The effect of different customer treatments on service fairness, customer satisfaction and customer behavior in a credit management environment

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THE EFFECT OF DIFFERENT CUSTOMER TREATMENTS

ON SERVICE FAIRNESS, CUSTOMER SATISFACTION AND

CUSTOMER BEHAVIOR IN A CREDIT MANAGEMENT

ENVIRONMENT

MASTER THESIS

Amsterdam Business School – UvA

Business Studies – Marketing Track

Thesis supervisor:

Prof. Dr. Ed Peelen

by

Wietse Roest

Student Number: 10499385

Date: September 9th, 2015

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Preface

A word and thanks to Prof. Dr. Ed Peelen. Your guiding feedback helped me to improve this thesis. I really appreciate your contributed time and effort in supervising me during the thesis process. I also want to say thank you to Kenny de Vilder. His feedback on editing and writing this thesis was for me of added value. The organization I work for also deserves a thank you. The opportunity to combine my job with an extensive study program is a one in a lifetime chance. Finally, a special thanks for Lisanne. The moments that I had to spend on my education were not always the best moments for you. The support you gave me and the patience you had encouraged me to bring this thesis to a good end.

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Abstract

In multiple industries, organizations are focusing on the delivery of service quality to

differentiate from competitors and deal with fierce competition. An important moment where organizations can prove their service quality is at the service encounter, where the customer directly interacts with an employee of the organization. One important factor of the service delivery during the service encounter is fairness of the service. The perceived fairness directly influences customer service satisfaction and customer buying intention.

This study examines both a tolerant and compelling customer treatment at the service encounter in a credit management context of a Dutch insurance organization. In this

environment customers that did not meet their payment obligation regarding the organization were exposed to one of the treatments. The effect of both treatments on customer perceived fairness, satisfaction, behavioral intention and actual customer behavior is measured. Results indicate that customers perceive both treatments as fair on interpersonal, informational, procedural and distributive justice. Moreover, customer satisfaction is also perceived as high for both treatments. An interesting finding is that no significant effect was found between the fairness constructs and customer behavioral intention but actual customer behavior is

significantly better among customers that experienced the compelling treatment compared to the tolerant treatment group.

This study helps managers in developing credit collection strategies that are perceived as fair by the customer, lead to satisfied and long term customers and increases effective collection of unpaid invoices.

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1. Introduction

When products and services are becoming more equal over time in terms of attributes and benefits, service organizations are trying to differentiate themselves from competition by focusing on service quality (Parasuraman, Zeithaml, & Berry, 1988). For instance, in the insurance industry, where insurance products can be seen as commodities. Organizations in this industry nowadays are focusing on the delivery of excellent service to customers, improving customer satisfaction and offering value for money.

The service encounter plays an important role in service delivery. “The service encounter is the dyadic interaction between a customer and a service provider” (Surprenant & Solomon, 1987, p. 87) and is viewed as increasingly important in service industries (Jayawardhena, 2010). According to Hartline, Wooldridge and Jones (2003, p. 43) customers “base their evaluations on their perceptions of the service encounter”. Earlier this was recognized when Bitner, Booms and Mohr (1994, p. 95) emphasized the importance of the service encounter. They stated that the service encounter is seen as “the moment of truth” by customers and is the most immediate evidence of service quality.

1.1 Immediate cause and research question

One of the situations in which service encounters often take place is the customer contact center of a service organization. According to Bitner (1990), satisfaction with service encounters are being formed based on a comparison of customer’s expectations with the actual perception of the service performance. According to the author this service encounter satisfaction needs to be distinguished from the individual’s general attitude toward the organization or the perceived service quality (Zeithaml, 1988).

As said, it is extremely important for organizations to focus on service delivery and improving its quality of the service, in order to differentiate themselves from competition. Moreover, service quality leads to an increase in customer loyalty. In their research on repurchase intentions Boulding, Kalra, Staelin and Zeithaml (1993) found a direct positive relationship between service quality and the willingness to recommend and the intention to repurchase. Other studies show that service quality influences behavioral intentions through value and satisfaction (Cronin, Brady, & Hult, 2000).

One important factor that directly influences both service satisfaction as well as customers buying intention is fairness of the service. Fairness or justice theory is based on the equity theory (Adams, 1965). This theory states that consumers assess the equality of their treatment

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8 during service encounters. Carr (2007) found that consumers make a judgment of

interpersonal, informational, procedural and distributive justice of the service which is positively related to customer service satisfaction. Han, Kwortnik Jr. and Wang (2008) also showed that fairness is an important factor positively related to customer satisfaction in multiple service contexts such as, offline banking, airlines, hotels and telecom providers. Other studies show that perceived justice explains re-patronage intentions, customers’ word-of-mouth, responses to service failure and customer satisfaction. (Blodgett, Granbois, & Walters, 1993) (Mccoll-Kennedy & Sparks, 2003) (del Río-Lanza, Vázquez-Casielles, & Díaz-Martín, 2009).

Despite the extensive research on service fairness in service recovery and complaint handling, research on service fairness related to customer satisfaction is limited (Aurier & Siadou-Martin, 2007). Moreover, it has never been researched in a context where customers do not meet their contractual obligations with the organization. For instance a context where customers not meet their payment obligations and in turn have some form of payment arrears. It is plausible that in this particular type of service encounters, when an organization is

contacting customers that have payment arrears, some form of tension exists. This tension arises from asking money from the customer on the one hand and keeping the customer satisfied about the service delivered at the service encounter on the other. Moreover, this type of service encounter could be a crucial moment in the organizations relationship with the customer because the relationship is already under pressure since the customer falls short in his payments to the organization. Therefore, this service encounter could be either positive in restoring the relationship or negative in terminating the relationship. It is therefore, from an academic and managerial point of view, important to know how customers perceive the fairness of this form of service delivery. Understanding the most effective treatment in terms of perceived fairness, customer satisfaction and customer behavior will help organizations in improving their service delivery. It was after all shown by research that systematic service fairness positively relates to repatronage intentions (Carr, 2007) and thus in restoring and sustaining the customer relationship with the organization.

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9 The cause described above leads to the following research question that will be answered in this master thesis:

“What is the effect of different customer treatments at a service encounter, on the perceived customer fairness, customer satisfaction and customer behavior in a context where the customer did not meet its contractual obligation”?

1.2 Data and methods

With the help of a quasi-experiment data will be collected and the proposed research question answered. Two different customer treatments are executed at a credit management department of an insurance organization within The Netherlands. After contacting the customer and executing the treatment a survey is sent to the customer. The returned surveys will be statistically analyzed in order to measure customers’ fairness perceptions, satisfaction and behavior.

1.3 Thesis composition

The next section will comprise an extensive literature review and ends with a conceptual model. Subsequently the research design and data collection methods will be described to test the conceptual model. In chapter four the analysis and results of the research will be given. The last section gives answer to the main research question of this thesis and provides recommendations based on these outcomes.

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2. Literature review

This section reviews literature relevant to answer the research question. First, the concept of fairness theory will be discussed. Next, the treatments used in the experiment will be

explained. Then, the underlying constructs from social psychology and organizational

research, as well as the most important findings on justice research in service contexts will be presented. Propositions are given accordingly. Fourth, the concepts of customer satisfaction and customer behavioral intention are discussed. Finally, the chapter ends with the

presentation of a conceptual model.

2.1 Fairness theory and constructs

In daily life there are multiple moments were people evaluate events as either fair or unfair. For instance in work situations when an employee evaluates the treatment of his supervisor or in commercial situations when a customer evaluates his treatment by the service organization. An important theory underlying peoples considerations about fairness of events is the fairness theory (Folger & Cropanzano, 2001). This theory comprises of two key aspects. The first is accountability, which means that when someone is treated unfair, he is holding someone accountable for this treatment and the negative consequences of this treatment. According to Folger and Cropanzano (2001), before someone can be held accountable for a negative event, three conditions should be met: First, the event is harmful to the person experiencing the event. Secondly, someone can be held accountable for the negative consequences since this person could have controlled the event and the third condition states that with the event some ethical and normative rules are violated.

The second aspect of fairness theory is counterfactual thinking. Counterfactual thinking is thinking “contrary to the facts” (Roese, 1997, p. 133). When engaging in counterfactual thinking someone is comparing the perceived treatment with how the treatment might have been experienced. With counterfactual thinking someone engages in three contrastive actions when evaluating a negative event experienced and therewith blaming accountability (Mccoll-Kennedy & Sparks, 2003). These three actions are: what could have happened, what should have happened and how it would have felt if something different was done to prevent the negative event.

Earlier research on fairness theory, developed in the field of social psychology, distinguishes a three-dimensional construct of fairness. This research states that negative perceptions of fairness arise from associations with distributive, procedural, and interactional justice (Folger

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11 & Cropanzano, 1998). Multiple researchers used this three-dimensional construct in their work on service fairness. For instance, in their article Seiders and Berry (1998) define service fairness as: “a customer’s perception of the degree of justice in a service firm behavior” (p. 9) and use the three-dimensional construct as applicable to measure and manage service fairness. Tax, Brown and Chandrashekaran (1998) studied service complaint experiences of customers. The authors found that consumers evaluate complaint incidents based on interactional justice, distributive justice and procedural justice. Other studies also rely on these three types of justice when studying service fairness and service recovery situations (Aurier & Siadou-Martin, 2007) (Mccoll-Kennedy & Sparks, 2003) (del Río-Lanza, Vázquez-Casielles, & Díaz-Martín, 2009).

Based on earlier work by Greenberg (1993) a four-dimensional model was developed and tested by Colquitt (2001). Instead of interactional justice he added interpersonal justice and informational justice as a third and fourth construct. For every construct he created separate justice measure items. These measure items were used by Carr (2007) to develop the FAIRSEV model in order to test the perceived fairness of a service and how it affects customer satisfaction.

According to the literature there exist some objections to the applicability of the fairness construct from social psychology in consumption and service contexts (Namkung & Jang, 2010) (Nikbin, Marimuthu, & Hyun, 2013). Deutsch (1985) for instance, argued that the assessment of input and output values by customers is difficult at the same time. Swan and Oliver (1985) also pointed to the difficulties of the input/outcome operationalization.

Furthermore, customers judge fairness based on understanding the situation and the potential to maximize benefits and rewards compared to their investments (Peter & Olson, 1993). However, earlier research on service fairness was done in physical service and consumption settings, for instance in restaurants (Namkung & Jang, 2010) (Nikbin et al., 2013). In these settings customers were offered an actual service and were expected to pay for this service. This research focuses yet on a part of the total service package, the delivery of service between a call center agent and a customer. The customer is not expected to pay for this part of the service. This is why this study uses the four-dimensional construct of Colquitt (2001) and its justice measure items based on social psychology literature.

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2.2 Customer treatments

Before the underlying justice constructs are discussed in more detail, an overview of the used customer treatments during the data collection is described. The reason for this explanation is to clarify the connection between literature and the propositions of this research.

In order to be able to test a customer’s perception of fairness and satisfaction at the service encounter two separate treatments are developed. The goal of these treatments is to test whether there is a significant difference between the two treatments and a customer’s

perception of fairness and satisfaction. The treatments are based on literature and constructed in such a way that both treatments score differently on the justice constructs.

The first treatment, called the tolerant treatment, is a more open procedure. This means that the customers’ payment arrears are not primed that obvious and customers are part of the decision making process in making a payment arrangement. It encompasses sentences like: “Probably a misunderstanding has occurred”, “In our administration we see that a premium invoice is still open” and “Were you informed about this invoice?” Additional information about the unpaid invoice is given. For instance, “It concerns the premium with expiration date X” and “We did not receive the payment because of the preauthorized debit was failed”. Also information about the insurance coverage is given. “Since the payment term is expired, there is unfortunately no insurance coverage until we receive the payment”. Furthermore, the customer is actively involved and has a choice in making the payment arrangement. “What could we arrange to settle the payment?”

The second treatment, called the compelling treatment, is a more closed procedure. The payment arrear is primed more obvious and because of that, the tone of voice is more

offensive. “Until now, you did not meet your payment obligation for your premium invoice”. As with the open treatment, information about the insurance coverage is given, “At this

moment there is no insurance coverage, when we have received your payment, your insurance coverage will be automatically restored”. Additional information is given upon request of the customer. The payment arrangement, or in other words the outcome, is also closed. The customer is forced to pay within 5 days, “I will ask you to pay the invoice within 5 days from now”. This request is only discussed when the customer clearly indicates that he is not in the possibility to pay or has a thorough explanation why he has not paid yet. For instance, resignation of the insurance service. Table 1 summarizes the two different treatments. The next section discusses, as mentioned above, literature on the different justice constructs and propositions are given accordingly.

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13 Tolerant (open style) Compelling (closed style)

“Probably a misunderstanding has occurred”

“In our administration we see that a premium invoice is still open”

“Were you informed about this open invoice?”

“Until now, you did not meet your payment obligation for your premium invoice”

“It concerns the premium with expiration date X” “We did not receive the payment because of the preauthorized debit was failed”

“Since the payment term is expired, there is unfortunately no insurance coverage until we receive the payment”

“At this moment there is no insurance coverage, when we have received your payment, your insurance coverage will be automatically restored, therefore there is some urgency to pay timely”

Additional information upon customers’ request “What could we arrange to settle the payment?” “I will ask you to pay the invoice within 5 days

from now”

Table 1: Two tested customer treatments

2.3 Interactional justice

In service contexts, “interactional justice concerns the manner in which the service problem is dealt with by service providers and the specific interactions between the service provider and the customer” (Mccoll-Kennedy & Sparks, 2003, p. 253). In other words, interactional justice is the fairness of the interactions between the service employee and the customer and how the service employee behaves in executing the procedures of service delivery. Greenberg (1993) found in his research among undergraduate students that besides the degree of

interpersonal sensitivity, which is the interaction, the validity of information given plays a role as well as a form of interactional justice. He argued that both aspects are involved in the perception of fairness. This means that interactional justice is not only about interpersonal aspects but also about the information provided during the service encounter. As mentioned before, Colquitt (2001) developed a four-dimensional construct, based on these findings. His construct consists of four categories where interactional justice comprises both interpersonal justice as well as informational justice.

2.3.1 Interpersonal justice

Interpersonal justice is defined by Tax et al. (1998) as “the perceived fairness of interpersonal treatment that people receive during the enactment of procedures” (p. 62). Earlier, Bies and Moag (1986) argued that honesty, politeness and respect are aspects of communication at which interactional justice can be evaluated and measured. In addition to these findings, Clemmer (1993) identified friendliness and interest as communication aspects as well. In service contexts, multiple studies on interpersonal behavior and justice were done. For instance, research on relationship marketing within the life insurance industry showed that a

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14 customer’s overall satisfaction with the service, was determined by the satisfaction of the client with the agent (Crosby & Stephens, 1987).

Bitner, Booms and Tetreault (1990) collected from different industries 700 incidents from customers at the service encounter and categorized them in satisfactory and dissatisfactory behavior of employees. Carr (2007) used the communication aspects politeness,

respectfulness and dignity to measure the interpersonal justice perceived by customers of an internal ICT market within an organization. He found a significant positive relationship between interpersonal fairness and customer satisfaction, mediated through overall service fairness.

In their research on financial services quality and fairness (Chen, Liu, Sheu, & Yang, 2012), the authors found similar results using the FAIRSERV model. At financial institutions in Taiwan they showed that interpersonal fairness has a positive significant effect on customer satisfaction, mediated through overall service fairness.

It is assumable that the mentioned communication aspects are more applied in the tolerant treatment compared to the compelling treatment, due to the tone of voice. It is the intention that customers that undergo the more tolerant approach perceive interpersonal fairness as higher. This indicates the following proposition:

P1: Interpersonal justice is perceived higher among customers that undergo a tolerant treatment compared to customers that undergo a compelling treatment.

2.3.2 Informational justice

Informational justice “refers to the adequacy and truthfulness of information which explains the causes of a negative event” (Nikbin, Ismail, Marimuthu, & Armesh, 2012, p. 312). In three studies Bies and Shapiro (1987) showed the effect of causal accounts on judgments of interactional fairness in organizational contexts. Causal accounts are: “explanations regarding a person's responsibility for his or her actions” (p. 201). They found that when such

explanations were given by a manager, interactional fairness was perceived higher by its employees. Shapiro, Buttner and Barry (1994) found that the specificity of an explanation plays a key role in peoples judgments of explanation adequacy. Whereby the more specific the outcome explained, the more positive the judgment of explanation adequacy. This corresponds with the perception of informational justice in a consumers environment as explained by Nikbin et al. (2012, p. 313): “A customer’s perception of informational justice is threatened by the lack of explanations provided to people about why procedures were used in a certain way or why outcomes were distributed in a certain manner”.

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15 Shapiro et al. (1994) also found that verbally communicated explanations were perceived as more adequate compared to written explanations. Although there only exists limited research on informational justice in service settings it was found by Mattila and Cranage (2005) that the informational justice perception of customers is positively influenced when customers are offered an informed choice after a service failure.

Based on the customer treatments, it is proposed that in a more compelling treatment the information given to a customer is perceived as better explained and more specific due to the straightforwardness of the message and information given. In turn this leads to a higher perceived justice. Therefore, the following proposition is tested:

P2: Informational justice is perceived higher among customers that undergo a compelling treatment compared to customers that undergo a tolerant treatment.

2.4 Procedural justice

Procedural justice “refers to the perceived fairness of the policies, procedures, and criteria used by decision makers in arriving at the outcome of a dispute or negotiation” (Blodgett, Hill, & Tax, 1997, p. 189). The first stream of literature on procedural justice came from Thibaut and Walker (1975). Based on their observations in courtrooms, they developed a control-oriented model with two criteria for procedural justice. The first criterion is process control, at which someone gets the possibility to express his views and arguments. This corresponds with the “voice effect” presented by Folger (1977). The procedure will be

perceived as more fair if someone has the opportunity to have “voice”. The second criterion is decision control at which someone has influence over the actual outcome. Multiple studies on procedural justice found the crucial fact that someone perceives his treatment as more fair when process control or “voice” is experienced. Even in situations when no direct control over the outcome is experienced or the outcome is unfavorable for the one that has “voice” (Lind, Kanfer, & Early, 1990).

During the second stream of research on procedural justice, Leventhal (1980) introduced additional criteria for fair procedures. Representation, accuracy, consistency, and

bias-suppression are criteria that influence and affect the perception of procedural justice (Colquitt, 2001) (Colquitt, Scott, Judge, & Shaw, 2006). When these criteria are met in procedures and policies, procedural fairness will be perceived as more fair. In service contexts Kelly,

Hoffman and Davis (1993) found that even when a satisfied solution was offered to a customer in service recovery strategy, customer’s evaluation was poor due to the process to arrive at the solution. Moreover, it was proven by Smith, Bolton and Wagner (1999) that

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16 customer satisfaction was positively influenced by procedural justice. More recently, Nikbin et al. (2012) showed with their research in the Malaysian mobile telecommunication industry that the higher procedural justice perceived, the lower switching intentions are to another provider.

Because of the open style of the tolerant treatment, it is assumable that a customer feels the opportunity to have both process and decision control. Moreover, in the tolerant treatment the customer is specifically asked to suggest the payment agreement. Therewith it is proposed that a customer that experiences a tolerant treatment is feeling a sense of having “voice” both over the process and the decision.

P3: Procedural justice is perceived higher among customers that undergo a tolerant treatment compared to customers that undergo a compelling treatment.

2.5 Distributive justice

Based on the work by Cohen-Charash and Spector (2001) a definition of distributive justice is provided by Carr (2007) as “the cognitive, affective and behavioral reaction to outcome distributions from a source”. The concept of distributive justice found its origin in social exchange theory with Adam’s equity theory (1965) as starting point. The fundamental idea of this concept is that when people engage in an exchange all the input are weighed against the output. If the outcome does not correspond with the expectations, than the outcome is

perceived as unequal or unfair. The equity rule is described by Leventhal (1976) as: “a single normative rule which dictates that rewards and resources be distributed accordance with recipients’ contributions” (p. 91). Colquitt (2001) argued in his study on justice measure items that the use of Leventhal’s conceptualization of the equity rule maximizes

generalizability. This means that this conceptualization measures the fairness of an outcome based on the effort or contributions of someone to arrive at that outcome. Therefore, these measure items are highly useful to study the fairness of an outcome at the service encounter. In service situations distributive justice is mainly researched in service recovery and

complaint handling situations. In their meta-analysis of 60 independent studies on satisfaction in complaint handling, the authors found that satisfaction with complaint handling was affected most by distributive justice (Orsingher, Valentini, & de Angelis, 2010).

The message given to the customer in the compelling treatment primes the lack of effort and contribution by the customer towards the organization (“Until now, you did not meet your payment obligation for your premium invoice”). In the tolerant treatment this message is not that primed clearly. Therefore, based on the equity rule a customer that experiences the

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17 compelling treatment, feels his lack of obligation and thus lack of input. Consequently he perceives his outcome in the form of a payment agreement as more fair. This leads to the following proposition to be tested:

P4: Distributive justice is perceived higher among customers that undergo a compelling treatment compared to customers that undergo a tolerant treatment.

2.6 Customer satisfaction

The concept of customer satisfaction has received much attention in literature over the last decades. Customer retention is for example a consequence of customer satisfaction

(Szymanski & Henard, 2001) . Therefore, it is important to understand the effect of service fairness on customer satisfaction. As mentioned before, the effect of perceived customer fairness on satisfaction is mainly researched in service recovery situations. For instance, Sparks and McColl-Kennedy (2001) showed a variety in satisfaction levels dependent on different types of recovery measures, corresponding with procedural, distributive and

interactional justice. Moreover, Aurier and Siadou-Martin (2007) mention that “If customers expect a fair treatment, both during service delivery and service recovery, (Bowen, Gilliland, & Folger, 1999) it is our belief that most of the research until now has focused on service recovery and complaint management”. Only the last years, some researchers did study the service fairness satisfaction relationship. In a study among customers of a restaurant the authors showed that service fairness had a positive effect on customer satisfaction (Aurier & Siadou-Martin, 2007), with procedural justice as key determinant. In research among

customers of an online bank service Zhu and Chen (2012) also found a positive relationship between perceived service fairness and customer satisfaction. It is therefore proposed, based on the propositions before, that in the specific context of a credit management environment: P5a: Customer satisfaction is perceived higher among customers that undergo a tolerant treatment compared to customers that undergo a compelling treatment.

P5b: Perceived interpersonal, informational, procedural and distributive justice have a direct positive effect on customer satisfaction

2.7 Customer behavioral intention

Emotional responses of consumers lead to behavioral intentions of consumers. For instance, Blodget et al. (1993) proved that perceived justice acts as the key determinant of negative word of mouth behavior by consumers and their repurchase intentions. Warshaw and Davis (1985) define behavioral intention: “as the degree to which a person has formulated

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18 environment, customers’ intensions to revisit a store were affected by perception of inequity (Huppertz, Arenson, & Evans, 1978). A significant positive relationship between service fairness and the intention to repatronage was found by Clemmer (1993). Finally, Namkung and Jang (2010) showed both a positive relationship between price fairness and interactional fairness on behavioral intentions in restaurant service settings. Because behavioral intention is not actual behavior, the actual behavior of customers is also measured in the research.

Especially in an environment where customers did not meet their contractual obligation it would be logical that customers experienced to the compelling treatment feel more urgency to pay and in turn behave that way. Therefore, based on earlier propositions it is for customer behavioral intention and customer behavior proposed that:

P6a: Customer behavioral intention is higher among customers that undergo a compelling treatment compared to customers that undergo a tolerant treatment.

P6b: Perceived interpersonal, informational, procedural and distributive justice have a direct positive effect on customer’s intention to pay

P6c: Customers that undergo the compelling treatment are meeting their payment agreement more precise than customers that undergo the tolerant treatment.

2.8 Conceptual model

All the propositions described above are summarized in the following conceptual model which will be tested in this master thesis.

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3. Data and method

In this chapter the method of research is presented. First, the research procedure is described. secondly, the research sample is clarified and response rates are given. Finally, the variables, measured with the questionnaire, are presented and control variables are given.

3.1 Research procedure

A quantitative explanatory research was executed at a credit management department of a Dutch insurance organization to answer the earlier mentioned research question: “What is the effect of different customer treatments at a service encounter, on the perceived customer fairness, customer satisfaction and customer behavior in a context where the customer did not meet its contractual obligation”?

The population for this research existed of all the customers of the insurance organization with an individual disability insurance and a payment arrear for this particular insurance at the time of research. A payment arrear in this research was defined as when a customer had omitted to pay at least one invoice which was expired for at least thirty days. Furthermore, the customer should have received two collection letters and the insurance coverage for the disability insurance was expired due to the terms of payment.

Sample elements were selected on a daily base through extreme case purposive sampling (Saunders, Lewis, & Thornhill, 2012), based on automatically sent collection letters. This particular sampling technique was chosen because only cases should be selected which met the criteria of the population. Moreover, the cases needed to provide the data necessary to answer the research question.

In order to test the proposed hypothesis both a tolerant and compelling customer treatment, consistent with the literature, were developed with the help of telephone scripting. The

scripting was based on a professional credit management training. Before the research started, both treatments were first tested with a pilot period of one week. Based on consumer reactions some adjustments were made in both the treatments. Both the two customer treatments were presented in chapter two as an introduction for the propositions to test.

Since the research was done in an operational setting, some factors had to be considered in the approach of the data collection. The most important factors were the operational capacity available and the variation in the daily amount of sent collection letters. Customers were contacted by a call center agent not earlier than four days after ship date of the collection letter and not after the next collection letter was sent, which was fourteen days after ship date. Before contacting, the customer details were requested in the organizations administrative

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20 system, to ensure the invoice had not been paid in the meantime and a telephone number of the customer was present. In the case of an absent telephone number, the customer details were enriched with a telephone number by searching the internet. Customers without a traceable telephone number or customers that fulfilled their payment obligation in the meantime were excluded from the experiment but not removed from the research list.

In the case a customer was not accessible and the invoice had not been paid in the meantime, a maximum of four attempted phone calls were executed on four different days. The

customers were excluded from the research if the invoice was paid in the meantime or if the customers remained inaccessible after four attempts.

During the moment of contact the call center agent executed the customer treatment, asked for the customer’s e-mail address and recorded the findings in the research list. At the end of the day a questionnaire was sent by e-mail to the sample elements that were contacted that particular day by the call center agent. When no response was received a reminder was sent after five days.

The questionnaire, to assess customers’ perceptions about the treatments, was first tested during a pilot period of one week. Adjustments were made in the questionnaire based on customers’ reactions. Both examples of the initial e-mail message and the remind e-mail are included in Appendix A. The questionnaire is included in Appendix B.

3.2 Research sample and timing

To be able to make statistical inferences a minimum of fifty respondents for each treatment was strived for. The execution of the research first focused on the tolerant treatment. When sufficient responses were collected the compelling treatment was started. The questionnaire was sent to 229 customers that experienced the tolerant treatment and to 227 customers that experienced the compelling treatment. Questionnaires could be started via the hyperlink in the sent e-mail. Anonymity and confidentiality of the customer’s privacy were guaranteed. From the tolerant treatment group 66 respondents started to fill out the questionnaire and 59

respondents completely finished it (response rate 25,8%). A total of 59 respondents that experienced the compelling treatment started the questionnaire and 56 respondents completely finished it (response rate 24,7%). Respondents of both treatments were mainly Dutch

entrepreneurs from a variety of different industries. A complete overview of the research sample is presented in the next chapter.

The period of data collection took twelve weeks within the months May, June and July in 2015. This moment of research displays the most representative results because it falls right

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21 after the yearly recalculation of insurance premiums at the beginning of the year and just before the summer holiday period in which not every customer with a payment arrear is equally accessible.

3.3 Research measures

All constructs, researched in the questionnaire, were measured on a 5-point Likert-scale. Only the distributive construct and behavioral intention construct contained a not applicable option the respondent had the opportunity to choose. Those measures were computed afterwards. The 5-point Likert scales ranged from 1= “strongly disagree” to 2= “strongly agree”. Next, the different variables and accompanying measures are presented.

Interpersonal justice was measured with the justice measure items (Colquitt, 2001), based on the work of Bies and Moag (1986). Politeness, respectfulness and the absence of improper remarks were communication aspects measured on a 5-point Likert scale. These three aspects all influence the interactions between the employee and the customer and are therefore used to assess customer’s perceived interpersonal justice during the service encounter.

In order to measure customer’s perceived informational justice, the justice measure items of Colquitt (2001), adapted from Bies and Moag (1986) and Shapiro et al. (1994), were slightly changed to the context and used. Customers were asked to score their perception of the clarification by the employee for being contacted. Moreover, it was also measured to what extend the reason of being contacted was obvious for the customer. At last, to what extend the explanation was tailored to the customers’ needs, in the opinion of the customer, completed the informational justice scale.

Four measure items relevant for the research context were chosen to study procedural justice. Measured on a 5-point Likert scale, the customer’s opportunity to have influence over the outcome as well as having “voice” were measures acquired by the work of Thibaut and Walker (1975). A treatment free of bias by the organizations employee and a treatment based on accurate information (Leventhal, 1980) were the remaining measures to complement the procedural justice construct.

Deviating from the previous measures, distributive justice had an additional not applicable option. Customers could already have paid at the moment they were contacted by the

employee. A payment agreement in this case would be illogical. Therefore, a ‘not applicable’ option was necessary to include for the outcome measure. Slightly tailored to the context and based on Adams equity theory (Adams, 1965), the perceived outcome was measured against customer’s input (Leventhal, 1980). Customer’s payment obligation, customer’s current

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22 situation and customer’s duration of their relationship with the organization were the input factors. The made payment agreement acted as the outcome factor.

Measured on a 5-point Likert scale, the customer satisfaction construct is adapted from earlier research on customer satisfaction at service encounters and service fairness (Bitner, 1990) (Aurier & Siadou-Martin, 2007) (Carr, 2007) (Zhu & Chen, 2012). Satisfaction about the interactions with the call center agent and customer’s satisfaction with the payment agreement were measured to assess the satisfaction about the treatment a customer received. The overall firm satisfaction perceived by the customer as well as customer’s satisfaction about the price-quality ratio of the insurance product were measured to assess customers’ overall perceptions of satisfaction with the firm and the insurance product.

Adapted from the work of Cronin et al. (2000), the behavioral intention construct is measured based on one question. The likelihood that the customer pays the invoice according to the agreement made during the service encounter. As with the distributive justice construct, a not applicable option was added to the Likert scale. In the case a customer had already paid an intention to pay is obviously illogical.

3.4 Control variables

Although the study is not an actual quasi experiment since the absence of a control group, external validity is high because of the practical context of the research. The use of real customer treatments and customer reactions addresses a real life setting. From a managerial point of view this is a favorable situation because the results from this context show the best guidelines for practical implementation and business results in the future. However, to

guarantee sufficient internal validity some control variables were added to the survey. Control variables are customers’ complaints in the past about the organization and contact about the specific invoice in an earlier stage.

The next chapter presents an overview of the research sample and gives the reliability of the research measures. Finally, the statistical results are presented to test the propositions and to answer the research question.

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4. Results

This section describes, with the help of different statistical tests, the analysis and results of the data collected from the treatments at the service encounter. It starts with the clarification of data cleaning and missing values. Next, the descriptive analysis is given. A factor analysis provides indications of validity and an analysis of scale reliability is executed. With the use of equation analysis and regression analysis propositions are tested. All the statistical analysis are executed with SPSS.

4.1 Descriptive data analysis

Responses were received from 66 customers out of the tolerant treatment and from 59 customers out of the compelling treatment. A total of 59 and 56 customers respectively completed the questionnaire. Since a minimum of 50 questionnaires for each treatment was strived for it was chosen to exclude the unfinished surveys from the data analysis.

As mentioned earlier, the distributive justice construct and the customer behavioral intention construct were measured on a 5-point Likert scale, with an additional not applicable option. In the case a respondent had chosen for the not applicable option this response was transformed into a missing value. The highest total of 44 missing values were found for the item “Offered me a reasonable payment agreement based on my history as a customer”.

Table 2 shows the descriptive analysis of the data. Among respondents a normal distribution of age exists with 40% of the respondents between 40 and 49 years old. The results about the duration of customer’s relationship with the organization indicates in general long term relationships. The outcomes are negatively skewed. This is confirmed in that 44,3% of the respondents have been customer of the organization for at least 10 years. Little more than 10% of the respondents did complain in the past about the organization. For both the tolerant as well as the compelling treatment 11,3% of the respondents had already contact with the organization about the unpaid invoice, before the employee did contact the customer. This description of the data shows a representative view of the organizations customer base for this type of insurance product.

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Variables Tolerant Treatment Compelling Treatment Total

# % # % # % Age < 30 years 30 – 39 years 40 – 49 years 50 – 59 years 60< years 4 3,5% 2 1,7% 6 5,2% 12 10,4% 12 10,4% 24 20,8% 21 18,3% 25 21,7% 46 40,0% 22 19,1% 16 13,9% 38 30,0% 0 0% 1 0,9% 1 0,9% 59 51,3% 56 48,7% 115 100,0% Customer relationship < 5 years 5 – 10 years 10< years 14 17 28 12,2% 14,8% 24,3% 16 17 23 13,9% 14,8% 20,0% 30 34 51 26,1% 29,4% 44,3% 59 51,3% 56 48,7% 115 100,0% Complaint history Ja Nee 6 53 5,2% 46,1% 7 49 6,1% 42,6% 13 102 11,3% 88,7% 59 51,3% 56 48,7% 115 100,0% Earlier contact invoice Ja Nee 13 46 11,3% 40,0% 13 43 11,3% 48,7% 26 89 22,6% 77,4% 59 51,3% 56 48,7% 115 100,0%

Table 2: Descriptive statistics

4.2 Reliability and validity

An exploratory factor analysis was executed to identify underlying factors in the items measured with the questionnaire and to identify possible data reduction. The measure items for interpersonal, informational, procedural and distributive justice were combined in the factor analysis. Since customer satisfaction and customer behavioral intention were both dependent variables of the four justice constructs, within the conceptual model, both were kept out the factor analysis.

The outcome of the factor analysis shows three factors with an eigenvalue of more than 1. Although four constructs were tested, the items from both the constructs informational justice and procedural justice loaded on one and the same factor. Except for the item “Gave me the opportunity to influence the outcome” which loaded also on the factor of distributive justice. An explanation that both informational and procedural justice loaded on only one factor, could be that the provision of proper information could be seen as a part of a fair procedure. The items of interpersonal justice and distributive justice both loaded on one separate factor. Despite three factors were found with an eigenvalue of more than 1, it was decided to use the four initial constructs. In literature a clear distinction is made between informational justice and procedural justice (Leventhal, 1980) (Shapiro et al., 1994). Moreover, Colquitt (2001) showed the validity for the justice measure items for all four scales.

Next, scale reliability was measured for the four justice scales as well as the customer satisfaction scale. Because the customer behavioral intention scale was measured only with the likelihood question, about paying upon agreement after the service encounter, no Cronbach’s Alpha was measured. Both the four justice scales and the customer satisfaction scale showed good reliability. None of the five constructs turned out to have a Cronbach’s

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25 Alpha below 0.80 which means that the items belonging to a scale are sufficiently measuring the same outcome. Table 3 shows the factor loadings from the factor analysis for every measure item. Reliability for the relevant scales are also displayed in the table. The SPSS output of the factor analysis is included in Appendix C.

Variables/Source Measures Factor Loadings Reliability (α)

Interpersonal justice (Bies & Moag, 1986; Colquitt, 2001) Treated me polite Treated me respectful Made no improper remarks ,904 ,862 ,874 ,936 Informational justice (Bies & Moag, 1986; Shapiro et al., 1994; Colquitt, 2001)

Clarified me the reason of contacting

Had an obvious reason to contact me

Tailored the explanation to my needs ,843 ,682 ,775 ,860 Procedural justice

(Thibaut & Walker, 1975;

Leventhal, 1980; Colquitt, 2001)

Contacted me based on accurate information

Treated me fee of bias Gave me the opportunity to tell my side of the story (“voice”)

Gave me the opportunity to influence the outcome

,692 ,802 ,774 ,498 ,844 Distributive justice (Leventhal, 1980; Colquitt, 2001) Offered me a reasonable payment agreement: - based on my payment obligation - based on my current situation - based on my history as customer ,912 ,923 ,884 ,959 Customer satisfaction ,850

Extraction Method: Principal Component Analysis Table 3: Exploratory Factor Analysis

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4.3 Equation analysis

To be able to test the propositions presented in chapter two, an equation analysis was performed on the justice variables. With the help of an independent samples t-test both the means of the tolerant treatment group and the compelling treatment group were compared for each justice variable as well as the customer satisfaction and customer behavioral intention variable. A Chi-Square test was used to test the difference in actual customer behavior between both treatments. Table 4 shows the summary of the equation analysis findings.

4.3.1 Interpersonal justice

First, before comparing means with an independent samples t-test, some assumptions should be met. The first assumption is a normal distribution of the data. A Kolmogorov-Smirnov test was used to test normality of the data for both treatments. Scores for interpersonal justice did deviate significantly from a normal distribution with D(59) = .259 and p = .000 for the tolerant treatment and D(56) = .273 and p = .000 for the compelling treatment. However, the central limit theorem allows us to assume normality since both samples are large enough (Field, 2013). Furthermore, the use of 1000 bootstrap samples helps to control for potential bias in the data. Testing for equal variances with Levene’s test shows no significant

differences in variances between both treatments (F = .588, p = .445).

The results of the independent samples t-test does not indicate significant differences on customers perception of interpersonal justice between the tolerant treatment (M = 12.86, SE = .36) and the compelling treatment (M = 12.70, SE = .37). The difference of 0.168, with 95% CI [-.818, 1,190] is not significant t(113) = .320, p = .752. Cohen’s d also shows almost no effect size (d = .062). Therewith proposition 1: “Interpersonal justice is perceived higher among customers that undergo a tolerant treatment compared to customers that undergo a compelling treatment” is not supported.

4.3.2 Informational justice

Distribution of the data for informational justice shows on both the tolerant treatment as well as the compelling treatment negative skewness, -1.11 (SE = .31) and -1.48 (SE = .32)

respectively. This asymmetrical distribution is supported with the results of the Kolmogorov-Smirnov test with D(59) = .183 and p = .000 for the tolerant treatment and D(56) = .219 and p = .000 for the compelling treatment. In running the independent samples t-test, the

bootstrapping method is used and equal variances assumed. (F = .075, p = .785)

As with interpersonal justice no significant difference is found on customers’ perception of informational justice between the tolerant treatment (M = 12.44, SE = .33) and the compelling

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27 treatment (M = 12.09, SE = .39). Although the mean for the tolerant treatment is slightly higher than for the compelling treatment, the mean difference of .351, with 95% CI [-0.626, 1,403] is not significant t(113) = .710, p = .508. The effect size also shows only a limited effect (d = .126). These results indicate that proposition 2: “Informational justice is perceived higher among customers that undergo a compelling treatment compared to customers that undergo a tolerant treatment.” is not supported.

4.3.3 Procedural justice

The Kolmogorov-Smirnov test, with D(59) = .123, p = .027 and D(56) = .195, p = .000 for both the tolerant treatment and compelling treatment, shows a significantly asymmetrical distribution. Scores of the independent samples t-test, with bootstrapping and the assumption of equal variances (F = .432, p = .513), provides a mean difference of -0.180 with 95% CI [-1.354, 1.097]. This represents a higher score among the compelling treatment group (M = 16.21, SE = .42 ) when it comes to procedural justice, compared with the tolerant treatment group (M = 16.03, SE = .45).

However, this difference is not significant t(113) = -.297, p = .739 and Cohen’s d shows no effect (d = -.056). Therefore, proposition 3, “Procedural justice is perceived higher among customers that undergo a tolerant treatment compared to customers that undergo a

compelling treatment” is not supported either. 4.3.4 Distributive justice

As seen for the other justice variables, the distributive justice data is significantly

asymmetrical. Both for the tolerant treatment (D(36) = .173, p = .008) and the compelling treatment (D(33) = .264, p = .000). Bootstrapping was used to correct for bias in the data. The scores of the independent t-test, with equal variances (F = .629, p = .431), show a mean difference of -0.869 with 95% CI [-2.390, 0.803]. Distributive justice is perceived slightly higher among customers from the compelling treatment group (M = 11.42, SE = .60) than among customers from the tolerant treatment group (M = 10.56, SE = .56).

Despite the difference is not significant with t(67) = -1.046, p = .304. The effect size shows at least a small effect (d = -.254). Proposition 4 “Distributive justice is perceived higher among customers that undergo a compelling treatment compared to customers that undergo a tolerant treatment” is not significantly supported but the effect is visible in the data.

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4.3.5 Customer satisfaction

The distribution of the customer satisfaction data is indicated as asymmetrical. For the tolerant treatment and compelling treatment D(59) = .199, p = .000 and D(56) = .169, p = .000 respectively. The outcomes of the independent samples t-test with bootstrapping does not support proposition 5a: “Customer satisfaction is perceived higher among customers that undergo a tolerant treatment compared to customers that undergo a compelling treatment.” Assuming equal variances (F = .585, p = .446), the mean difference of -.953 with 95% CI [-2.318, 0.418] between the tolerant group (M = 14.12, SE = .49) and the compelling group (M = 15.07, SE = .48) is not significant (t(113) = -1.385, p = .159). Despite no significant effect is proved, the mean difference shows, with a small effect size (d = -.263), that customers whom experienced the compelling treatment are even more satisfied than customers that experienced the tolerant treatment.

4.3.6 Customer behavioral intention

No significant difference is found between the tolerant treatment group (M = 4.21, SE = .14) and the compelling treatment group (M = 4.49, SE = .10) when it comes to the intention to pay. Remarkable is the outcome of the independent samples t-test. Although not a significant difference is found, the outcome indicates more difference than in the earlier equations, at which the upper level of the confidence interval is close to zero (MD = -.278 with 95% CI [0.674, 0.61] and t(99) = 1.565, p = .127). This is also seen in the medium effect size (d = -.391). Because of the asymmetrical data distribution, the t-test was performed with the bootstrapping method and equal variances were assumed (F = 1.486, p = .226).

4.3.7 Customer behavior

As mentioned in chapter 2, customer behavioral intention is not the same as actual customer behavior. Therefore the difference in customer behavior is tested between the tolerant treatment group and the compelling treatment group. A Chi-Square test is performed to test this possible difference. Results from the test show that customers from the compelling treatment are showing better payment behavior than customers from the tolerant treatment group. In both the categories, ‘customer pays on the day as agreed on’ as well as ‘customer pays within 2 to 4 days after the day that was agreed on’, customers from the compelling treatment count higher scores. Moreover, customers from the tolerant treatment group did often not pay at all compared to the compelling treatment group. The Chi-Square outcomes indicates a significant association between the type of treatment and whether a customer is behaving according to the agreement χ2 (4) = 10.642, p = .031.

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29 All the SPSS output from the several independent t-tests and the Chi-Square analysis is recorded in Appendix D.

Proposition Variable Mean Difference 95% CI t p d Proposition supported 1 Interpersonal Justice 0.168 [-.860, 1,210] .320 .749 .062 No 2 Informational Justice 0.351 [-0.626, 1,403] .710 .479 .126 No 3 Procedural Justice -0.180 [-1.354, 1,097] -.297 .739 -.056 No 4 Distributive Justice -0.869 [-2.390, 0.803] -1.046 .304 -.254 No 5a Customer Satisfaction -0.953 [-2.318, 0.418] -1.385 .159 -.263 No 6a Customer Behavioral Intention -0.278 [-0.674, 0.61] -1.565 .127 -.391 No

Proposition Variable χ2 p Proposition supported

6c Customer Behavior 10.642 .031 Yes

Table 4: Independent t-test and Chi-Square results

4.4 Correlation and regression analysis

A correlation matrix as seen in table 5 was composed to show the correlation between all the variables and its possible significance. Remarkable are the correlations between the justice variables. On the 0.01 significance level, positive correlations exist between interpersonal justice and informational justice, procedural justice and customer satisfaction. Positive

correlations with distributive justice and customer behavioral intention exist on the 0.05 level. Informational justice correlates significantly on the 0.01 level with procedural and distributive justice as well as with customer satisfaction and customer behavioral intention.

Where procedural justice positively correlates with distributive justice, customer satisfaction and the intention of customers to pay, it is negative correlated with actual customer behavior on the 0.01 significance level. Distributive justice is positively correlated with customer satisfaction, customer behavioral intention and customer behavior. As expected, customer satisfaction is positively correlated with the intention to pay but, although on the 0.05 level, negatively correlated with actual behavior. Customer behavioral intention is negatively correlated with actual behavior.

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Variable M SD 1 2 3 4 5 6 7 8 9 10 11 12 13

1. Age 3.03 .888**

2. Customer Relationship Duration 2.18 .823 .412**

3. Complaint History 1.89 .318 -.141 -.021

4. Earlier Contact About Invoice 1.77 .420 .092 .171 .267**

5. Reason Payment Arrear 2.83 2.070 -.021 -.089 .023 -.096

6. Customer Treatment 1.49 .502 .001 -.069 -.037 -.014 -.087 7. Interpersonal Justice 12.78 2.800 .225* .135 .179 -.027 -.075 -.030 8. Informational Justice 12.27 2.647 .142* .114 .276** .079 -.115 -.067 .682** 9. Procedural Justice 16.12 3.239 .078 .113 .226** .117 -.167 .028 .519** .849** 10. Distributive Justice 10.97 3.447 .037 -.073 .069 .017 .058 .127 .308* .600** .722** 11. Customer Satisfaction 14.58 3.702 .007 -.044 .354** .080 -.060 .129 .407** .680** .742** .726**

12. Customer Behavioral Intention 4.35 .899 -.052 .023 .135 .130 -.289** .155 .239* .320** .383** .409** .401**

13. Customer Behavior 2.26 1.326 .091 -.046 -.183 -.132 .264** -.244** -.050 -.129 -.269** .342** -.189* -.277**

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4.4.1 Customer satisfaction

A multiple regression analysis is performed to draw a conclusion about the effect of service fairness on customer satisfaction. Dummy variables are used to include categorical predictors in the regression model (Field, 2013). The variables customer’s complaint history and

customer’s earlier contact about the invoice are included as a dummy variable.

The enter method is used to include the variables in a hierarchical manner. Model fit for both models are significant (p = 0.00) and collinearity exists only for informational and procedural justice, but this is tolerable since collinearity is not too severe. The outcomes of both models are reported in table 6 with standard errors based on bootstrapping.

The predictors of step 2 account for 74,3% of the variance in customer satisfaction, which indicates that when scores on the predictor variables go up, customer satisfaction also increases. Moreover, the addition of the variables customers’ complaint history and customers’ earlier contact show a significant increase of R-square.

Significant predictor variables on customer satisfaction within the model are distributive justice (p = 0.02) and customers’ complaint history (p = 0.034). In both models, although not significant, informational justice (p = 0.165) also has an positive effect on customer

satisfaction.

These results indicate that proposition 5b: “Perceived interpersonal, informational,

procedural and distributive justice have a direct positive effect on customer satisfaction” is only partial supported but the regression model shows a positive significant relationship.

4.4.2 Customer behavioral intention

To test the effect of the justice aspects on customer behavioral intention again a multiple regression is performed and dummy variables used which are hierarchical included with the enter method. No significant improvement of R-square is found with the addition of

customers’ complaint history and customers’ earlier contact (p = 0.212). The fit of the model is significant (p = 0.006) and again collinearity exists for informational and procedural justice which is tolerable since it is not too severe.

As the outcomes in table 7 show, only 20,7% of the predictor variables account for variance in customers behavioral intention. Furthermore, no one of the predictor variables show significant effect on the outcome variable. Therewith, proposition 6b: “Perceived

interpersonal, informational, procedural and distributive justice have a direct positive effect on customers’ intention to pay” is not supported. SPSS output for both regression analysis are recorded in Appendix E.

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32 b SE B β p Step 1 Constant -1.105 1.475 .426 Interpersonal Justice 0.002 0.12 .001 .980 Informational Justice 0.619 0.336 .408 .077 Procedural Justice 0.181 0.220 .154 .434 Distributive Justice _0.445 _0.127 _.369 _.002 Step 2 Constant -2.629 1.705 .126 Interpersonal Justice 0.024 0.115 .017 .791 Informational Justice 0.471 0.325 .310 .165 Procedural Justice 0.203 0.228 .173 .396 Distributive Justice _0.477 _0.123 _.396 _.002 Complaint History _2.229 _1.004 _.190 _.034

Customer earlier contact _0.519 _0.857 _.051 _.521

R2 = .703 for step 1; and Δ R2 = .041 for step 2 (p<0.05) Table 6: Fairness- satisfaction regression

Outcome variable: Customer satisfaction Standard errors based on bootstrapping

b SE B β p Step 1 Constant 2.430 0.908 .000 Interpersonal Justice _0.002 _0.073 _.130 _.356 Informational Justice _0.619 _0.115 _.084 _.757 Procedural Justice _0.181 _0.086 _.064 _.829 Distributive Justice _0.445 _0.039 _.273 _.108

R2 = .207 for step 1 Table 7: Fairness- intention regression

Outcome variable: Customer behavioral intention Standard errors based on bootstrapping

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5. Discussion

This study researched the effect of two customer treatments on perceived fairness, customer satisfaction, customer behavioral intention and actual customer behavior. The research, conducted in a context where customers did not meet their obligations regarding the

organization, showed some remarkable results. In this chapter the significance of the findings are discussed and practical implications are given. Finally, limitations of the study are

described and recommendations for further research are presented.

5.1 Equation outcomes

Although no significant mean difference was found between the two treatments on

interpersonal justice, the tolerant treatment group scored, as proposed, slightly higher than the compelling treatment group. Based on the customers ratings of interpersonal justice an

assumable reason for finding no significant difference could be the use of communication aspects (Bies & Moag, 1986). The results show that even in a more compelling message still the communication aspects as politeness and respectfulness can be utilized. An additional explanation could be the execution of the research. Since the research was executed in a real life context, it could be of disadvantage for the organization when customers are treated impolite and disrespectful on purpose.

As for interpersonal justice no significant difference was found between the tolerant

treatment group and the compelling treatment group for informational justice. This indicates that in both treatments sufficient explanations are given about procedures and outcomes (Nikbin et al., 2012). Moreover, for both treatments explanations are perceived as sufficiently adequate and specific (Shapiro et al., 1994). These outcomes also show that if a message is communicated more straightforward, as with the compelling treatment, this not necessarily leads to a higher perceived informational justice. The message tailoring could be an

explanation for this outcome. In the tolerant treatment information was more given upon request of the customer and therefore more tailored to the customer needs. Tailored messages can be perceived as more adequate en better explained (Shapiro et al., 1994), which

eventually leads to higher informational justice for the tolerant treatment group.

No significant effect is found between the tolerant treatment and the compelling treatment when it comes to procedural justice. This indicates that customers from the tolerant treatment group perceived sufficient possibilities to have voice and influence the outcome (Thibaut & Walker, 1975). On the other hand customers from the compelling group also rated procedural

The effect of different customer treatments on service fairness, customer satisfaction and customer behavior in a credit management environment