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Antecedents and consequences ofbuyer-seller relationship qualityin the financial services industry

Lova Rajaobelina and Jasmin BergeronSchool of Business and Management, University of Quebec in Montreal,

Montreal, Canada

Abstract

Purpose The purpose of this study is to develop a model that investigates the antecedents and theconsequences of buyer-seller relationship quality in the financial services.

Design/methodology/approach Data were collected from a survey of more than 400 dyads (414financial advisors and 772 clients in Canada) and were analyzed using structural equation modeling(SEM).

Findings The results notably show that, for both financial advisors and clients, customerorientation has an impact on buyer-seller relationship quality, whereas buyer-seller similarity doesnot. The link between relationship quality and both consequences (purchase intention andword-of-mouth) is significant for the two samples.

Research limitations/implications Limitations and research directions refer to the measure ofword-of-mouth construct, which is only weakly reliable, and the need to consider a multilevel approach.

Practical implications The study can be helpful for financial advisors to build effective strategiesfor enhancing their relationships with clients.

Originality/value The study is one of the few to consider both perceptions (financial advisors andclients) in order to analyze buyer-seller relationship quality in the financial services sector.

Keywords Buyer-seller relationships, Financial services, Relationship marketingPaper type Research paper

IntroductionIn an age of increased depersonalization and automation impacting upon financialservice quality and delivery, the relevance of the relationship concept could bebrought into question (OLoughlin et al., 2004). Also, not all customers prefer to engagein a close relationship with their current service provider (OLoughlin et al., 2004;Shekhar and Gupta, 2008). Therefore, it may be neither possible nor profitable to createclose, personal and long-term relationships with all consumers (OMalley and Tynan,2000) and a combined transactional and relationship marketing approach may benecessary in order to recruit new customers and retain existing ones (Walsh, 2002; inOLoughlin et al., 2004).

However, the literature proposes that the relationship marketing approach stilltakes a preponderant place in the financial services context (Athanassopoulou, 2006;OLoughlin et al., 2004). Both academics and practitioners have given credit to theconcept of relationship marketing during the last decade (Srinivasan and Moorman,2005). Relationship marketing stresses the development and maintenance oflong-lasting relationships between the firm and its customers (Sheth and Parvatiyar,1995). Long-term customer relationships are considered to be one of the most important

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/0265-2323.htm

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quality

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Received December 2008Revised April 2009

Accepted May 2009

International Journal of BankMarketing

Vol. 27 No. 5, 2009pp. 359-380

q Emerald Group Publishing Limited0265-2323

DOI 10.1108/02652320910979889


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business assets for business organizations (Collier and Bienstock, 2006). Many studies(Crosby et al., 1990; Morgan and Hunt, 1994; De Wulf et al., 2001) state the positiveimpact of relationship marketing on sellers performance as the increase of selling,market share and profit.

Owing to the intangibility and complexity of the characteristics related to servicedelivery, it is important to manage adequately the relationships with customers in thefinancial services sector (Eisingerich and Bell, 2007; OLoughlin et al., 2004; Shekharand Gupta, 2008). Even though the clients may be affected by their relationship withthe firm, they are even more affected by their interpersonal relationship (Palmatieret al., 2007).

Relationship quality is considered as an overall assessment of the strength of arelationship (Garbarino and Johnson, 1999) and captures the essence of relationshipmarketing (Jap et al., 1999, Ural, 2007). It plays a critical role in the study of long-termrelationship maintenance (Finn, 2005). A strong relationship is an intangible asset,which cannot be easily duplicated by competitors (Wong et al., 2007).

The financial services sector is a competitive sector, which needs to strengthen therelationship with the clients in order to dissociate from the other companies. Financialadvisors should more than ever develop a good and sustainable relationship with theirclients. Indeed, mutual benefits result from maintaining good relationships. From thecustomers perspective, the positive benefits of relationship marketing can only becarried out if customers are willing to engage in long-term relationships (Gwinner et al.,1998). For financial services companies, since researchers have concluded that it is fivetimes more expensive to acquire new customers than to keep existing ones(Athanassopoulou, 2006), the development of a strong customer relationship canimprove customer loyalty, which in turn leads to increased profits for the firm(Reichheld, 1993, Athanassopoulou, 2006).

The objective of this study is to investigate the antecedents affecting relationship

quality and its consequences between financial advisors and their customers.Comprehending the essentials of what determines relationship quality can provideuseful management insights into developing effective strategies that allow financialservices companies to retain customers. To best of our knowledge, this study is thefirst to consider both perceptions (financial advisors and clients) in order to reach ourgoal.

The rest of the article proceeds as follows: in the next section, we describe theconcept of relationship quality. Then, we overview the hypotheses development.Further, we present the methodology followed by the results. Finally, we discuss theresults and offer conclusions including future research directions.

Relationship quality

The relationship marketing literature is abundant (e.g. Berry, 1983; Morgan and Hunt,1994) and particularly in the banking sector (e.g. Perrien et al., 1993; Ricard andPerrien, 1999). Regarding the literature of relationship quality in the financial services(see Table I), Crosby et al. (1990) were the pioneers followed by Wray et al. (1994). Thelatest study that encompassed the subject was conducted by Wong et al. (2007) inHong-Kong. We also incorporate in Table I some studies from experience-basedservices sector (e.g. restoration, airlines) in order to compare with the financial servicessector which is inherently credence quality. Financial products as complex services

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Authors Relationship qualityAntecedents of relationshipquality

Consequences of relationshipquality Context

Crosby et al. (1990) Trust, satisfaction Similarity, expertise, relationalselling (cooperative intentions,mutual disclosure, andintensive follow-up contact)

Nonea Whole life insurance (151policyholders in the USA)

Wray et al. (1994) Trust, satisfaction Ethics, expertise, relationshipduration, selling orientation,customer orientation

None Financial services (564customers in the USA)

Smith (1998) Trust, commitment,satisfaction

Relationship duration None 366 members of thePurchasing Management

Association of CanadaShamdasani andBalakrishnan (2000)

Trust, satisfaction Expertise, customerknowledge, friendliness,similarity

Loyaltyb 325 Singaporean clients of hairsalons

Lages et al. (2005) Amount of informationsharing, communicationquality, long-term orientation,satisfaction

Expertise None Relationship quality betweenthe exporting firm and theimporter (sample of 111 UKexporters)

Kim et al. (2006) Trust, satisfaction Customer orientation,communication, relationshipbenefits

Commitment, loyalty,word-of-mouth

887 dinner patrons at 21luxury restaurants in Korea

Macintosh (2007) Trust, satisfaction Customer orientation,expertise

Word-of-mouth, loyalty 220 Canadian businesstravelers regarding theirrelationships with their travelagents

Wong et al. (2007) Trust, satisfaction Information sharing Will ingness to refer ,anticipation of futureinteraction

207 consumers of financialservices in Hong Kong

Cheng et al. (2008) Trust, satisfaction Customer orientation,

expertise, interpersonalrelationship

Commitment, loyalty Airline relationship quality:

252 domestic passengers inTaiwan

Notes: a Sales effectiveness and anticipation of future interaction were non-significant; b Loyalty represents repeat purchases, proportion of purchases,purchase sequence, and probability of purchase

TableI.

Literatureon

relationship

quality

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are different from standardized services such as those offered by hotels and airlines(Materson, 2008). Customers may, for example, expect the same level of service fromhotels and airlines from one period to another. On the contrary, they are harder toplease regarding mutual funds or stocks (Materson, 2008). Therefore, the

generalization of findings from standardized services to complex services isquestionable. We then mostly dwell on the three studies (shaded in Table I) in thefinancial services.

Relationship quality has been defined as a bundle of intangible values resulting inan expected long-term relationship between related parties (Levitt, 1981; Zineldin,2000; Fruchter and Sigue, 2005). Hennig-Thurau and Klee (1997, p. 751) describedrelationship quality between customers and firms as the degree of appropriateness of arelationship to fulfill the needs of the customer associated with the relationship. Thisnotion is generally recognized as a higher-order construct (Ural, 2007). Crosby et al.(1990) suggest that relationship quality consists of two dimensions: trust in thesalesperson and satisfaction with the salesperson. In addition, there seems to be anagreement on defining this concept as a second-order factor of trust and satisfaction inthe banking literature (Crosby et al., 1990; Wray et al., 1994; Wong et al., 2007) but alsoin different context such as restoration, airline relationship, hair salons (Shamdasaniand Balakrishnan, 2000; Kim et al., 2006; Macintosh, 2007; Cheng et al., 2008).

The literature has brought trust as one of the main factors which play an importantrole in influencing a customer to develop and maintain relationship with the serviceprovider (Liang and Wang, 2006; Shekhar and Gupta, 2008). It is generally thought tobe a key determinant of the quality of buyer-seller relationships (Ndubisi, 2007; Swanet al., 1999). Ganesan (1994) and Doney and Cannon (1997) contends that trust engulfstwo dimensions:

(1) objective credibility, defined as the belief that the other has the expertise toperform the job; and

(2) benevolence, defined as the belief that the other has motives beneficial to thetarget when new conditions arise for which a commitment was not made.

In addition, satisfaction with the relationship is regarded as an important outcome ofbuyer seller relationships (Liang and Wang, 2006). Nowadays, customer satisfactionstill represents an imperative cornerstone for customer-oriented business practicesacross a multitude of companies operating in diverse industries (Szymanski andHenard, 2001) and can be considered the essence of success in our highly competitivebusiness world (Jamal and Naser, 2002). Relationship satisfaction is defined as aconsumers affective state resulting from an overall appraisal of his or her relationshipwith a retailer (Crosby et al., 1990; Liang and Wang, 2006). Jap (2001) describedrelationship satisfaction as a positive affective state resulting from the appraisal of all

aspects of a working relationship. Geyskens et al. (1999) conceptualized twodimensions of satisfaction: non-economic (e.g. communication skills, expertise) andeconomic (e.g. sales, return on investment).

However, there is no consensus concerning the antecedents and the consequences ofthe relationship quality. In Crosby et al. (1990) study, the consequences of relationshipquality were not significant. Also, Wray et al. (1994) did not consider the relationshipquality consequences. Finally, although the consequences of relationship quality werecovered in Wong et al.s (2007) work, only the information sharing was treated as an

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antecedent. Therefore, this study fills the gap by considering most of the antecedentsand consequences by adapting Crosby et al.s model. Furthermore, relationship quality,from the customers perspective, has received attention by researchers during the lastdecade but no study has dealt with this issue from the financial advisors perspective.

Hypotheses developmentAntecedentsAccording to the literature (see Table I), expertise seems to be the most citedantecedent of relationship quality. Also, customer orientation is often mentioned.Similarity and customer knowledge are sometimes considered as antecedents. Someconstructs such as communication, information sharing, and duration relationship areseldom stated. In this study, client knowledge, customer orientation, expertise, andsimilarity will be considered.

Client knowledge. In the academic literature, customer knowledge has emerged inthe past two decades in research on the quality of services and on relationship

marketing. In relationship marketing, customer knowledge has often been consideredas a significant dimension of the force of the relationship between a service providerand its clients (Paulin et al., 2000). Knowing the customer is an essential component ofthe buyer-seller relationship. It is a determining element of the quality of a sound andefficient relationship (Blanchard et al., 2001) and it contributes to creating a unique andinimitable competitive advantage. Teas (1988) observes that knowing the client, andunderstanding his/her situation influence the quality of the relationship. Therefore, wehypothesize that:

H1. Client knowledge is positively related to relationship quality.

Customer orientation. Customer orientation is initially developed in personal sellingmanagement and is often regarded as an indicator of the quality of buyer-seller

relationships (Cheng et al., 2008). Brown et al. (2002) describe customer orientation as apersonality variable that reflects the service sellers disposition to meet customerneeds. A pillar of a customer-orientation approach is that salespeople must understandcustomers needs, expectations, and concerns (Saxe and Weitz, 1982). Salespeople thatare customer oriented are concerned with satisfying their needs better than would theircompetitors (Wray et al., 1994). Bejou et al. (1996) use artificial neural network analysisto investigate the determinants of relationship quality and find that the degree ofcustomer orientation has a significant impact on the customers trust and satisfaction.Hence, we posit the following hypothesis:

H2. Sellers customer orientation is positively related to relationship quality.

Expertise. Domain expertise is typically assessed by a service providers level of

knowledge and experience with regard to the focal product or service. Crosby et al.(1990) indicate that a salespersons expertise reflects the identification of relevantcompetencies associated with the goods or service transaction. In their study ofinsurance salesperson-customer relationships, they find that an insurancesalespersons expertise has a significant effect on relationship quality. Experiencedand knowledgeable employees can reduce customers perceived uncertainty andanxiety, which may lead to higher customer satisfaction and trust. The level ofexpertise possessed by employees including knowledge, experience or skills relevant to


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a particular domain or activity is a vital determinant of relationship quality. Thus, thefollowing hypothesis is offered.

H3. Sellers domain expertise is positively related to relationship quality.

Similarity. Buyer-seller similarity has been examined in a large number of empiricalstudies across literatures in marketing and social psychology, and has been debated insales research for over 35 years (Dwyer et al., 1998; Evans, 1963; Lichtenthal andTellefsen, 2001). Some researchers found statistically significant relationships betweensimilarity and performance criteria, such as greater relationship investment, trust,satisfaction, and sales (Crosby et al., 1990; Smith, 1998). As relationship quality is asecond-order of trust and satisfaction, we propose the following hypothesis:

H4. Buyer-seller similarity is positively related to relationship quality.

Consequences

Loyalty, purchase intention (anticipation of future interaction for Wong et al., 2007) andword-of-mouth (sometimes defined as willingness to refer) (see Table I) are the mostcommon consequences. Notwithstanding, commitment is sometimes cited. In thispaper, purchase intention and word-of-mouth will be considered as consequences.

Purchase intention. Purchase intention refers to the degree of perceptual convictionof a customer to repurchase a particular product (or service) or to repurchase anyproduct (or service) at a particular organization. The essence of purchase intentionencompasses concepts such as probabilities and expectations. Kellerman (1987)identified purchase intention as an outcome goal of dyadic encounters. Given that thecost of retaining an existing customer is less expensive than prospecting for a newcustomer (Spreng et al., 1995), purchase intention is a very important consideration for

businesses. The best predictor of the likelihood that a customer will seek future contactwith a financial services provider is the quality of the relationship to date (Wong et al.,2007). We therefore suggest that:

H5. There is a positive relationship between relationship quality and purchaseintention.

Word-of-mouth. Getting existing customers to provide referrals should be one of theeffective ways to add new business (Collier and Bienstock, 2006). A referral from acustomer can often open the gates and allow a salesperson access to previouslyunreachable prospects. Huntley (2006) found that when the quality of relationship ishigh, customers are more willing to recommend the sellers offerings to colleagues andthey purchase more from the seller. Maintaining high-quality relationships with

customers appears to increase their willingness to provide referrals (Finn, 2005). Thisleads to the following hypothesis:

H6. There is a positive relationship between relationship quality andword-of-mouth.

Our six hypotheses related to the antecedents and the consequences of buyer-sellerrelationship quality in the financial services sector lead to the following proposedmodel (Figure 1).

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MethodIn this research, the financial services sector is used as the arena for investigating theantecedents and consequences of perceived relationship value in buyer-sellerrelationships. We collected data from financial advisors and their clients[1].

MeasuresIn this study, relationship quality was measured with two global indicators,satisfaction and trust, which were developed by adding the three individual items

representing satisfaction and trust. Sanzo et al. (2003) argued that satisfactionmeasures[2] should include an evaluation of the economic and non-economic aspects ofthe relationship. In the case of financial services, economic satisfaction is relevant sinceit depends greatly on the salespersons advice. One item was employed to assesseconomic satisfaction. Non-economic satisfaction implies a positive affective responsetowards relationships psychological aspects, in such a way that a satisfied customerenjoys dealing with the salesperson, given the belief that the latter is concerned fortheir welfare and will be willing to exchange relevant information (Geyskens et al.,1999). One item of non-economic satisfaction was derived from Lagace et al. (1991).A last item was added to capture satisfaction at a global level.

As stated in Swan et al.s (1999) meta-analysis of customer trust in the salesperson,the measures of trust have covered three levels of abstraction. First, some measuresfocused on specific salesperson behaviors, such as keep promises (Crosby et al.,1990). Second, other authors used attributes that are broader than a specific behavior,such as dependable. A third level of abstraction included general trust measures thatdo not reference to either specific behaviors or attributes, such as trustworthy. Weemployed three items to represent each level.

Three items were derived from Bergeron (2004) to measure a salespersonsknowledge of the clients needs, objectives, and expectations. Saxe and Weitz (1982)introduced what is probably the most accepted scale of customer orientation: the SOCO

Figure 1.Proposed model


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scale, a tool for measuring the customer orientation of salespeople. Michaels and Day(1985) successfully adapted the self-report SOCO scale for customer samples. Thus,two items were borrowed from Saxe and Weitz (1982) and Michaels and Day (1985) tomeasure customer orientation.

To assess the expertise construct, we adapted three items from the scale developedby Bergeron et al. (2001) in the banking industry. To measure internal characteristics,we adapted Crosby et al.s (1990) scale of buyer-seller similarity. To answer the call ofLichtenthal and Tellefsen (2001), we also added one question regarding buyer-sellersimilarity of business-related characteristics. The two-item purchase intention scalewas based on the work of Ramsey and Sohi (1997).

Bergeron and Vachon (2008) think that measures should include aspects linked tointentions (e.g. I intent to buy from this salesperson again) and expectations (e.g. Iexpect to purchase from this salesperson again). Since we wanted to adopt amultidimensional conceptualization of WOM, two items were derived from Bergeronet al.s (2001) study. These indicators measured the likeliness of positive and negativeWOM communications in the future. An additional indicator, derived from Boles et al.(1997), assessed the probability of providing referrals to the salesperson if he/she askedfor them. All measures were on a seven-point scale with anchors of strongly disagree(1) to strongly agree (7). Measures are presented in Table II.

Procedures and samplesBefore collecting data, a three-step pretest procedure was followed. First, a draft of thequestionnaire was shown to three university professors specialized in bankingresearch who suggested significant changes to font, character spacing, and questionwording. Second, the questionnaire was analyzed and reviewed by two banking sectorexperts. Again, face validity was examined and slight corrections were made. Third, areiterative pretest procedure was conducted with 48 financial advisors, as well as 12customers.

Since the data from buyers and sellers were collected separately, we will examineeach sample independently. Of the 675 questionnaires sent to financial advisors, a totalof 418 were returned by the subjects, representing a response rate of nearly 62 percent.Among those, four cases were eliminated for outliers[3], leaving 414 data casesavailable for analysis.

Approximately 1,672[4] questionnaires were distributed to clients. A total of 778questionnaires were returned to the university in a postage-paid envelope giving agood response rate for customers of 46 percent. Of that number, six respondents werediscarded for outliers, resulting in a final sample of 772 customers.

Sample characteristicsSample characteristics results are presented in Table III. The financial advisor samplerepresented ten different financial institutions. In this sample, there were more women(271; 65.6 percent) than men (142; 34.4 percent). A majority of financial advisors (75percent) were aged 35 to 54. Nearly half (47 percent) of the sampled financial advisershad a household annual income of more than $80,000 and 33.2 percent had an income of$50,000 to $79,000. The respondents were well educated, with over 63 percent having auniversity degree or the equivalent. The average tenure with their firm was 13.9 years,

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with an average of approximately 9.3 years experience selling financial products andservices.

The client sample consisted of 404 men (52.5 percent) and 366 women (47.5 percent).Their median age ranged from 44 to 54 years and their median household annualincome ranged between $50,000 and $60,000. More than 43 percent of the respondentshad a university degree. On average, each respondent had done business with his/herfinancial advisor for four years.

Constructs Authors

Relationship qualityTrust Swan et al. (1999)

Usually keeps his/her promisesIs dependableIs trustworthy

Satisfaction Lagace et al. (1991)Satisfied with the information provided Sanzo et al. (2003)Satisfied overall with the financial advisorSatisfied with the monetary benefits provided

Antecedents of relationship qualityCustomer knowledge Bergeron (2004)Knew the clients financial needsKnew what the client expectsKnew the clients financial objectives

Customer orientation Michaels and Day (1985); Saxe and Weitz (1982)Was sincerely interested in satisfying theclients needsHelped the client achieve his/her financial goals

Expertise Bergeron et al. (2001)Had a good financial expertiseKnew well his products and services

Buyer-seller similarity Crosby et al. (1990)Appearance Lichtenthal and Tellefsen (2001)BehavioursPersonality

Consequences of relationship quality Purchase intention Ramsey and Sohi (1997)Intend to do business with financial advisor againExpect to purchase financial products and/orservices from financial advisor in the future

Word-of-mouth Bergeron et al. (2001); Boles et al. (1997)Client will talk positively about financial advisorto people he/she knowsClient would provide referrals (e.g. friends, family,and colleagues) to financial advisor if he/sheasked for them

Notes: 7 points scale: 1 strongly disagree and 7 strongly agreeTable II.

Measurement summary


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Analysis and results Reliability and validityThree sets of analyses were conducted to assess for scale reliability for each sample: thecorrected item-total correlation, the Cronbach alpha and the composite reliability (seeTable IV). Results indicate that all the items had corrected item-total correlationsgreater than 0.35, which represents the cutoff suggested by McKelvey (1976). Cronbachalphas and composite reliability indexes were computed. Results[5] presented inTable IV show that the alphas varied between 0.726 (for the customer orientation

Variable Range Bankers (%/sd) Customers (%/sd)

n 414 100.0 772 100.0Gender Male 142 34.4 404 52.5

Female 271 65.6 366 47.5Age 18 to 34 years 91 22.4 140 18.5

35 to 44 years 152 37.3 178 23.545 to 54 years 151 37.1 215 28.555 years 13 3.2 223 29.5

Height to 1.65m 194 48.7 264 37.31.66m to 1.75m 129 32.4 274 38.71.77m 75 18.8 170 24.0

Income to $49,999 81 19.8 279 38.1(household) $50,000 to $79,999 138 33.2 231 31.6

$80,000 to $99,999 91 22.3 99 13.5$100,000 101 24.7 123 16.8

Level of studies Primary/High school 84 20.4 249 32.5

Some college 68 16.5 186 24.3University 260 63.1 332 43.2Civil status Single 52 12.7 150 19.5

Boyfriend/girlfriend 135 32.9 159 20.7Married 180 43.9 338 44.0Separated, divorced orwidowed 43 10.5 121 15.8

Financial advisorsTotal selling experience (years) 9.3 7.5Selling experience with the firm(years) 13.9 10.1Average number of client metper week 11.9 5.7Average number of times they

contacted their client per year 2.8 1.7ClientsExperience with the institution(years) 14.1 11.1Experience with the advisor(years) 4.0 4.8Average number of institutionswith which the client dealt 2.0 0.9Average percentage of bankingbusiness done with thisinstitution 72.6 30.4

Table III.Samples characteristics

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Factorloadings

fromCFA*

Correcteditem-total

correlations

AlphasofCronbach

(a)

Compositereliability

index(C.R.)

Averagevariance

ex

tracted

Constructs

Financial

advisor

Client

Financial

advisor

Client

Financial

advisor

Client

Financial

advisor

Client

Financial

advis

or

Client

Relationshipquality

0.760

0.856

0.765

0.888

0.42

9

0.686

Trust

0.794

0.846

0.783

0.839

0.51

4

0.538

Usuallykeepshis/herpromises

0.693

0.844

0.600

0.696

Isdependable

0.935

0.920

0.728

0.808

Istrustworthy

0.730

0.642

0.551

0.622

Satisfaction

0.731

0.874

0.731

0.873

0.41

7

0.563

Satisfiedwiththeinformationprovided

0.725

0.789

0.559

0.752

Satisfiedoverallwiththe

financial

advisor

0.758

0.933

0.594

0.829

Satisfiedwiththemonetarybenefits

provided

0.651

0.768

0.522

0.706

Antecedentsofrelationshipquality

Customerknowledge

0.798

0.917

0.793

0.917

0.46

5

0.617

Knewtheclientsfinancialneeds

0.842

0.889

0.621

0.820

Knewwhattheclientexpects

0.641

0.873

0.613

0.833

Knewtheclientsfinancialobjectives

0.762

0.872

0.688

0.844

Customerorientation

0.726

0.856

0.648

0.845

0.43

2

0.561

Wassincerelyinterested

insatisfying

theclientsneeds

0.645

0.831

0.480

0.735

Helpedtheclientachieve

his/her

financialgoals

0.795

0.842

0.480

0.735

Expertise

0.753

0.876

0.753

0.865

0.51

0

0.615

Hadagoodfinancialexp

ertise

0.867

0.933

0.604

0.763

Knewwellhisproductsandservices

0.708

0.806

0.604

0.763

(continued)

Table IV.Measures and relevant

factor and reliabilityanalyses


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Factorloadings

fromCFA*

Correcteditem-total

correlations

AlphasofCronbach

(a)

Compositereliability

index(C.R.)

Averagevariance

ex

tracted

Constructs

Financial

advisor

Client

Financial

advisor

Client

Financial

advisor

Client

Financial

advisor

Client

Financial

advis

or

Client

Buyer-sellersimilarity

0.800

0.856

0.792

0.856

0.50

8

0.541

Appearance

0.705

0.681

0.633

0.674

Behaviors

0.876

0.889

0.722

0.765

Personality

0.781

0.863

0.576

0.782

Consequencesofrelations

hipquality

Purchaseintention

0.787

0.860

0.763

0.860

0.49

4

0.597

Intendtodobusinesswithfinancial

advisoragain

0.780

0.930

0.621

0.768

Expecttopurchasefinan

cialproducts

and/orservicesfromfina

ncialadvisor

inthefuture

0.781

0.779

0.621

0.768

Word-of-mouth

0.387

0.850

0.364

0.848

0.29

5

0.622

Clientwilltalkpositively

about

financialadvisortopeoplehe/she

knows

0.741

0.937

0.287

0.752

Clientwouldproviderefe

rrals(e.g.

friends,family,andcolleagues)to

financialadvisorifhe/sheaskedfor

them

0.352

0.811

0.287

0.752

Notes:*Allfactorloadingsweresignificant(0.0

5)

Table IV.

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construct in the financial advisor sample) to 0.917 (for the customer knowledgeconstruct in the client sample), and were above the recommended 0.7 threshold level(Nunnally, 1978). Composite reliability indexes ranged from 0.648 to 0.917, which alsoexceeds the 0.6 threshold necessary for measurement reliability (Bagozzi and Yi, 1988;

Fornell and Larcker, 1981).Convergent validity can be assessed by examining the factor loadings and squared

multiple correlations from the confirmatory factor analysis. Following Hair et al.s(1998) recommendations, factor loadings greater than 0.5 are considered to be verysignificant. All of the items in the research model had significant factor loadingsgreater than 0.5. Also, squared multiple correlations between the individual items andtheir prior factors were high (above 0.5 in all cases[6]). Thus, all factors in themeasurement model had adequate reliability and convergent validity.

Furthermore, we also assessed discriminant validity by following the suggestions ofFornell and Larcker (1981): if proportion of variance extracted[7] in each constructexceeds the square of the F coefficients representing its correlation with other factors,discriminant validity is demonstrated. After comparing the correlations between other

constructs and there respective variance extracted estimates, we found an adequatediscriminant validity. Only one pair of scales (customer orientation and word-of-mouth)did not respect the condition and then constitutes a weakness of this paper. Thecorrelation between them was (F 0:817 for financial advisors and F 0:884 forclients, F2 0:667 for financial advisors and F2 0:781 for clients; see Table V). Thevariance extracted estimates for customer orientation were 0.432(financialadvisor)/0.561(client) and 0.295(financial advisor)/0.622(client) for word-of-mouth.

Measurement modelConfirmatory factor analyses (CFAs) were used to test the adequacy of themeasurement model using EQS 6.1. The results indicated a good fit between the modeland the observed data. The overall fit indices of the measurement model werex2 189.679 (d.f.142) (financial advisor)/220.175 (client) (d.f.142), p 0.000/0.000,GFI 0.941/0.936, AGFI 0.913/0.906, CFI 0.990/0.995, SRMR 0.045/0.040,

Customerknowledge

Customerorientation Expertise Similarity

Purchaseintention

Word-of-mouth

For financial advisor sampleCustomer knowledge 1Customer orientation 0.554 1Expertise 0.584 0.424 1Similarity 0.228 0.051 0.155 1Purchase intention 0.498 0.634 0.410 0.106 1

Word-of-mouth 0.666 0.817 0.493 0.224 0.835 1 For client sampleCustomer knowledge 1Customer orientation 0.825 1Expertise 0.762 0.773 1Similarity 0.351 0.380 0.345 1Purchase intention 0.752 0.832 0.647 0.284 1Word-of-mouth 0.744 0.884 0.694 0.402 0.829 1

Table V.Correlation matrix among

factors


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RMSEA 0.030/0.030. Although the chi-square is not significant in each model, it isknown to be sensitive to sample size and trivial discrepancies (Fornell and Larcker,1981; Doney and Cannon, 1997) and thus, is a poor indicator of model fit (Singh, 2000).In contrast, other fit indices such as CFI, SRMR, and RMSEA are more appropriate for

assessing model fit (Bagozzi and Yi, 1988).The comparative fit indices (CFI) for the two samples were 0.990 and 0.995, which

constitute another good indication that each measurement models represented anadequate fit to its respective data (higher than 0.950) (Kline, 2005). Both GFI and AGFIexceeded the recommended 0.9 threshold level (Bollen, 1989). In addition, SRMR andRMSEA were lower than 0.06 and 0.05, respectively (Hu and Bentler, 1999; Kline, 2005).

Structural modelOverall model results. The hypothesized relationships in the model were testedsimultaneously using structural equation modeling. The resulting x2 were 217.933(financial advisor) and 318.859 (client) with 159 degrees of freedom and the ratio ofx2 to

degrees of freedom below 1:3 (p 0.000/0.000; GFI 0.933/0.910; AGFI 0.911/0.881;RMSEA 0.032/0.041; NFI 0.957/0.981; CFI 0.988/0.990), suggesting that thehypothesized model fits the data. In Table V, the resulting standardized parameterestimates are presented. The proposed integrated model explains 70.8 percent (forfinancial advisor) and 91 percent (for clients) of the variance in relationship quality, 71.1percent (for financial advisor) and 83.1 percent (for clients) of the variance in purchaseintention, and 87.6 percent (for financial advisor) and 81.8 percent (for client) of thevariance in the word-of mouth construct. We can conclude that the model presentedexplains pretty well the variance of the main constructs.

Hypotheses testing. In Tables VI-VII, we present the resulting standardizedparameter estimates. Within the model, the estimates of the structural coefficientsprovide the basic tests of the proposed theory. Following the proposed model, we first

addressed the antecedents of relationship quality and then discussed links amongrelationship quality, purchase intention, and word-of-mouth.

Antecedents of relationship quality. H1 through H4 address the relationships amongrelationship quality and its antecedents. H2, which predicts that customer orientationhas a positive impact on relationship quality is supported for both financial advisors(g 0:635, p , 0:05) and clients (g 0:837, p , 0:05). H4, which suggests thatseller-buyer similarity affects relationship quality is not supported (g 0:061 (n.s.)(financial advisor)/0.056 (n.s.) (client)). H1 and H3 which expect that customerknowledge and expertise have positive impact on relationship quality show differentresults. Whereas customer knowledge and expertise have an impact on relationshipquality for financial advisors (g 0:181, p , 0:05; g 0:156, p , 0:05), these linksare not significant for clients.

Relationship quality and its consequences. H5 and H6 pertain to the relationshipsamong relationship quality, purchase intention, and word-of-mouth. H5 predicts apositive relationship between relationship quality and purchase intention. Ashypothesized, the path estimate is positive and significant for both entities(g 0:839=0:912, p , 0:05), thus confirming H5. H6 expects that relationshipquality will display a positive relationship with word-of-mouth. Path estimate isconsistent with this prediction as evidenced by a positive path estimate(g 0:936=0:905, p , 0:05). Hence, H6 is supported.

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Discussion and implicationsThis paper has described the antecedents of relationship quality and its consequencesin the financial services sector, more exactly between the financial advisors and theirclients. To our knowledge, it is the first study to examine at the same time both entities

perceptions of the relationship quality.Customer orientation is the only antecedent, which has a significant impact on

relationship quality for both financial advisors and clients (see Figure 2). This resultvalidates Kim et al.s (2006) study in luxury restaurants or Cheng et al.s (2008) paper inairline relationship quality. Likewise, the results corroborate Wray et al.s (1994) workon relationship quality in financial services. Consequently, financial advisors shouldhave customer-oriented attitudes and behaviors. They should maintain theirrelationships with their clients by taking time to meet their needs and personalizing

Relationship

Standardized parameterestimates(first number: financialadvisor sample/secondnumber: client sample)

t-value(first number: financialadvisor sample/secondnumber: client sample)

Hypothesis testing(financial advisorsample/client sample)

Customerknowledge ! RQ(H1 ) 0.181/0.104 2.004*/1.233(ns)

Supported/Notsupported

Customerorientation ! RQ(H2 ) 0.635/0.837 5.030*/7.169 * Supported/SupportedExpertise ! RQ(H3 ) 0.156/0.035 2.008*/0.512(ns)

Supported/Notsupported

Similarity ! RQ(H4 ) 0.061/0.056 1.185(ns)/1.825(ns)

Not Supported/NotSupported

RQ ! Purchase

intention (H5 ) 0.839/0.912 6.509*

/15.353*

Supported/SupportedRQ !Word-of-mouth(H6 ) 0.936/0.905 6.665*/15.350 * Supported/Supported

Notes: * Significant; ns non-significant

Table VI.The structural model

results

Fit statistics Value (Financial advisor/client) Recommended value

x2 217.933/318.859Degrees of freedom 159/159p value 0.000/0.000 $ 0.05

x2

/d.f. 1.370/2.005 # 3GFI 0.933/0.910 $ 0.90 (Bollen, 1989)AGFI 0.911/0.881 $ 0.90 (Bollen, 1989)CFI 0.988/0.990 $ 0.95 (Kline, 2005)NFI 0.957/0.981 $ 0.90RMSEA 0.032/0.041 # 0.05 (Hu and Bentler, 1999; Kline, 2005)SRMR 0.048/0.045 # 0.06 (Hu and Bentler, 1999; Kline, 2005)

Table VII.The structural model

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the offer. Customer-oriented employees should always keep clients interests in mind,take steps to clarify their needs and expectations, and engage in activities andbehaviors to satisfy the clients needs in a helpful way. Promoting customer orientationamong the personnel can be fruitful, coming from the person in charge of the financialadvisors. Also, during the recruitment stage, it is worthy to consider the employeescustomer orientation aptitudes such as being empathic. Finally, some coaching ortraining focusing on improving employees customer orientation can be appropriate.Doing consulting simulations or being supervised can help the employees to improvetheir skills.

Surprisingly, there is no significant link between similarity and relationship qualityaccording to both financial advisors and clients. Contrary to the results of Crosby et al.

(1990), similarity does not constitute an antecedent of relationship quality though webased our scale on items provided by these authors. Similarity may be an importantissue during the beginning of a relationship between financial advisors and theirclients; but after a while (the average length of relationship being four years in ourstudy), it would not be considered as a major factor.

We could also emphasize some interesting results related to the influence of clientknowledge and expertise on relationship quality. Whereas we found an impact of clientknowledge and expertise on relationship quality according to financial advisors, the

Figure 2.Summary of results

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results are different for clients. Again, we could explain this non-significant influenceof client knowledge and expertise on relationship quality by the relationship duration(average of fours years). In fact, duration may reassure the clients while reducing theirfears of financial advisors incompetency and of lack of knowledge.

Our study empirically supported the link between relationship quality and purchaseintention and thus confirmed the findings of Wong et al.s (2007) study in Hong-Kongfinancial services. Also, we found that word-of-mouth is linked to relationship quality.In conclusion, relationship quality, a higher order construct made up of trust andsatisfaction, strengthens buyer-seller relationship. It improves customer purchaseintention, which in turn leads to increased profits for companies. Clients will have along-term relationship with their respective financial advisors and will be prone toprovide referrals.

Conclusion, limitations and research opportunitiesThis research adds to the relationship quality literature by focusing on the perceptionsof both entities. As expected, customer orientation constitutes a principal antecedent ofrelationship quality. In this study, the buyer-seller similarity does not show asignificant influence on relationship quality.

Our study also has limitations that suggest avenues for further research. First of all,the word-of-mouth construct for financial advisors was not reliable[8] and presented alack of discriminant validity with customer orientation construct. In addition, weconsider purchase intention as a consequence of relationship quality but not loyalty,which is more common in the literature. Then, we can extend the current model inincluding other antecedents such as relationship value[9] (Ulaga and Eggert, 2006),communication, or length of relationship. We could also attempt to find out if there is asignificant link between relationship quality and financial advisors outcomes.

Future research could fruitfully examine potential differences in male-male,

male-female, female-male, and female-female financial advisors-clients relationships.Such study could reveal and make us understand possible barriers to effectivebuyer-seller relationships. Finally, we could consider a multilevel analysis (with clientsin level-1 and financial advisors in level-2) as we asked the financial advisors to givethe questionnaire to their four clients. We sincerely hope that the results and insightspresented in this research will be used as a spring-board for future research in adomain growing in theoretical and practical importance.

Notes

1. Financial advisors would give a questionnaire to their four clients for self-administration.

2. Note that the measures were the same for the clients and the financial advisors. Only theformulation changed. For example, if the measure of satisfaction for the client is (1) I am

satisfied with the information provided by the financial advisor, the measure of satisfactionfor financial advisor will be: the client is satisfied with the information that I provided.

3. Outliers are cases with the largest contribution to normalized multivariate Kurtosis.

4. A total of 418 participating bankers X four clients.

5. Only the word-of-mouth construct for financial advisors was weakly reliable (Cronbachalpha and composite reliability below the threshold level).

6. The exception is the squared multiple correlation of item wom2 (for financial advisor) whichis 0.124.


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7. The formula for Average Variance Extracted (AVE) is:

AVE

Pl2

iP

l2iP

dl: factor loading; d: error of measurement.

8. The item wom2 poorly loads with the factor word-of-mouth for financial advisors. We could

not delete this item as we only have two items for this measure.9. Most definitions present customer-perceived value as a trade-off between benefits and

sacrifices perceived by the customer in a suppliers offering (Zeithaml, 1988; Monroe, 1990).

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About the authorsLova Rajaobelina, MBA, is a PhD Candidate at the Business School of the University of Quebecin Montreal, Canada. He is also a researcher for the financial services management departmentChair at the same university. His research interests are in bank marketing, hospitality andtourism strategies, online consumer marketing, and research methodologies.

Jasmin Bergeron is Professor of Marketing at the Business School of the University of Quebec

in Montreal, Canada. He has delivered more than 1000 practical conferences in more than 50financial institutions around the world. He has also published articles in journals such as the

Journal of the Academy of Marketing Science, the Journal of Retailing, the Journal of BusinessResearch, and the Journal of Service Research, among others. He has also authored several bookson selling and negotiating strategies in the financial industry. Jasmin Bergeron is thecorresponding author and can be contacted at [email protected]

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