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Key drivers of satisfaction affecting attitudinal and behavioral loyalty: Combining
quantitative and qualitative research methodologies
Key Words: Satisfaction, Loyalty, attitudes, behavior, Structural Equation Models,
Interviews
Abstract
The paper researches the link between satisfaction and loyalty in a B2B setting in the
healthcare industry, in particular the clinical pathology area via a longitudinal study
design. It suggests that attribute satisfaction predicts global satisfaction, which in turn
predicts behavioral loyalty. Based on recent recommendations, the researcher
operationalized attitudinal loyalty with a single-item scale in order to focus on a specific
behavioral aspect of loyalty, the continuation of the usage of the service provider.
Furthermore, two key moderating variables were included in the study to test the validity
of the structural equation model used to investigate the satisfaction loyalty relationship.
While the moderator variables were not significant, the research concludes that future
researchers should consider moderator variables when conducting satisfaction loyalty
research in B2B settings. In addition, the authors followed up with interviews to better
understand why physicians switched to other Service providers.
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Current state of research of Satisfaction-Loyalty link in B2B settings
Over the past twenty years, customer satisfaction and customer loyalty have
become critical concepts in the marketing and management efforts of organizations.
While in the 1980s, increased satisfaction was a goal in and by itself, during the 1990s
researcher started to place greater emphasis on the link between customer satisfaction,
loyalty and ultimately profitability (Anderson and Mittal 2000). Reichheld and Sasser
assume that a reduction in customer defection of 5% can have an impact of 25% of
existing revenues (Reichheld and Sasser 1990). The main reason is that the acquisition of
new customers is more costly than the retention of existing customers.
Recently more and more researchers have conducted empirical research to show
the causal link between satisfaction and loyalty in B2B settings (Anderson and Sullivan
1993, Fornell, Johnson, Anderson and Bryant 1996). However, von Wangenheim (2003)
suggests that more research we need to investigate the impact of moderating variables on
the link between satisfaction and loyalty. In addition, little research has been conducted
in the context of the healthcare industry, which currently accounts for 13% of GDP of the
US and is projected to grow to 16% in 2010 (The Institute for the Future 2003, p. 30).
While the link between satisfaction, attitudinal loyalty and behavioral loyalty appears
self-evident, this link has been difficult to demonstrate empirically. Mittal and Wagner
(2001) proposed and validated the following reasons why the link is so difficult to detect
statistically:
1. In B2C settings, customer demographics such as gender may result in differential
levels of attitudinal and behavioral loyalty.
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2. Satisfaction ratings may be affected by measurement errors in particular response
biases. Grisaffe (2004) identified no less than twelve problems when applying
customer satisfaction measurements.
3. The relationship between satisfaction and attitudinal and behavioral loyalty may
have a functional form that is different from the “classical” linear relationship (i.e.
non-linearity of the relationship between satisfaction and loyalty).
4. In addition, moderating variables may impact the relationship between
satisfaction and loyalty (von Wangenheim 2003)
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Methodology
This paper investigates the impact of two moderating variables that may influence the
link between satisfaction and loyalty in the context of B2B relationships in the healthcare
industry. In particular, the impact of satisfaction on the profitable segments of
commercial clinical laboratories will be investigated.
In a typical setting, a patient gets a script or a requisition from the physician to draw
blood. The patient then goes to a patient service center where the blood is drawn. The
specimen is sent to a clinical laboratory, tested and the report sent back to the physician.
Patients with a PPO have the option to choose a clinical laboratory, while patients with an
HMO are contractually bound to a specific national or regional laboratory.
This research assumes that two variables influence the relationship between
satisfaction and loyalty
1. The size of the physician organization: Larger organizations are often bound by
contracts with clinical laboratories and therefore have longer relationships with
commercial labs.
1. The physician specialty: Oftentimes, family practices and physicians practicing
internal medicine are more likely to use the same clinical laboratory for routine
tests than their more specialized colleagues do who are more prone to ordering
esoteric rather than routine tests such as routine panels.
Measures
This paper distinguishes between attitudinal and behavioral loyalty. Attitudinal loyalty
measures the intention to continue a relationship with the service provider, while
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behavioral loyalty measures the actual loyalty over time. The key behavioral aspect of
loyalty in this study is the continued sending of specimens to a specific clinical
laboratory.
In a recent paper (Grisaffe 2001) summarized the debate over the appropriate way
of measuring loyalty. This debate is now known as the Neal-Brandt debate. Neal
ascertains “If I purchase in a product category 10 times in one year, I am 100% loyal. If I
purchase the brand only five out of the times, I am 50% loyal” (Grisaffe 2001, p. 55).
Neal makes a case for measuring loyalty at a behavioral rather than at a cognitive
level. Tucker (1964) and Lawrence (1969) introduced the loyalty measurement based on
this type of classical behaviorism in the 1960s.
Burke on the other hand takes the more recent cognitive approach first introduced
by Pessemier (1959), and Jacoby, and Olson (1970) and uses a three item-questionnaire
to investigate loyalty. The three-item questionnaire intends to improve the reliability of
the loyalty measure. Measuring loyalty is currently predominated by the cognitive
approach.
This research combines the cognitive and the behavioral approaches of measuring
loyalty to determine how intention to continue using a service provider way predicts
actual behavior.
This research also abandons the multi-item scale for measuring cognitive loyalty
and instead focuses on specific intentions. The intention measured in this study is to use
the service 12 months from the administration of the satisfaction survey. Abandoning
multi-items scales in favor of single-item scales are particularly justified in longitudinal
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studies where intentions and actual behavior are observed, as is the case in this study
(Mittal and Wagner 2001).
More recently, Soederlund and Oehman demonstrated that multi-item scales of
intentions are not equally related to actual behavior. They suggest that “researchers
should be concerned with the particular intention constructs they use the selection of one
particular intention indicator over another will generate different conclusions about the
role satisfaction has as a determinant of satisfaction” (Soederlund and Oehman, 2003 p.
53). This is particularly important when intention measures are correlated with actual
behavior. The authors also deplore that “behavioral data are seldom collected by
satisfaction researchers” and that “intentions are often used as proxy for behavior”
(Soederlund and Oehman, 2003, p. 53. The latter is the case for the paradigmatic cross-
sectional study design of Zeithaml and colleagues who reviewed the impact of service
quality on loyalty (Zeithaml, Berry and Parasuraman 1996). These authors developed a
thirteen-item scale and correlated it with intention to repurchase a product.
Study Design
This study is a longitudinal study that correlates satisfaction and intention and
observes if the original intention truly translates into actual behavioral loyalty. Most
previous studies have suffered from the methodological weakness of cross-sectional
studies and used attitudinal loyalty as a proxy for behavioral loyalty. In addition, this
study follows up with physicians to determine if and why they discontinued using the
services of the commercial service provider. In addition, this study introduces two
moderating variables: Size of the organization and physician specialties. Practitioners
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assume that these variables are important in the context of the measurement of the
satisfaction – loyalty link. This research attempts to validate these assumptions with
empirical data.
Model Development
In this section, we develop a model that relates satisfaction to attitudinal loyalty and
behavioral loyalty. Rather than developing a general model, we propose a firm specific
model. Mittal and Wagner (2001, p. 134) state, “this course is appropriate, as it is at this
level that managers must make decisions”. The proposed model uses a structural equation
model that links attribute satisfaction to overall satisfaction, attitudinal loyalty and
behavioral loyalty. A structural equation model takes into account that satisfaction,
attitudinal loyalty and behavioral loyalty are not error free measures. Instead, it assumes
that all three constructs are error-prone and this error is explicitly included in the
structural model. It combines the analysis of the measurement model with a confirmatory
approach to factor analysis. Thus, it consists of both a measurement model and a
regression-based path model. By comparing the z-transformed correlations between the
two levels of the moderating variables, it also allows for a review of the impact of
moderating variables. Structural equation modeling thus includes three distinct
methodologies in one and is the preferred tool to investigate the hypotheses of this study.
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Research Questions and hypotheses
Experience in the clinical laboratory diagnosis industry shows that physicians are
primarily concerned with three aspects of specimen handling:
1. The time it takes from drawing the blood to receiving a report called lead-time in
operations management and turnaround time in the clinical diagnostic industry.
2. The availability of customer service in regards to any questions about the test
result, the status of the test results and the delivery of report.
3. The intangible perception of the overall handling of specimen by the service
providers may have a strong influence on global satisfaction. The intangible
perceptions include the professionalism of the phlebotomy staff, the cleanliness of
the phlebotomy facility, the professionalism of the courier who pick up the
specimen etc. Clinical laboratorists call these intangible aspects the overall
integrity of the handling of the specimen. Lost or misplaced specimen, for
example, can become a major issue in this industry because they can result in the
redraw of blood and even worse the contamination of results with at times
disastrous and life-threatening consequences. As discussed earlier physicians
make about 70% of all clinical diagnostic decisions based on test results (Iselin
2007). Therefore, “specimen handling” is a key driver of overall satisfaction.
These considerations motivated then the following seven research questions:
1. Is satisfaction with turnaround time positively related to overall satisfaction
2. Does the professional handling of specimens positively relate to overall
satisfaction?
3. Does customer service positively relate to overall satisfaction?
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4. Does overall satisfaction positively relate to intended loyalty?
5. Does intended behavioral loyalty positively relate to actual behavioral
loyalty?
6. Does the size of the office affect the relationship between overall satisfaction
and attitudinal loyalty?
7. Does the focus of the physician (general vs. specialized) influence the
relationship between overall satisfaction and attitudinal loyalty?
Construct Operationalization
One of the US largest providers of clinical laboratory services provided the data for this
research. Clients are defined as physicians who send tests to the regional laboratories to
perform clinical diagnostic tests. The company selects respondents based on a random
sample generator and sends out the surveys two weeks after it released the test results
back to the physician. The survey measures attribute satisfaction such as satisfaction with
turnaround time, customer services, sales and billing, overall satisfaction, intention to use
the service provider in 12 months from the time of the administration of the survey. The
critical variables analyzed in this study are attribute satisfaction with turnaround time,
specimen handling and customer service as well as overall satisfaction, attitudinal
loyalty, behavioral loyalty and the two moderating variables: size of the physician and
physician specialty.
Attribute and overall satisfaction are measured on a five-point scale (5 = Outstanding,
1 = Unsatisfactory).The survey question was: “How would you rate the quality of
COMPANY’S pathology testing?”.
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Attitudinal loyalty is measured on a five point scale: “How likely are you to
continue to use COMPANY, 12 months from now?” 1 = Not at All Likely, 2 = Not Very
Likely, 3 = Somewhat Likely, 4 = Very Likely, 5 = Extremely Likely.
Behavioral loyalty is measured by reviewing if the physician truly continued to
process with the service provider and was still engaged in an ongoing relationship with
the service provider 12 months after filling out the survey. A special staff reviewed the
ordering volume for each respondent for a period of 18 months following the receipt of
the survey response using a Shewart control chart. A Shewart control chart determines
variability in test ordering and downward trends in the sending of specimens.
The researcher observed if the volume had ceased after 12 months. The researcher
used two methods to determine if a physician had truly switched to a competitor.
First, the researcher reviewed the quality chart data for an additional 6 months to
determine if the physician had truly defected. Some of the volume is seasonally
determined and a six-month period was deemed sufficient to determine if a significant
drop in the volume, ordering pattern was due to seasonality.
Furthermore, sales people reviewed the records of the physician and made on-site
visits after 18 months to determine if and why the physician had seized to process with
the service provider. The additional, qualitative validity check ensured that the
quantitative correlation between attitudinal and absolute loyalty was valid and gave
additional actionable information to the service provider about the reasons for defection.
In regards to the timing of the measurement of behavioral loyalty, no clear standards
have evolved in the literature. For this study, and for the lifecycle of this organization, the
researcher chose the period of one year, to observe the absolute defection. The sales force
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and the management team had direct input into this decision. By asking the physician if
he/she intended to continue processing with the service provider for another year and by
reviewing the actual records, this research could establish a clear quantitative link
between attitudinal and behavioral loyalty.
In addition, the institution instituted a process of content validity to determine if the
observed behavior loyalty/disloyalty truly reflected the measurement findings. The
information obtained by the content validation also gave valuable, actionable information
about the reasons underlying the switching to a competitor. It also allowed for identifying
why intentions did not translate into actual behavior. The research hereby addressed one
of Grisaffe’s “dozen problems with applied customer measurement” namely “believing
that customer satisfaction measurement is doing” (Grisaffe, 2004, p. 5).
Sample
The company sent 1200 surveys to the physicians’ offices. The potential for non-
response bias using the time stamp of the response could not be tested because the
surveys were sent to the company’s headquarters and time stamps of early and late
responders were not captured.
Two segmentation criteria were used to evaluate if there is potential bias of
survey responses: Physician function, and county in which physician practices.
The top seven categories of practitioner function responding to the survey are almost
the same as in the main population. No significant difference between the population
frame and the survey respondents could be found (Chi-square = 1.54, d.f. = 6, p = 0.957),
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Research Model
As previously mentioned, structural equation modeling was used to investigate
the extent to which differences in observed satisfaction ratings translate into attitudinal
loyalty and behavioral loyalty. Structural Equation Modeling allows testing the
hypotheses simultaneously and at the same time determines to what degree the
hypothesized data reflect the moments of the actual data. In addition, it allows for the
assessment of the reliability in the more stringent form of a confirmatory factor analysis.
The output of the final model is shows in Figure 1.
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Figure 1: Structural Equation Model
.97
TAT1
.96
TAT2.89
SO.97
CSO
.94
CSA
.97
SP
.93
ST
e3 e4 e5
Turnaround Time Specimen HandlingClient Services
.96.98.94
.57
Sat. Overall
.42
AttitudinalLoyalty
.00
BehavioralLoyalty
.62
.65.67
e8
e9
e10
.19 .25.42
.65
.07
Structural ModelChi Square = 46.012
P =.205D.F. =39
GFI =.958AGFI =.930NFI =.982TLI =.996
e1 e2 e6 e7
.97.98.98.98
.79
TAT 4
.89
e11
Legend:TAT1 = TAT Overall; TAT2 = TAT Standard Tests; TAT4 = TAT Esoteric Tests; SO = Special Handling Overall; SP = Protection of Specimen; ST = Timing of Special Handling; CSO = Client Services Overall; CSA = Client Services Accessible.
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Overall Goodness-of-fit of the hypothesized model
Overall, the model fit the data structure well. The chi-square of the model is 46.012 with
39 degrees of freedom and p = .215. All critical fit indexes show values above the
recommended critical value of .90 (GFI = .958, AGFI = .930, NFI = .982, TLI = .996).
The individual hypotheses could therefore be evaluated within the framework of the
Structural Equation Model
Major Findings
The results of the hypothesis tests are as follows. Table 1 summarizes the unstandardized
and standardized regression coefficients, the critical ratios, and p-values of the variables
included in hypotheses one through five
1. HA1: The standardized regression path leading from turnaround time to overall
satisfaction is positive and significant (r = .186, d.f. = 39, p < .008). The null
hypothesis is rejected and thus confirming the alternative hypothesis
2. HA2: The standardized regression path leading from specimen handling to overall
satisfaction is positive and significant (r = .245, d.f. = 39, p < .001). The null
hypothesis is rejected and thus confirming the alternative hypothesis
3. HA3: The standardized regression path leading from client services to overall
satisfaction is positive and significant (r = .423, d.f. = 39, p < .001). The null
hypothesis is thus rejected and thus confirming the alternative hypothesis
4. HA4: The standardized regression path leading from overall satisfaction to
attitudinal loyalty is positive and significant (r = .65, d.f. = 39, p < .001). The null
hypothesis is thus rejected and thus confirming the alternative hypothesis.
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5. HA5: The standardized regression path leading from attitudinal loyalty to
behavioral loyalty is not significant (r = 0.069, d.f. = 39, p > .05). The null
hypothesis failed to be rejected and thus the alternative hypothesis could not be
confirmed.
The results show that turnaround time, special handling and customer service are
attributes that are critical in determining overall satisfaction with the services provided.
These three constructs explain 57% of the variation in overall satisfaction. It is
noteworthy that the standard regression coefficient for client services is twice as large as
that of turnaround time. This is an indication that satisfaction with service quality is
highly critical and almost more important than the satisfaction with the tangible product
itself, i.e. the time to receive the final test result. Furthermore, overall satisfaction is
significantly correlated with attitudinal loyalty. The model explains 42% of the variation
in attitudinal loyalty. Based on Cohen’s tables (1985) the effect size of the relationship is
very large. Finally, the relationship between attitudinal loyalty and behavioral loyalty is
much smaller than anticipated.
Table 1: Tests of hypotheses 1 through 5: Unstandardized and Standardized coefficients
Dependent VariableIndependent Variable Estimate S.E. C.R. P Label
Overall Satisfaction <-- Turnaround Time 0.195 0.074 2.65 0.008 par-6Overall Satisfaction <-- Specimen Handling 0.238 0.072 3.301 0.001 par-7Overall Satisfaction <-- Client Services 0.426 0.073 5.871 0 par-8Attitudinal Loyalty <-- Overall Satisfaction 0.523 0.045 11.574 0 par-9Behavioral Loyalty <-- Attitudinal Loyalty 0.024 0.026 0.94 0.347 par-10
Standardized Regression Weights EstimateOverall Satisfaction <-- Turnaround Time 0.186Overall Satisfaction <-- Specimen Handling 0.245Overall Satisfaction <-- Client Services 0.423Attitudinal Loyalty <-- Overall Satisfaction 0.65Behavioral Loyalty <-- Attitudinal Loyalty 0.069
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Tests of the moderating variables
In a further step, the impact of the two moderating variables on the relationship between
attitudinal and behavioral loyalty were evaluated.
The individual results for the physician function are as follows.
1. The standardized regression path from attitudinal loyalty to behavioral
loyalty for the general physician group is not significant (r = .022, p
=.288). The null hypothesis failed to be rejected.
2. The standardized regression path from attitudinal loyalty to behavioral
loyalty for the specialist physician group is not significant (r = .0014, (p
=. 353). The null hypothesis failed to be rejected.
3. The difference between the two standardized regression paths is not
significant. Thus, it can be concluded that physician function does not
significantly moderate the relationship between attitudinal and
behavioral loyalty.
The individual results for organizational size are as follows:
1. The standardized regression path from attitudinal loyalty to behavioral
loyalty of the one physician office is not significant (r = 001, p = .905).
The null hypothesis failed to be rejected.
2. The standardized regression path from attitudinal loyalty to behavioral
loyalty of the more than one physician office is not significant (r = .012,
p = 905). The null hypothesis failed to be rejected.
3. The difference between the two standardized regression paths is not
significant. Thus, it can be concluded that office size does not
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significantly moderate the relationship between attitudinal and
behavioral loyalty.
In sum, the analysis could not yield any significant moderating effect of either
physician function or organizational size. Table 2 summarizes the findings.
Table 2: The impact of the moderating variables on behavioral loyalty
Cross-Validation of the original model
MODERATOR 1: OFFICE SIZE MODERATOR 2: SPECIALIZATIONOne physician, r = .001 (p =. 975)
> One physician, r = .012 (p =. 905)
∆ = Not significant
Generalist, r = .022 (p =.288)
Specialist, r = .0014 (p =.353)
∆ = Not significant
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The main challenge to the generalization of empirical findings based on a model
generation approach in structural equation modeling is the over fitting of a model to a
specific data set (Hoyle 1995). In effect, a just identified model will always yield a
perfect fit. The structural equation model literature therefore strongly advises to cross-
validate these findings with a separate data set (see for example Arbuckle and Woetke
1995, Hoyle 1995, and Rigdon 2005). As a result, the findings were cross-validated with
a different sample.
Three months after the administration of the original survey, the same instrument
was used to measure the satisfaction with services provided by the company in its main
business unit. This business unit processes almost three times the volume of the business
unit used for the original roll out of the survey. The survey instrument and the mode of
administration were identical to the one used to generate the original model. This survey
yielded a similar response rate (16.5%) and 461 usable responses.
It is important to point out that there is one key difference in the services provided
by the two business units. While the business unit of the original study sends esoteric, i.e.
specialty tests to another so-called reference lab, the business unit of the cross-validation
study is a reference lab itself. Reference labs typically perform non-routine tests, so
called esoteric tests. As a result, the turnaround time for esoteric tests of the two sites
differs by an average of two days. The implication for the cross-validation study is that
the turnaround time for esoteric tests is of lesser importance in measuring the construct
turnaround time in the business unit of the cross-validation study. The variable
“turnaround time of esoteric tests” (TAT 4) was thus dropped from the model.
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An additional constraint in the form of a path leading from client services to
attitudinal loyalty was included to achieve an acceptable goodness of fit. This additional
path can be explained by the importance of client services in this industry. It may well be
that client services and especially the individualized caring attention provided by the
client service reps increases the level of loyalty felt by the sales force. Comments on the
survey of the cross-validation study repeatedly mention the importance of the relationship
between the physician and the client service rep that typically helps them in resolving any
issues.
With the exception of these two changes, the cross-validation model replicates the
revised model very well. Figure 2 shows a graphical representation of the cross-validated
model. With 55 distinct sample moments and 26 distinct parameters to be estimated, the
total number of degrees of freedom is 29.
Table 3 shows the covariance and variance matrix, the outlier analysis, and the
analysis of multivariate normality.
Table 3: Cross-Validated Model: Covariance-Variance matrix
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CovariancesEstimate S.E. C.R. P Label
Turnaround Time <--> Client Services 0.305 0.029 10.688 0 par-3Specimen Handling <--> Turnaround Time 0.341 0.032 10.531 0 par-4Specimen Handling <--> Client Services 0.368 0.036 10.231 0 par-5
VariancesEstimate S.E. C.R. P Label
Specimen Handling 0.676 0.057 11.903 0 par-14Turnaround Time 0.431 0.033 13.112 0 par-15Client Services 0.544 0.042 13.098 0 par-16e8 0.358 0.024 14.948 0 par-17e9 0.273 0.018 15.125 0 par-18e3 0.033 0.006 5.736 0 par-19e4 0.058 0.007 8.774 0 par-20e5 0.202 0.015 13.346 0 par-21e10 0.171 0.011 15.166 0 par-22e1 0.038 0.01 3.976 0 par-23e2 0.052 0.01 5.386 0 par-24e6 0.026 0.011 2.243 0.025 par-25e7 0.069 0.012 5.999 0 par-26
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Figure 2: Cross-validated Structural Model
.92
TAT1
.89
TAT2.94
SO.96
CSO
.89
CSA
.91
SP
.77
ST
e3 e4 e5
Turnaround Time Specimen HandlingClient Services
.88.95.97
.44
Sat. Overall
.39
AttitudinalLoyalty
.00
BehavioralLoyalty
.63
.63.61
e8
e9
e10
.28 .31.17
.48
.00
Structural ModelChi Square = 32.083
P =.316D.F. =29
GFI =.986AGFI =.974NFI =.992TLI =.999
e1 e2 e6 e7
.94.98.95.96
.21
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Overall Goodness-of-Fit of the cross-validated model
The chi-square of the cross-validated model is 32.083 with 29 degrees of freedom
and p = .316. All critical fit indexes show values above the recommended critical value of
.90 (GFI = .986, AGFI = .974, NFI = .992, TLI = .999). The revised model is based on 66
distinct sample moments and 27 distinct parameters to be estimated. This results in a total
number of degrees of freedom of 29.
Further evidence of the validity of the revised model is the fact that the
hypothesized relationships are replicated in the cross-validated model as well. While the
absolute coefficients change, the critical paths are significant in both models. Sampling
theory assumes that there is some variation in the strengths of the relationship from
sample to sample. Table 4 compares the five critical standard regression coefficients for
the revised and the cross-validated model.
Table 4: Comparison of the revised and cross-validated model
In summary, the cross-validated model provides strong evidence that the revised
structural model generalizes to different data sets and that the findings of the hypothesis
tests are not merely due to the fitting of the model to a particular data set.
Dependent Independent Estimate Estimate Variable Variable Revised Model Cross-validated ModelOverall Satisfaction <-- Turnaround Time 0.186 0.285Overall Satisfaction <-- Specimen Handling 0.245 0.307Overall Satisfaction <-- Client Services 0.423 0.168Attitudinal Loyalty <-- Overall Satisfaction 0.65 0.481Behavioral Loyalty <-- Attitudinal Loyalty 0.069 0.003
23
A Qualitative Post Mortem – Comments on the Findings
The findings of the “post mortem” discussions with the sales people were particularly
interesting. Of the total number of service switchers, 11.6% complained about service or
pricing issues. By contrast, 88.3% of the service switchers mentioned structural reasons.
In the clinical laboratory industry, the sending of specimens is often dictated by the
insurance of the patient. Patients with PPO have the choice of a clinical pathology
laboratory service provider, while the insurance company contracted by the insurance
provider binds HMO patient. Thus, it may not so much the service failure on the part of
the service provider, but the pressure of the underlying industrial organizational structure
that may have caused the weak link between attitudinal and behavioral loyalty. Future
research on the link between satisfaction and loyalty should take this finding into
account. This finding also puts into question the relatively strong link of cross-sectional
studies as opposed to longitudinal studies in regards to the strength of the satisfaction –
loyalty link. More studies should be conducted to investigate the method-method
contamination in cross-sectional study designs particularly as they relate to the link of
satisfaction and loyalty in B2B settings.
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Limitations of the study
This study has several limitations. As mentioned above, it is a study in a specific
industry, i.e. in the medical services industry. External generalization is therefore difficult
to justify. The study is also limited in that it reviews only one key player in the industry.
This makes external generalization even more difficult. However, it does enhance the
internal validity of the finding.
In addition to the limitations in research design, the study has shown that the
original sample size may be too low for the effect size of the relationship between
attitudinal and behavioral loyalty.
Finally, the measurement instrument of satisfaction has shown some weaknesses.
Most importantly, the factorial structure changed depending on the type of statistical
analysis used and the sample size. While the exploratory factor analysis yielded that
reporting and turnaround are part of the same construct, the structural equation model
showed consistently that reporting does not load on the same factor. In addition,
turnaround time is not a stable construct across different situations. This has partly to do
with the fact that not all business units service the clients in an identical way. For
example, some business units process esoteric tests in-house, while others send them to a
referral lab. Consequently, the factorial structure of the measurement instrument was less
stable than anticipated. The instability of the factorial structure raises the question if it is
advantageous to use a standardized service quality scale rather than an industry-specific
satisfaction scale.
Any replication of this research needs to take the shortcomings of the
measurement system into consideration.
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Implications for researchers
The findings of this study offer several implications for researchers.
First, the study supports previous research that suggests that overall satisfaction is
driven by service quality (measured as client satisfaction with problem handling) and
product quality (the turnaround time of result reporting). This study showed that the
impact of satisfaction with problem resolution provided by the call center operations on
overall satisfaction is stronger than the impact of satisfaction with the actual product.
This indicates that customers perceive value in the ability of a company to provide
effective problem resolution. The direct effect on attitudinal loyalty also indicates that the
way that companies handle problems may actually increase attitudinal loyalty.
Second, the analysis makes clear that the link between attribute satisfaction, overall
satisfaction and attitudinal satisfaction is real. Satisfaction matters in that it is an
important antecedent of attitudinal loyalty.
Third, the link between attitudinal loyalty and behavioral loyalty is weaker than
anticipated given the extensive literature research. While Hennig-Thurau and Klee (1997,
p. 739) assume a correlation between .18 and .26, and Bolton (1998) finds a correlation
that is around .30, the current study estimates the relationship to be less than .10.
As mentioned above, given the findings of meta-analysis such a divergence of
estimates is not unusual given the sampling error of correlation coefficients from study to
study (Hunter and Schmidt, 1990). There is some justification for the conclusion that
future research needs to ensure that the research design has sufficient power to detect the
expected relationship. This study suggests that samples sizes for a replication should not
be lower than 400 assuming that the effect size is small in the sense of Cohen (1988).
26
This implies sample sizes may need to be much larger than are typical in this research
field. Rather than using sample sizes between 50 and 200, the link between attitudinal
and behavioral loyalty may need investigation with sample sizes that exceed 400.
In addition, the current research opens up the question why the correlation between
attitudinal loyalty (intention) and behavioral loyalty is so low. One possibility is that
psychological factors of satisfaction play a secondary role in explaining behavioral
loyalty and defection (Reichheld 1996).
Another possibility is that additional psychological factors influence behavioral
loyalty. The marketing relationship literature, for example, puts heavy emphasis on trust
and commitment as two variables that are important in explaining attitudinal and
behavioral loyalty. The service quality literature emphasizes the importance of service
quality. Thus, future research may include satisfaction, service quality, trust and
commitment as factors that determine attitudinal and behavioral loyalty.
Alternatively, researchers need to take more closely into consideration the type of
exchange that is prevalent in their research environment. The loyalty concept was
adapted from the consumer behavior literature, which historically analyzed true
transactional exchanges. Business-to-business transactions occur via relationships that are
more formalized. Thus, the concept of loyalty may need to take into consideration the
type of discretionary behavior that the formalized relationship allows. Clients have the
option to either end the relationship altogether such as switching providers, or if they
have multiple providers, shift volume from one provider to another. Behavioral loyalty
may thus have an absolute and a relative aspect. Absolute loyalty would mean that a
relationship exists at all. In case where a company has several service providers, relative
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loyalty means that businesses can switch volumes from one provider to another.
Anecdotal evidence in the clinical laboratory industry shows that physician tend to switch
volumes if a service provider loses a specimen. Research has not yet sufficiently analyzed
this possibility.
Management Implications
It is also possible that the strength of the correlation between attitudinal and
behavioral loyalty is a function of the level of satisfaction itself. Follow-up studies
showed that the bulk of the defection was due to external factors rather than service
failures.
The level of performance of the organization itself can be a reason why defection may
be high or low. Thus, the performance of the company in the eye of the customer itself
may moderate the link between attitudinal and behavioral loyalty.
In other words, companies that perform at a low level have two problems: On the one
hand, they have to manage defection due to external factors, and on the other hand, they
need to manage disloyalty due to service failure and dissatisfaction.
By contrast, companies that perform at a high level are able to keep those customers
that otherwise willingly defect to a competitor. Therein lays an important implication for
managers. Satisfaction may act as an “insurance policy”. While the company cannot
hedge against external, structural factors such as contractual arrangements that make
customers switch and that are out of the control of the organization, high levels of
satisfaction may ensure that existing customers do not voluntarily switch to a competitor.
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Suggestions for Future Research
This study opens up new avenues for reviewing the attitudinal and behavioral
consequences of satisfaction:
1. Future studies should include other antecedent factors such as service quality,
trust, and commitment.
2. There is an opportunity to replicate this study in different industries such as the
financial services industry in order to assess what the correlation between
attitudinal and behavioral loyalty is. Care should be taken to measure the
impact of both absolute and relative defection and loyalty.
3. The impact of the performance of the organization on the strength of the
correlation between attitudinal and behavioral loyalty needs to be assessed. As
mentioned above, performance in the eye of the customer may be a moderating
factor.
4. This longitudinal study showed a weaker link between satisfaction and loyalty
than cross-sectional studies. The link may be strongly moderated by the
underlying structure of the industrial organization. As a result, more studies
should compare the impact of the legal and industrial environment of the
organization in their design to investigate the satisfaction-loyalty link.
5. Pressure of the underlying industrial organizational structure may cause to
weaken the link between attitudinal and behavioral loyalty. Future research on
the link between satisfaction and loyalty should take this into account.
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Limitations and Extensions
A key limitation of this study is that this is a firm-specific study, and that additional
studies need to show if a generalization to other firms and industries are valid. As pointed
out earlier, functional relationships other than a linear relationship may explain the low
correlation between attitudinal and absolute behavioral loyalty. However, given the low
level of service related issues and the impact of external, situational factors that drive
behavior in this study, a different function will not yield a higher explained variance.
Another key limitation is the fact that relative behavioral loyalty was measured via a
survey item, which arouses the question if method variance accounts for the significant
difference between the measure of absolute and relative behavioral loyalty. In the
healthcare industry, firms will have to set up specific tracking systems to separate and
differentiate between HMO and PPO specimen volumes.
The findings appear to be contrasting Keaveny’s (1995, p. 78) findings that only 6%
of switching behavior is explained by factors “beyond the control of either the customer
or the service provider”. However, Keaveny’s research was conducted in a B2C setting,
which in its ideal form is characterized by consumers with full decision-making power
who can freely decide about individual transactions or contractual obligations.
The research suggests that future research should be conducted to identify the degree
to which external factors determine the strength of the link between satisfaction and
switching behavior. This research suggests that the type of relationship (contractual vs.
other types of relationships) and the decision-making power within the relationship are
important factors that moderate the strength of the link between satisfaction and loyalty.
In addition, this research suggests that loyal behavior can be viewed as two-dimensional:
30
Absolute behavioral loyalty occurs when a customer or client switches suppliers
altogether. Relative behavioral loyalty occurs when a customer or client moves volumes
from one supplier to another without abandoning the relationship altogether.
Thus, the link between satisfaction and loyalty needs to take into consideration an
internal and an external dimension.
The internal dimension analyses the nature of the relationship between satisfaction
and loyalty. In this context, questions about the linearity vs. non-linearity of the
satisfaction – loyalty link, thresholds of satisfaction, or customer segmentations play a
key role.
The external dimension takes into consideration the type of relationship between the
customer and supplier. This external condition sets the foundation for the internal
dimension and determines if and to what degree the internal dynamics of the satisfaction
– loyalty work. It is suggested that part of the lack of the empirical validation of the
satisfaction – loyalty in B2B settings can be explained by the fact that not enough
attention has been paid to the interplay between external and internal dimensions of that
link.
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