A Comprehensive Framework for Service Quality: An Investigation of Critical Conceptual and Measureme

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A Comprehensive Framework for Service Quality
An Investigation of Critical Conceptual and
Measurement Issues Through a Longitudinal Study
Pratibha A. DabholkarC. David ShepherdDayle I.
Thorpe2000Journal of Retailing, Vol 76(2),
pp139-173
Yoo Jae Heung ITMGT, ICU
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Make it Clean
CS
BI
Personal Attention
Personal Attention
Expectation
Perception
Feature
SQ
Feature
Comfort
Feature
Comfort
Comfort
Gap (Disconfirmation)
Reliability
Reliability
Reliability
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Objective

• Propose and test a comprehensive framework of
  service quality
• Conceptual
• Critical measure issue

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Summary of Conceptual Issues

• Are relevant factors related to service quality
  better conceived as its components or its
  antecedents?
• Which format increase our understanding of SQ and
  which offers greater predictive power?
• What role does CS play in the framework of SQ
  prediction behavioral consequences?
• Does it add greater predictive power over that of
  SQ? If so, does CS act as independent determinant
  or does it mediate the effect of SQ on behavior?

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Measurement Issues in SQ

• Debate on whether SQ should be measured as
  perceptions or as disconfirmation (difference
  between perceptions and expectation)
• SQ measurement is also tied to disconfirmation.
  The computed difference scores for
  disconfirmation have problems of reliability,
  discriminant validity, and variance restriction
• Two-part measures (measures of expectations and
  perceptions (Parasuraman, Berry, Zeithaml)
• Direct disconfirmation measures (Brown,
  Churchill, Peter)
• The validity of a cross-sectional vs. a
  longitudinal research design (one where
  expectations are measured before the service is
  delivered and perceptions are captured after the
  services) for measuring SQ.
• For researchers using the disconfirmation
  approach, it is not clear whether both
  expectations and perceptions should be measured
  in a cross-sectional or whether a longitudinal
  design is more appropriate.

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Summary Measurement Issues in SQ

• Are perception measures superior to
  disconfirmation measures?
• Is measured disconfirmation superior to computed
  disconfirmation?
• Is a cross-sectional design adequate or does a
  longitudinal design offer significant advantage?

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Why antecedents model?

• No study to date has conducted a rigorous
  examination of the factors as antecedents and
  none has explored which model is better for
  understanding SQ
• Why antecedents model?
• Component view failed to capture the effect of
  the relevant factors and failed to capture
  customers overall evaluations of SQ as a
  separate, multi-item construct.
• P1 Factors relevant to SQ act as antecedents to
  overall evaluation of SQ rather than acting as
  its components

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Role of CS within the SQ Framework

• Does CS have an incremental effect over that of
  SQ on behavioral intentions, and is this effect
  an independent or a mediating one?
• (SQ - CS or CS-SQ, CS and SQ are independent)
• It is expected that CS will have a mediating role
  on behavioral intentions rather than an effect
  independent of SQ
• P2a CS mediates the influence of SQ on
  behavioral intension (BI) rather than both
  variables having an independent effect on BI.
• P2b CS mediates the influence of SQ on BI rather
  SQ mediating the influence of CS on BI.

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Disconfirmation vs. Perceptions

• Traditionally, SQ has been conceptualized as
  disconfirmation process but disconfirmation
  paradigm suffers due to problems with measuring
  expectations.
• Increasingly, researchers are simply measuring
  perceptions as indicators of SQ
• Some researchers have compared computed
  difference scores w/ perceptions to conclude that
  perceptions are a better predictor of SQ than
  disconfirmation.
• P3a Perception measures are superior to measured
  disconfirmation (direct score ask the gap
  directly)
• P3b Perception measures are superior to computed
  disconfirmation (difference score measure
  expectation and perception respectively then
  compute the gap)

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Difference Scores vs. Measured Disconfirmation

• Debate in the SQ on the use of difference(computed
  ) score vs. the measured disconfirmation (Brown,
  Churchill, Peter, 1993 Parasuraman, Berry,
  Zeithaml, 1993)
• Difference Score Perception Expectation
• Measured Score direct mental estimation of
  perceptions compared to expectation
• If both types of disconfirmation are similar in
  their predictive ability, explicit expectations
  data are not needed. Also if measured
  disconfirmation has the same diagnostic power as
  computed disconfirmation, then psychometric
  problems associated with the latter can be
  avoided. It is clear that there is greater
  support for measured disconfirmation
• P4 Measured disconfirmation is superior to
  computed disconfirmation

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Cross-Sectional vs. Longitudinal Design

• The majority of empirical conducted to measure SQ
  have been cross-sectional where both expectation
  and perceptions were measured after the service
  had been delivered.
• This approach assumes that expectations before
  the service are identical to expectations after
  the service and does not account for the fact
  that expectations may change over time or after
  the delivery of the service
• Longitudinal studies are cumbersome, costly, and
  by definition, time consuming
• P5 A cross-sectional research design is superior
  to a longitudinal design for measuring service
  quality.

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Testing Models

Testing Measures
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Methodology
How to make the ugly Frog to a gorgeous Prince?
How?
How??
How???
How????
There is no Magic !
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Institutional customers of the pictorial
directory division churches
Context
Sample
Longitudinal study 397 usable pairs
Qualitative Research
3 FGI ascertaining 4 Factors Reliability,
personal attention, Comfort, Features
Scale Development for Exogenous Variables
Expectation, Perception, Measured Disconfirmation
Measures of Endogenous Variables
Overall SQ, CS, Intention
Data Collection Procedures
Telephone interviews, contacted twice First call
expectation before service delivery Second call
Perceptions and measured disconfirmation after
service
Confirmatory Factor Analysis
For 21 items, CFI( Comparative Fit Index), Drop
items, re-examination Chronbachs alpha for
reliability
Other Tests to Validate Constructs and Measures
Discriminant validity of CS and SQ constructs Was
ascertained using a chi-square difference test
Variance Restriction and Other Patterns
S.D were similar ranged form 0.78 to 1.59 The
gaps for all the items followed a similar
pattern Irrespective of how they were measured.
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Result of CFA and Reliability Test
Chi-Square difference significant at P

0.9 Cronbachs Alpha 0.7
Distinct and Reliable Construct
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Correlations
Additional Chi-square difference test for pairs
of constructs with higher correlations (The
result was significant at p
discriminant validity among the constructs
Collinearity test for the exogenous variables and
VIF (variance inflation factor) test showed Lack
of Collinearity among These variables for all
three mesures (VIF Correlations SQ and CS with CS SQ and CS with
BI CS with BI
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Analytic Results

• Model Fit Regression in SEM with LISREL
• Shows relative importance of the predictor
  variables, as long as the fit is acceptable.
• Compared to traditional SEM, this technique has
  the added advantage of parsimony and is more
  robust against violations of assumptions about
  multivariate normality
• F-value from Regression analysis , RMR, NNFI, CFI
  used for check model fit
• RMSEA is not an appropriate fit index given
  extremely low degrees of freedom
• Check the Significant Gammas and absence of
  negative Gamma for check model specification
  Effect sizes and R2 value to assess the strength
  of models
• Basic simple correlations and sophisticated
  omitted paths analysis

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Test P1 (1/P1)
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Result of Testing (2/P1)

• Model Fit F-value, SD RMR, NNFI, CFI
• Significant Gamma and Absence of negative Gamma
• R2 values for BI
• Correlation of factor with SQ than BI

R2 are higher when CS acts mediating role (see
2/P2)
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Testing P2
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Result of Testing P2 (2/P2)
Acceptable
Fewer Significant Gamma
Higher effect size than Independent Model
Unacceptable
R2 are not much different but this is higher than
the case of SQ as a determininant (see 2/P1)
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Testing Overall Framework
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Result of Testing Overall Framework

• Good Fits
• Factors act as antecedents
• CS plays a mediating role

Computed disconfirmation model is not tested
because part of that dataset is identical to the
perceptions data, and would give misleading
results
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Testing Measures

• Computed Disconfirmation is evaluated against the
  other two measures for test of P3b, P4, P5
• Measured Disconfirmation is evaluated against
  Perception for a test of P3a
• Using same criteria that were used for comparing
  models Parsimony in Data collection

See the Proposition Map if you want to remind
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Computed vs. Perception and measured
Disconfirmation (P3b, P4, P5)
Low effect size

• Computed Disconfirmation shows Lowest F-value,
  Unacceptable NNFI
• Computed and Measured - Negative or
  Non-significant Gamma whereas Perception has No
  in Antecedents Model
• Computed is far lower R2 value for SQ as well as
  value for BI despite the overlap of the data
• Computed - lower effect size
• Computed - not easier to collect data because
  it requires twice (Not parsimonious)

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Measured Disconfirmation vs. Perceptions(P3a)
Higher F-value
Higher R2 but the difference Perception has Higher Effect Size
Perception has Better fit (although both are
unacceptable)
So, It is partially Supported
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Conclusion/Discussion
It looks more clear, now! Thank You!