The Role of Consumer Innate Satisfaction in the Satisfaction-Loyalty relationship
1. Introduction
Experienced marketing managers often intuitively recognize the fact that some customers
are consistently easier to please than others (Grace 2005; Mooradian and Olver, 1997).
Unfortunately, this intuitive observation has received little attention in either practitioner or
marketing literature; there is a research gap with respect to categorizing, describing or identifying
these types of customers. Possible explanations for this gap include a lack of an appropriate
theoretical framework, difficulties in defining and measuring unobservable characteristics of
consumers, and the expanding theories of customer satisfaction that provide little guidance for
practical implementation. Regardless, efforts are expended, sometimes at a great cost, to achieve
customer satisfaction. The relatively common rule of thumb is that satisfaction is a main driver of
customer loyalty (Anderson and Mittal 2000; Mittal, Ross and Baldasare 1998;), and that it is five
to ten times cheaper to retain current customers than to acquire new ones (Slater and Narver, 2000).
Even further, evidence shows that the satisfaction-loyalty link is not straightforward, i.e. not all
satisfied customers are loyal and some satisfied customers defect to competitors (Jones and Sasser,
1995). It is commonplace for customers with the same satisfaction level to express different loyalty
intentions; for example, an evaluation of ‘somewhat satisfied’ may predict that only certain
customers will recommend the product but not repurchase it while others will do neither.. In fact, a
recent review of the satisfaction-loyalty literature concludes that the main effect of satisfaction on
loyalty is indeed weak and that customer satisfaction by itself, can hardly change customer loyalty
in a significant way (Kumar, Pozza and Ganesh, 2013). Thus, investing in ways to increase
customer satisfaction without fully understanding its role and its relationship with loyalty is not
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economically efficient. Our research therefore asks firms to question the assumption that increases
in customer satisfaction will lead to higher loyalty and ask the question: what else could account for
this difficulty in translating satisfaction into loyalty? One plausible explanation for this
unpredictability could be the random aspects of consumer behavior, unobservable by researchers.
Another could be that consumers may have different thresholds to becoming brand loyal that are
not fully captured by satisfaction surveys (Jin and Su, 2009). This paper provides further insights
into these issues by describing and investigating the role of a customer’s predisposition to being
satisfied in the satisfaction-loyalty relationship. In doing so, we seek to answer research calls to
explicate the evaluation of satisfaction or loyalty as partially a result of interplay between various
factors as well as consumer predispositions (Nijssen et al. 2003; Westbrook, 1980).
In an effort to characterize satisfaction and its relationship with loyalty, past research has
examined several possibilities, including: a) consumer related issues (i.e. age, gender, income etc.);
b) relational characteristics (i.e., transaction costs, length of relationship); and c) marketplace
issues (i.e. online versus offline) (see Finn 2012 and Kumar et al. 2013 for a review). However,
there is a dearth of research which investigates the role that dispositional (personality- based)
characteristics play in the relationship between satisfaction and loyalty. This is unfortunate since
understanding predispositions seems extremely informative in other areas of marketing, such as
product adoption (Im, Mason and Houston 2007; Kleijnen,de Ruyter and Andreassen 2005), eshopping (Das et al. 2003) or service failure, where recent evidence shows that innate individual
resources, such as consumer emotional intelligence needs to be recognized more (Gabbott et al.
2010). Other service areas have recognized that individual predispositions can play a role in
dysfunctional customer behavior (Lloyd and Reynolds 2003), service quality evaluations (Iglesias
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2004; Hellen and Saaksjarvi 2011), loyalty formation (Evanschitzky and Wunderlich 2006), or
patronizing a certain sporting event (Pons, Murali and Nyeck 2006). Whereas existing research has
utilized personality traits to further understand satisfaction, linking specific ones to behavioral
measures has proven to be more cumbersome (Mooradian and Olver 1997; Westbrook 1980). Our
paper is a step towards filling this gap in the literature by exploring the concept of a predisposition
to be satisfied (thereafter innate satisfaction) (Grace, 2005) and propose its moderating role on the
relationship between satisfaction and loyalty. Furthermore, to extend the nomological network of
innate satisfaction and contextualize its critical significance, we explore group differences between
innately satisfied and dissatisfied customers following service failure and recovery.
According to Grace (2005), innate satisfaction is a consumer dispositional characteristic
that describes a tendency to be more or less satisfied in a general consumption context. Specifically,
we define innate satisfaction as a generalized trait that reflects a person’s inherently pleasing-prone
personality, predisposition and cognitive style and therefore can be applied to multiple purchase
situations. We view it as an orientation that results from a consumer’s basic and compound traits as
well as learning history, and influences attitudes and behavior across consumption situations. Our
conceptualization identifies innately satisfied or dissatisfied consumers, who embrace this trait by
systematically processing satisfaction judgments according to their predisposition, ultimately
driving their post-purchase attitudes and behavior. Understanding the true impact of satisfaction on
customer loyalty and uncovering the role of a customer’s innate level of satisfaction can help
marketers fine tune customer targeting and help more accurately predict consumer post purchase
behavior. As we show in this paper, a measure of a customer’s satisfaction predisposition has a
significant effect on the relationship between satisfaction and customer loyalty. Specifically, we
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find that the impact of customer satisfaction on loyalty is stronger for customers who are innately
satisfied compared to customers who are innately dissatisfied, but this happens only at lower levels
of satisfaction. Furthermore, we find that this disposition carries over to service recovery results and
emotions associated with it but not to perceptions of service failure. Our study finds significant
group differences between innately satisfied and dissatisfied individuals in a service failure and
recovery context.
We begin with a brief review of the customer satisfaction and loyalty literatures. Following
this, we introduce consumer innate satisfaction, and specify the theoretical relationships between
the relevant constructs. Next, we present two empirical studies and their results. We end with a
discussion of our findings and future research directions.
2. Theoretical background and hypotheses
2.1 Customer Satisfaction
Customer satisfaction has been the subject of extant research over the past three decades
(i.e. Oliver 1980, 1997; Kumar et al. 2013; Szymanski and Henard 2001). Conceptually, research
identifies satisfaction as a global or overall evaluation which is transaction specific or attribute level
(Bowman and Narayandas 2001; Lam et al. 2004; Mittal and Kamakura 2001). The transaction
specific approach reflects an immediate post purchase evaluative judgment of the most recent
transactional experience with the product, service, or organization (Oliver, 1993). Attribute-level
satisfaction reflects functional, symbolic and experiential benefits that consumers search for in a
product or service (Mittal, Ross and Baldasare, 1998). Based on Oliver (1997) we define customer
satisfaction as the overall consumer’s evaluative summary of purchase and consumption
experiences associated with a specific product or service to date (Auh and Johnson, 2005).
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2.2 Customer Loyalty
Although extant literature investigates customer loyalty from various perspectives, the two
most common approaches are attitudinal and behavioral. Dick and Basu (1994) define loyalty as a
behavioral response that is expressed over time through the decisions that are made among
alternatives. However, this approach focuses solely on results or outcomes of loyalty such as repeat
purchasing, without accounting for the fact that individuals may be repeating their purchasing out
of habit due to switching costs or other barriers (Bendapudi and Berry, 1997). Oliver (1997) defines
loyalty as a deeply held commitment to repurchase a product or repatronize a service in the future.
The foundation of this attitudinal approach is that true loyalty develops from a positive attitude
toward the firm.
Attitudinal approaches to loyalty focus on brand recommendations (Boulding et al., 1993),
resistance to superior products (Narayandas, 1996), repurchase intention (Cronin and Taylor, 1992;
Anderson and Sullivan, 1993), positive word of mouth (Zeithaml et al., 1996) and willingness to
pay a price premium (Zeithaml et al., 1996; Narayandas, 1996). Alternatively, some researchers
argue that loyalty is a multidimensional construct, encompassing both attitudinal and behavioral
dimensions (Homburg and Giering, 2001, Picon et al., 2014). To date, there still is a lack of
consensus regarding the most appropriate conceptualization of loyalty. Attitudinal measures have
the advantage of taking into account the feelings of loyalty, fidelity and commitment, which cannot
be captured in a behavioral measure. Conversely, the multidimensional view of loyalty incorporates
behavioral intentions such as price indifference and complaining response (de Ruyter et al. 1998,
Rundle-Thiele, 2005); these are often viewed as separate constructs in the marketing literature, and
distinct outcomes of attitudinal loyalty (Jaiswal and Niraj, 2011). In this paper, we focus on
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attitudinal loyalty, inferred from repurchase intentions and positive recommendation intentions
(Suh and Yi, 2006).
2.3 The relationship between Customer Satisfaction and Customer Loyalty
The literature pertaining to the relationship between customer satisfaction and loyalty can be
organized into four broad categories. The first category consists of studies that provide empirical
evidence of a positive relationship between satisfaction and loyalty, without elaboration. Amongst
these are studies that specifically investigate overall satisfaction and attitudinal loyalty (Jones and
Sasser, 1995; Lam et al., 2004; Mittal, Kumar and Tsiros, 1999) and report a positive relationship.
A second stream of literature takes into consideration contingent effects of the satisfaction-loyalty
relationship, by identifying variables that modify its strength. While some studies discuss the
factors which moderate the satisfaction-loyalty link (i.e. Bloemer and Kasper 1995; Mittal and
Kamakura 2001; Oliva, Oliver and McMillan 1992), research seems to uncover conflicting or
paradoxical results (i.e. McKenna 1999). In some studies, the satisfaction and loyalty association
ranges from almost insignificant (e.g., cars) to highly significant (e.g., local telephone services)
(Jones and Sasser 1995); hence there are several calls for research in the area (i.e. Dong et al 2011;
Homburg and Giering 2001; Streukens and de Ruyter, 2004). A third category of research examines
the presence of mediating factors between the two constructs. Some authors suggest potential
mediators such as expectations (Yi and La, 2004), trust (Agustin and Singh, 2005), and
commitment (Garbarino and Johnson, 1999). Finally, the fourth set of literature pertains to the
functional form linkage between satisfaction and loyalty (Dong et al. 2011). Most of the existing
studies propose a linear structural relationship between satisfaction and repurchase intentions.
However, Ngobo (1999) and Jones and Sasser (1995) provide evidence of an industry type driven
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nonlinear relationship. More recently, Dong et al. (2011) characterize the functional form of
satisfaction and repurchase intention with both linear and nonlinear specifications, since they vary
across segments in industry, and consumer economic and demographic variables or market
characteristics. Dong and colleagues urge researchers to empirically validate the functional form
before incorporating moderating effects. Other research hypothesizes a non-linear, asymmetrical
relationship between satisfaction and loyalty dimensions of repurchase and recommendation (Lam
et al. 2004). Overall, extant literature leads to the expectation of a nonlinear and asymmetric link
between satisfaction and repurchase or recommendation intention.
2.4 Customer Innate Satisfaction
Research finds that innate individual predispositions, such as consumer emotional
intelligence and consumer happiness (Gabott et al 2011; Hellen and Saaksjarvi 2011) emerge as
predictors of service quality and commitment. Dispositions can influence the manner in which
individuals perceive events in life (gaining a new job, buying a new house, etc.) and the way they
collect, process, and evaluate information in a particular instance (Levin and Stokes 1989; Necowitz
and Roznowski 1994). Dispositional characteristics may also predetermine individuals to self-select
into particular environments (Diener, Larsen and Emmons 1984; Judge et al. 2000). Also, Larsen
and Ketelaar (1991) indicate that dispositions influence the extent to which individuals are sensitive
to positive and negative events. In the marketing literature, with the exception of Grace (2005),
limited research conceptualizes innate satisfaction, especially in an empirical realm..
2.5 Innate satisfaction as a moderator
We contend that satisfaction can be more meaningfully investigated if placed within the
context of a consumer predisposition.
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Social sciences research suggests that predispositional variance between consumers relate to
differences in attention to positive and negative signals (Mooradian and Olver, 1997).Based on
Accessibility-Diagnosticity Theory (Skworonksi and Carlston, 1989), an innate satisfaction
predisposition may impact the salience of the discrepancy between reported satisfaction and
expected baseline satisfaction experienced by the consumer.
In general, discrepancies associated with negative satisfaction experiences will be more
salient to innately satisfied consumers, as they are perceived to be novel, different from the regular
baseline. In this case, the level of disparity will act as an important cue that receives greater
attention for subsequent judgments, and will be more important for the innately satisfied
consumers. As the level of satisfaction increases, the discrepancy and the salience of the
discrepancy decreases for innately satisfied consumers. Therefore, the relationship between
satisfaction and loyalty behavior strengthens. At lower levels of satisfaction, a negative experience
will not be noticed more than regularly by the innately dissatisfied consumers. Therefore, although
it is present, the salience of the discrepancy is lower for innately dissatisfied consumers. According
to Attribution Theory (Wong and Weiner 1981), unexpected negative experiences should lead to
internal search for factors (i.e. consumers will attribute dissatisfaction to idiosyncratic factors, not
external factors), and the less pleasant, unexpected experiences will lead to stronger consumer
responses. As the satisfaction level increases, the discrepancy becomes less and less for innately
dissatisfied consumers, and therefore, the relationship between satisfaction and loyalty intentions
strengthens, but at a lower rate compared to innately satisfied consumers. In sum, we expect a
difference in loyalty intentions to be observed at lower levels of satisfaction judgments.
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Conversely, for innately satisfied consumers, positive satisfaction experiences may be a
natural, expected, regular experience in a positive consumer world. In these cases, the loyalty
evaluations are guided by the strongest, sufficient cue (Skowronksi and Carlston, 1989). The level
of satisfaction acts as a sufficient cue (Skowronski and Carlston, 1989) and is stronger than any
other cue in predicting consumer subsequent loyalty intentions. This is corroborated with
attributional research (Wong and Weiner, 1981) that stipulates that in case of success
(i.e.satisfaction), consumers will direct their attributional inferences regarding the causes of the
success toward external to consumer factors. Thus, satisfaction will be attributed to product or
service performance whereas the salience of the discrepancy, which is an internal factor, will be
very low. In this case, satisfaction will not affect post purchase loyalty intentions in a way
significantly differently than regularly expected. For the innately dissatisfied consumers, the
salience of the current high level of satisfaction can theoretically determine stronger positive
reactions, compared to innately satisfied consumers, as this is perceived as a non-ordinary
occurrence. However, similar to innately satisfied consumer attributions, this positive instance will
be attributed to factors external to consumers. Therefore, we predict that, at higher levels of
satisfaction, the salience of discrepancy is overlooked, whereas increasing satisfaction levels act as
sufficient enough cues to determine an increase in loyalty intentions for both types of customers,
albeit with diminishing returns for all consumers (Anderson, 1998, Anderson and Mittal, 2000).
Hence, as innate satisfaction is important in characterizing the effects of satisfaction we
propose a moderating influence of innate satisfaction on the satisfaction-loyaty relationship.
Specifically, we expect that:
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H1) Customer innate satisfaction moderates the impact of satisfaction on willingness to recommend
only when customer satisfaction is low. Specifically, for low levels of satisfaction, the impact of
satisfaction on willingness to recommend is stronger for innately satisfied customers compared to
innately dissatisfied customers.
H2) Customer innate satisfaction moderates the impact of satisfaction on repurchase intentions only
when customer satisfaction is low. Specifically, for low levels of satisfaction, the impact of
satisfaction on repurchase intentions is stronger for innately satisfied customers compared to
innately dissatisfied customers.
3. Method
3.1 Study 1: Measures, sample and data collection
Data were collected through Amazon Turk, in exchange for a small financial incentive.
Respondents completed an online survey in which they reported customer satisfaction scores for a
service/product currently used in 16 different service/product categories. The categories were:
checking account, automobile insurance, streaming video service, internet-phone-TV bundle,
prepaid cell service, dental services, gym membership, hair stylist, digital camera, travel agent, ebooks, laundry detergent, razor blades, magazine subscription, microwave oven and laptop
computer. These categories were selected after conducting a pre-test involving a focus group (10
respondents) to understand commonly used products/services in terms of everyday interaction.
Satisfaction (S) was measured using a 7-point three item scale, based on Fornell et al.
(1996) and Gustaffson et al. (2005) which included overall satisfaction, expectations and
comparison to the ideal for each service/product. Each respondent also answered a three item, 7point willingness to recommend scale (Aksoy et al. 2011, van Hoye 2008) for the products/services
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in each of the 16 categories that included likelihood to recommend without being asked, encourage
others to buy and recommend when asked about the service/product. The mean score of the three
items is used as the respondent’s willingness to recommend (W). Finally, repurchase intentions (R)
were measured using a single item 7-point scale (based on Fornell et al. 1996). In addition,
demographic data such as income, age, gender and education level were also collected.
Uncovering innate satisfaction
We use an innovative approach to estimate a customer’s innate tendency to be satisfied or
dissatisfied in a consumption context. We leverage the customer satisfaction data from the multiple
categories mentioned above and partial out the systematic and random components. We start by
calculating the average satisfaction level for consumers in each product/service category. Next, we
subtract the average satisfaction for the product/service category from an individual customer’s
satisfaction score for that product/service to obtain the customer’s deviation from the mean category
score. This deviation is important because it may result from: a) differences in product/service
experiences compared to the mean experience for that category and b) tendency to be more (or less)
satisfied in general. We compute these satisfaction deviations for each of the sixteen
product/service categories for each respondent. Note that for an individual respondent while we can
expect random differences in product/service experiences for the various categories, the differences
in predisposition are systematic. Finally, we compute the mean satisfaction deviation for the
individual respondent across the sixteen product categories, so that the random differences in
product/service experiences are averaged out and the tendency to be satisfied (or dissatisfied)
remains. Thus, this mean deviation represents each respondent’s innate satisfaction (I) score.
Customers with a negative I score are assumed to be innately dissatisfied or harder to please and
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customers with a positive I score are assumed to be innately satisfied or easier to please. This
measure of innate satisfaction is more accurate than a self-reported measure of innate satisfaction
since it statistically extracts idiosyncratic responses to product experiences.
Sample characteristics
A total of 409 complete respondent-category observations were collected. The sample
consisted of about half male (48.7 %) and half (51.3 %) female respondents primarily (64.1 %)
aged 31 or older. A majority of the respondents (79.5 %) had household incomes of less than
$75,000. The descriptive statistics for the data are presented in Table 1.
--- insert table 1 about here--Analysis of the correlation matrix provides indication that there is enough variation in the data to
proceed with subsequent testing. In addition, since our data came from the same source, we
proceeded to detect whether common method bias is significant in these data. Although CMV is
less problematic in more complex estimations that involve multiple independent variables or
interactions (Siemsen et al, 2010), we further examined the potential for CMV As suggested by
Lindell and Whitney (2001) We performed a marker-variable test for CMV. In order for this test,
data were collected in regards to ‘the material purchase’ orientation of a customer (Howell and Hill,
2009), a theoretically unrelated construct. The lowest correlation value (.23, between material
purchase and innate satisfaction) was then used for dissatenuation. All the correlations that were
previously significant remained significant after their correlation has been dissatenuated. Overall,
these results led to the conclusion that CMV is not a significant issue in the present study. Finally,
all constructs exhibit high composite reliabilities, indicating internal consistency (Fornell and
Larcker, 1981). The Cronbach’s alpha for the Satisfaction scale is .91, whereas the Cronbach’s
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alpha for willingness to recommend is .934. Overall, these statistics indicate that the psychometric
properties of the constructs are sufficiently strong to enable interpretation of the restricted
maximum likelihood estimates.
Dependent variables
Based on past literature we measure loyalty with two distinct measures: the mean scores
from the three item scale for willingness to recommend (W) and single item repurchase intentions
(R) scale. We estimate the model separately for each of these variables.
3.2 Model and Estimation
Based on the literature, we specify a quadratic model relating satisfaction and loyalty using S and
S2 as independent variables. We also specify a quadratic simple effect for innate satisfaction using I
and I2. Next, given our hypothesis regarding the differential moderating role of innate satisfaction
on the relationship between satisfaction and loyalty, the multiplicative interaction terms I*S, I*S2
and I2*S are included in the model.
The estimated model is given by the equation:
Loyalty = β0 + β1 S + β2 I+β3 IS + β4 S 2 + β5 I2 + β6 IS 2 + β7 I2 S + 𝑋𝑐
(1)
where the dependent variable Loyalty is either willingness to recommend (W) or repurchase
intentions (R) ; the independent variables are as described in section 3.2; and Xc represents the
vector of control variables that include category specific dummies, age, household income and
gender. Since each respondent provided data for multiple categories, we used mixed models using
restricted maximum likelihood estimation methods to control for these within-respondent
correlations. Table (2) shows the results of the mixed effects regression model for the two loyalty
dependent variables.
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---insert table 2 about here--We can see from Table 2 that innate satisfaction has a significant impact on willingness to
recommend. Specifically, while the simple effect of innate satisfaction is negative and significant,
the first order interaction I*S is found to be positive and significant (β3 >0). This provides support
for the moderating role of innate satisfaction on the relationship between satisfaction and
willingness to recommend. Specifically, since β3 >0, the impact of satisfaction is stronger for
innately satisfied customers compared to innately dissatisfied customers. In addition, since the
higher order interaction term I*S2 is negative and significant (β6 <0), the moderating role of innate
satisfaction is stronger at lower levels of satisfaction compared to higher levels of satisfaction.
Appendix A shows that the difference in the impact of satisfaction for innately satisfied (IH ) and
innately dissatisfied (IL ) customers is given by
𝐷𝑖𝑓𝑓 𝑊 = β3 (IH − IL ) + 2β6 (IH − IL )S
(2)
Substituting the values of the parameters and testing the difference at low and high levels of
satisfaction, we find that (see appendix A for details) while the difference is positive and significant
at low levels of satisfaction (𝐷𝑖𝑓𝑓𝑆𝑊
>0), the difference is not significant at high levels of
𝐿
satisfaction (𝐷𝑖𝑓𝑓𝑆𝑊
=0). Hence, hypothesis H1 is supported.
𝐻
Similarly, looking at the intention to repurchase model we find that α 3 >0 and α 6 <0. Since we also
find (see appendix A) that 𝐷𝑖𝑓𝑓𝑆𝑅𝐿 >0 and 𝐷𝑖𝑓𝑓𝑆𝑅𝐻 =0, hypothesis H2 is also supported. As shown in
Figures 1 and 2, the impact of satisfaction is stronger (as evidenced by steeper slope) for innately
satisfied customers compared to innately dissatisfied customers. This effect is present only at lower
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levels of satisfaction. At higher levels of satisfaction, there seems to be no significant differences in
the slopes.
3.3 Study 2
Another objective of this paper was to extend the domain of innate satisfaction and to
explore its importance in a specific service context of service failure and recovery (i.e. Bitner,
Booms and Tetreault, 1990; Vasquez-Casielles et al., 2007, Ganesh et al., 2000). The participants
were students in an undergraduate marketing class at a large Southwestern University. Participants
were first asked to rate their satisfaction and intention to recommend with sixteen products/services
categories. Then, the respondents were asked to monitor over the course of a semester instances of
service experiences. Once the respondents experienced a service failure, they filled in a
questionnaire regarding the magnitude of the failure as measured by Hess, Ganesan and Klein
(2003) and rated affective and behavioral responses to the service failure as measured by Schoefer
(2008) and Smith and Bolton (2002). Furthermore, the respondents were instructed to monitor the
service recovery and record the perceptions of service recovery quality as well as their affective and
behavioral responses to service recovery as measured by Bougie, Pieters and Zeelenberg (2003). In
addition, the respondents recorded their post recovery satisfaction, attitudinal and behavioral loyalty
(Ganesh et al. 2000) following the service recovery and the mood at the time the questionnaire was
completed. The respondents reported on their experiences once a week, for 10 weeks. At the end of
the semester, 44 students, 26 males and 18 females, similar in age, completed the entire process.
Results
Following the specification for computing Innate Satisfaction from study 1 and 2, we
calculated the scores of innate satisfaction for each of the respondents. We then investigated how
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innately satisfied customers differ from innately dissatisfied customers in a service failure and
recovery setting. We first median-split the innate satisfaction data and created a dummy variable
that specifies innately satisfied/ dissatisfied customers. Data analyses focused on group differences
in relation to service failure and service recovery reactions by means of one-way ANOVAs.
---insert table 3 about here---
Results show (table 3) that innately satisfied respondents do not significantly differ in their
reactions to service failure compared to innately dissatisfied respondents: both groups experienced
similar levels of negative emotions (anger, pity, disappointment) and do not differ in their intention
to complain to the provider or to complain to third parties. Results also show that, following service
recovery, controlling for age, gender and mood, there is a significant statistical difference between
the two groups with respect to interactional, procedural and outcome fairness of service recovery,
between positive and negative emotions reported, as well as post recovery satisfaction. Innately
dissatisfied individuals reported generally lower satisfaction, lower positive emotions, heightened
negative emotions and less perceptions of fairness. Furthermore, following recovery, innately
dissatisfied customers reported higher intentions to not recommend their service provider (MID
=6.19 vs MIS=4.23, p<.005) but not significant other complaining actions (to the provider or third
parties). Finally, the two groups differ significantly in terms of their expressions of attitudinal and
behavioral loyalty. This brings empirical support towards the contention that innately satisfied and
innately dissatisfied consumers react to service failure and recovery in a significantly different
manner.
4. Discussion and Implications
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The current study draws attention towards the novel concept of predisposition towards
satisfaction of a customer, and provides a measure of it, that can be a valid tool for measuring true
levels of satisfaction and subsequent post purchase behaviors. We thus extend the current
satisfaction-loyalty literature and provide a response to repeated calls to address this complex
relationship from new perspectives (Nijssen et al. 2003). Our results show that, indeed, this
predisposition manifests in such a way that can account for differential responses to satisfaction
judgments among consumers: predisposition towards satisfaction significantly alters the loyalty
responses at the same level of satisfaction judgments. Our results confirm the potential of consumer
dispositional approach (Westbrook 1980, Nijseen et al. 2003) across a range of product/service
categories. Given the broad range of categories selected, our results could be open to a
hedonic/utilitarian alternative explanation. To rule out this explanation we performed additional
analyses, that showed no indication that consumer disposition may modify the satisfaction-loyalty
relation differently for hedonic versus utilitarian products. This result strengthens the generalized
conceptualization of our construct.
Our model is parsimonious and rigorous in that it covers the emerging views of relationship
among the two focal constructs. Based on theory, we uncovered evidence for a concave
asymmetrical relationship between satisfaction and loyalty, answering calls for research in this
regard (Dong et al. 2011). Finally, our measure of predisposition can prove an alternative to
measuring unobservable consumer traits and an alternative to repurchase or recommendation
thresholds (Jin and Su, 2009) that serve as indicators to identify customers with intrinsic
retainability.
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Managerially, identification of consumer segments that are predisposed to satisfaction or
dissatisfaction can inform service providers about directions to monitor in the future. Marketing
managers may find this important since different customer segments may be more or less loyal
under similar levels of product/service satisfaction. This has implications for satisfaction and
loyalty management, as well as targeting and message planning. Budget allocation can be
ineffective when there are customer segments that are prone to be more loyal and need to be
identified (i.e. Iyer and Muncy, 2005). Investing in improving customer service with the hope of
delivering satisfaction seems to pay off better in the case of innately satisfied customers.
Innate satisfaction can be employed to manage service expectations through company
communications. From a practitioner point of view, if the target market is primarily consisting of
innately satisfied customers, communication can be employed to help consumers make better sense
of the product/service experience, helping reduce cognitive dissonance and increase their
satisfaction levels. As the impact of satisfaction on loyalty is greater for these customers, increasing
their level of satisfaction is the optimal strategy. Also, communication can be employed to educate
consumers about their predispositions and how it can affect their future choices. If the customer
base is made of primarily of innately dissatisfied customers, then is important to educate consumers
about the true value of the satisfaction they are experiencing with the brand, while emphasizing the
importance of acting on their satisfaction. Given the increased number of brand choices that
customers face and the age of the ‘never satisfied consumer’ (McKenna 1999), a blanket strategy to
increase customer satisfaction irrespective of customer predispositions may not be the right
strategy.
Managers often initially plan their budget with multiple objectives in mind, such as
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attracting customers, reinforcing attitudes and building loyalty. Knowledge of the customers’ innate
predisposition should be even more important for products/services where word of mouth and
recommendations are critical for product adoption. Managers should understand that striving for
achieving satisfaction should remain priority; however, the impact of satisfaction may vary
depending on customer predispositions. Communicating on the market about satisfaction should
also take into consideration the origin of satisfaction ratings. High satisfaction ratings expressed
predominantly by innately dissatisfied consumers should be indicative of optimal product/service
performance. In this case, efforts should be made to transform these customers into brand
advocates. For example, advertising messages can be tailored to express these brand advantages. On
the other hand, low satisfaction ratings expressed predominantly by innately satisfied customers
should always be seen as a warning signal. Advertising should then be used to manage the brand
quality reputation on the market. Finally, innately satisfied customers differ than innately
dissatisfied customers in their responses to service failure and recovery. Innately dissatisfied
customers have stronger and more negative reactions even after service recovery but, while they
have lower attitudinal loyalty (perhaps in line with their disposition) they do show higher
behavioral loyalty intentions. In case repeat purchase and transactional satisfaction is of interest,
then recovery seems more essential for innate dissatisfied customers.
5. Limitations and future research
Given the fact that this is among the first reported empirical study on consumer satisfaction
predisposition and its implications for brand loyalty, future research should try to replicate our
findings in other product/service specific settings. Classification of customers on innate satisfaction
levels is context specific, as there is a lack of established standard that allow comparisons to be
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made. However, we believe that our objective measure (based on repeated customer expressed
judgments) may alleviate this limitation. An extension to the current study should incorporate
consumer predisposition to be satisfied to more holistic models relating satisfaction and loyalty
(Kumar et al. 2013). Alternative measures of loyalty such as switching intentions, repeat purchase
should be collected to investigate the differential impact of innate satisfaction.
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Table 1: Descriptive Statistics
M
SD
W
R
S
1. Willingness to Recommend (W)
4.80
1.60
-
2. Repurchase Intentions (R)
5.82
1.38
0.71*
-
3. Satisfaction (S)
5.30
1.14
0.78*
0.77*
-
4. Innate Satisfaction (I)
0
0.63
0.46*
0.38*
0.56*
* p<0.05 (two-tailed)
26
Table 2: Parameter Estimates from mixed effects models
Dependent variables
Willingness to
Recommend (W)
Repurchase
Intention(R)
Intercept
-3.378**
-4.286***
Satisfaction (S)
2.047***
2.850***
Innate Satisfaction(I)
-3.756**
-2.769*
I*S
1.248*
0.926*
S2
-0.096**
-0.183***
I2
-1.097*
-0.324
I*S2
-0.101*
-0.079*
I2*S
0.179*
0.03
Independent variables a
a
Model estimated with product category dummies, age, income and gender controls.
* p<0.05 (one-tailed), ** p<0.01 (one-tailed), *** p<0.001 (one-tailed)
27
Table 3: Differences between Innately Dissatisfied and Satisfied Consumers (Service Failure and Recovery)
Group N
Mean Std Sig. Variable
Group N
Mean
IL
23 5.66
1.68
IL
24 6.19
Negative
WOM
IH
20 5.20
1.98
IH
20 4.23
Total
43 5.45
1.82 .42
Total
44 5.30
Failure
IL
24 9.13
1.33
Complaint
IL
24 5.32
positive
to Provider IH
IH
20 9.10
2.19
20 5.45
emotions
Total
44 9.11
1.78 95
Total
44 5.38
IL
24 3.86
3.03
Complaint
IL
24 1.75
Procedure
to third
fairness
IH
20 6.50
3.53
IH
20 2.24
parties
Total
44 5.06
3.49 .012
Total
44 1.97
IL
24 2.68
2.02
IL
24 3.33
Outcome
Post
fairness
recovery
IH
20 5.04
3.73
IH
20 5.42
satisfaction
Total
44 3.75
3.12 .017
Total
44 4.28
IL
24 3.63
2.86
24 2.89
Positive
Attitudinal IL
emotions
Loyalty
IH
20 6.27
3.50
IH
20 4.75
recovery
Total
44 4.83
3.40 .010
Total
44 3.73
IL
24 4.85
2.66
24 5.82
Negative
Behavioral IL
Emotions
Loyalty
IH
20 2.63
1.94
IH
20 4.03
recovery
Total
44 3.84
2.59 .003
Total
44 5.01
IL : Innately Dissatisfied, IH : Innately Satisfied. Bold variables are significant at p<.05
Variable
Failure
negative
emotions
Std
1.97
2.35
2.34
2.86
2.98
2.88
0.94
2.53
1.84
2.68
3.59
3.26
1.69
2.46
2.26
2.63
3.12
2.97
Sig.
.005
.887
.423
.039
.007
.049
28
Figure 1: Impact of Satisfaction on Willingness to Recommend
Willingness to Recommend
7
6
5
4
3
Innately Dissatisfied
Innately Satisfied
2
1
Low
Medium
Satisfaction
High
Figure 2: Impact of Satisfaction on Repurchase Intention
Intention to Repurchase
7
6
5
Innately Dissatsifeid
4
3
Innately Satisfied
Low
Medium
Satisfaction
High
29
Appendix A: Hypothesis Testing Details
The estimated model is given by the equation:
Loyalty = β0 + β1 S + β2 I+β3 IS + β4 S 2 + β5 I2 + β6 IS 2 + β7 I2 S + 𝑋𝑐
(A1)
where the dependent variable Loyalty (L)is either willingness to recommend (W) or repurchase
intentions (R)
The impact of satisfaction on loyalty can be better understood by taking the first derivative of
equation (A1)above.
𝑑𝐿
= β1 + β3 I + 2β4 S + 2β6 IS + β7 I2
𝑑𝑆
(A2)
This impact for innately satisfied individuals can be computed by substituting I=IH
𝑑𝐿𝐼𝐻
2
= β1 + β3 IH + 2β4 S + 2β6 IH S + β7 IH
𝑑𝑆
(A3)
The impact for innately dissatisfied individuals can be expressed as
dLIL
= β1 + β3 IL + 2β4 S + 2β6 IL S + β7 IL2
dS
(𝐴4)
The difference between the two is given by
𝑑𝐿𝐼𝐻 𝑑𝐿𝐼𝐿
2
𝐷𝑖𝑓𝑓 =
−
= β3 (IH − IL ) + 2β6 (IH − IL )S + β7 (IH
− IL2 )
𝑑𝑆
𝑑𝑆
(A5)
Since the mean level of innate satisfaction is zero, and since IH = 1.5*(standard deviation of innate
2
satisfaction) and IL = -1.5*(standard deviation of innate satisfaction), IH
− IL2 = 0. Hence, equation
(A5) reduces to
𝐷𝑖𝑓𝑓 = β3 (IH − IL ) + 2β6 (IH − IL )S
(A6)
Given that the standard deviation for innate satisfaction is found to be 0.63, we have IH=0.945 and
IL= -0.945. The mean levels of satisfaction are found to be 5.3 and the standard deviation is 1.14.
Hence, low levels of satisfaction are given by SL=5.3-1.5*1.14= 3.59 and high levels of satisfaction
are given by SH 5.3+1.5*1.14= 7. The difference between the impact of satisfaction for innately
satisfied and innately dissatisfied customers at low levels of customer satisfaction is therefore
obtained by substituting IH , IL and SL into equation (A6) and is given by
𝐷𝑖𝑓𝑓𝑆𝐿 = 1.89( β3 ) + 13.57( β6 )
(A7)
Similarly, substituting IH, IL and SH into equation (A6) results in
30
𝐷𝑖𝑓𝑓𝑆𝐻 = 1.89(β3 ) + 26.46(β6 )
(A8)
Note that equations (A7) and (A8) are of the form 𝑋 = a ∗ β3 + b ∗ β6 . In order to test hypothesis
relating to X (such as X>0), we need to compute
𝑡𝑋 =
a ∗ β3 + b ∗ β6
(𝐴9)
𝑎2 𝑉𝑎𝑟(β3 ) + 𝑏 2 𝑉𝑎𝑟(β6 ) + 2abCov(β3 , β6 )
In order to find support for our hypothesis that suggests that the impact of satisfaction is stronger
for innately satisfied customers compared to innately dissatisfied customers and that this
differential effect manifests only at low levels of satisfaction, we must find that
𝐷𝑖𝑓𝑓𝑆𝑊
> 0 𝑎𝑛𝑑 𝐷𝑖𝑓𝑓𝑆𝑊
=0
𝐿
𝐻
(𝐴10)
𝐷𝑖𝑓𝑓𝑆𝑅𝐿 > 0 𝑎𝑛𝑑 𝐷𝑖𝑓𝑓𝑆𝑅𝐻 = 0
(𝐴11)
Note that the superscript on the variable Diff represents the dependent variable (W for willingness
to purchase and R for intention to repurchase) and the subscript represents the level of satisfaction
at which the difference in impact between innately satisfied and innately dissatisfied customers is
computed. In order to test the hypothesis listed in (A10) and (A11), we compute the relevant tstatistics using the formula in (A9) and compare with the one-tailed critical t-value of 1.66. The
results are presented in table A1.
Table A1: Test of significant differences
Computed Value
T-value
𝐷𝑖𝑓𝑓𝑆𝑊
𝐿
0.99
2.49**
𝐷𝑖𝑓𝑓𝑆𝑊
𝐻
-0.31
-0.88
𝐷𝑖𝑓𝑓𝑆𝑅𝐿
0.68
1.92*
𝐷𝑖𝑓𝑓𝑆𝑅𝐻
-0.34
-0.99
** p<0.01, *p<0.05
As we can see from table A1, we find support for both sets of hypothesis. Hence, H1 and H2 are
supported.
31