in the interest of the research.
3.2 Measurement Evaluation
Data preparation for analysis is an important first step when data is collected
(Wickham, 2014), this section shows the sample and how the data is prepared for the analysis.
3.2.1 Sample
17 analysis, resulting in a total of 109 people participating in the experiment (63.3% male; Mage
= 22.72; SD = 3.74). People could either click on a link or fill in the link in their internet browser (74.31%) or scan a QR-code to take part in the experiment.
3.2.2 Data Cleaning
Detecting and removing inconsistencies in the data, also called data cleaning, improves the quality of the data (Rahm & Do, 2000). One item of a respondent contained an impossible value (8) and was after reviewing recoded to the correct value (7). There were no further impossible values or inconsistencies in the data.
3.2.3 Recoding
The scores of four questions for perceived price unfairness were recoded (1=7, 7=1 et cetera), recoding ensured that a higher score equals a higher perceived price unfairness. All the scores for perceived price uncertainty were recoded (1=7, 7=1 et cetera) to ensure that a higher score equals a higher degree of uncertainty.
3.2.4 Reliability Analysis
According to Bagozzi & Yi (1988), it is wise to assess the reliability of the constructs and individual measures, they say that there are multiple types of reliability that can be measured by using confirmatory factor analysis. Composite reliability of the latent variable (CR) and the average variance extracted (AVE) are good measures to assess the reliability of constructs and individual factor loadings (FL) can show the reliability of an individual item to a latent construct (Bagozzi & Yi, 1988). Furthermore, Cronbach’s Alpha can be used as a measure of reliability (Field, Miles, & Field, 2012). Table 1 shows the values for the reliability analysis. The constructs for the dependent and mediating variables can be found in the first column.
18 The cut-off values for the Cronbach’s Alpha, composite reliability and the average variance extracted for purchase satisfaction, switching intention and perceived price uncertainty amply exceed the cut-off value. However, two items for both perceived price unfairness as perceived price uncertainty have a factor loading below .7. The item with a factor loading of .44 is dropped from the construct ‘perceived price unfairness’ because it is way below the cut-off value of .7 and negatively influences the CA, CR and AVE. The other items that fell below the .7 threshold are included in the analysis because somewhat lower values are acceptable (Morgan, Gliner, & Harmon, 2005) and it keeps the underlying constructs intact.
3.2.5 Descriptives
The individual items were averaged for both dependent variables, purchase satisfaction (M = 4.02, SD = 1.92) and switching intention (M = 4.12, SD = 1.48), and used for further analysis. Furthermore, the mean and standard deviation were calculated for the mediating variables, perceived price unfairness (M = 4.66, SD = 1.28) and perceived price uncertainty (M = 4.71, SD = 1.47), and also used for further analysis.
19 TABLE 1
Confirmatory Factor Analysis Dependent and Mediating Variables
Construct Measurement Items FL CA CR AVE
Purchase Satisfaction Reflective measure
11-point scale
How do you feel about the purchase? Anchors: 1 = “dissatisfied”, 11 = “satisfied”
.81 .9 .9 .69
How do you feel about the purchase? Anchors: 1 = “unhappy”, 11 = “happy”
.86
How do you feel about the purchase? Anchors: 1 = “disappointed”, 11 = “delighted”
How do you feel about the purchase? Anchors: 1 = “ displeased”, 11 = “pleased”
.79
.87
Switching Intention Reflective measure
7-point scale
Anchors: 1 = “strongly disagree”, 7 = “strongly agree”
I have no intention to go again to this supermarket
I intend to buy my products at another supermarket in the future
.82 .94
.87 .87 .77
Price Unfairness Reflective measure
7-point scale
Anchors: 1 = “very unfair” or “strongly disagree”, 7 = “very fair” or “strongly agree”
How would you rate the fairness of Supermarché's pricing practice in this situation? (recoded)
How fair do you consider Supermarché's way of pricing in this situation? (recoded)
I feel this sort of pricing is unfair
This pricing practice is fair to the buyer (recoded)
An objective third party would say this pricing practice is fair (dropped) .83 .94 .86 .65 .44 .89 .89 .68
Price Uncertainty Reflective measure
7-point scale
How sure are you of your knowledge of what the prices at Supermarché at different times are? (recoded)
.59 .74 .76 .52
Anchors: 1 = “not at all sure” or “not at all confident”, 7 = “completely sure” or “completely confident”
How sure are you of your knowledge of whether the prices at Supermarché at a given time are good, bad, or indifferent? (recoded)
To what extent are you confident of your personal view on whether the prices at Supermarché at a given time are good, bad, or indifferent? (recoded)
.89
20
4. RESULTS
4.1 Direct Effects
This research conducted a 2 (systematic time-based dynamic pricing vs. unsystematic time-based dynamic pricing) x 2 (moderator: fresh products vs. non-fresh products) ANOVA to predict purchase satisfaction and switching intention. Figure 4 demonstrates the effects of the dynamic pricing type on purchase satisfaction and switching intention. In contrast with H1a, purchase satisfaction did not differ significantly for participants in the systematic condition (M = 3.92; SD = 1.8) in comparison to participants in the unsystematic condition (M = 4.14; SD = 2.06; F(1, 107) = .37; p = .54). This finding means that participants in the systematic condition were not more satisfied with their purchase in comparison to participants in the unsystematic condition. In support of H1b, switching intention is significantly lower for participants in the systematic group (M = 3.62; SD = 1.35) in comparison to participants in the unsystematic group (M = 4.68; SD = 1.44; F(1, 107) = 15.72; p <.001). This finding indicates that people have a higher intention to switch supermarkets when they buy products with unsystematic dynamic prices in comparison to products with systematic time-based dynamic prices.
FIGURE 4
Average Purchase Satisfaction and Switching Intention for Dynamic Pricing Type Groups
4.2 Mediation
Perceived price unfairness was lower for people in the systematic condition (M = 4.44;
SD = 1.38) compared to people in the unsystematic condition (M = 5.13; SD = 1.24; F(1, 107)
21 systematic prices (M = 4.07; SD = 1.39) than participants who saw the unsystematic prices (M = 5.46; SD = 1.19; F(1, 107) = 30.85; p <.001). Perceived price unfairness has a significant negative impact on purchase satisfaction (ß = -.47; SE = .15; p < .01) and a significant positive impact on switching intention (ß = .66; SE = .08; p < .001). Perceived price uncertainty has no significant direct effect on purchase satisfaction (ß = -.15; SE = .12; p > .1), however a significant positive effect on switching intention (ß = -.47; SE = .15; p < .01).
The difference between a systematic and unsystematic price did not produce a significant direct effect on purchase satisfaction. Baron & Kenny (1986) claim that the main effect must be significant in order to have a possible mediating effect. However, this assumption does not hold when there are competing effects of the independent variable to the moderator and the moderator to the dependent variable (Zhao, Lynch, & Chen, 2010). A competing mediating effect is likely in this case because the effect of systematic relative to unsystematic time-based dynamic pricing on perceived price unfairness and perceived price uncertainty is negative and the effect of the mediating variables on purchase satisfaction is positive. Therefore, a mediation analysis is performed of perceived price unfairness and perceived price uncertainty for the type of time-based dynamic pricing on both purchase satisfaction as switching intention. Based on Zhao et al., (2010), the effect of the independent variable on the mediator (a) multiplied by the effect of the mediator on the dependent variable (b) must be significant in order to have a mediation. The mediating models were bootstrapped with 5000 simulations because a few thousand is preferred (Hayes, 2018). The effect, standard errors and p-values are presented in Table 2.
As predicted with H2a, there is a significant mediation effect of perceived price unfairness on the effect of systematic relative to unsystematic dynamic pricing, this effect is only indirect (ß = .36; p < .01) and competing. Furthermore there is a significant partial mediating effect of perceived price unfairness on the effect of systematic relative to unsystematic dynamic pricing on switching intention (ß = -.42; p < .01). This finding is consistent with H2b.
22 TABLE 2
Mediation Analysis of Price Unfairness & Price Uncertainty on Type of Dynamic Pricing on Purchase Satisfaction and Switching Intention
Price Unfairness (mediator) and Purchase Satisfaction (dependent variable) ß SE p-value
Systematic versus Unsystematic Dynamic Pricing Purchase Satisfaction (c) -.22 .37 .544 Systematic versus Unsystematic Dynamic Pricing Price Unfairness (a) -.69 .25 .007 Price Unfairness Purchase Satisfaction (b) -.52 .16 .002
a*b .36 .007
Systematic versus Unsystematic Dynamic Pricing (Price Unfairness) Purchase Satisfaction (c’)
-.53 .38 .170
Price Unfairness (mediator) and Switching Intention (dependent variable) ß SE p-value
Systematic versus Unsystematic Dynamic Pricing Switching Intention (c) -1.06 .27 .000 Systematic versus Unsystematic Dynamic Pricing Price Unfairness (a) -.69 .25 .007 Price Unfairness Switching Intention (b) .6 .08 .007
a*b -.42 .010
Systematic versus Unsystematic Dynamic Pricing (Price Unfairness) Switching Intention (c’)
-.65 .23 .006
Price Uncertainty (mediator) and Purchase Satisfaction (dependent variable) ß SE p-value
Systematic versus Unsystematic Dynamic Pricing Purchase Satisfaction (c) -.22 .37 .544 Systematic versus Unsystematic Dynamic Pricing Price Uncertainty (a) -1.39 .25 .000 Price Uncertainty Purchase Satisfaction (b) -.25 .14 .085
a*b .34 .096
Systematic versus Unsystematic Dynamic Pricing (Price Uncertainty) Purchase Satisfaction (c’)
-.57 ..42 .179
Price Uncertainty (mediator) and Switching Intention (dependent variable) ß SE p-value
Systematic versus Unsystematic Dynamic Pricing Switching Intention (c) -1.06 .27 .000 Systematic versus Unsystematic Dynamic Pricing Price Uncertainty (a) -1.39 .25 .000 Price Uncertainty Switching Intention (b) .17 .1 .102
a*b -.23 .083
Systematic versus Unsystematic Dynamic Pricing (Price Uncertainty) Switching Intention (c’)
-.83 .3 .003
4.3 Moderation
Besides the competing mediating effect of the type of time-based dynamic pricing on purchase satisfaction, there is also a marginal significant moderating effect of the type of basket (fresh relative to non-fresh) on the main effect (F(1, 105) = 1.71; p < .1). Purchase satisfaction of participants in the systematic condition that bought fresh products was significantly higher (M = 4.42; SD = 2.03) compared to participants who bought non-fresh products (M = 3.36; SD = 1.32; p < .05). However, participants who bought fresh products in the unsystematic condition did not differ significantly on purchase satisfaction (M = 4.05; SD
= 2.24) with participants in the non-fresh condition (M = 4.25; SD = 1.88; p = > .1).
23 crossover interaction effect. In contrast to Hypothesis 4b, there is no moderation effect of the basket type on switching intention (F(1, 105) = 6.17; p > .1). Switching intention was in the systematic condition higher for customers who bought fresh products (M = 3.79; SD = 1.47) compared to customers who bought non-fresh products (M = 3.43; SD = 1.2). On the other hand, switching intention was lower in the unsystematic condition for customers who bought fresh products (M = 4.44; SD = 1.55) compared to customers who bought non-fresh products (M = 4.96; SD = 1.28). However, these differences are not significant (p > .1). Switching intention for fresh products was marginally significantly higher (p < .1) and for non-fresh products significantly higher (p < .001) in the unsystematic condition compared to the systematic condition. The graph can be found in Figure 5.
FIGURE 5
Moderation of Basket Type on the Effect of Dynamic Pricing Type on Purchase Satisfaction and Switching Intention
4.4 Moderated Mediation
24 compared to participants that bought non-fresh products (M = 5.59; SD = 1.26; p > .1). The uncertainty for participants in both the fresh (p < .01) as for those in the non-fresh condition (p < .001) was significantly lower in the systematic condition compared to the unsystematic condition. The graph can be found in Figure 6.
FIGURE 6
Moderated Mediation of Basket Type on the Effect of Dynamic Pricing Type on Uncertainty
The second moderating effect in the mediation process is an effect of basket type on the effect of perceived price uncertainty on purchase satisfaction, while including the type of dynamic pricing. On average, purchase satisfaction for non-fresh products is 4.4 when there is no perceived price uncertainty and an increase in perceived price uncertainty has no significant influence on this effect (ß = -.07; p > .1). The type of dynamic pricing did not have an influence on this effect (ß = -.56; p > .1). Purchase satisfaction is on average higher (ß = 2.74; SE = 1.24; p < .05) for participants who bought fresh products compared to participants who bought non-fresh products when there is no perceived price uncertainty. However, purchase satisfaction decreases with .46 when perceived price uncertainty increases with 1. This finding shows that there is a moderating effect in the mediation process (F(1, 104) = 2.31
p = < .1). The results indicate that price uncertainty does not affect purchase satisfaction for
25 FIGURE 7
Moderated Mediation of Basket Type on the Effect of Uncertainty on Purchase Satisfaction
4.5 Hypotheses
Table 3 summarizes the hypotheses, whether these are supported and shows the p-value for the effects. Inconsistently with Hypothesis 1a, systematic dynamic pricing did not lead to a higher purchase satisfaction relative to unsystematic dynamic pricing, however did lead to a lower switching intention in support of Hypothesis 1b. Price unfairness mediated both the effects of the type of dynamic pricing on purchase satisfaction and switching intention, in support of Hypotheses 2a and 2b. Price uncertainty marginal significantly mediated the effects on purchase satisfaction and switching intention in support of Hypotheses 3a and 3b. Fresh relative to non-fresh products created a marginal significant moderating effect on the effect of systematic relative to unsystematic dynamic pricing on purchase satisfaction, however not on switching intention. These findings mean that Hypothesis 4a is marginally supported and Hypothesis 4b is not supported.
TABLE 3
Summary of Hypotheses
Hypotheses Support p-value
H1a: Systematic versus Unsystematic Dynamic Pricing Purchase Satisfaction Not supported .544 H1b: Systematic versus Unsystematic Dynamic Pricing Switching Intention Supported .000 H2a: H1a is mediated by Price Unfairness Supported .007 H2b: H1b is mediated by Price Unfairness Supported .01 H3a: H1a is mediated by Price Uncertainty Marginally
supported
.096
H3b: H1b is mediated by Price Uncertainty Marginally supported
.083
H4a: H1a is moderated by Fresh versus Non-fresh Products Marginally supported
.086
26
5. ADDITIONAL ANALYSIS
5.1 Post-hoc Results
Next to the dependent variables mentioned in the results section, gender and three other dependent variables are measured, namely complaint intention, benevolence trust and repurchase intention. The confirmatory factor analysis matrix for these measures can be found in Appendix B. Based on the low values for complaint intention in the confirmatory factor analysis, this construct was dropped from the analysis.
5.1.1 Direct Effects
Repurchase intention is higher for people in the systematic condition (M = 4.27; SD = 1.38) compared to people in the unsystematic condition (M = 3.15; SD = 1.4; F(1, 107) = 30.85; p < .001). Benevolence trust was not significantly higher for participants in the systematic condition (M = 3.27; SD = 1.11) compared to participants in the unsystematic condition (M = 2.94; SD = 1.1; F(1, 107) = 2.44; p > .1) These findings mean that there is a direct positive effect of systematic relative to unsystematic time-based dynamic pricing on repurchase intention and no direct effect on benevolence trust. The bar plots in Figure 8 show the differences in the mean repurchase intention and benevolence trust between the dynamic pricing conditions.
FIGURE 8
Average Repurchase Intention and Benevolence Trust for Dynamic Pricing Type Groups
5.1.2 Mediation
27 = .09; p < .001) and benevolence trust (ß = -.41; SE = .09; p < .001). An increase in perceived price uncertainty also leads to a lower repurchase intention (ß = -.28; SE = .09; p < .01), however did not lead to a lower benevolence trust (ß = -.09; SE = .07; p > .1). When looking at mediation, perceived price unfairness partially mediates the effect of the type of dynamic pricing on repurchase intention (ß = .37; p < .01) and only indirectly mediates the effect of the type of dynamic pricing on benevolence trust (ß = -.41; p < .01). Perceived price uncertainty did not produce a significant mediating effect on the effect of systematic relative to unsystematic dynamic pricing on repurchase intention (ß = .19; p > .1) or benevolence trust (ß = .07; p > .1). The exact effects, standard errors and p-values can be found in Table 4.
TABLE 4
Mediation Analysis of Price Unfairness and Price Uncertainty on Type of Dynamic Pricing on Purchase Satisfaction and Switching Intention
5.1.3 Moderation
Gender produced a marginal significant moderating effect on switching intention (F(1, 105) = 6.28 ;1 p < .1) and a significant effect on repurchase intention (F(1, 105) = 7.63; p < .05). Switching intention was higher for male participants in the systematic condition (M =
Price Unfairness (mediator) and Repurchase Intention (dependent variable) ß SE p-value
Systematic versus Unsystematic Dynamic Pricing Repurchase Intention (c) 1.12 .27 .000 Systematic versus Unsystematic Dynamic Pricing Price Unfairness (a) -.69 .25 .007 Price Unfairness Repurchase Intention (b) -.53 .09 .000
a*b .37 .007
Systematic versus Unsystematic Dynamic Pricing (Price Unfairness) Repurchase Intention (c’)
.75 .24 .004
Price Unfairness (mediator) and Benevolence Trust (dependent variable) ß SE p-value
Systematic versus Unsystematic Dynamic Pricing Benevolence Trust (c) .33 .21 .121 Systematic versus Unsystematic Dynamic Pricing Price Unfairness (a) -.69 .25 .007 Price Unfairness Benevolence Trust (b) -.41 .07 .000
a*b .29 .006
Systematic versus Unsystematic Dynamic Pricing (Price Unfairness) Benevolence Trust (c’)
.04 .19 .831
Price Uncertainty (mediator) and Repurchase Intention (dependent variable) ß SE p-value
Systematic versus Unsystematic Dynamic Pricing Repurchase Intention (c) 1.12 .27 .000 Systematic versus Unsystematic Dynamic Pricing Price Uncertainty (a) -1.39 .25 .000 Price Uncertainty Repurchase Intention (b) -.13 .1 .198
a*b .19 .200
Systematic versus Unsystematic Dynamic Pricing (Price Uncertainty) Repurchase Intention (c’)
.94 .3 .002
Price Uncertainty (mediator) and Benevolence Trust (dependent variable) ß SE p-value
Systematic versus Unsystematic Dynamic Pricing Benevolence Trust (c) .33 .21 .121 Systematic versus Unsystematic Dynamic Pricing Price Uncertainty (a) -1.39 .25 .000 Price Uncertainty Benevolence Trust (b) -.05 .08 .546
a*b .07 .57
Systematic versus Unsystematic Dynamic Pricing (Price Uncertainty) Benevolence Trust (c’)
28 3.81; SD = 1.37) compared to female participants (M = 3.25; SD = 1.26). On the other hand, female participants had a higher switching intention in the unsystematic condition (M = 4.9;
SD = 1.46) compared to males (M = 4.53; SD = 1.44). However, these differences are not
significant (p > .1). Switching intention was significantly lower in the systematic condition compared to the unsystematic condition for male participants (p < .05), but especially for female participants (p < .001). Female participants had a marginal significant higher repurchase intention (M = 4.75; SD = 1.3) in the systematic condition compared to males (M = 4.03; SD = 1.37; p < .1), however a lower insignificant repurchase intention (M = 2.9; SD = 1.38) in the unsystematic condition compared to male participants (M = 3.32; SD = 1.4; p > .1). Repurchase intention was higher in the systematic condition compared to the unsystematic condition for males (p < .05), but especially for females (p < .001). The interaction effects can be found in Figure 9.
FIGURE 9
Moderation of Gender on the Effect of Dynamic Pricing Type on Switching Intention (left) and Repurchase Intention (right)
5.2 Tests of Regression Assumptions
Several assumptions must be satisfied in regression analysis to ensure the quality of the models (Leeflang, Wieringa, Bijmolt, & Pauwels, 2015), therefore five assumptions are tested regarding the validity of the models.
5.2.1 Multicollinearity
29 issue because it can damage the estimation (Hayes, 2018). It can be assessed using VIF-scores for the moderator and the independent variable. There is multicollinearity when the VIF-scores exceed 10 (Field et al., 2012). The model in section 4.4 with the moderating variable ‘fresh versus non-versus products’ in the mediation analysis with perceived price uncertainty on purchase satisfaction exceeds the cut-off value mildly. The model has VIF-scores between 11.85 and 13.03 that shows that there is some multicollinearity. So, the estimation is likely to be damaged mildly. The other models do not suffer from multicollinearity.
5.2.2 Autocorrelation
Autocorrelation occurs when the residuals exhibit a systematic specific pattern over time (Leeflang et al., 2015). In this research, autocorrelation is not an issue because this research makes no use of time-series, but cross-sectional data.
5.2.3 Normality
The assumption of normality states that the error terms in the estimations are normally distributed (Hayes, 2018). The error term for the different models can be assumed to be normal in this study because the sample is greater than 30 (Field et al., 2012).
5.2.4 Heteroscedasticity
The variance of the parameters is biased when there is heteroscedasticity in the sample (Leeflang et al., 2015). The Breusch-Pagan test detects if the error term is correlated to one or more independent variables creating heteroscedasticity. Some models suffered from an unequal variance of the disturbance term, resulting in a wrong estimate of variance of the effects. Using robust standard errors solves this problem and generates the real p-value (Hayes & Cai, 2007). These are used for the models that suffered from heteroscedasticity.
5.2.5 Endogeneity
30
6. DISCUSSION
The aim of this study was to investigate the effects of two forms of dynamic pricing on purchase satisfaction and switching intention, namely systematic time-based dynamic pricing and unsystematic based dynamic pricing. This research predicted that systematic time-based dynamic pricing had a less negative and maybe even a positive effect on purchase satisfaction and a less positive and maybe even a more negative effect on switching intention relative to unsystematic time-based dynamic pricing. Furthermore, this research predicted that perceived price unfairness and perceived price uncertainty mediate the main effects and the difference between fresh and non-fresh products moderates the main effects. This research tested the hypotheses with an experiment where participants received a story as if they went to a supermarket and bought fresh or non-fresh products. The experiment randomly divided participants in a group were the basket had a systematic decreasing price throughout the day and a group were the basket price developed unsystematically throughout the day. Furthermore, participants were divided in a group that bought fresh products and a group that bought non-fresh products.
Kannan & Kopalle (2001) claimed that customers are more likely to accept prices that they can easily understand. However, in contrast to the prediction, systematic relative to unsystematic time-based dynamic pricing did not positively influence purchase satisfaction directly. This was due to competing mediating effects. Consistent with the hypothesis and Kannan & Kopalle (2001), customers had a lower switching intention when exposed to systematic dynamic pricing in comparison to unsystematic dynamic pricing.
Based on the literature (Antón et al., 2007; Garbarino & Maxwell, 2010; Haws & Bearden, 2006), perceived price unfairness is likely to influence the effect of the type of dynamic pricing on purchase satisfaction and switching intention. In line with these findings and with equity theory (Darke & Dahl, 2003), perceived price unfairness mediates the effect of systematic relative to unsystematic time-based dynamic pricing on purchase satisfaction and switching intention. These findings are in line with the predictions.
31 In line with the literature (Kannan & Kopalle, 2001) and the prediction, fresh relative to non-fresh products produced a moderating effect on the effect of the type of dynamic pricing on purchase satisfaction. Purchase satisfaction with a systematic price is higher when people buy fresh products relative to people that buy non-fresh products. In contrast to the hypothesis and the proposition of Kannan & Kopalle (2001), fresh relative to non-fresh products did not lead to a moderating effect of the type of dynamic pricing on switching intention. Weisstein et al., (2013) found that dynamic pricing on non-perishable products in an online setting can increase negative reactions, however the story in the current research told participants that they bought products in a supermarket (offline). The internet facilitates a dramatic reduction in search and switching costs (Wu et al., 2011). So, the effort for a customer to switch supermarkets might diminished a moderating effect of basket type on switching intention.
In line with Garbarino & Maxwell (2010), systematic relative to unsystematic dynamic pricing has a negative effect on repurchase intention. Furthermore, this effect is mediated by perceived price unfairness, in line with Jin et al. (2014) where they found that perceived price unfairness and repurchase intention are negatively correlated. Benevolence trust is not directly influenced by systematic relative to unsystematic dynamic pricing, however, this effect was mediated by perceived price unfairness. This finding is in line with literature that states that price unfairness negatively affects trust (Garbarino & Maxwell, 2010).
Not related to the hypotheses and surprisingly, there were moderating effects of basket type in the mediation process related to uncertainty. The first one is the moderating effect of the type of basket on the type of dynamic pricing on uncertainty. Participants had a higher uncertainty for fresh products in the systematic condition compared to non-fresh products, but the uncertainty did not differ in the unsystematic condition. This effect might be due to that supermarkets often change the prices of fresh products based on the quality (Buisman & Haijema, 2019) and people might think that the same thing can also happen in this case. The second one was the effect of the type of basket on the effect of uncertainty on purchase satisfaction. Uncertainty did affect purchase satisfaction of customers that bought fresh products, however not those that bought non-fresh products, insinuating that uncertainty matters less for customers for non-fresh products. This is not in line with ambiguity aversion (Frisch & Baron, 1988; Trautmann et al., 2008) and decision theory (Eichberger & Jürgen, 2018).
32 gender differences in dynamic pricing, however, differences in sensitivity to perceived price unfairness might have caused this difference. Researchers found that women in the USA were more sensitive to perceived price unfairness compared to men, however this finding did not apply to Germany and South-Korea (Maxwell et al., 2009).
6.1 Theoratical Implications
This research adds to the literature regarding time-based dynamic pricing in several ways. First, it shows that there is a difference in attitudes and responses of people on unsystematic and systematic time-based dynamic pricing in the supermarket industry. Previous research showed that dynamic pricing can cause a negative influence on purchase satisfaction, repurchase intention benevolence trust and a positive influence on switching intention (Garbarino & Maxwell, 2010). This research shows that systematic time-based dynamic pricing relative to unsystematic time-based dynamic pricing directly or indirectly influence all these concepts.
Second, it shows that perceived price unfairness and perceived price uncertainty are related to dynamic pricing. Researchers already linked perceived price unfairness to dynamic pricing (e.g. Garbarino & Maxwell, 2010; Haws & Bearden, 2006), however, this research is the first that showed the difference in perceived price unfairness and perceived price uncertainty between systematic time-based dynamic pricing and unsystematic time-based dynamic pricing.
Third, this research displays the difference between dynamic pricing of fresh products and non-fresh products. It shows that there are differences in the amount of purchase satisfaction and perceived uncertainty someone has when buying fresh relative to non-fresh products in a supermarket.
Fourth, this research found minor gender differences in repurchase intention and switching intention after participants were exposed to systematic or unsystematic dynamic pricing and thus shows that males and females can repond differently to dynamic pricing.
6.2 Managerial Implications
Managers in the supermarket industry can use this research to optimize their pricing strategy for fresh and non-fresh products. It provides insights in the attitudes and responses of customers when they are exposed to dynamic pricing.
33 calculate purchase satisfaction, possible switchers, the amount of people who repurchases at their supermarket chain and the decrease in trust that customers can have when exposed to systematic relative to unsystematic dynamic pricing. When managers choose an unsystematic time-based dynamic pricing strategy, they must be aware that customers’ switching intention is probably higher and repurchase intention probably lower compared to systematic time-based dynamic pricing.
Second it helps managers understand that perceived price unfairness and to some extend uncertainty are partly the underlying causes of the negative attitudes and responses towards unsystematic relative to systematic time-based dynamic pricing. Managers need to find ways to decrease perceived price unfairness and perceived price uncertainty.
Third it shows managers that there are small differences in purchase satisfaction for fresh relative to non-fresh products when implementing dynamic pricing, however not for switching intention or repurchase intention. Fresh products should have a systematic decreasing price, because this type of dynamic pricing creates a lower switching intention and a higher repurchase intention compared to unsystematic dynamic pricing and does not affect purchase satisfaction. Futhermore Buisman & Haijema (2019) found that a systematic decreasing price based on quality can decrease waste and Herbon et al. (2012) show that such a price can increase profits. Managers that focus more on purchase satisfaction compared to switching intention and repurchase intention can choose to implement unsystematic dynamic pricing on non-fresh products. Customers have in this case a higher purchase satisfaction, however have a higher intention to switch and a lower intention to purchase again at the store.
6.3 Limitations and Future Research
This study used a convenient sample consisting mostly of students that is not representative for the population in the Netherlands. It would be wise to make use of a more differentiated sample group in order to make it more representative for the population in the Netherlands.
Another limitation is that reverse causality cannot be ruled out for the mediating effects on the dependent variables. Future research could randomly divide people in an unfair or uncertain condition to solve this problem.
34 study in a supermarket. It would be interesting to conduct the experiment in a real supermarket, with real money, in order to make it more realistic for participants.
This research did not include information for the participant about the motivation from the supermarket in the experiment to conduct the dynamic pricing strategy. Future research could include the reason why the supermarket is using dynamic prices to help participants understand why dynamic pricing is implemented. It can be interesting to look at the perceived price unfairness, perceived price uncertainty and customer attitudes and responses when including information from the retailer because retailers might want to explain why they implement dynamic pricing.
Furthermore, this research explained the dynamic pricing strategy in the post-purchase stage. Future research could look at the difference between customer attitudes and responses when receiving information about the strategy in the pre-purchase and the post-purchase stage for systematic relative to unsystematic time-based dynamic pricing. It could help managers to find ways to tackle negative attitudes and responses of customers.
6.4 Conclusion
35
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39
APPENDIX A: Experiment
Start of Block: Intro
Thank you for participating in this experiment. You will read a story and receive some questions regarding prices in supermarkets. The results will be used for my Master Thesis at the University of Groningen. The experiment will take approximately 7 minutes.
The questions need to be answered individually and in one session. The answers will remain anonymous and will only be used for this study.
You can win a Bol.com voucher of €10 by participating in this experiment. You can fill in your email adress at the end of the experiment if you want to win this voucher. You can only participate once.
You can leave your email address at the end of the survey if you have questions regarding the experiment. I will contact you if you have done so.
Again, thanks for participating in this experiment.
Kind regards,
Peter Neef
End of Block: Intro
Start of Block: Demographics
Q1 What is your gender?
o
Male (1)o
Female (2)Page Break
Q2 What was your age on November 1, 2019?
________________________________________________________________
40 End of Block: Demographics
Start of Block: Text 2/3
You will read a story on the next pages. Read it carefully, this is essential for the experiment.
End of Block: Text 2/3 Start of Block: Groups
Fresh Condition: Imagine you want to buy fresh products (e.g. fruit, vegetables, dairy products, meat) and decided exactly what kind of products you will buy. You decide to go to a new supermarket in your town, ‘Supermarché’.
41 Non-Fresh Condition: Imagine you want to buy non-fresh products (e.g. canned food, spreads, herbs, toilet paper, detergent) and decided exactly what kind of products you will buy. You decide to go to a new supermarket in your town, ‘Supermarché’.
You go to Supermarché on Tuesday at 10:00 (10AM) and use your own money to purchase the products for a price of €14. An example of the basket is shown in the figure below.
End of Block: Groups Start of Block: Purchase
42 Unsystematic
After the purchase, you find out that Supermarché just introduced a new pricing system. Prices decrease and increase every hour during the day, each day, with no pattern. The prices differ every week. You can see the change of the price of your basket in the figures below. The price you paid on Tuesday was €14 at 10AM, however at 6PM, the price for the same basket was €10.
End of Block: Purchase Start of Block: Measures
Intro You will now get some questions regarding your purchase, please read them carefully.
43 PS How do you feel about the purchase?
1 (1) 2 (2) 3 (3) 4 (4) 5 (5) 6 (6) 7 (7) 8 (8) 9 (9) 10 (10) 11 (11) Dissatisfied
o
o
o
o
o
o
o
o
o
o
o
Satisfied Unhappyo
o
o
o
o
o
o
o
o
o
o
Happy Disappointedo
o
o
o
o
o
o
o
o
o
o
Delighted Displeasedo
o
o
o
o
o
o
o
o
o
o
Pleased Page BreakCI If this pricing situation happened to you, how likely is that you would...
