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Assist. Urban Sebjan, Ph.D. student Faculty of Economics and Business University of Maribor urban.sebjan@um.si
Domen Merc, M.Sc. student Faculty of Economics and Business University of Maribor domen.merc@student.um.si
Denis Rogan, M.Sc. student Faculty of Economics and Business University of Maribor denis.rogan@student.um.si
Izvirni znanstveni članek Original Scientific Paper
THE IMPACT OF FAIR INSURANCE SERVICES ON THE QUALITY AND STRENGTH OF THE RELATIONSHIP BETWEEN CUSTOMERS AND INSURANCE COMPANIES
Abstract: Due to increasing competition in the insurance market and the complexity of insurance services, the perceived fairness of insurance services and the implications of the relationship between users and insurance companies are coming more and more under scrutiny. This study finds that perceived fairness of insurance services positively and significantly impacts on the strength and quality of the relationship between the users of insurance services and the insurance company. At the same time, we find that the strength of this relationship has a positive and significant impact on its quality. The findings are important for insurance companies, which must, despite the complexity of insurance services, provide insurance services to users that are understandable and unambiguous while at the same time ensuring social and financial security.
Keywords: insurance services, relationship, fair services, structural equation modelling (SEM), conceptual model
VPLIV POŠTENIH ZAVAROVALNIH STORITEV NA KAKOVOST IN MOČ ODNOSA MED UPORABNIKI IN ZAVAROVALNICAMI
Povzetek: Zaradi vse večje konkurence na zavarovalniškem trgu in kompleksnosti zavarovalnih storitev je v ospredju vprašanje o zaznani poštenosti zavarovalnih storitev in posledicah v odnosu med uporabnikom in zavarovalnico. Ker živimo v času, ko se družba ukvarja z vprašanji »krize« etičnosti, družbene odgovornosti in moralnih vrednot v gospodarstvu, smo se v študiji osredotočili na proučevanje poštene zavarovalne storitve, katere področje postaja vedno bolj aktualno. Glavni namen prispevka je identificirati pošteno zavarovalno storitev v povezavi z odnosom med zavarovalnico in uporabnikov. Osredotočili smo se na vprašanje, kako zaznana poštena zavarovalna storitev vpliva na moč in kakovost
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odnosa med zavarovalnico in uporabniki njenih storitev. V zavarovalništvu je malo raziskanega o poštenih zavarovalnih storitvah, zato je bil cilj študije proučiti, kako uporabniki zaznavajo poštenost zavarovalnih storitev, kako lahko zaznana poštena storitev prispeva k moči odnosa in kakovosti odnosa med zavarovalnico in uporabnikom ter proučiti vpliv moči odnosa na kakovost odnosa med zavarovalnico in uporabnikom. V prispevku predstavimo konceptualni model, s katerim smo ob pomoči modeliranja s strukturnimi enačbami ugotovili, da zaznana poštena zavarovalna storitev pozitivno in značilno vpliva na moč odnosa in kakovost odnosa med uporabnikom zavarovalnih storitev in zavarovalnico. Ugotovili smo, da moč odnosa pozitivno in značilno vpliva na kakovost odnosa med uporabnikom zavarovalnih storitev in zavarovalnico. Rezultati raziskave nakazujejo, da ima zaznana poštena zavarovalna storitev močan vpliv na kakovost odnosa v primerjavi z zaznano močjo odnosa med uporabnikom in zavarovalnico. To pomeni, da se kakovost odnosa bolj odraža v trenutni situaciji in v storitvah, ki jih uporabniki uporabljajo stalno. Moč odnosa pa se odraža skozi daljše časovno obdobje, kar pomeni, da stranka nima vedno dovolj jasno izoblikovanega mnenja o moči odnosa z zavarovalnico, zaznano moč odnosa pa lahko dodatno ovira pogosta zamenjava zavarovalnic ali kratkoročni odnosi med uporabnikom in zavarovalnico. Če primerjamo zavarovalne storitve z bančnimi, lahko prav tako ugotovimo, da uporabniki nimajo tako pogostih stikov z zavarovalnico kot na primer z bančnimi ustanovami, saj se zavarovanja običajno sklepajo za daljši čas, običajno za eno leto (ali tudi več let), ko je treba vnovič obnoviti zavarovanje po preteku jamčenja za zavarovalno tveganje. Naj poudarimo, da se pri proučevanju poštenih storitev pojavlja različno izrazoslovje (npr. poštenost, pravičnost itd.), kar nakazuje tudi znanstveni pregled raziskovalcev, ki proučujejo odnos med uporabnikom in organizacijo in vedenje uporabnika storitev (npr. zadovoljstva, nakupne namere, zaupanja itd.). Zato so v prihodnje potrebne dodatne razprave o jasni opredelitvi poštene storitve in o izboljšanju merjenja moči odnosa med zavarovalnico in stranko. Ugotovitve so pomembne za zavarovalnice, ki morajo kljub kompleksnosti zavarovalnih storitev uporabnikom nuditi zavarovalne storitve, ki bodo za uporabnike razumljive ter jim bodo nudile pošteno socialno in finačno varnost. Le na ta način bodo zavarovalnice lahko vplivale na kakovost odnosa in moč odnos ter zadržale svoje obstoječe stranke.
Ključne besede: zavarovalne storitve, odnos, poštena storitev, modeliranje s strukturnimi enačbami, konceptualni model
1. INTRODUCTION
The insurance sector has a special influence on economic development all over the world. Despite the important role of insurance in society, it is possible to detect saturation in the Slovenian insurance market, reflected in increasing competition among companies: "Increasing illiquidity, numerous corporate bankruptcies and rising unemployment have led to lower demand for several years. Insurance is a service that consumers forgo relatively quickly. Initially, this was only seen in the life insurance sector. Last year, however, that trend was more evident in the nonlife insurance sector. Total premiums collected by insurance companies were down 3.7% in the year 2014 on the previous year" (SIA, 2014: 9).
Although the Slovenian insurance market has shrunk due to the financial crisis, we can still perceive a power sales orientation of insurance companies. However, power sales-driver orientation of insurance can also create negative behavioural effects, which may be reflected in the asymmetry of information, fraud and exploitation conditions of insurance.
Since we live in a time when companies have to deal with ethical dilemmas, social responsibility and moral values within the economy, our study focuses on the fairness of insurance services. Despite the many different insurance services, what all of them have in common is that they are intangible (Edvardsson et al., 2005; Armstrong & Kotler, 2011). Seiders and Berry (l998) found that fairness is especially important for service firms because their products are intangible and difficult to evaluate, forcing consumers to rely on trust. Users find many financial services complex and difficult to understand, including insurance services (e.g., insurance conditions, insurance coverage, additional conditions). This raises doubts about the fairness of insurance services, thus demanding an examination of the relationship between users and insurance companies.
In this study the central research question is about how the perceived fairness of insurance services affects the quality and strength of the relationship between customers and insurance companies. In the area of financial services, most work has been done on the honesty of banking services (Chen et al., 2012; Worthington & Devlin, 2013); less research has been carried out on the fairness of services in the insurance sector. Although some researchers have associated fairness of insurance with the behaviour of customers (satisfaction, trust, behavioural intention, emotions) and the dimensions of a service (value) through various sales channels (Zhu & Chen, 2012; Chen et al.,
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2012; Chen & Chou, 2012; Namkung & Jang, 2010; Virvilaite et al., 2015), few results have been published on the links between a fair insurance service and the relationship between the service users and the insurance company.
Our study focused on two key research objectives: to determine how perceived fairness of service affects the quality and strength of the user-company relationship, and to investigate the connection between the quality and the power of this relationship. In the remainder of the present paper, we will set out some theoretical foundations for service fairness and the strength and quality of the usercompany relationship. We will then continue with the empirical examination of the conceptual model and present the results of empirical research in which we explore the relationships between the constructs of a conceptual model. At the end of the article we present the findings and provide suggestions for future research.
2. LITERATURE REVIEW AND RESEARCH HYPOTHESES
Fairness is defined in opposition to unfairness, a lived phenomenon in all situations (Hegge, 2012). For customers, fairness is the absence of unfairness. Because customers generally expect fair treatment, their reactions to unfairness can be pronounced (Seiders & Berry, 1998). Carr writes: "Service fairness or justice is a multidimensional construct based on equity theory" (2007); it is also a multidimensional concept depending on different dimensions (e.g., distributive, procedural, interpersonal and informational fairness, price fairness) (Namkung et al., 2009; Kyootai et al., 2011; Ting, 2013; Devlin et al., 2014). The concept of fairness is rooted in the theory of justice, which is in turn adapted from equity theory, suggesting that over-rewarded and under-rewarded relationship outcomes cause distress, which people strive to reduce (Adams, 1965). Individuals seek a fair input/output balance and become satisfied whenever they feel their inputs are fairly rewarded. Perception of unfairness can lead to distress and dissatisfaction. In contrast, perception of fairness results in positive emotions and satisfaction (Patterson et al., 1997; Szymanski & Henard, 2001). It might seem at first sight that the concepts of justice and fairness are the same, and that there is no reason to distinguish them or to say that one is more essential than the other. According to Rawls (1958) this impression is false: he considers justice only as an institutional virtue. Justice is not to be confused with an all-inclusive vision of a good society; it is only one part of any such conception.
Service consumers want services performed correctly at the first time of asking and in a timely manner (Vargo & Lusch, 2004; Carr, 2007). Perceived service fairness is an important influence on service quality assessment from the customer perspective. As a major aspect of any service facility, customers expect fair treatment. If customers are involved in service operations, fairness significantly influences the perceived service quality and becomes crucial for customer satisfaction. Consequently, quantifying fairness in order to evaluate the degree of perceived fairness should be an integral part of service quality assessment (Sandmann, 2013).
Fairness perceptions play an important role in customer behaviour. Many researchers have studied service fairness from different perspectives (price fairness, fair processes and procedures, outcome fairness, interactional fairness and fair treatment by staff). The most common conclusion has been that only price fairness and interactional fairness are significantly associated with behavioural intention, while two dimensions - distributive justice (fair outcomes) and interactional justice (fair treatment by staff) - have the largest effects on customer loyalty and relationship quality (Clark & Yancey, 2009; Namkung & Cheong, 2010; Kwortnik & Han, 2011). Some studies have found that consumer perceptions of interactional fairness, price fairness and information fairness affect behavioural/loyalty intentions through engendering satisfaction (Namkung & Jang, 2009; Wang & Mattila, 2011; Matute-Valle-jo et al., 2011; Zhu & Chen, 2012): "Fair service not only has a significant impact on customer satisfaction, but also plays a role equivalent to service quality in determining customers' trust and perceived value, which in turn lead to customer satisfaction" (Chen et al., 2012: 399). Service fairness is an antecedent of consumption emotions (positive and negative) that in turn influence customers' satisfaction and behavioural intentions (Su & Hsu, 2013). Xia et al. (2010) also investigated promotion fairness and price fairness. They found that perceived price-promotion fairness had a significant effect on perceived price fairness. Perceived price fairness in service industries can be viewed as a threshold factor in order to maintain satisfied and loyal customers (Martín-Consuegra et al., 2007). Lee et al. (2011) suggested that overall fairness is critical for the enhancement of positive customer attitudes.
Interactional fairness is of the greatest importance for customers. Service companies should therefore manage interactions with customers effectively at every point of contact. All customers should be equally treated because preferential
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treatment of certain customers can destroy the relationship. If service providers are successful in providing a fair service, they are also more likely to be considered high-quality service providers. Although there is a direct effect of service fairness on customer loyalty, customer loyalty is mainly affected by relationship quality. As customers stay with the same provider for longer periods, trust and commitment increase, and these customers are therefore more likely to stay with the same provider and to refer the provider to others (Giovanis et al., 2013).
Peppers and Rogers (2004) indicate that fairness of services (especially procedural fairness) has a strong effect on development of trust and commitment, and contributes to the development of an effective long-term relationship. Because customer relationships are characterized by social distance, customers must depend on organizations employed to act on their behalf (Culnan & Armstrong, 1999). Nguyen and Mutum (2012) argue that it is important to develop processes and procedures which the other party in the relationship judges to be fair, in order to sustain the relationship. Furthermore, such processes and procedures can partially or fully mediate the respective relationships of service fairness and service quality to customer loyalty. Service fairness is a means of retaining customers, which organizations can achieve by keeping promises in the provision of services, reflecting the quality of the relationship between the insurance company and the users. Thus, service fairness influences loyalty indirectly by strengthening the supplier-customer relationship quality, which has been proven to be a better predictor of customer attitude and behaviour (Vrontis et al., 2013). Research shows that the perception of relationship fairness also enhances relationship quality (Kumar et al., 1995). Clark and Yancey (2009) found that that distributive, procedural, and interactional justice all positively impact customers' transaction-specific post-recovery satisfaction with the service organization, and ultimately the quality of the customer-firm relationship.
Relationship quality is broadly regarded as a concept that is related to customers' satisfaction with the service provider (Dorsch et al., 1998; Yi-Shun et al., 2011), their trust in the service provider and their commitment to continue the customer-firm relationship (Dorsch et al., 1998; Hewett et al., 2002; Kumar et al., 1995; Yi-Shun, 2011). Generally, relationship quality describes the overall depth and climate of a relationship (Bove & Johnson, 1999; Amy & Amrik, 2006). Wong and Sohal (2006) found out in their research that relationship strength is positively
correlated with relationship quality in the retail sector. Banyte et al. (2014) also found that patients' satisfaction as well as trust had a strong influence on the relationship strength with and loyalty to a clinic's doctors. Appendix A provides an overview of research in the field of service fairness in relation to user behaviour.
Figure 1 depicts a relationship-strength model based on the results in the literature. The major constructs in the model are insurance-service fairness, relationship strength and relationship quality; the three hypothesized paths depict the interrelationships between these constructs:
Hypothesis 1. Perceived service fairness is positively related to relationship strength.
Hypothesis 2. Perceived service fairness is positively related to relationship quality.
Hypothesis 3. Relationship strength is positively related to relationship quality.
3. RESEARCH METHODOLOGY AND SAMPLE
The data were collected through an online questionnaire from 9 December 2014 to 23 February 2015. The target population comprised random users who were legally able to buy insurance services in Slovenia, aged 18 years and older. Statistical units included in the sample were selected at random, so that all units had the same opportunity to be selected in the sample. In the online questionnaire anyone who had insurance coverage and was in a business relationship with an insurance company could participate. All returned online questionnaires were correctly _ completed. For testing of the hypotheses, data were collected based on a random sample of 200 customers of insurance services in Slovenia. In terms of demographics: 45.0% were male (n = 90) and 55.0% female (n = 110).
To ensure content validity, the measures for our constructs were adapted from the extant literature to suit the context of service fairness and the relationship between customer and organization. Insurance-service fairness was measured using items adapted from Devlin et al. (2014), Nam-kung and Cheong (2010) and Chen et al. (2012). Items for relationship strength were adapted from Ozkan-Tektas (2014). Relationship quality was measured by adapting items from Ndubisi (2007).
Finally, the preliminary questionnaires were pre-tested on six insurance-service customers to check their understanding of statements. The suggestions made were considered in the
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questionnaire's final version, which consisted of 21 items. The respondents were requested to indicate the extent to which they agreed or disagreed by checking the appropriate response to each questionnaire item. All items were measured on a seven-point Likert scale as follows: 1 = not agree at all, 4 = neutral, and 7 = absolutely agree. The questionnaire also included four questions gathering demographics of the sample population: age, gender, education and income.
Table 1 summarizes the demographic information on the sample. The largest group of respondents were between 18 and 25 years old (38.0%), followed by those between 26 and 35 years old (26.0%) and 46 and 55 years old (14.0%), and those who were 56 years and over (13.0%). The smallest group of respondents was between 36 and 45 years old (9.0%). The largest group had a college-level education or above (58.0%), followed by those who were educated up to secondary school (33.0%) and vocational school (7.0%).
The smallest group of respondents were educated up to grade school (2.0%). The largest group of respondents had a monthly income of between 701 and 1100 EUR (33.0%), followed by those who had a monthly income of between 301 and 700 EUR (22.0%) and below 300 EUR (20.0%).
4. RESULTS
Statistical Package for the Social Sciences (SPSS) and WarpPLS softwares were used to analyse the reliability and validity of the data and to conduct PLS path modelling (PLS-SEM) (Hair et al., 2014).
Factor analysis was conducted in two phases. In the first phase, we performed principal compon ent analysis (PCA). PCA was also used to
establish a scale dimensionality by checking the factorial structure of items (indicators). The Kaiser-Meyer-Olkin statistics (KMO) were calculated and the Bartlett's Test of Sphericity (BTS) carried out. The KMO represents the ratio of the squared correlation between variables to the squared partial correlation between variables, varying between 0 and 1. The KMO value has to be greater than 0.5 to be acceptable (Field, 2011: 647). BTS is a test of the assumption of sphericity. This test examines whether a variance-covariance matrix is proportional to an identity matrix. Therefore, it effectively tests whether the diagonal elements of the variance-covariance matrix are equal, and that the off-diagonal elements are approximately zero (Field, 2011: 781-782). The independence of the factors and simpler factor structure were obtained with principal component analysis and the varimax method. PCA showed that the service fairness (KMO=0.881, BTS=1193.99, p < 0.001) is composed of two factors. The construct 'Service fairness' is composed of the factor 'honesty of employee' (SF1) and the factor 'honesty components of insurance services' (SF2). The first factor explains 39.0% of the variance, the second factor 34.1%. In total 73.1% of the variance is explained. Relationship quality (KMO=0.856, BTS=385.879, p < 0.001) and relationship strength (KMO=0.500, BTS=33.511, p < 0.001) are composed of a single factor. The construct 'Relationship quality' explains 86.8% of the variance, while the construct 'Relationship strength' explains 77.2% of the variance. To improve the reliability of measurement of the construct 'Relationship strength' we had to discard three variables.
In the second phase we conducted the confirmatory factory analysis (CFA), which was used to ascertain the efficiency of the measurement mo-
Figure 1: Conceptual model with hypotheses
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Table 1: Sample characteristics
Variables		fi	fi (%)
Gender	Male	90	45.0
	Female	110	55.0
Monthly income	Below 300 EUR	40	20.0
	301 to 700 EUR	44	22.0
	701 to 1,100 EUR	66	33.0
	1,101 to 1,400 EUR	32	16.0
	1,401 EUR and over	18	9.0
Education level	Grade school	4	2.0
	Vocational school	14	7.0
	Secondary school	66	33.0
	College and over	116	58.0
Age (year old)	18 to 25	76	38.0
	26 to 35	52	26.0
	36 to 45	18	9.0
	46 to 55	28	14.0
	56 and over	26	13.0
Source: Authors.
dels, and SEM was used to test the conceptual framework and assumptions. The properties of the measurement model are detailed in Table 2.
The reading of Cronbach's alpha (Cronbach, 1951) and the composite reliability for all the variables are greater than 0.50, in relation to the expected factors, showing higher reliability among the indicators. The p AVE values for this model exceeded 0.5 for the reflective constructs (Hair et al., 2010; Fornell & Larcker, 1981; Bagozzi & Yi, 1988), thus indicating convergent validity for all constructs. Composite reliabilities pcCR for the three reflectively measured constructs ranged from 0.871 to 0.962, exceeding the minimum requirement of 0.7 (Hair et al., 2010; Fornell & Larcker, 1981; Bagozzi & Yi, 1988). Since all the pcCR values were higher than the pcAVE values, there was convergent validity for all studied constructs. The internal consistency of the items in relation to the single trait within the instrumental was tested using Cronbach's a, ranging from 0.705 to 0.948. Cohen (1988) explains variation R2 for the endogenous latent variables, where 0.26, 0.13 and 0.02 represent strong, medium or low impact, respectively.
The examination of the endogenous constructs' predictive power shows that 'Relationship quality', which is the primary outcome measure of the model, has a substantial R2 value of 0.615. Based on the variance explained by the model for a particular endogenous variable (R2), we examined the predictability value of the structural model (Cohen, 1988; Stone, 1974). The prediction of 'Relation-
ship strength' was comparably weak (R2 = 0.272). Since collinearity was possible between latent variables, variance inflation factors (VIFs) were used to detect it, with the rule that this value should be less than 3.3 in every dimension or latent variable (Cenfetelli & Bassellier, 2009; Petter et al., 2007). All VIFs were less than 3.3. Q2 coefficients are also known as Stone-Geisser Q-squared coefficients, named after their principal original proponents (Geisser, 1974; Stone, 1974). Acceptable predictive validity in connection with an endogenous latent variable is suggested by a Q-squared coefficient greater than zero (Kock, 2013). From Table 3 it is evident that all values of the latent-variable Q2 coefficient are greater than zero.
The results of the discriminant validity are shown in Table 4. Discriminant validity was checked by comparing the shared variances between factors with the square root of pcAVE for each construct. The inter-construct correlations are all positive and significant. The findings shown in Table 4 suggest discriminant validity, since all diagonal elements are greater than the non-diagonal elements in the corresponding rows and columns.
We also examined the fit of the data to the research model by analysing the coefficient of goo-dness-of-fit (GoF) (Tenenhaus et al., 2005). Tenen-haus et al. (2005) defined the GoF as the square root of the product between what they refer to as the average communality index and the average R-squared (ARS). GoF is defined as small (0.35), medium (0.50) and large (0.61) (Wetzels et al.,
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Table 2: Confirmatory factor analysis results
Model path	Factor loadinga	Variance explained	Meanb	SD
Service fairness (SF)"		73.0%		
SF1 ^ Service fairness	0.938		5.22	1.127
SF2 ^ Service fairness	0.948		5.28	1.030
Relationship strength (RS)		77.2%		
RS1 ^ Relationship strength	0.879		4.72	1.449
RS2 ^ Relationship strength	0.879		5.09	1.236
Relationship quality (RQ)		86.8%		
RQ1 ^ Relationship quality	0.949		5.55	1.193
RQ2 ^ Relationship quality	0.940		5.23	1.320
RQ3 ^ Relationship quality	0.967		5.15	1.293
RQ4 ^ Relationship quality	0.866		5.32	1.312
Notes: a All factor loadings are significant at 0.001. b Measured on a seven-point scale, ranging from 1 = strongly disagree to 7 = strongly agree, Standard deviation (SD). "two-level factor.
Source: Authors.
2009). The model in this study had a GoF value of 0.611; this indicates that the model fit was good. WarpPLS provided us with additional criteria for verifying data consistency within the research model. Thus assessing the model's fit with the data, it is recommended that the p-values for all the average path coefficients (APC = 0.472), average adjusted R-squared (AARS=0.436) and the average R-squared (ARS = 0.444) all be lower than 0.001. Average values of the variance inflation factor (AVIF = 1.290) and average full colli-nearity VIF (AFVIF = 2.050) were lower than 5.0. Values of Sympson's paradox ratio (SPR = 1.000) and R-squared contribution ratio (RSCR = 1.000) achieve the ideal value, which was proposed as 1.000. The data contribute statistical suppression ratio (SSR = 1.000) and nonlinear causality bivariate direction ratio (NLBCDR = 1.000), both indicators exceeding the suggested value of 0.7 (Schepers et al., 2005; Kock, 2013).
In Figure 2 we present the relationships between individual constructs. The results fully supported the hypotheses on the effect of service fairness
Table 3: Indicators of Quality of Research Model
on relationship quality (H1, y1 = 0.603, p < 0.001). In relation to the first hypothesis it was confirmed that the higher the perceived fairness of insurance services, the higher the perceived quality of the relationship that users are experiencing with the insurance company. As expected, insurance-service fairness had a positive effect on relationship strength, supporting H2 (y2 = 0.522, p < 0.001). The positive and statistically significant relationship between service fairness and relationship strength indicated that the higher the perceived fairness of insurance services, the more users will perceive a strong relationship with the insurer. Finally, relationship strength also had a positive and significant effect on the relationship quality (H3; 63 = 0.291, p < 0.01); thus, hypothesis H3 is supported. This means that the more users perceive strength in the relationship, the more perceive quality. We found that all the relationships between the constructs in the proposed model are positive and statistically significant.
Impact assessment /2 (effect size) was used to assess the impact of latent variables in a struc-
Constructs	Cronbach a	pcCR	pcAVE	R2	Q2	VIF
1. SF	0.875	0.941	0.889	(-)	(-)	2.233
2. RS	0.705	0.871	0.772	0.272	0.282	1.452
3. RQ	0.948	0.962	0.865	0.615	0.619	2.465
Notes: Service fairness (SF), relationship strength (RS), relationship quality (RQ). Source: Authors.
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Table 4: Descriptive Statistics and Correlation Matrix of all Variables
Constructs	Mean	SD	1	2	3
1. SF	5.25	1.008	0.943*		
2. RS	4.91	1.180	0.476	0.879*	
3. RQ	5.31	1.193	0.738	0.548	0.9330*
Notes: All correlations are significant at 0.01. "Square roots of average variancesextracted (AVEs) shown on diagonal. Source: Authors.
tural model (Cohen, 1988). The strongest effect was that of the independent variable 'Service fairness' on the dependent variable 'Relationship quality' (/2 = 0.447). The impact of the independent variable 'Relationship strength' on the dependent variable 'Relationship quality' was low (/2 = 0.168). The relationship between constructs for the proposed model is reported in Table 5.
5. DISCUSSION OF IMPLICATIONS AND FURTHER RESEARCH
In this study we investigated the fairness of insurance services and its impact on relationships quality and relationship strength. We emphasize that there is not currently a lot of relevant literature available on fairness or fair relations in the insurance industry, and so this study makes an important contribution to the field. Examining the fairness of insurance services is essential in this day and age, because insurance companies are trying to attract new and maintain existing customers with new offers, which are therefore becoming increasingly complex and difficult to understand. Lack of transparency can result, which can cast doubt on customers' perception of the credibility of an insurance company and the fairness of its services, as it may lose transparency in its complexity. Insurance companies create a relationship with their customers through the process of implementation of services. In this study we established that insurance services characterized by fairness have a positive impact on relationship quality and relationship strength,
IF igure 2: Co nceptual model with results
Y2 = 0.522***
INSURANCE SERVICE FAIRNESS (SF)
Y1 = 0.603
RELATI STRENC R2=	ONSHIP TH (RS) 1272
	ß3 = 0.291**
RELATIONSHIP QUALITY (RQ) R2= 0.615	
Notes: GoF=0.611, APC=0.472 (p < 0.001), ARS=0.444 (p < 0.001), AARS=0.436 (p < 0.001), AVIF=1.290, AF-VIF=2.050, SPR=1.000, RSCR=1.000, SSR=1.000, NLBCDR=1.000, ***p < 0.001, **p < 0.01. Source: Authors.
which confirms the findings of other researche rs (Kumar et al., 1995; Clark & Yancey, 2009) who have focused on relationship quality, but not on relationship strength. Establishing the existence of links between fairness of insurance services and relationship stsength is an important scientific contrib ution. "The more customers and consum -ers perceive fairness in an insurance service, the more positively they perceive the quality of the relationship. At the sametime, the greater the perception of fairness in insurance services, the more strongly customers perceive the link with the insurance company, and so insurance companies reduce the likelihood that customers will choose to change insurance company and insur-
Table 5: Standardized Path Coefficients for Proposed Model
Hypothesized path	Hypothesis	Expected sign	Path coefficient	/2	S.E.	Results
V, (SF ^ RQ)	H1	+	0.603***	0.447	0.086	Supported
V2 (SF ^ RS)	H2	+	0.522***	0.272	0.088	Supported
ß3 (RS ^ RQ)	H3	+	0.291**	0.168	0.093	Supported
Notes: ***p < 0.001, **p < 0.01; /2 effect size; S.E. standard error; Service fairness (SF), Relationship strength (RS), Relationship quality (RQ).
Source: Authors.
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ance service. Therefore, we agree with Maicas Lopez et al. (2006) that those customers who maintain a long-lasting relationship with the organization (length), use the service more (depth), and invest in complementary services (breadth) will be less predisposed to switch. The strength of a customer-contact employee relationship will affect the volume to which the customer says positive things about the organization and recommends it to other people (Wong & Sohal, 2006).
It is up to insurance companies to present comprehensive services to customers, including through offer simulations. The biggest problem in the insurance industry is still represented by three factors: insurance premium, insurance coverage and evaluation of the damage event. This is because the damage assessment is based on the insurance coverage for which the customer paid the insurance premium. In the event of damage/ accidents and consequent damage assessment/ casualty, insurance companies can rely on additional clauses included in the insurance contract, but users often do not know about these because they are often not informed, or are informed inadequately. Taking into account the additional clauses in the contract when assessing claims in cases/incidents, customers often do not receive the level of damage compensation they expect. In these cases users perceive a value of insurance claim that is higher than the actual value that the insurance company pays; customers therefore become dissatisfied and suspicious, and start to question the fairness of the insurance company's services. Additional to these factors, discounts are also important, but they are not the same for all insured persons; discounts may be added to policies at insurance-company employees' discretion (up to a certain mandate), and they alone decide the level for particular customers. In this way the insurance company may lose its reputation and consequently suffer loss of confidence among existing customers or even loss of users, who must then be replaced. In today's highly competitive environment, loss of customers is very costly, so the managers and employees of insurance companies have to continually focus on trust and the fulfilment of given commitments (Shemwell & Cronin, 1995), since these not only affect the quality, but also the strength of the relationship with users.
In the context of the conceptual model, we found that the quality of the relationship significantly influenced the strength of the relationship between customers and the insurance company, which is consistent with previous findings in the literature (Wong & Sohal, 2006). The conceptual model plays a central role in the correlation of fairness
and relationship quality, noticeably less so in the correlation between relationship strength and relationship quality. This is because customers are mainly focused on the perception of the benefits/ costs of the insurance service, which are subsequently reflected in the relationship between the customer and the insurance company.
In the theoretical review of the field, we also found some inconsistencies in the interpretation of relationship quality and relationship strength. Although many researchers define the relationship strength as the dimension governed by the level of trust and (effective) commitment towards the individual service provider (Morgan & Hunt, 1994; Shemwell & Cronin, 1995; Bove & Johnson, 1999; Buttle, 2009), Vazquez-Casielles et al. (2010) represent the relationship quality in the same terms. If we consider our findings and the conclusions of Wong and Sohal (2006), we can perceive that a continuous quality of the relationship with the insurance company deepens this relationship, whereby this relationship is also strengthened. Therefore, it is necessary to distinguish between the quality and strength (depth) of the relationship.
In evaluating our research we must point out its limitations. The results of the survey relate only to the users of insurance services, and therefore cannot be generalized for the entire population. The sample included only a limited number of insurance-service customers, so the sample will need to be increased in the future. Since the sample included only Slovenian customers of insurance services, the conceptual model could also be tested on a sample of foreign users, especially in those countries where insurance services are highly developed. We could also include in the conceptual model some of the demographic variables of insurance-service customers, such as age, education, personal income, and location of residence (e.g., city, suburbia, rural). This study was based on existing research on the fairness of insurance services; further research could check the fairness of services in other fields, such as education, health care and telecommunications. The conceptual model was based on only three constructs; it would be reasonable to extend it to include relationship economic value, relationship value outcomes, customer switching behaviour, and relationship satisfaction.
The field investigated here is complex and demanding, as it concerns a comprehensive social problem, which is not unique to the insurance sector in Slovenia but relates to the entire global social system. Insurance and other financial institutions must devote themselves to the deve-
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lopment of honest services, which will be reflected and perceived in fair premiums, discounts, offers, advertising, etc. Insurance companies should strictly and continuously measure customers' perception of the fairness of the service in different areas and different sales channels, especially in contact with customers. At the same time, it is necessary to define the key dimensions of a fair insurance service, which must relate to so-called external processes, where the customer is in contact with staff, as well as so-called internal processes. The dimensions of a perceived 'fair insurance service' can be usefully included and integrated by insurance companies in the processes of customer relationship management (CRM).
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Appendix A: Review of literature on components of service fairness
SF — SQ
SF — S
SF — PV
SF — T
SF — BI
SF — LI
IF — LI
IF — S
OF— S
OF—T
OF—PV
n.s.
PF—OF
IF—OF
DF—OF
DF—LI
DF—T
PF—T
*/n.s.
INF—T
*/n.s.
DF—S
PF—S
n.s.
INF—S
n.s.
INF—SQ
PF—SQ
DF—SQ
IN—S
IF—S
CF—RI
CF—TI
DF—BI
PF—BI
n.s.
INF—>BI
n.s.
PF—PE
n.s.
PF— NE
OCF— PE
OCF— NE
n.s.
INF— PE
INF— NE
n.s.
OCF— BI
n.s.
Notes: n.s. - not significant at 0.05, *p < 0.05; **p < 0.01; ***p < 0.001, service fairness (SF), service quality (SQ), perceived value (PV), trust (T), satisfaction (S), loyalty intention (LI), overall fairness (OF), distributive fairness (DF), procedural fairness (PF), informational fairness (IF), interactional fairness (INF), interpersonal fairness (IN), corporate fairness (CF), relational interaction (RI), transactional interaction, behavioural intention (BI), positive emotion (PE), negative emotion (NE), outcome fairness (OCF).
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