TESTING AN EXPANDED ATTITUDE MODEL OF GOAL-DIRECTED BEHAVIOR IN...
TESTING AN EXPANDED ATTITUDE MODEL OF
GOAL-DIRECTED BEHAVIOR IN A LOYALTY CONTEXT
Journal
of Consumer Satisfaction, Dissatisfaction and Complaining Behavior
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Abstrak
Marketers have known for some time that satisfaction is closely
linked to loyalty intentions (Oliver 1997, 1999). However, Johnson et al.
(2006) argue that the drivers of customer loyalty intentions are complex and
dynamic, changing and evolving over time. The current study therefore
approaches the explanation of loyalty intentions in a different way.
Specifically, the argument is made and a model is tested and supported of
loyalty intentions based upon attitudinal, Goal-Directed conceptualizations.
Specifically, we adapt the Model of Goal-Directed Behavior (MGB) posited by Perugini and Bagozzi (2001) and
apply the model to loyalty in a B2B service context. First, the goal-directed,
attitudinal explanatory model represented by the MGB conceptualization is supported
by the current research. Second, two proposed extensions of the MGB are
supported involving unique forms of attitude and perceived behavioral control.
Finally, a multi-stage loyalty conceptualization is generally supported by the
data. The managerial and research implications of the study are presented and
discussed. [PUBLICATION ABSTRACT]
INTRODUCTION
The quest for loyalty is one
of the most frequently sought after strategic marketing objectives today
(Evanschitzky and Wunderlich 2006; Oliver 1999). Indeed, there appears little
controversy in the marketing literature surrounding the general belief that
customer loyalty can differentiate firms and generate sustained profits
(Evanschitzky and Wunderlich 2006; Keiningham, Vavra, Aksoy, and Wallard 2005;
Oliver 1999; Rust, Lemon, and Narayandas 2005; Vavra 1992). Oliver (1999)
argues for the determined study of loyalty with the same enthusiasm researchers
have devoted to a better understanding of customer satisfaction.
None-the-less, there are
still a number of important gaps in our understanding of the construct. First,
there continues to exist no agreement on the definition of loyalty
(Evanschitzky and Wunderlich 2006; Oliver 1999; Uncles et al. 2003). Second,
much remains to be learned of the nature of the relationship between loyalty
and antecedent influences such as satisfaction, value and trust (Agustin and Singh
2005), and why they appear so inconsistent in fostering loyalty. This
observation represents somewhat of a "loyalty riddle" for marketers
today. Third, there appears little explicit consideration of the more general
body of knowledge related to judgment and decision-making (J/DM) in marketing
inquiries related to consumer loyalty. Fourth, the role of moderating impacts
on loyalty are still poorly understood (Evanschitzky and Wunderlich 2006).
Together, such gaps in our understanding (1) make it difficult to consistently
define loyalty both constitutively and operationally across studies, (2) which
subsequently hampers our understanding of how loyalty forms, and (3) further
attenuates relating loyalty to other marketing theories and marketing
performance outcomes.
The following study first
calls for moving beyond strictly behavioral views of loyalty (i.e., that people
act) toward models that help us to better understand how loyalty judgments form
(i.e., how people form the motivation to act). Specifically, we call for
commensurability in adopting Oliver's (1997, 1999) attitude-based constitutive
definition of customer loyalty. We then propose an attitude-based model of how
such loyalty decisions are made based on a special case of Perugini and Bagozzi's (2001)
Model of GoalDirected Behaviors (MGB), which purports to well-reconcile with
emerging more general models of judgment and decision making (J/DM). The
contribution of the proposed model lies in its ability to help us solve the
riddle of why loyalty seems so hard to engender in so many target audiences.
Specifically, instead of relying solely on the often inconsistent influence of
satisfaction on loyalty (Oliver 1999), marketers can instead identify the
specific psychological antecedents (both cognitive and affective) motivating
loyalty intentions and subsequent behaviors in marketing strategic management.
Together, the results constitute a framework that may help begin the process of
unifying loyalty research with the extant body of knowledge across social science
disciplines. Thus, the presented study contributes to the body of knowledge for
both marketing practitioners and theoreticians, and potentially beyond.
This study builds upon the
argument that a cognitive and affective perspective of loyalty is consistent
with the emerging marketing literature. For example, studies are emerging
demonstrating the key role of affective commitment in strengthening loyalty to
a brand (Fullerton 2003, Matilla 2006). Ratner and Herbst (2005) conduct four
experiments and conclude that an emotional reaction to a negative outcome can
affect switching behaviors. Olsen et al. (2005) conduct a study identifying the
importance of taking ambivalence into consideration when measuring satisfaction
and modeling satisfaction-loyalty relationships. Yu and Dean (2001) also
suggest that the emotional component of satisfaction is a driver of loyalty.
Consistent with the emerging literature, the following study builds upon the
assertion that marketing models attempting to explain the evolution of loyalty
intentions need to consider affective considerations. However, we argue that in
order to consider affective considerations in loyalty models, loyalty theory
must be reconciled with the underlying literatures related to both judgment and
decision making (J/DM) and attitudes. Such reconciliation suggests modeling the
formation of loyalty intentions in new ways.
THEORY
The marketing literature most
generally identifies the existence of two competing views of loyalty. The first
view, which we will call the behavioral perspective, considers the domain of
the concept of loyalty to essentially be constrained to retention of the brand
(e.g., East et al. 2005). This perspective focuses almost exclusively on repeat
purchase. The emphasis is entirely on behavioral measures. The gist of this
perspective appears to be to focus on operationalizing the loyalty concept in
as parsimonious a manner as possible. This perspective appears most popularly
advocated by Reichheld's (2003) argument to simply measure word-of-mouth
behaviors in order to measure customer loyalty.
That said, Uncles et al.
(2003) suggests that loyalty represents a paradox. They explain the
decision-making process underlying the behavioral perspective essentially
involves passive acceptance of brands. An alternative perspective involves
considering loyalty to be an attitudinal-based phenomenon, which we will refer
to as the attitudinal perspective. While both perspectives appear to assume
goal-directed behavior, the behavioral perspective identified above appears to
be a relatively more (short-term) sales-directed perspective. The attitudinal
perspective can arguably be characterized as a more defensive alternative that
presupposes the efficacy of customer relationship management initiatives.
The attitudinal perspective
is the approach taken in the current research based upon the Morgan and Rego
(2006) study demonstrating the misguided nature of the behavioral perspective
as identified above. Our review of the literature suggests that Oliver's (1999,
p. 34) definition of loyalty represents a viable candidate for commensurable
agreement as a constitutive definition for attitudinally-based loyalty: "a
deeply held commitment to rebuy or repatronize a preferred product/service
consistently in the future, thereby causing repetitive same-brand or same
brand-set purchasing, despite situational influences and marketing efforts
having the potential to cause switching behavior." We view this definition
as adequately capturing both behaviorally- and attitudinally-based loyalty
models identified above. Consistent with Jacoby and Chestnut's (1978) original
expectancy-value based assertions, Oliver (1999) suggests that loyalty includes
four hierarchical phases: cognitive, affective, conative (i.e., behavioral intention),
and action. However, this conceptualization differs in the argument that
consumers become "loyal" at each attitudinal phase relating to
different elements of the attitude development structure.
Consumers are theorized to
become loyal first in a cognitive sense, then later in an affective sense, and
finally in a behavioral sense (i.e., action inertia). In addition, defining
loyalty as a construct with cognitive, affective, conative, and action phases
suggests that explanatory models should incorporate similar constructs. Not
inconsistent with Oliver's (1997, 1999) conceptualization of hierarchical
loyalty we have defined loyalty in the current research at two general levels:
repurchase and fortitude. In addition, consistent with Reichheld (2003), we
include word of mouth as a dependent variable in our model. Table 1 presents
Oliver's (1997, 1999) theoretical model of loyalty, and how they relate to our
conceptualization.
In summary, the current study
investigates customer loyalty consistent with a goal-directed, attitudinal
perspective that seeks to incorporate affective considerations in the
decision-making model. However, prior to presenting the research model
investigated herein, we first address the efficacy of our research strategy
when compared to emerging theoretical considerations from the judgment and
decision making (J/DM) and attitude literature.
Reconciling Loyalty Theory
with the J/DM and Attitude Literatures
The vast majority of evidence
related to choice under risk or uncertainty is based upon theories that are
cognitive in nature (Lowenstein et al. 2001).
That is, people assess choice alternatives and then integrate this information
through some form of expectation-based calculus to arrive at a decision. As
attitude theories have origins in multi-attribute utility theory (MAUT),
attitude theory appears accurately described as having evolved based upon the
consequentiality perspective as described by Lowenstein et al. (2001).
Such a perspective suggests that consumers base decisions on the consequences
of their actions. However, one of the problems associated with the
consequentialist perspective has been its inability to easily incorporate
affective considerations (Kahneman and Tversky 2000, Tversky and Kahneman
2000). Hsee et al. (2004), however, are proponents of a perspective that
purports to reconcile affective considerations with the consequentialist
perspective.
The Hsee et al. (2004)
perspective allows for the contemplation of affective considerations within the
context of attitude models (as a form of J/DM models). In fact, the MGB
represents an effort to specifically accomplish this end. MGB differs from
previous attitude models such as the Theory of Planned Behavior by (1)
introducing emotions into traditional attitudinal explanations of goal-directed
behavior through positive and negative emotions (As), and (2) accounting for
motivation through the capture of desires as part of the model. These
anticipated emotions influence volitional processes (i.e., cognitive planning),
which in turn influence the development of desires (i.e., motivation, which
subsequently influence instrumental behaviors). Figure 1 presents MGB specific
to loyalty inquiries. However, the current research extends beyond simply
applying MGB to loyalty considerations, but also includes extensions of the MGB
which are appropriate for this context.
Proposed Extensions to the
MGB Conceptualization & Research Hypotheses
A review of the literature
identifies two potential extensions of the MGB conceptualization that also
merit consideration within the current research. The first extension involves
the appropriate conceptualization and operationalization of the Attitude^sub
Act^ construct. Perugini and Bagozzi (2001, p.
81) define "attitudes" in the MGB conceptualization consistent with
that typically associated with the Theory of Planned Behavior (see Eagly and
Chaiken 1993): "Attitude is conceived as a 'psychological tendency' that
is expressed by evaluating a particular entity with some degree of favor or
disfavor." However, evidence is emerging supporting calls for specifically
considering separate affective evaluations when measuring attitudes (Hagger and
Chatzisarantis 2005, Okada 2005, Van Den Berg et al. 2005). Voss et al. (2003)
report a multidimensional, parsimonious, reliable, and valid scale of consumer
attitudes toward brands and product categories based on a two-dimensional
conceptualization of the construct: hedonic and utilitarian attitude
components. We suggest that the measures employed by Perugini and Bagozzi (2001) to
measure Attitude^sub Act^ may obscure the unique contributions of hedonic and
utilitarian consumer attitudes toward the act of consumption. This distinction
is important given the previously identified linkages of the model to affective
and cognitive considerations. Therefore, the current research first tests the
validity of a two dimensional conceptualization of Attitude^sub Act^ based upon
the scale developed by Voss et al. (2003), and then assesses whether
utilitarian and hedonic attitudinal proclivities differentially influence
respondents' motivations as desires. We further suspect that utilitarian
attitude forms will be more influential in the current research setting based
upon discussions with knowledgeable industry managers.
The second proposed extension
concerns perceived behavioral control. Perugini and Bagozzi (2001)
operationalize PBC using measures of both difficulty and control in a
unidimensional scale. However, Trafimow et al (2002) present evidence that PBC
is best conceptualized as a two-dimensional construct: perceived control and
perceived difficulty, with perceived difficulty generally being a better
predictor of behavioral intentions than perceived control. In the current study
an assessment is performed to determine whether difficulty and control
differentially influence the MGB as unique exogenous constructs. Figure 2 and
Table 2 present the model assessed in the current research that incorporates
both the original MGB conceptualization and the modifications proposed herein.
As discussed above, the
current study includes utilitarian and hedonic dimensions of attitude. The
utilitarian dimension of an attitude is defined by Voss et al. (2003) as being
derived from the functions performed by products, being that the scale was
developed in a product context. In the present context, the utilitarian
dimension of attitude would be the function derived from the act of being
loyal. Loyalty would be functional in that it would make the choice process
easier. Information processing theorists suggest decision makers value effort
reduction in the decision process (Bettman, Luce, and Payne 1998). An
increasing utilitarian dimension should therefore be associated with increasing
desire (i.e., motivation) to be loyal.
H1a: Utilitarian attitude
will be positively associated with increasing desire to be loyal.
The hedonic dimension of an
attitude is defined as the sensations derived from the experience of using the
product (Voss et al. 2003). In the present context, the hedonic dimension would
represent the sensation derived from the act of being loyal. Loyalty is
associated with decreasing cognitive effort. Research offers evidence that
increasing cognitive effort is associated with increasing negative affect
(Garbarino and Edell 1997). Assuming a converse process, reductions in decision
effort should be associated with increasing positive affect. Therefore:
H1b: Hedonic attitude will be
positively associated with increasing desire to be loyal.
The MGB suggests that
subjective norms will also be related to desires (Perugini and Bagozzi 2001).
Subjective norms refer to consumers' overall perceptions of what relevant
others (e.g., friends, family) think he or she should do (Evans, Christiansen,
and Gill 1996). Evidence suggests that what we perceive others to think we
should do will impact our desires (Perugini and Bagozzi 2001).
These significant others would involve professional peers and colleagues within
the context of the current research. Therefore:
H2: Subjective norms will be
positively associated with increasing desire to be loyal.
As previously discussed,
research offers evidence that negative and positive anticipated emotions impact
desires (Perugini and Bagozzi 2001).
Anticipated emotions are the emotions that a person expects to experience by
achieving a sought after goal (Bagozzi,
Baumgartner, and Pieters 1998). These are not the emotions experienced while
being loyal, but are the anticipated emotions that consumers weigh when
deciding whether to pursue a goal of being loyal. According to theory the
process would work like this: first, consumers form a goal; second they
consider the consequences of achieving the goal with the corresponding emotions
arising termed anticipated emotions (Perugini and Bagozzi 2001).
Research offers evidence that when a consumer considers a goal, anticipated
emotions influence the level of motivation for that goal. In the MGB,
anticipated emotions are positive when associated with achievement of a goal
and negative when associated with not achieving a goal. Evidence suggests
desire is influenced both by the expected emotions of achieving and not
achieving a goal. Therefore:
H3: Positive anticipated
emotions associated with achieving the goal of being loyal will be positively
associated with an increasing desire to be loyal.
H4: Negative anticipated
emotions associated with failure to achieve the goal of being loyal will be
positively associated with an increasing desire to be loyal.
Perceived behavioral control
is defined as the extent to which consumers consider the performance of a
behavior to be under their voluntary control (Trafimow et al. 2002). In the
context of loyalty, perceived behavioral control represents the degree to which
a consumer believes loyalty to a firm is within their control. In the current
B2B context, perceived behavioral control could be impacted by the degree to
which the respondent believes they are able to make the decision. Desires can
be generally defined as motivation. A consumer's belief that they have the
control necessary to be loyal would seem to be a baseline necessity for them to
desire to be loyal. Otherwise it would be a wasteful consideration. Perugini and Bagozzi (2001)
offer evidence that perceived behavioral control has a positive association
with desires. Therefore:
H5a: Perceived behavioral
control of loyalty will be positively associated with desire to be loyal.
Perceived difficulty is
defined by Trafimow et al. (2002) as the degree to which consumers consider a
behavior to be easy or difficult to perform. In the current B2B context, the
politics of the workplace might make it more difficult for a buyer to be loyal
to a particular firm. If a consumer believes that being loyal is easier, the
likelihood that they will be loyal should increase. Therefore:
H5b: Perceived difficulty of
loyalty will be negatively associated with desire to be loyal.
Perceived behavioral control
and perceived difficulty should be directly related to loyalty intentions. Both
control and difficulty should lead directly to intentions because they are a
baseline necessary for intentions to form. If consumers believe that they have
control and that the task is easy there is the possibility of intention, even
if there is no motivation. Therefore:
H6a: Perceived behavioral
control of loyalty will be positively associated with loyalty repurchase
intentions.
H6b: Perceived behavioral
control of loyalty will be positively associated with loyalty fortitude
intentions.
H6c: Perceived behavioral
control of loyalty will be positively associated with word-of-mouth behavioral
intentions.
H6d: Perceived difficulty of
loyalty will be negatively associated with loyalty repurchase intentions.
H6e: Perceived difficulty of
loyalty will be negatively associated with loyalty fortitude intentions.
H6f: Perceived difficulty of
loyalty will be negatively associated with word-of-mouth behavioral intentions.
Recent research expands
traditional models by suggesting that desire mediates the relationship between
attitude and intention (Perugini and Bagozzi 2001).
According to this view, intention does not encompass the motivation that is
necessary for an intention to be formed. Desire represents this motivation.
Research offers evidence that desire is a distinct construct from intention (Perugini and Bagozzi 2004), and does mediate the relationship
between an attitude and behavior (Perugini and Bagozzi 2001).
Desire is defined as "a state of mind whereby an agent has a personal
motivation to perform an action or to achieve a goal" (Perugini andBagozzi 2004, p. 71). Desire is said to
"represent the motivational state of mind wherein appraisals and reasons
to act are transformed into a motivation to do so (Perugini and Bagozzi 2001, p.
84)."
A basic assumption underlying
this research is that consumers have a goal to be loyal. Loyalty makes decision
making more efficient. Once consumers determine an initial solution, they can
reduce choice effort by remaining loyal. Research suggests that the reduction
of effort is an underlying motivation for decision makers (Bettman, Luce, and
Payne 1998). In today's marketplace which is characterized by hyperchoice
(e.g., Mick, Broniarczlyk, and Haidt 2004), loyalty is one strategy for
reducing effort. Thus loyalty can be modeled as a goal. The MGB suggests that
desires will mediate the relationship between attitude and behavioral
intentions. Therefore:
H7a: Desire to be loyal will
be positively related to loyalty repurchase intentions.
H7b: Desire to be loyal will
be positively related to loyalty fortitude intentions.
H7c: Desire to be loyal will
be positively related to word-of-mouth behavioral intentions.
Some research suggests that
different stages of loyalty have a hierarchical structure. Oliver (1999)
suggests that cognitive loyalty precedes affective loyalty, affective loyalty
precedes conative loyalty, and conative loyalty precedes action loyalty.
Consistent with this view, we suggest that loyalty repurchase intentions will
precede loyalty fortitude intentions, and loyalty fortitude intentions will
precede word of mouth behavioral intentions. Consumers demonstrating brand
repurchase loyalty buy because of compelling information (e.g., quality), a
cognitive orientation. Consumers demonstrating brand fortitude loyalty buy
because of a commitment to the brand with both cognitive and affective
dimensions. Behavioral word of mouth intentions are an outcome of the other two
stages of loyalty. Therefore:
H8: Loyalty repurchase
intentions will have a positive relationship with loyalty fortitude intentions.
H9: Loyalty fortitude
intentions will have a positive relationship with word-of-mouth behavioral
intentions.
METHODS
The Population and Obtained
Sample
The population selected for
empirically assessing the research model presented as Figure 2 involved key
decision makers of accounting firms concerned with the purchase of professional
liability insurance (PLI). The selection of this particular target population
was made for several reasons. First, PLI and the accounting discipline both
represent "pure" service considerations, a rigorous loyalty situation
in which to test the research hypotheses. Second, the recent enactment of the
Sarbanes-Oxley legislation underscores the need for PLI due to changes in the
ethical perceptions of the accounting industry (Fletcher 2003). Consequently,
PLI should be important and somewhat involving to respondents. A list of
"Managing Partners" from the American Institute of Certified Public
Accountants (AICPA) was purchased after discussions with industry managers and
researchers to identify the best possible sampling frame. Two thousand surveys
were sent to managing partners of accounting firms across all fifty states in
the US, offering a $10 gift card as an incentive to encourage participation.
All potential respondents received a follow-up postcard one week later.
A total of 210 completed
surveys were returned, plus 79 that were nondeliverable for one reason or
another. This yielded a working response rate of 2.8%. However, 20 surveys were
discarded because respondents failed to completely answer the loyalty scales.
The authors are encouraged by the response rate given generally declining
survey response rates (Dillman 2007), as well as the correspondence of the
study with the beginning of the 2004 tax season. Dillman (2007) identifies the
challenges associated with meaningfully measuring nonresponse error, which we
attempted to address by increasing our response rate through the reminder
postcard. The original cover letter accompanying the survey instrument also
encouraged recipients to personally complete the survey. In addition, prior to
model testing, the obtained sample was scrutinized to compare it with the
population of interest based upon demographic variables. Discussions were
solicited with industry experts possessing extensive long-term experience in
the PLI industry relative to accountants, with all agreeing that the obtained
sample very closely corresponds to the known demographic characteristics of the
population of interest (largely Caucasian, middle-aged males). The vast
majority of respondents have extensive accounting experience (between 11 and 40
years), with more than half having held their current PLI policies for less
than 11 years, signifying little reason to believe that non-targeted
respondents completed any of the returned surveys. Thus, the conclusion is
supported that the obtained sample appears representative of the population of
interest.
The Measures Employed to
Operationalize the Constructs
Appendix A presents the
measures used in the reported study, as well as their constitutive definitions
and sources. Those measures specifically developed for this study were based
upon constitutive definitions found in the literature. In addition, all of the
measures employed are of a global and reflective nature (see Jarvis, McKenzie,
and Podsakoff 2003). That is, they assert to directly measure the underlying
core of the domain in a relatively comprehensive manner. The measures used in
the current research therefore possess a measure of face validity in that most
of the scales rely on measures previously reported in this line of inquiry.
However, before moving on to
a discussion of the reliability and validity of the measures, two issues merit
discussion. First, loyalty intentions were divided into two general categories
of repurchase intentions versus fortitude forms of loyalty in a manner not
inconsistent with the Oliver's (1997, 1999) conceptualization (see Table 1). In
addition, word-of-mouth behaviors were treated as an independent endogenous
dependent variable consistent with the recommendations of Reichheld (2003) and
Soderlund (2006). Second, Bagozzi et al. (2001)
argue that it remains unclear whether satisfaction is phenomenologically
distinct from other positive emotions. Specifically, they point to the fact
that satisfaction is neither a basic emotion nor a central emotional category
in leading theories of emotions. In other words, these authors suggest that
satisfaction may just be another reflective indicator of positive emotions.
Based on this assertion, we included positive anticipated satisfaction as a
measure of positive anticipated emotions (see Appendix A). We therefore test
the proposition that anticipated satisfaction may operate as a reflective
indicator of positive anticipated emotions.
The reliability and variance
extracted validity scores associated with the measures are reported in Appendix
A. Hair et al. (1998) suggest calculating construct reliability estimates and
variance-extracted measures. Raines-Eudy (2000) states that calculated share
variance scores exceeding 50% are the recommended criteria for model
constructs. Appendix A demonstrates that the scales used for the model
constructs all exceed recommended criteria for reliability and validity in
measurement except for previous behavior. Consequently, previous behavior was
dropped from subsequent analyses. In addition, following Anderson and Gerbing
(1988), we employed the two-step approach for demonstrating discriminant
validity. Table 3 presents the PHI matrix of the confirmatory factor analysis
of the measurement model (χ^sup 2^ = 1068.79, RMSEA = 0.042, CFI = .98, and
SRMR = 0.055). Table 4 presents the χ^sup 2^ difference tests further
supporting discriminant validity among the constructs most likely to lack
discriminant validity in measurement. Thus, evidence is apparent for sufficient
reliability and validity in measurement to proceed to hypothesis testing using
SEM.
Statistical Considerations
All of the hypotheses are
tested using structural equation analyses (SEM) using LISREL 8.80. The data was
normalized prior to analysis using the normalization algorithm in PRELIS
consistent with need for normality in multivariate analyses (Hair et al. 1998).
As is often inherent in survey-based research, a small number of missing values
in responses were noted related to measures of the endogenous and exogenous
constructs.1 This can be problematic in that LISREL provides full information
and multiple imputation based on the assumption that data are missing at
random. Missing values associated with the exogenous model variables were
addressed prior to analyses by using the linear interpolation method within the
SPSS Missing Values statistical software package.
McQuitty (2004) posits that
it is possible to estimate the power associated with the test of an entire
(SEM) model. He argues that addressing this matter is essential as statistical
power directly affects the confidence with which test results can be
interpreted. The obtained sample in the current research provides statistical
power in excess of .9 based on the indices provided by McQuitty (2004).2
Further encouragement is provided by the recent results of Curren et al (2003)
who assert that both sample estimates and confidence intervals of RMSEA's are
accurate using SEM with sample sizes of ≥ 200. Thus, sufficient statistical
power exists to proceed to analyses using SEM.
RESULTS
Table 5 presents results of
hypothesis testing of the research model presented as Figure 2 and Table 2. The
results support a number of conclusions:
1. Desires are shown to be
most strongly influenced by utilitarian forms of attitude, followed by positive
anticipated emotions (including anticipated satisfaction), subjective norms,
and hedonic forms of attitude.
2. Initial repurchase forms
of loyalty intentions are a function of the motivation to be loyal (i.e.,
desire) and the perceived difficulty associated with the act of repurchase.
3. Subsequent fortitude forms
of loyalty intentions are a function of motivation as desires and repurchase
forms of loyalty intentions, followed by perceptions of control. This result
merits further discussion as the sign associated with the finding is negative,
suggesting that the greater the perceived control the manager has over the
behavior, the less likely the manager's intention to demonstrate fortitude forms
of loyalty. Some insight into this finding is apparent by examining the PHI
matrix presented as Table 3. As expected, control is negatively related to
perceptions of difficulty. However, control is also negatively related to
fortitude forms of loyalty, desires, and positive anticipated emotions, while
positively associated with negative anticipated emotions. Discussions with
industry managers with long-term experience speculate that this finding may be
capturing the negative affect associated with the additional accountability
that often accompanies greater control.
4. Word-of-mouth intentions
are found to be solely driven by fortitude forms of loyalty in the current
research.
In summary, the results offer
some support for the major assertions of the current research. First, many of
the components of the Goal-Directed, attitudinal explanatory model represented
by the MGB conceptualization are supported by the current research (Please see
Table 2). Second, the proposed MGB extensions involving unique forms of
attitude and perceived behavioral control are supported herein. Finally, a
multi-stage loyalty conceptualization is generally supported by the data.
STUDY LIMITATIONS
In spite of the interesting
nature of the results reported herein, we caution readers to recognize that a
single study should never be sufficient evidence to create fully-accepted new
theory. No research project is devoid of limitation, and the current study claims
no exception. Identifiable limitations might first include the obtained sample
size in spite of our arguments supporting conclusions of representativeness,
reliability, validity, and sample power sufficiency.
Second, the study would have
benefited from measures of behaviors, not just behavioral intentions. Behavior,
in this context, is the act of remaining with a provider. Given that most do
remain with the provider for an extended amount of time, it makes sense to
measure loyalty intentions rather than behavior. Behavior would not necessarily
represent loyalty as it could be due to several factors (e.g., inertia,
switching costs) which have little to do with psychological attachment to the
firm. For this reason, we use loyalty intentions, rather than behavior, as our
dependent variable. The use of intentions as opposed to behavior may help to
explain some of our weaker findings such as perceived behavioral control.
Third, our study did not
measure the degree to which switching costs or unique knowledge may have
explained loyalty behaviors. However, given our focus on attitudinal and
behavioral measures of loyalty, we have some evidence that loyalty instead of
switching costs explained intentions.
Fourth, we did not test
frequency and recency effects given the selected research setting. Recency and
frequency effects were omitted based on the recommendations of managers
suggesting that the decision to continue a PLI policy was not universally
consistent across time. In choosing a context, we wanted one that offered the
opportunity for loyalty to develop; suggesting businesses need to be familiar
with the service provider. However, the downside of this is that frequency of
past behavior is limited, given that firms rarely change their insurance
provider. In other words, many people make this decision once, and then revisit
it only when there is a need.
Finally, given the dearth of
such considerations within B2B contexts, much of the theory developed in the
current research is generalized from consumer research. However, given the
theoretical development of the research model from the literatures of a wide
variety of social sciences, coupled with supported empirical efficacy of
results, the finding associated with this study arguably provide a significant
step forward in our understanding of the formation of loyalty intentions. We
encourage efforts to replicate the results reported herein.
RESEARCH AND MANAGERIAL
IMPLICATIONS
We began this study with the
proposition that models of customer loyalty can and should incorporate affect
based on emerging evidence from the J/DM and attitude literatures. We reject
the notion that inquiries into loyalty should be grounded in purely behavioral
considerations, and develop and test a model of loyalty based upon an arguably
more valid attitudinal approach. So, what is gained based on the current
research?
We begin by looking at the
research implications. The preceding literature review makes clear that typical
approaches to explain loyalty are initially attenuated by irregular constitutive
and operational definitions. Significant advances in our understanding must
begin with commensurable agreement as to the appropriate conceptualization and
operationalization of the loyalty construct. The second typical practice that
attenuates our understanding of customer loyalty concerns the common absence of
reconciliation with underlying models of J/DM in marketing explanations of
loyalty decisions. We suggest that this study provides a framework that can
help overcome both of these impediments to the advancement of our understanding
of loyalty. Academicians and/or practitioners who care to understand the
motivational influences underlying loyalty judgments and decisions can
replicate and extend the models proposed herein to inform their understanding
of loyalty, and particularly the underlying motivations.
There are also a number of
very interesting puzzles that remain to be answered specific to customer
loyalty. The first puzzle involves whether or not a direct path should be
modeled between affect and behavior, or whether affect always operates via an
interaction with cognition. Such an assumption appears implicit in the
prevailing models of loyalty based upon the argument that quality perceptions
[arrow right] satisfaction judgments [arrow right] loyalty intention.
Assertions concerning experienced utility aside, Loewenstein et al (2001) and
Anderson (2003) present arguments that affective influences are difficult to
reconcile with explanatory models of J/DM under conditions of risk. The gist of
their argument is as follows: Objective or judged probabilities may be the same
across two situations, yet feelings in the two situations may diverge,
potentially yielding different preferences (i.e., those more affectively
influenced). Prospect theory requires that they yield equivalent preferences.
While interesting, our own review of the literature suggests that this issue
remains unresolved and therefore constitutes an important area of future
inquiry, particularly in light of experienced utility. We summarize that
loyalty research must eventually be reconciled with both cognitive and
affective explanations of J/DM.
This call is consistent with
Rottenstreich and Shu (2004) who identify what is known about the connections
between affect and decision making based upon the traditional expected
utility-based paradigm and its derivatives. First, in terms of the deliberate,
calculative versus affective, more automatic valuation processes: (1) the use
of different valuation processes may contribute to preference pliability, (2)
valuation by feelings are relatively scope-insensitive yielding step-function
valuation functions, whereas calculative valuation is relatively
scopeinsensitive yielding more linear value functions, and (3) valuation by
feeling appears to yield greater loss aversion than calculative valuation.
second, the weighting function can also be impacted by affect under
uncertainty: (1) affectrichness potentially yields pronounced certainty and
impossibility effects and extreme insensitivity to intermediate probability
variations, (2) affectrichness may contribute to the elevation of w in models
such as prospect theory, (3) the influence of affect is likely to depend upon
the imagery evoked, and (4) affect, especially in the form of mood, appears to
bias judgments of likelihood. Finally, the introduction of emotions to
traditional explanatory models is complicated by the observation that emotional
reactions can occur at the time the decision is made, after the decision but
before the consequences are realized, or after the consequences are realized.
These conclusions support an
expectation that virtually all expected utility-based J/DM explanatory models
can be expected to vary across situations and circumstances. Therefore, it is
not at all surprising that loyalty appears irrational when studied by models
that do not explicitly account for affective considerations. This probable
model inconsistency across settings and circumstances underscores our call for
greater emphasis on replication in support of loyalty studies. Such a call is
further supported by Norenzayan and Heine's (2005) compelling argument that
generalization of results of psychological studies beyond one's sample is
inherently risky due to an absence of psychological universels. A stronger
emphasis on the identification of relevant psychological universals underlying
loyalty judgments and decisions also appears as a worthwhile research endeavor
supporting such calls.
The second obvious puzzle
facing loyalty researchers concerns how to reconcile the plethora of
empirically supported known antecedents (e.g., satisfaction, trust, switching
costs, brand equity, value, etc.) to loyalty with attitude-based models such as
are presented herein. We suggest that the surest path to reconciling the myriad
of results reported related to loyalty is to agree upon an underlying J/DM
model. Thus, we call upon future marketing research related to the loyalty
construct to identify and defend results based upon a comparison to underlying
models of J/DM. In order to answer such a call, we assert that some formal
consideration of the cognitive and affective antecedents to loyalty
intentions/behaviors is desirable.
This suggests including
modeling perspectives related to attitudinally-based, goaldirected explanatory
models. For example, the recent work of Hagger and Chatzisarantis (2005)
suggests considering higher-order constructs in attitude models. Given the
growing emphasis on the multidimensional nature of the constructs underlying
attitudinal models of J/DM, such a modeling emphasis may prove to be
particularly promising. Complementing this assertion, Perugini (2005) highlights the importance of
predictive models of implicit versus explicit attitudes, which appears a
promising means of extending the current research (also see Neumann et al.
2004). MacDonald and Nail's (2005) arguments related to attitude change and the
public-private attitude distinction also merit consideration in future
research. Bagozzi et al. (2002) point out that a number of
individual and/or situational variables have the potential to influence
motivation, including involvement's ability to influence elaboration and
thereby influence the research model. The authors also recommend consideration
of the relevant conditional influences impacting such models.
The final puzzle that we
identify concerns how motivation operates in models of the formation of loyalty
intentions/behaviors. Perugini and Bagozzi (2004) make a strong argument
differentiating motivation as desire from intentions. We suspect that there is
more to learn about the various unique and synergistic roles of desires, PBC,
and/or intentions in terms of motivational content.
The current research also
offers insight for managers who want to positively influence the customers'
intentions to be loyal. While word-ofmouth has been identified as the important
measure of customer loyalty/satisfaction (Reichheld 2003), it is also useful
for managers to understand those factors impacting the customer's intention to
want to use word-of-mouth to promote a specific manufacturer's market offering
over that of a competitor. As predicted, we find that word-of-mouth intentions
were influenced by loyalty fortitude intentions, which in turn, are influenced
by loyalty repurchase intentions and the customer's desire to be loyal.
Furthermore, a customer's loyalty repurchase intentions are influenced by the
customer's desire to be loyal as well as the difficulty the customer
experiences when trying to be loyal. Finally, the customer's desire to be loyal
is influenced by others significant to the customer (subjective norm) the
anticipated positive emotion associated with being loyal, as well as hedonic
utilitarian and hedonic attitudes towards being loyal. As such, the
organization must focus on factors that impact the customer's utility that they
receive in the exchange, while simultaneously heightening the experience of
customers so that the customer developed positive feelings toward being loyal
to the organization. Finally, it may be beneficial to the organization to
direct marketing actions toward the totality of targeted customers. By
enhancing the image of the firm to all potential customers, word of mouth
activities might enhance loyalty intentions through the identified influence of
subjective norms. This effect should occur through influencing their desire to
be loyal, and desire's impact on the formulation of repurchase and fortitude
loyalty intentions. In much the same way, organizations that can enhance the
customer's positive anticipated emotions about being loyal to the organization
and hedonic attitude should also experience customers with higher levels of
word of mouth intentions. Managers would need to focus on ensuring the customer
has enjoyable, exciting, delightful exchanges when re-purchasing the market
offering. Such exchanges should highlight to the customer the benefits of
increasing loyalty to the organization.
Managerially, the results
first suggest that at least some of the discussion related to the appropriate
conceptualization and measurement of customer loyalty found in the consumer
literature can be generalized to B2B settings. second, the results also
generally support the recent arguments of Morgan and Rego (2006) calling into
question the efficacy of Reichheld's (2003) behavioralist arguments. Third, the
results suggest caution in using satisfaction measurements. If loyalty is a
goal as is argued herein, then it remains unclear whether satisfaction is an
anticipate emotion as envisioned by Bagozzi et al. (2003), or an outcome of a service
interaction as traditionally envisioned, or perhaps even both. It appears that
much remains to be learned about how to measure and incorporate satisfaction in
managerial decision making. Fourth, if managerial perceptions of control are
indeed related to negative affect, as found herein, then methods appear
warranted to minimize such negative emotional associations with control in
support of long-term marketing relationships. Finally, consistent with the
general marketing literature, firms offering service products such as PLI
insurance may best be served by seeking to foster fortitude-related forms of
loyalty in support of relationship marketing initiatives.
Footnote
1 Most variables were in the
range of 2-8%.
2 McQuitty (2004) goes on to
assert that recommendations for minimum sample sizes (in excess of 100) or 5-10
times the number of variables or estimated parameters may be outdated.
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