Dynamic Pricing in Online Business. What Pricing Strategy Should Be Used in Digital Business?


Textbook, 2018
146 Pages
Anonymous

Excerpt

Table of Contents

Abstract

List of Abbreviations

List of Figures

List of Tables

1 Introduction

2 Theoretical Foundation and Literature Review
2.1 Definition and Classification of Dynamic Pricing
2.2 Literature Review
2.3 Identified Research Gap

3 Derivation of Hypotheses and Conceptual Model
3.1 Consumer Reactions on Dynamic Pricing
3.2 Status-based Dynamic Pricing
3.3 Influence Factors on Price Fairness Perception
3.4 Consumer Reactions on Price Fairness Perception
3.5 Control Variable
3.6 Conceptual Model

4 Design of Empirical Study
4.1 Survey Method
4.2 Survey Scenarios
4.3 Survey Items

5 Data Analysis and Results
5.1 Survey Sample
5.2 Measurement Reliability
5.3 Hypotheses Testing
5.4 Further Analyses

6 Concluding Discussion
6.1 Discussion of Results
6.2 Managerial Implications
6.3 Limitations and Further Research

References

Appendix
Appendix A: Survey
Appendix B: Results of Pre-test
Appendix C: Construct Validity
Appendix D: Demographic statistics
Appendix E: Exploratory Factor Analysis
Appendix F: Reliability statistics
Appendix G: ANOVA - Assumptions & Results (H1)
Appendix H: Moderation Assumptions (Regression)
Appendix I: Results of Moderation Analysis (PROCESS)
Appendix J: Results of Moderation Analysis (Regression)
Appendix K: Moderation Analysis (Two-way ANOVA)
Appendix L: Mediation Assumptions (Regression)
Appendix M: Results of Mediation Analysis (PROCESS)
Appendix N: Frequency Table (NOBEL)
Appendix O: Frequency Table & Histogram (PSENS)
Appendix P: Frequency Table & Histogram (PEXP)

Abstract

The diffusion of dynamic pricing in retail industry is rapidly increasing. Retailers spot their advantage of increasing their overall profit. However, companies often face the problem of negative consumer reactions resulting from dynamic pricing. One type of dynamic pricing is status-based dynamic pricing where prices are set based on the customer status (e.g., new vs. existing). There is room for investigation in literature if consumers, depending on their existing relationship to the seller, react different to dynamic pricing than new consumers. Hence, this master thesis aims to contribute to existing literature as well as to detect managerial implications for e-commerce retailers by focusing on how new and existing customers react to dynamic pricing techniques .

In order to address this research question, a conceptual framework is established. Thus, status-based dynamic pricing is assumed to impact existing and new customers’ price fairness perception negatively. The relationship is supposed to be influenced by different moderators included in the model. The moderators are represented by individual consumer characteristics (price sensitivity and norm belief), the relationship quality to a reference person and towards the seller (prior trust), as well as by prior experience with dynamic pricing. Finally, the consumers’ price fairness perception is assumed to positively mediate the relationship between dynamic pricing and repurchase intention.

A web-based survey was conducted to collect consumer data. To examine the differences in price fairness perception relative to new and existing consumers, a one-way analysis of variance (ANOVA) is applied. The moderation analysis is conducted via PROCESS Model 1 and respective robustness checks are considered with a two-way ANOVA and regression analyses. The impact of dynamic pricing on the consumers’ repurchase intention via price fairness perception is assessed with a mediation analysis conducted by PROCESS Model 4.

The findings imply that status-based dynamic pricing leads to price unfairness perception for new and existing consumers. However, new consumers are observed to possess higher fairness perception than existing consumers. On top, an indirect effect of status-based dynamic pricing on repurchase intention via price fairness perception is revealed. A dynamic pricing event leading to higher price fairness perception will hence result in higher repurchase intention. Additionally, a direct effect of status-based dynamic pricing on repurchase intention is detected. New consumers consequently possess higher repurchase intentions compared to existing consumers. This finding can guide managers when practicing status-based dynamic pricing by reducing negative consumer reactions. Although prior experience with dynamic pricing did not exhibit the assumed moderator effect in the model, the variable is found to moderate the relationship of price fairness perception and repurchase intention positively. Managers can use this finding by incorporating it in upcoming pricing decisions.

List of Abbreviations

Abbildung in dieser Leseprobe nicht enthalten

List of Figures

Figure 1: Conceptual Demarcation of Dynamic Pricing and Study Focus

Figure 2: Conceptual Model

Figure 3: Message Visual of the Friend (new customer/ existing customer)

Figure 4: Blog Post of a Stranger (existing customer/new customer)

Figure 5: Moderation Analysis (Conceptual- and Statistical Diagram)

Figure 6: Mediator Analysis (Statistical Diagram)

Figure 7: Results of Hypotheses Tests

Figure 8: Overview significant Results

Figure 9: Scatterplot PFAIR and RELATION

List of Tables

Table 1: Overview of Existing Studies on Dynamic Pricing

Table 2: Scenario Development

Table 3: Cronbach’s alpha

Table 4: Results of Moderation Analysis (Process Model 1)

1 Introduction

During the last decade, companies had to set their strategic focus on digital transformation in order to fulfill the market’s need. It is nowadays almost inevitable for firms to digitize their corporate infrastructure, processes, products and services in order to increase market segment share (Bharadwaj et al. 2013).

According to the Global Pricing Study 2016 [1] of Simon-Kucher & Partners, 60% of the participating companies see the growing online business as a significant opportunity to increase their sales. However, also 60% of the companies perceive the digitization as a threat to their prices and margin.

To address this threat, 40% of German companies vary their prices dynamically on the Internet and the trend is ongoing (Kohn 2017; Wenk-Fischer and Zirbes 2016) . Dynamic pricing in the online environment is a strategic pricing tool, in which businesses flexible adapt their prices based on algorithms over time, across customer segments or depending on supply and demand (Haws and Bearden 2006; Krämer and Kalka 2017, p. 92). It is recognized as a possibility for companies to exploit the consumers’ maximum willingness to pay (WTP) (Grewal et al. 2011) resulting in an overall increase in the companies’ profits (Elmaghraby and Keskinocak 2003; Kohn 2017; Sahay 2007). Studies confirm those findings and state that companies which use dynamic pricing tools were able to reach 27% higher profits as the competition (Global Pricing Study 2016).

Simultaneously, dynamic pricing is a widely discussed topic in politics and economical magazines as well as an ongoing topic for the federal ministry of consumer protection (Kolbrück 2016). Due to frequently changing prices, the federal ministry of consumer protection sees the price transparency for consumers at risk. Consumers are no longer able to estimate prices properly. Hence, they do not know if the price displayed is actually cheap or might be 30% cheaper in one hour (Remmel 2016). Dynamic pricing has received special public attention when customers noticed that different prices were charged for the same product at the same retailer for different consumers (e.g., on Amazon) (Adamy 2000). Often customers react with dissatisfaction and complaining (e.g., Xia, Monroe, and Cox 2004; Haws and Bearden 2006). Moreover, consumers dissatisfaction can have a long-term impact on the buyer-seller relationship as well as the companies’ reputation and profits (e.g., Garbarino and Maxwell 2010; Lii and Sy 2009).

The ministry for consumers finds that 91% of customers have an aversion to dynamically adapted prices (N.N. 2017). Hence, they perceive it as unfair when paying a different price for the same product than other consumers (Weisstein, Monroe, and Kukar-Kinney 2013). Consumers’ price fairness perception is stated to be a crucial factor in the success of implementing dynamic pricing strategies (Heda, Mewborn, and Caine 2017; Hinz, Hann, and Spann 2011; Lii and Sy 2009). Consultants of McKinsey confirm this finding and note that companies can only stay successful on the market in the long-term when consumers perceive the company’s prices permanently as fair (N.N. 2016).

The importance of reasonable pricing is in the center of attention for marketing researchers as well as for practitioners (Gijsbrechts 1993; Global Pricing Study 2016). An important and often discussed topic is dynamic pricing across different customer groups (here and in the following: status-based dynamic pricing) (Li and Jain 2016). Within this dynamic pricing technique, new and existing customers are charged different prices with one customer group being the advantaged one (Feinberg, Krishna, and Zhang 2002; Grewal, Hardesty, and Iyer 2004). Previous literature has mostly focused on new consumer reactions when they pay a lower price relative to existing consumers (Feinberg, Krishna, and Zhang 2002; Malc, Mumel, and Pisnik 2016). However, literature is scarce in examination of new consumers being disadvantaged over existing customers (Grewal, Hardesty, and Iyer 2004).

Moreover, price comparisons between consumers have proven to be a crucial factor for consumers’ price fairness perception (Haws and Bearden 2006). Gelbrich (2011) examines the different consumer reactions of comparing their price to a person with whom the customer has a positive, neutral, or negative relationship with (here and in the following: relationship quality). However, the author does not connect relationship quality with status-based dynamic pricing in the study. Thus, Gelbrich (2011) highlights this as a need for further research (p. 220). Furthermore, prior trust is repeatedly stated in literature as a key concept when it comes to the relationship between buyers and sellers (Sirdeshmukh, Singh, and Sabol 2002). However, little research is done so far concerning the influence of new and existing consumers’ prior trust towards the seller in a status-based dynamic pricing context (Garbarino and Maxwell 2010).

So far there is a lack of investigation how different customer status groups (new vs. existing customers) react on status-based dynamic pricing. In particular, this applies to the gap of missing information about the influence of status-based dynamic pricing and relationship quality (Gelbrich 2011) as well as prior trust on price fairness perception (Garbarino and Maxwell 2010). Hence, the study will have three main focus areas consisting of the combination of status-based dynamic pricing with the buyer-seller relationship (expressed in prior trust) and the buyer-buyer relationship (relationship quality). To round off the analysis, consumers’ individual characteristics (expressed in price sensitivity and norm belief) as well as consumers’ prior experience with dynamic pricing will be included in the study. Subsequent consumer reactions (expressed in repurchase intention) will be further assessed.

Therefore, the focus of the study will face status-based dynamic pricing (i.e., new customer vs. existing customer) and examines if regular customers have a different price fairness perception as well as a distinctive responding action (i.e., repurchase intention) when they are disadvantaged compared to new customers and vice versa. Moreover, a focus will be set whether the relationship quality to the comparative other party influences the consumers’ price fairness perception. Hence, the aim of this study is to explore how new and existing customers react to dynamic pricing techniques. For companies this might be an interesting fact to know in order to adapt their pricing strategy to the different consumer groups. Therefore, this master thesis aims to contribute to existing literature as well as to detect managerial implications for e-commerce retailers.

The present study starts with a classification of dynamic pricing. The study’s focus is highlighted and a definition of status-based dynamic pricing which is used throughout the study is provided (2.1). Next, existing literature on dynamic pricing with the focus of consumer reactions is reviewed (2.2) which leads to the identified research gap (2.3). In order to investigate the research aim, hypotheses are derived on the basis of equity theory as well as on previous findings in chapter 3. Subsequently the hypotheses are summarized in the conceptual model (3.6). In chapter 4 the research method used for the data collection is outlined. Chapter 5 is dedicated for the data analysis in order to assess the proposed hypotheses. Finally, chapter 6 concludes with the discussion of the results, implications for practice, limitations of the study as well as suggestions for further research.

2 Theoretical Foundation and Literature Review

The following subchapter (2.1) serves to recapture the role of dynamic pricing in digital businesses and to outline a definition for the underlying master thesis. Furthermore, new and existing customer segments are discussed and put in relation to status-based dynamic pricing. Moreover, in the second subchapter (2.2) relevant literature concerning status-based dynamic pricing and related constructs is reviewed. Lastly, the third subchapter (2.3) closes with an overview of the reviewed literature and the derived research gap.

2.1 Definition and Classification of Dynamic Pricing

In practice, dynamic pricing states a common tool for companies to manage their revenues. However, literature is two-sided whether revenue management and dynamic pricing are two distinct concepts of demand management (Boyd and Bilegan 2003) or if dynamic pricing is an integrated part of revenue management (Klein and Steinhardt 2008, p. 177; Martens and Hilbert 2011; Talluri and van Ryzin 2004). A demarcation of dynamic pricing from revenue management is crucial, since both concepts partially overlap (Chiang, Jason, and Xu 2007; Klein and Steinhardt 2008, pp. 176-179). Researchers differentiate ‘quantity-based’ and ‘price-based’ revenue management with dynamic pricing being the latter one. On the one hand, ‘quantity-based’ revenue management focuses in price setting on profit maximization depending on capacity restrictions (Gallego and van Ryzin 1994). On the other hand, ‘price-based’ revenue management concentrates on setting an optimal price without considering capacity constraints (Talluri and van Ryzin 2004, pp. 175-177). Within the scope of the present study, dynamic pricing is considered as price-based revenue management. However, an accurate distinction is difficult since the interaction of supply and demand always influences price setting.

Dynamic pricing is differentiated into two broad constructs. Firstly, ‘price-discovery’ mechanisms which include price-negotiating processes, such as bidding, to fix a closing price (Elmaghraby and Keskinocak 2003; Hinz, Hann, and Spann 2011). In contrast to that, the second method constitutes the ‘posted-price’ mechanism in which products are offered at ‘take-it-or-leave-it’ prices (Elmaghraby and Keskinocak 2003). For most retailers, the latter method is the most appealing one, since prices can be individually set depending on customers’ information (Garbarino and Lee 2003). Due to the internet, it is easier to track customers’ data, to detect competitors’ prices and to respond to market fluctuations (Elmaghraby and Keskinocak 2003). With the help of algorithms firms can integrate those information into their price setting and adjust prices of identical products according to the customers’ WTP (Krämer and Kalka 2017, p. 89). Dynamic pricing in the sense of ‘posted-price’ mechanism can therefore be defined as setting prices individually depending on consumers’ demographics (e.g., place of residence, income), search behavior, last purchase quantities, purchase spending or customers’ preferences (Haws and Bearden 2006; Hinz, Hann, and Spann 2011; Huang, Chang, and Yi-Hsuan Chen 2005; Krämer and Kalka 2017, p. 92; Weisstein, Monroe, and Kukar-Kinney 2013) with the goal of profit maximization (Klein and Steinhardt 2008, p. 176). Hence, within the focus of the present study, the definition of dynamic pricing in the sense of ‘posted-price’ mechanism is considered.

This kind of dynamic pricing is recognized as a variation of the traditional form of price discrimination (Garbarino and Lee 2003). In the news, the expression of price discrimination is often synonymously used for dynamic pricing (Österreichisches Institut für angewandte Telekommunikation 2015). Pigou (1952) differentiates between three degrees of price discrimination (pp. 278-279). In the first-degree (individual level) of price discrimination, the price is set on an individual level based on the gathered customer information. In the context of dynamic pricing, researchers mostly refer to the individual level (first-degree) of price discrimination (Garbarino and Lee 2003). In the second degree of price discrimination, consumers are given discounts based on their purchase quantity (here and in the following, Pigou 1952, pp. 278-279). However, this kind of price discrimination does not fall in the sense of dynamic pricing as it is defined in the underlying study. Lastly, the third-degree discrimination segments the market based on the consumers’ price sensitivity. Hence, consumers with high price sensitivity receive a lower price than consumers that possess a low price sensitivity (Garbarino and Lee 2003; Grewal, Hardesty, and Iyer 2004; Huang, Chang, and Yi-Hsuan Chen 2005; Iyer et al. 2002). Since the present study examines price adaptions based on the consumer status, the first-degree price discrimination is applied.

The variation of prices depending on different customer groups is a widespread method of dynamic pricing. A companies’ customer segmentation can hence comprise age groups (e.g., special offers for seniors or students) or customer status groups (e.g., new vs. existing (synonymously used as regular or frequent) customers). However, the examination of consumer reactions on the latter one is stated as most discussed and relevant (Li and Jain 2016). A prominent practical example with regard to charging different prices in the online environment depending on the customer status, is the case of Amazon in 2000 (here and in the following, Huang, Chang, and Yi-Hsuan Chen 2005). Therefore, Amazon was accused to charge existing customers a higher price than new customers for consumer durable products. This price discrimination resulted in a consumer backlash (Adamy 2000).

There is a split in literature concerning the outcome of offering different prices to customers depending on their status. Economic literature proposes that a profit increase might be reached by setting a higher price for existing than for new customers, assuming the case that loyal (or regular) customers are less price sensitive (Shaffer and Zhang 2000). At the same time, companies try to attract new consumers with offering them lower prices. Thus, managers assume that new consumers will become loyal customers when seeing the advantages (here and in the following, Feinberg, Krishna, and Zhang 2002). However, research in consumer behavior demonstrates that existing customers perceive higher prices as more unfair when they learn that they paid more than new consumers.

To sum it up, the research focus of the underlying study will be on status-based dynamic pricing. Companies identify with the help of cookies and clickstream data, purchase and visit histories of the customers (here and in the following, adapted from Grewal, Hardesty, and Iyer 2004, p. 88). Therefore, the consumers are separated in first time or regular buyers when entering the retailer’s site. As this information is used to set different prices to the exact same product, dynamic pricing is defined in the underlying study as a price discrimination strategy that enables a single company to adapt prices dynamically for the same product or service across customer groups (based on: Garbarino and Maxwell 2010; Weisstein, Monroe, and Kukar-Kinney 2013).

Abbildung in dieser Leseprobe nicht enthalten

Figure 1: Conceptual Demarcation of Dynamic Pricing and Study Focus

Source: author’s own illustration

2.2 Literature Review

In the upcoming chapter an overview of existing literature concerning the interface of dynamic pricing and consumer’s reactions is exposed (here and in the following labeled as ‘behavioral price research’ (Cheng and Monroe 2013)). The underlying variables of the study (i.e., status-based dynamic pricing, relationship quality, prior trust, norm belief, price sensitivity, prior experience with dynamic pricing, price fairness perception, repurchase intention) are deducted based on the literature review. The variables are extracted from focusing on the core construct of price fairness perception and considering the antecedents as well as the consequences of it. In the following, findings of several studies concerning the dedicated variables are opposed.

The reviewed literature is selected based on the quality of the journal according to the ‘VHB-Jourqual 3’ Ranking as well as on relevance for the underlying study. As the study highlights three main focus areas (status-based dynamic pricing, buyer-buyer relationship, and buyer-seller relationship), behavioral pricing literature is selected to meet one of these criteria or to contribute to related constructs influencing price fairness perceptions. Dynamic pricing literature that originates from other research areas such as computer science or operations research will be mostly excluded as it mainly focuses on data driven price setting without considering customers’ reactions (e.g., Araman and Caldentey 2011; Özer, Ozer, and Phillips 2012).

The conceptual paper by Xia, Monroe, and Cox (2004) as well as the empirical paper by Haws and Bearden (2006) serve as fundaments for subsequent studies concerning behavioral pricing literature (e.g., Bolton, Keh, and Alba 2010, p. 564; Garbarino and Maxwell 2010; Gelbrich 2011). Hence, the papers support the following literature review. Xia, Monroe, and Cox (2004) summarize in their paper empirical findings on price fairness perceptions. They develop a conceptual model of antecedents and consequences of consumers’ price fairness perceptions. They establish a well-known conceptual understanding of price fairness, which is used by several researchers as a foundation for their studies (e.g., Haws and Bearden 2006). Xia, Monroe, and Cox (2004) define the construct of price fairness perception as the consumer’s judgment whether a price is assessed as just, reasonable, or acceptable depending on the difference (or lack of difference) between the vendor’s price and a reference price (p. 3). The reference price in dynamic pricing can encompass a price paid by the consumer or other consumers in last purchases for a similar or equal product (here and in the following, Xia, Monroe, and Cox 2004, p. 2). In addition to this, a reference party can also state a comparison to prices other companies offer for the same or equal products.

Xia, Monroe, and Cox (2004) propose that judgments of price fairness are a result of price comparison processes. The authors propose that when perceived inequality in prices appears, crucial factors in fairness perception are the extent of similarity between the transactions and the type of reference parties involved (here and in the following, Xia, Monroe, and Cox 2004, p. 2). Several variables such as social norms or the stage of the buyer-seller-relationship (expressed in trust) are further influencing the consumers’ price judgment. Depending on the direction of price inequality, price fairness perception results in possible positive (e.g., word-of-mouth (WOM)), or negative consumer actions (e.g., withdraw from a purchase).

As a basis Xia, Monroe, and Cox (2004) propose that price comparison processes and consequently price fairness perception result from price inequalities experienced by consumers. Such price inequality perception can result from status-based dynamic pricing (Grewal, Hardesty, and Iyer 2004). Literature concerning new and existing consumer reactions based on the price difference is outlined in the following. Feinberg, Krishna, and Zhang (2002) examine in their research a potential ‘betrayal effect’ as well as ‘jealousy effect’. In their study, the authors assume that customers who are in favor of one specific firm are at the same time potential switchers (here and in the following, Feinberg, Krishna, and Zhang 2002, p. 280). Thus, the researchers illuminate in their study a market with two competing music download services. The authors propose that existing consumers’ preference declines towards the firm when the company offers price reductions to new consumers (‘betrayal effect’). In addition to this, the authors assume that existing consumers perceive less favor to their company when they recognize that other companies offer price advantages to their existing consumers. Hence, the authors derive empirical evidence for a betrayal as well as a jealousy effect in their study. Therefore, existing consumers feel betrayed when they find that the company offers lower prices for other companies’ switchers. Additionally, they perceive less favor to their company when they recognize that at the same time other companies offer lower prices to their existing consumers (Feinberg, Krishna, and Zhang 2002, p. 288).

Following these findings, Grewal, Hardesty, and Iyer (2004) assess new and existing consumer reactions on status-based dynamic pricing in the tourist industry (p. 90). In particular, the authors examine consumer reactions based on the comparison to the other contrary customer status (i.e., new vs. existing customer). Therefore, they include post-purchase trust, price fairness perception as well as repurchase intention as dependent variables (DV) in their model. They find that existing consumers perceive lower price fairness as well as lower repurchase intention when they are disadvantaged relative to a new consumer than vice versa (here and in the following, Grewal, Hardesty, and Iyer 2004, p. 92). With regard to trust, they observe that trust of frequent consumers diminishes when they recognize that the trusted company processes norm violating pricing strategies (i.e., charging an existing customer more).

Moreover, Ashworth and McShane (2012) take a step backwards and research why the observation of other consumers paying a lower price has such a strong impact on price fairness perception. They define feelings of deservingness and sellers’ disrespect as descriptive variables of perceived price unfairness. The authors execute three different studies in their paper. Study one and study three consider promotional price adaptions, which are not considered as dynamic pricing per definition (see chapter 2.1). Therefore, only the findings of study two are relevant for the underlying study. The authors examine consumer reactions in relation to advantaged new consumers when purchasing a camera. Although, they do not explicitly state that the participants of the survey are existing customers, their pricing strategy can be considered as status-based dynamic pricing. Ashworth and McShane (2012) confirm their proposal and derive evidence that disadvantaged consumers perceive sellers’ disrespect and violation of equally deservingness as sources of unfairness (p. 151). Moreover, the authors state that price inequalities are considered fair when the reason for the discount is reasonable and clearly distinguishable. Darke and Dahl (2003) find that in the case of price reductions for loyal consumers, price fairness perception occurs as long as the price difference is appropriate (i.e., a discount of ten percent is considered just by the participants, but a reduction of fifty percent is too high).

Bolton, Keh, and Alba (2010) conduct four studies, whereas study one to three examine price discrimination among different consumers at the same time without declaring the reason for the price inequality (pp. 566-571). In the fourth study, the authors examine status-based dynamic pricing within different cultures. The authors consider cultural differences as crucial factors in explaining price fairness perceptions. Chinese and U.S. consumers’ price fairness perception differs when they are new consumers and are advantaged over existing consumers. Thus, U.S consumers perceive the price difference as more fair than Chinese consumers.

Lastly, Martin, Ponder, and Lueg (2009) refer in their study to the price fairness perception model of Xia, Monroe, and Cox (2004). The authors examine in offline scenarios existing and new consumer reactions to price differentiation based on the customer status. The authors consider price fairness perceptions and post customer loyalty depending on offering reasons (no reason vs. justified/ unjustified reason) for the price change. They propose that a reason for the price increase which is external to the firm (i.e., increase of supplier costs) is more justified than a reason which is internal to the firm (i.e., interest to raise profit margins) (Martin, Ponder, and Lueg 2009, p. 590). In contrast to the studies of Grewal, Hardesty, and Iyer (2004) as well as Feinberg, Krishna, and Zhang (2002), participants do not compare their higher paid price to the contrary other consumer status (i.e., comparison of an existing consumer to a new consumer and vice versa) (here and in the following, Martin, Ponder, and Lueg 2009, p. 592). The authors find that offering consumers a reason for a price increase, whether justified or unjustified, results in higher fairness perception than offering no reason. In line with Grewal, Hardesty, and Iyer (2004), they observe that existing consumers perceive higher price unfairness in the case of disadvantaged price differences compared to new consumers. However, at the same time existing consumers are more loyal after a single price increase than non-regular consumers (here and in the following, Martin, Ponder, and Lueg 2009). The authors only examined consumers’ reactions to a single price increase and assume that loyal consumers react differently when the price is increased multiple times.

Next to status-based dynamic pricing, literature concerning the price comparison process which is assumed to influence price fairness perceptions is reviewed (Xia, Monroe, and Cox 2004). Haws and Bearden (2006) lean their conceptual understanding of price fairness on Xia, Monroe, and Cox (2004) and include in their model perceived price fairness as a mediator, resulting in purchase satisfaction. Thereupon, Haws and Bearden (2006) test different types of reference transactions in three studies. Within those studies they consider the influence of price inequality observed between different sellers, consumers, and in dissimilar time spans. As a result, they observe that price discrimination between consumers have the greatest impact on price unfairness perception. With respect to this, consumers that only compare prices between different sellers and not between consumers perceive the disadvantaged price inequality as less unfair (Haws and Bearden 2006, pp. 308-309).

Gelbrich (2011) build on these findings and differentiates in her model between different types of relationship qualities to the comparative other party (neutral, positive, negative). In her study, the respondents are in an advantaged price inequality situation and pay a lower price than their reference person. The price inequity occurs in different clothing stores of the same retailer. Hence, Gelbrich (2011) examines how the participants’ reactions (i.e., emotions) vary across the different relationship qualities. She finds that consumers in an advantaged price inequality situation cannot fully enjoy their advantage when they are in a positive relationship to the comparative other (e.g., good friend, family member) (here and in the following, Gelbrich 2011, p. 220). In contrast to this, when the advantaged consumer possesses a negative relationship to the disadvantaged other, he/she experiences even more satisfaction knowing that the other person paid more. When having a neutral relationship to the disadvantaged other, the outcome is only recognized as a reference price and the advantaged customer is delighted about her/his paid price. Gelbrich (2011) requests in her conclusion for further research of the role of relationship quality in other dynamic pricing events such as status-based dynamic pricing (p. 220).

Malc, Mumel and Pisnik (2016) include relationship quality as an independent variable (IV) in their study. Thus, they assess in four different shopping scenarios the consumers’ price fairness perception across different reference transactions. Therefore, participants were either given a higher price than they paid one year ago, five years ago, paid by a stranger or a friend as reference. The authors hypothesize that relationship quality has the highest influence on price fairness perception. Although, the authors’ proposition concerning a high impact of relationship quality cannot reach the empirical statistic level, they detect a trend that price unfairness perception is higher when the consumers compare their price to a friend than to an unknown person (here and in the following, Malc, Mumel, and Pisnik 2016, p. 3696). When consumers were given a higher price than they paid five years ago, price fairness perception was high regardless of disadvantaged price inequality. Nevertheless, this is not the case for the purchase done one year ago. Furthermore, the authors derive empirical evidence that a participants’ higher income results in higher price fairness perception. They assume that consumers with a higher salary possess low price sensitivity and hence perceive price differences as more fair. Lastly, they observe that the degree of perceived price fairness impacts the severity of consumer reactions (i.e., purchase intention, terminating the relationship with the seller).

Next to the concept of status-based dynamic pricing and relationship quality, social norms are depicted as another important antecedent of price fairness perception (Xia, Monroe, and Cox 2004). Social norms are defined as shared consumers’ expectations about companies’ behavior (Heide and John 1992). In the context of pricing, social norms are rules consumers agree companies should follow in the price setting process (here and in the following, Garbarino and Maxwell 2010). The constructs of social norms and fairness are closely related and frequently social norms are considered as rules of fairness. When those rules are violated, the action is esteemed unfair (Maxwell 2002). Ashworth and McShane (2012) postulate this finding as the belief of equally deservingness. Therefore, consumers expect the same as others and paying more than other consumers clearly abuses this expectation.

Wright (2002) states that consumers’ meta-knowledge about the market and companies’ pricing techniques develop over time. Therefore, a practice that is primarily judged as unfair slowly evolves into a new norm and is accepted and rarely perceived as unfair by consumers (Kahneman, Knetsch, and Thaler 1986a). Kannan and Kopalle (2001) follow this thought and propose that different prices in the tourist industry are perceived as fair but price inequality in other industries are perceived as unfair. The authors state that this is due to consumers acceptance of dynamic pricing techniques in the tourist industry (Kannan and Kopalle 2001, p. 72). Xia, Monroe, and Cox (2004) as well as Bolton, Keh, and Alba (2010) propose but not empirically test that the consumers prior experience with dynamic pricing in certain industries is a crucial source of becoming a norm in the respective industry. Bolton, Keh, and Alba (2010) point this out as a topic that needs further research (p. 575). Consequently, judgments of fairness depend on social norms that have developed in diverse markets differently (Maxwell and Garbarino 2010; Richards, Liaukonyte, and Streletskaya 2016; Xia, Monroe, and Cox 2004).

Garbarino and Maxwell (2010) refer to the research call of Grewal, Hardesty, and Iyer (2004) who propose, but not empirically test, social norms as key factors of fairness perceptions resulting from dynamic pricing (p. 97). Therefore, Garbarino and Maxwell (2010) determine social norms as the reason for consumers’ accepting several dynamic pricing tactics while rejecting others (p. 1066). In their research, the authors relate to Maxwell and Garbarino (2010) who classify several dynamic pricing techniques as normally accepted, while detecting others as violation of existing norms. In line with Haws and Bearden (2006), Garbarino and Maxwell (2010) refine their empirical findings to the consumers’ expectation that the same retailer should offer the same price, but independent sellers are not normatively forced to do it.

Furthermore, Garbarino and Maxwell (2010) include norm violating pricing as an IV in their model and price fairness perception, trust and purchase intention as DV. They examine status-based dynamic pricing and declare it as norm violating pricing. They further propose that the participants’ strength to belief in the norm that ‘all consumers should pay the same price’ negatively moderates the relationship of status-based dynamic pricing on perceived price fairness. The authors consider ‘self/self’ price comparisons, where the participants experience the price discrimination on their own via two retailer accounts (new and existing). The authors observe that the norm violating pricing practice results in lower perceived price fairness and lower intentions to purchase at the retailer (here and in the following, Garbarino and Maxwell 2010, pp. 1069-10.70). In contrast to that, pricing practices that are not perceived as norm violation (i.e., different prices at diverse retailers) lead to comparatively higher intentions to purchase, trust, and price fairness perception. Within a norm violating pricing process, the authors confirm that a strong norm belief lowers the consumer’s price fairness perception. Lastly, Campbell (1999) finds that firms’ motives of setting prices possess a great impact in consumers’ price fairness judgments. In the case that the seller practices a pricing rule, which is considered as unfair, consumers, perceive, even when they pay the reference price, price unfairness.

The concept of trust is repeatedly stated in literature as a crucial construct in order to understand the relationship between buyers and sellers (Morgan and Hunt 1994; Sirdeshmukh, Singh, and Sabol 2002). Xia, Monroe, and Cox (2004) propose that the buyer-seller relationship (expressed in trust) plays a crucial role in price fairness judgments. The authors suggest that customers with high trust towards the company may observe a price increase as justified even when the reason for the price change is not known (Xia, Monroe, and Cox 2004). With reference to Mayer, Davis, and Schoorman (1995), trust is defined as the consumers’ willingness to take the risk of being vulnerable, based on the expectation that the seller “will perform a particular action important to the trustor, irrespective of the ability to monitor or control that other party” (p. 712).

Xia, Monroe, and Cox (2004) propose in their conceptual paper that the meaning and impact of trust on price fairness perceptions vary in different phases of a buyer-seller relationship. In the beginning of a buyer-seller relationship, the customer has no prior transaction experience with the seller and may rely on the vendor’s reputation to form initial trust. In this stage, customers are primarily concerned about the transaction itself (e.g., delivery and return policy, product quality) and might base their trust on the sellers’ competence (Campbell 1999). The competence stage reflects the consumers’ cognition that the seller is trustworthy and is able to meet its promises (Garbarino and Lee 2003, p. 500; Mayer, Davis, and Schoorman 1995, pp. 717-719; Selnes and Gønhaug 2000, p. 259). In this case, the vendors’ good reputation can buffer customers’ potential negative perceptions of price inequality. Therefore, Campbell (1999) derive empirical evidence that consumers perceive price inequality situations as more fair when the company enjoys a good reputation than for companies that possess a poor standing. McKnight, Cummings, and Chervany (1998) argue that initial trust can be high since consumers trust the vendor until they experience something unsatisfactory.

With repeated purchases, customers gain more knowledge about the seller and base their trust on previous transactions and the sellers’ benevolence (Xia, Monroe, and Cox 2004). The benevolence stage of trust constitutes the degree to which consumers perceive and evaluate the company’s willingness to be favorable to consumers (Garbarino and Lee 2003, p. 500; Selnes and Gønhaug, 2000, p. 259; Singh and Sirdeshmukh, 2000). Grewal, Hardesty, and Iyer (2004), consider benevolence trust as the key dimension of trust (p. 89). Kannan and Kopalle (2001) state in their conceptual paper that dynamic pricing strategies impact consumers’ trust towards the seller negatively. In the setting of online dynamic pricing, Garbarino and Lee (2003) split overall trust in benevolence- and competence trust and derive empirical evidence for overall trust being an additive function of both. They confirm the before-mentioned proposition of Kannan and Kopalle (2001) and find that dynamic pricing methods decrease benevolence trust and consequently overall trust. Xia, Monroe, and Cox (2004) underline this by stating that these two dimensions of trust (benevolence- and competence) are crucial for the formation of overall trust.

Garbarino and Maxwell (2010) examine in their study the impact of prior trust on the relationship of status-based dynamic pricing on price fairness perception, benevolence trust, complaint intentions as well as purchase intention. Their findings reveal that prior trust buffers negative reactions of consumers resulting from status-based dynamic pricing. Lastly, Xia, Monroe, and Cox (2004) define the ultimate stage of a buyer-seller relationship when trust is fully developed and based on identification. In this case, the relationship resists rather high obstacles and trust buffers price unfairness perception. However, this type of relationship is rare in business associations and trust rather stays at a competence or benevolence level.

Finally, Xia, Monroe, and Cox (2004, p. 2) postulate that responding consumer actions of dynamic pricing depend on the direction of price inequality. In existing research, scenarios are mostly designed as participants being in a disadvantaged price inequality situation. In this kind of setting consumers discover that they paid a higher price than a comparative reference party for the same product or service (e.g., Ashworth and McShane 2012; Garbarino and Maxwell 2010). Researchers find that perceived price unfairness results from disadvantaged price inequality (Grewal, Hardesty, and Iyer 2004) and leads to outcomes such as declining satisfaction (Haws and Bearden 2006) and switching behavior (Antón, Camarero, and Carrero 2007). In contrast to this, consumers’ perception and reaction to advantaged price inequality are less sharp (Gelbrich 2011). Whereas, it is easy for consumers to articulate what is unfair, it is difficult for them to declare what is fair (here and in the following, Xia, Monroe, and Cox 2004). Perceptions of unfairness are usually clearer, stronger, and more specific than perceptions of fairness.

Richards, Liaukonyte, and Streletskaya (2016) focus in their study on both inequality directions. Hence, they observe that advantaged consumers perceive higher purchase intention compared to disadvantaged consumers (p. 149). Bolton, Keh, and Alba (2010) expand their research and compare equal and advantaged price inequality outcomes. Similar as the findings of Richards, Liaukonyte, and Streletskaya (2016), they detect that the advantaged consumer perceives higher fairness than the disadvantaged one. Their approach additionally observes that charging the same prices leads to the highest price fairness perception and intention to repurchase (Bolton, Keh, and Alba 2010, p. 571). Repurchase intention is repeatedly stated as an important consumer outcome in dynamic pricing (Weisstein, Monroe, and Kukar-Kinney 2013). Prior studies observe a negative connection resulting from price-disadvantaged consumers on repurchase intention (Garbarino and Maxwell 2010; Grewal, Hardesty, and Iyer 2004). Weisstein, Monroe, and Kukar-Kinney (2013) examine if the negative effect of disadvantaged consumers on repurchase intention changes when the perceived transaction dissimilarity is increased through price framing (i.e., presenting price offers in different formats) (p. 2). Hence, the authors observe a positive effect of increasing transaction dissimilarity and price fairness perception as well as repurchase intention. They additionally emphasize price fairness perception being an important antecedent of repurchase intention (Weisstein, Monroe, and Kukar-Kinney 2013).

2.3 Identified Research Gap

In the previous literature review findings from different researchers concerning the impact of dynamic pricing on consumer reactions are drawn. Different antecedents of price fairness perception and consequent consumer reactions are outlined. With regard to insights drawn from the literature review, this study aims to contribute to existing dynamic pricing literature in four ways.

First, the underlying study includes status-based dynamic pricing. New and existing consumers reactions in a disadvantaged price event will be assessed. The comparison to the contrary other consumer status will be further included. Hence, the study aims to contribute to prior literature with providing a more in-depth understanding of the respective consumer outcomes (Grewal, Hardesty, and Iyer 2004).

Second, literature is scarce in investigation of the impact of relationship quality in price comparison processes. Through inclusion of the relationship quality in price comparison processes, the underlying study is first to combine status-based dynamic pricing and relationship quality. Gelbrich (2011) highlights this as a topic that requires further research (p. 220). Thus, the present study is aiming at closing the research gap of new and existing consumer reactions on dynamic pricing with a focus on relationship quality and the status of the reference party.

Third, little research is done concerning the role of prior trust for new and existing consumers in status-based dynamic pricing. By including prior trust as a moderator in the model, the underlying study contributes to prior literature concerning the buyer-seller relationship.

Fourth, in line with prior research, price fairness perception is considered as the key construct from consumer reactions to status-based dynamic pricing (Grewal, Hardesty, and Iyer 2004). The underlying study aims to provide a more in-depth understanding of price fairness perception by including moderators that have not been examined in the context of status-based dynamic pricing so far . Primarily, such moderating variables are represented by prior experience with dynamic pricing as well as relationship quality to the comparative other. Other moderators that are included in the study are price sensitivity and the consumers’ norm belief. Hence, the underlying study enhances the understanding of the price fairness construct as it was already suggested by other researchers (Campbell 1999; Weisstein, Monroe, and Kukar-Kinney 2013). Lastly, repurchase intention is included as the central consumer reaction of price fairness perceptions in the underlying study.

Table 1 summarizes the literature that was reviewed in detail and is considered as most relevant for the underlying research aim. At the end of the table the study’s focus is highlighted. By including status-based dynamic pricing (column 1), the customer status of the reference person (column 2), as well as the relationship to the comparative other consumer (column 3), the present study will deliver a comprehensive view of existing and new consumers reactions on the dynamic pricing process based on their customer status. The constructs of norm belief (column 4) and prior trust (column 5) are included in table 1, as well since they are repeatedly stated in the literature within the context of status-based dynamic pricing. Moreover, prior experience with dynamic pricing (column 6) as well as price sensitivity (column 7) are included. Lastly, repurchase intention (column 9) as a consequence of price fairness perception (column 8) will be examined in the context of the different customer status groups.

Abbildung in dieser Leseprobe nicht enthalten

Table 1: Overview of Existing Studies on Dynamic Pricing

Source: author’s own illustration.

3 Derivation of Hypotheses and Conceptual Model

The preceding introduction of the main constructs and literature review serve as a basis for the subsequent chapter. In the following, equity theory is outlined as the main underlying theory for the study. Furthermore, the theory is connected to the hypotheses development. Lastly, the conceptual model summarizes the relation of the different constructs with the belonging hypotheses.

3.1 Consumer Reactions on Dynamic Pricing

In order to observe, how new and existing consumers react to dynamic pricing practices and to reveal potential differences in their behaviors, it is essential to first set theoretical foundations for consumer-related outcomes. In the context of pricing, a key construct states the consumers’ perception of price fairness (Haws and Bearden 2006; Xia, Monroe, and Cox 2004).

Perceptions of price (un)fairness are originally explained by the theory of cognitive dissonance (here and in the following, Huppertz, Arenson, and Evans 1978, p. 250). A state of cognitive dissonance exists when a person’s cognitive elements are not consistent with each other. This state causes mental distress and consumers perceive the need to reestablish consonance by developing reasons for the inequity (McGuire 1966, p. 14). Equity theory comprises a distinct interpretation and application of cognitive dissonance theory. Since, it is proposed that equity theory serves as the most comprehensive foundation of the construct of perceived price fairness, the focus will be set on this theory in the following (Adams 1965; Ashworth and McShane 2012; Malc, Mumel, and Pisnik 2016).

Compared to the theory of cognitive dissonance, equity theory focuses more on intergroup or interpersonal relationships than on product-person relationships (Huppertz, Arenson, and Evans 1978, pp. 250-251). The idea of equity theory is that people value balance between inputs and outcomes in an exchange relationship involving two parties (here and in the following, Adams 1965). In the sense of the underlying study, the exchange relationship demonstrates the buyer-seller relationship. Moreover, equity theory comprises the concept of social comparison. Therefore, individuals compare relative to other parties their ratio of inputs in relation to their outputs from the exchange (here and in the following, Huppertz, Arenson, and Evans 1978, p. 250). Inputs are defined as the consumer’s contribution to the exchange relationship. The buyer’s contribution to the seller entitles him/her to expect rewards in return. In the scope of the present study, inputs can be represented by trust or loyalty (Darke and Dahl 2003, p. 334). Outcomes are defined as the positive or negative consequences a buyer perceives as return to their contribution. Within the pricing context, the outcome will be the price the seller offers to the buyer (Xia, Monroe, and Cox 2004, p. 2).

Therefore, equity theory distinguishes between equitable and inequitable outcome distributions (Peters and van den Bos 2008; Xia, Monroe, and Cox 2004, p. 1). On the one hand equity occurs when all parties obtain the same relation of inputs to outputs (here and in the following, Adams 1965). On the other hand, inequity occurs when the ratio of inputs/outputs is inconsistent with the input or output of the reference party. Similar as in the case of cognitive dissonance, a person who perceives inequity aims at reducing this state (here and in the following, Huppertz, Arenson, and Evans 1978, p. 250). In order to restore equity, individuals may leave the exchange module or increase inputs when they are low compared to the inputs of the reference party. With regard to comparisons to other consumers in the context of pricing, equity theory indicates that all customers should pay the same price for the same product or service (Darke and Dahl 2003). Therefore, equity theory implies that consumers’ unfairness perceptions are a consequence from consumers paying different prices for the same product (Xia, Monroe, and Cox 2004).

On the basis of equity theory, Xia, Monroe, and Cox (2004) depict in their conceptual model price comparison processes as a key antecedent to price fairness perception. Based on literature review, it can be assumed that strong reactions result from the comparison with other consumers on price fairness judgments. Thus, the direction of price inequality (i.e., advantaged vs. disadvantaged) is crucial for subsequent consumer reactions (Haws and Bearden 2006; Xia, Monroe, and Cox 2004). As the literature review has revealed, disadvantaged price inequality constitutes the highest potential of price unfairness perception (Ashworth and McShane 2012). Moreover, for companies it is a key threat that consumers observe that they have paid a higher price than other consumers (Richards, Liaukonyte, and Streletskaya 2016). In the present study the focus will therefore be on dynamic pricing resulting in disadvantaged price inequality for existing as well as for new consumers.

In order to understand the differences in price fairness perceptions among different consumers, it is crucial to receive an in-depth understanding of the concept itself. Huppertz, Arenson, and Evans (1978), as one of the first authors, show in their seminal paper the impact of price fairness perception on consumers’ willingness to purchase. When examine consumers’ price fairness perceptions, the researchers concentrate on the price difference (i.e., magnitude) between the paid price and a reference price (economic fairness) (Huppertz, Arenson, and Evans 1978, pp. 256-258). Thus, in economic theory, consumers are described as maximisers of their self-interested utility aiming at small prices (Mill 1969, pp. 203‑226).

However, Maxwell (2002) postulates economic fairness as only one segment of price fairness (p. 192). Pruitt and Carnevale (1993) propose consumers are not only self-interested utility maximisers but are also social conscious. Therefore, consumers not only judge a price’s fairness based on economic fairness but also on social fairness (i.e., price is socially accepted based on norms and rules) (Pruitt and Carnevale 1993). Consumers perceive consistent price practices as a key factor of price fairness. Hence, prices should be set in line with existing rules (i.e., community standards) to be perceived as fair (Diller 2008, p. 166). A violation of those rules will lead to price unfairness perception (Garbarino and Maxwell 2010).

In the present study, new and existing consumers’ reactions on dynamic pricing based on the customer status are examined. Setting individual prices for different customer groups is recognized as a norm violation in general (Garbarino and Maxwell 2010). However, research is scarce depending on how the different customer segments react. Especially, research in different reactions when consumers compare themselves to friends or unknown persons or other new or existing customers, has been rare.

The fairness effect will be isolated reviewed based on the different design disfavoring the existing consumer versus disfavoring the new consumer as well as with respect to the different dimensions of the other variables.

3.2 Status-based Dynamic Pricing

Equity theory suggests that fairness perceptions are developed when persons compare their own input and output ratio with the one of the comparison other (Adams 1965). Hence, any perceived difference between the consumers’ ratios of inputs and outputs regardless of disadvantaged or advantaged price inequality leads to perceived price unfairness (Malc, Mumel, and Pisnik 2016).

Consumers who contribute a higher input than other consumers feel treated unfairly by receiving the same outcome as the reference person. One type of larger input constitutes customer loyalty to the respective company (Darke and Dahl 2003). Therefore, an existing consumer who has already bought at the retailer and receives the same price as another new consumer who has not contributed to the firm’s profit yet, might feel treated unfairly. Moreover, the existing consumer may perceive even higher price unfairness when the new consumer receives a lower price (Feinberg, Krishna, and Zhang 2002, p. 279).

Due to the price difference, disadvantaged new consumers might perceive price unfairness as well when comparing with advantaged existing consumers. However, since they contribute less input to the company compared to the existing consumers, their price unfairness perception might be relatively lower than for existing consumers. They might perceive their higher price as more justified (Ashworth and McShane 2012). The hypothesis derivation is supported by the findings of Grewal, Hardesty, and Iyer (2004) who find lower price fairness perceptions for disadvantaged existing consumers compared to disadvantaged new consumers in the tourist industry. Status-based dynamic pricing is selected as an IV in the underlying study. It is presumed that existing consumers perceive higher prices relative to a new consumer as unfair. Furthermore, higher prices paid by new consumers in relation to existing customers result in perceived price unfairness as well. However, price unfairness perceptions are stronger for existing customers than for new customers:

H1: Higher prices paid by existing customers relative to new customers trigger stronger negative price fairness judgments than higher prices paid by new customers relative to existing customers.

3.3 Influence Factors on Price Fairness Perception

In the following, several moderating variables are introduced that are proposed to impact the relationship of status-based dynamic pricing on price fairness perceptions. The moderating factors are categorized in consumers’ price comparison process with focus on the relationship quality to the reference person (3.3.1), the buyer-seller-relationship that is expressed in prior trust (3.3.2), the consumers’ individual characteristics (i.e., norm belief and price sensitivity) (3.3.3) as well as the consumers’ prior experience with dynamic pricing (3.3.4). The five moderators are deducted from the literature review and aim to provide a more in-depth comprehension of consumers’ price fairness perception.

3.3.1 Relationship Quality to the Reference Person

Price comparison processes are viewed as a critical part in price fairness judgments (Xia, Monroe, and Cox 2004). Haws and Bearden (2006) statistically confirm that price differences between consumers lead to the lowest fairness perception. Moreover, due to the digitalization comparisons between consumers are increasing and become more relevant for companies to consider (Garbarino and Lee 2003; Weisstein, Monroe, and Kukar-Kinney 2013).

In particular, the relationship quality to the comparative other plays a crucial part in that process (Tesser and Campbell 1982). Most of recent studies concerning dynamic pricing only consider a neutral person as a reference party (here and in the following, Peters and van den Bos 2008). However, in everyday life it is more common for consumers to exchange information with people they know than with unknown others. Hence, the underlying study distinguishes two types of relationships to the reference person (i.e., positive or neutral).

On the one hand, consumers who are in a neutral relationship do not possess any social ties and hardly know each other (Brandstätter 2000; Gelbrich 2011). With regard to equity theory, Walster, Berscheid, and Walster (1973) argue that people are in general selfish. This line of argumentation states that consumers only consider their own goal achievement and do not take the other parties’ objectives into account (Clark 1984; Gruder 1971). This behavior can be traced back to a competitive motive that surpassing others strengthens the own self-esteem (Aspinwall and Taylor 1993). Following this thought, in the context of pricing, consumers therefore compare their own outcome (i.e., price) relative to the other party’s outcome to form fairness judgments (here and in the following, Gelbrich 2011, p. 209). The other consumer’s price solely presents a reference point for evaluating individual outcome achievement. Hence, any disadvantaged price difference compared to a reference party would be judged as unfair.

On the other hand, consumers in a positive relationship are considered to have strong social ties and may be represented by friends or family (Gelbrich 2011, p. 209). This kind of relationship is based on mutual understanding and support (Clark and Mills 1993). Therefore, extended research within social comparisons shows that in this case consumers not only consider their own goal achievement but also the other consumer’s outcome (in the present study the paid price) in fairness judgments (Clark 1984). It is argued that when consumers are opposed to friends, they do not only consider their outcome but are also concerned about the outcome of their friends when judging fairness (Clark and Mills 1979). Hence, the relationship quality between the consumer A and the counterpart B might influence consumer reactions when opposed to price inequality (Gelbrich 2011).

With regard to the reviewed literature, findings of Gelbrich (2011) reveal that consumers in advantaged price inequality could not fully enjoy the lower price when he/she is in a positive relationship to the comparative other. The unfairness perception resulting from the other persons’ disadvantaged price inequality also influences the individual feelings about the price (Gelbrich 2011, p. 209). Therefore, relationship quality is included as a moderator in the underlying conceptual model. It is assumed that a comparison to an advantaged friend weakens the negative effect of the price discrimination more than a comparison to an unknown person:

H2: A positive relationship quality weakens the negative effect of status-based price differentiation on price fairness judgments more than a neutral relationship quality.

3.3.2 Prior Trust

Consumers’ prior trust in the company has been depicted as another important moderator impacting the relationship of status-based dynamic pricing on price fairness perception. Research has proven that fairness and trust are highly interrelated (Colquitt 2001). Based on literature review, buyer’s prior trust can mitigate the negative effect of dynamic pricing on price fairness perception (here and in the following, Campbell 1999). Thus, consumers infer more positive motives for a price increase when they have trust in the firm. Hence, they accept price increases more than consumers that possess low or no trust in the seller (Bolton, Kannan, and Bramlett 2000; Hess, Ganesan, and Klein 2003). Findings of Garbarino and Maxwell (2010) confirm that consumers with high prior trust perceive higher price fairness in disadvantaged price inequality than consumers with lower prior trust. Therefore, high initial trust is depicted as a moderator in the underlying study and is proposed to weaken the negative effect of status-based dynamic pricing on price fairness perceptions:

H3a: High prior trust towards the retailer buffers the negative effect of status-based price differentiation on price fairness judgments more than low prior trust.

3.3.3 Individual Characteristics

In the following, hypotheses for the consumers’ norm belief and the individual price sensitivity are formulated. The belief in social norms and the individuals’ price sensitivity are further indicators why differences in price fairness perceptions among different consumers might occur although they are in the same disadvantaged price inequality situation.

Norm Belief

Within the scope of pricing, social norms are the unwritten rules a society states a company should follow in the price setting process (Garbarino and Maxwell 2010, p. 1067). Xia, Monroe, and Cox (2004) include in their price fairness framework social norms as a crucial antecedent of price fairness perceptions. Thus, consumers perceive a price as more fair when the unwritten rules of price setting are followed (Maxwell 2002). In contrast to that, breaking those social norms leads to price unfairness perception (Grewal, Hardesty, and Iyer 2004).

In the case of the present study, consumers’ reactions on status-based dynamic pricing are examined. According to literature, consumers perceive this kind of price discrimination as a norm-breaking event that generally leads to price unfairness perception (here and in the following, Garbarino and Maxwell 2010). However, it is personal assessment whether individuals believe that prices should be the same for all customer groups or not. Moreover, every person might have a stronger or weaker belief in this kind of norm. With regard to equity theory, Adams (1965) proposes that the intensity of consumers’ reactions on a norm-breaking event varies with the extent of inequity (p. 283). Garbarino and Maxwell (2010) test this idea and derive empirical evidence that the stronger the consumer’s norm belief is, the more price unfairness perception resulting from norm-violating price increases occurs. Therefore, the strength of norm belief is included as a moderator in the underlying conceptual model. It is proposed that a strong norm belief intensifies the negative effect of status-based price differentiation on price fairness perception compared to a weak norm belief:

H3b: A strong norm belief intensifies the negative effect of status-based price differentiation on price fairness judgments more than a low norm belief.

Price Sensitivity

Next to norm belief, price sensitivity is depicted as another individual characteristic assumed to explain differences in consumers’ price fairness perceptions. Prior researches have revealed that an individuals’ price sensitivity is appropriate to explain individual responses to a price increase (Bucklin, Gupta, and Han 1995; Wakefield and Inman 2003, p. 201). The construct is defined as the degree of an individuals’ perception and reaction to differences in prices (Monroe 1979, pp. 40-41; Wakefield and Inman 2003, pp. 201-202). Monroe (1973) states that price sensitivity reflects consumers’ subjective reactions to price inequality situations. Thus, price sensitivity is included as a moderator in the present study. Price sensitivity is proposed to impact the relationship of status-based dynamic pricing on price fairness perception. It is assumed that consumers with high price sensitivity perceive higher price unfairness than consumers with low price sensitivity in the same disadvantaged situation:

H3c: A high price sensitivity intensifies the negative effect of status-based price differentiation on price fairness judgments more than low price sensitivity.

3.3.4 Prior Experience

Prior literature already implied that pricing norms can be established over time. Therefore, pricing techniques which are first perceived as unfair, can develop into a new pricing norm that is no longer perceived as unfair (Kahneman, Knetsch, and Thaler 1986b, p. 740). Consumers begin to expect dynamically adapted prices and consequently perceive them as less unfair (Kannan and Kopalle 2001, p. 72). According to Bolton, Keh, and Alba (2010), pricing techniques will be more accepted with increasing practice. Subsequently, the consumers’ experience with dynamic pricing grows and the pricing practice might be perceived as more fair with frequently usage (Fassnacht and Mahadevan 2010). Bolton, Keh, and Alba (2010) depict the influence of prior experiences with dynamic pricing as a topic that requires further research (p. 575).

Therefore, prior experience with dynamic pricing is depicted as the fifth moderator assumed to influence the relationship between status-based dynamic pricing and price fairness perception. Prior experience is defined as a particular situation faced by a consumer (e.g., Chiou and Wan 2007). According to Bagozzi, Gopinath, and Nyer (1999), emotions that are evoked in certain purchase situations are strong sources for further consumer outcomes. A certain prior experience with dynamic pricing might therefore impact the consumer’s price fairness perception when observing another dynamic pricing situation (based on Bolton, Kannan, and Bramlett 2000). Bolton, Kannan, and Bramlett (2000) examine prior experience within prior price- and service experiences of a company. However, to the best of the author’s knowledge this research paper is the first to include prior experience within a dynamic pricing context. It is assumed that a positive experience with dynamic pricing will weaken price unfairness perceptions resulting from another dynamic pricing event since the customer was advantaged before:

H3d: A positive prior experience with dynamic pricing weakens the negative effect of status-based price differentiation on price fairness judgments more than a negative prior experience.

3.4 Consumer Reactions on Price Fairness Perception

With reference to prior research, perceived price (un-) fairness is closely linked to consumer-related outcomes (here and in the following, Weisstein, Monroe, and Kukar-Kinney 2013). Considering price fairness perceptions, subsequent consumer behaviors are constantly positive across several studies. Such outcomes can be expressed in price acceptance (Lichtenstein, Bloch, and Black 1988), consumer satisfaction in general (Oliver and Swan 1989a, 1989b; Xia, Monroe, and Cox 2004), purchase satisfaction (Haws and Bearden 2006), and (re-) purchase intentions (Campbell 1999). Repurchase intention is repeatedly stated as being a key consequence from several other consumer outcomes such as purchase satisfaction (Anderson and Sullivan 1993). For managers, it is of high interest to assess the consumers’ intention to repurchase in the future since it can be an important predictor for future financial performance of the company (e.g., Morgan and Rego 2006). With regard to marketing literature, price fairness perception is highlighted as an important antecedent of repurchase intention and a positive relationship is observed (e.g., Weisstein, Monroe, and Kukar-Kinney 2013).

The examination of repurchase intention as a DV is considered as meaningful in the present study since the participants across different scenarios might react different on the dynamic pricing outcome. On the one hand, the disadvantaged existing consumers might perceive high price unfairness resulting in low levels of repurchase intention. They might feel betrayed by their trusted seller and do not consider buying at the same seller again. However, on the other hand, new disadvantaged consumers might perceive lower levels of price unfairness (see H1). Therefore, based on equity theory, it is conceivable that new consumers try to restore equity by increasing their inputs when their inputs are lower compared to the other party (i.e., existing customers) (Huppertz, Arenson, and Evans 1978, p. 250). Thus, new customers might consider repurchasing at the seller to receive a status as an existing customer as well. In order to measure this train of thought, price fairness perception is included as a mediator between status-based dynamic pricing and repurchase intention in the conceptual model. Therefore, it is hypothesized that consumers’ price fairness perception mediates the relationship of status-based dynamic pricing and repurchase intention positively:

H4: Perceived price fairness positively mediates the relationship between status-based dynamic pricing and repurchase intention.

3.5 Control Variable

With respect to the reviewed literature, the variable income is included as a control variable in the conceptual model. Personal income has shown to influence consumers’ perceived price fairness significantly (Malc, Mumel, and Pisnik 2016). Therefore, consumers with higher personal income might perceive price differences as more fair than consumers with a lower salary. Moreover, consumers with a high salary might react more reserved to price discrimination as people with low salary (Malc, Mumel, and Pisnik 2016, p. 3696).

3.6 Conceptual Model

Figure 2 illustrates the present study’s conceptual model which consists of the derived hypotheses and their interactions. The model will serve as guidance for the following empirical research. In order to facilitate the reading of the model, the arrows illustrate the assumed direction of the effects. The signs next to the hypotheses demonstrate the expected positive or negative effect.

The figure shows the dynamic pricing technique based on the consumer status on the left side. Status-based dynamic pricing is proposed to impact consumers’ price fairness perception negatively. Whereas it is propositioned that existing consumers perceive higher price unfairness perceptions than new consumers in a disadvantaged dynamic pricing event (H1). Moreover, several moderators are supposed to impact the relationship of status-based dynamic pricing on price fairness perceptions. Firstly, the relationship quality to the reference party is proposed to influence price fairness perceptions positively (H2). Prior trust to the company (H3a) is assumed to buffer the negative effect of status-based dynamic pricing on price fairness perceptions. Further, norm belief (H3b) and price sensitivity (H3c) are assumed to impact the relationship of the IV on the mediator negatively. Prior experience with dynamic pricing (H3d) is further proposed to influence the relationship positively. Lastly, the consumers’ price fairness perception is supposed to impact repurchase intention positively (H4). The control variable income is depicted and pointed out in the bottom right corner.

Abbildung in dieser Leseprobe nicht enthalten

Figure 2: Conceptual Model

Source: author’s own illustration

4 Design of Empirical Study

The following chapter constitutes the design of the empirical study used to assess the hypotheses that are summarized in the conceptual model. In the underlying study three variables with two characteristics each will be manipulated (i.e., prior trust, status-based dynamic pricing, relationship quality). Therefore, the study applies 2 x 2 x 2 (status-based dynamic pricing: existing, new x relationship quality: positive, neutral x prior trust: high, low) between subjects-design.

4.1 Survey Method

In order to measure the different hypotheses within eight scenarios, a substantial number of participants has to be reached. For that purpose, the design of an online survey is depicted. Web-based questionnaires can be assessed as very reliable in reaching a great number of participants in a short period of time (1.5 weeks). The survey method is considered appropriate as it is simple to realize with regard to time and cost (Samuel Craig, and Douglas 2001, pp. 88-89). However, web-based surveys exclude those potential participants who do not have Internet access or reject transferring personal data via the Internet (Hudson et al. 2004, p. 238). Nevertheless, those kinds of constraints are negligible within the scope of the underlying study. The content of the survey is only relevant for participants that possess Internet access and occasionally purchase online. Therefore, an online survey via Qualtrics was designed. The study was structured into four main sections that are outlined in the following chapters.

4.2 Survey Scenarios

There are several crucial components in the scope of dynamic pricing that impact consumers’ price fairness perception. In order to assure that differences in price fairness perception result from the variables applied in the survey scenarios, other additional variables need to be constant across all scenarios. This process is essential to increase internal validity of the results (Bortz 1999, pp. 8-9).

First of all, the depicted industry in which price inequality appears, matters. Dynamic pricing is already broadly applied in the hotel or airline industry (Kannan and Kopalle 2001). Price unfairness perception might be very low since participants are used to price adaptions in this industry. Therefore, for the underlying study another industry is depicted. Dynamic pricing in the electronic products market is gaining in application for practitioners and in attention for news and customers (Álvarez 2016; Brand 2013). In order to ensure the relevance of the study and to provide valuable insights for managers, the focus is set on dynamic pricing of electronic products for online retailers (here and in the following, Brand 2013; N.N. 2015). A television is depicted as the product of interest in the scenarios since it is a product all age groups and genders can identify with. Moreover, it is a representative product that is often affected by dynamic pricing in practice.

Secondly, the condition for price comparisons is crucial. With regard to the definition of dynamic pricing, price comparisons must concern the same product at the same retailer (Xia, Monroe, and Cox 2004). Therefore, price comparisons between two stores are excluded. Due to the manipulation of the variable relationship quality, the participant will either draw price comparisons to a person he/she has a neutral or a positive relationship with. On the one hand, the reference person, the participant has a neutral relationship with, is represented by a stranger. On the other hand, friends are selected as the reference person the consumer has a positive relationship with. Aside from this being one of the most common manipulation form when communal versus neutral relationships are compared (e.g., Clark and Mills 1979; Clark and Mills 1993), it is assumed that participants can best identify themselves with friends assuming that they share common interests. At the same time the friend or stranger possesses the contrary customer status as the participant. As already revealed in the hypothesis derivation, the underlying study focuses on disadvantaged price inequality. Hence, the participant (either new or existing customer) is disadvantaged compared to a friend or stranger who is an existing or new customer.

Third of all, the level of price inequity between consumers possesses a crucial role on the consequent price fairness perception (Xia, Monroe, and Cox 2004). According to literature, Blattberg, Briesch, and Fox (1995) suggest utilizing a constant price discount in order to compare the results independent of differences in price discounts. Moreover, small price increases (e.g., 10%) might be more likely accepted by consumers and thus perceived as fair (Bolton, Kannan, and Bramlett 2000; Darke and Dahl 2003; Hess, Ganesan, and Klein 2003). Therefore, Blattberg, Briesch, and Fox (1995) recommend consulting a 20% discount (p. G130). Diller (2008, p. 165) as well as Haws and Bearden (2006, p. 306) consider a 20% price inequality as a meaningful threshold as well. Therefore, in the underlying study a 20% price difference is applied across all scenarios. To sum it up, the disadvantaged price inequality of 20% price difference between consumers for electronic products (i.e., a television) will be constant across all scenarios. Therefore, the assumed differences in price fairness perceptions can mainly traced to the three manipulated variables illustrated in the scenarios (i.e., status-based dynamic pricing, relationship quality, prior trust).

In order to test hypotheses H1 to H3a, eight scenarios have to be designed. Therefore, each scenario possesses one characteristic of the three manipulated variables status-based dynamic pricing, relationship quality, prior trust. In the following, the manipulation of the three variables is described in more detail.

First of all, in order to assess whether existing or new consumers perceive less price unfairness perception, the participants in all scenarios demonstrate either an existing or a new customer. The participants are transferred in the situation to purchase a television at the online store ‘elektrowelt.de’. In order to not distract the participants, there is no emphasis on the specific model or brand of the television. The online store ‘elektrowelt.de’ is fictional. An existing online retailer might have implied risks of participants’ positive/negative connotations towards the retailer which might influence their price fairness perception. New consumers are expressed in the scenario as the consumers who purchase the first time at the retailer. Existing customers are those who have already purchased at the retailer before. The scenario development concerning new and existing consumers is slightly adapted from Grewal, Hardesty, and Iyer (2004).

Secondly, the variable prior trust in the retailer is manipulated as well. Since, the online-retailer ‘elektrowelt.de’ does not exist it is not possible for the participants to form prior trust towards the retailer. Therefore, the direction of prior trust (i.e., high, low) is given in the scenario.

Lastly, the manipulation of relationship quality to the reference person is implemented by a message of the participant’s ‘friend Anna’ or by a post of an ‘unknown person’ on the Internet. In each of the cases Anna or the unknown person represents a new or an existing consumer (i.e., the opposite consumer status compared to the participant’s consumer status). Therefore, the other person announces that he/she receives a lower price (i.e., 300 Euro instead of 375 Euro) because of his/her customer status (new vs. existing) at ‘elektrowelt.de’. For the communication with the friend or stranger different communication forms were depicted. For the communication with the participant’s friend, a WhatsApp Message was generated (see Figure 3). The instant messaging application is found to be a private and intimate way of communicating with friends (Karapanos, Teixeira, and Gouveia 2016). Additionally, it is currently the most popular way of communicating with friends and family (Hackenbruch 2017). Therefore, the WhatsApp message of the friend was created via ‘fakewhats.com’ and was further edited with Adobe Photoshop using the design of a smartphone. A small profile picture as well as a name for the reference person (Anna) was edited. With those adjustments, the communication to the participants’ friend should illustrate a personal impression.

[...]


[1] Simon-Kucher & Partners interviews thousands of companies every two years about their pricing strategies, price pressure and competition. This study is unique in its design. A total of 2,186 companies from more than 40 countries and 25 industries participated in the Pricing Study 2016, including 758 from Germany, Austria and Switzerland.

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Details

Title
Dynamic Pricing in Online Business. What Pricing Strategy Should Be Used in Digital Business?
Year
2018
Pages
146
Catalog Number
V425082
ISBN (eBook)
9783960953289
ISBN (Book)
9783960953296
Language
English
Notes
Diese Arbeit wurde mit einer 1,7 bewertet. Insbesondere wurde das breit angelegte konzeptionelle Modell sehr gelobt.
Tags
Dynamic Pricing, Status-based Dynamic Pricing, Consumer Dissatisfaction, Price Transparency, New Customers, Regular Customers
Quote paper
Anonymous, 2018, Dynamic Pricing in Online Business. What Pricing Strategy Should Be Used in Digital Business?, Munich, GRIN Verlag, https://www.grin.com/document/425082

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