Accepted Manuscript

The ugly side of customer management – Consumer reactions tofirm-initiated contract terminations

Anke Lepthien, Dominik Papies, Michel Clement, ValentynaMelnyk

PII: S0167-8116(17)30006-XDOI: doi: 10.1016/j.ijresmar.2017.02.001Reference: IJRM 1205

To appear in: International Journal of Research in Marketing

Received date: 6 May 2014

Please cite this article as: Anke Lepthien, Dominik Papies, Michel Clement, ValentynaMelnyk , The ugly side of customer management – Consumer reactions to firm-initiatedcontract terminations. The address for the corresponding author was captured as affiliationfor all authors. Please check if appropriate. Ijrm(2017), doi: 10.1016/j.ijresmar.2017.02.001

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The Ugly Side of Customer Management –

Consumer Reactions to Firm-initiated Contract Terminations

Anke Lepthien

Institute of Marketing, University of Hamburg, Germany

& Institute of Human Resource Management and Organizations

Hamburg University of Technology, Germany

Dominik Papies

School of Business and Economics, University of Tuebingen, Germany

Michel Clement

Institute of Marketing, University of Hamburg, Germany

Valentyna Melnyk

School of Communication, Journalism and Marketing, Massey University, New Zealand

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ARTICLE INFO

Article history:

First received on May 6, 2014 and was under review for 6½ months.

Senior Editor: Don R. Lehmann

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Contact Information:

Anke Lepthien is PhD-Student at the University of Hamburg, Institute of Marketing, Moorweidenstr. 18, 20148 Hamburg, Germany, e-mail: anke.lepthien@uni-hamburg.de

Dominik Papies is Professor of Marketing at the University of Tuebingen School of Business and Economics, Nauklerstr. 47, 72074 Tuebingen, Germany, e-mail: dominik.papies@uni-tuebingen.de

Michel Clement is Professor of Marketing and Media at the University of Hamburg, Institute of Marketing & Research Center for Media and Communication, Moorweidenstr. 18, 20148 Hamburg, Germany, e-mail: michel.clement@uni-hamburg.de

Valentyna Melnyk is Professor of Marketing and Consumer Research at Massey University, School of Communication, Journalism & Marketing, Private Bag 102904, Auckland 0745, New Zealand, e-mail: V.Melnyk@massey.ac.nz

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The Ugly Side of Customer Management –

Consumer Reactions to Firm-initiated Contract Terminations

Abstract

Many firms apply “customer demarketing” strategies and dismiss unprofitable

customers. However, empirical research on the consequences of dismissing customers is

scarce. We address this research gap and develop and empirically analyze a theoretical

framework of consumer reactions to customer demarketing based on the theory of dual

entitlement. We conduct seven experiments in which we identify the main effect of observing

customer demarketing and analyze several moderating and mediating factors. In addition, we

use a field study to illustrate the consequences of experiencing a contract termination in a

real-world setting, in which a firm terminated contracts for more than 10,000 customers. The

results show that consumers disapprove of customer demarketing, regardless of whether they

experience it themselves or only observe it, regardless of the responsibility for the cause of

the contract termination, and regardless of the social proximity to the dismissed customers.

The effect is, however, somewhat weaker if customer demarketing is perceived to be a

common occurrence, if alternative offers are made, and if the financial cause is framed as a

loss. Furthermore, firms can dampen the negative effect of customer demarketing by offering

substantial monetary compensation to dismissed customers. We identify perceived fairness of

the firm’s behavior as the underlying process that causes the negative effects of customer

demarketing, and this also holds when we control for potential alternative mediators (i.e.,

warmth and competence perceptions).

Keywords: Customer Management, Customer Demarketing, Fairness, CLV

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1. Introduction

Unprofitable customers account for up to 50% of bank customers and up to 40% of mail-

order company customers (e.g., Ang & Taylor 2005). Shah et al. (2012) find that up to 35% of

customers who cross-buy are unprofitable, accounting for up to 88% of a firm’s total loss from its

customers. As we embrace the notion that firms should focus their marketing activities on the

most profitable customer segments (e.g., Homburg, Droll, & Totzek 2008, Rust, Kumar, &

Venkatesan 2011), what should they do about unprofitable customers – particularly when all

attempts to turn them into profitable customers fail?

The business press provides frequent evidence of companies that dismissed unprofitable

customers by terminating their contracts (with or without an option of a new (inferior) contract).

The literature refers to this strategy as “customer demarketing” (Kotler & Levy 1971, Bradley &

Blythe 2014) or “customer abandonment” (Haenlein, Kaplan, & Schoder 2006).1 Table 1 contains

a sample of companies that have terminated contracts with customers, and we provide an

example for a termination letter in Appendix B.

TABLE 1 about here

Although Kotler and Levy introduced the concept of selective demarketing to the

literature as early as 1971, academic research on customer demarketing remains scarce. Lehmann

(1999, p. 15) encourages researchers to “consider the impact of customer deletion decisions and

efforts, rather than just attempts to attract customers. (Because 80% to 90% of customers are

unprofitable, this is a potentially major area)”. However, we find very little research that

empirically addresses the consequences of selective demarketing (see Table 2; a notable

exception is the research by Haenlein & Kaplan (2010, 2011, 2012), who study the consumer

1 We will use the term “customer demarketing” throughout the manuscript.

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response to different customer demarketing strategies). One reason for this dearth of research

may be managers’ unwillingness to share data on this delicate topic.

Hence, despite its frequent occurrence, the consequences of customer demarketing are not

well understood. Formally terminating customer contracts not only is likely to adversely affect

the brand attitude of these customers but also may spill over to other current or potential

customers. This effect will be amplified if news about the contract terminations is picked up by

the press or by multipliers on the Internet (e.g., blogs, forums), as Table 1 demonstrates. Hence,

dismissing unprofitable customers is likely to reduce losses, but it will also negatively affect the

brand attitude of individuals who are subject to customer demarketing actions.

We identify and address two major research gaps. First, it is not clear to what extent

customer demarketing negatively affects individuals who experience or merely observe a contract

termination of somebody else. Second, it is largely unknown what companies should or should

not do to decrease those negative effects. We base our analysis and framework on the theory of

dual entitlement (Kahneman, Knetsch, & Thaler 1986), which posits that consumers evaluate the

adequacy or fairness of a transaction that they observe. Across eight studies (seven experiments

and one large field study), we empirically investigate the effect of customer demarketing on two

key outcome variables, one of which is attitudinal (i.e., brand attitude), while the other is

behavioral (i.e., WoM). Based on the dual entitlement framework, we study the role of perceived

fairness as the primary underlying process. Further, we assess two alternative mediators (i.e.,

warmth and competence perceptions) and investigate several moderators that may decrease the

negative effect of customer demarketing on the outcomes. In addition, our field study provides a

unique opportunity to study the effect of experiencing customer demarketing in cooperation with

a leading service provider from the consumer utilities sector (electricity, natural gas, water) in

Europe that canceled contracts with a large number of customers who were identified as

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unprofitable because of changes in the regulatory framework. This setting allows us to illustrate

the effect of customer demarketing on key variables in a real-world setting.

Our study makes several contributions to the literature. First, we demonstrate the extent to

which consumers respond negatively to customer demarketing. Second, as Table 2 reveals, our

knowledge of the underlying psychological mechanisms that cause the negative response (i.e., its

potential mediators) is limited. The only study that has addressed this, Haenlein & Kaplan

(2010), proposes the attitudinal response to customer demarketing as a mediator. We build on

these findings and shed further light on the underlying processes of consumers’ response (both

attitudinal and behavioral) to customer demarketing by investigating a new mediator (perceived

fairness). We also assess whether perceived fairness serves as a mediator if we control for

potential alternative mediators (i.e., warmth and competence perceptions). Third, we make an

important academic and managerial contribution by investigating moderating variables that can

or cannot decrease the effect of customer demarketing. Importantly, for the managerial

contribution, most of those moderators can be actively utilized by the firm. Finally, we

complement the experimental studies with a field study in which we observe responses of

customers whose contracts were terminated in a real customer demarketing setting.

2. Theory and Hypotheses Development

2.1 Literature Overview

Most research in the customer demarketing context is of a conceptual or theoretical

nature. Mittal, Sarkees, & Murshed (2008) suggest implementation strategies of customer

demarketing, and Shin, Sudhir, & Yoon (2012) and Pazgal, Sobermann, & Thomadsen (2013)

underline that dismissing customers can increase profit. These papers are valuable starting points

to empirically investigate the effects of customer demarketing. They are complemented by

simulation studies (e.g., Haenlein, Kaplan, & Schoder 2006) that analyze the consequences of

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including customer demarketing as an option in CLV calculation. Other research has addressed

the boundary conditions of the profitability of demarketing, e.g., Kim & Lee (2007) show in an

analytical model that demarketing in cases of strong network externalities is less likely to be

profitable. Conversely, non-paying (unprofitable) customers may be needed to increase the size

of a network to attract paying customers (Gupta & Mela 2008).

Empirical research on the topic of customer demarketing is scarce. A notable exception is

research by Haenlein & Kaplan (2010, 2011, 2012), who empirically analyze consumer reactions

to different ways of customer demarketing by assessing the effects of different demarketing

strategies relative to one another. However, to assess the extent to which customer demarketing is

damaging for the firm and whether potential moderators may be able to offset its negative effects,

it is important to know the effect of demarketing compared to no demarketing, and this is the

void that our research addresses. We summarize all studies we are aware of in Table 2.

TABLE 2 about here

2.2 Main Effect and Mediators

We argue that the unilateral termination of a contract by a firm (even if the firm

simultaneously offers the option to enter a new contract) is likely to be perceived as unjust or

unfair. The dual entitlement principle (Kahneman, Knetsch, & Thaler 1986) suggests that

consumers have a notion about the adequate outcome of a transaction they observe between a

firm and its customers. A firm is entitled to a “reference profit”, while consumers are entitled to

an adequate “reference price”. Societies develop “a consensus as to what constitutes an equitable

relationship” (Walster, Berscheid, & Walster 1973, p.152). Indeed, it has been shown in several

domains that fairness evaluations (e.g., Kahneman, Knetsch, & Thaler 1986) are an important

ingredient in human decision making (e.g., price fairness; Bolton, Keh, & Alba 2010), service

recovery (Patterson, Cowley, & Prasongsukarn 2006), or inter-firm relationships (Samaha,

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Palmatier, & Dant 2011)), which may be even more important than a financial gain (Turillo et al.

2002). Consumers make judgments about the fairness of the transactions they observe and will

view deviations from the reference entitlements as unfair. Thus, a firm terminating contracts with

its customers will be perceived as unfair because these actions are inconsistent with how a firm is

supposed to treat its customers (Porath, MacInnis, & Folkes 2011). When individuals perceive

treatment as unjust or unfair, they will penalize the party who violates some consensual fairness

standard, even if this penalization is associated with costs (Turillo et al. 2002). Hence, customer

demarketing is likely to result in attitudinal (e.g., negative emotions) and behavioral reactions

(e.g., looking at means of “revenge” via negative WoM or consumer brand sabotage) towards the

firm (Bolton, Warlop, & Alba 2003; Haenlein & Kaplan 2010; Kähr et al. 2016).

Importantly, consumers do not need to be directly involved in a relationship with a

company to react to customer demarketing. Based on social identification theory (Tajfel &

Turner 1986), consumers who observe a relationship between a firm and its customers are likely

to identify with one of the two parties involved, more likely with the customers. This

identification is strengthened by direct or vicarious experiences of conflicts or failures of the

party with which one can identify (Ashforth & Mael 1989).

Based on these considerations, we expect both customers experiencing demarketing and

those merely observing it to show similar attitudinal responses to customer demarketing. We

expect the behavioral reaction to be in the same direction but weaker in magnitude among

observing customers because they are less personally involved and are typically not faced with

any direct decisions with respect to the firm (Haenlein & Kaplan 2010).

Our framework (Figure 1) suggests that because of the unfairness perceptions, consumers

who observe or experience customer demarketing will adjust their attitude to the brand.

Furthermore, a consumer may communicate her disapproval to other – active or potential –

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customers, thus initiating negative word-of-mouth (negative WoM; Anderson 1998). Both brand

attitude and intentions to engage in negative WoM have been identified as predictors of the

respective behaviors and will serve as dependent variables in our analyses. In sum, we expect that

customer demarketing will have negative effects on brand attitude and negative WoM intentions

with regard to the brand that dismisses the customers, and we expect that these effects are

mediated by fairness perceptions. We therefore hypothesize:

H1: Customer demarketing (a) decreases brand attitude and (b) increases intentions to

engage in negative WoM.

H2: The effect of customer demarketing on (a) brand attitude and (b) intentions to engage

in negative WoM is mediated by the perceived fairness of the firm’s behavior.

FIGURE 1 about here

It is possible that perceived fairness may not be the only channel through which customer

demarketing transmits its harmful consequences. Recent evidence suggests that consumers also

perceive brands and companies along the dimensions of warmth and competence (Aaker et al.

2012; Kervyn et al. 2012). Specifically, the warmth dimension reflects traits related to perceived

intent, including trustworthiness and morality, while competence includes traits related to ability

and efficacy (Aaker et al. 2010). Importantly, warmth and competence perceptions are automatic

and may not only affect fairness perceptions (which are more cognitive in nature, e.g., Xia,

Monroe, & Cox 2004) but also independently increase both attitude and behavioral intentions

towards brands (Kervyn et al. 2012; Hess & Melnyk 2016). We argue that dismissing customers

to increase profitability may decrease warmth perceptions because “self-profit” at the expense of

“other-profit” falls into the morality domain, which is the fundamental component of warmth

judgment (Wojciszke et al. 1998). With respect to competence, although “self-profit” tendencies

(i.e., the decision to dismiss some customers to ensure profitability of the firm) may, ceteris

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paribus, increase competence perceptions, the initial capabilities of the firm will also influence

competence judgments (Cuddy et al. 2008). That is, consumers may perceive the firm as

incompetent by attracting unprofitable customers in the first place, which implies a negative

effect of customer demarketing on perceived competence. In sum, because warmth and

competence perceptions increase both attitude and behavioral intentions towards brands (Kervyn

et al. 2012; Hess & Melnyk 2016), both constructs may mediate the impact of customer

demarketing on our dependent variables:

H3: The effect of customer demarketing on (a) brand attitude and (b) intentions to engage

in negative WoM is mediated by the perception of the firm’s warmth.

H4: The effect of customer demarketing on (a) brand attitude and (b) intentions to engage

in negative WoM is mediated by the perception of the firm’s competence.

It is important to note that previous research has argued that by being automatic and

difficult to control (Cuddy et al. 2008), warmth and competence perceptions inform other, more

cognitive judgments, including fairness (Kervyn et al. 2012). Although we do not propose formal

hypotheses, we also expect that warmth and competence have a direct effect on fairness.

2.3 Moderators

To increase firms’ understanding of how to mitigate negative effects of customer

demarketing, we investigate several moderators of the effect of customer demarketing on the

outcome variables. One factor relates to the question of who is responsible for the cause of the

customer demarketing action (e.g., see Gelbrich (2010) for the case of service failures). In

making causal attributions, the most important judgment concerns the locus of causality (internal

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or external factors) (Xenikou, Furnham & McCarrey 1997).2 Hence, if a consumer observes a

contract termination by a firm, she will seek to identify the party that is responsible for this event

and can be held accountable (McColl-Kennedy & Sparks 2003). Generally, in the case of

negative events, consumers are more likely to make internal attributions, i.e., place blame on the

firm, as opposed to external attributions, i.e., place blame on a situation or other customers

(Tomlinson & Mayer 2009). However, if a consumer engages in fraudulent or unethical behavior

(e.g., makes excessive use of mobile downloads that reduce the available bandwidth for other

users in the area), other consumers are less likely to disapprove of customer demarketing because

they will be more likely to make an external attribution of the firm’s need to terminate the

contract. If, in contrast, the firm bears full responsibility for the customer demarketing because,

e.g., the unprofitability of certain customers is due to the firm’s cost structure, consumers may

strongly disapprove of the contract termination. We argue that the attribution of responsibility

(i.e., locus of causality) serves as a moderator of the effect of customer demarketing.

H5: Attribution of responsibility moderates the effects of customer demarketing on

fairness, such that customer demarketing has a weaker effect on perceived fairness when the

responsibility for demarketing is attributed to the customer as opposed to the firm.

A cue such as observing customer demarketing will primarily influence those consumers

who view the cue as relevant to their own decision making. This may especially be the case when

demarketing affects consumers who are socially close to the observer (Haenlein 2013) because

individuals feel more empathy when members of one’s social in-group have negative experiences

as opposed to members of a social out-group (Tarrant, Dazely, & Cottom 2009). We therefore

2 Weiner (1985) later added the dimensions of stability and controllability; however, those dimensions are independent of the locus of causality and are more applicable to person-to-person interactions rather than person-to-company interactions. Therefore, in this paper, we focus on the locus of causality (i.e., who is responsible) dimension of attribution.

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expect that social proximity (i.e., the extent to which other people are perceived as socially

similar (Trope et al. 2007)) will increase the relevance of a transaction and deviations from the

entitlements. This is also supported by the construal level, which suggests that consumers will

mentally represent socially close objects and events with more detail and specificity, which is

equivalent to low-level construal (Trope et al. 2007). We argue that the effect of customer

demarketing is moderated by the social proximity to those who are affected by demarketing:

H6: Social proximity moderates the effects of observing customer demarketing on

fairness, such that customer demarketing in socially close groups has a stronger effect on

perceived fairness than customer demarketing in socially distant groups.

External factors that can influence the reference point of what consumers perceive to be

common or frequent may also influence fairness perception. One of those factors is commonness

of a particular observed behavior because it can change or distort fairness judgments via the

availability heuristic (Tversky & Kahneman 1973). For example, research suggests that in

contexts (e.g., industries or countries) where differential treatments are part of the norm,

consumers perceive differential customer treatment as less unfair and react less negatively to non-

preferred treatment (Mayser & von Wangenheim 2013). Therefore, we expect the negative effect

of customer demarketing to be less severe when consumers perceive demarketing to be rather

common as opposed to very uncommon.

H7: Perceptions of customer demarketing as common (at the marketplace) moderate the

effects of observing customer demarketing on fairness, such that they decrease the effect of

customer demarketing on perceived fairness.

Finally, it is important to consider mechanisms of coping with unfairness. Research

suggests that one coping mechanism for “feeling duped” is the activation of a downwards

comparison, i.e., comparing the current situation to the worst scenario and being comforted by

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the relatively moderate loss (Markman et al. 1993; Vohs et al. 2007). When the firm can activate

this framing by specifying the loss if the customer did not have the contract in the first place, the

termination may be perceived as less unfair (cf. prospect theory, Kahneman and Tversky 1979).

The firm can active this framing through the information released in the context of the customer

demarketing, e.g., it can highlight how much was gained for a customer (lost for the firm) so far

due to a contract or the potential loss (if they did not have the contract in the first place).

Frames may also differ depending on the object of the losses and gains, but the direction

of this effect is not clear a priori. On the one hand, conventional wisdom suggests that consumers

should care about their own gains and losses more than those of a firm. This view is consistent

with the idea of the “fear-of-loss” framework (Camerer 2005), which suggests that loss aversion

is akin to an emotional state that is activated only if the object of potential loss is regarded as

self-relevant. Thus, a customer’s loss should have a stronger effect on perceived fairness. This

suggests that highlighting customers’ losses (if they did not have the contract in the first place) is

more likely to increase fairness perception than highlighting the corresponding customers’ gains

(due to having had the contract). On the other hand, new circumstances (e.g., termination of a

contract) activate competitive behavior, i.e., a customer attempts to obtain a slightly superior

position to the other party (Poppe and Valkenberg 2003). This competitive motivation may in

turn enhance attention to any potential losses made by the firm, which could also bring some

satisfaction from mental retaliation and hence decrease unfairness. Further, Kahneman, Knetsch

& Thaler (1986) find that fairness perceptions are susceptible to framing effects, e.g., people do

not judge firms as unfair that introduce wage cuts because they were losing money. Therefore,

framing in terms of the firm’s losses is also more likely to enhance fairness perceptions than the

framing in terms of the firm’s gains. Irrespective of the object of the frame, we expect loss

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framing to reduce unfairness more than framing that highlights gains. Nevertheless, we explicitly

control for the effects of the object of the frame on the fairness perceptions.

H8: Loss (versus gain) framing of customer demarketing moderates the effects of

customer demarketing on fairness perceptions, such that the effect is less negative in the loss

framing compared to the gain framing.

The perception of unfairness arises because the dismissed customers are deprived of the

possibility to extract the utility from the transaction to which they are entitled. We suggest there

may be two ways that a firm is able to implement customer demarketing while giving customers

the opportunity to extract utility from the transaction to keep the negative effects on fairness at

bay. First, the firm offers the customer a new contract under different terms that are now

profitable for the firm (e.g., because of higher prices). An example for such a new contract would

be a situation in which a bank moves a customer to its low-cost subsidiary (Bott and Reuter

2000). This may also indicate to the customer or the observer that the firm cares about the

customer’s entitlement, which in turn may reduce negative emotions towards the firm (Porath,

MacInnis, & Folkes 2011). We therefore hypothesize:

H9: Making alternative offers for dismissed customers moderates the effect of customer

demarketing on fairness perceptions, such that the presence of alternative offers (versus no offer)

increases perceived fairness

The second approach is monetary compensation (e.g., by offering a one-time payment). It

gives customers the possibility to derive the utility to which they are entitled to and may serve as

a signal that the firm acknowledges responsibility for granting the customer her due entitlement.

This acknowledgment may reduce negative emotions towards the firm (Porath, MacInnis, &

Folkes 2011), therefore mitigating the negative effects of customer demarketing:

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H10: Monetary compensation for dismissed customers moderates the effect of customer

demarketing on fairness perceptions, such that customer demarketing with monetary

compensation (versus no monetary compensation) increases perceived fairness.

3. Experimental Analysis

We investigate the effect of customer demarketing in seven experimental studies. Table 3

provides an overview, and Appendix B & F provides the manipulations. We use Study 1 to test

H1&H2 and to assess warmth and competence as potential alternative mediators (H3&H4). We

test H5 in Study 2, whereas H6 is analyzed in Study 3. The moderating role of commonness (H7)

is analyzed in Study 4. In Study 5, we look at H8. We analyze H9 and H10 in Studies 6 and 7,

respectively. In all studies, we measure the mediators and dependent variables with 7-point

Likert-type scales. We analyze the results for (moderated) mediation with Process Models 4, 6

and 7, respectively (Hayes 2013) and report the respective unstandardized regression coefficients.

TABLE 3 about here

3.1. Study 1: Main Effect and Mediation

Procedure. In Study 1, we assess the main effect of customer demarketing and the role of

the perceived fairness as mediator. In addition, we shed some light on warmth and competence

perceptions as potential alternative mediators to explain why consumers respond negatively to

customer demarketing (H3 & H4). We recruited respondents, representative of the German

market, through a market research firm for a short online experiment and randomly assigned

them to one treatment in a 2-group (demarketing vs. no demarketing) between-subjects design.

The final sample consisted of 2813 respondents (mean age = 45.49, SD = 12.53; 48.4% female).

3 Across all online experiments that follow, we applied the same consistent rule on the elimination of respondents who provided random answers to ensure sufficient data quality. Specifically, at the end of the corresponding online questionnaire, we asked respondents to indicate the share of truthful answers they provided

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Materials. Participants read a newspaper article about a mobile phone provider. It stated

that the firm would release the earnings report next week and information on the profitability of

their customers (control condition). In the customer demarketing condition, the article also

contained the information that the contracts with the unprofitable customers were terminated.

Manipulation check. Respondents indicated their agreement to the statement that in the

newspaper article, a firm had terminated contracts with unprofitable customers. As intended, the

mean in the customer demarketing condition (M = 5.98; SD = 1.75) was significantly higher (t =

19.66, p<.001) than that in the control condition (M = 2.12; SD = 1.52).

Measures. We measure perceived fairness of the firm’s behavior with a three-item scale:

the behavior of Brand A is reasonable, Brand A treats its customers fairly, and the behavior of

Brand A is just (α = .96; Bolton, Keh & Alba 2010). We measure attitude toward Brand A with a

four-item scale: Brand A is “good”, “pleasant”, “likeable”, and “appealing” (α = .98; Spears and

Singh 2004). We measure the intention to engage in negative WoM (nWoM) with a three-item

scale: “I would not recommend Brand A to someone who seeks my advice”, “I say negative

things about Brand A to other people”, and “I would not recommend Brand A to others.”

(α = .91; Price & Arnould 1999). In addition, we measure warmth and competence perceptions

following Aaker et al. (2012) by asking to which extent the firm was seen as warm and friendly

(α = .95) and competent and capable (α = .96).

FIGURE 2 & TABLE 4 about here

Results. H1 specifies the direct effect of customer demarketing on brand attitude and

(Wlömert & Papies 2016). If a respondent stated on an 11-point scale (1 = only random answers to 11 = only truthful answers) that she provided less than or equal to 50% truthful answers, the respondent was excluded. We also excluded those respondents who selected the identical scale point across all answers throughout the questionnaire. This rule resulted in the exclusion of 29 respondents (9.4%) in Study 1, 31 respondents (6.6% of the sample) in Study 2, 49 respondents (8.4%) in Study 4, 23 respondents (4.6%) in Study 5, 24 respondents (6.0%) in Study 6, and 38 respondents (7.2%) in Study 7. We also assessed whether our results depended on the specification of these exclusion rules, which was not the case. The results of these robustness checks are available upon request.

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intention to engage in negative WoM. ANOVA results indicate that customer demarketing

(versus no demarketing) indeed affects fairness perceptions (F = 191.24, p<.001), attitude (F =

168.22, p<.001), and intention to engage in nWoM (F = 147.09, p<.001), which supports H1.

Table 4 reveals that the effects are in the hypothesized direction.

Figure 2 provides insights into the mediating relationships. In Figure 2a we once again see

the strong negative main effect of customer demarketing on perceived fairness (-2.484). On top

of that, fairness shows a strong relation to both brand attitude and intention to negative WoM.

This supports the theory that fairness is a mediator of the negative effect of customer

demarketing. The indirect effect via fairness on attitude is -.99 (= -2.484*.399), the indirect on

negative WoM is 1.04 (=-2.484*-.421). The bootstrapped standard errors of the indirect effects

indicate that these effects are significant. Table A1 in the Appendix contains detailed results. A

comparison of the total effect of customer demarketing on brand attitude (i.e., -2.3) to the indirect

effect via fairness suggests that 43% of the total effect is mediated by fairness alone.4 The same

holds for the corresponding effect on negative WoM.

The case is less clear for the role of perceived warmth and competence. We see a

significant mediation both via warmth (-.563 = -2.109*.267) and via competence (-.604

= -2.019*.299) if brand attitude is the dependent variable. The corresponding effect sizes,

however, are smaller compared to fairness. The mediation via warmth accounts for 24%, and the

mediation via competence accounts for 26% of the effect of customer demarketing, which

highlights the strong role of fairness because fairness alone accounts for 43% of the effect. For

the behavioral measure of nWoM, we do not find evidence that warmth or competence

perceptions function as a mediator. These findings provide only partial support for H3 & H4 and

4 Indirect effect (= -.990) / total effect (= -2.3) = .43.

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support the notion that fairness is indeed a key mediator for the effects of customer demarketing.

While these analyses consider a parallel mediation of warmth, competence, and fairness, previous

research implies that warmth and competence may also be antecedents to perceived fairness

(Cuddy et al. 2008; Kervyn et al. 2012). Specifically, because warmth and competence

evaluations are automatic and happen in a “split second” (Cuddy et al. 2008), it is reasonable to

expect that those automatic evaluations are likely to inform fairness evaluations, which are much

more cognitive in nature (e.g., Xia, Monroe, & Cox 2004). The results of a serial mediation

(Figure 2b)5 support the notion that both warmth and competence positively affect fairness

perceptions. However, even after controlling for warmth and competence as antecedents of

perceived fairness, customer demarketing still exerts a substantial negative influence on

perceived fairness (Appendix, Tables A1/A2).

Discussion. This study finds that consumers disapprove of demarketing, and they

disapprove because they perceive customer demarketing as unfair. In addition, this study

investigates warmth and competence perceptions as potential alternative mediators. The results

show that perceived warmth as well as competence are strongly affected by customer

demarketing, but the evidence for their role as mediators is mixed. Warmth and competence

mediate the effects for attitudinal responses but not behavioral responses (nWoM). Overall, the

mediation via warmth and competence is weaker than that via fairness. In sum, the results support

the theory that fairness indeed is a key mediator that explains why consumers react to customer

demarketing. We therefore focus on perceived fairness as a mediator in the following studies.

Figure 2c and Table A3 (Appendix) summarize the results with fairness as mediator.

3.2 Study 2: The Main Effect of Customer Demarketing & Attribution

5 We thank an anonymous reviewer for bringing up this idea.

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Procedure. We test H5 in an online experiment using a 2 (customer demarketing vs. no

customer demarketing) x 2 (attribution to customers vs. attribution to the firm) between-subjects

design. We recruited respondents, representative of the German market, through a market

research firm and randomly assigned them to one of the four experimental groups. The final

sample consisted of 437 (mean age: 44.44 (SD: 12.46), 48.7% female).

Material. Participants read a newspaper article about the same mobile phone provider that

we used in the previous study. The article informed the reader that the firm would release the

annual earnings report next week (control condition). In the customer demarketing condition, the

article contained information that some customers were identified as unprofitable and stated that

the contracts with the unprofitable customers were terminated. On a separate page, respondents

read an article that was labeled as background information on the topic of customer profitability.

A fictitious expert stated that either the firm is responsible for customers being unprofitable due

to poor calculations (“attribution to the firm” condition) or that the customers are responsible for

being unprofitable due to misuse of the service (“attribution to the customer” condition). We

measured our dependent variables afterwards.

Manipulation check. Respondents indicated their agreement to the statement that in the

newspaper article, the firm had terminated contracts with unprofitable customers. As intended,

the mean in the customer demarketing conditions (M = 5.79, SD = 1.75) is significantly (t =

13.90; p<.001) higher than the mean of the no-demarketing conditions (M = 3.48, SD = 1.73). To

assess the effectiveness of the attribution manipulation, respondents stated on a 7-point Likert

scale whether the article reported that the customers were responsible for being unprofitable for

the firm. As intended, the mean in the attribution-to-customer conditions (M = 5.22, SD = 1.77)

is significantly (t = 10.18; p<.001) higher than the mean of the attribution-to-firm conditions (M

= 3.36, SD = 2.06). Respondents also stated whether the article reported that the firm was

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responsible for customers being unprofitable for the firm. As intended, the mean in the

attribution-to-firm conditions (M = 5.25, SD = 1.85) is significantly (t = 12.02; p<.001) higher

than the mean of the attribution-to-customers conditions (M = 3.08, SD = 1.91).6

Measures. We use the same measures as before to assess perceived fairness (α = .96),

attitude toward Brand A (α = .97), and intention to engage in negative WoM (α = .82).

Results. We estimate a model allowing for moderated mediation (Hayes 2013, Process

Model 7). Figure 3 shows the results. The significant effect of customer demarketing on fairness

(-1.126) indicates that fairness perception is reduced by more than 1 point on a 7-point Likert

scale due to customer demarketing in the attribution-to-customer condition, which is in line with

Study 1. The substantial effects of fairness on brand attitude and negative WoM indicate that

demarketing does have an unfavorable effect on brand attitude and negative WoM via fairness as

a mediator. This again supports H2.

FIGURE 3 about here

H5 posits that the evaluation of customer demarketing depends on the attribution of

responsibility. However, despite the significant differences in the objective level of customer’s

fault across the conditions (i.e., attribution manipulation), the interaction effect between customer

demarketing and attribution on the dependent variables is only marginally significant (p=.07).

The interaction effect (-.520) suggests that respondents tend to consider the objective

information, but the confidence intervals are wide and include zero, which is more consistent

with the notion of self-serving bias and the fundamental attribution error (Ross 1977). This

suggests that – even despite objective facts – people automatically attribute blame for negative

events such as customer demarketing to the actions of the firm rather than situational factors or

6 In a robustness check, we assess whether the results change when we exclude respondents who incorrectly recalled whether customer demarketing took place. The substantive findings do not change.

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the actions of other customers (Mezulis et al. 2004). The conditional indirect effects (which

capture for each level of the moderator the total effect via the mediator fairness) mirror this

picture. The indirect effect in the attribution-to-customer condition is -.862 (=-1.126*.766) for

brand attitude and .611 (-1.126*-.543) for nWoM. The corresponding effects in the attribution-to-

firm condition are -1.261 for brand attitude and .894 for nWoM. The differences between the two

conditions are not significant, as evidenced by the index of moderated mediation, where the

confidence interval includes zero. See Table A4 in the Appendix for detailed results. The effect

sizes suggest that 79% of the total on brand attitude is mediated by fairness in the firm condition

(74% in the customer condition). For nWoM as dependent variable, 73% of the total effect is

mediated by fairness in the firm condition (47% in the customer condition).

Discussion. Customer demarketing has negative effects, regardless of whether the

customer or the firm is to be blamed objectively. Shifting the responsibility to the unprofitable

customers may somewhat alleviate the negative effects of demarketing, but this strategy clearly

fails to offset the negative effects of customer demarketing.

3.2 Study 3: The Effect of Customer Demarketing and Social Proximity

Procedure. Study 3 assesses the moderating effect of social proximity (H6). A total of 190

business studies undergraduates (mean age = 22.93, SD = 2.83; 50.0% female) of a large German

university were randomly assigned to one treatment in a 2 (socially close vs. socially distant) x 2

(demarketing vs. no demarketing) between-subjects paper-and-pencil experiment.

Materials. Participants read a newspaper article about a test market of a mobile phone

provider that was not yet active in the German market. It stated that some customers in the test

market were unprofitable. In the customer demarketing condition, the article concluded by stating

that the contracts with some of the unprofitable test market participants were terminated. We

manipulated social proximity by placing the test market participants who were described in the

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newspaper article in either a social in-group or out-group. In the in-group condition, students of

the university where the study was conducted were the test market participants. In the out-group

condition, participants of the test market were law students of a fictitious private law school from

a culturally different part of the country that relied on high tuition fees and portrayed itself as an

elite school. This type of school was chosen as an out-group because pretests had established that

business undergraduates perceive law students as culturally different. Furthermore, this particular

area in Germany was perceived as culturally different from where the study was conducted.

Manipulation check. To assess whether this manipulation of in-group vs. out-group was

effective, we rely on findings from Berger and Heath (2008), who identify aspects (similarity,

liking, and cost of being confused as a member of the group) of perceived social proximity. We

asked four questions relating to these aspects (“The students in the newspaper article are very

similar to me and my friends”; “I feel very positive about the students in the newspaper article”;

“I can identify very well with the students in the newspaper article”; “Being mistaken for one of

the students in the newspaper article would be very unpleasant for me and my friends”). As

intended, we find a significantly (t = 4.84; p<.01) lower mean for the out-group (M = 2.58; SD =

1.27) than the in-group conditions (M = 3.58; SD = 1.46) for the average of the four items.

Measures. We use the same measures as before to assess the perceived fairness of the

firm’s behavior (α=.89), brand attitude (α=.91), and intention to engage in nWoM (α=.82).

FIGURE 4 about here

Results. The results (Figure 4) replicate our findings from the previous studies and show a

negative effect of customer demarketing on perceived fairness. Again, perceived fairness

mediates the effect, and the indirect effect of customer demarketing on brand attitude (nWoM)

is -.83 (.677) when contracts of socially distant customers are terminated (the direct effect of

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customer demarketing on attitude (.113) is insignificant).7 The bootstrapped confidence intervals

(Table A5 Appendix) indicate these effects as significant. The theory proposes that respondents

will disapprove less of customer demarketing of socially distant customers (H6). However, the

results do not support this because respondents disapprove of customer demarketing regardless of

social proximity, as evidenced by the negative (-.122) but insignificant interaction.

Discussion. Study 3 corroborates the findings from Studies 1&2. Further, it shows that

observing demarketing has a similarly negative effect on attitude and negative WoM regardless

of the social proximity to the customers who were dismissed. Consistent with the social

identification theory (Tajfel & Turner 1986), the latter result suggests that customers are likely to

identify themselves with other customers even if they are not socially close. This suggests that

firms cannot rely on a communication strategy to mitigate the effects of customer demarketing

that portrays the dismissed customers as a non-representative group that is different from other

customers. One alternative explanation may be that although our manipulation of social

proximity resulted in significant differences between the conditions, it was relatively weak in

order to keep it as realistic as possible (e.g., it did not include close friends or family). It may

well be that contrasting extreme levels of social proximity (e.g., a stranger versus a family

member) will increase the magnitude of the effect; we also expect that consumers may be less

forgiving if customer demarketing concerns their close family members. However, importantly

for this research, the results suggest that the negative effect of demarketing is sustained even at

relatively low levels of social proximity.

3.4 Study 4: The Moderating Role of Perceived Commonness

7 The sign of the direct effects of customer demarketing on attitude and nWoM appears to be inconsistent with the previous studies. However, the effect is clearly insignificant, i.e., small with very wide confidence intervals. This suggests that the effect of customer demarketing may be fully mediated by fairness, which may also be due to a weak manipulation of social proximity.

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Procedure. Study 4 tests whether the effect of customer demarketing depends on whether

it was perceived to be a common or uncommon event. We recruited respondents, representative

of the German market, through a market research firm and assigned them to one treatment in a 2

(common vs. uncommon) x 2 (demarketing vs. no demarketing) between-subjects design. The

final sample consisted of 537 respondents (mean age = 43.65 (SD = 12.80); 46.9% female).

Materials. Participants read a newspaper article about the same mobile phone provider

that we used above. It said that the firm would release the earnings report (control condition) and

information about an internal study on potentially unprofitable customers. In the customer

demarketing condition, the article added that some customers were identified as unprofitable and

that the contracts with the unprofitable customers were terminated. On a separate page,

respondents read background information on the topic of customer profitability. In the condition

“common” (“uncommon”), the fictitious expert that was interviewed stated that it was quite

common (very uncommon) for firms to terminate contracts with unprofitable customers.

Manipulation check. Respondents indicated their agreement to the statement that in the

article, a firm had terminated contracts with unprofitable customers. As intended, the mean in the

customer demarketing condition (M = 5.81; SD = 1.73) is significantly higher (t = 13.19, p<.001)

than that in the control condition (M = 3.70; SD = 1.97). In addition, respondents indicated their

agreement to the statement that it is very uncommon for firms to terminate contracts with some of

its customers. As intended, the mean in the uncommon condition (M = 5.45; SD = 1.85) is higher

(t = 17.25; p<.001) than that in the common one (M = 2.66; SD = 1.90).

Measures. We use the same measures as before to assess the perceived fairness of the

firm’s behavior (α = .97), brand attitude (α = .97), and intention to engage in nWoM (α = .89).

FIGURE 5 about here

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Results. We test H7 using a moderated mediation (Process Model 7; Hayes 2013). The

results (Figure 5) replicate the negative effect of customer demarketing from the previous studies.

As theory suggests, customer demarketing is viewed as less unfair when consumers perceive it to

be rather common versus very uncommon, i.e., the interaction (.562) is significant. Accordingly,

the conditional indirect effects via fairness differ significantly from one another (index of

moderated mediation is significant), i.e., the indirect effect on attitude is -1.140 (-1.579) if

demarketing is perceived as common (uncommon). The indirect effect on nWoM is .743 (1.030)

if demarketing is viewed as common (uncommon). Table A6 (Appendix) has detailed results.

The effect sizes suggest that 86% of the total effect on brand attitude is mediated by fairness in

the uncommon condition (70% in the common condition). For nWoM, 51% of the total effect is

mediated by fairness in the uncommon condition (40% in the common condition).

Discussion. Study 4 identifies commonness of customer demarketing as a moderator of

the negative demarketing effect, i.e., the slope is less negative in the common condition than in

the condition in which customer demarketing is perceived to be uncommon. The interaction,

however, is not strong enough to offset the negative effect of customer demarketing, i.e., even if

it is perceived as common, we see a negative effect on fairness and brand attitude. This implies

that consumers are likely to react to demarketing even if it is perceived as common.

3.5 Study 5: Framing Justification for Customer Demarketing as a Loss versus Gain

In Study 5, we investigate H8, i.e., whether a firm can reduce the effect of customer

demarketing using different demarketing letter framing (i.e., losses vs. gains, while controlling

for whether those losses vs. gains are associated with the firm vs. the customer). Furthermore, to

extend the previous studies, we manipulate customer demarketing as being experienced.

Procedure. We recruited respondents, representative of the German market, through a

market research firm for a short online experiment. Respondents were randomly assigned to one

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of the four scenarios in a 2 (loss vs. gain) x 2 (firm vs. customer) between-subjects design. The

final sample consisted of 482 respondents (mean age = 42.76 (SD = 12.59); 47.7% female).

Materials. We asked respondents to imagine a situation in which they arrived home after

a normal day and found a letter from their wireless provider in their mail. Respondents then read

the letter that said that calculations had shown that based on the respondents’ consumption level,

the firm took a loss by providing the service to them, or the firm would have made a gain if the

firm had not served them. The consumer scenarios read that the consumer had made a gain

compared to the charges of other wireless carriers, or the consumer would have incurred a loss if

she had used similar services at other wireless carriers. In all conditions, the value of either a loss

or a gain is indicated with 200€ per year. We measured our dependent variables afterwards.

Manipulation check. To assess whether the manipulation of gain/loss and firm/customer

was effective, we asked for the degree of agreement (1 = completely disagree, 7 = completely

agree) with four statements that described the letter as referring either to (1) losses or (2) gains

incurred (3) by the firm or (4) by the customer. As intended, consumers perceived more strongly

that the letter referred to losses in the loss condition (M = 4.47, SD = 1.44) than in the gain

condition (M = 4.06, SD = 1.35), and the difference was significant (t=3.26, p=.001). Consumers

in the customer condition perceived the letter to refer to customers (M =3.56; SD = 1.32; t=5.19,

p<.001) more strongly than those in the firm condition (M=2.91; SD = 1.44).

Measures. We use the same measures as in the previous studies to assess the perceived

fairness of the firm (α=.94), brand attitude (α=.96), and intention to engage in nWoM (α=.86).

FIGURE 6 about here

Results. We test H8 by estimating a moderated mediation (Process Model 7; Hayes 2013,

Figure 6). Consistent with Studies 1-4, we find that perceived fairness mediates the relation

between customer demarketing framing and the dependent variables attitude and negative WoM.

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The indirect effect of loss vs. gain via fairness is .409 in the firm condition and .135 in the

customer condition. Hence, the indirect effect is weaker in the customer condition, although this

difference fails to be significant at the 5% level, as evidenced by the insignificant interaction

(-.418) and the insignificant index of moderated mediation (see Table A7 in the Appendix).

Discussion. These results suggest that letter framing can influence firm perceptions,

particularly with respect to its fairness, i.e., the framing of the justification for the customer

demarketing in terms of loss for a company seems to increase perceptions of firm fairness. This is

consistent with the idea that fairness perceptions are susceptible to framing effects and provides

further support for the suggestion that a firm’s action will be perceived as less unfair and more

acceptable when its profits are threatened (Kahneman, Knetsch, & Thaler 1986).

3.6 Study 6: Offering Alternatives

Procedure. We designed Study 6 to assess to what extent firms can alleviate the negative

effect of customer demarketing by making alternative offers to customers whose contracts were

terminated (H9). We recruited respondents through a professional market research firm and

randomly assigned them to one of four treatments (no demarketing; demarketing without

alternative offer; demarketing with new contract in subsidiary; demarketing with contract in other

firm). The sample consisted of 373 respondents (mean age = 40.27 (SD = 12.16); 53.1% female).

Materials. Participants read a newspaper article about a bank. We measured our

dependent variables afterwards. In the control condition, the article stated that the bank would

present its earnings report. In the customer demarketing condition without alternative offers, it

said that some customers were identified as unprofitable, and their contracts would be terminated.

In the alternative offer condition, the article added that all customers whose contracts were

terminated would receive an offer for a new contract with the bank’s online-only subsidiary.

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Manipulation check. Respondents indicated their agreement to the statement that in the

newspaper article, a firm had terminated contracts with unprofitable customers. As intended, the

mean in the demarketing conditions (M = 5.42; SD = 1.88) is significantly higher (t = 21.23,

p<.001) than that in the control condition (M = 1.94; SD = 1.27). The respondents in the

demarketing conditions indicated their agreement with the statement that the bank offered an

alternative contract to the customers with the terminated contracts. As intended, the mean in the

condition with compensation (M = 6.09; SD = 1.48) is significantly higher (t = 22.83, p<.001)

than that in the demarketing condition without compensation (M = 1.82; SD = 1.43).

Measures. We use the same measures as before to assess the perceived fairness of the

firm’s behavior (α=.98), brand attitude (α=.97), and intention to engage in nWoM (α=.84).

Results. This study (Table 4) again replicates the main negative effect of observing

customer demarketing, i.e., mean fairness perceptions in the customer demarketing condition

(M = 2.11) are lower than those in the control condition (M = 4.04), and the difference is

significant (t = 11.38; p < .001). However, an alternative offer made by the firm reduces the

negative effect, i.e., fairness perceptions are somewhat higher (M = 2.46; t = 1.93; p = .054) in

the case in which the firm makes an alternative offer than in the case of customer demarketing

without an alternative offer.

Discussion. Firms engaging in customer demarketing can somewhat decrease their

negative effect by making an alternative offer to affected customers. This will not offset the

negative effect of customer demarketing but will weaken it. However, these offers are typically

more profitable for the company, and provided that the company is interested in keeping the

customer under a new contract, this could be a viable solution strategy.

3.7 Study 7: The Effect of Customer Demarketing with Monetary Compensation

Procedure. Study 7 tests the effect of monetary compensation (H10). We recruited

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respondents through a market research firm and randomly assigned them to one of three

monetary compensated demarketing treatments (15€, 50€, 100€8) and two control groups

(demarketing without compensation, no demarketing) in a between-subjects online experiment.

The final sample consisted of 491 subjects (mean age = 48.93; SD = 13.93; 49.5% female).

Materials. Participants read a newspaper article containing information about a fictitious

mobile phone service provider in Germany and assessed the dependent variables afterwards. In

the customer demarketing conditions, the article stated that some customers were unprofitable

and their contracts had been terminated. In the monetary compensation manipulation, we added

that the dismissed customers had received monetary compensation of 15€, 50€, or 100€. The

control group read that the provider would present its earnings report.

Manipulation check. To check if the monetary manipulation was effective, we asked

respondents whether the dismissed customers received a monetary compensation. As intended,

we find a significant difference in the mean (p<.001) for the conditions with compensation than

for both control groups (demarketing without monetary compensation, no demarketing).

Measures. We use the same measures as in the previous studies to assess the perceived

fairness of the firm’s behavior (α=.97), brand attitude (α=.96), intention to engage in nWoM

(α=.86) and intention to sign a contract with the firm (single item).

Results. Our results replicate the negative impact of customer demarketing on perceived

fairness, attitude, and negative WoM (Table 4).9 Theory predicts that the negative effect of

demarketing is lower if the firm offers monetary compensation. Our results show that this is

8 We chose this maximum amount because it had been used by service providers in the telecommunication industry to compensate for customer demarketing (see the example of 1&1 in Table 1).

9 We also ran ANOVAs for the whole sample and controlled for whether the respondents indicated correctly whether there was monetary compensation. For all three dependent variables, the control variable was insignificant (fairness: p=.83; attitude: p=.06; nWoM: p=.58). We also estimated our results with only those respondents who correctly indicated whether compensation was given (n=370). However, the results do not change. For the sake of parsimony and consistency with the other studies, we chose to report the results for all the respondents.

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indeed the case (Table 4). Interestingly, however, the amount of the compensation really matters.

Only for substantial amounts (i.e., 100€), we find that perceived fairness (M = 2.50) increases

compared to no compensation (M = 2.02). The difference is significant (t = 2.13; p = .034). At

the same time, offering a small compensation of only 15€ seems worse (M = 1.65) than offering

no compensation, although the effect fails to be significant at the 5% level (t = 1.90; p = .059).

Discussion. Offering monetary compensation to customers whose contracts are terminated

can reduce the harm caused by observing demarketing. Hence, monetary compensation is an

effective tool in restoring distributive justice and increasing fairness perceptions (Tax et al.

1998). However, contrary to conventional wisdom, it does not completely eliminate the negative

effects of customer demarketing. Importantly, our results suggest that the effect of monetary

compensation on fairness and the dependent variables can vary in a non-monotonic way with the

compensation amount. For example, a small amount of monetary compensation may be perceived

as too little and insulting when compared with what consumers feel is fair given a specific

contract and/or social norms, leading to detrimental trivialization effects of such a compensation

versus no compensation at all (Gneezy & Rustichini 2000; Liu et al. 2015). Hence, the financial

burden that is needed to reduce the negative effects is high, and firms should use monetary

compensation only in selected situations, e.g., when they expect high public visibility of the

contract terminations. Compensation strategies require a careful evaluation of customers’

perceptions with respect to the value of the alternative offer.

4. Illustrating the Effect of Customer Demarketing – A Field Study

4.1 Background

In 2010, a European service provider from the household utilities industry (electricity,

natural gas, and water) identified a segment of customers whose contracts no longer guaranteed a

sufficient profitability margin due to new conditions in the regulatory framework. The service

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provider terminated the contracts with these customers by sending them a letter stating that the

current contract would be terminated.

4.2 Procedure

We initiated a research collaboration with the firm a few weeks after the company had

terminated the contracts to learn about the consequences of this customer demarketing

implementation. In 2011, approximately twelve months after the demarketing letters were sent

out, we surveyed 7,829 households from the region where the service provider was active and

where the contracts were terminated. The paper-and-pencil survey was labeled as academic

research carried out by a large German university. Neither the survey nor the mailing contained

any hints as to the service provider, and they contained no questions on or references to customer

demarketing or contract terminations to avoid any salience effects.

At the time of the survey, all households were subscribers of this utility, either with the

industry partner or with a competing provider. The addresses of the respondents were randomly

drawn from the customer database as follows. We took a random sample of customers who left

the company after the contract termination, a random sample of customers who decided to sign a

new contract with the firm, and a random sample of customers who did not react to the contract

termination. The service provider was legally obliged to continue a basic service to the last group

with limited service levels even if they did not choose a new provider or entered new contract

with the old provider.10 In addition, we contacted a random sample of customers who were not

affected by the customer demarketing implementation because they had contracts that did not

contain those components in question; these customers served as a control group. All

10 One could argue that because customers were offered an alternative contract, this contract termination is not truly customer demarketing. Study 6 (above), however, suggests that the consequences are similar; hence, we treat this case as customer demarketing, while being aware that this may be a special case of customer demarketing.

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questionnaires had an identical design; a small code that was not informative to the respondents

allowed us to identify to which group a response belonged. All households were located in the

same metropolitan region in Germany. We received 407 responses from customers who were not

affected by the contract termination and 711 responses from dismissed customers (= 1,118

responses; 14.28% response rate).11 For the following analyses, we combine all households who

received customer demarketing into our treatment group. All surveyed households that had not

received the contract termination letter served as a control group.

4.3 Measures

To avoid that respondents associated the questionnaire with the particular service

provider, we did not use the provider’s brand name. Instead, we initially asked respondents who

were customers of that service provider at the time of the survey to name their current provider of

that particular utility. All other respondents were asked to name the firm that provided that utility

to their household two years prior. All questions then related to the brand the respondents named.

This procedure ensured that the object of all questions is always the same provider (i.e., the focal

firm). We applied the same measurements as before to assess the perceived fairness of the firm’s

behavior (α=.89), brand attitude (α=.91), and nWoM intention (α=.82). In addition, we provided

an alphabetical list to respondents that contained the 10 largest brands (our focal brand was

among them) that provided this utility in this region. We then asked the respondents to indicate

their intention to sign a contract with each brand if they had to choose a provider today. We use

this as an additional dependent variable.

11 To assess non-response, we test whether early respondents differed from late respondents in our focal variables (Armstrong & Overton 1977). We cannot identify a significant difference. Additionally, we do not detect significant differences between households that were affected by demarketing compared to those that were not. In addition, we test for an interaction between demarketing treatment and response time, again without a significant result. We therefore conclude that non-response should not be a problem for our results.

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4.4 Results

When assessing the impact of the contract termination on our dependent variables, it is

important to note that we were not involved in the planning and execution of the customer

demarketing intervention. This also implies that the assignment of respondents to the treatment

condition was not random. Rather, customers received the customer demarketing treatment when

they had a certain type of contract with the firm. This particular contract was offered until two

years before the termination, and at that time, this particular contract was the only contract

offered by the firm. We therefore cannot exclude the existence of a selection bias. To address

potential self-selection problems, we complement our analysis with a propensity score matching

procedure that is often used in similar (quasi-experimental) settings (e.g., Rosenbaum & Rubin

1985). The general idea is that for every household that was affected by customer demarketing,

we identify a household that was not affected but is as similar as possible in all other aspects, and

this assessment is based on the propensity to be subject to customer demarketing. We then

compare our dependent variable only between the households affected by demarketing and their

identified matching partners from the non-affected households. We follow the literature on

propensity score matching and estimate the propensity scores with a probit model, using a Kernel

estimator (Epanechnikov, bandwidth of .06) and common support (i.e., only households with

similar propensity scores are included in the comparison; Caliendo & Kopeinig 2008). The

matching removes most of the observable differences in the covariates between the two treatment

groups (Appendix D), which becomes evident in a standardized bias after a matching of less than

5%, suggesting a good matching quality (Caliendo & Kopeinig 2008). The results of the matched

and unmatched sample are very similar. To conserve space, we present only the main findings in

Table 9 (see Appendix C-E for details), which are based on a comparison between all customers

from our sample who received customer demarketing (treatment) and those who did not (control).

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Does experiencing real customer demarketing have a measurable effect on key variables

such as brand attitude or nWoM? The answer is unequivocal: yes, it does. On a 7-point scale,

demarketing reduces the perceived fairness 1.68 points, and the indirect effect via fairness show

strong effects on attitude and nWoM (Table 5). The effect on the intention to sign a new contract

with this provider appears to be the strongest, which highlights the detrimental effect of customer

demarketing on the firm’s relationship to the customer base. In the unmatched sample, the effects

are almost the same (lower part table 9). We estimate several other versions of the model that

included different sets of the covariates. However, none of our focal coefficients changes in a

meaningful way. We therefore report the most parsimonious model.

TABLE 5 about here

In line with the previous studies, we find that consumers disapprove of demarketing

because they perceive it as unfair, as evidenced by the significant indirect effects of the mediation

via fairness. Hence, this field study supports the findings from the experiments, and the size of

the effects (e.g., the effect on fairness) is quite similar to the experimental studies. One aspect of

this field study deserves attention. The survey was conducted one year after the customer

demarketing “treatment”, which is a long time frame compared to lab experiments, where the

dependent variables are measured usually minutes after the treatment. Nonetheless, we find

strong effects. This suggests that customer demarketing leaves sustainable and long-lasting scars

on the relationship between a company and its customers.

5. Discussion

Across seven experiments and a field study, we shed light on consumer reactions to

customer demarketing and investigate the underlying mechanisms (mediators) and ways to

mitigate the negative effects (moderators). Specifically, we empirically demonstrate the extent to

which customer demarketing affects both attitudinal (reduces brand attitude) as well as

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behavioral (increases negative WoM) customer responses. Our results have a number of

important theoretical and managerial implications.

5.1. Theoretical Implications

First, our study demonstrates how remarkably sustainable the effects of customer

demarketing are. For example, we find the negative effects of customer demarketing in a field

study in which the measurement was performed one year after the contract terminations.

Importantly, and consistent with social identification theory (Tajfel & Turner 1986), merely

observing customer demarketing in the marketplace induces negative consumer reactions, both

attitudinal and behavioral, i.e., one does not need to experience customer demarketing directly to

respond in these ways. Thus, our empirical insights add to the mostly conceptual or theoretical

literature about customer demarketing (e.g., Mittal, Sarkees, & Murshed 2008, Kotler & Levy

1971) and extend the empirical work by Haenlein & Kaplan (2010, 2011, 2012) by testing new

mediators and moderators. The fact that the results are consistent across all studies, cover close to

2800 respondents in the experimental studies alone and are independent of the method of analysis

in the field study (i.e., matched or unmatched sample) provides confidence in the robustness of

the results. The findings are summarized in Table 3.

Second, our results on the underlying mechanisms suggest that customer demarketing

produces negative effects because it is perceived as unfair. This also holds when we introduce

two alternative mediators (warmth and competence). Overall, our results suggest that if firms

successfully address consumers’ fairness concerns, they can mitigate the negative effects of

customer demarketing. Furthermore, on a broader level, our findings suggest that excessive

customer demarketing that is perceived as unfair may contribute to anti-corporate sentiments

among some consumers (e.g., Cronin et al. 2012). Although it is not at the center of our study,

our findings also contribute to the stream of literature on the antecedents of warmth and

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competence perceptions of the company (Aaker et al. 2012; Kervyn et al. 2012) by uncovering

how customer demarketing affects those perceptions.

Third, this research contributes to the stream of literature on customer lifetime value

(CLV; Venkatesan & Kumar 2004) by providing insights about strategic options to manage

unprofitable customers. Our results shed light on both (1) insignificant and (2) significant

moderators of the negative effects of customer demarketing. With respect to the first group of

moderators, we find that the effects of customer demarketing are largely independent of whether

the objective reason for demarketing is attributed to the firm or the customer. This is consistent

with the notion of self-serving bias, i.e., despite objective facts, people automatically attribute

blame for negative events such as customer demarketing to the actions of the firm rather than to

situational factors or the actions of other customers (Mezulis et al. 2004). Furthermore, the effects

of customer demarketing remain strong regardless of the social distance to those customers

whose contracts were terminated. This finding is consistent with the idea that when observing

demarketing, consumers are likely to identify with other customers, even ones who are socially

distant from them, and react accordingly (Tajfel & Turner 1986).

With respect to the second group of moderators, we identify several moderators that could

substantially reduce the negative effect of customer demarketing. Specifically, consistent with the

idea that the commonness of an observed behavior changes fairness judgments via the availability

heuristic (Tversky & Kahneman 1973), the effects of demarketing will be less negative if firms

succeed in communicating that customer demarketing is quite common. Furthermore, consistent

with loss aversion theory, the negative effect will also be reduced if the company frames the

cause for customer demarketing as a loss to the firm. This finding is also in line with the dual

entitlement principle (Kahneman, Knetsch & Thaler 1986), suggesting that transactions are

perceived as unfair if one of the two parties involved is deprived of their entitlement. Hence,

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firms can mitigate the negative effect of demarketing when they raise awareness that there is a

loss involved for the firm if the firm does not take action.

Finally, firms can mitigate the negative effects of demarketing by making an alternative

offer, which may be an attractive option if the firm is interested in keeping the customer under

the terms of a new contract. Our results provide further evidence for the economics theory

suggesting that monetary compensation is an effective tool in restoring distributive justice and

increasing fairness perceptions (Tax et al. 1998). However, the effect of monetary compensation

on fairness and the dependent variables can vary in a non-monotonic way with the compensation

amount. For example, a small monetary compensation may be perceived as too little and insulting

when compared with what consumers feel is fair given a specific contract and/or social norms,

leading to detrimental trivialization effects of such a compensation versus no compensation at all

(Gneezy & Rustichini 2000; Liu et al. 2015). Our results are consistent with Gneezy & Rustichini

(2000) and suggest that too small compensation could be worse than no compensation. In sum,

firms can reduce the effects when the dismissed customers receive substantial monetary

compensation, while small compensations could backfire. A substantial compensation will allow

the customer to extract utility from the relationship of which she would otherwise be deprived.

Additionally, such compensation would signal that the firm seriously considers the customer’s

position. This will mitigate – albeit not eliminate – the negative effects of customer demarketing.

5.2. Managerial Implications

Our management implications address the question whether a firm should terminate

contracts with customers at all. Our results highlight the negative consequences of customer

demarketing and hence suggest that prevention is important. This implies that firms should

carefully monitor their customer acquisition strategy to avoid attracting unprofitable customers in

the first place. At the same time, firms must monitor their existing customers’ risk of becoming

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unprofitable. Hence, a firm should calculate and predict customers’ lifetime values (CLV). This

requires predicting and monitoring customers’ usage behavior, particularly in industries where

unlimited usage plans are common but costs depend on usage (e.g., telecommunication). A firm

can then provide early incentives to customers to either become profitable or to systematically

reduce switching costs for those customers at risk. This is against many managers’ intuition,

particularly when the management compensation is linked to indicators such as “number of new

customers” (Kaplan 2012). The relevance of these processes is highlighted by a recent case of a

German bank (“number 26”, Table 1) that gives customers a free checking account and free cash

withdrawal at all national ATMs. However, the bank incurred positive marginal costs for each

cash withdrawal, which rendered customers who frequently withdrew money unprofitable. The

firm terminated the contracts with these customers, which led to substantial negative WoM.

Careful monitoring of the CLV may have reduced the harm. Thus, only if CLV is negative and

when all means of making the future CLV positive have failed should a firm consider customer

demarketing. Firms must also act within accepted ethical boundaries; otherwise, unfairness

perceptions and their consequences are likely to be even more severe. Thus, corporate social

responsibility requires the careful evaluation of customer demarketing.

When firms choose a customer demarketing strategy, they must be prepared that

consumers will perceive such an action as unfair, cold, and incompetent and penalize the firm.

This harms the firm’s brand image and evokes the tendency of individuals to engage in negative

WoM. This impact holds not only for consumers directly experiencing demarketing but also for

those merely observing it, e.g., by reading about it in the media. While our results show that it is

difficult to mitigate the negative effects of customer demarketing, they also offer important

insights for managers not only on effective strategies to decrease the negative effects but also on

strategies that (although commonly used) are ineffective. We summarize those below.

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Implications for what firms should do. (1) Firms should communicate the commonness of

customer demarketing in the marketplace, as it helps to reduce its negative effects across the

board. (2) Firms should make an alternative offer. Provided a firm wants to keep the customer

under new conditions, it is likely to be a very effective strategy that increases customer

profitability and mitigates the negative effects of customer demarketing. (3) Firms should

consider substantial financial compensation if it is sufficiently important to dismiss customers.

(4) Firms should justify demarketing by focusing on the framing of the loss to the firm (if

possible, by quantifying the monetary value of the loss). As these types of framing do not damage

the perception of the firm’s competence, they help significantly reduce the unfairness perception

and resulting negative effects of customer demarketing.

Implications for what firms should NOT do. (1) The company should not underestimate

the negative effects demarketing has on customers who are merely observe it or learn about it

from the press. (2) Firms should not offer small compensation, as they may backfire. (3) Firms

should not publicly attribute blame to the customers because due to self-serving bias, most

customers will blame it on the firm anyway. (4) Firms should not distance existing customers

from dismissed ones and communicate “they are different”, as consumers are still likely to

identity with other consumers rather than with the company. (5) Firms should not justify

customer demarketing by focusing on gains framing (i.e., how much the customer has gained

from this company so far, or how much a company can gain by dismissing the customer), as gain

frames tend to amplify the negative effects of customer demarketing.

6. Limitations and Future Research

As with any research, this research has a number of limitation that offer potential avenues

for further research. First, it was important for this study that the manipulations were perceived as

realistic as possible, which could have weakened the strength of the manipulation (particularly in

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Study 3). However, future studies may want to increase the strength of manipulation or use actual

data (if available) on the reactions to customer demarketing. Second, although empirical evidence

suggests that the directions of behavioral intentions are good predictors of the directions of actual

behavior (Chandon et al. 2005), the intentions might differ from actual behavior. We hope that

researchers will increasingly find opportunities to collect behavioral data in this very sensitive

field. Third, our data do not allow us to estimate the cash flow effects that occur from terminating

customers. Although simulations have shown the potential benefits on profitability due to

selective demarketing (e.g., Haenlein, Kaplan, & Schoder 2006), an empirical test is still a

challenge because it requires a monetary quantification of the negative spill-over effects to other

(potential) customers. Hence, a promising avenue for a future research would be addressing the

effects of customer demarketing on firm’s profitability – especially as disruptive behavior (using

social media) of individuals is an element that should be included in the calculation of the CLV.

Additionally, customer demarketing may increase the prevalence of anti-corporate attitudes,

which may amplify these effects. Fourth, while we show that fairness perceptions are an

important driver of the negative effects of customer demarketing – even if we control for

potential alternative mediators – future research should explore other potential mediators. For

example, as openness about firm’s losses seem to reduce unfairness perceptions, it is worthwhile

to test how the number or type of communication before termination helps to close the unfairness

gap between firms and customers. Fifth, as we conducted our studies in Germany, we cannot

generalize the findings across different cultures. Further, although in our field study we identified

segments of customers that differed in their behavioral reactions to customer demarketing, the

study’s setup did not allow us to clearly disentangle cause and effect and hence reliably describe

the differences among those segments. This offers opportunities for future research to examine

difference among the types of demarked/terminated customers. Finally, although our results

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across industries suggest that even for less vital services (Internet), customer demarketing is

associated with strong negative effects, the effects could be even stronger for utilities providers.

This opens avenues for future research to address the generalizability of our findings to other

settings, such as entertainment services, utilities providers, or B2B.

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TABLES AND FIGURES

TABLE 1: Examples of Customer Demarketing

Industry Company Product/Service Year Background Customer Demarketing Method Source Telecommunication O2 Mobile phone

service 2011 High expenses for heavy users of

a mobile tariff with a cost cap Termination http://www.teltarif.de/o2-o-kuendigung-

rauswurf-vielnutzer-poweruser-heavy-user-kosten-airbag/news/42631.html

Telecommunication Deutsche Telekom

ISDN 2009 Dismissing unattractive existing contracts to save costs

Termination http://www.spiegel.de/wirtschaft/0,1518,626542,00.html

Telecommunication EPlus Data plan for mobile phones

2008 Permanent data activity reduces the network quality

Termination http://www.zdnet.de/news/39189273/e-plus-kuendigt-kunden-mit-daten-flatrate.htm

Telecommunication Sprint Nextel Data plan for mobile phones

2007 Calling customer-service lines too often

Letters notifying some customers that their service would be canceled one month later due to excessive calls to customer service (sample letter: Appendix A)

http://news.cnet.com/8301-10784_3-9739869-7.html

Telecommunication Comcast Broadband internet 2007 In regions where it is too expensive to invest in the network infrastructure, it is cheaper to discontinue service to heavy users

Cutting off service – often without warning – when high-speed Internet customers exceed an unstated download limit

http://www.infoworld.com/d/adventures-in-it/comcast-silently-terminates-broadband-customers-405

Telecommunication Verizon Data plan for mobile phones

2007 Unusually high data activity over an extended period

Disconnect the mobile number http://www.dslreports.com/shownews/82762

Telecommunication 1&1 Broadband Internet 2005 Excessive data activity on unlimited subscription plans

Customer service offers 100 Euro if the customer terminates the contract

http://www.heise.de/newsticker/meldung/Goldener-Handschlag-fuer-Power-Sauger-108291.html

Energy Provider RheinEnergy Natural gas and electricity

2011 Unprofitability of existing contracts

Written notice of termination because of rearrangement of offerings

http://blog.guhlke.com/2011/07/rheinenergie-kundigt-fairregio-duo.html

Financial Service Provider

Number26 Checking account 2016 Unprofitability of existing contracts

Written notice of termination without explanation

http://www.handelsblatt.com/finanzen/steuern-recht/recht/fintech-number26-chef-verteidigt-girokonto-kuendigungen/13674190.html

Financial Service Provider

BHW Building savings contract

2007 Unprofitability of existing building saving contracts because of high fixed interest rates

Termination and payout http://www.vedix.de/bhw-bausparkasse-kundigt-kunden-563/

Retail Filene’s Basement

Off-price men's and women's apparel, home goods, and accessories

2003 Returned too many items and made too many complaints about service

Letter from the chain's corporate parent telling them they were no longer welcome in the stores

http://www.sunjournal.com/node/647822

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TABLE 2: Related literature

Study Research design Dependent Variables

Main Effect of Marketing

Moderators Mediators Observing Demarketing

Experiencing Demarketing

Haenlein & Kaplan 2010

Online experiment with 773 US customers

Intentions (exit, voice, loyalty, purchase) Fairness Value

no Tie strength Abandonment strategy Customer type (current vs. potential) Firm positioning strategy

Attitude to demarketing

yes no

Haenlein & Kaplan 2011

Online experiment with (773+192) US customers

Intentions (exit, purchase, boycott)

no Tie strength Abandonment strategy Remaining vs. potential customers

no yes no

Haenlein & Kaplan 2012

Online experiment with 385 US customers

Intentions (exit, voice, loyalty)

no Tie strength Abandonment strategy

no yes no

This study 7 online experiments with 2,791 respondents Field study with 1,118 households

Intentions (negative WoM) Brand attitude

yes Social proximity Attribution Commonness Message Framing

Fairness Warmth Competence

yes yes

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TABLE 3: Overview of Empirical Studies Study 1 Study 2 Study 3 Study 4 Study 5 Study 6 Study 7

Fairness, Warmth & Competence

Attribution Social proximity Commonness Loss - Gain Alternative offer Compensation

Factor 1 (factor levels) Demarketing (yes/no)

Demarketing (yes/no)

Demarketing (yes/no)

Demarketing (yes/no)

Framing1 (Loss/Gain)

Demarketing (yes/no/alternative offer)

Demarketing (with compensation/no compensation/no)

Factor 2 (factor levels) Attribution (customer/firm)

Social proximity (high/low)

Commonness of occurrence (common/ uncommon)

Framing (Firm/Customer)

n 281 437 190 537 482 373 491

Hypothesis / Theoretical expectations

H1 Customer demarketing → brand attitude (-) Customer demarketing → nWoM (+)

● ●

● ●

● ●

● ●

● ●

● ●

H2 Perceived fairness mediates ● ● ● ● ●

H3 Perceived warmth mediates ○/●

H4 Perceived competence mediates ○/●

H5 Attribution of responsibility moderates the effects of customer demarketing

○

H6 Social proximity moderates the effects of customer demarketing

○

H7 Commonness moderates the negative effects of customer demarketing

●

H8 Losses (versus gains) framing increase fairness perceptions

●

H9 Alternative contract offer moderates the effects of customer demarketing

●

H10 Monetary compensation moderates the effects of customer demarketing

●

Note: ● (○) indicates hypothesis was supported (not supported). ○/● denotes partial support. 1 In this study, we manipulate customer demarketing to be experienced; in all studies, customer demarketing is observed.

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TABLE 4: Results (means and SD) for Studies 1-7

Study Factor 1 Factor 2 Fairness Brand Attitude

nWoM

Study 1 No Customer Demarketing [138] 4.83 (1.27) 4.59 (1.30) 2.82 (1.51) Customer Demarketing [143] 2.35 (1.70) 2.29 (1.64) 5.23 (1.80)

Study 2 No Customer Demarketing

Attribution Firm [112] 3.51 (1.46) 3.51 (1.54) 4.35 (1.49) Attribution Customer [106] 4.01 (1.58) 3.83 (1.57) 3.67 (1.59)

Customer Demarketing Attribution Firm [107] 1.87 (1.26) 1.92 (1.12) 5.58 (1.45) Attribution Customer [112] 2.89 (1.56) 2.67 (1.46) 4.97 (1.48)

Study 3 No Customer Demarketing

Socially close [30/39/38]* 4.27 (1.16) 4.38 (1.35) 3.59 (1.76) Socially distant [39/42/39]* 4.30 (.994) 4.11 (1.30) 3.72 (1.57)

Customer Demarketing Socially close [40/38/40]* 2.63 (1.18) 3.34 (1.23) 4.12 (1.46) Socially distant [42/42/41]* 2.90 (1.36) 3.42 (1.33) 4.31 (1.38)

Study 4 No Customer Demarketing

Uncommon [132] 4.21 (1.45) 4.03 (1.45) 3.18 (1.47) Common [129] 3.87 (1.42) 3.86 (1.35) 3.70 (1.50)

Customer Demarketing Uncommon [141] 2.19 (1.50) 2.20 (1.43) 5.19 (1.70) Common [135] 2.40 (1.45) 2.23 (1.38) 5.57 (1.54)

Study 5 Gain frame

Firm frame [122] 1.77 (1.13) 2.01 (1.22) 5.92 (1.35) Customer frame [117] 1.92 (1.26) 2.52 (1.38) 5.60 (1.53)

Loss frame Firm frame [123] 2.40 (1.53) 2.56 (1.45) 5.63 (1.56) Customer frame [120] 2.13 (1.29) 2.47 (1.47) 5.58 (1.61)

Study 6 No Customer Demarketing [132] 4.04 (1.32) 3.86 (1.43) 3.34 (1.46) Customer Demarketing [123] 2.11 (1.39) 2.04 (1.25) 5.22 (1.65) Alternative Offer [118] 2.46 (1.52) 2.53 (1.51) 4.96 (1.57)

Study 7

No Customer Demarketing [88] 3.94 (1.20) 3.90 (1.24) 3.34 (1.48)

Customer Demarketing [111] 2.02 (1.53) 2.12 (1.37) 5.22 (1.81)

Compensation 15€ [95] 1.66 (1.20) 1.82 (1.14) 5.45 (1.67)

Compensation 50€ [98] 2.18 (1.48) 2.16 (1.22) 5.27 (1.62)

Compensation 100€ [99] 2.50 (1.70) 2.54 (1.45) 5.06 (1.65)

Note: We report means per cell (standard deviation in brackets). Values in square brackets are sample sizes. * Sample sizes vary between conditions because of missing values.

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TABLE 5: Estimation Results Field Study Matched

sample Fairness Attitude nWoM Intention

Coeff. (s.e.)

95% CI Coeff. (s.e.)

95% CI Coeff. (s.e.)

95% CI Coeff. (s.e.)

95% CI

Demarketing -1.682 (.116)

-1.911 -1.454 -.289 (.072)

-.430 -.148 .541 (.130)

.286 .796 -.554(.140)

-.828 -.280

Fairness .791 (.020)

.752 .830 -.771 (.036)

-.841 -.701 .896(.038)

.821 .971

Constant 4.093 (.096)

3.906 4.281 .513 (.097)

.323 .702 6.299 (.174)

5.957 6.641 .400 (.189)

.028 .771

Indirect effects Via Fairness -1.330

(.093) -1.514 -1.155 1.305

(.105) 1.114 1.537 -1.624

(.124)-1.876 -1.398

R²=.22, F(1,761)=208.812,

p=.000

R²=.75, F(2,760)=1117.610,

p=.000

R²=.49, F(2,758)=362.377,

p=.000

R²=.59, F(2,601)=434.787,

p=.000 Unmatched

sample Fairness Attitude nWoM Intention

Coeff. (s.e.)

95% CI Coeff. (s.e.)

95% CI Coeff. (s.e.)

95% CI Coeff.(s.e.)

95% CI

Demarketing -1.672 (.108)

-1.885 -1.460 -.326 (.069)

-.462 -.191 .605 (.119)

.373 .838 -.852(.141)

-1.129 -.575

Fairness .793 (.019)

.756 .830 -.731 (.032)

-.795 -.668 .811 (.038)

.736 .886

Constant 4.124 (.088)

3.952 4.296 .582 (.092)

.401 .763 6.128 (.158)

5.818 6.439 .899 (.189)

.527 1.271

Indirect effects Via Fairness -1.326

(.094) -1.513 -1.143 1.228

(.096) 1.039 1.413 -1.450

(.128)-1.706 -1.204

R²=.21, F(1,907)=238.841,

p=.000

R²=.73, F(2,906)=1247.634,

p=.000

R²=.47, F(2,905)=408.757,

p=.000

R²=.53, F(2,703)=399.341,

p=.000 Note: The coefficients are unstandardized OLS regression coefficients with confidence intervals (standard errors in parentheses) obtained from Process Model 4 (Hayes 2013). We obtain the standard errors (95% confidence intervals) for the indirect effect via bootstrapping.

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FIGURE 1: Conceptual Framework of Customer Demarketing in Commercial Contexts

Brand Attitude Negative WoM

Perceived Fairness Warmth

Competence

Customer Demarketing

Attribution Social Proximity Commonness

Loss/Gain

Alternative Offer Compensation

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FIGURE 2: Results Study 1 Figure 2a: Parallel Mediation

Figure 2b: Serial Mediation

Figure 2c: Mediation via fairness

Note: The coefficients are unstandardized OLS regression coefficients (standard errors in parentheses) obtained from Process Model 4 (2a, 2c) and Model 6 (2b), respectively (Hayes 2013). Significant coefficients (p<.05) are printed in bold. Detailed estimation results are reported in Appendix Table A1 (parallel mediation), Table A2 (serial mediation) and Table A3 (mediation via fairness).

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FIGURE 3: Results Study 2

Note: The coefficients are unstandardized OLS regression coefficients (standard errors in parentheses) obtained from Process Model 7 (Hayes 2013). Significant coefficients (p<.05) are printed in bold. Detailed estimation results are reported in Table A4 (Appendix).

FIGURE 4: Results Study 3

Note: The coefficients are unstandardized OLS regression coefficients (standard errors in parentheses) obtained from Process Model 7 (Hayes 2013). Significant coefficients (p<.05) are printed in bold. Detailed estimation results are reported in Table A5 (Appendix).

FIGURE 5: Results Study 4

Note: The coefficients are unstandardized OLS regression coefficients (standard errors in parentheses) obtained from Process Model 7 (Hayes 2013). Significant coefficients (p<.05) are printed in bold. Detailed estimation results are reported in Table A6 (Appendix).

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FIGURE 6: Results Study 5

Note: The coefficients are unstandardized OLS regression coefficients (standard errors in parentheses) obtained from Process Model 7 (Hayes 2013). Significant coefficients (p<.05) are printed in bold. Detailed estimation results are reported in Table A7 (Appendix).

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